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Linear quadratic bumpless transfer A method for bumpless transfer using ideas from LQ theory is presented and shown to reduce to the Hanus conditioning scheme under certain conditions.

State resetting for bumpless switching in supervisory control In this paper the realization and implementation of a multi-controller scheme made of a finite set of linear single-input-single-output controllers, possibly having different state dimensions, is studied. The supervisory control framework is considered, namely a minimal parameter dependent realization of the set of controllers such that all controllers share the same state space is used. A specific state resetting strategy based on the behavioral approach to system theory is developed in order to master the transient upon controller switching.

Interpolation for gain-scheduled control with guarantees Here, a methodology is presented which considers the interpolation of linear time-invariant (LTI) controllers designed for different operating points of a nonlinear system in order to produce a gain-scheduled controller. Guarantees of closed-loop quadratic stability and performance at intermediate interpolation points are presented in terms of a set of linear matrix inequalities (LMIs). The proposed interpolation scheme can be applied in cases where the system must remain at the operating points most of the time and the transitions from one point to another rarely occur, e.g., chemical processes, satellites.

On controller initialization in multivariable switching systems We consider a class of switched systems which consists of a linear MIMO and possibly unstable process in feedback interconnection with a multicontroller whose dynamics switch. It is shown how one can achieve significantly better transient performance by selecting the initial condition for every controller when it is inserted into the feedback loop. This initialization is obtained by performing the minimization of a quadratic cost function of the tracking error, controlled output, and control signal. We guarantee input-to-state stability of the closed-loop system when the average number of switches per unit of time is smaller than a specific value. If this is not the case then stability can still be achieved by adding a mild constraint to the optimization. We illustrate the use of our results in the control of a flexible beam actuated in torque. This system is unstable with two poles at the origin and contains several lightly damped modes, which can be easily excited by controller switching.

Air-Fuel Ratio Control of Spark Ignition Engines Using a Switching LPV Controller The three way catalytic converter (TWC) is a critical component for the mitigation of tailpipe emissions of modern spark ignition internal combustion (IC) engines. Because the TWC operates effectively only when the air-fuel ratio is very close to stoichiometric, accurate control of the air-fuel ratio is required. This paper uses a switching linear parameter varying (LPV) controller to regulate the air-fuel ratio. For controller design purposes, the dynamics of the fuel path is modeled as a time-varying first-order plus dead time (FOPDT) model, varying with the engine operating point, i.e., engine speed and air flow. Large variation of the FOPDT model across the engine operating range leads to a conservative LPV controller. Therefore, the operating range is divided into smaller subregions, an individual LPV controller is designed for each, and the LPV controllers are then switched based on the operating point. The LPV controllers are found by solving a convex optimization problem with linear matrix inequalities (LMIs) which can be efficiently solved using available LMI techniques. The resulting closed-loop system has guaranteed performance over the operating range of the engine. Simulations show the improved air-fuel ratio regulation of the switching LPV controller over the engine's operating range compared to that of an H∞ controller which is scheduled based on air flow only as well as a non-switching LPV controller.

Optimal switching surface design for state-feedback switching LPV control In this paper, we deal with the problem of simultaneous design of state-feedback switching linear parameter-varying (LPV) controllers and switching surfaces for LPV plants to further improve the performance of the switching LPV controllers. The LPV plants that we consider have polynomially parameter-dependent state-space matrices. Using slack variable approach, we first propose a method to design state-feedback switching LPV controllers, and then formulate simultaneous design problem as an optimization problem involving bilinear matrix inequalities (BMIs). An algorithm is then proposed to solve the problem by iteratively fixing either switching surface variables or controller variables while optimizing the other. A simple numerical example is given to demonstrate the effectiveness of the proposed approach.

Stability of Time-Delay Feedback Switched Linear Systems We address stability of state feedback switched linear systems in which delays are present in both the feedback state and the switching signal of the switched controller. For switched systems with average dwell-time switching signals, we provide a condition, in terms of upper bounds on the delays and in terms of a lower bound on the average dwell-time, to guarantee asymptotic stability of the closed loop. The condition also implies that, in general, feedback switched linear systems are robust with respect to both small state delays and small switching delays. Our approach combines existing multiple Lyapunov function techniques with the merging switching signal technique, which gives relationships between the average dwell times of two mismatched switching signals and their mismatched times. A methodology for numerical solution based on linear matrix inequality is also included.

On the security of public key protocols Recently the use of public key encryption to provide secure network communication has received considerable attention. Such public key systems are usually effective against passive eavesdroppers, who merely tap the lines and try to decipher the message. It has been pointed out, however, that an improperly designed protocol could be vulnerable to an active saboteur, one who may impersonate another user or alter the message being transmitted. Several models are formulated in which the security of protocols can be discussed precisely. Algorithms and characterizations that can be used to determine protocol security in these models are given.

A Game-Theoretical Approach for User Allocation in Edge Computing Environment Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently.

Interpolating view and scene motion by dynamic view morphing We introduce the problem of view interpolation for dynamic scenes. Our solution to this problem extends the concept of view morphing and retains the practical advantages of that method. We are specifically concerned with interpolating between two reference views captured at different times, so that there is a missing interval of time between when the views were taken. The synthetic interpolations produced by our algorithm portray one possible physically-valid version of what transpired in the scene during the missing time. It is assumed that each object in the original scene underwent a series of rigid translations. Dynamic view morphing can work with widely-spaced reference views, sparse point correspondences, and uncalibrated cameras. When the camera-to-camera transformation can be determined, the synthetic interpolation will portray scene objects moving along straight-line, constant-velocity trajectories in world space

A simplified dual neural network for quadratic programming with its KWTA application. The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and the computational complexity of the neural network. The simplified dual neural network is shown to be globally convergent to the exact optimal solution. The complexity of the neural network architecture is reduced with the number of neurons equal to the number of inequality constraints. Its application to k-winners-take-all (KWTA) operation is discussed to demonstrate how to solve problems with this neural network.

Simultaneous wireless information and power transfer in modern communication systems Energy harvesting for wireless communication networks is a new paradigm that allows terminals to recharge their batteries from external energy sources in the surrounding environment. A promising energy harvesting technology is wireless power transfer where terminals harvest energy from electromagnetic radiation. Thereby, the energy may be harvested opportunistically from ambient electromagnetic sources or from sources that intentionally transmit electromagnetic energy for energy harvesting purposes. A particularly interesting and challenging scenario arises when sources perform simultaneous wireless information and power transfer (SWIPT), as strong signals not only increase power transfer but also interference. This article provides an overview of SWIPT systems with a particular focus on the hardware realization of rectenna circuits and practical techniques that achieve SWIPT in the domains of time, power, antennas, and space. The article also discusses the benefits of a potential integration of SWIPT technologies in modern communication networks in the context of resource allocation and cooperative cognitive radio networks.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Distributed Resource Allocation for D2D Communications Underlaying Cellular Networks in Time-Varying Environment. In this letter, we address joint channel and power allocation in a device-to-device (D2D) network underlaying a cellular network in a time-varying environment. A fully distributed solution, which does not require information exchange, is proposed to allocate channel and power levels to D2D pairs while ensuring the quality of service (QoS) of the cellular user equipments (CUEs). The problem is modeled as a Stackelberg game with pricing. At the leader level, base station sets prices for the channels to ensure the QoS of the CUEs. At the follower level, D2D pairs use an uncoupled stochastic learning algorithm to learn the channel indices and power levels while minimizing the weighted aggregate interference and the price paid. The follower game is shown to be an ordinal potential game. We perform simulations to study the convergence of the algorithm.

Stochastic Power Adaptation with Multiagent Reinforcement Learning for Cognitive Wireless Mesh Networks As the scarce spectrum resource is becoming overcrowded, cognitive radio indicates great flexibility to improve the spectrum efficiency by opportunistically accessing the authorized frequency bands. One of the critical challenges for operating such radios in a network is how to efficiently allocate transmission powers and frequency resource among the secondary users (SUs) while satisfying the quality-of-service constraints of the primary users. In this paper, we focus on the noncooperative power allocation problem in cognitive wireless mesh networks formed by a number of clusters with the consideration of energy efficiency. Due to the SUs' dynamic and spontaneous properties, the problem is modeled as a stochastic learning process. We first extend the single-agent $(Q)$-learning to a multiuser context, and then propose a conjecture-based multiagent $(Q)$-learning algorithm to achieve the optimal transmission strategies with only private and incomplete information. An intelligent SU performs $(Q)$-function updates based on the conjecture over the other SUs' stochastic behaviors. This learning algorithm provably converges given certain restrictions that arise during the learning procedure. Simulation experiments are used to verify the performance of our algorithm and demonstrate its effectiveness of improving the energy efficiency.

Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey. This paper presents a comprehensive literature review on applications of economic and pricing theory for resource management in the evolving fifth generation (5G) wireless networks. The 5G wireless networks are envisioned to overcome existing limitations of cellular networks in terms of data rate, capacity, latency, energy efficiency, spectrum efficiency, coverage, reliability, and cost per information transfer. To achieve the goals, the 5G systems will adopt emerging technologies such as massive multiple-input multiple-output, mmWave communications, and dense heterogeneous networks. However, 5G involves multiple entities and stakeholders that may have different objectives, e.g., high data rate, low latency, utility maximization, and revenue/profit maximization. This poses a number of challenges to resource management designs of 5G. While the traditional solutions may neither efficient nor applicable, economic and pricing models have been recently developed and adopted as useful tools to achieve the objectives. In this paper, we review economic and pricing approaches proposed to address resource management issues in the 5G wireless networks including user association, spectrum allocation, and interference and power management. Furthermore, we present applications of economic and pricing models for wireless caching and mobile data offloading. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to the 5G wireless networks.

Effective Capacity in Wireless Networks: A Comprehensive Survey. Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physical-layer channel models, however, do not explicitly consider quality-of-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks, such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications, such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

Joint channel and power allocation for device-to-device underlay Device-to-device transmission is one of the enabling technologies of 5G, with a potential of significantly improving the spectral efficiency. Spectral reuse in D2D underlay necessitates interference management. A challenge in D2D underlay systems is the increased number of D2D and interfering links and CSI feedback requirement. In this work, we propose a solution for D2D channel allocation, which requires only the neighbor information of D2D communicating nodes. We aim to maximize the supported D2D pairs with a constraint on the interference caused at the base station, at each subchannel. We formulate the channel allocation problem as a Mixed integer programming (MIP). We also combine it with an iterative power control scheme in order to fit more D2D pairs in the channels. We also propose suboptimal channel + power allocation algorithms and evaluate and compare their performances by simulations. Numerical results reveal that the proposed algorithms perform quite close to the MIP-based solution and power control significantly increases the number of served D2D pairs.

Convert Harm Into Benefit: A Coordination-Learning Based Dynamic Spectrum Anti-Jamming Approach This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern.

Footprints: history-rich tools for information foraging Inspired by Hill and Hollans original work [7], we have beendeveloping a theory of interaction history and building tools toapply this theory to navigation in a complex information space. Wehave built a series of tools - map, paths, annota- tions andsignposts - based on a physical-world navigation metaphor. Thesetools have been in use for over a year. Our user study involved acontrolled browse task and showed that users were able to get thesame amount of work done with significantly less effort.

Very Deep Convolutional Networks for Large-Scale Image Recognition. In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Chimp optimization algorithm. •A novel optimizer called Chimp Optimization Algorithm (ChOA) is proposed.•ChOA is inspired by individual intelligence and sexual motivation of chimps.•ChOA alleviates the problems of slow convergence rate and trapping in local optima.•The four main steps of Chimp hunting are implemented.

Space-time modeling of traffic flow. This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5min average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.

A simplified dual neural network for quadratic programming with its KWTA application. The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and the computational complexity of the neural network. The simplified dual neural network is shown to be globally convergent to the exact optimal solution. The complexity of the neural network architecture is reduced with the number of neurons equal to the number of inequality constraints. Its application to k-winners-take-all (KWTA) operation is discussed to demonstrate how to solve problems with this neural network.

G2-type SRMPC scheme for synchronous manipulation of two redundant robot arms. In this paper, to remedy the joint-angle drift phenomenon for manipulation of two redundant robot arms, a novel scheme for simultaneous repetitive motion planning and control (SRMPC) at the joint-acceleration level is proposed, which consists of two subschemes. To do so, the performance index of each SRMPC subscheme is derived and designed by employing the gradient dynamics twice, of which a convergence theorem and its proof are presented. In addition, for improving the accuracy of the motion planning and control, position error, and velocity, error feedbacks are incorporated into the forward kinematics equation and analyzed via Zhang neural-dynamics method. Then the two subschemes are simultaneously reformulated as two quadratic programs (QPs), which are finally unified into one QP problem. Furthermore, a piecewise-linear projection equation-based neural network (PLPENN) is used to solve the unified QP problem, which can handle the strictly convex QP problem in an inverse-free manner. More importantly, via such a unified QP formulation and the corresponding PLPENN solver, the synchronism of two redundant robot arms is guaranteed. Finally, two given tasks are fulfilled by 2 three-link and 2 five-link planar robot arms, respectively. Computer-simulation results validate the efficacy and accuracy of the SRMPC scheme and the corresponding PLPENN solver for synchronous manipulation of two redundant robot arms.

Distributed Kalman consensus filter with event-triggered communication: Formulation and stability analysis. •The problem of distributed state estimation in sensor networks with event-triggered communication schedules on both sensor-to-estimator channel and estimator-to-estimator channel is studied.•An event-triggered KCF is designed by deriving the optimal Kalman gain matrix which minimizes the mean squared error.•A computational scalable form of the proposed filter is presented by some approximations.•An appropriate choice of the consensus gain matrix is provided to ensure the stochastic stability of the proposed filter.

Intention-detection strategies for upper limb exosuits: model-based myoelectric vs dynamic-based control The cognitive human-robot interaction between an exosuit and its wearer plays a key role in determining both the biomechanical effects of the device on movements and its perceived effectiveness. There is a lack of evidence, however, on the comparative performance of different control methods, implemented on the same device. Here, we compare two different control approaches on the same robotic suit: a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a dynamic-based control that provides support against gravity using an inverse dynamic model. Tested on a cohort of four healthy participants, assistance from the exosuit results in a marked reduction in the effort of muscles working against gravity with both control approaches (peak reduction of 68.6±18.8%, for the dynamic arm model and 62.4±25.1% for the myoprocessor), when compared to an unpowered condition. Neither of the two controllers had an affect on the performance of their users in a joint-angle tracking task (peak errors of 15.4° and 16.4° for the dynamic arm model and myoprocessor, respectively, compared to 13.1o in the unpowered condition). However, our results highlight the remarkable adaptability of the myoprocessor to seamlessly adapt to changing external dynamics.

Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses Large-scale multi-objective optimization problems (MOPs) that involve a large number of decision variables, have emerged from many real-world applications. While evolutionary algorithms (EAs) have been widely acknowledged as a mainstream method for MOPs, most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables. More recently, it has been reported that traditional multi-objective EAs (MOEAs) suffer severe deterioration with the increase of decision variables. As a result, and motivated by the emergence of real-world large-scale MOPs, investigation of MOEAs in this aspect has attracted much more attention in the past decade. This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles. From the key difficulties of the large-scale MOPs, the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables. From the perspective of methodology, the large-scale MOEAs are categorized into three classes and introduced respectively: divide and conquer based, dimensionality reduction based and enhanced search-based approaches. Several future research directions are also discussed.

A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis.

A Multi-Layered Immune System For Graph Planarization Problem This paper presents a new multi-layered artificial immune system architecture using the ideas generated from the biological immune system for solving combinatorial optimization problems. The proposed methodology is composed of five layers. After expressing the problem as, a suitable representation in the first layer, the search space and the features of the problem are estimated and extracted in the second and third layers, respectively. Through taking advantage of the minimized search space from estimation and the heuristic information from extraction, the antibodies (or solutions) are evolved in the fourth layer and finally the fittest antibody is exported. In order to demonstrate the efficiency of the proposed system, the graph planarization problem is tested. Simulation results based on several benchmark instances show that the proposed algorithm performs better than traditional algorithms.

From evolutionary computation to the evolution of things Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

Implementing a GPU-based parallel MAX-MIN Ant System The MAX–MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore’s law, and the availability of graphics processing units (GPUs) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of GPU-based ACO implementations. In this paper, we discuss a range of novel ideas for improving the GPU-based parallel MMAS implementation, allowing it to better utilize the computing power offered by two subsequent Nvidia GPU architectures. Specifically, based on the weighted reservoir sampling algorithm we propose a novel parallel implementation of the node selection procedure, which is at the heart of the MMAS and other ACO algorithms. We also present a memory-efficient implementation of another key-component – the tabu list structure – which is used in the ACO’s solution construction stage. The proposed implementations, combined with the existing approaches, lead to a total of six MMAS variants, which are evaluated on a set of Traveling Salesman Problem (TSP) instances ranging from 198 to 3795 cities. The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7.18x and 21.79x smaller, respectively. The fastest of the proposed MMAS variants is able to generate over 1 million candidate solutions per second when solving a 1002-city instance. Moreover, we show that, combined with the 2-opt local search heuristic, the proposed parallel MMAS finds high-quality solutions for the TSP instances with up to 18,512 nodes.

Recent Advances in Evolutionary Computation Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of “biological evolution” toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc., in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily “evolutionary”. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed.

Evolutionary computation: comments on the history and current state Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.

Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization Smart mobile devices (SMDs) can meet users' high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth and inevitably increases the latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve a balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels, and transmission power of each SMD in each time slot. It jointly considers the execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and the maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.

Computer intrusion detection through EWMA for autocorrelated and uncorrelated data Reliability and quality of service from information systems has been threatened by cyber intrusions. To protect information systems from intrusions and thus assure reliability and quality of service, it is highly desirable to develop techniques that detect intrusions. Many intrusions manifest in anomalous changes in intensity of events occurring in information systems. In this study, we apply, tes...

Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems An efficient optimization method called 'Teaching-Learning-Based Optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods.

Understanding Taxi Service Strategies From Taxi GPS Traces Taxi service strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical time-stamped GPS traces. Mining GPS traces to understand the service strategies of skilled taxi drivers can benefit the drivers themselves, passengers, and city planners in a number of ways. This paper intends to uncover the efficient and inefficient taxi service strategies based on a large-scale GPS historical database of approximately 7600 taxis over one year in a city in China. First, we separate the GPS traces of individual taxi drivers and link them with the revenue generated. Second, we investigate the taxi service strategies from three perspectives, namely, passenger-searching strategies, passenger-delivery strategies, and service-region preference. Finally, we represent the taxi service strategies with a feature matrix and evaluate the correlation between service strategies and revenue, informing which strategies are efficient or inefficient. We predict the revenue of taxi drivers based on their strategies and achieve a prediction residual as less as 2.35 RMB/h,1 which demonstrates that the extracted taxi service strategies with our proposed approach well characterize the driving behavior and performance of taxi drivers.

Adaptive fuzzy tracking control for switched uncertain strict-feedback nonlinear systems. •Adaptive tracking control for switched strict-feedback nonlinear systems is proposed.•The generalized fuzzy hyperbolic model is used to approximate nonlinear functions.•The designed controller has fewer design parameters comparing with existing methods.

Energy harvesting algorithm considering max flow problem in wireless sensor networks. In Wireless Sensor Networks (WSNs), sensor nodes with poor energy always have bad effect on the data rate or max flow. These nodes are called bottleneck nodes. In this paper, in order to increase the max flow, we assume an energy harvesting WSNs environment to investigate the cooperation of multiple Mobile Chargers (MCs). MCs are mobile robots that use wireless charging technology to charge sensor nodes in WSNs. This means that in energy harvesting WSNs environments, sensor nodes can obtain energy replenishment by using MCs or collecting energy from nature by themselves. In our research, we use MCs to improve the energy of the sensor nodes by performing multiple rounds of unified scheduling, and finally achieve the purpose of increasing the max flow at sinks. Firstly, we model this problem as a Linear Programming (LP) to search the max flow in a round of charging scheduling and prove that the problem is NP-hard. In order to solve the problem, we propose a heuristic approach: deploying MCs in units of paths with the lowest energy node priority. To reduce the energy consumption of MCs and increase the charging efficiency, we also take the optimization of MCs’ moving distance into our consideration. Finally, we extend the method to multiple rounds of scheduling called BottleNeck. Simulation results show that Bottleneck performs well at increasing max flow.

Parallel control for continuous-time linear systems: A case study. In this paper, a new parallel controller is developed for continuous-time linear systems. The main contribution of the method is to establish a new parallel control law, where both state and control are considered as the input. The structure of the parallel control is provided, and the relationship between the parallel control and traditional feedback controls is presented. Considering the situati...

Generative Adversarial Networks for Parallel Transportation Systems. Generative Adversaria Networks (GANs) have emerged as a promising and effective mechanism for machine learning due to its recent successful applications. GANs share the same idea of producing, testing, acquiring, and utilizing data as well as knowledge based on artificial systems, computational experiments, and parallel execution of actual and virtual scenarios, as outlined in the theory of parall...

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges AbstractRecent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of “what to fuse”, “when to fuse”, and “how to fuse” remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/.

Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance Object detection is a critical problem for advanced driving assistance systems (ADAS). Recently, convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered features. However, due to the challenging driving environment (e.g., large object scale variation, object occlusion, and bad light conditions), popular CNN detectors do not achieve very good object detection accuracy over the KITTI autonomous driving benchmark dataset. In this paper, we propose three enhancements for CNN-based visual object detection for ADAS. To address the large object scale variation challenge, deconvolution and fusion of CNN feature maps are proposed to add context and deeper features for better object detection at low feature map scales. In addition, soft non-maximal suppression (NMS) is applied across object proposals at different feature scales to address the object occlusion challenge. As the cars and pedestrians have distinct aspect ratio features, we measure their aspect ratio statistics and exploit them to set anchor boxes properly for better object matching and localization. The proposed CNN enhancements are evaluated with various image input sizes by experiments over KITTI dataset. The experimental results demonstrate the effectiveness of the proposed enhancements with good detection performance over KITTI test set.

MFR-CNN: Incorporating Multi-Scale Features and Global Information for Traffic Object Detection. Object detection plays an important role in intelligent transportation systems and intelligent vehicles. Although the topic of object detection has been studied for decades, it is still challenging to accurately detect objects under complex scenarios. The contributing factors for challenges include diversified object and background appearance, motion blur, adverse weather conditions, and complex i...

Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds Varying density of point clouds increases the difficulty of 3D detection. In this paper, we present a context-aware dynamic network (CADNet) to capture the variance of density by considering both point context and semantic context. Point-level contexts are generated from original point clouds to enlarge the effective receptive filed. They are extracted around the voxelized pillars based on our extended voxelization method and processed with the context encoder in parallel with the pillar features. With a large perception range, we are able to capture the variance of features for potential objects and generate attentive spatial guidance to help adjust the strengths for different regions. In the region proposal network, considering the limited representation ability of traditional convolution where same kernels are shared among different samples and positions, we propose a decomposable dynamic convolutional layer to adapt to the variance of input features by learning from the local semantic context. It adaptively generates the position-dependent coefficients for multiple fixed kernels and combines them to convolve with local features. Based on our dynamic convolution, we design a dual-path convolution block to further improve the representation ability. We conduct experiments on KITTI dataset and the proposed CADNet has achieved superior performance of 3D detection outperforming SECOND and PointPillars by a large margin at the speed of 30 FPS.

Visual Human–Computer Interactions for Intelligent Vehicles and Intelligent Transportation Systems: The State of the Art and Future Directions Research on intelligent vehicles has been popular in the past decade. To fill the gap between automatic approaches and man-machine control systems, it is indispensable to integrate visual human-computer interactions (VHCIs) into intelligent vehicles systems. In this article, we review existing studies on VHCI in intelligent vehicles from three aspects: 1) visual intelligence; 2) decision making; and 3) macro deployment. We discuss how VHCI evolves in intelligent vehicles and how it enhances the capability of intelligent vehicles. We present several simulated scenarios and cases for future intelligent transportation system.

Artificial intelligence applications in the development of autonomous vehicles: a survey The advancement of artificial intelligence ( AI ) has truly stimulated the development and deployment of autonomous vehicles ( AVs ) in the transportation industry. Fueled by big data from various sensing devices and advanced computing resources, AI has become an essential component of AVs for perceiving the surrounding environment and making appropriate decision in motion. To achieve goal of full automation ( i.e., self-driving ( , it is important to know how AI works in AV systems. Existing research have made great efforts in investigating different aspects of applying AI in AV development. However, few studies have offered the research community a thorough examination of current practices in implementing AI in AVs. Thus, this paper aims to shorten the gap by providing a comprehensive survey of key studies in this research avenue. Specifically, it intends to analyze their use of AIs in supporting the primary applications in AVs: 1) perception; 2) localization and mapping; and 3) decision making. It investigates the current practices to understand how AI can be used and what are the challenges and issues associated with their implementation. Based on the exploration of current practices and technology advances, this paper further provides insights into potential opportunities regarding the use of AI in conjunction with other emerging technologies: 1) high definition maps, big data, and high performance computing; 2) augmented reality( AR ) / virtual reality ( VR ) enhanced simulation platform; and 3) 5G communication for connected AVs. This paper is expected to offer a quick reference for researchers interested in understanding the use of AI in AV research.

Wireless sensor network survey A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.

Toward a theory of intrinsically motivating instruction First, a number of previous theories of intrinsic motivation are reviewed. Then, several studies of highly motivating computer games are described. These studies focus on what makes the games fun, not on what makes them educational. Finally, with this background, a rudimentary theory of intrinsically motivating instruction is developed, based on three categories: challenge, fantasy, and curiosity. Challenge is hypothesized to depend on goals with uncertain outcomes. Several ways of making outcomes uncertain are discussed, including variable difficulty level, multiple level goals, hidden information, and randomness. Fantasy is claimed to have both cognitive and emotional advantages in designing instructional environments. A distinction is made between extrinsic fantasies that depend only weakly on the skill used in a game, and intrinsic fantasies that are intimately related to the use of the skill. Curiosity is separated into sensory and cognitive components, and it is suggested that cognitive curiosity can be aroused by making learners believe their knowledge structures are incomplete, inconsistent, or unparsimonious.

Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles This paper proposes an operating framework for aggregators of plug-in electric vehicles (PEVs). First, a minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands. The same algorithm is applicable for negotiating bilateral contracts. Second, a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day. Simulation results are used to evaluate the proposed algorithms, and to demonstrate the potential impact of an aggregated PEV fleet on the power system.

Sub-modularity and Antenna Selection in MIMO systems In this paper, we show that the optimal receive antenna subset selection problem for maximizing the mutual information in a point-to-point MIMO system is sub-modular. Consequently, a greedy step-wise optimization approach, where at each step, an antenna that maximizes the incremental gain is added to the existing antenna subset, is guaranteed to be within a (1-1/e)-fraction of the global optimal value independent of all parameters. For a single-antenna-equipped source and destination with multiple relays, we show that the relay antenna selection problem to maximize the mutual information is modular and a greedy step-wise optimization approach leads to an optimal solution.

Driver Gaze Zone Estimation Using Convolutional Neural Networks: A General Framework and Ablative Analysis Driver gaze has been shown to be an excellent surrogate for driver attention in intelligent vehicles. With the recent surge of highly autonomous vehicles, driver gaze can be useful for determining the handoff time to a human driver. While there has been significant improvement in personalized driver gaze zone estimation systems, a generalized system which is invariant to different subjects, perspe...

Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objecti...

HMM-Based Asynchronous <italic>H</italic>_∞ Filtering for Fuzzy Singular Markovian Switching Systems With Retarded Time-Varying Delays This article reports our study on asynchronous H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering for fuzzy singular Markovian switching systems with retarded time-varying delays via the Takagi-Sugeno fuzzy control technique. The devised parallel distributed compensation fuzzy filter modes are described by a hidden Markovian model, which runs asynchronously with that of the original fuzzy singular Markovian switching delayed system. The fuzzy asynchronous filtering dealt with in this article contains synchronous and mode-independent filtering as special cases. Novel admissibility and filtering conditions are derived in terms of linear matrix inequalities so as to ensure the stochastic admissibility and the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance level. Simulation examples including a singlelink robot arm are employed to demonstrate the correctness and effectiveness of the proposed fuzzy asynchronous filtering technique.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Model Cards for Model Reporting. Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance characteristics. In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. While we focus primarily on human-centered machine learning models in the application fields of computer vision and natural language processing, this framework can be used to document any trained machine learning model. To solidify the concept, we provide cards for two supervised models: One trained to detect smiling faces in images, and one trained to detect toxic comments in text. We propose model cards as a step towards the responsible democratization of machine learning and related artificial intelligence technology, increasing transparency into how well artificial intelligence technology works. We hope this work encourages those releasing trained machine learning models to accompany model releases with similar detailed evaluation numbers and other relevant documentation.

Explanations and Expectations: Trust Building in Automated Vehicles. Trust is a vital determinant of acceptance of automated vehicles (AVs) and expectations and explanations are often at the heart of any trusting relationship. Once expectations have been violated, explanations are needed to mitigate the damage. This study introduces the importance of timing of explanations in promoting trust in AVs. We present the preliminary results of a within-subjects experimental study involving eight participants exposed to four AV driving conditions (i.e. 32 data points). Preliminary results show a pattern that suggests that explanations provided before the AV takes actions promote more trust than explanations provided afterward.

Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to? Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysi...

Explaining Classifications For Individual Instances We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities. It is based on the decomposition of a model's predictions on individual contributions of each attribute. Our method works for the so-called black box models such as support vector machines, neural networks, and nearest neighbor algorithms, as well as for ensemble methods such as boosting and random forests. We demonstrate that the generated explanations closely follow the learned models and present a visualization technique that shows the utility of our approach and enables the comparison of different prediction methods.

Explanation in Artificial Intelligence: Insights from the Social Sciences. There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a ‘good’ explanation. There exist vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations to the explanation process. This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

ImageNet Large Scale Visual Recognition Challenge. The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.

One billion word benchmark for measuring progress in statistical language modeling. We propose a new benchmark corpus to be used for measuring progress in statistical language modeling. With almost one billion words of training data, we hope this benchmark will be useful to quickly evaluate novel language modeling techniques, and to compare their contribution when combined with other advanced techniques. We show performance of several well-known types of language models, with the best results achieved with a recurrent neural network based language model. The baseline unpruned Kneser-Ney 5-gram model achieves perplexity 67.6; a combination of techniques leads to 35% reduction in perplexity, or 10% reduction in cross-entropy (bits), over that baseline. The benchmark is available as a code.google.com project; besides the scripts needed to rebuild the training/held-out data, it also makes available log-probability values for each word in each of ten held-out data sets, for each of the baseline n-gram models.

Shared Steering Torque Control for Lane Change Assistance: A Stochastic Game-Theoretic Approach. The challenging issue of “human-machine copilot” opens up a new frontier to enhancing driving safety. However, driver-machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering systems, which degrade the system&#39;s path-tracking ability and reduce driving safety. This paper proposes a novel stochastic game-based shared control framework to model the...

An aggregation approach to short-term traffic flow prediction In this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the naïve, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting.

Smallest Enclosing Disks (Balls And Ellipsoids) A simple randomized algorithm is developed which computes the smallest enclosing disk of a finite set of points in the plane in expected linear time. The algorithm is based on Seidel's recent Linear Programming algorithm, and it can be generalized to computing smallest enclosing balls or ellipsoids of point sets in higher dimensions in a straightforward way. Experimental results of an implementation are presented.

Continuous finite-time stabilization of the translational and rotational double integrators A class of bounded continuous time-invariant finite-time stabilizing feedback laws is given for the double integrator. Lyapunov theory is used to prove finite-time convergence. For the rotational double integrator, these controllers are modified to obtain finite-time-stabilizing feedbacks that avoid "unwinding." Most of the available techniques for feedback stabilization lead to closed-loop systems with Lipschitzian dynamics. The convergence in such systems is at best exponential with infinite settling time. In other words, none of the solutions starting in an open neighborhood of the origin converge to the origin in finite time. In fact, finite- time convergence implies nonuniqueness of solutions (in reverse time) which is not possible in the presence of Lipschitz-continuous dynamics. Our goal is to develop techniques for obtaining continuous finite- time-stabilizing feedback controllers. The present paper focuses on the double integrator as an illustrative example of this objective. Since the double integrator is controllable, open-loop control strategies can be used to drive the state to the origin in finite time (1, p. 38). One such control strategy is the minimum energy control (2), which transfers the state of the system from the initial conditions to the origin in a given time . This control strategy minimizes the control energy cost and is given by (2, pp. 466-475)

Distributed adaptive containment control of uncertain nonlinear multi-agent systems in strict-feedback form. This paper presents a distributed containment control approach for uncertain nonlinear strict-feedback systems with multiple dynamic leaders under a directed graph topology where the leaders are neighbors of only a subset of the followers. The strict-feedback followers with nonparametric uncertainties are considered and the local adaptive dynamic surface controller for each follower is designed using only neighbors’ information to guarantee that all followers converge to the dynamic convex hull spanned by the dynamic leaders where the derivatives of leader signals are not available to implement controllers, i.e., the position information of leaders is only required. The function approximation technique using neural networks is employed to estimate nonlinear uncertainty terms derived from the controller design procedure for the followers. It is shown that the containment control errors converge to an adjustable neighborhood of the origin.

Distributed Kalman consensus filter with event-triggered communication: Formulation and stability analysis. •The problem of distributed state estimation in sensor networks with event-triggered communication schedules on both sensor-to-estimator channel and estimator-to-estimator channel is studied.•An event-triggered KCF is designed by deriving the optimal Kalman gain matrix which minimizes the mean squared error.•A computational scalable form of the proposed filter is presented by some approximations.•An appropriate choice of the consensus gain matrix is provided to ensure the stochastic stability of the proposed filter.

Social Robots for (Second) Language Learning in (Migrant) Primary School Children Especially these days, innovation and support from technology to relieve pressure in education is highly urgent. This study tested the potential advantage of a social robot over a tablet in (second) language learning on performance, engagement, and enjoyment. Shortages in primary education call for new technology solutions. Previous studies combined robots with tablets, to compensate for robot’s limitations, however, this study applied direct human–robot interaction. Primary school children (N = 63, aged 4–6) participated in a 3-wave field experiment with story-telling exercises, either with a semi-autonomous robot (without tablet, using WOz) or a tablet. Results showed increased learning gains over time when training with a social robot, compared to the tablet. Children who trained with a robot were more engaged in the story-telling task and enjoyed it more. Robot’s behavioral style (social or neutral) hardly differed overall, however, seems to vary for high versus low educational abilities. While social robots need sophistication before being implemented in schools, our study shows the potential of social robots as tutors in (second) language learning.

Secure Consensus of Multiagent Systems With Input Saturation and Distributed Multiple DoS Attacks The secure consensus problem of multiagent systems with input saturation and denial-of-service (DoS) attacks constraints is an interesting research problem. To solve this problem, the assumptions that all the agents own the same saturation level and suffer the same DoS attacks from a single adversary are usually made. This brief removes the above assumptions and investigates the secure consensus problem of multiagent systems with different saturation levels and multiple DoS attacks. The studied multiagent systems have different layers, in which different saturation levels are studied for different layers. Moreover, the DoS attacks are launched from different adversaries and will cause different effects for different agents. To achieve the desired objective, two different consensus protocols for agents in different levels are first designed using the low-gain technique. Then, the investigated dynamic system under DoS attacks is modeled by a switched system by categorizing the DoS attacks into several types. The controller is proposed by solving parametric algebraic Riccati equation (ARE). Sufficient conditions for the DoS attack duration on each channel are derived.

Input-to-State Stabilizing Control under Denial-of-Service The issue of cyber-security has become ever more prevalent in the analysis and design of networked systems. In this paper, we analyze networked control systems in the presence of Denial-of-Service (DoS) attacks, namely attacks that prevent transmissions over the network. We characterize frequency and duration of the DoS attacks under which input-to-state stability (ISS) of the closed-loop system can be preserved. To achieve ISS, a suitable scheduling of the transmission times is determined. It is shown that the considered framework is flexible enough so as to allow the designer to choose from several implementation options that can be used for trading-off performance vs. communication resources. Examples are given to substantiate the analysis.

Survey on Recent Advances in Networked Control Systems. Networked control systems (NCSs) are systems whose control loops are closed through communication networks such that both control signals and feedback signals can be exchanged among system components (sensors, controllers, actuators, and so on). NCSs have a broad range of applications in areas such as industrial control and signal processing. This survey provides an overview on the theoretical dev...

Neural network-based event-triggered MFAC for nonlinear discrete-time processes. This paper is concerned with the event-triggered data-driven control problem for nonlinear discrete-time systems. An event-based data-driven model-free adaptive controller design algorithm together with constructing an adaptive event-trigger condition is developed. Different from the existing data-driven model-free adaptive control approach, an aperiodic neural network weight update law is introduced to estimate the controller parameters, and the event-trigger mechanism is activated only if the event-trigger error exceeds the threshold. Furthermore, by combining the equivalent-dynamic-linearization technique with the Lyapunov method, it is proved that both the closed-loop control system and the weight estimation error are ultimately bounded. Finally, two simulation examples are provided to demonstrate the effectiveness of the derived method.

Observer-Based Event-Triggered Containment Control for MASs Under DoS Attacks This article studies the observer-based event-triggered containment control problem for linear multiagent systems (MASs) under denial-of-service (DoS) attacks. In order to deal with situations where MASs states are unmeasurable, an improved separation method-based observer design method with less conservativeness is proposed to estimate MASs states. To save communication resources and achieve the containment control objective, a novel observer-based event-triggered containment controller design method based on observer states is proposed for MASs under the influence of DoS attacks, which can make the MASs resilient to DoS attacks. In addition, the Zeno behavior can be eliminated effectively by introducing a positive constant into the designed event-triggered mechanism. Finally, a practical example is presented to illustrate the effectiveness of the designed observer and the event-triggered containment controller.

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Massive MIMO for next generation wireless systems Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.

Deep Residual Learning for Image Recognition Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Communication theory of secrecy systems THE problems of cryptography and secrecy systems furnish an interesting application of communication theory.1 In this paper a theory of secrecy systems is developed. The approach is on a theoretical level and is intended to complement the treatment found in standard works on cryptography.2 There, a detailed study is made of the many standard types of codes and ciphers, and of the ways of breaking them. We will be more concerned with the general mathematical structure and properties of secrecy systems.

A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization In recent years, there has been a growing interest for the experimental analysis in the field of evolutionary algorithms. It is noticeable due to the existence of numerous papers which analyze and propose different types of problems, such as the basis for experimental comparisons of algorithms, proposals of different methodologies in comparison or proposals of use of different statistical techniques in algorithms’ comparison.In this paper, we focus our study on the use of statistical techniques in the analysis of evolutionary algorithms’ behaviour over optimization problems. A study about the required conditions for statistical analysis of the results is presented by using some models of evolutionary algorithms for real-coding optimization. This study is conducted in two ways: single-problem analysis and multiple-problem analysis. The results obtained state that a parametric statistical analysis could not be appropriate specially when we deal with multiple-problem results. In multiple-problem analysis, we propose the use of non-parametric statistical tests given that they are less restrictive than parametric ones and they can be used over small size samples of results. As a case study, we analyze the published results for the algorithms presented in the CEC’2005 Special Session on Real Parameter Optimization by using non-parametric test procedures.

Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion This article presents the results of video-based Human Robot Interaction (HRI) trials which investigated people's perceptions of different robot appearances and associated attention-seeking features and behaviors displayed by robots with different appearance and behaviors. The HRI trials studied the participants' preferences for various features of robot appearance and behavior, as well as their personality attributions towards the robots compared to their own personalities. Overall, participants tended to prefer robots with more human-like appearance and attributes. However, systematic individual differences in the dynamic appearance ratings are not consistent with a universal effect. Introverts and participants with lower emotional stability tended to prefer the mechanical looking appearance to a greater degree than other participants. It is also shown that it is possible to rate individual elements of a particular robot's behavior and then assess the contribution, or otherwise, of that element to the overall perception of the robot by people. Relating participants' dynamic appearance ratings of individual robots to independent static appearance ratings provided evidence that could be taken to support a portion of the left hand side of Mori's theoretically proposed `uncanny valley' diagram. Suggestions for future work are outlined.

Finite-approximation-error-based discrete-time iterative adaptive dynamic programming. In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite horizon discrete-time nonlinear systems with finite approximation errors. First, a new generalized value iteration algorithm of ADP is developed to make the iterative performance index function converge to the solution of the Hamilton-Jacobi-Bellman equation. The ...

An evolutionary programming approach for securing medical images using watermarking scheme in invariant discrete wavelet transformation. •The proposed watermarking scheme utilized improved discrete wavelet transformation (IDWT) to retrieve the invariant wavelet domain.•The entropy mechanism is used to identify the suitable region for insertion of watermark. This will improve the imperceptibility and robustness of the watermarking procedure.•The scaling factors such as PSNR and NC are considered for evaluation of the proposed method and the Particle Swarm Optimization is employed to optimize the scaling factors.

Attitudes Towards Social Robots In Education: Enthusiast, Practical, Troubled, Sceptic, And Mindfully Positive While social robots bring new opportunities for education, they also come with moral challenges. Therefore, there is a need for moral guidelines for the responsible implementation of these robots. When developing such guidelines, it is important to include different stakeholder perspectives. Existing (qualitative) studies regarding these perspectives however mainly focus on single stakeholders. In this exploratory study, we examine and compare the attitudes of multiple stakeholders on the use of social robots in primary education, using a novel questionnaire that covers various aspects of moral issues mentioned in earlier studies. Furthermore, we also group the stakeholders based on similarities in attitudes and examine which socio-demographic characteristics influence these attitude types. Based on the results, we identify five distinct attitude profiles and show that the probability of belonging to a specific profile is affected by such characteristics as stakeholder type, age, education and income. Our results also indicate that social robots have the potential to be implemented in education in a morally responsible way that takes into account the attitudes of various stakeholders, although there are multiple moral issues that need to be addressed first. Finally, we present seven (practical) implications for a responsible application of social robots in education following from our results. These implications provide valuable insights into how social robots should be implemented.

A performance evaluation of routing protocols for vehicular ad hoc networks with swarm intelligence. Swarm intelligence work on artificial intelligence which is defined as collective behavior of self organized but centralized system. Movie effect, swarm robotics and network routing are applications of swarm intelligence. Vehicular ad-hoc network (VANET) is also self controlled, high dynamic network system. Road safety and traffic management are the main applications of VANETs. If we integrate swarm intelligence with VANETs, it will show outstanding results in terms of latency, throughput, data delivery cost and ratio. There are some issues in VANETs like data aging, heavy cost and message prioritization which can be solved with the help of swarm intelligence. In this paper, we will come across some good results in VANET when we apply some algorithm of swarm intelligence. There are some algorithms of swarm intelligence that can be applied in VANETs like artificial bee colony and AntNet. Swarm intelligence can be implemented in multicasting and data center routing. Moreover, we will also analysis AODV, DSR routing protocol with the smarm intelligence routing protocol in the network.

On the History of the Minimum Spanning Tree Problem It is standard practice among authors discussing the minimum spanning tree problem to refer to the work of Kruskal(1956) and Prim (1957) as the sources of the problem and its first efficient solutions, despite the citation by both of Boruvka (1926) as a predecessor. In fact, there are several apparently independent sources and algorithmic solutions of the problem. They have appeared in Czechoslovakia, France, and Poland, going back to the beginning of this century. We shall explore and compare these works and their motivations, and relate them to the most recent advances on the minimum spanning tree problem.

Smart home energy management system using IEEE 802.15.4 and zigbee Wireless personal area network and wireless sensor networks are rapidly gaining popularity, and the IEEE 802.15 Wireless Personal Area Working Group has defined no less than different standards so as to cater to the requirements of different applications. The ubiquitous home network has gained widespread attentions due to its seamless integration into everyday life. This innovative system transparently unifies various home appliances, smart sensors and energy technologies. The smart energy market requires two types of ZigBee networks for device control and energy management. Today, organizations use IEEE 802.15.4 and ZigBee to effectively deliver solutions for a variety of areas including consumer electronic device control, energy management and efficiency, home and commercial building automation as well as industrial plant management. We present the design of a multi-sensing, heating and airconditioning system and actuation application - the home users: a sensor network-based smart light control system for smart home and energy control production. This paper designs smart home device descriptions and standard practices for demand response and load management "Smart Energy" applications needed in a smart energy based residential or light commercial environment. The control application domains included in this initial version are sensing device control, pricing and demand response and load control applications. This paper introduces smart home interfaces and device definitions to allow interoperability among ZigBee devices produced by various manufacturers of electrical equipment, meters, and smart energy enabling products. We introduced the proposed home energy control systems design that provides intelligent services for users and we demonstrate its implementation using a real testbad.

Bee life-based multi constraints multicast routing optimization for vehicular ad hoc networks. A vehicular ad hoc network (VANET) is a subclass of mobile ad hoc networks, considered as one of the most important approach of intelligent transportation systems (ITS). It allows inter-vehicle communication in which their movement is restricted by a VANET mobility model and supported by some roadside base stations as fixed infrastructures. Multicasting provides different traffic information to a limited number of vehicle drivers by a parallel transmission. However, it represents a very important challenge in the application of vehicular ad hoc networks especially, in the case of the network scalability. In the applications of this sensitive field, it is very essential to transmit correct data anywhere and at any time. Consequently, the VANET routing protocols should be adapted appropriately and meet effectively the quality of service (QoS) requirements in an optimized multicast routing. In this paper, we propose a novel bee colony optimization algorithm called bees life algorithm (BLA) applied to solve the quality of service multicast routing problem (QoS-MRP) for vehicular ad hoc networks as NP-Complete problem with multiple constraints. It is considered as swarm-based algorithm which imitates closely the life of the colony. It follows the two important behaviors in the nature of bees which are the reproduction and the food foraging. BLA is applied to solve QoS-MRP with four objectives which are cost, delay, jitter, and bandwidth. It is also submitted to three constraints which are maximum allowed delay, maximum allowed jitter and minimum requested bandwidth. In order to evaluate the performance and the effectiveness of this realized proposal using C++ and integrated at the routing protocol level, a simulation study has been performed using the network simulator (NS2) based on a mobility model of VANET. The comparisons of the experimental results show that the proposed algorithm outperformed in an efficient way genetic algorithm (GA), bees algorithm (BA) and marriage in honey bees optimization (MBO) algorithm as state-of-the-art conventional metaheuristics applied to QoS-MRP problem with the same simulation parameters.

On the Spatiotemporal Traffic Variation in Vehicle Mobility Modeling Several studies have shown the importance of realistic micromobility and macromobility modeling in vehicular ad hoc networks (VANETs). At the macroscopic level, most researchers focus on a detailed and accurate description of road topology. However, a key factor often overlooked is a spatiotemporal configuration of vehicular traffic. This factor greatly influences network topology and topology variations. Indeed, vehicle distribution has high spatial and temporal diversity that depends on the time of the day and place attraction. This diversity impacts the quality of radio links and, thus, network topology. In this paper, we propose a new mobility model for vehicular networks in urban and suburban environments. To reproduce realistic network topology and topological changes, the model uses real static and dynamic data on the environment. The data concern particularly the topographic and socioeconomic characteristics of infrastructures and the spatiotemporal population distribution. We validate our model by comparing the simulation results with real data derived from individual displacement survey. We also present statistics on network topology, which show the interest of taking into account the spatiotemporal mobility variation.

Effective crowdsensing and routing algorithms for next generation vehicular networks The vehicular ad hoc network (VANET) has recently emerged as a promising networking technique attracting both the vehicular manufacturing industry and the academic community. Therefore, the design of next generation VANET management schemes becomes an important issue to satisfy the new demands. However, it is difficult to adapt traditional control approaches, which have already proven reliable in ad-hoc wireless networks, directly. In this study, we focus on the development of vehicular crowdsensing and routing algorithms in VANETs. The proposed scheme, which is based on reinforcement learning and game theory, is designed as novel vertical and horizontal game models, and provides an effective dual-plane control mechanism. In a vertical game, network agent and vehicles work together toward an appropriate crowdsensing process. In a horizontal game, vehicles select their best routing route for the VANET routing. Based on the decentralized, distributed manner, our dual-plane game paradigm captures the dynamics of the VANET system. Simulations and performance analysis verify the efficiency of the proposed scheme, showing that our approach can outperform existing schemes in terms of RSU’s task success ratio, normalized routing throughput, and end-to-end packet delay.

An enhanced QoS CBT multicast routing protocol based on Genetic Algorithm in a hybrid HAP-Satellite system A QoS multicast routing scheme based on Genetic Algorithms (GA) heuristic is presented in this paper. Our proposal, called Constrained Cost–Bandwidth–Delay Genetic Algorithm (CCBD-GA), is applied to a multilayer hybrid platform that includes High Altitude Platforms (HAPs) and a Satellite platform. This GA scheme has been compared with another GA well-known in the literature called Multi-Objective Genetic Algorithm (MOGA) in order to show the proposed algorithm goodness. In order to test the efficiency of GA schemes on a multicast routing protocol, these GA schemes are inserted into an enhanced version of the Core-Based Tree (CBT) protocol with QoS support. CBT and GA schemes are tested in a multilayer hybrid HAP and Satellite architecture and interesting results have been discovered. The joint bandwidth–delay metrics can be very useful in hybrid platforms such as that considered, because it is possible to take advantage of the single characteristics of the Satellite and HAP segments. The HAP segment offers low propagation delay permitting QoS constraints based on maximum end-to-end delay to be met. The Satellite segment, instead, offers high bandwidth capacity with higher propagation delay. The joint bandwidth–delay metric permits the balancing of the traffic load respecting both QoS constraints. Simulation results have been evaluated in terms of HAP and Satellite utilization, bandwidth, end-to-end delay, fitness function and cost of the GA schemes.

On the ratio of optimal integral and fractional covers It is shown that the ratio of optimal integral and fractional covers of a hypergraph does not exceed 1 + log d , where d is the maximum degree. This theorem may replace probabilistic methods in certain circumstances. Several applications are shown.

Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution Recently, the rapid advance of vehicular networks has led to the emergence of diverse delay-sensitive vehicular applications such as automatic driving, auto navigation. Note that existing resource-constrained vehicles cannot adequately meet these demands on low / ultra-low latency. By offloading parts of the vehicles’ compute-intensive tasks to the edge servers in proximity, mobile edge computing is envisioned as a promising paradigm, giving rise to the vehicular edge computing networks (VECNs). However, most existing works on task offloading in VECNs did not take the load balancing of the computation resources at the edge servers into account. To address these issues and given the high dynamics of vehicular networks, we introduce fiber-wireless (FiWi) technology to enhance VECNs, due to its advantages on centralized network management and supporting multiple communication techniques. Aiming to minimize the processing delay of the vehicles’ computation tasks, we propose a software-defined networking (SDN) based load-balancing task offloading scheme in FiWi enhanced VECNs, where SDN is introduced to provide supports for the centralized network and vehicle information management. Extensive analysis and numerical results corroborate that our proposed load-balancing scheme can achieve superior performance on processing delay reduction by utilizing the edge servers’ computation resources more efficiently.

A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots Autonomous mobile robots navigating in changing and dynamic unstructured environments like the outdoor environments need to cope with large amounts of uncertainties that are inherent of natural environments. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present a novel reactive control architecture for autonomous mobile robots that is based on type-2 FLC to implement the basic navigation behaviors and the coordination between these behaviors to produce a type-2 hierarchical FLC. In our experiments, we implemented this type-2 architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2-based control system dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1-based control system while achieving a significant rule reduction compared to the type-1 system.

Multi-stage genetic programming: A new strategy to nonlinear system modeling This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling nonlinear systems. The proposed strategy is based on incorporating the individual effect of predictor variables and the interactions among them to provide more accurate simulations. According to the MSGP strategy, an efficient formulation for a problem comprises different terms. In the first stage of the MSGP-based analysis, the output variable is formulated in terms of an influencing variable. Thereafter, the error between the actual and the predicted value is formulated in terms of a new variable. Finally, the interaction term is derived by formulating the difference between the actual values and the values predicted by the individually developed terms. The capabilities of MSGP are illustrated by applying it to the formulation of different complex engineering problems. The problems analyzed herein include the following: (i) simulation of pH neutralization process, (ii) prediction of surface roughness in end milling, and (iii) classification of soil liquefaction conditions. The validity of the proposed strategy is confirmed by applying the derived models to the parts of the experimental results that were not included in the analyses. Further, the external validation of the models is verified using several statistical criteria recommended by other researchers. The MSGP-based solutions are capable of effectively simulating the nonlinear behavior of the investigated systems. The results of MSGP are found to be more accurate than those of standard GP and artificial neural network-based models.

Placing Virtual Machines to Optimize Cloud Gaming Experience Optimizing cloud gaming experience is no easy task due to the complex tradeoff between gamer quality of experience (QoE) and provider net profit. We tackle the challenge and study an optimization problem to maximize the cloud gaming provider's total profit while achieving just-good-enough QoE. We conduct measurement studies to derive the QoE and performance models. We formulate and optimally solve the problem. The optimization problem has exponential running time, and we develop an efficient heuristic algorithm. We also present an alternative formulation and algorithms for closed cloud gaming services with dedicated infrastructures, where the profit is not a concern and overall gaming QoE needs to be maximized. We present a prototype system and testbed using off-the-shelf virtualization software, to demonstrate the practicality and efficiency of our algorithms. Our experience on realizing the testbed sheds some lights on how cloud gaming providers may build up their own profitable services. Last, we conduct extensive trace-driven simulations to evaluate our proposed algorithms. The simulation results show that the proposed heuristic algorithms: (i) produce close-to-optimal solutions, (ii) scale to large cloud gaming services with 20,000 servers and 40,000 gamers, and (iii) outperform the state-of-the-art placement heuristic, e.g., by up to 3.5 times in terms of net profits.

Adversarial Example Generation with Syntactically Controlled Paraphrase Networks. We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder-decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) fool pretrained models and (2) improve the robustness of these models to syntactic variation when used to augment their training data.

Hardware Circuits Design and Performance Evaluation of a Soft Lower Limb Exoskeleton Soft lower limb exoskeletons (LLEs) are wearable devices that have good potential in walking rehabilitation and augmentation. While a few studies focused on the structure design and assistance force optimization of the soft LLEs, rarely work has been conducted on the hardware circuits design. The main purpose of this work is to present a new soft LLE for walking efficiency improvement and introduce its hardware circuits design. A soft LLE for hip flexion assistance and a hardware circuits system with scalability were proposed. To assess the efficacy of the soft LLE, the experimental tests that evaluate the sensor data acquisition, force tracking performance, lower limb muscle activity and metabolic cost were conducted. The time error in the peak assistance force was just 1%. The reduction in the normalized root-mean-square EMG of the rectus femoris was 7.1%. The net metabolic cost in exoskeleton on condition was reduced by 7.8% relative to walking with no exoskeleton. The results show that the designed hardware circuits can be applied to the soft LLE and the soft LLE is able to improve walking efficiency of wearers.

Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network Bus arrival time prediction intends to improve the level of the services provided by transportation agencies. Intuitively, many stochastic factors affect the predictability of the arrival time, e.g., weather and local events. Moreover, the arrival time prediction for a current station is closely correlated with that of multiple passed stations. Motivated by the observations above, this paper propo...

Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. •The model of support vector regression with adaptive genetic algorithm and the seasonal mechanism is proposed.•Parameters selection and seasonal adjustment should be carefully selected.•We focus on latest and representative holiday daily data in China.•Two experiments are used to prove the effect of the model.•The AGASSVR is superior to AGA-SVR and BPNN.

Regression conformal prediction with random forests Regression conformal prediction produces prediction intervals that are valid, i.e., the probability of excluding the correct target value is bounded by a predefined confidence level. The most important criterion when comparing conformal regressors is efficiency; the prediction intervals should be as tight (informative) as possible. In this study, the use of random forests as the underlying model for regression conformal prediction is investigated and compared to existing state-of-the-art techniques, which are based on neural networks and k-nearest neighbors. In addition to their robust predictive performance, random forests allow for determining the size of the prediction intervals by using out-of-bag estimates instead of requiring a separate calibration set. An extensive empirical investigation, using 33 publicly available data sets, was undertaken to compare the use of random forests to existing state-of-the-art conformal predictors. The results show that the suggested approach, on almost all confidence levels and using both standard and normalized nonconformity functions, produced significantly more efficient conformal predictors than the existing alternatives.

Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units. We present ARU, an Adaptive Recurrent Unit for streaming adaptation of deep globally trained time-series forecasting models. The ARU combines the advantages of learning complex data transformations across multiple time series from deep global models, with per-series localization offered by closed-form linear models. Unlike existing methods of adaptation that are either memory-intensive or non-responsive after training, ARUs require only fixed sized state and adapt to streaming data via an easy RNN-like update operation. The core principle driving ARU is simple --- maintain sufficient statistics of conditional Gaussian distributions and use them to compute local parameters in closed form. Our contribution is in embedding such local linear models in globally trained deep models while allowing end-to-end training on the one hand, and easy RNN-like updates on the other. Across several datasets we show that ARU is more effective than recently proposed local adaptation methods that tax the global network to compute local parameters.

AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction Spatio-temporal (ST) prediction (e.g. crowd flow prediction) is of great importance in a wide range of smart city applications from urban planning, intelligent transportation and public safety. Recently, many deep neural network models have been proposed to make accurate prediction. However, manually designing neural networks requires amount of expert efforts and ST domain knowledge. How to automatically construct a general neural network for diverse spatio-temporal predication tasks in cities? In this paper, we study Neural Architecture Search (NAS) for spatio-temporal prediction and propose an efficient spatio-temporal neural architecture search method, entitled AutoST. To our best knowledge, the search space is an important human prior to the success of NAS in different applications while current NAS models concentrated on optimizing search strategy in the fixed search space. Thus, we design a novel search space tailored for ST-domain which consists of two categories of components: (i) optional convolution operations at each layer to automatically extract multi-range spatio-temporal dependencies; (ii) learnable skip connections among layers to dynamically fuse low- and high-level ST-features. We conduct extensive experiments on four real-word spatio-temporal prediction tasks, including taxi flow and crowd flow, showing that the learned network architectures can significantly improve the performance of representative ST neural network models. Furthermore, our proposed efficient NAS approach searches 8-10x faster than state-of-the-art NAS approaches, demonstrating the efficiency and effectiveness of AutoST.

Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting. Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets. We take the automatic relevance determination (ARD) view and devise a network to exploit the exogenous variables in addition to time series. In particular, our ARD network can incorporate the uncertainty of the exogenous variables and eventually helps identify useful exogenous variables and suppress those irrelevant for forecasting. The distribution of multi-step ahead forecasts are approximated by Monte Carlo simulation. We show in experiments that our model produces accurate and sharp probabilistic forecasts. The estimated uncertainty of our forecasting also realistically increases over time, in a spontaneous manner.

Transfer Knowledge between Cities The rapid urbanization has motivated extensive research on urban computing. It is critical for urban computing tasks to unlock the power of the diversity of data modalities generated by different sources in urban spaces, such as vehicles and humans. However, we are more likely to encounter the label scarcity problem and the data insufficiency problem when solving an urban computing task in a city where services and infrastructures are not ready or just built. In this paper, we propose a FLexible multimOdal tRAnsfer Learning (FLORAL) method to transfer knowledge from a city where there exist sufficient multimodal data and labels, to this kind of cities to fully alleviate the two problems. FLORAL learns semantically related dictionaries for multiple modalities from a source domain, and simultaneously transfers the dictionaries and labelled instances from the source into a target domain. We evaluate the proposed method with a case study of air quality prediction.

Space-time modeling of traffic flow. This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5min average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.

Model-based periodic event-triggered control for linear systems Periodic event-triggered control (PETC) is a control strategy that combines ideas from conventional periodic sampled-data control and event-triggered control. By communicating periodically sampled sensor and controller data only when needed to guarantee stability or performance properties, PETC is capable of reducing the number of transmissions significantly, while still retaining a satisfactory closed-loop behavior. In this paper, we will study observer-based controllers for linear systems and propose advanced event-triggering mechanisms (ETMs) that will reduce communication in both the sensor-to-controller channels and the controller-to-actuator channels. By exploiting model-based computations, the new classes of ETMs will outperform existing ETMs in the literature. To model and analyze the proposed classes of ETMs, we present two frameworks based on perturbed linear and piecewise linear systems, leading to conditions for global exponential stability and @?"2-gain performance of the resulting closed-loop systems in terms of linear matrix inequalities. The proposed analysis frameworks can be used to make tradeoffs between the network utilization on the one hand and the performance in terms of @?"2-gains on the other. In addition, we will show that the closed-loop performance realized by an observer-based controller, implemented in a conventional periodic time-triggered fashion, can be recovered arbitrarily closely by a PETC implementation. This provides a justification for emulation-based design. Next to centralized model-based ETMs, we will also provide a decentralized setup suitable for large-scale systems, where sensors and actuators are physically distributed over a wide area. The improvements realized by the proposed model-based ETMs will be demonstrated using numerical examples.

Affective social robots For human-robot interaction to proceed in a smooth, natural manner, robots must adhere to human social norms. One such human convention is the use of expressive moods and emotions as an integral part of social interaction. Such expressions are used to convey messages such as ''I'm happy to see you'' or ''I want to be comforted,'' and people's long-term relationships depend heavily on shared emotional experiences. Thus, we have developed an affective model for social robots. This generative model attempts to create natural, human-like affect and includes distinctions between immediate emotional responses, the overall mood of the robot, and long-term attitudes toward each visitor to the robot, with a focus on developing long-term human-robot relationships. This paper presents the general affect model as well as particular details of our implementation of the model on one robot, the Roboceptionist. In addition, we present findings from two studies that demonstrate the model's potential.

Rich Models for Steganalysis of Digital Images We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary $\\pm {\\hbox{1}}$ embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes.

Heterogeneous ensemble for feature drifts in data streams The nature of data streams requires classification algorithms to be real-time, efficient, and able to cope with high-dimensional data that are continuously arriving. It is a known fact that in high-dimensional datasets, not all features are critical for training a classifier. To improve the performance of data stream classification, we propose an algorithm called HEFT-Stream (H eterogeneous E nsemble with F eature drifT for Data Streams ) that incorporates feature selection into a heterogeneous ensemble to adapt to different types of concept drifts. As an example of the proposed framework, we first modify the FCBF [13] algorithm so that it dynamically update the relevant feature subsets for data streams. Next, a heterogeneous ensemble is constructed based on different online classifiers, including Online Naive Bayes and CVFDT [5]. Empirical results show that our ensemble classifier outperforms state-of-the-art ensemble classifiers (AWE [15] and OnlineBagging [21]) in terms of accuracy, speed, and scalability. The success of HEFT-Stream opens new research directions in understanding the relationship between feature selection techniques and ensemble learning to achieve better classification performance.

Orientation-aware RFID tracking with centimeter-level accuracy. RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, we in this paper propose OmniTrack, an orientation-aware RFID tracking approach. Our study discovers the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tag's orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeter-level location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

A haptic texture database for tool-mediated texture recognition and classification While stroking a rigid tool over an object surface, vibrations induced on the tool, which represent the interaction between the tool and the surface texture, can be measured by means of an accelerometer. Such acceleration signals can be used to recognize or to classify object surface textures. The temporal and spectral properties of the acquired signals, however, heavily depend on different parameters like the applied force on the surface or the lateral velocity during the exploration. Robust features that are invariant against such scan-time parameters are currently lacking, but would enable texture classification and recognition using uncontrolled human exploratory movements. In this paper, we introduce a haptic texture database which allows for a systematic analysis of feature candidates. The publicly available database includes recorded accelerations measured during controlled and well-defined texture scans, as well as uncontrolled human free hand texture explorations for 43 different textures. As a preliminary feature analysis, we test and compare six well-established features from audio and speech recognition together with a Gaussian Mixture Model-based classifier on our recorded free hand signals. Among the tested features, best results are achieved using Mel-Frequency Cepstral Coefficients (MFCCs), leading to a texture recognition accuracy of 80.2%.

Picbreeder: evolving pictures collaboratively online Picbreeder is an online service that allows users to collaboratively evolve images. Like in other Interactive Evolutionary Computation (IEC) programs, users evolve images on Picbreeder by selecting ones that appeal to them to produce a new generation. However, Picbreeder also offers an online community in which to share these images, and most importantly, the ability to continue evolving others' images. Through this process of branching from other images, and through continually increasing image complexity made possible by the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC systems. Participation requires no explicit talent from the users, thereby opening Picbreeder to the entire Internet community. This paper details how Picbreeder encourages innovation, featuring images that were collaboratively evolved.

On the effect of mirroring in the IPOP active CMA-ES on the noiseless BBOB testbed Mirrored mutations and active covariance matrix adaptation are two recent ideas to improve the well-known covariance matrix adaptation evolution strategy (CMA-ES)---a state-of-the-art algorithm for numerical optimization. It turns out that both mechanisms can be implemented simultaneously. In this paper, we investigate the impact of mirrored mutations on the so-called IPOP active CMA-ES. We find that additional mirrored mutations improve the IPOP active CMA-ES statistically significantly, but by only a small margin, on several functions while never a statistically significant performance decline can be observed. Furthermore, experiments on different function instances with some algorithm parameters and stopping criteria changed reveal essentially the same results.

Importance of Matching Physical Friction, Hardness, and Texture in Creating Realistic Haptic Virtual Surfaces. Interacting with physical objects through a tool elicits tactile and kinesthetic sensations that comprise your haptic impression of the object. These cues, however, are largely missing from interactions with virtual objects, yielding an unrealistic user experience. This article evaluates the realism of virtual surfaces rendered using haptic models constructed from data recorded during interactions with real surfaces. The models include three components: surface friction, tapping transients, and texture vibrations. We render the virtual surfaces on a SensAble Phantom Omni haptic interface augmented with a Tactile Labs Haptuator for vibration output. We conducted a human-subject study to assess the realism of these virtual surfaces and the importance of the three model components. Following a perceptual discrepancy paradigm, subjects compared each of 15 real surfaces to a full rendering of the same surface plus versions missing each model component. The realism improvement achieved by including friction, tapping, or texture in the rendering was found to directly relate to the intensity of the surface's property in that domain (slipperiness, hardness, or roughness). A subsequent analysis of forces and vibrations measured during interactions with virtual surfaces indicated that the Omni's inherent mechanical properties corrupted the user's haptic experience, decreasing realism of the virtual surface.

Psychophysical Dimensions of Tactile Perception of Textures This paper reviews studies on the tactile dimensionality of physical properties of materials in order to determine a common structure for these dimensions. Based on the commonality found in a number of studies and known mechanisms for the perception of physical properties of textures, we conclude that tactile textures are composed of three prominent psychophysical dimensions that are perceived as roughness/smoothness, hardness/softness, and coldness/warmness. The roughness dimension may be divided into two dimensions: macro and fine roughness. Furthermore, it is reasonable to consider that a friction dimension that is related to the perception of moistness/dryness and stickiness/slipperiness exists. Thus, the five potential dimensions of tactile perception are macro and fine roughness, warmness/coldness, hardness/softness, and friction (moistness/dryness, stickiness/slipperiness). We also summarize methods such as psychological experiments and mathematical approaches for structuring tactile dimensions and their limitations.

An evaluation of direct attacks using fake fingers generated from ISO templates This work reports a vulnerability evaluation of a highly competitive ISO matcher to direct attacks carried out with fake fingers generated from ISO templates. Experiments are carried out on a fingerprint database acquired in a real-life scenario and show that the evaluated system is highly vulnerable to the proposed attack scheme, granting access in over 75% of the attempts (for a high-security operating point). Thus, the study disproves the popular belief of minutiae templates non-reversibility and raises a key vulnerability issue in the use of non-encrypted standard templates. (This article is an extended version of Galbally et al., 2008, which was awarded with the IBM Best Student Paper Award in the track of Biometrics at ICPR 2008).

Evolutionary computation: comments on the history and current state Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete

Crowd sensing of traffic anomalies based on human mobility and social media The advances in mobile computing and social networking services enable people to probe the dynamics of a city. In this paper, we address the problem of detecting and describing traffic anomalies using crowd sensing with two forms of data, human mobility and social media. Traffic anomalies are caused by accidents, control, protests, sport events, celebrations, disasters and other events. Unlike existing traffic-anomaly-detection methods, we identify anomalies according to drivers' routing behavior on an urban road network. Here, a detected anomaly is represented by a sub-graph of a road network where drivers' routing behaviors significantly differ from their original patterns. We then try to describe the detected anomaly by mining representative terms from the social media that people posted when the anomaly happened. The system for detecting such traffic anomalies can benefit both drivers and transportation authorities, e.g., by notifying drivers approaching an anomaly and suggesting alternative routes, as well as supporting traffic jam diagnosis and dispersal. We evaluate our system with a GPS trajectory dataset generated by over 30,000 taxicabs over a period of 3 months in Beijing, and a dataset of tweets collected from WeiBo, a Twitter-like social site in China. The results demonstrate the effectiveness and efficiency of our system.

Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users...

Space-time modeling of traffic flow. This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5min average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.

Dynamic transfer among alternative controllers and its relation to antiwindup controller design Advanced control strategies and modern consulting provide new challenges for the classical problem of bumpless transfer. It can, for example, be necessary to transfer between an only approximately known existing analog controller and a new digital or adaptive controller without accessing any states. Transfer ought to be bidirectional and not presuppose steady state, so that an immediate back-transfer is possible if the new controller should drive the plant unstable. We present a scheme that meets these requirements. By casting the problem of bidirectional transfer into an associated tracking control problem, systematic analysis and design procedures from control theory can be applied. The associated control problem also has a correspondence to the design of antiwindup controllers. The paper includes laboratory and industrial applications.

Completely Pinpointing the Missing RFID Tags in a Time-Efficient Way Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45% of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time.

Robust Sparse Linear Discriminant Analysis Linear discriminant analysis (LDA) is a very popular supervised feature extraction method and has been extended to different variants. However, classical LDA has the following problems: 1) The obtained discriminant projection does not have good interpretability for features. 2) LDA is sensitive to noise. 3) LDA is sensitive to the selection of number of projection directions. In this paper, a novel feature extraction method called robust sparse linear discriminant analysis (RSLDA) is proposed to solve the above problems. Specifically, RSLDA adaptively selects the most discriminative features for discriminant analysis by introducing the l2;1 norm. An orthogonal matrix and a sparse matrix are also simultaneously introduced to guarantee that the extracted features can hold the main energy of the original data and enhance the robustness to noise, and thus RSLDA has the potential to perform better than other discriminant methods. Extensive experiments on six databases demonstrate that the proposed method achieves the competitive performance compared with other state-of-the-art feature extraction methods. Moreover, the proposed method is robust to the noisy data. IEEE

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

An evaluation of direct attacks using fake fingers generated from ISO templates This work reports a vulnerability evaluation of a highly competitive ISO matcher to direct attacks carried out with fake fingers generated from ISO templates. Experiments are carried out on a fingerprint database acquired in a real-life scenario and show that the evaluated system is highly vulnerable to the proposed attack scheme, granting access in over 75% of the attempts (for a high-security operating point). Thus, the study disproves the popular belief of minutiae templates non-reversibility and raises a key vulnerability issue in the use of non-encrypted standard templates. (This article is an extended version of Galbally et al., 2008, which was awarded with the IBM Best Student Paper Award in the track of Biometrics at ICPR 2008).

Fingerprint Matching Using Feature Space Correlation We present a novel fingerprint alignment and matching scheme that utilizes ridge feature maps to represent, align and match fingerprint images. The technique described here obviates the need for extracting minutiae points or the core point to either align or match fingerprint images. The proposed scheme examines the ridge strength (in local neighborhoods of the fingerprint image) at various orientations, using a set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints. A standard deviation map corresponding to the variation in local pixel intensities in each of the 8 filtered images, is generated. The standard deviation map is sampled at regular intervals in both the horizontal and vertical directions, to construct the ridge feature map. The ridge feature map provides a compact fixed-length representation for a fingerprint image. When a query print is presented to the system, the standard deviation map of the query image and the ridge feature map of the template are correlated, in order to determine the translation offsets necessary to align them. Based on the translation offsets, a matching score is generated by computing the Euclidean distance between the aligned feature maps. Feature extraction and matching takes ~ 1 second in a Pentium III, 800 MHz processor. Combining the matching score generated by the proposed technique with that obtained from a minutiae-based matcher results in an overall improvement in performance of a fingerprint matching system.

Do GANs actually learn the distribution? An empirical study. Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of (Goodfellow et al 2014) suggested they do, if they were given sufficiently large deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al (to appear at ICML 2017) raised doubts whether the same holds when discriminator has finite size. It showed that the training objective can approach its optimum value even if the generated distribution has very low support ---in other words, the training objective is unable to prevent mode collapse. The current note reports experiments suggesting that such problems are not merely theoretical. It presents empirical evidence that well-known GANs approaches do learn distributions of fairly low support, and thus presumably are not learning the target distribution. The main technical contribution is a new proposed test, based upon the famous birthday paradox, for estimating the support size of the generated distribution.

Safe mutations for deep and recurrent neural networks through output gradients While neuroevolution (evolving neural networks) has been successful across a variety of domains from reinforcement learning, to artificial life, to evolutionary robotics, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights will likely break existing functionality. This paper proposes a solution: a family of safe mutation (SM) operators that facilitate exploration without dramatically altering network behavior or requiring additional interaction with the environment. The most effective SM variant scales the degree of mutation of each individual weight according to the sensitivity of the network's outputs to that weight, which requires computing the gradient of outputs with respect to the weights (instead of the gradient of error, as in conventional deep learning). This safe mutation through gradients (SM-G) operator dramatically increases the ability of a simple genetic algorithm-based neuroevolution method to find solutions in high-dimensional domains that require deep and/or recurrent neural networks, including domains that require processing raw pixels. By improving our ability to evolve deep neural networks, this new safer approach to mutation expands the scope of domains amenable to neuroevolution.

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner workings of such computational brains is both fascinating basic science that is interesting in its own right-similar to why we study the human brain-and will enable researchers to further improve DNNs. One path to understanding how a neural network functions internally is to study what each of its neurons has learned to detect. One such method is called activation maximization (AM), which synthesizes an input (e.g. an image) that highly activates a neuron. Here we dramatically improve the qualitative state of the art of activation maximization by harnessing a powerful, learned prior: a deep generator network (DGN). The algorithm (1) generates qualitatively state-of-the-art synthetic images that look almost real, (2) reveals the features learned by each neuron in an interpretable way, (3) generalizes well to new datasets and somewhat well to different network architectures without requiring the prior to be relearned, and (4) can be considered as a high-quality generative method (in this case, by generating novel, creative, interesting, recognizable images).

Very Deep Convolutional Networks for Large-Scale Image Recognition. In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Long short-term memory. Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

A survey on sensor networks The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.

A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems Recently, wireless technologies have been growing actively all around the world. In the context of wireless technology, fifth-generation (5G) technology has become a most challenging and interesting topic in wireless research. This article provides an overview of the Internet of Things (IoT) in 5G wireless systems. IoT in the 5G system will be a game changer in the future generation. It will open a door for new wireless architecture and smart services. Recent cellular network LTE (4G) will not be sufficient and efficient to meet the demands of multiple device connectivity and high data rate, more bandwidth, low-latency quality of service (QoS), and low interference. To address these challenges, we consider 5G as the most promising technology. We provide a detailed overview of challenges and vision of various communication industries in 5G IoT systems. The different layers in 5G IoT systems are discussed in detail. This article provides a comprehensive review on emerging and enabling technologies related to the 5G system that enables IoT. We consider the technology drivers for 5G wireless technology, such as 5G new radio (NR), multiple-input–multiple-output antenna with the beamformation technology, mm-wave commutation technology, heterogeneous networks (HetNets), the role of augmented reality (AR) in IoT, which are discussed in detail. We also provide a review on low-power wide-area networks (LPWANs), security challenges, and its control measure in the 5G IoT scenario. This article introduces the role of AR in the 5G IoT scenario. This article also discusses the research gaps and future directions. The focus is also on application areas of IoT in 5G systems. We, therefore, outline some of the important research directions in 5G IoT.

Collaborative privacy management The landscape of the World Wide Web with all its versatile services heavily relies on the disclosure of private user information. Unfortunately, the growing amount of personal data collected by service providers poses a significant privacy threat for Internet users. Targeting growing privacy concerns of users, privacy-enhancing technologies emerged. One goal of these technologies is the provision of tools that facilitate a more informative decision about personal data disclosures. A famous PET representative is the PRIME project that aims for a holistic privacy-enhancing identity management system. However, approaches like the PRIME privacy architecture require service providers to change their server infrastructure and add specific privacy-enhancing components. In the near future, service providers are not expected to alter internal processes. Addressing the dependency on service providers, this paper introduces a user-centric privacy architecture that enables the provider-independent protection of personal data. A central component of the proposed privacy infrastructure is an online privacy community, which facilitates the open exchange of privacy-related information about service providers. We characterize the benefits and the potentials of our proposed solution and evaluate a prototypical implementation.

On controller initialization in multivariable switching systems We consider a class of switched systems which consists of a linear MIMO and possibly unstable process in feedback interconnection with a multicontroller whose dynamics switch. It is shown how one can achieve significantly better transient performance by selecting the initial condition for every controller when it is inserted into the feedback loop. This initialization is obtained by performing the minimization of a quadratic cost function of the tracking error, controlled output, and control signal. We guarantee input-to-state stability of the closed-loop system when the average number of switches per unit of time is smaller than a specific value. If this is not the case then stability can still be achieved by adding a mild constraint to the optimization. We illustrate the use of our results in the control of a flexible beam actuated in torque. This system is unstable with two poles at the origin and contains several lightly damped modes, which can be easily excited by controller switching.

Simultaneous wireless information and power transfer in modern communication systems Energy harvesting for wireless communication networks is a new paradigm that allows terminals to recharge their batteries from external energy sources in the surrounding environment. A promising energy harvesting technology is wireless power transfer where terminals harvest energy from electromagnetic radiation. Thereby, the energy may be harvested opportunistically from ambient electromagnetic sources or from sources that intentionally transmit electromagnetic energy for energy harvesting purposes. A particularly interesting and challenging scenario arises when sources perform simultaneous wireless information and power transfer (SWIPT), as strong signals not only increase power transfer but also interference. This article provides an overview of SWIPT systems with a particular focus on the hardware realization of rectenna circuits and practical techniques that achieve SWIPT in the domains of time, power, antennas, and space. The article also discusses the benefits of a potential integration of SWIPT technologies in modern communication networks in the context of resource allocation and cooperative cognitive radio networks.

Scalable and Privacy-Preserving Data Sharing Based on Blockchain. With the development of network technology and cloud computing, data sharing is becoming increasingly popular, and many scholars have conducted in-depth research to promote its flourish. As the scale of data sharing expands, its privacy protection has become a hot issue in research. Moreover, in data sharing, the data is usually maintained in multiple parties, which brings new challenges to protect the privacy of these multi-party data. In this paper, we propose a trusted data sharing scheme using blockchain. We use blockchain to prevent the shared data from being tampered, and use the Paillier cryptosystem to realize the confidentiality of the shared data. In the proposed scheme, the shared data can be traded, and the transaction information is protected by using the (p, t)-threshold Paillier cryptosystem. We conduct experiments in cloud storage scenarios and the experimental results demonstrate the efficiency and effectiveness of the proposed scheme.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

A Delay-Sensitive Multicast Protocol for Network Capacity Enhancement in Multirate MANETs. Due to significant advances in wireless modulation technologies, some MAC standards such as 802.11a, 802.11b, and 802.11g can operate with multiple data rates for QoS-constrained multimedia communication to utilize the limited resources of MANETs more efficiently. In this paper, by means of measuring the busy/idle ratio of the shared radio channel, a method for estimating one-hop delay is first su...

Multiple QoS Parameters-Based Routing for Civil Aeronautical Ad Hoc Networks. Aeronautical ad hoc network (AANET) can be applied as in-flight communication systems to allow aircraft to communicate with the ground, in complement to other existing communication systems to support Internet of Things. However, the unique features of civil AANETs present a great challenge to provide efficient and reliable data delivery in such environments. In this paper, we propose a multiple q...

Performance Improvement of Cluster-Based Routing Protocol in VANET. Vehicular ad-hoc NETworks (VANETs) have received considerable attention in recent years, due to its unique characteristics, which are different from mobile ad-hoc NETworks, such as rapid topology change, frequent link failure, and high vehicle mobility. The main drawback of VANETs network is the network instability, which yields to reduce the network effciency. In this paper, we propose three algorithms: cluster-based life-time routing (CBLTR) protocol, Intersection dynamic VANET routing (IDVR) protocol, and control overhead reduction algorithm (CORA). The CBLTR protocol aims to increase the route stability and average throughput in a bidirectional segment scenario. The cluster heads (CHs) are selected based on maximum lifetime among all vehicles that are located within each cluster. The IDVR protocol aims to increase the route stability and average throughput, and to reduce end-to-end delay in a grid topology. The elected intersection CH receives a set of candidate shortest routes (SCSR) closed to the desired destination from the software defined network. The IDVR protocol selects the optimal route based on its current location, destination location, and the maximum of the minimum average throughput of SCSR. Finally, the CORA algorithm aims to reduce the control overhead messages in the clusters by developing a new mechanism to calculate the optimal numbers of the control overhead messages between the cluster members and the CH. We used SUMO traffic generator simulators and MATLAB to evaluate the performance of our proposed protocols. These protocols significantly outperform many protocols mentioned in the literature, in terms of many parameters.

SCOTRES: Secure Routing for IoT and CPS. Wireless ad-hoc networks are becoming popular due to the emergence of the Internet of Things and cyber-physical systems (CPSs). Due to the open wireless medium, secure routing functionality becomes important. However, the current solutions focus on a constrain set of network vulnerabilities and do not provide protection against newer attacks. In this paper, we propose SCOTRES-a trust-based system ...

Communication Solutions for Vehicle Ad-hoc Network in Smart Cities Environment: A Comprehensive Survey In recent years, the explosive growth of multimedia applications and services has required further improvements in mobile systems to meet transfer speed requirements. Mobile Ad-hoc Network was formed in the 1970s. It is a set of mobile devices that have self-configuring capable to establish parameters to transmit data without relying on an pre-installed infrastructure systems. Today, MANET is strongly applied in many fields such as healthcare, military, smart agriculture, and disaster prevention. In the transportation area, in order to meet the unique characteristics of the vehicle network, such as movement pattern, high mobility with the support of RSUs, MANET has evolved into Vehicle Ad-hoc Networks, also called VANET. Due to the mobility of the nodes, like MANET, ​​the performance of VANET is relatively low and depends on the communication technologies. Designing more flexible, reliable, and smarter routing protocols to improve VANET performance for smart urban is a significant challenge. In this study, we conduct a survey of communication solutions for VANET in recent years. The results indicated a common framework for designing VANET communication solutions based on three main approaches: multi-metric, UAV/Cloud/Internet, and Intelligent. Moreover, with each proposed solution, we also analyse to show the focus of the research and the results achieved. Finally, we discuss and point out possible future research directions. We hope that the research results in this work will be important guidelines for future research in the communication area for VANET.

A Network Lifetime Extension-Aware Cooperative MAC Protocol for MANETs With Optimized Power Control. In this paper, a cooperative medium access control (CMAC) protocol, termed network lifetime extension-aware CMAC (LEA-CMAC) for mobile ad-hoc networks (MANETs) is proposed. The main feature of the LEA-CMAC protocol is to enhance the network performance through the cooperative transmission to achieve a multi-objective target orientation. The unpredictable nature of wireless communication links results in the degradation of network performance in terms of throughput, end-to-end delay, energy efficiency, and network lifetime of MANETs. Through cooperative transmission, the network performance of MANETs can be improved, provided a beneficial cooperation is satisfied and design parameters are carefully selected at the MAC layer. To achieve a multi-objective target-oriented CMAC protocol, we formulated an optimization problem to extend the network lifetime of MANETs. The optimization solution led to the investigation of symmetric and asymmetric transmit power policies. We then proposed a distributed relay selection process to select the best retransmitting node among the qualified relays, with consideration on a transmit power, a sufficient residual energy after cooperation, and a high cooperative gain. The simulation results show that the LEA-CMAC protocol can achieve a multi-objective target orientation by exploiting an asymmetric transmit power policy to improve the network performance.

Distinctive Image Features from Scale-Invariant Keypoints This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

ImageNet Large Scale Visual Recognition Challenge. The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.

A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems Recently, wireless technologies have been growing actively all around the world. In the context of wireless technology, fifth-generation (5G) technology has become a most challenging and interesting topic in wireless research. This article provides an overview of the Internet of Things (IoT) in 5G wireless systems. IoT in the 5G system will be a game changer in the future generation. It will open a door for new wireless architecture and smart services. Recent cellular network LTE (4G) will not be sufficient and efficient to meet the demands of multiple device connectivity and high data rate, more bandwidth, low-latency quality of service (QoS), and low interference. To address these challenges, we consider 5G as the most promising technology. We provide a detailed overview of challenges and vision of various communication industries in 5G IoT systems. The different layers in 5G IoT systems are discussed in detail. This article provides a comprehensive review on emerging and enabling technologies related to the 5G system that enables IoT. We consider the technology drivers for 5G wireless technology, such as 5G new radio (NR), multiple-input–multiple-output antenna with the beamformation technology, mm-wave commutation technology, heterogeneous networks (HetNets), the role of augmented reality (AR) in IoT, which are discussed in detail. We also provide a review on low-power wide-area networks (LPWANs), security challenges, and its control measure in the 5G IoT scenario. This article introduces the role of AR in the 5G IoT scenario. This article also discusses the research gaps and future directions. The focus is also on application areas of IoT in 5G systems. We, therefore, outline some of the important research directions in 5G IoT.

A communication robot in a shopping mall This paper reports our development of a communication robot for use in a shopping mall to provide shopping information, offer route guidance, and build rapport. In the development, the major difficulties included sensing human behaviors, conversation in a noisy daily environment, and the needs of unexpected miscellaneous knowledge in the conversation. We chose a networkrobot system approach, where a single robot's poor sensing capability and knowledge are supplemented by ubiquitous sensors and a human operator. The developed robot system detects a person with floor sensors to initiate interaction, identifies individuals with radio-frequency identification (RFID) tags, gives shopping information while chatting, and provides route guidance with deictic gestures. The robotwas partially teleoperated to avoid the difficulty of speech recognition as well as to furnish a new kind of knowledge that only humans can flexibly provide. The information supplied by a human operator was later used to increase the robot's autonomy. For 25 days in a shopping mall, we conducted a field trial and gathered 2642 interactions. A total of 235 participants signed up to use RFID tags and, later, provided questionnaire responses. The questionnaire results are promising in terms of the visitors' perceived acceptability as well as the encouragement of their shopping activities. The results of the teleoperation analysis revealed that the amount of teleoperation gradually decreased, which is also promising.

Comment on "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes" Comparison of generative and discriminative classifiers is an ever-lasting topic. As an important contribution to this topic, based on their theoretical and empirical comparisons between the naïve Bayes classifier and linear logistic regression, Ng and Jordan (NIPS 841---848, 2001) claimed that there exist two distinct regimes of performance between the generative and discriminative classifiers with regard to the training-set size. In this paper, our empirical and simulation studies, as a complement of their work, however, suggest that the existence of the two distinct regimes may not be so reliable. In addition, for real world datasets, so far there is no theoretically correct, general criterion for choosing between the discriminative and the generative approaches to classification of an observation x into a class y; the choice depends on the relative confidence we have in the correctness of the specification of either p(y|x) or p(x, y) for the data. This can be to some extent a demonstration of why Efron (J Am Stat Assoc 70(352):892---898, 1975) and O'Neill (J Am Stat Assoc 75(369):154---160, 1980) prefer normal-based linear discriminant analysis (LDA) when no model mis-specification occurs but other empirical studies may prefer linear logistic regression instead. Furthermore, we suggest that pairing of either LDA assuming a common diagonal covariance matrix (LDA-驴) or the naïve Bayes classifier and linear logistic regression may not be perfect, and hence it may not be reliable for any claim that was derived from the comparison between LDA-驴 or the naïve Bayes classifier and linear logistic regression to be generalised to all generative and discriminative classifiers.

Adaptive dynamic programming and optimal control of nonlinear nonaffine systems. In this paper, a novel optimal control design scheme is proposed for continuous-time nonaffine nonlinear dynamic systems with unknown dynamics by adaptive dynamic programming (ADP). The proposed methodology iteratively updates the control policy online by using the state and input information without identifying the system dynamics. An ADP algorithm is developed, and can be applied to a general class of nonlinear control design problems. The convergence analysis for the designed control scheme is presented, along with rigorous stability analysis for the closed-loop system. The effectiveness of this new algorithm is illustrated by two simulation examples.

Adaptive Fuzzy Control With Prescribed Performance for Block-Triangular-Structured Nonlinear Systems. In this paper, an adaptive fuzzy control method with prescribed performance is proposed for multi-input and multioutput block-triangular-structured nonlinear systems with immeasurable states. Fuzzy logic systems are adopted to identify the unknown nonlinear system functions. Adaptive fuzzy state observers are designed to solve the problem of unmeasured states, and a new observer-based output-feedb...

Intention-detection strategies for upper limb exosuits: model-based myoelectric vs dynamic-based control The cognitive human-robot interaction between an exosuit and its wearer plays a key role in determining both the biomechanical effects of the device on movements and its perceived effectiveness. There is a lack of evidence, however, on the comparative performance of different control methods, implemented on the same device. Here, we compare two different control approaches on the same robotic suit: a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a dynamic-based control that provides support against gravity using an inverse dynamic model. Tested on a cohort of four healthy participants, assistance from the exosuit results in a marked reduction in the effort of muscles working against gravity with both control approaches (peak reduction of 68.6±18.8%, for the dynamic arm model and 62.4±25.1% for the myoprocessor), when compared to an unpowered condition. Neither of the two controllers had an affect on the performance of their users in a joint-angle tracking task (peak errors of 15.4° and 16.4° for the dynamic arm model and myoprocessor, respectively, compared to 13.1o in the unpowered condition). However, our results highlight the remarkable adaptability of the myoprocessor to seamlessly adapt to changing external dynamics.

Using Ontologies for Test Suites Generation for Automated and Autonomous Driving Functions In this paper, we outline a general automated testing approach to be applied for verification and validation of automated and autonomous driving functions. The approach makes use of ontologies of environment the system under test is interacting with. Ontologies are automatically converted into input models for combinatorial testing, which are used to generate test cases. The obtained abstract test cases are used to generate concrete test scenarios that provide the basis for simulation used to verify the functionality of the system under test. We discuss the general approach including its potential for automation in the automotive domain where there is growing need for sophisticated verification based on simulation in case of automated and autonomous vehicles.

Using Ontology-Based Traffic Models for More Efficient Decision Making of Autonomous Vehicles The paper describes how a high-level abstract world model can be used to support the decision-making process of an autonomous driving system. The approach uses a hierarchical world model and distinguishes between a low-level model for the trajectory planning and a high-level model for solving the traffic coordination problem. The abstract world model used in the CyberCars-2 project is presented. It is based on a topological lane segmentation and introduces relations to represent the semantic context of the traffic scenario. This makes it much easier to realize a consistent and complete driving control system, and to analyze, evaluate and simulate such a system.

Ontology-based methods for enhancing autonomous vehicle path planning We report the results of a first implementation demonstrating the use of an ontology to support reasoning about obstacles to improve the capabilities and performance of on-board route planning for autonomous vehicles. This is part of an overall effort to evaluate the performance of ontologies in different components of an autonomous vehicle within the 4D/RCS system architecture developed at NIST. Our initial focus has been on simple roadway driving scenarios where the controlled vehicle encounters potential obstacles in its path. As reported elsewhere [C. Schlenoff, S. Balakirsky, M. Uschold, R. Provine, S. Smith, Using ontologies to aid navigation planning in autonomous vehicles, Knowledge Engineering Review 18 (3) (2004) 243–255], our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to plan to avoid the object. We describe the results of the first implementation that integrates the ontology, the reasoner and the planner. We describe our insights and lessons learned and discuss resulting changes to our approach.

Online Verification of Automated Road Vehicles Using Reachability Analysis An approach for formally verifying the safety of automated vehicles is proposed. Due to the uniqueness of each traffic situation, we verify safety online, i.e., during the operation of the vehicle. The verification is performed by predicting the set of all possible occupancies of the automated vehicle and other traffic participants on the road. In order to capture all possible future scenarios, we apply reachability analysis to consider all possible behaviors of mathematical models considering uncertain inputs (e.g., sensor noise, disturbances) and partially unknown initial states. Safety is guaranteed with respect to the modeled uncertainties and behaviors if the occupancy of the automated vehicle does not intersect that of other traffic participants for all times. The applicability of the approach is demonstrated by test drives with an automated vehicle at the Robotics Institute at Carnegie Mellon University.

AVFI: Fault Injection for Autonomous Vehicles Autonomous vehicle (AV) technology is rapidly becoming a reality on U.S. roads, offering the promise of improvements in traffic management, safety, and the comfort and efficiency of vehicular travel. With this increasing popularity and ubiquitous deployment, resilience has become a critical requirement for public acceptance and adoption. Recent studies into the resilience of AVs have shown that though the AV systems are improving over time, they have not reached human levels of automation. Prior work in this area has studied the safety and resilience of individual components of the AV system (e.g., testing of neural networks powering the perception function). However, methods for holistic end-to-end resilience assessment of AV systems are still non-existent.

Automatically testing self-driving cars with search-based procedural content generation Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving car software in real traffic is not only expensive but also dangerous, and has already caused fatalities. Virtual tests, in which self-driving car software is tested in computer simulations, offer a more efficient and safer alternative compared to naturalistic field operational tests. However, creating suitable test scenarios is laborious and difficult. In this paper we combine procedural content generation, a technique commonly employed in modern video games, and search-based testing, a testing technique proven to be effective in many domains, in order to automatically create challenging virtual scenarios for testing self-driving car soft- ware. Our AsFault prototype implements this approach to generate virtual roads for testing lane keeping, one of the defining features of autonomous driving. Evaluation on two different self-driving car software systems demonstrates that AsFault can generate effective virtual road networks that succeed in revealing software failures, which manifest as cars departing their lane. Compared to random testing AsFault was not only more efficient, but also caused up to twice as many lane departures.

Acclimatizing the Operational Design Domain for Autonomous Driving Systems The operational design domain (ODD) of an automated driving system (ADS) can be used to confine the environmental scope of where the ADS is safe to execute. ODD acclimatization is one of the necessary steps for validating vehicle safety in complex traffic environments. This article proposes an approach and architectural design to extract and enhance the ODD of the ADS based on the task scenario an...

Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques Automated vehicles (AVs) must be thoroughly evaluated before their release and deployment. A widely used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the other primary vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in nonaccelerated cases can be accurately estimated. The cross-entropy method is used to recursively search for the optimal skewing parameters. The frequencies of the occurrences of conflicts, crashes, and injuries are estimated for a modeled AV, and the achieved accelerated rate is around 2000 to 20 000. In other words, in the accelerated simulations, driving for 1000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to greatly reduce the development and validation time for AVs.

A survey of socially interactive robots This paper reviews “socially interactive robots”: robots for which social human–robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the different forms of “social robots”. We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report [T. Fong, I. Nourbakhsh, K. Dautenhahn, A survey of socially interactive robots: concepts, design and applications, Technical Report No. CMU-RI-TR-02-29, Robotics Institute, Carnegie Mellon University, 2002].

A General Equilibrium Model for Industries with Price and Service Competition This paper develops a stochastic general equilibrium inventory model for an oligopoly, in which all inventory constraint parameters are endogenously determined. We propose several systems of demand processes whose distributions are functions of all retailers' prices and all retailers' service levels. We proceed with the investigation of the equilibrium behavior of infinite-horizon models for industries facing this type of generalized competition, under demand uncertainty.We systematically consider the following three competition scenarios. (1) Price competition only: Here, we assume that the firms' service levels are exogenously chosen, but characterize how the price and inventory strategy equilibrium vary with the chosen service levels. (2) Simultaneous price and service-level competition: Here, each of the firms simultaneously chooses a service level and a combined price and inventory strategy. (3) Two-stage competition: The firms make their competitive choices sequentially. In a first stage, all firms simultaneously choose a service level; in a second stage, the firms simultaneously choose a combined pricing and inventory strategy with full knowledge of the service levels selected by all competitors. We show that in all of the above settings a Nash equilibrium of infinite-horizon stationary strategies exists and that it is of a simple structure, provided a Nash equilibrium exists in a so-called reduced game.We pay particular attention to the question of whether a firm can choose its service level on the basis of its own (input) characteristics (i.e., its cost parameters and demand function) only. We also investigate under which of the demand models a firm, under simultaneous competition, responds to a change in the exogenously specified characteristics of the various competitors by either: (i) adjusting its service level and price in the same direction, thereby compensating for price increases (decreases) by offering improved (inferior) service, or (ii) adjusting them in opposite directions, thereby simultaneously offering better or worse prices and service.

Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles This paper proposes an operating framework for aggregators of plug-in electric vehicles (PEVs). First, a minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands. The same algorithm is applicable for negotiating bilateral contracts. Second, a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day. Simulation results are used to evaluate the proposed algorithms, and to demonstrate the potential impact of an aggregated PEV fleet on the power system.

An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.

Driver Gaze Zone Estimation Using Convolutional Neural Networks: A General Framework and Ablative Analysis Driver gaze has been shown to be an excellent surrogate for driver attention in intelligent vehicles. With the recent surge of highly autonomous vehicles, driver gaze can be useful for determining the handoff time to a human driver. While there has been significant improvement in personalized driver gaze zone estimation systems, a generalized system which is invariant to different subjects, perspe...

Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objecti...

The Drone Scheduling Problem: A Systematic State-of-the-Art Review Drones are receiving popularity with time due to their advanced mobility. Although they were initially deployed for military purposes, they now have a wide array of applications in various public and private sectors. Further deployment of drones can promote the global economic recovery from the COVID-19 pandemic. Even though drones offer a number of advantages, they have limited flying time and weight carrying capacity. Effective drone schedules may assist with overcoming such limitations. Drone scheduling is associated with optimization of drone flight paths and may include other features, such as determination of arrival time at each node, utilization of drones, battery capacity considerations, and battery recharging considerations. A number of studies on drone scheduling have been published over the past years. However, there is a lack of a systematic literature survey that provides a holistic overview of the drone scheduling problem, existing tendencies, main research limitations, and future research needs. Therefore, this study conducts an extensive survey of the scientific literature that assessed drone scheduling. The collected studies are grouped into different categories, including general drone scheduling, drone scheduling for delivery of goods, drone scheduling for monitoring, and drone scheduling with recharge considerations. A detailed review of the collected studies is presented for each of the categories. Representative mathematical models are provided for each category of studies, accompanied by a summary of findings, existing gaps in the state-of-the-art, and future research needs. The outcomes of this research are expected to assist the relevant stakeholders with an effective drone schedule design.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Ethical Considerations Of Applying Robots In Kindergarten Settings: Towards An Approach From A Macroperspective In child-robot interaction (cHRI) research, many studies pursue the goal to develop interactive systems that can be applied in everyday settings. For early education, increasingly, the setting of a kindergarten is targeted. However, when cHRI and research are brought into a kindergarten, a range of ethical and related procedural aspects have to be considered and dealt with. While ethical models elaborated within other human-robot interaction settings, e.g., assisted living contexts, can provide some important indicators for relevant issues, we argue that it is important to start developing a systematic approach to identify and tackle those ethical issues which rise with cHRI in kindergarten settings on a more global level and address the impact of the technology from a macroperspective beyond the effects on the individual. Based on our experience in conducting studies with children in general and pedagogical considerations on the role of the institution of kindergarten in specific, in this paper, we enfold some relevant aspects that have barely been addressed in an explicit way in current cHRI research. Four areas are analyzed and key ethical issues are identified in each area: (1) the institutional setting of a kindergarten, (2) children as a vulnerable group, (3) the caregivers' role, and (4) pedagogical concepts. With our considerations, we aim at (i) broadening the methodology of the current studies within the area of cHRI, (ii) revalidate it based on our comprehensive empirical experience with research in kindergarten settings, both laboratory and real-world contexts, and (iii) provide a framework for the development of a more systematic approach to address the ethical issues in cHRI research within kindergarten settings.

Bitcoin Fee Decisions in Transaction Confirmation Queueing Games Under Limited Multi-Priority Rule In the Bitcoin system, transaction fees serve not only as the fundamental economic incentive to stimulate miners, but also as an important tuner for the Bitcoin system to define the priorities in the transaction confirmation process. In this paper, we aim to study the priority rules for queueing transactions based on their associated fees, and in turn users' decision-making in formulating their fees in the transaction confirmation queueing game. Based on the queueing theory, we first analyzed the waiting time of users under non-preemptive limited multi-priority (LMP) rule, which is formulated to adjust users' waiting time over different priorities. We then established a game-theoretical model, and analyze users' equilibrium fee decisions. Towards the end, we conducted computational experiments to validate the theoretical analysis. Our research findings can not only help understand users' fee decisions under the LMP rule, but also offer useful managerial insights in optimizing the queueing rules of Bitcoin transactions.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Adaptive Multi-Kernel SVM With Spatial-Temporal Correlation for Short-Term Traffic Flow Prediction Accurate estimation of the traffic state can help to address the issue of urban traffic congestion, providing guiding advices for people’s travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction algorithm based on an adaptive multi-kernel support vector machine (AMSVM) with spatial–temporal correlation, which is named as AMSVM-STC. First, we explore both the nonlinearity and randomness of the traffic flow, and hybridize Gaussian kernel and polynomial kernel to constitute the AMSVM. Second, we optimize the parameters of AMSVM with the adaptive particle swarm optimization algorithm, and propose a novel method to make the hybrid kernel’s weight adjust adaptively according to the change tendency of real-time traffic flow. Third, we incorporate the spatial–temporal correlation information with AMSVM to predict the short-term traffic flow. We evaluate our algorithm by doing thorough experiment on real data sets. The results demonstrate that our algorithm can do a timely and adaptive prediction even in the rush hour when the traffic conditions change rapidly. At the same time, the proposed AMSVM-STC outperforms the existing methods.

Introduction to the special section on intelligent systems for socially aware computing

A survey on graph kernels. Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification.

TL-GDBN: Growing Deep Belief Network With Transfer Learning A deep belief network (DBN) is effective to create a powerful generative model by using training data. However, it is difficult to fast determine its optimal structure given specific applications. In this paper, a growing DBN with transfer learning (TL-GDBN) is proposed to automatically decide its structure size, which can accelerate its learning process and improve model accuracy. First, a basic DBN structure with single hidden layer is initialized and then pretrained, and the learned weight parameters are frozen. Second, TL-GDBN uses TL to transfer the knowledge from the learned weight parameters to newly added neurons and hidden layers, which can achieve a growing structure until the stopping criterion for pretraining is satisfied. Third, the weight parameters derived from pretraining of TL-GDBN are further fine-tuned by using layer-by-layer partial least square regression from top to bottom, which can avoid many problems of traditional backpropagation algorithm-based fine-tuning. Moreover, the convergence analysis of the TL-GDBN is presented. Finally, TL-GDBN is tested on two benchmark data sets and a practical wastewater treatment system. The simulation results show that it has better modeling performance, faster learning speed, and more robust structure than existing models. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</italic> —Transfer learning (TL) aims to improve training effectiveness by transferring knowledge from a source domain to target domain. This paper presents a growing deep belief network (DBN) with TL to improve the training effectiveness and determine the optimal model size. Facing a complex process and real-world workflow, DBN tends to require long time for its successful training. The proposed growing DBN with TL (TL-GDBN) accelerates the learning process by instantaneously transferring the knowledge from a source domain to each new deeper or wider substructure. The experimental results show that the proposed TL-GDBN model has a great potential to deal with complex system, especially the systems with high nonlinearity. As a result, it can be readily applicable to some industrial nonlinear systems.

Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks Traffic flow forecasting has been a long-standing topic in intelligent transportation systems, and a renewed interest has been seen in recent years due to the development of artificial intelligence techniques. New deep neural networks have been developed to model traffic flow, but it is very challenging to predict citywide traffic flow at the road level in fine temporal scale owing to the influenc...

Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction. An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi's experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.

Image quality assessment: from error visibility to structural similarity. Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000.

A survey of socially interactive robots This paper reviews “socially interactive robots”: robots for which social human–robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the different forms of “social robots”. We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report [T. Fong, I. Nourbakhsh, K. Dautenhahn, A survey of socially interactive robots: concepts, design and applications, Technical Report No. CMU-RI-TR-02-29, Robotics Institute, Carnegie Mellon University, 2002].

Energy Efficiency Resource Allocation For D2d Communication Network Based On Relay Selection In order to solve the problem of spectrum resource shortage and energy consumption, we put forward a new model that combines with D2D communication and energy harvesting technology: energy harvesting-aided D2D communication network under the cognitive radio (EHA-CRD), where the D2D users harvest energy from the base station and the D2D source communicate with D2D destination by D2D relays. Our goals are to investigate the maximization energy efficiency (EE) of the network by joint time allocation and relay selection while taking into the constraints of the signal-to-noise ratio of D2D and the rates of the Cellular users. During this process, the energy collection time and communication time are randomly allocated. The maximization problem of EE can be divided into two sub-problems: (1) relay selection problem; (2) time optimization problem. For the first sub-problem, we propose a weighted sum maximum algorithm to select the best relay. For the last sub-problem, the EE maximization problem is non-convex problem with time. Thus, by using fractional programming theory, we transform it into a standard convex optimization problem, and we propose the optimization iterative algorithm to solve the convex optimization problem for obtaining the optimal solution. And, the simulation results show that the proposed relay selection algorithm and time optimization algorithm are significantly improved compared with the existing algorithms.

A communication robot in a shopping mall This paper reports our development of a communication robot for use in a shopping mall to provide shopping information, offer route guidance, and build rapport. In the development, the major difficulties included sensing human behaviors, conversation in a noisy daily environment, and the needs of unexpected miscellaneous knowledge in the conversation. We chose a networkrobot system approach, where a single robot's poor sensing capability and knowledge are supplemented by ubiquitous sensors and a human operator. The developed robot system detects a person with floor sensors to initiate interaction, identifies individuals with radio-frequency identification (RFID) tags, gives shopping information while chatting, and provides route guidance with deictic gestures. The robotwas partially teleoperated to avoid the difficulty of speech recognition as well as to furnish a new kind of knowledge that only humans can flexibly provide. The information supplied by a human operator was later used to increase the robot's autonomy. For 25 days in a shopping mall, we conducted a field trial and gathered 2642 interactions. A total of 235 participants signed up to use RFID tags and, later, provided questionnaire responses. The questionnaire results are promising in terms of the visitors' perceived acceptability as well as the encouragement of their shopping activities. The results of the teleoperation analysis revealed that the amount of teleoperation gradually decreased, which is also promising.

Comment on "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes" Comparison of generative and discriminative classifiers is an ever-lasting topic. As an important contribution to this topic, based on their theoretical and empirical comparisons between the naïve Bayes classifier and linear logistic regression, Ng and Jordan (NIPS 841---848, 2001) claimed that there exist two distinct regimes of performance between the generative and discriminative classifiers with regard to the training-set size. In this paper, our empirical and simulation studies, as a complement of their work, however, suggest that the existence of the two distinct regimes may not be so reliable. In addition, for real world datasets, so far there is no theoretically correct, general criterion for choosing between the discriminative and the generative approaches to classification of an observation x into a class y; the choice depends on the relative confidence we have in the correctness of the specification of either p(y|x) or p(x, y) for the data. This can be to some extent a demonstration of why Efron (J Am Stat Assoc 70(352):892---898, 1975) and O'Neill (J Am Stat Assoc 75(369):154---160, 1980) prefer normal-based linear discriminant analysis (LDA) when no model mis-specification occurs but other empirical studies may prefer linear logistic regression instead. Furthermore, we suggest that pairing of either LDA assuming a common diagonal covariance matrix (LDA-驴) or the naïve Bayes classifier and linear logistic regression may not be perfect, and hence it may not be reliable for any claim that was derived from the comparison between LDA-驴 or the naïve Bayes classifier and linear logistic regression to be generalised to all generative and discriminative classifiers.

G2-type SRMPC scheme for synchronous manipulation of two redundant robot arms. In this paper, to remedy the joint-angle drift phenomenon for manipulation of two redundant robot arms, a novel scheme for simultaneous repetitive motion planning and control (SRMPC) at the joint-acceleration level is proposed, which consists of two subschemes. To do so, the performance index of each SRMPC subscheme is derived and designed by employing the gradient dynamics twice, of which a convergence theorem and its proof are presented. In addition, for improving the accuracy of the motion planning and control, position error, and velocity, error feedbacks are incorporated into the forward kinematics equation and analyzed via Zhang neural-dynamics method. Then the two subschemes are simultaneously reformulated as two quadratic programs (QPs), which are finally unified into one QP problem. Furthermore, a piecewise-linear projection equation-based neural network (PLPENN) is used to solve the unified QP problem, which can handle the strictly convex QP problem in an inverse-free manner. More importantly, via such a unified QP formulation and the corresponding PLPENN solver, the synchronism of two redundant robot arms is guaranteed. Finally, two given tasks are fulfilled by 2 three-link and 2 five-link planar robot arms, respectively. Computer-simulation results validate the efficacy and accuracy of the SRMPC scheme and the corresponding PLPENN solver for synchronous manipulation of two redundant robot arms.

Adaptive fuzzy tracking control for switched uncertain strict-feedback nonlinear systems. •Adaptive tracking control for switched strict-feedback nonlinear systems is proposed.•The generalized fuzzy hyperbolic model is used to approximate nonlinear functions.•The designed controller has fewer design parameters comparing with existing methods.

Ethical Considerations Of Applying Robots In Kindergarten Settings: Towards An Approach From A Macroperspective In child-robot interaction (cHRI) research, many studies pursue the goal to develop interactive systems that can be applied in everyday settings. For early education, increasingly, the setting of a kindergarten is targeted. However, when cHRI and research are brought into a kindergarten, a range of ethical and related procedural aspects have to be considered and dealt with. While ethical models elaborated within other human-robot interaction settings, e.g., assisted living contexts, can provide some important indicators for relevant issues, we argue that it is important to start developing a systematic approach to identify and tackle those ethical issues which rise with cHRI in kindergarten settings on a more global level and address the impact of the technology from a macroperspective beyond the effects on the individual. Based on our experience in conducting studies with children in general and pedagogical considerations on the role of the institution of kindergarten in specific, in this paper, we enfold some relevant aspects that have barely been addressed in an explicit way in current cHRI research. Four areas are analyzed and key ethical issues are identified in each area: (1) the institutional setting of a kindergarten, (2) children as a vulnerable group, (3) the caregivers' role, and (4) pedagogical concepts. With our considerations, we aim at (i) broadening the methodology of the current studies within the area of cHRI, (ii) revalidate it based on our comprehensive empirical experience with research in kindergarten settings, both laboratory and real-world contexts, and (iii) provide a framework for the development of a more systematic approach to address the ethical issues in cHRI research within kindergarten settings.

Model-Less Hybrid Position/Force Control: A Minimalist Approach for Continuum Manipulators in Unknown, Constrained Environments. Continuum manipulators are designed to operate in constrained environments that are often unknown or unsensed, relying on body compliance to conform to obstacles. The interaction mechanics between the compliant body and unknown environment present significant challenges for traditional robot control techniques based on modeling these interactions exactly. In this letter, we describe a hybrid posit...

Li-function activated ZNN with finite-time convergence applied to redundant-manipulator kinematic control via time-varying Jacobian matrix pseudoinversion. This paper presents and investigates the application of Zhang neural network (ZNN) activated by Li function to kinematic control of redundant robot manipulators via time-varying Jacobian matrix pseudoinversion. That is, by using Li activation function and by computing the time-varying pseudoinverse of the Jacobian matrix (of the robot manipulator), the resultant ZNN model is applied to redundant-manipulator kinematic control. Note that there are nine novelties and differences of ZNN from the conventional gradient neural network in the research methodology. More importantly, such a Li-function activated ZNN (LFAZNN) model has the property of finite-time convergence (showing its feasibility to redundant-manipulator kinematic control). Simulation results based on a four-link planar robot manipulator and a PA10 robot manipulator further demonstrate the effectiveness of the presented LFAZNN model, as well as show the LFAZNN application prospect.

Kinematic model to control the end-effector of a continuum robot for multi-axis processing This paper presents a novel kinematic approach for controlling the end-effector of a continuum robot for in-situ repair/inspection in restricted and hazardous environments. Forward and inverse kinematic (IK) models have been developed to control the last segment of the continuum robot for performing multi-axis processing tasks using the last six Degrees of Freedom (DoF). The forward kinematics (FK) is proposed using a combination of Euler angle representation and homogeneous matrices. Due to the redundancy of the system, different constraints are proposed to solve the IK for different cases; therefore, the IK model is solved for bending and direction angles between (-pi/2 to + pi/2) radians. In addition, a novel method to calculate the Jacobian matrix is proposed for this type of hyper-redundant kinematics. The error between the results calculated using the proposed Jacobian algorithm and using the partial derivative equations of the FK map (with respect to linear and angular velocity) is evaluated. The error between the two models is found to be insignificant, thus, the Jacobian is validated as a method of calculating the IK for six DoF.

Optimization-Based Inverse Model of Soft Robots With Contact Handling. This letter presents a physically based algorithm to interactively simulate and control the motion of soft robots interacting with their environment. We use the finite-element method to simulate the nonlinear deformation of the soft structure, its actuators, and surroundings and propose a control method relying on a quadratic optimization to find the inverse of the model. The novelty of this work ...

Robust Zeroing Neural-Dynamics and Its Time-Varying Disturbances Suppression Model Applied to Mobile Robot Manipulators. This paper proposes a novel robust zeroing neural-dynamics (RZND) approach as well as its associated model for solving the inverse kinematics problem of mobile robot manipulators. Unlike existing works based on the assumption that neural network models are free of external disturbances, four common forms of time-varying disturbances suppressed by the proposed RZND model are investigated in this pa...

A New Inequality-Based Obstacle-Avoidance MVN Scheme and Its Application to Redundant Robot Manipulators This paper proposes a new inequality-based criterion/constraint with its algorithmic and computational details for obstacle avoidance of redundant robot manipulators. By incorporating such a dynamically updated inequality constraint and the joint physical constraints (such as joint-angle limits and joint-velocity limits), a novel minimum-velocity-norm (MVN) scheme is presented and investigated for robotic redundancy resolution. The resultant obstacle-avoidance MVN scheme resolved at the joint-velocity level is further reformulated as a general quadratic program (QP). Two QP solvers, i.e., a simplified primal–dual neural network based on linear variational inequalities (LVI) and an LVI-based numerical algorithm, are developed and applied for online solution of the QP problem as well as the inequality-based obstacle-avoidance MVN scheme. Simulative results that are based on PA10 robot manipulator and a six-link planar robot manipulator in the presence of window-shaped and point obstacles demonstrate the efficacy and superiority of the proposed obstacle-avoidance MVN scheme. Moreover, experimental results of the proposed MVN scheme implemented on the practical six-link planar robot manipulator substantiate the physical realizability and effectiveness of such a scheme for obstacle avoidance of redundant robot manipulator.

Event-Triggered Finite-Time Control for Networked Switched Linear Systems With Asynchronous Switching. This paper is concerned with the event-triggered finite-time control problem for networked switched linear systems by using an asynchronous switching scheme. Not only the problem of finite-time boundedness, but also the problem of input-output finite-time stability is considered in this paper. Compared with the existing event-triggered results of the switched systems, a new type of event-triggered...

Tabu Search - Part I

Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider a MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources􀀀the transmit precoding matrices of the MUs􀀀and the computational resources􀀀the CPU cycles/second assigned by the cloud to each MU􀀀in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

Symbolic model checking for real-time systems We describe finite-state programs over real-numbered time in a guarded-command language with real-valued clocks or, equivalently, as finite automata with real-valued clocks. Model checking answers the question which states of a real-time program satisfy a branching-time specification (given in an extension of CTL with clock variables). We develop an algorithm that computes this set of states symbolically as a fixpoint of a functional on state predicates, without constructing the state space. For this purpose, we introduce a μ-calculus on computation trees over real-numbered time. Unfortunately, many standard program properties, such as response for all nonzeno execution sequences (during which time diverges), cannot be characterized by fixpoints: we show that the expressiveness of the timed μ-calculus is incomparable to the expressiveness of timed CTL. Fortunately, this result does not impair the symbolic verification of "implementable" real-time programs-those whose safety constraints are machine-closed with respect to diverging time and whose fairness constraints are restricted to finite upper bounds on clock values. All timed CTL properties of such programs are shown to be computable as finitely approximable fixpoints in a simple decidable theory.

The industrial indoor channel: large-scale and temporal fading at 900, 2400, and 5200 MHz In this paper, large-scale fading and temporal fading characteristics of the industrial radio channel at 900, 2400, and 5200 MHz are determined. In contrast to measurements performed in houses and in office buildings, few attempts have been made until now to model propagation in industrial environments. In this paper, the industrial environment is categorized into different topographies. Industrial topographies are defined separately for large-scale and temporal fading, and their definition is based upon the specific physical characteristics of the local surroundings affecting both types of fading. Large-scale fading is well expressed by a one-slope path-loss model and excellent agreement with a lognormal distribution is obtained. Temporal fading is found to be Ricean and Ricean K-factors have been determined. Ricean K-factors are found to follow a lognormal distribution.

Cost-Effective Authentic and Anonymous Data Sharing with Forward Security Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to reauthenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Adaptive fuzzy tracking control for switched uncertain strict-feedback nonlinear systems. •Adaptive tracking control for switched strict-feedback nonlinear systems is proposed.•The generalized fuzzy hyperbolic model is used to approximate nonlinear functions.•The designed controller has fewer design parameters comparing with existing methods.

Hardware Circuits Design and Performance Evaluation of a Soft Lower Limb Exoskeleton Soft lower limb exoskeletons (LLEs) are wearable devices that have good potential in walking rehabilitation and augmentation. While a few studies focused on the structure design and assistance force optimization of the soft LLEs, rarely work has been conducted on the hardware circuits design. The main purpose of this work is to present a new soft LLE for walking efficiency improvement and introduce its hardware circuits design. A soft LLE for hip flexion assistance and a hardware circuits system with scalability were proposed. To assess the efficacy of the soft LLE, the experimental tests that evaluate the sensor data acquisition, force tracking performance, lower limb muscle activity and metabolic cost were conducted. The time error in the peak assistance force was just 1%. The reduction in the normalized root-mean-square EMG of the rectus femoris was 7.1%. The net metabolic cost in exoskeleton on condition was reduced by 7.8% relative to walking with no exoskeleton. The results show that the designed hardware circuits can be applied to the soft LLE and the soft LLE is able to improve walking efficiency of wearers.

Secure Federated Learning in 5G Mobile Networks Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage of ML. We seamlessly integrate Federated Learning (FL) into the 3GPP5G Network Data Analytics (NWDA) architecture, and add a Multi-Party Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and find that it has much lower communication overhead than previous work, without affecting ML performance.

Network anomaly detection using IP flows with Principal Component Analysis and Ant Colony Optimization. It is remarkable how proactive network management is in such demand nowadays, since networks are growing in size and complexity and Information Technology services cannot be stopped. In this manner, it is necessary to use an approach which proactively identifies traffic behavior patterns which may harm the network's normal operations. Aiming an automated management to detect and prevent potential problems, we present and compare two novel anomaly detection mechanisms based on statistical procedure Principal Component Analysis and the Ant Colony Optimization metaheuristic. These methods generate a traffic profile, called Digital Signature of Network Segment using Flow analysis (DSNSF), which is adopted as normal network behavior. Then, this signature is compared with the real network traffic by using a modification of the Dynamic Time Warping metric in order to recognize anomalous events. Thus, a seven-dimensional analysis of IP flows is performed, allowing the characterization of bits, packets and flows traffic transmitted per second, and the extraction of descriptive flow attributes, like source IP address, destination IP address, source TCP/UDP port and destination TCP/UDP port. The systems were evaluated using a real network environment and showed promising results. Moreover, the correspondence between true-positive and false-positive rates demonstrates that the systems are able to enhance the detection of anomalous behavior by maintaining a satisfactory false-alarm rate. Display Omitted Anomaly detection issue is addressed based on network traffic profiling.Proposal and comparison of detection methods belonging to distinct algorithm classes.Detection mechanism constructed over an adaptation of a pattern matching technique.Use of real and simulated traffic to evaluate the proposed methods.Traffic patterns that may harm the network operations are proactively identified.

A multi-step outlier-based anomaly detection approach to network-wide traffic. We propose a multi-step outlier-based anomaly detection approach to network-wide traffic.We propose a feature selection algorithm to select relevant non-redundant subset of features.We propose a tree-based clustering algorithm to generate non-redundant overlapped clusters.We introduce an efficient score-based outlier estimation technique to detect anomalies in network-wide traffic.We establish a fast distributed feature extraction framework to extract significant features from raw network-wide traffic.We conduct extensive experiments using the proposed algorithms with synthetic and real-life network-wide traffic datasets. Outlier detection is of considerable interest in fields such as physical sciences, medical diagnosis, surveillance detection, fraud detection and network anomaly detection. The data mining and network management research communities are interested in improving existing score-based network traffic anomaly detection techniques because of ample scopes to increase performance. In this paper, we present a multi-step outlier-based approach for detection of anomalies in network-wide traffic. We identify a subset of relevant traffic features and use it during clustering and anomaly detection. To support outlier-based network anomaly identification, we use the following modules: a mutual information and generalized entropy based feature selection technique to select a relevant non-redundant subset of features, a tree-based clustering technique to generate a set of reference points and an outlier score function to rank incoming network traffic to identify anomalies. We also design a fast distributed feature extraction and data preparation framework to extract features from raw network-wide traffic. We evaluate our approach in terms of detection rate, false positive rate, precision, recall and F-measure using several high dimensional synthetic and real-world datasets and find the performance superior in comparison to competing algorithms.

Deep Anomaly Detection with Deviation Networks Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection applications. This paper introduces a novel anomaly detection framework and its instantiation to address these problems. Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e.g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail. Extensive results show that our method can be trained substantially more data-efficiently and achieves significantly better anomaly scoring than state-of-the-art competing methods.

FLaaS: Federated Learning as a Service ABSTRACTFederated Learning (FL) is emerging as a promising technology to build machine learning models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on user devices, avoiding user data to be transferred to centralized servers, and can be enhanced with differential privacy mechanisms. Although FL has been recently deployed in real systems, the possibility of collaborative modeling across different 3rd-party applications has not yet been explored. In this paper, we tackle this problem and present Federated Learning as a Service (FLaaS), a system enabling different scenarios of 3rd-party application collaborative model building and addressing the consequent challenges of permission and privacy management, usability, and hierarchical model training. FLaaS can be deployed in different operational environments. As a proof of concept, we implement it on a mobile phone setting and discuss practical implications of results on simulated and real devices with respect to on-device training CPU cost, memory footprint and power consumed per FL model round. Therefore, we demonstrate FLaaS's feasibility in building unique or joint FL models across applications for image object detection in a few hours, across 100 devices.

Massive MIMO Systems for 5G and beyond Networks - Overview, Recent Trends, Challenges, and Future Research Direction. The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G-and beyond-networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.

Artificial-Intelligence-Enabled Intelligent 6G Networks With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G and upcoming 5G networks) may not be able to completely meet quickly rising traffic demands. Accordingly, efforts from both industry and academia have already been put to the research on 6G networks. Recently, artificial intelligence (Ai) has been utilized as a new paradigm for the design and optimization of 6G networks with a high level of intelligence. Therefore, this article proposes an Ai-enabled intelligent architecture for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment and intelligent service provisioning, where the architecture is divided into four layers: intelligent sensing layer, data mining and analytics layer, intelligent control layer and smart application layer. We then review and discuss the applications of Ai techniques for 6G networks and elaborate how to employ the Ai techniques to efficiently and effectively optimize the network performance, including Ai-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. We highlight important future research directions and potential solutions for Ai-enabled intelligent 6G networks, including computation efficiency, algorithms robustness, hardware development and energy management.

A fast and elitist multiobjective genetic algorithm: NSGA-II Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN3) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN2) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed

Multi-Armed Bandit-Based Client Scheduling for Federated Learning By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms.

Statistical tools for digital forensics A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.

Toward Social Learning Environments We are teaching a new generation of students, cradled in technologies, communication and abundance of information. The implications are that we need to focus the design of learning technologies to support social learning in context. Instead of designing technologies that “teach” the learner, the new social learning technologies will perform three main roles: 1) support the learner in finding the right content (right for the context, for the particular learner, for the specific purpose of the learner, right pedagogically); 2) support learners to connect with the right people (again right for the context, learner, purpose, educational goal etc.), and 3) motivate / incentivize people to learn. In the pursuit of such environments, new areas of sciences become relevant as a source of methods and techniques: social psychology, economic / game theory, multi-agent systems. The paper illustrates how social learning technologies can be designed using some existing and emerging technologies: ontologies vs. social tagging, exploratory search, collaborative vs. self-managed social recommendations, trust and reputation mechanisms, mechanism design and social visualization.

Snow walking: motion-limiting device that reproduces the experience of walking in deep snow We propose \"Snow Walking,\" a boot-shaped device that reproduces the experience of walking in deep snow. The main purpose of this study is reproducing the feel of walking in a unique environment that we do not experience daily, particularly one that has depth, such as of deep snow. When you walk in deep snow, you get three feelings: the feel of pulling your foot up from the deep snow, the feel of putting your foot down into the deep snow, and the feel of your feet crunching across the bottom of deep snow. You cannot walk in deep snow easily, and with the system, you get a unique feeling not only on the sole of your foot but as if your entire foot is buried in the snow. We reproduce these feelings by using a slider, electromagnet, vibration speaker, a hook and loop fastener, and potato starch.

Safe mutations for deep and recurrent neural networks through output gradients While neuroevolution (evolving neural networks) has been successful across a variety of domains from reinforcement learning, to artificial life, to evolutionary robotics, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights will likely break existing functionality. This paper proposes a solution: a family of safe mutation (SM) operators that facilitate exploration without dramatically altering network behavior or requiring additional interaction with the environment. The most effective SM variant scales the degree of mutation of each individual weight according to the sensitivity of the network's outputs to that weight, which requires computing the gradient of outputs with respect to the weights (instead of the gradient of error, as in conventional deep learning). This safe mutation through gradients (SM-G) operator dramatically increases the ability of a simple genetic algorithm-based neuroevolution method to find solutions in high-dimensional domains that require deep and/or recurrent neural networks, including domains that require processing raw pixels. By improving our ability to evolve deep neural networks, this new safer approach to mutation expands the scope of domains amenable to neuroevolution.

Energy harvesting algorithm considering max flow problem in wireless sensor networks. In Wireless Sensor Networks (WSNs), sensor nodes with poor energy always have bad effect on the data rate or max flow. These nodes are called bottleneck nodes. In this paper, in order to increase the max flow, we assume an energy harvesting WSNs environment to investigate the cooperation of multiple Mobile Chargers (MCs). MCs are mobile robots that use wireless charging technology to charge sensor nodes in WSNs. This means that in energy harvesting WSNs environments, sensor nodes can obtain energy replenishment by using MCs or collecting energy from nature by themselves. In our research, we use MCs to improve the energy of the sensor nodes by performing multiple rounds of unified scheduling, and finally achieve the purpose of increasing the max flow at sinks. Firstly, we model this problem as a Linear Programming (LP) to search the max flow in a round of charging scheduling and prove that the problem is NP-hard. In order to solve the problem, we propose a heuristic approach: deploying MCs in units of paths with the lowest energy node priority. To reduce the energy consumption of MCs and increase the charging efficiency, we also take the optimization of MCs’ moving distance into our consideration. Finally, we extend the method to multiple rounds of scheduling called BottleNeck. Simulation results show that Bottleneck performs well at increasing max flow.

Implementing a GPU-based parallel MAX-MIN Ant System The MAX–MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore’s law, and the availability of graphics processing units (GPUs) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of GPU-based ACO implementations. In this paper, we discuss a range of novel ideas for improving the GPU-based parallel MMAS implementation, allowing it to better utilize the computing power offered by two subsequent Nvidia GPU architectures. Specifically, based on the weighted reservoir sampling algorithm we propose a novel parallel implementation of the node selection procedure, which is at the heart of the MMAS and other ACO algorithms. We also present a memory-efficient implementation of another key-component – the tabu list structure – which is used in the ACO’s solution construction stage. The proposed implementations, combined with the existing approaches, lead to a total of six MMAS variants, which are evaluated on a set of Traveling Salesman Problem (TSP) instances ranging from 198 to 3795 cities. The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7.18x and 21.79x smaller, respectively. The fastest of the proposed MMAS variants is able to generate over 1 million candidate solutions per second when solving a 1002-city instance. Moreover, we show that, combined with the 2-opt local search heuristic, the proposed parallel MMAS finds high-quality solutions for the TSP instances with up to 18,512 nodes.

A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests In this paper, we focus on the experimental analysis on the performance in artificial neural networks with the use of statistical tests on the classification task. Particularly, we have studied whether the sample of results from multiple trials obtained by conventional artificial neural networks and support vector machines checks the necessary conditions for being analyzed through parametrical tests. The study is conducted by considering three possibilities on classification experiments: random variation in the selection of test data, the selection of training data and internal randomness in the learning algorithm. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which justifies the need of using non-parametric statistics in the experimental analysis.

A Multi-Layered Immune System For Graph Planarization Problem This paper presents a new multi-layered artificial immune system architecture using the ideas generated from the biological immune system for solving combinatorial optimization problems. The proposed methodology is composed of five layers. After expressing the problem as, a suitable representation in the first layer, the search space and the features of the problem are estimated and extracted in the second and third layers, respectively. Through taking advantage of the minimized search space from estimation and the heuristic information from extraction, the antibodies (or solutions) are evolved in the fourth layer and finally the fittest antibody is exported. In order to demonstrate the efficiency of the proposed system, the graph planarization problem is tested. Simulation results based on several benchmark instances show that the proposed algorithm performs better than traditional algorithms.

From evolutionary computation to the evolution of things Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

Improving Dendritic Neuron Model With Dynamic Scale-Free Network-Based Differential Evolution Some recent research reports that a dendritic neuron model (DNM) can achieve better performance than traditional artificial neuron networks (ANNs) on classification, prediction, and other problems when its parameters are well-tuned by a learning algorithm. However, the back-propagation algorithm (BP), as a mostly used learning algorithm, intrinsically suffers from defects of slow convergence and e...

Recent Advances in Evolutionary Computation Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of “biological evolution” toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc., in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily “evolutionary”. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed.

Evolutionary computation: comments on the history and current state Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.

Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization Smart mobile devices (SMDs) can meet users' high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth and inevitably increases the latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve a balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels, and transmission power of each SMD in each time slot. It jointly considers the execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and the maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.

Computer intrusion detection through EWMA for autocorrelated and uncorrelated data Reliability and quality of service from information systems has been threatened by cyber intrusions. To protect information systems from intrusions and thus assure reliability and quality of service, it is highly desirable to develop techniques that detect intrusions. Many intrusions manifest in anomalous changes in intensity of events occurring in information systems. In this study, we apply, tes...

Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems An efficient optimization method called 'Teaching-Learning-Based Optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods.

Understanding Taxi Service Strategies From Taxi GPS Traces Taxi service strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical time-stamped GPS traces. Mining GPS traces to understand the service strategies of skilled taxi drivers can benefit the drivers themselves, passengers, and city planners in a number of ways. This paper intends to uncover the efficient and inefficient taxi service strategies based on a large-scale GPS historical database of approximately 7600 taxis over one year in a city in China. First, we separate the GPS traces of individual taxi drivers and link them with the revenue generated. Second, we investigate the taxi service strategies from three perspectives, namely, passenger-searching strategies, passenger-delivery strategies, and service-region preference. Finally, we represent the taxi service strategies with a feature matrix and evaluate the correlation between service strategies and revenue, informing which strategies are efficient or inefficient. We predict the revenue of taxi drivers based on their strategies and achieve a prediction residual as less as 2.35 RMB/h,1 which demonstrates that the extracted taxi service strategies with our proposed approach well characterize the driving behavior and performance of taxi drivers.

Adaptive fuzzy tracking control for switched uncertain strict-feedback nonlinear systems. •Adaptive tracking control for switched strict-feedback nonlinear systems is proposed.•The generalized fuzzy hyperbolic model is used to approximate nonlinear functions.•The designed controller has fewer design parameters comparing with existing methods.

Energy harvesting algorithm considering max flow problem in wireless sensor networks. In Wireless Sensor Networks (WSNs), sensor nodes with poor energy always have bad effect on the data rate or max flow. These nodes are called bottleneck nodes. In this paper, in order to increase the max flow, we assume an energy harvesting WSNs environment to investigate the cooperation of multiple Mobile Chargers (MCs). MCs are mobile robots that use wireless charging technology to charge sensor nodes in WSNs. This means that in energy harvesting WSNs environments, sensor nodes can obtain energy replenishment by using MCs or collecting energy from nature by themselves. In our research, we use MCs to improve the energy of the sensor nodes by performing multiple rounds of unified scheduling, and finally achieve the purpose of increasing the max flow at sinks. Firstly, we model this problem as a Linear Programming (LP) to search the max flow in a round of charging scheduling and prove that the problem is NP-hard. In order to solve the problem, we propose a heuristic approach: deploying MCs in units of paths with the lowest energy node priority. To reduce the energy consumption of MCs and increase the charging efficiency, we also take the optimization of MCs’ moving distance into our consideration. Finally, we extend the method to multiple rounds of scheduling called BottleNeck. Simulation results show that Bottleneck performs well at increasing max flow.

Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers This paper proposes a novel Adaptive Charged System Search (ACSS) algorithm for the optimal tuning of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). The five stages of this algorithm, namely the engagement, exploration, explanation, elaboration and evaluation, involve the adaptation of the acceleration, velocity, and separation distance parameters to the iteration index, and the substitution of the worst charged particles' fitness function values and positions with the best performing particle data. The ACSS algorithm solves the optimization problems aiming to minimize the objective functions expressed as the sum of absolute control error plus squared output sensitivity function, resulting in optimal fuzzy control systems with reduced parametric sensitivity. The ACSS-based tuning of T-S PI-FCs is applied to second-order servo systems with an integral component. The ACSS algorithm is validated by an experimental case study dealing with the optimal tuning of a T-S PI-FC for the position control of a nonlinear servo system.

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system The performance of the fuzzy controllers depends highly on the proper selection of some design parameters which is usually tuned iteratively via a trial and error process based primarily on engineering intuition. With the recent developments in the area of global optimization, it has been made possible to obtain the optimal values of the design parameters systematically. Nevertheless, it is well known that unless a priori knowledge is available about the optimization search-domain, most of the available time-domain objective functions may result in undesirable solutions. It is consequently important to provide guidelines on how these parameters affect the closed-loop behavior. As a result, some alternative objective functions are presented for the time-domain optimization of the fuzzy controllers, and the design parameters of a PID-type fuzzy controller are tuned by using the proposed time-domain objective functions. Finally, the real-time application of the optimal PID-type fuzzy controller is investigated on the robust stabilization of a laboratory active magnetic bearing system. The experimental results show that the designed PID-type fuzzy controllers provide much superior performances than the linear on-board controllers while retaining lower profiles of control signals.

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications An overview on recent developments in fault diagnosis is carried out.Machine learning, data mining and evolving soft computing techniques are discussed.Real liquid level control, wind turbine and servo system applications are offered.An overview on nature-inspired optimal control of industrial processes is given.New research challenges with strong industrial impact are highlighted. Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.

Harmony search algorithm for solving Sudoku Harmony search (HS) algorithm was applied to solving Sudoku puzzle. The HS is an evolutionary algorithm which mimics musicians' behaviors such as random play, memory-based play, and pitch-adjusted play when they perform improvisation. Sudoku puzzles in this study were formulated as an optimization problem with number-uniqueness penalties. HS could successfully solve the optimization problem after 285 function evaluations, taking 9 seconds. Also, sensitivity analysis of HS parameters was performed to obtain a better idea of algorithm parameter values.

Recent advances on the use of meta-heuristic optimization algorithms to optimize the type-2 fuzzy logic systems in intelligent control Finding the appropriate values of parameters and structure of type-2 fuzzy logic systems is a difficult and complex task. Many types of meta-heuristic algorithms have been used to find the complex structure and appropriate parameter values of the type-2 fuzzy systems and more recently hybrid meta-heuristic algorithms. In this paper, we review recent advances (2012 to date) on the application of meta-heuristic algorithms and hybrid meta-heuristic algorithms, for the optimization of type-2 fuzzy logic systems in intelligent control. It was found that the major meta-heuristic algorithms used for optimizing the design of type-2 fuzzy logic systems in intelligent control were genetic algorithms and particle swarm optimization as well as hybrid meta-heuristic algorithms. Researchers can use this review as a starting point for further advancement as well as an exploration of other meta-heuristic algorithms that have received little or no attention from researchers.

Adaptive population extremal optimization-based PID neural network for multivariable nonlinear control systems. The connection weights parameters play important roles in adjusting the performance of PID neural network (PIDNN) for complex control systems. However, how to obtain an optimal set of initial values of these connection weight parameters in a multivariable PIDNN called MPIDNN is still an open issue for system designers and engineers. This paper formulates this issue as a typical constrained optimization problem firstly by minimizing the cumulative sum of the product of exponential time and the system errors, and a real-time penalty function for overshoots of the system outputs, and then proposes an adaptive population extremal optimization-based MPIDNN method called PEO-MPIDNN for the optimal control issue of multivariable nonlinear control systems. The simulation results for two typical multivariable nonlinear control systems have demonstrated the superiority of the proposed PEO-MPIDNN to real-coded genetic algorithm (RCGA) and particle swarm optimization (PSO)-based MPIDNN, traditional MPIDNN with back propagation algorithm, and population extremal optimization-based multivariable PID control algorithm in terms of transient-state, steady-state, and robust control performance.

A novel real-coded population-based extremal optimization algorithm with polynomial mutation: A non-parametric statistical study on continuous optimization problems. As a recently developed optimization method inspired by far-from-equilibrium dynamics of self-organized criticality, extremal optimization (EO) has been successfully applied to a variety of combinatorial optimization problems while its applications in continuous optimization problems are relatively rare. Additionally, there are only few studies concerning the effects of mutation operation on EO algorithms although mutation operation plays a crucial role in controlling the optimization dynamics and consequently affecting the performance of EO-based algorithms. This paper proposes a novel real-coded population-based EO algorithm with polynomial mutation (RPEO-PLM) for continuous optimization problems. The basic idea behind RPEO-PLM is the population-based iterated optimization consisting of generation of a real-coded random initial population, evaluation of individual and population fitness, generation of a new population based on polynomial mutation, and updating the population by accepting the new population unconditionally. One of the most attractive advantages is its relative simplicity compared with other popular evolutionary algorithms due to its fewer adjustable parameters needing to be tuned and only selection and mutation operations. Furthermore, the experimental results on a large number of benchmark functions with the different dimensions by using non-parametric statistical tests including Friedman and Quade tests have shown that the proposed RPEO-PLM algorithm outperforms other popular population-based evolutionary algorithms, e.g., real-coded genetic algorithm (RCGA) with adaptive directed mutation (RCGA-ADM), RCGA with polynomial mutation (RCGA-PLM), intelligent evolutionary algorithm (IEA), a hybrid particle swarm optimization and EO algorithm (PSO–EO), the original population-based EO (PEO), and an improved RPEO algorithm with random mutation (IRPEO-RM) in terms of accuracy.

A survey on industrial applications of fuzzy control Fuzzy control has long been applied to industry with several important theoretical results and successful results. Originally introduced as model-free control design approach, model-based fuzzy control has gained widespread significance in the past decade. This paper presents a survey on recent developments of analysis and design of fuzzy control systems focused on industrial applications reported after 2000.

Adaptive dynamic programming for finite-horizon optimal control of discrete-time nonlinear systems with ε-error bound. In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an ε-error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.

BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the Internet of Things are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this article. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting and multicriteria decision making are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

Student Models that Invite the Learner In: The SMILI:() Open Learner Modelling Framework In recent years, the learner models of some adaptive learning environments have been opened to the learners they represent. However, as yet there is no standard way of describing and analysing these 'open learner models'. This is, in part, due to the variety of issues that can be important or relevant in any particular learner model. The lack of a framework to discuss open learner models poses several difficulties: there is no systematic way to analyse and describe the open learner models of any one system; there is no systematic way to compare the features of open learner models in different systems; and the designers of each new adaptive learning system must repeatedly tread the same path of studying the many diverse uses and approaches of open learner modelling so that they might determine how to make use of open learner modelling in their system. We believe this is a serious barrier to the effective use of open learner models. This paper presents such a framework, and gives examples of its use to describe and compare adaptive educational systems.

Spatial augmented reality as a method for a mobile robot to communicate intended movement. •Communication strategies are to allow robots to convey upcoming movements to humans.•Arrows for conveying direction of movement are understood by humans.•Simple maps depicting a sequence of upcoming movements are useful to humans.•Robots projecting arrows and a map can effectively communicate upcoming movement.

Learning Discriminative Features with Multiple Granularities for Person Re-Identification. The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we obtain a top result of Rank-1/mAP=96.6%/94.2% with this method after re-ranking.

Attitudes Towards Social Robots In Education: Enthusiast, Practical, Troubled, Sceptic, And Mindfully Positive While social robots bring new opportunities for education, they also come with moral challenges. Therefore, there is a need for moral guidelines for the responsible implementation of these robots. When developing such guidelines, it is important to include different stakeholder perspectives. Existing (qualitative) studies regarding these perspectives however mainly focus on single stakeholders. In this exploratory study, we examine and compare the attitudes of multiple stakeholders on the use of social robots in primary education, using a novel questionnaire that covers various aspects of moral issues mentioned in earlier studies. Furthermore, we also group the stakeholders based on similarities in attitudes and examine which socio-demographic characteristics influence these attitude types. Based on the results, we identify five distinct attitude profiles and show that the probability of belonging to a specific profile is affected by such characteristics as stakeholder type, age, education and income. Our results also indicate that social robots have the potential to be implemented in education in a morally responsible way that takes into account the attitudes of various stakeholders, although there are multiple moral issues that need to be addressed first. Finally, we present seven (practical) implications for a responsible application of social robots in education following from our results. These implications provide valuable insights into how social robots should be implemented.

Robust and Secure Multiple Watermarking for Medical Images. This paper presents a robust and secure region of interest and non-region of interest based watermarking method for medical images. The proposed method applies the combination of discrete wavelet transform and discrete cosine transforms on the cover medical image for the embedding of image and electronic patient records (EPR) watermark simultaneously. The embedding of multiple watermarks at the same time provides extra level of security and important for the patient identity verification purpose. Further, security of the image and EPR watermarks is enhancing by using message-digest (MD5) hash algorithm and Rivest---Shamir---Adleman respectively before embedding into the medical cover image. In addition, Hamming error correction code is applying on the encrypted EPR watermark to enhance the robustness and reduce the possibility bit error rates which may result into wrong diagnosis in medical environments. The robustness of the method is also extensively examined for known attacks such as salt & pepper, Gaussian, speckle, JPEG compression, filtering, histogram equalization. The method is found to be robust for hidden watermark at acceptable quality of the watermarked image. Therefore, the hybrid method is suitable for avoidance of the patient identity theft/alteration/modification and secure medical document dissemination over the open channel for medical applications.

A feature-based robust digital image watermarking scheme A robust digital image watermarking scheme that combines image feature extraction and image normalization is proposed. The goal is to resist both geometric distortion and signal processing attacks. We adopt a feature extraction method called Mexican hat wavelet scale interaction. The extracted feature points can survive a variety of attacks and be used as reference points for both watermark embedding and detection. The normalized image of an image (object) is nearly invariant with respect to rotations. As a result, the watermark detection task can be much simplified when it is applied to the normalized image. However, because image normalization is sensitive to image local variation, we apply image normalization to nonoverlapped image disks separately. The disks are centered at the extracted feature points. Several copies of a 16-bit watermark sequence are embedded in the original image to improve the robustness of watermarks. Simulation results show that our scheme can survive low-quality JPEG compression, color reduction, sharpening, Gaussian filtering, median filtering, row or column removal, shearing, rotation, local warping, cropping, and linear geometric transformations.

Imperceptible visible watermarking based on postcamera histogram operation real-world scene captured via digital devices, such as a digital still camera, video recorder and mobile device, is a common behavior in recent decades. With the increasing availability, reproduction and sharing of media, the intellectual property of digital media is incapable of guaranty. To claim the ownership of digital camera media, the imperceptible visible watermarking (IVW) mechanism was designed based on the observation that most camera devices contain the postcamera histogram operation. The IVW approach can achieve advantages both the content readability of invisible watermarking methodology and the visual ownership identification of visible watermarking methodology. The computational complexity of IVW is low and can be effectively applied to almost any of the digital electronic devices when capturing the real-world scene without additional instruments. The following results and analysis demonstrate the novel scheme is effective and applicable for versatile images and videos captured.

Digital watermarking: Applicability for developing trust in medical imaging workflows state of the art review. Medical images can be intentionally or unintentionally manipulated both within the secure medical system environment and outside, as images are viewed, extracted and transmitted. Many organisations have invested heavily in Picture Archiving and Communication Systems (PACS), which are intended to facilitate data security. However, it is common for images, and records, to be extracted from these for a wide range of accepted practices, such as external second opinion, transmission to another care provider, patient data request, etc. Therefore, confirming trust within medical imaging workflows has become essential. Digital watermarking has been recognised as a promising approach for ensuring the authenticity and integrity of medical images. Authenticity refers to the ability to identify the information origin and prove that the data relates to the right patient. Integrity means the capacity to ensure that the information has not been altered without authorisation.

Medical image region based watermarking for secured telemedicine. Exchange of Medical images over public networks entail a methodology to offer secrecy for the image along with confirmation for image integrity. In this paper, a region-based Firefly optimized algorithm and hybridization of DWT and Schur transforms in conjunction with multiple watermarking is recommended to endow with security requisites such as authenticity of the ownership of medical image besides origin of source for interchange of medical images in telemedicine applications. Secrecy and authenticity are offered by inserting robust multiple watermarks in the region-of-noninterest (RONI) of the medical image by means of a blind method in the discrete wavelet transform and Schur transform (DWT-Schur). The capability of imperceptibility, robustness and payload are the main parameters for the assessment of watermarking algorithm, with MRI, Ultrasound plus X-ray gray-scale medical image modalities. Simulation results make obvious the efficacy of the projected algorithm in offering the essential security benefits for applications allied to telemedicine.

A privacy-aware deep learning framework for health recommendation system on analysis of big data In recent technological advancement, the health recommendation system is gaining attention among the public to acquire health care services online. Traditional health recommendations are insecure due to the lack of security constraints caused by the intruders and not suitable to suggest appropriate recommendations. Thus, it creates hesitation in the minds of the people to share sensitive medical information. Hence, it is essential to design a privacy-preserving health recommendation system that should guarantee privacy and also suggest top-N recommendation to the user based on their preferences and earlier feedback. To cope with these issues, we propose a stacked discriminative de-noising convolution auto-encoder–decoder with a two-way recommendation scheme that provides secure and efficient health data to the end-users. In this scheme, privacy is assured to users through the modified blowfish algorithm. For structuring the big data collected from the patient, the Hadoop transform is used. Here, the two-way system analyzes and learns more effective features from the explicit and implicit information of the patient individually, and finally, all the learned features are fused to provide an efficient recommendation. The performance of the proposed system is analyzed with different statistical metrics and compared with recent approaches. From the result analysis, it is evident that the proposed system performs better than the earlier approaches.

Watermarking techniques used in medical images: a survey. The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. As a result of this, there is a need for medical image watermarking (MIW). However, MIW needs to be performed with special care for two reasons. Firstly, the watermarking procedure cannot compromise the quality of the image. Secondly, confidential patient information embedded within the image should be flawlessly retrievable without risk of error after image decompressing. Despite extensive research undertaken in this area, there is still no method available to fulfill all the requirements of MIW. This paper aims to provide a useful survey on watermarking and offer a clear perspective for interested researchers by analyzing the strengths and weaknesses of different existing methods.

Footprints: history-rich tools for information foraging Inspired by Hill and Hollans original work [7], we have beendeveloping a theory of interaction history and building tools toapply this theory to navigation in a complex information space. Wehave built a series of tools - map, paths, annota- tions andsignposts - based on a physical-world navigation metaphor. Thesetools have been in use for over a year. Our user study involved acontrolled browse task and showed that users were able to get thesame amount of work done with significantly less effort.

Theory and Experiment on Formation-Containment Control of Multiple Multirotor Unmanned Aerial Vehicle Systems. Formation-containment control problems for multiple multirotor unmanned aerial vehicle (UAV) systems with directed topologies are studied, where the states of leaders form desired formation and the states of followers converge to the convex hull spanned by those of the leaders. First, formation-containment protocols are constructed based on the neighboring information of UAVs. Then, sufficient con...

Numerical Comparison of Some Penalty-Based Constraint Handling Techniques in Genetic Algorithms We study five penalty function-based constraint handling techniques to be used with genetic algorithms in global optimization. Three of them, the method of superiority of feasible points, the method of parameter free penalties and the method of adaptive penalties have already been considered in the literature. In addition, we introduce two new modifications of these methods. We compare all the five methods numerically in 33 test problems and report and analyze the results obtained in terms of accuracy, efficiency and reliability. The method of adaptive penalties turned out to be most efficient while the method of parameter free penalties was the most reliable.

Adaptive Learning in Tracking Control Based on the Dual Critic Network Design. In this paper, we present a new adaptive dynamic programming approach by integrating a reference network that provides an internal goal representation to help the systems learning and optimization. Specifically, we build the reference network on top of the critic network to form a dual critic network design that contains the detailed internal goal representation to help approximate the value funct...

Mobile-to-mobile energy replenishment in mission-critical robotic sensor networks Recently, much research effort has been devoted to employing mobile chargers for energy replenishment of the robots in robotic sensor networks. Observing the discrepancy between the charging latency of robots and charger travel distance, we propose a novel tree-based charging schedule for the charger, which minimizes its travel distance without causing the robot energy depletion. We analytically evaluate its performance and show its closeness to the optimal solutions. Furthermore, through a queue-based approach, we provide theoretical guidance on the setting of the remaining energy threshold at which the robots request energy replenishment. This guided setting guarantees the feasibility of the tree-based schedule to return a depletion-free charging schedule. The performance of the tree-based charging schedule is evaluated through extensive simulations. The results show that the charger travel distance can be reduced by around 20%, when compared with the schedule that only considers the robot charging latency.

Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems. This paper addresses the finite-time tracking problem of nonlinear pure-feedback systems. Unlike the literature on traditional finite-time stabilization, in this paper the nonlinear system functions, including the bounding functions, are all totally unknown. Fuzzy logic systems are used to model those unknown functions. To present a finite-time control strategy, a criterion of semiglobal practical...

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Adaptive generation of challenging scenarios for testing and evaluation of autonomous vehicles. •A novel framework for generating test cases for autonomous vehicles is proposed.•Adaptive sampling significantly reduces the number of simulations required.•Adjacency clustering identifies performance boundaries of the system.•Approach successfully applied to complex unmanned underwater vehicle missions.

Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components Autonomous vehicles are complex systems that are challenging to test and debug. A requirements-driven approach to the development process can decrease the resources required to design and test these systems, while simultaneously increasing the reliability. We present a testing framework that uses signal temporal logic (STL), which is a precise and unambiguous requirements language. Our framework e...

Automatic Virtual Test Technology for Intelligent Driving Systems Considering Both Coverage and Efficiency The testing of the intelligent driving systems is faced with the challenges of efficiency because real traffic scenarios are infinite, uncontrollable and difficult to be precisely defined. Based on the complexity index of scenario that designed to measure the test effect indirectly, a new combinational testing algorithm of test cases generation is proposed to make a balance among multiple objects including test coverage, the number of test cases and test effect. Then a joint simulation platform based on Matlab, PreScan and Carsim is built up to realize the construction of 3D test environment, execution of test scenarios and evaluation of test results automatically and seamlessly. The strategy proposed in this paper is validated by applying it to a traffic jam pilot system. The result shows that the proposed strategy can improve the overall complexity of the designed test scenarios effectively, which can help us detect system faults faster and easier. And the time required to conduct tests is reduced obviously by means of automation.

Ontology-based methods for enhancing autonomous vehicle path planning We report the results of a first implementation demonstrating the use of an ontology to support reasoning about obstacles to improve the capabilities and performance of on-board route planning for autonomous vehicles. This is part of an overall effort to evaluate the performance of ontologies in different components of an autonomous vehicle within the 4D/RCS system architecture developed at NIST. Our initial focus has been on simple roadway driving scenarios where the controlled vehicle encounters potential obstacles in its path. As reported elsewhere [C. Schlenoff, S. Balakirsky, M. Uschold, R. Provine, S. Smith, Using ontologies to aid navigation planning in autonomous vehicles, Knowledge Engineering Review 18 (3) (2004) 243–255], our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to plan to avoid the object. We describe the results of the first implementation that integrates the ontology, the reasoner and the planner. We describe our insights and lessons learned and discuss resulting changes to our approach.

Integrated Simulation and Formal Verification of a Simple Autonomous Vehicle. This paper presents a proof-of-concept application of an approach to system development based on the integration of formal verification and co-simulation. A simple autonomous vehicle has the task of reaching an assigned straight path and then follow it, and it can be controlled by varying its turning speed. The correctness of the proposed control law has been formalized and verified by interactive theorem proving with the Prototype Verification System. Concurrently, the system has been co-simulated using the Prototype Verification System and the MathWorks Simulink tool: The vehicle kinematics have been simulated in Simulink, whereas the controller has been modeled in the logic language of the Prototype Verification System and simulated with the interpreter for the same language available in the theorem proving environment. With this approach, co-simulation and formal verification corroborate each other, thus strengthening developers' confidence in their analysis.

A Retargetable Fault Injection Framework for Safety Validation of Autonomous Vehicles Autonomous vehicles use Electronic Control Units running complex software to improve passenger comfort and safety. To test safety of in-vehicle electronics, the ISO 26262 standard on functional safety recommends using fault injection during component and system-level design. A Fault Injection Framework (FIF) induces hard-to-trigger hardware and software faults at runtime, enabling analysis of fault propagation effects. The growing number and complexity of diverse interacting components in vehicles demands a versatile FIF at the vehicle level. In this paper, we present a novel retargetable FIF based on debugger interfaces available on many target systems. We validated our FIF in three Hardware-In-the-Loop setups for autonomous driving based on the NXP BlueBox prototyping platform. To trigger a fault injection process, we developed an interactive user interface based on Robot Operating System, which also visualized vehicle system health. Our retargetable debugger-based fault injection mechanism confirmed safety properties and identified safety shortcomings of various automotive systems.

Test Scenario Generation and Optimization Technology for Intelligent Driving Systems In this paper, we propose a new scenario generation algorithm called Combinatorial Testing Based on Complexity (CTBC) based on both combinatorial testing (CT) method and Test Matrix (TM) technique for intelligent driving systems. To guide the generation procedure in the algorithm and evaluate the validity of the generated scenarios, we further propose a concept of complexity of test scenario. CTBC...

Reduction of Uncertainties for Safety Assessment of Automated Driving Under Parallel Simulations Many achievements concerning developments in the field of automated driving have been made. However, automated driving still faces the challenge of safety validation. Conventional methods are not suitable any more for this highly complex automation system. Therefore, the method named Virtual Assessment of Automation in Field Operation (VAAFO) is motivated. In this approach, automated driving system has no access to actuators but rather runs parallel to the human driver. Consequently, this approach is divided into two modules: online trajectory comparison and offline safety assessment. This paper focuses on the second module, in which uncertainties in world model are reduced and then the safety of Automated Vehicle (AV) is evaluated. Retrospective post-processing combined with Joint Integrated Probabilistic Data Association (JIPDA) tracker is put forward to reduce existence uncertainties. State uncertainties are reduced by an Unscented Rauch-Tung-Striebel smoother (URTSS). Furthermore, inverse TTC and remaining lateral distance are utilized to assess the safety of AV in the corrected world model. The results demonstrate that retrospective post-processing combined with JIPDA can reduce existence uncertainties greatly. URTSS is very useful for reducing state uncertainties. The studied case illustrates that the safety of AV can be assessed by parallel running and critical scenarios are found accordingly.

Accurate Self-Localization in RFID Tag Information Grids Using FIR Filtering Grid navigation spaces nested with the radio-frequency identification (RFID) tags are promising for industrial and other needs, because each tag can deliver information about a local two-dimensional or three-dimensional surrounding. The approach, however, requires high accuracy in vehicle self-localization. Otherwise, errors may lead to collisions; possibly even fatal. We propose a new extended finite impulse response (EFIR) filtering algorithm and show that it meets this need. The EFIR filter requires an optimal averaging interval, but does not involve the noise statistics which are often not well known to the engineer. It is more accurate than the extended Kalman filter (EKF) under real operation conditions and its iterative algorithm has the Kalman form. Better performance of the proposed EFIR filter is demonstrated based on extensive simulations in a comparison to EKF, which is widely used in RFID tag grids. We also show that errors in noise covariances may provoke divergence in EKF, whereas the EFIR filter remains stable and is thus more robust.

A Survey on Transfer Learning A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

The set cover with pairs problem We consider a generalization of the set cover problem, in which elements are covered by pairs of objects, and we are required to find a minimum cost subset of objects that induces a collection of pairs covering all elements. Formally, let U be a ground set of elements and let ${\cal S}$ be a set of objects, where each object i has a non-negative cost wi. For every $\{ i, j \} \subseteq {\cal S}$, let ${\cal C}(i,j)$ be the collection of elements in U covered by the pair { i, j }. The set cover with pairs problem asks to find a subset $A \subseteq {\cal S}$ such that $\bigcup_{ \{ i, j \} \subseteq A } {\cal C}(i,j) = U$ and such that ∑i∈Awi is minimized. In addition to studying this general problem, we are also concerned with developing polynomial time approximation algorithms for interesting special cases. The problems we consider in this framework arise in the context of domination in metric spaces and separation of point sets.

Telecommunications Power Plant Damage Assessment for Hurricane Katrina– Site Survey and Follow-Up Results This paper extends knowledge of disaster impact on the telecommunications power infrastructure by discussing the effects of Hurricane Katrina based on an on-site survey conducted in October 2005 and on public sources. It includes observations about power infrastructure damage in wire-line and wireless networks. In general, the impact on centralized network elements was more severe than on the distributed portion of the grids. The main cause of outage was lack of power due to fuel supply disruptions, flooding and security issues. This work also describes the means used to restore telecommunications services and proposes ways to improve logistics, such as coordinating portable generator set deployment among different network operators and reducing genset fuel consumption by installing permanent photovoltaic systems at sites where long electric outages are likely. One long term solution is to use of distributed generation. It also discusses the consequences on telecom power technology and practices since the storm.

When Vehicles See Pedestrians With Phones: A Multicue Framework for Recognizing Phone-Based Activities of Pedestrians The intelligent vehicle community has devoted considerable efforts to model driver behavior, and in particular, to detect and overcome driver distraction in an effort to reduce accidents caused by driver negligence. However, as the domain increasingly shifts toward autonomous and semiautonomous solutions, the driver is no longer integral to the decision-making process, indicating a need to refocus...

Pricing-Based Channel Selection for D2D Content Sharing in Dynamic Environment In order to make device-to-device (D2D) content sharing give full play to its advantage of improving local area services, one of the important issues is to decide the channels that D2D pairs occupy. Most existing works study this issue in static environment, and ignore the guidance for D2D pairs to select the channel adaptively. In this paper, we investigate this issue in dynamic environment where...

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.github.com/lehaifeng/T-GCN</uri> .

Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization. Crowd Flow Prediction (CFP) is one major challenge in the intelligent transportation systems of the Sydney Trains Network. However, most advanced CFP methods only focus on entrance and exit flows at the major stations or a few subway lines, neglecting Crowd Flow Distribution (CFD) forecasting problem across the entire city network. CFD prediction plays an irreplaceable role in metro management as a tool that can help authorities plan route schedules and avoid congestion. In this paper, we propose three online non-negative matrix factorization (ONMF) models. ONMF-AO incorporates an Average Optimization strategy that adapts to stable passenger flows. ONMF-MR captures the Most Recent trends to achieve better performance when sudden changes in crowd flow occur. The Hybrid model, ONMF-H, integrates both ONMF-AO and ONMF-MR to exploit the strengths of each model in different scenarios and enhance the models' applicability to real-world situations. Given a series of CFD snapshots, both models learn the latent attributes of the train stations and, therefore, are able to capture transition patterns from one timestamp to the next by combining historic guidance. Intensive experiments on a large-scale, real-world dataset containing transactional data demonstrate the superiority of our ONMF models.

Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling Ride-hailing applications are becoming more and more popular for providing drivers and passengers with convenient ride services, especially in metropolises like Beijing or New York. To obtain the passengers' mobility patterns, the online platforms of ride services need to predict the number of passenger demands from one region to another in advance. We formulate this problem as an Origin-Destination Matrix Prediction (ODMP) problem. Though this problem is essential to large-scale providers of ride services for helping them make decisions and some providers have already put it forward in public, existing studies have not solved this problem well. One of the main reasons is that the ODMP problem is more challenging than the common demand prediction. Besides the number of demands in a region, it also requires the model to predict the destinations of them. In addition, data sparsity is a severe issue. To solve the problem effectively, we propose a unified model, Grid-Embedding based Multi-task Learning (GEML) which consists of two components focusing on spatial and temporal information respectively. The Grid-Embedding part is designed to model the spatial mobility patterns of passengers and neighboring relationships of different areas, the pre-weighted aggregator of which aims to sense the sparsity and range of data. The Multi-task Learning framework focuses on modeling temporal attributes and capturing several objectives of the ODMP problem. The evaluation of our model is conducted on real operational datasets from UCAR and Didi. The experimental results demonstrate the superiority of our GEML against the state-of-the-art approaches.

Regression conformal prediction with random forests Regression conformal prediction produces prediction intervals that are valid, i.e., the probability of excluding the correct target value is bounded by a predefined confidence level. The most important criterion when comparing conformal regressors is efficiency; the prediction intervals should be as tight (informative) as possible. In this study, the use of random forests as the underlying model for regression conformal prediction is investigated and compared to existing state-of-the-art techniques, which are based on neural networks and k-nearest neighbors. In addition to their robust predictive performance, random forests allow for determining the size of the prediction intervals by using out-of-bag estimates instead of requiring a separate calibration set. An extensive empirical investigation, using 33 publicly available data sets, was undertaken to compare the use of random forests to existing state-of-the-art conformal predictors. The results show that the suggested approach, on almost all confidence levels and using both standard and normalized nonconformity functions, produced significantly more efficient conformal predictors than the existing alternatives.

Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction Predicting urban flow is essential for city risk assessment and traffic management, which profoundly impacts people's lives and property. Recently, some deep learning models, focusing on capturing spatio-temporal (ST) correlations between urban regions, have been proposed to predict urban flows. However, these models overlook latent region functions that impact ST correlations greatly. Thus, it is necessary to have a framework to assist these deep models in tackling the region function issue. However, it is very challenging because of two problems: 1) how to make deep models predict flows taking into consideration latent region functions; 2) how to make the framework generalize to a variety of deep models. To tackle these challenges, we propose a novel framework that employs matrix factorization for spatio-temporal neural networks (MF-STN), capable of enhancing the state-of-the-art deep ST models. MF-STN consists of two components: 1) a ST feature learner, which obtains features of ST correlations from all regions by the corresponding sub-networks in the existing deep models; and 2) a region-specific predictor, which leverages the learned ST features to make region-specific predictions. In particular, matrix factorization is employed on the neural networks, namely, decomposing the region-specific parameters of the predictor into learnable matrices, i.e., region embedding matrices and parameter embedding matrices, to model latent region functions and correlations among regions. Extensive experiments were conducted on two real-world datasets, illustrating that MF-STN can significantly improve the performance of some representative ST models while preserving model complexity.

DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g. police) and public service operators (e.g. subway/bus operator) to protect people's safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the deep trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly-complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.

Traffic Speed Prediction: An Attention-Based Method. Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction.

Flight Delay Prediction Based on Aviation Big Data and Machine Learning Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance-broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the overfitting problem.

Bike Flow Prediction with Multi-Graph Convolutional Networks. One fundamental issue in managing bike sharing systems is bike flow prediction. Due to the hardness of predicting flow for a single station, recent research often predicts flow at cluster-level. However, they cannot directly guide fine-grained system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy using the breakthroughs of deep learning techniques. We propose a multi-graph convolutional neural network model to predict flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective. More specifically, we construct multiple graphs for a bike sharing system to reflect heterogeneous inter-station relationships. Afterward, we fuse multiple graphs and apply the convolutional layers to predict station-level future bike flow. The results on realistic bike flow datasets verify that our multi-graph model can outperform state-of-the-art prediction models by reducing up to 25.1% prediction error.

Model-based periodic event-triggered control for linear systems Periodic event-triggered control (PETC) is a control strategy that combines ideas from conventional periodic sampled-data control and event-triggered control. By communicating periodically sampled sensor and controller data only when needed to guarantee stability or performance properties, PETC is capable of reducing the number of transmissions significantly, while still retaining a satisfactory closed-loop behavior. In this paper, we will study observer-based controllers for linear systems and propose advanced event-triggering mechanisms (ETMs) that will reduce communication in both the sensor-to-controller channels and the controller-to-actuator channels. By exploiting model-based computations, the new classes of ETMs will outperform existing ETMs in the literature. To model and analyze the proposed classes of ETMs, we present two frameworks based on perturbed linear and piecewise linear systems, leading to conditions for global exponential stability and @?"2-gain performance of the resulting closed-loop systems in terms of linear matrix inequalities. The proposed analysis frameworks can be used to make tradeoffs between the network utilization on the one hand and the performance in terms of @?"2-gains on the other. In addition, we will show that the closed-loop performance realized by an observer-based controller, implemented in a conventional periodic time-triggered fashion, can be recovered arbitrarily closely by a PETC implementation. This provides a justification for emulation-based design. Next to centralized model-based ETMs, we will also provide a decentralized setup suitable for large-scale systems, where sensors and actuators are physically distributed over a wide area. The improvements realized by the proposed model-based ETMs will be demonstrated using numerical examples.

Power assist method for HAL-3 using EMG-based feedback controller We have developed the exoskeletal robotics suite HAL (Hybrid Assistive Leg) which is integrated with human and assists suitable power for lower limb of people with gait disorder. This study proposes the method of assist motion and assist torque to realize a power assist corresponding to the operator's intention. In the method of assist motion, we adopted Phase Sequence control which generates a series of assist motions by transiting some simple basic motions called Phase. We used the feedback controller to adjust the assist torque to maintain myoelectricity signals which were generated while performing the power assist walking. The experiment results showed the effective power assist according to operator's intention by using these control, methods.

Development of an orthosis for walking assistance using pneumatic artificial muscle: a quantitative assessment of the effect of assistance. In recent years, there is an increase in the number of people that require support during walking as a result of a decrease in the leg muscle strength accompanying aging. An important index for evaluating walking ability is step length. A key cause for a decrease in step length is the loss of muscle strength in the legs. Many researchers have designed and developed orthoses for walking assistance. In this study, we advanced the design of an orthosis for walking assistance that assists the forward swing of the leg to increase step length. We employed a pneumatic artificial muscle as the actuator so that flexible assistance with low rigidity can be achieved. To evaluate the performance of the system, we measured the effect of assistance quantitatively. In this study, we constructed a prototype of the orthosis and measure EMG and step length on fitting it to a healthy subject so as to determine the effect of assistance, noting the increase in the obtained step length. Although there was an increase in EMG stemming from the need to maintain body balance during the stance phase, we observed that the EMG of the sartorius muscle, which helps swing the leg forward, decreased, and the strength of the semitendinosus muscle, which restrains the leg against over-assistance, did not increase but decreased. Our experiments showed that the assistance force provided by the developed orthosis is not adequate for the intended task, and the development of a mechanism that provides appropriate assistance is required in the future.

Hierarchical Compliance Control of a Soft Ankle Rehabilitation Robot Actuated by Pneumatic Muscles. Traditional compliance control of a rehabilitation robot is implemented in task space by using impedance or admittance control algorithms. The soft robot actuated by pneumatic muscle actuators (PMAs) is becoming prominent for patients as it enables the compliance being adjusted in each active link, which, however, has not been reported in the literature. This paper proposes a new compliance control method of a soft ankle rehabilitation robot that is driven by four PMAs configured in parallel to enable three degrees of freedom movement of the ankle joint. A new hierarchical compliance control structure, including a low-level compliance adjustment controller in joint space and a high-level admittance controller in task space, is designed. An adaptive compliance control paradigm is further developed by taking into account patient's active contribution and movement ability during a previous period of time, in order to provide robot assistance only when it is necessarily required. Experiments on healthy and impaired human subjects were conducted to verify the adaptive hierarchical compliance control scheme. The results show that the robot hierarchical compliance can be online adjusted according to the participant's assessment. The robot reduces its assistance output when participants contribute more and vice versa, thus providing a potentially feasible solution to the patient-in-loop cooperative training strategy.

Learning Feature Recovery Transformer for Occluded Person Re-Identification One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e., the interference of noise during feature matching and the loss of pedestrian information brought by the occlusions. In this paper, we propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously, which mainly consists of visibility graph matching and feature recovery transformer. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its k-nearest neighbors in the gallery to recover the complete features. Extensive experiments across different person Re-ID datasets, including occluded, partial and holistic datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly outperforms state-of-the-art results by at least 6.2% Rank- 1 accuracy and 7.2% mAP scores on the challenging Occluded-Duke dataset.

Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots In this paper, a system that allows applying precision agriculture techniques is described. The application is based on the deployment of a team of unmanned aerial vehicles that are able to take georeferenced pictures in order to create a full map by applying mosaicking procedures for postprocessing. The main contribution of this work is practical experimentation with an integrated tool. Contributions in different fields are also reported. Among them is a new one-phase automatic task partitioning manager, which is based on negotiation among the aerial vehicles, considering their state and capabilities. Once the individual tasks are assigned, an optimal path planning algorithm is in charge of determining the best path for each vehicle to follow. Also, a robust flight control based on the use of a control law that improves the maneuverability of the quadrotors has been designed. A set of field tests was performed in order to analyze all the capabilities of the system, from task negotiations to final performance. These experiments also allowed testing control robustness under different weather conditions. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Efficient Boustrophedon Multi-Robot Coverage: an algorithmic approach This paper presents algorithmic solutions for the complete coverage path planning problem using a team of mobile robots. Multiple robots decrease the time to complete the coverage, but maximal efficiency is only achieved if the number of regions covered multiple times is minimized. A set of multi-robot coverage algorithms is presented that minimize repeat coverage. The algorithms use the same planar cell-based decomposition as the Boustrophedon single robot coverage algorithm, but provide extensions to handle how robots cover a single cell, and how robots are allocated among cells. Specifically, for the coverage task our choice of multi-robot policy strongly depends on the type of communication that exists between the robots. When the robots operate under the line-of-sight communication restriction, keeping them as a team helps to minimize repeat coverage. When communication between the robots is available without any restrictions, the robots are initially distributed through space, and each one is allocated a virtually-bounded area to cover. A greedy auction mechanism is used for task/cell allocation among the robots. Experimental results from different simulated and real environments that illustrate our approach for different communication conditions are presented.

Touring a sequence of polygons Given a sequence of k polygons in the plane, a start point s, and a target point, t, we seek a shortest path that starts at s, visits in order each of the polygons, and ends at t. If the polygons are disjoint and convex, we give an algorithm running in time O(kn log (n/k)), where n is the total number of vertices specifying the polygons. We also extend our results to a case in which the convex polygons are arbitrarily intersecting and the subpath between any two consecutive polygons is constrained to lie within a simply connected region; the algorithm uses O(nk2 log n) time. Our methods are simple and allow shortest path queries from s to a query point t to be answered in time O(k log n + m), where m is the combinatorial path length. We show that for nonconvex polygons this "touring polygons" problem is NP-hard.The touring polygons problem is a strict generalization of some classic problems in computational geometry, including the safari problem, the zoo-keeper problem, and the watchman route problem in a simple polygon. Our new results give an order of magnitude improvement in the running times of the safari problem and the watchman route problem: We solve the safari problem in O(n2 log n) time and the watchman route problem (through a fixed point s) in time O(n3 log n), compared with the previous time bounds of O(n3) and O(n4), respectively.

New exact method for large asymmetric distance-constrained vehicle routing problem. In this paper we revise and modify an old branch-and-bound method for solving the asymmetric distance-constrained vehicle routing problem suggested by Laporte et al. in 1987. Our modification is based on reformulating distance-constrained vehicle routing problem into a travelling salesman problem, and on using assignment problem as a lower bounding procedure. In addition, our algorithm uses the best-first strategy and new tolerance based branching rules. Since our method is fast but memory consuming, it could stop before optimality is proven. Therefore, we introduce the randomness, in case of ties, in choosing the node of the search tree. If an optimal solution is not found, we restart our procedure. As far as we know, the instances that we have solved exactly (up to 1000 customers) are much larger than the instances considered for other vehicle routing problem models from the recent literature. So, despite of its simplicity, this proposed algorithm is capable of solving the largest instances ever solved in the literature. Moreover, this approach is general and may be used for solving other types of vehicle routing problems. (C) 2012 Elsevier B.V. All rights reserved.

A Survey on Vehicle Routing Problem with Loading Constraints In the classical vehicle routing problem (VRP), a fleet of vehicles is available to serve a set of customers with known demand. Each customer is visited by exactly one vehicle as well as the objective is to minimize the total distance or the total charge incurred. Recent years have seen increased attention on VRP integrated with additional loading constraints, known as 2L-CVRP or 3L-CVRP. In this paper we present a survey of the state-of-the-art on 2L-CVRP/3L-CVRP.

Finding shortest safari routes in simple polygons Let P be a simple polygon, and let P be a set of disjoint convex polygons inside P, each sharing one edge with P. The safari route problem asks for a shortest route inside P that visits each polygon in P. In this paper, we first present a dynamic programming algorithm with running time O(n3) for computing the shortest safari route in the case that a starting point on the route is given, where n is the total number of vertices of P and polygons in P. (Ntafos in [Comput. Geom. 1 (1992) 149-170] claimed a more efficient solution, but as shown in Appendix A of this paper, the time analysis of Ntafos' algorithm is erroneous and no time bound is guaranteed for his algorithm.) The restriction of giving a starting point is then removed by a brute-force algorithm, which requires O(n4) time. The solution of the safari route problem finds applications in watchman routes under limited visibility.

Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage. In this letter, the efficient deployment of multiple unmanned aerial vehicles (UAVs) acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the 3-D locations of the UAVs is determined in a way that the total coverage area ...

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.

Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization Smart mobile devices (SMDs) can meet users' high expectations by executing computational intensive applications but they only have limited resources, including CPU, memory, battery power, and wireless medium. To tackle this limitation, partial computation offloading can be used as a promising method to schedule some tasks of applications from resource-limited SMDs to high-performance edge servers. However, it brings communication overhead issues caused by limited bandwidth and inevitably increases the latency of tasks offloaded to edge servers. Therefore, it is highly challenging to achieve a balance between high-resource consumption in SMDs and high communication cost for providing energy-efficient and latency-low services to users. This work proposes a partial computation offloading method to minimize the total energy consumed by SMDs and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of SMDs, allocated bandwidth of available channels, and transmission power of each SMD in each time slot. It jointly considers the execution time of tasks performed in SMDs and edge servers, and transmission time of data. It also jointly considers latency limits, CPU speeds, transmission power limits, available energy of SMDs, and the maximum number of CPU cycles and memories in edge servers. Considering these factors, a nonlinear constrained optimization problem is formulated and solved by a novel hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSP) to produce a close-to-optimal solution. GSP achieves joint optimization of computation offloading between a cloud data center and the edge, and resource allocation in the data center. Real-life data-based experimental results prove that it achieves lower energy consumption in less convergence time than its three typical peers.

Computer intrusion detection through EWMA for autocorrelated and uncorrelated data Reliability and quality of service from information systems has been threatened by cyber intrusions. To protect information systems from intrusions and thus assure reliability and quality of service, it is highly desirable to develop techniques that detect intrusions. Many intrusions manifest in anomalous changes in intensity of events occurring in information systems. In this study, we apply, tes...

Comment on "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes" Comparison of generative and discriminative classifiers is an ever-lasting topic. As an important contribution to this topic, based on their theoretical and empirical comparisons between the naïve Bayes classifier and linear logistic regression, Ng and Jordan (NIPS 841---848, 2001) claimed that there exist two distinct regimes of performance between the generative and discriminative classifiers with regard to the training-set size. In this paper, our empirical and simulation studies, as a complement of their work, however, suggest that the existence of the two distinct regimes may not be so reliable. In addition, for real world datasets, so far there is no theoretically correct, general criterion for choosing between the discriminative and the generative approaches to classification of an observation x into a class y; the choice depends on the relative confidence we have in the correctness of the specification of either p(y|x) or p(x, y) for the data. This can be to some extent a demonstration of why Efron (J Am Stat Assoc 70(352):892---898, 1975) and O'Neill (J Am Stat Assoc 75(369):154---160, 1980) prefer normal-based linear discriminant analysis (LDA) when no model mis-specification occurs but other empirical studies may prefer linear logistic regression instead. Furthermore, we suggest that pairing of either LDA assuming a common diagonal covariance matrix (LDA-驴) or the naïve Bayes classifier and linear logistic regression may not be perfect, and hence it may not be reliable for any claim that was derived from the comparison between LDA-驴 or the naïve Bayes classifier and linear logistic regression to be generalised to all generative and discriminative classifiers.

Beamforming for MISO Interference Channels with QoS and RF Energy Transfer We consider a multiuser multiple-input single-output interference channel where the receivers are characterized by both quality-of-service (QoS) and radio-frequency (RF) energy harvesting (EH) constraints. We consider the power splitting RF-EH technique where each receiver divides the received signal into two parts a) for information decoding and b) for battery charging. The minimum required power that supports both the QoS and the RF-EH constraints is formulated as an optimization problem that incorporates the transmitted power and the beamforming design at each transmitter as well as the power splitting ratio at each receiver. We consider both the cases of fixed beamforming and when the beamforming design is incorporated into the optimization problem. For fixed beamforming we study three standard beamforming schemes, the zero-forcing (ZF), the regularized zero-forcing (RZF) and the maximum ratio transmission (MRT); a hybrid scheme, MRT-ZF, comprised of a linear combination of MRT and ZF beamforming is also examined. The optimal solution for ZF beamforming is derived in closed-form, while optimization algorithms based on second-order cone programming are developed for MRT, RZF and MRT-ZF beamforming to solve the problem. In addition, the joint-optimization of beamforming and power allocation is studied using semidefinite programming (SDP) with the aid of rank relaxation.

Inter-class sparsity based discriminative least square regression Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring the correlations among samples. The second one is that the used label matrix, i.e., zero–one label matrix is inappropriate for classification. To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), for multi-class classification. Different from other methods, the proposed method pursues that the transformed samples have a common sparsity structure in each class. For this goal, an inter-class sparsity constraint is introduced to the least square regression model such that the margins of samples from the same class can be greatly reduced while those of samples from different classes can be enlarged. In addition, an error term with row-sparsity constraint is introduced to relax the strict zero–one label matrix, which allows the method to be more flexible in learning the discriminative transformation matrix. These factors encourage the method to learn a more compact and discriminative transformation for regression and thus has the potential to perform better than other methods. Extensive experimental results show that the proposed method achieves the best performance in comparison with other methods for multi-class classification.

Social Robots for (Second) Language Learning in (Migrant) Primary School Children Especially these days, innovation and support from technology to relieve pressure in education is highly urgent. This study tested the potential advantage of a social robot over a tablet in (second) language learning on performance, engagement, and enjoyment. Shortages in primary education call for new technology solutions. Previous studies combined robots with tablets, to compensate for robot’s limitations, however, this study applied direct human–robot interaction. Primary school children (N = 63, aged 4–6) participated in a 3-wave field experiment with story-telling exercises, either with a semi-autonomous robot (without tablet, using WOz) or a tablet. Results showed increased learning gains over time when training with a social robot, compared to the tablet. Children who trained with a robot were more engaged in the story-telling task and enjoyed it more. Robot’s behavioral style (social or neutral) hardly differed overall, however, seems to vary for high versus low educational abilities. While social robots need sophistication before being implemented in schools, our study shows the potential of social robots as tutors in (second) language learning.

Kernel-Based Positioning in Wireless Local Area Networks The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods.

Magnetic field feature extraction and selection for indoor location estimation. User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices, which allows us to exploit not only infrastructures made for everyday use, such as WiFi, but also natural infrastructure, as is the case of natural magnetic field. In this paper we present an extension and improvement of our current indoor localization model based on the feature extraction of 46 magnetic field signal features. The extension adds a feature selection phase to our methodology, which is performed through Genetic Algorithm (GA) with the aim of optimizing the fitness of our current model. In addition, we present an evaluation of the final model in two different scenarios: home and office building. The results indicate that performing a feature selection process allows us to reduce the number of signal features of the model from 46 to 5 regardless the scenario and room location distribution. Further, we verified that reducing the number of features increases the probability of our estimator correctly detecting the user's location (sensitivity) and its capacity to detect false positives (specificity) in both scenarios.

A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing. Traditional radio-map-based localization methods need to sample a large number of location fingerprints offline, which requires huge amount of human and material resources. To solve the high sampling cost problem, an automatic radio-map construction algorithm based on crowdsourcing is proposed. The algorithm employs the crowd-sourced information provided by a large number of users when they are walking in the buildings as the source of location fingerprint data. Through the variation characteristics of users' smartphone sensors, the indoor anchors (doors) are identified and their locations are regarded as reference positions of the whole radio-map. The AP-Cluster method is used to cluster the crowdsourced fingerprints to acquire the representative fingerprints. According to the reference positions and the similarity between fingerprints, the representative fingerprints are linked to their corresponding physical locations and the radio-map is generated. Experimental results demonstrate that the proposed algorithm reduces the cost of fingerprint sampling and radio-map construction and guarantees the localization accuracy. The proposed method does not require users' explicit participation, which effectively solves the resource-consumption problem when a location fingerprint database is established.

Magnetic, Acceleration Fields and Gyroscope Quaternion (MAGYQ)-based attitude estimation with smartphone sensors for indoor pedestrian navigation. The dependence of proposed pedestrian navigation solutions on a dedicated infrastructure is a limiting factor to the deployment of location based services. Consequently self-contained Pedestrian Dead-Reckoning (PDR) approaches are gaining interest for autonomous navigation. Even if the quality of low cost inertial sensors and magnetometers has strongly improved, processing noisy sensor signals combined with high hand dynamics remains a challenge. Estimating accurate attitude angles for achieving long term positioning accuracy is targeted in this work. A new Magnetic, Acceleration fields and GYroscope Quaternion (MAGYQ)-based attitude angles estimation filter is proposed and demonstrated with handheld sensors. It benefits from a gyroscope signal modelling in the quaternion set and two new opportunistic updates: magnetic angular rate update (MARU) and acceleration gradient update (AGU). MAGYQ filter performances are assessed indoors, outdoors, with dynamic and static motion conditions. The heading error, using only the inertial solution, is found to be less than 10 degrees after 1.5 km walking. The performance is also evaluated in the positioning domain with trajectories computed following a PDR strategy.

A Robust Crowdsourcing-Based Indoor Localization System. WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS.

Optimization-Based Wi-Fi Radio Map Construction for Indoor Positioning Using Only Smart Phones. Fingerprinting-based Wi-Fi indoor positioning has great potential for positioning in GPS-denied areas. However, establishing a fingerprinting map (also called a radio map) prior to positioning (site survey) is normally a labor-intensive task. This paper proposes a method for easy site survey without need for any extra hardware. The user can conduct the site survey adopting only a smart phone. The collected inertial-based readings are processed using the pedestrian dead-reckoning algorithms to generate a raw trajectory. Then a factor graph optimization method is proposed to re-estimate the trajectory by adding constraints originated from collected Wi-Fi fingerprints and landmark positions. The proposed method is verified through an experiment in a mall. The mean positioning error is 1.10 m and the maximum error is 2.25 m. This level of positioning accuracy is considered sufficient for radio map generation purposes. A classical baseline algorithm, the k-Nearest Neighbor (kNN) algorithm, is adopted to test the positioning performance of the radio map (RM), which also validates the quality of the constructed RM from the proposed method.

Walk&Sketch: create floor plans with an RGB-D camera Creating floor plans for large areas via manual surveying is labor-intensive and error-prone. In this paper, we present a system, Walk&Sketch, that creates floor plans of an indoor environment by a person walking through the environment at a normal strolling pace and taking videos using a consumer RGB-D camera. The method computes floor maps represented by polylines from a 3D point cloud based on precise frame-to-frame alignment. It aligns a reference frame with the floor and computes the frame-to-frame offsets from the continuous RGB-D input. Line segments at a certain height are extracted from the 3D point cloud, and are merged to form a polyline map, which can be further modified and annotated by users. The explored area is visualized as a sequence of polygons, providing users with the information on coverage. Experiments have done in various areas of an office building and have shown encouraging results.

A survey on sensor networks The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.

Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider a MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources􀀀the transmit precoding matrices of the MUs􀀀and the computational resources􀀀the CPU cycles/second assigned by the cloud to each MU􀀀in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

Space-time modeling of traffic flow. This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5min average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.

A novel full structure optimization algorithm for radial basis probabilistic neural networks. In this paper, a novel full structure optimization algorithm for radial basis probabilistic neural networks (RBPNN) is proposed. Firstly, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to heuristically select the initial hidden layer centers of the RBPNN, and then the recursive orthogonal least square (ROLS) algorithm combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. Finally, the effectiveness and efficiency of our proposed algorithm are evaluated through a plant species identification task involving 50 plant species.

Passive Image-Splicing Detection by a 2-D Noncausal Markov Model In this paper, a 2-D noncausal Markov model is proposed for passive digital image-splicing detection. Different from the traditional Markov model, the proposed approach models an image as a 2-D noncausal signal and captures the underlying dependencies between the current node and its neighbors. The model parameters are treated as the discriminative features to differentiate the spliced images from the natural ones. We apply the model in the block discrete cosine transformation domain and the discrete Meyer wavelet transform domain, and the cross-domain features are treated as the final discriminative features for classification. The support vector machine which is the most popular classifier used in the image-splicing detection is exploited in our paper for classification. To evaluate the performance of the proposed method, all the experiments are conducted on public image-splicing detection evaluation data sets, and the experimental results have shown that the proposed approach outperforms some state-of-the-art methods.

Collective feature selection to identify crucial epistatic variants. In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Heterogeneous Graph Attention Network for Unsupervised Multiple-Target Domain Adaptation Domain adaptation, which transfers the knowledge from label-rich source domain to unlabeled target domains, is a challenging task in machine learning. The prior domain adaptation methods focus on pairwise adaptation assumption with a single source and a single target domain, while little work concerns the scenario of one source domain and multiple target domains. Applying pairwise adaptation metho...

Space-time super-resolution. We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple low-resolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By "temporal super-resolution," we mean recovering rapid dynamic events that occur faster than regular frame-rate. Such dynamic events are not visible (or else are observed incorrectly) in any of the input sequences, even if these are played in "slow-motion." The spatial and temporal dimensions are very different in nature, yet are interrelated. This leads to interesting visual trade-offs in time and space and to new video applications. These include: 1) treatment of spatial artifacts (e.g., motion-blur) by increasing the temporal resolution and 2) combination of input sequences of different space-time resolutions (e.g., NTSC, PAL, and even high quality still images) to generate a high quality video sequence. We further analyze and compare characteristics of temporal super-resolution to those of spatial super-resolution. These include: How many video cameras are needed to obtain increased resolution? What is the upper bound on resolution improvement via super-resolution? What is the temporal analogue to the spatial "ringing" effect?

Transient attributes for high-level understanding and editing of outdoor scenes We live in a dynamic visual world where the appearance of scenes changes dramatically from hour to hour or season to season. In this work we study \"transient scene attributes\" -- high level properties which affect scene appearance, such as \"snow\", \"autumn\", \"dusk\", \"fog\". We define 40 transient attributes and use crowdsourcing to annotate thousands of images from 101 webcams. We use this \"transient attribute database\" to train regressors that can predict the presence of attributes in novel images. We demonstrate a photo organization method based on predicted attributes. Finally we propose a high-level image editing method which allows a user to adjust the attributes of a scene, e.g. change a scene to be \"snowy\" or \"sunset\". To support attribute manipulation we introduce a novel appearance transfer technique which is simple and fast yet competitive with the state-of-the-art. We show that we can convincingly modify many transient attributes in outdoor scenes.

Semantic Understanding of Scenes through the ADE20K Dataset. Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. Totally there are 25k images of the complex everyday scenes containing a variety of objects in their natural spatial context. On average there are 19.5 instances and 10.5 object classes per image. Based on ADE20K, we construct benchmarks for scene parsing and instance segmentation. We provide baseline performances on both of the benchmarks and re-implement state-of-the-art models for open source. We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance. We show that the networks trained on ADE20K are able to segment a wide variety of scenes and objects.

Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures. This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also perform text-to-video generation which, to the best of our knowledge, makes it the first approach of its kind. It combines a Variational Autoencoder(VAE) with a Recurrent Attention Mechanism in a novel manner to create a temporally dependent sequence of frames that are gradually formed over time. The recurrent attention mechanism in Sync-DRAW attends to each individual frame of the video in sychronization, while the VAE learns a latent distribution for the entire video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101 suggest that Sync-DRAW is efficient in learning the spatial and temporal information of the videos and generates frames with high structural integrity, and can generate videos from simple captions on these datasets.

Dynamic Facial Expression Generation on Hilbert Hypersphere With Conditional Wasserstein Generative Adversarial Nets In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded as points on a hypersphere. By proposing a conditional version of manifold-valued Wasserstein generative adversarial network (GAN) for motion generation on the hypersphere, w...

GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution We show that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR). While most existing SR approaches attempt to generate realistic textures through learning with adversarial loss, our method, Generative LatEnt bANk (GLEAN), goes beyond existing practices by directly leveraging rich and diverse priors encapsulated in a pre-trained GAN. But unlike prevalent GAN inversion methods that require expensive image-specific optimization at runtime, our approach only needs a single forward pass to generate the upscaled image. GLEAN can be easily incorporated in a simple encoder-bank-decoder architecture with multi-resolution skip connections. Switching the bank allows the method to deal with images from diverse categories, e.g., cat, building, human face, and car. Images upscaled by GLEAN show clear improvements in terms of fidelity and texture faithfulness in comparison to existing methods as shown in Fig. 1.

End-To-End Time-Lapse Video Synthesis From A Single Outdoor Image Time-lapse videos usually contain visually appealing content but are often difficult and costly to create. In this paper, we present an end-to-end solution to synthesize a time-lapse video from a single outdoor image using deep neural networks. Our key idea is to train a conditional generative adversarial network based on existing datasets of time-lapse videos and image sequences. We propose a multi-frame joint conditional generation framework to effectively learn the correlation between the illumination change of an outdoor scene and the time of the day. We further present a multi-domain training scheme for robust training of our generative models from two datasets with different distributions and missing timestamp labels. Compared to alternative time-lapse video synthesis algorithms, our method uses the timestamp as the control variable and does not require a reference video to guide the synthesis of the final output. We conduct ablation studies to validate our algorithm and compare with state-of-the-art techniques both qualitatively and quantitatively.

Sequence to Sequence Learning with Neural Networks. Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.

A General Equilibrium Model for Industries with Price and Service Competition This paper develops a stochastic general equilibrium inventory model for an oligopoly, in which all inventory constraint parameters are endogenously determined. We propose several systems of demand processes whose distributions are functions of all retailers' prices and all retailers' service levels. We proceed with the investigation of the equilibrium behavior of infinite-horizon models for industries facing this type of generalized competition, under demand uncertainty.We systematically consider the following three competition scenarios. (1) Price competition only: Here, we assume that the firms' service levels are exogenously chosen, but characterize how the price and inventory strategy equilibrium vary with the chosen service levels. (2) Simultaneous price and service-level competition: Here, each of the firms simultaneously chooses a service level and a combined price and inventory strategy. (3) Two-stage competition: The firms make their competitive choices sequentially. In a first stage, all firms simultaneously choose a service level; in a second stage, the firms simultaneously choose a combined pricing and inventory strategy with full knowledge of the service levels selected by all competitors. We show that in all of the above settings a Nash equilibrium of infinite-horizon stationary strategies exists and that it is of a simple structure, provided a Nash equilibrium exists in a so-called reduced game.We pay particular attention to the question of whether a firm can choose its service level on the basis of its own (input) characteristics (i.e., its cost parameters and demand function) only. We also investigate under which of the demand models a firm, under simultaneous competition, responds to a change in the exogenously specified characteristics of the various competitors by either: (i) adjusting its service level and price in the same direction, thereby compensating for price increases (decreases) by offering improved (inferior) service, or (ii) adjusting them in opposite directions, thereby simultaneously offering better or worse prices and service.

Mobile cloud computing: A survey Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. In this paper, we provide an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues. We conclude the paper with a critical analysis of challenges that have not yet been fully met, and highlight directions for future work.

Eye-vergence visual servoing enhancing Lyapunov-stable trackability Visual servoing methods for hand---eye configuration are vulnerable for hand's dynamical oscillation, since nonlinear dynamical effects of whole manipulator stand against the stable tracking ability (trackability). Our proposal to solve this problem is that the controller for visual servoing of the hand and the one for eye-vergence should be separated independently based on decoupling each other, where the trackability is verified by Lyapunov analysis. Then the effectiveness of the decoupled hand and eye-vergence visual servoing method is evaluated through simulations incorporated with actual dynamics of 7-DoF robot with additional 3-DoF for eye-vergence mechanism by amplitude and phase frequency analysis.

An improved E-DRM scheme for mobile environments. With the rapid development of information science and network technology, Internet has become an important platform for the dissemination of digital content, which can be easily copied and distributed through the Internet. Although convenience is increased, it causes significant damage to authors of digital content. Digital rights management system (DRM system) is an access control system that is designed to protect digital content and ensure illegal users from maliciously spreading digital content. Enterprise Digital Rights Management system (E-DRM system) is a DRM system that prevents unauthorized users from stealing the enterprise's confidential data. User authentication is the most important method to ensure digital rights management. In order to verify the validity of user, the biometrics-based authentication protocol is widely used due to the biological characteristics of each user are unique. By using biometric identification, it can ensure the correctness of user identity. In addition, due to the popularity of mobile device and Internet, user can access digital content and network information at anytime and anywhere. Recently, Mishra et al. proposed an anonymous and secure biometric-based enterprise digital rights management system for mobile environment. Although biometrics-based authentication is used to prevent users from being forged, the anonymity of users and the preservation of digital content are not ensured in their proposed system. Therefore, in this paper, we will propose a more efficient and secure biometric-based enterprise digital rights management system with user anonymity for mobile environments.

Intention-detection strategies for upper limb exosuits: model-based myoelectric vs dynamic-based control The cognitive human-robot interaction between an exosuit and its wearer plays a key role in determining both the biomechanical effects of the device on movements and its perceived effectiveness. There is a lack of evidence, however, on the comparative performance of different control methods, implemented on the same device. Here, we compare two different control approaches on the same robotic suit: a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a dynamic-based control that provides support against gravity using an inverse dynamic model. Tested on a cohort of four healthy participants, assistance from the exosuit results in a marked reduction in the effort of muscles working against gravity with both control approaches (peak reduction of 68.6±18.8%, for the dynamic arm model and 62.4±25.1% for the myoprocessor), when compared to an unpowered condition. Neither of the two controllers had an affect on the performance of their users in a joint-angle tracking task (peak errors of 15.4° and 16.4° for the dynamic arm model and myoprocessor, respectively, compared to 13.1o in the unpowered condition). However, our results highlight the remarkable adaptability of the myoprocessor to seamlessly adapt to changing external dynamics.

A survey of dynamic spectrum allocation based on reinforcement learning algorithms in cognitive radio networks Cognitive radio is an emerging technology that is considered to be an evolution for software device radio in which cognition and decision-making components are included. The main function of cognitive radio is to exploit “spectrum holes” or “white spaces” to address the challenge of the low utilization of radio resources. Dynamic spectrum allocation, whose significant functions are to ensure that cognitive users access the available frequency and bandwidth to communicate in an opportunistic manner and to minimize the interference between primary and secondary users, is a key mechanism in cognitive radio networks. Reinforcement learning, which rapidly analyzes the amount of data in a model-free manner, dramatically facilitates the performance of dynamic spectrum allocation in real application scenarios. This paper presents a survey on the state-of-the-art spectrum allocation algorithms based on reinforcement learning techniques in cognitive radio networks. The advantages and disadvantages of each algorithm are analyzed in their specific practical application scenarios. Finally, we discuss open issues in dynamic spectrum allocation that can be topics of future research.

Stochastic Power Adaptation with Multiagent Reinforcement Learning for Cognitive Wireless Mesh Networks As the scarce spectrum resource is becoming overcrowded, cognitive radio indicates great flexibility to improve the spectrum efficiency by opportunistically accessing the authorized frequency bands. One of the critical challenges for operating such radios in a network is how to efficiently allocate transmission powers and frequency resource among the secondary users (SUs) while satisfying the quality-of-service constraints of the primary users. In this paper, we focus on the noncooperative power allocation problem in cognitive wireless mesh networks formed by a number of clusters with the consideration of energy efficiency. Due to the SUs' dynamic and spontaneous properties, the problem is modeled as a stochastic learning process. We first extend the single-agent $(Q)$-learning to a multiuser context, and then propose a conjecture-based multiagent $(Q)$-learning algorithm to achieve the optimal transmission strategies with only private and incomplete information. An intelligent SU performs $(Q)$-function updates based on the conjecture over the other SUs' stochastic behaviors. This learning algorithm provably converges given certain restrictions that arise during the learning procedure. Simulation experiments are used to verify the performance of our algorithm and demonstrate its effectiveness of improving the energy efficiency.

Applications of Economic and Pricing Models for Resource Management in 5G Wireless Networks: A Survey. This paper presents a comprehensive literature review on applications of economic and pricing theory for resource management in the evolving fifth generation (5G) wireless networks. The 5G wireless networks are envisioned to overcome existing limitations of cellular networks in terms of data rate, capacity, latency, energy efficiency, spectrum efficiency, coverage, reliability, and cost per information transfer. To achieve the goals, the 5G systems will adopt emerging technologies such as massive multiple-input multiple-output, mmWave communications, and dense heterogeneous networks. However, 5G involves multiple entities and stakeholders that may have different objectives, e.g., high data rate, low latency, utility maximization, and revenue/profit maximization. This poses a number of challenges to resource management designs of 5G. While the traditional solutions may neither efficient nor applicable, economic and pricing models have been recently developed and adopted as useful tools to achieve the objectives. In this paper, we review economic and pricing approaches proposed to address resource management issues in the 5G wireless networks including user association, spectrum allocation, and interference and power management. Furthermore, we present applications of economic and pricing models for wireless caching and mobile data offloading. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to the 5G wireless networks.

Effective Capacity in Wireless Networks: A Comprehensive Survey. Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physical-layer channel models, however, do not explicitly consider quality-of-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks, such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications, such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

Joint channel and power allocation for device-to-device underlay Device-to-device transmission is one of the enabling technologies of 5G, with a potential of significantly improving the spectral efficiency. Spectral reuse in D2D underlay necessitates interference management. A challenge in D2D underlay systems is the increased number of D2D and interfering links and CSI feedback requirement. In this work, we propose a solution for D2D channel allocation, which requires only the neighbor information of D2D communicating nodes. We aim to maximize the supported D2D pairs with a constraint on the interference caused at the base station, at each subchannel. We formulate the channel allocation problem as a Mixed integer programming (MIP). We also combine it with an iterative power control scheme in order to fit more D2D pairs in the channels. We also propose suboptimal channel + power allocation algorithms and evaluate and compare their performances by simulations. Numerical results reveal that the proposed algorithms perform quite close to the MIP-based solution and power control significantly increases the number of served D2D pairs.

Convert Harm Into Benefit: A Coordination-Learning Based Dynamic Spectrum Anti-Jamming Approach This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern.

Footprints: history-rich tools for information foraging Inspired by Hill and Hollans original work [7], we have beendeveloping a theory of interaction history and building tools toapply this theory to navigation in a complex information space. Wehave built a series of tools - map, paths, annota- tions andsignposts - based on a physical-world navigation metaphor. Thesetools have been in use for over a year. Our user study involved acontrolled browse task and showed that users were able to get thesame amount of work done with significantly less effort.

Very Deep Convolutional Networks for Large-Scale Image Recognition. In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Chimp optimization algorithm. •A novel optimizer called Chimp Optimization Algorithm (ChOA) is proposed.•ChOA is inspired by individual intelligence and sexual motivation of chimps.•ChOA alleviates the problems of slow convergence rate and trapping in local optima.•The four main steps of Chimp hunting are implemented.

Space-time modeling of traffic flow. This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5min average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.

A simplified dual neural network for quadratic programming with its KWTA application. The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and the computational complexity of the neural network. The simplified dual neural network is shown to be globally convergent to the exact optimal solution. The complexity of the neural network architecture is reduced with the number of neurons equal to the number of inequality constraints. Its application to k-winners-take-all (KWTA) operation is discussed to demonstrate how to solve problems with this neural network.

G2-type SRMPC scheme for synchronous manipulation of two redundant robot arms. In this paper, to remedy the joint-angle drift phenomenon for manipulation of two redundant robot arms, a novel scheme for simultaneous repetitive motion planning and control (SRMPC) at the joint-acceleration level is proposed, which consists of two subschemes. To do so, the performance index of each SRMPC subscheme is derived and designed by employing the gradient dynamics twice, of which a convergence theorem and its proof are presented. In addition, for improving the accuracy of the motion planning and control, position error, and velocity, error feedbacks are incorporated into the forward kinematics equation and analyzed via Zhang neural-dynamics method. Then the two subschemes are simultaneously reformulated as two quadratic programs (QPs), which are finally unified into one QP problem. Furthermore, a piecewise-linear projection equation-based neural network (PLPENN) is used to solve the unified QP problem, which can handle the strictly convex QP problem in an inverse-free manner. More importantly, via such a unified QP formulation and the corresponding PLPENN solver, the synchronism of two redundant robot arms is guaranteed. Finally, two given tasks are fulfilled by 2 three-link and 2 five-link planar robot arms, respectively. Computer-simulation results validate the efficacy and accuracy of the SRMPC scheme and the corresponding PLPENN solver for synchronous manipulation of two redundant robot arms.

Distributed Kalman consensus filter with event-triggered communication: Formulation and stability analysis. •The problem of distributed state estimation in sensor networks with event-triggered communication schedules on both sensor-to-estimator channel and estimator-to-estimator channel is studied.•An event-triggered KCF is designed by deriving the optimal Kalman gain matrix which minimizes the mean squared error.•A computational scalable form of the proposed filter is presented by some approximations.•An appropriate choice of the consensus gain matrix is provided to ensure the stochastic stability of the proposed filter.

Intention-detection strategies for upper limb exosuits: model-based myoelectric vs dynamic-based control The cognitive human-robot interaction between an exosuit and its wearer plays a key role in determining both the biomechanical effects of the device on movements and its perceived effectiveness. There is a lack of evidence, however, on the comparative performance of different control methods, implemented on the same device. Here, we compare two different control approaches on the same robotic suit: a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a dynamic-based control that provides support against gravity using an inverse dynamic model. Tested on a cohort of four healthy participants, assistance from the exosuit results in a marked reduction in the effort of muscles working against gravity with both control approaches (peak reduction of 68.6±18.8%, for the dynamic arm model and 62.4±25.1% for the myoprocessor), when compared to an unpowered condition. Neither of the two controllers had an affect on the performance of their users in a joint-angle tracking task (peak errors of 15.4° and 16.4° for the dynamic arm model and myoprocessor, respectively, compared to 13.1o in the unpowered condition). However, our results highlight the remarkable adaptability of the myoprocessor to seamlessly adapt to changing external dynamics.

An Approach of Secret Sharing Technique Based on Convolution Neural Network and DNA Sequence for Data Security in Wireless Communication Now-a-days the information security is the prime factor in wireless communication. One of the major and fruitful security techniques in cryptography is data encryption with keys. The efficiency of the encryption depends on the resistance ability of the encryption key and the encryption algorithm. Threshold cryptography is one type of cryptographic technique where the information (message and encryption key) is divied into some specific number of shares and on the contrary the information can only be reconstructed by accumulating an allowable set of shares. In this paper we have presented convolution neural network with DNA sequence based (n, n) threshold cryptographic technique. Convolutional Neural Network is one of the important deep neural networks and it has multiple layers. Here pooling layer is used for key generation purposes. We have used maxpooling operation and average pooling operation for encryption key generation and authentication purpose. A new mask generation algorithm is introduced for ‘n’ numbers of share. This mask generation algorithm is based on unit matrix of order. n x n, AND operation and OR operation between plain text and key. This threshold value is also necessary for reconstraction of information. DNA sequence is used for imposing the nonlinearity in cipher text. Different types of experimental results and its analysis prove that the scheme has great potential and ability to achieve the authenticity and confidentiality in any data security platform.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Ethical Considerations Of Applying Robots In Kindergarten Settings: Towards An Approach From A Macroperspective In child-robot interaction (cHRI) research, many studies pursue the goal to develop interactive systems that can be applied in everyday settings. For early education, increasingly, the setting of a kindergarten is targeted. However, when cHRI and research are brought into a kindergarten, a range of ethical and related procedural aspects have to be considered and dealt with. While ethical models elaborated within other human-robot interaction settings, e.g., assisted living contexts, can provide some important indicators for relevant issues, we argue that it is important to start developing a systematic approach to identify and tackle those ethical issues which rise with cHRI in kindergarten settings on a more global level and address the impact of the technology from a macroperspective beyond the effects on the individual. Based on our experience in conducting studies with children in general and pedagogical considerations on the role of the institution of kindergarten in specific, in this paper, we enfold some relevant aspects that have barely been addressed in an explicit way in current cHRI research. Four areas are analyzed and key ethical issues are identified in each area: (1) the institutional setting of a kindergarten, (2) children as a vulnerable group, (3) the caregivers' role, and (4) pedagogical concepts. With our considerations, we aim at (i) broadening the methodology of the current studies within the area of cHRI, (ii) revalidate it based on our comprehensive empirical experience with research in kindergarten settings, both laboratory and real-world contexts, and (iii) provide a framework for the development of a more systematic approach to address the ethical issues in cHRI research within kindergarten settings.

Retransmission DBTMA Protocol with Fast Retransmission Strategy to Improve the Performance of MANETs The Mobile Ad-hoc Networks (MANETs) are often visible to the exposed terminal problem and hidden terminal problem, which exist due to non-transitivity in media access control schemes. This affects the utilization of channel and throughput in media access control protocols, e.g., dual busy tone multiple access protocol (DBTMA). Hence, to improve the fairness and throughput performance of the DBTMA it is very necessary to address the problems associated with hidden and exposed terminals. Hence, in this paper, the quality of service (QoS) is improved by enhancing the capability of the DBTMA for better network service in the MANETs. The proposed method uses an improved DBTMA called Retransmission Dual Busy Tone Multiple Access (RDBTMA) protocol. This is based on two elements namely: busy tones and Ready To Send/Clear to Send (RTS/CTS) dialogues. In addition to this fast retransmission, a strategy is used further to improve its effectiveness. The retransmission strategy is adopted using negative acknowledgment after the collision occurred by the hidden nodes. A hidden node, where the collision occurs at access point, listens to the NACK signal and uses the signal to determine the requirement fast retransmission scheme. The proposed method is simulated and compared against existing methods in terms of various network parameters. The results show that the proposed RDBTMA protocol is effective in terms of the improved QoS in terms of network throughput (21.9%), packet delivery ratio (17.8%), 14.9% less packet loss, and 38% less route discovery delay than the existing methods.

Multiple QoS Parameters-Based Routing for Civil Aeronautical Ad Hoc Networks. Aeronautical ad hoc network (AANET) can be applied as in-flight communication systems to allow aircraft to communicate with the ground, in complement to other existing communication systems to support Internet of Things. However, the unique features of civil AANETs present a great challenge to provide efficient and reliable data delivery in such environments. In this paper, we propose a multiple q...

Performance Improvement of Cluster-Based Routing Protocol in VANET. Vehicular ad-hoc NETworks (VANETs) have received considerable attention in recent years, due to its unique characteristics, which are different from mobile ad-hoc NETworks, such as rapid topology change, frequent link failure, and high vehicle mobility. The main drawback of VANETs network is the network instability, which yields to reduce the network effciency. In this paper, we propose three algorithms: cluster-based life-time routing (CBLTR) protocol, Intersection dynamic VANET routing (IDVR) protocol, and control overhead reduction algorithm (CORA). The CBLTR protocol aims to increase the route stability and average throughput in a bidirectional segment scenario. The cluster heads (CHs) are selected based on maximum lifetime among all vehicles that are located within each cluster. The IDVR protocol aims to increase the route stability and average throughput, and to reduce end-to-end delay in a grid topology. The elected intersection CH receives a set of candidate shortest routes (SCSR) closed to the desired destination from the software defined network. The IDVR protocol selects the optimal route based on its current location, destination location, and the maximum of the minimum average throughput of SCSR. Finally, the CORA algorithm aims to reduce the control overhead messages in the clusters by developing a new mechanism to calculate the optimal numbers of the control overhead messages between the cluster members and the CH. We used SUMO traffic generator simulators and MATLAB to evaluate the performance of our proposed protocols. These protocols significantly outperform many protocols mentioned in the literature, in terms of many parameters.

SCOTRES: Secure Routing for IoT and CPS. Wireless ad-hoc networks are becoming popular due to the emergence of the Internet of Things and cyber-physical systems (CPSs). Due to the open wireless medium, secure routing functionality becomes important. However, the current solutions focus on a constrain set of network vulnerabilities and do not provide protection against newer attacks. In this paper, we propose SCOTRES-a trust-based system ...

AQ-Routing: mobility-, stability-aware adaptive routing protocol for data routing in MANET–IoT systems Internet of Things, is an innovative technology which allows the connection of physical things with the digital world through the use of heterogeneous networks and communication technologies. In an IoT system, a major role is played by the wireless sensor network as its components comprise: sensing, data acquiring, heterogeneous connectivity and data processing. Mobile ad-hoc networks are highly self reconfiguring networks of mobile nodes which communicate through wireless links. In such a network, each node acts both as a router and host at the same time. The interaction between MANETs and Internet of Things opens new ways for service provision in smart environments and challenging issues in its networking aspects. One of the main issues in MANET–IoT systems is the mobility of the network nodes: routing protocol must react effectively to the topological changes into the algorithm design. We describe the design and implementation of AQ-Routing, and analyze its performance using both simulations and measurements based on our implementation. In general, the networking of such a system is very challenging regarding routing aspects. Also, it is related to system mobility and limited network sensor resources. This article builds upon this observation an adaptive routing protocol (AQ-Routing) based on Reinforcement Learning (RL) techniques, which has the ability to detect the level of mobility at different points of time so that each individual node can update routing metric accordingly. The proposed protocol introduces: (i) new model, developed via Q-learning technique, to detect the level of mobility at each node in the network; (ii) a new metric, called $$Q_{\textit{metric}},$$ which account for the static and dynamic routing metrics, and which are combined and updated to the changing network topologies. The protocol can efficiently handle network mobility by a way of preemptively adapting its behaviour thanks to the mobility detection model. The presented results of simulation provide an effective approach to improve the stability of links in both static and mobile scenario and, hence, increase the packet delivery ratio in the global MANET–IoT system.

A Network Lifetime Extension-Aware Cooperative MAC Protocol for MANETs With Optimized Power Control. In this paper, a cooperative medium access control (CMAC) protocol, termed network lifetime extension-aware CMAC (LEA-CMAC) for mobile ad-hoc networks (MANETs) is proposed. The main feature of the LEA-CMAC protocol is to enhance the network performance through the cooperative transmission to achieve a multi-objective target orientation. The unpredictable nature of wireless communication links results in the degradation of network performance in terms of throughput, end-to-end delay, energy efficiency, and network lifetime of MANETs. Through cooperative transmission, the network performance of MANETs can be improved, provided a beneficial cooperation is satisfied and design parameters are carefully selected at the MAC layer. To achieve a multi-objective target-oriented CMAC protocol, we formulated an optimization problem to extend the network lifetime of MANETs. The optimization solution led to the investigation of symmetric and asymmetric transmit power policies. We then proposed a distributed relay selection process to select the best retransmitting node among the qualified relays, with consideration on a transmit power, a sufficient residual energy after cooperation, and a high cooperative gain. The simulation results show that the LEA-CMAC protocol can achieve a multi-objective target orientation by exploiting an asymmetric transmit power policy to improve the network performance.

Distinctive Image Features from Scale-Invariant Keypoints This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

ImageNet Large Scale Visual Recognition Challenge. The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements.

A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems Recently, wireless technologies have been growing actively all around the world. In the context of wireless technology, fifth-generation (5G) technology has become a most challenging and interesting topic in wireless research. This article provides an overview of the Internet of Things (IoT) in 5G wireless systems. IoT in the 5G system will be a game changer in the future generation. It will open a door for new wireless architecture and smart services. Recent cellular network LTE (4G) will not be sufficient and efficient to meet the demands of multiple device connectivity and high data rate, more bandwidth, low-latency quality of service (QoS), and low interference. To address these challenges, we consider 5G as the most promising technology. We provide a detailed overview of challenges and vision of various communication industries in 5G IoT systems. The different layers in 5G IoT systems are discussed in detail. This article provides a comprehensive review on emerging and enabling technologies related to the 5G system that enables IoT. We consider the technology drivers for 5G wireless technology, such as 5G new radio (NR), multiple-input–multiple-output antenna with the beamformation technology, mm-wave commutation technology, heterogeneous networks (HetNets), the role of augmented reality (AR) in IoT, which are discussed in detail. We also provide a review on low-power wide-area networks (LPWANs), security challenges, and its control measure in the 5G IoT scenario. This article introduces the role of AR in the 5G IoT scenario. This article also discusses the research gaps and future directions. The focus is also on application areas of IoT in 5G systems. We, therefore, outline some of the important research directions in 5G IoT.

A communication robot in a shopping mall This paper reports our development of a communication robot for use in a shopping mall to provide shopping information, offer route guidance, and build rapport. In the development, the major difficulties included sensing human behaviors, conversation in a noisy daily environment, and the needs of unexpected miscellaneous knowledge in the conversation. We chose a networkrobot system approach, where a single robot's poor sensing capability and knowledge are supplemented by ubiquitous sensors and a human operator. The developed robot system detects a person with floor sensors to initiate interaction, identifies individuals with radio-frequency identification (RFID) tags, gives shopping information while chatting, and provides route guidance with deictic gestures. The robotwas partially teleoperated to avoid the difficulty of speech recognition as well as to furnish a new kind of knowledge that only humans can flexibly provide. The information supplied by a human operator was later used to increase the robot's autonomy. For 25 days in a shopping mall, we conducted a field trial and gathered 2642 interactions. A total of 235 participants signed up to use RFID tags and, later, provided questionnaire responses. The questionnaire results are promising in terms of the visitors' perceived acceptability as well as the encouragement of their shopping activities. The results of the teleoperation analysis revealed that the amount of teleoperation gradually decreased, which is also promising.

Comment on "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes" Comparison of generative and discriminative classifiers is an ever-lasting topic. As an important contribution to this topic, based on their theoretical and empirical comparisons between the naïve Bayes classifier and linear logistic regression, Ng and Jordan (NIPS 841---848, 2001) claimed that there exist two distinct regimes of performance between the generative and discriminative classifiers with regard to the training-set size. In this paper, our empirical and simulation studies, as a complement of their work, however, suggest that the existence of the two distinct regimes may not be so reliable. In addition, for real world datasets, so far there is no theoretically correct, general criterion for choosing between the discriminative and the generative approaches to classification of an observation x into a class y; the choice depends on the relative confidence we have in the correctness of the specification of either p(y|x) or p(x, y) for the data. This can be to some extent a demonstration of why Efron (J Am Stat Assoc 70(352):892---898, 1975) and O'Neill (J Am Stat Assoc 75(369):154---160, 1980) prefer normal-based linear discriminant analysis (LDA) when no model mis-specification occurs but other empirical studies may prefer linear logistic regression instead. Furthermore, we suggest that pairing of either LDA assuming a common diagonal covariance matrix (LDA-驴) or the naïve Bayes classifier and linear logistic regression may not be perfect, and hence it may not be reliable for any claim that was derived from the comparison between LDA-驴 or the naïve Bayes classifier and linear logistic regression to be generalised to all generative and discriminative classifiers.

Adaptive dynamic programming and optimal control of nonlinear nonaffine systems. In this paper, a novel optimal control design scheme is proposed for continuous-time nonaffine nonlinear dynamic systems with unknown dynamics by adaptive dynamic programming (ADP). The proposed methodology iteratively updates the control policy online by using the state and input information without identifying the system dynamics. An ADP algorithm is developed, and can be applied to a general class of nonlinear control design problems. The convergence analysis for the designed control scheme is presented, along with rigorous stability analysis for the closed-loop system. The effectiveness of this new algorithm is illustrated by two simulation examples.

Adaptive Fuzzy Control With Prescribed Performance for Block-Triangular-Structured Nonlinear Systems. In this paper, an adaptive fuzzy control method with prescribed performance is proposed for multi-input and multioutput block-triangular-structured nonlinear systems with immeasurable states. Fuzzy logic systems are adopted to identify the unknown nonlinear system functions. Adaptive fuzzy state observers are designed to solve the problem of unmeasured states, and a new observer-based output-feedb...

Intention-detection strategies for upper limb exosuits: model-based myoelectric vs dynamic-based control The cognitive human-robot interaction between an exosuit and its wearer plays a key role in determining both the biomechanical effects of the device on movements and its perceived effectiveness. There is a lack of evidence, however, on the comparative performance of different control methods, implemented on the same device. Here, we compare two different control approaches on the same robotic suit: a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a dynamic-based control that provides support against gravity using an inverse dynamic model. Tested on a cohort of four healthy participants, assistance from the exosuit results in a marked reduction in the effort of muscles working against gravity with both control approaches (peak reduction of 68.6±18.8%, for the dynamic arm model and 62.4±25.1% for the myoprocessor), when compared to an unpowered condition. Neither of the two controllers had an affect on the performance of their users in a joint-angle tracking task (peak errors of 15.4° and 16.4° for the dynamic arm model and myoprocessor, respectively, compared to 13.1o in the unpowered condition). However, our results highlight the remarkable adaptability of the myoprocessor to seamlessly adapt to changing external dynamics.

UAV-Enabled Data Collection for Wireless Sensor Networks With Distributed Beamforming This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of ground nodes (GNs) via distributed beamforming. We consider two scenarios with delay-tolerant and delay-sensitive applications, in which the GNs send the common/shared messages to the UAV via adaptive- and fixed-rate transmissions, respectively. For the two scenarios, we aim to maximize the average data-rate throughput and minimize the transmission outage probability, respectively, by jointly optimizing the UAV’s trajectory design and the GNs’ transmit power allocation over time, subject to the UAV’s flight speed constraints and the GNs’ individual average power constraints. However, the two formulated problems are both non-convex and thus generally difficult to be optimally solved. To tackle this issue, we first consider the relaxed problems in the ideal case with the UAV’s flight speed constraints ignored, for which the well-structured optimal solutions are obtained to reveal the fundamental performance upper bounds. It is shown that for the two approximate problems, the optimal trajectory solutions have the same multi-location-hovering structure, but with different optimal power allocation strategies. Next, for the general problems with the UAV’s flight speed constraints considered, we propose efficient algorithms to obtain high-quality solutions by using the techniques from convex optimization and approximation. Finally, numerical results show that our proposed designs significantly outperform other benchmark schemes, in terms of the achieved data-rate throughput and outage probability under the two scenarios. It is also observed that when the mission period becomes sufficiently long, our proposed designs approach the performance upper bounds when the UAV’s flight speed constraints are ignored.

Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications FSA (artificial fish-swarm algorithm) is one of the best methods of optimization among the swarm intelligence algorithms. This algorithm is inspired by the collective movement of the fish and their various social behaviors. Based on a series of instinctive behaviors, the fish always try to maintain their colonies and accordingly demonstrate intelligent behaviors. Searching for food, immigration and dealing with dangers all happen in a social form and interactions between all fish in a group will result in an intelligent social behavior.This algorithm has many advantages including high convergence speed, flexibility, fault tolerance and high accuracy. This paper is a review of AFSA algorithm and describes the evolution of this algorithm along with all improvements, its combination with various methods as well as its applications. There are many optimization methods which have a affinity with this method and the result of this combination will improve the performance of this method. Its disadvantages include high time complexity, lack of balance between global and local search, in addition to lack of benefiting from the experiences of group members for the next movements.

A dynamic N threshold prolong lifetime method for wireless sensor nodes. Ubiquitous computing is a technology to assist many computers available around the physical environment at any place and anytime. This service tends to be invisible from users in everyday life. Ubiquitous computing uses sensors extensively to provide important information such that applications can adjust their behavior. A Wireless Sensor Network (WSN) has been applied to implement such an architecture. To ensure continuous service, a dynamic N threshold power saving method for WSN is developed. A threshold N has been derived to obtain minimum power consumption for the sensor node while considering each different data arrival rate. We proposed a theoretical analysis regarding the probability variation for each state considering different arrival rate, service rate and collision probability. Several experiments have been conducted to demonstrate the effectiveness of our research. Our method can be applied to prolong the service time of a ubiquitous computing network to cope with the network disconnection issue.

Fuzzy Mathematical Programming and Self-Adaptive Artificial Fish Swarm Algorithm for Just-in-Time Energy-Aware Flow Shop Scheduling Problem With Outsourcing Option Flow shop scheduling (FSS) problem constitutes a major part of production planning in every manufacturing organization. It aims at determining the optimal sequence of processing jobs on available machines within a given customer order. In this article, a novel biobjective mixed-integer linear programming (MILP) model is proposed for FSS with an outsourcing option and just-in-time delivery in order to simultaneously minimize the total cost of the production system and total energy consumption. Each job is considered to be either scheduled in-house or to be outsourced to one of the possible subcontractors. To efficiently solve the problem, a hybrid technique is proposed based on an interactive fuzzy solution technique and a self-adaptive artificial fish swarm algorithm (SAAFSA). The proposed model is treated as a single objective MILP using a multiobjective fuzzy mathematical programming technique based on the ε-constraint, and SAAFSA is then applied to provide Pareto optimal solutions. The obtained results demonstrate the usefulness of the suggested methodology and high efficiency of the algorithm in comparison with CPLEX solver in different problem instances. Finally, a sensitivity analysis is implemented on the main parameters to study the behavior of the objectives according to the real-world conditions.

Energy-Efficient Relay-Selection-Based Dynamic Routing Algorithm for IoT-Oriented Software-Defined WSNs In this article, a dynamic routing algorithm based on energy-efficient relay selection (RS), referred to as DRA-EERS, is proposed to adapt to the higher dynamics in time-varying software-defined wireless sensor networks (SDWSNs) for the Internet-of-Things (IoT) applications. First, the time-varying features of SDWSNs are investigated from which the state-transition probability (STP) of the node is calculated based on a Markov chain. Second, a dynamic link weight is designed for DRA-EERS by incorporating both the link reward and the link cost, where the link reward is related to the link energy efficiency (EE) and the node STP, while the link cost is affected by the locations of nodes. Moreover, one adjustable coefficient is used to balance the link reward and the link cost. Finally, the energy-efficient routing problem can be formulated as an optimization problem, and DRA-EERS is performed to find the best relay according to the energy-efficient RS criteria derived from the designed link weight. The simulation results demonstrate that the path EE obtained by DRA-EERS through an available coefficient adjustment outperforms that by Dijkstra's shortest path algorithm. Again, a tradeoff between the EE and the throughput can be achieved by adjusting the coefficient of the link weight, i.e., increasing the impact of the link reward to improve the EE, and otherwise, to improve the throughput.

Energy-Efficient Optimization for Wireless Information and Power Transfer in Large-Scale MIMO Systems Employing Energy Beamforming In this letter, we consider a large-scale multiple-input multiple-output (MIMO) system where the receiver should harvest energy from the transmitter by wireless power transfer to support its wireless information transmission. The energy beamforming in the large-scale MIMO system is utilized to address the challenging problem of long-distance wireless power transfer. Furthermore, considering the limitation of the power in such a system, this letter focuses on the maximization of the energy efficiency of information transmission (bit per Joule) while satisfying the quality-of-service (QoS) requirement, i.e. delay constraint, by jointly optimizing transfer duration and transmit power. By solving the optimization problem, we derive an energy-efficient resource allocation scheme. Numerical results validate the effectiveness of the proposed scheme.

Accurate Self-Localization in RFID Tag Information Grids Using FIR Filtering Grid navigation spaces nested with the radio-frequency identification (RFID) tags are promising for industrial and other needs, because each tag can deliver information about a local two-dimensional or three-dimensional surrounding. The approach, however, requires high accuracy in vehicle self-localization. Otherwise, errors may lead to collisions; possibly even fatal. We propose a new extended finite impulse response (EFIR) filtering algorithm and show that it meets this need. The EFIR filter requires an optimal averaging interval, but does not involve the noise statistics which are often not well known to the engineer. It is more accurate than the extended Kalman filter (EKF) under real operation conditions and its iterative algorithm has the Kalman form. Better performance of the proposed EFIR filter is demonstrated based on extensive simulations in a comparison to EKF, which is widely used in RFID tag grids. We also show that errors in noise covariances may provoke divergence in EKF, whereas the EFIR filter remains stable and is thus more robust.

Evolutionary computation: comments on the history and current state Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete

Supporting social navigation on the World Wide Web This paper discusses a navigation behavior on Internet information services, in particular the World Wide Web, which is characterized by pointing out of information using various communication tools. We call this behavior social navigation as it is based on communication and interaction with other users, be that through email, or any other means of communication. Social navigation phenomena are quite common although most current tools (like Web browsers or email clients) offer very little support for it. We describe why social navigation is useful and how it can be better supported in future systems. We further describe two prototype systems that, although originally not designed explicitly as tools for social navigation, provide features that are typical for social navigation systems. One of these systems, the Juggler system, is a combination of a textual virtual environment and a Web client. The other system is a prototype of a Web- hotlist organizer, called Vortex. We use both systems to describe fundamental principles of social navigation systems.

Proofs of Storage from Homomorphic Identification Protocols Proofs of storage (PoS) are interactive protocols allowing a client to verify that a server faithfully stores a file. Previous work has shown that proofs of storage can be constructed from any homomorphic linear authenticator (HLA). The latter, roughly speaking, are signature/message authentication schemes where `tags' on multiple messages can be homomorphically combined to yield a `tag' on any linear combination of these messages. We provide a framework for building public-key HLAs from any identification protocol satisfying certain homomorphic properties. We then show how to turn any public-key HLA into a publicly-verifiable PoS with communication complexity independent of the file length and supporting an unbounded number of verifications. We illustrate the use of our transformations by applying them to a variant of an identification protocol by Shoup, thus obtaining the first unbounded-use PoS based on factoring (in the random oracle model).

Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking Real-time tracking of human body motion is an important technology in synthetic environments, robotics, and other human-computer interaction applications. This paper presents an extended Kalman filter designed for real-time estimation of the orientation of human limb segments. The filter processes data from small inertial/magnetic sensor modules containing triaxial angular rate sensors, accelerometers, and magnetometers. The filter represents rotation using quaternions rather than Euler angles or axis/angle pairs. Preprocessing of the acceleration and magnetometer measurements using the Quest algorithm produces a computed quaternion input for the filter. This preprocessing reduces the dimension of the state vector and makes the measurement equations linear. Real-time implementation and testing results of the quaternion-based Kalman filter are presented. Experimental results validate the filter design, and show the feasibility of using inertial/magnetic sensor modules for real-time human body motion tracking

Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics. In this paper, a novel approach based on the Q-learning algorithm is proposed to solve the infinite-horizon linear quadratic tracker (LQT) for unknown discrete-time systems in a causal manner. It is assumed that the reference trajectory is generated by a linear command generator system. An augmented system composed of the original system and the command generator is constructed and it is shown that the value function for the LQT is quadratic in terms of the state of the augmented system. Using the quadratic structure of the value function, a Bellman equation and an augmented algebraic Riccati equation (ARE) for solving the LQT are derived. In contrast to the standard solution of the LQT, which requires the solution of an ARE and a noncausal difference equation simultaneously, in the proposed method the optimal control input is obtained by only solving an augmented ARE. A Q-learning algorithm is developed to solve online the augmented ARE without any knowledge about the system dynamics or the command generator. Convergence to the optimal solution is shown. A simulation example is used to verify the effectiveness of the proposed control scheme.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network. In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F-score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

Energy harvesting algorithm considering max flow problem in wireless sensor networks. In Wireless Sensor Networks (WSNs), sensor nodes with poor energy always have bad effect on the data rate or max flow. These nodes are called bottleneck nodes. In this paper, in order to increase the max flow, we assume an energy harvesting WSNs environment to investigate the cooperation of multiple Mobile Chargers (MCs). MCs are mobile robots that use wireless charging technology to charge sensor nodes in WSNs. This means that in energy harvesting WSNs environments, sensor nodes can obtain energy replenishment by using MCs or collecting energy from nature by themselves. In our research, we use MCs to improve the energy of the sensor nodes by performing multiple rounds of unified scheduling, and finally achieve the purpose of increasing the max flow at sinks. Firstly, we model this problem as a Linear Programming (LP) to search the max flow in a round of charging scheduling and prove that the problem is NP-hard. In order to solve the problem, we propose a heuristic approach: deploying MCs in units of paths with the lowest energy node priority. To reduce the energy consumption of MCs and increase the charging efficiency, we also take the optimization of MCs’ moving distance into our consideration. Finally, we extend the method to multiple rounds of scheduling called BottleNeck. Simulation results show that Bottleneck performs well at increasing max flow.

Exploring First Impressions of the Perceived Social Intelligence and Construal Level of Robots that Disclose their Ability to Deceive If a robot tells you it can lie for your benefit, how would that change how you perceive it? This paper presents a mixed-methods empirical study that investigates how disclosure of deceptive or honest capabilities influences the perceived social intelligence and construal level of a robot. We first conduct a study with 198 Mechanical Turk participants, and then a replication of it with 15 undergraduate students in order to gain qualitative data. Our results show that how a robot introduces itself can have noticeable effects on how it is perceived–even from just one exposure. In particular, when revealing having ability to lie when it believes it is in the best interest of a human, people noticeably find the robot to be less trustworthy than a robot that conceals any honesty aspects or reveals total truthfulness. Moreover, robots that are forthcoming with their truthful abilities are seen in a lower construal than one that is transparent about its deceptive abilities. These results add much needed knowledge to the understudied area of robot deception and could inform designers and policy makers of future practices when considering deploying robots that deceive.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Ethical Considerations Of Applying Robots In Kindergarten Settings: Towards An Approach From A Macroperspective In child-robot interaction (cHRI) research, many studies pursue the goal to develop interactive systems that can be applied in everyday settings. For early education, increasingly, the setting of a kindergarten is targeted. However, when cHRI and research are brought into a kindergarten, a range of ethical and related procedural aspects have to be considered and dealt with. While ethical models elaborated within other human-robot interaction settings, e.g., assisted living contexts, can provide some important indicators for relevant issues, we argue that it is important to start developing a systematic approach to identify and tackle those ethical issues which rise with cHRI in kindergarten settings on a more global level and address the impact of the technology from a macroperspective beyond the effects on the individual. Based on our experience in conducting studies with children in general and pedagogical considerations on the role of the institution of kindergarten in specific, in this paper, we enfold some relevant aspects that have barely been addressed in an explicit way in current cHRI research. Four areas are analyzed and key ethical issues are identified in each area: (1) the institutional setting of a kindergarten, (2) children as a vulnerable group, (3) the caregivers' role, and (4) pedagogical concepts. With our considerations, we aim at (i) broadening the methodology of the current studies within the area of cHRI, (ii) revalidate it based on our comprehensive empirical experience with research in kindergarten settings, both laboratory and real-world contexts, and (iii) provide a framework for the development of a more systematic approach to address the ethical issues in cHRI research within kindergarten settings.

An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning Short-term traffic volume prediction, which can assist road users in choosing appropriate routes and reducing travel time cost, is a significant topic of intelligent transportation system. To overcome the error magnification phenomena of traditional combination methods and to improve prediction performance, this paper proposes an improved Bayesian combination model with deep learning (IBCM-DL) for traffic flow prediction. First, an IBCM framework is established based on the new BCM framework proposed by Wang. Then, correlation analysis is used to analyze the relevance between the historical traffic flow and the traffic flow within the current interval. Three sub-predictors including the gated recurrent unit neural network (GRUNN), autoregressive integrated moving average (ARIMA), and radial basis function neural network (RBFNN) are incorporated into the IBCM framework to take advantage of each method. The real-world traffic volume data captured by microwave sensors located on the expressways of Beijing was used to validate the proposed model in multiple scenarios. The overall results illustrate that the IBCM-DL model outperforms the other state-of-the-art methods in terms of accuracy and stability.

Introduction to the special section on intelligent systems for socially aware computing

A survey on graph kernels. Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification.

TL-GDBN: Growing Deep Belief Network With Transfer Learning A deep belief network (DBN) is effective to create a powerful generative model by using training data. However, it is difficult to fast determine its optimal structure given specific applications. In this paper, a growing DBN with transfer learning (TL-GDBN) is proposed to automatically decide its structure size, which can accelerate its learning process and improve model accuracy. First, a basic DBN structure with single hidden layer is initialized and then pretrained, and the learned weight parameters are frozen. Second, TL-GDBN uses TL to transfer the knowledge from the learned weight parameters to newly added neurons and hidden layers, which can achieve a growing structure until the stopping criterion for pretraining is satisfied. Third, the weight parameters derived from pretraining of TL-GDBN are further fine-tuned by using layer-by-layer partial least square regression from top to bottom, which can avoid many problems of traditional backpropagation algorithm-based fine-tuning. Moreover, the convergence analysis of the TL-GDBN is presented. Finally, TL-GDBN is tested on two benchmark data sets and a practical wastewater treatment system. The simulation results show that it has better modeling performance, faster learning speed, and more robust structure than existing models. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</italic> —Transfer learning (TL) aims to improve training effectiveness by transferring knowledge from a source domain to target domain. This paper presents a growing deep belief network (DBN) with TL to improve the training effectiveness and determine the optimal model size. Facing a complex process and real-world workflow, DBN tends to require long time for its successful training. The proposed growing DBN with TL (TL-GDBN) accelerates the learning process by instantaneously transferring the knowledge from a source domain to each new deeper or wider substructure. The experimental results show that the proposed TL-GDBN model has a great potential to deal with complex system, especially the systems with high nonlinearity. As a result, it can be readily applicable to some industrial nonlinear systems.

A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction This paper proposes a unified spatio-temporal model for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-v...

DNN-based prediction model for spatio-temporal data. Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data, which has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend). In this paper, we propose a <u>Deep</u>-learning-based prediction model for <u>S</u>patio-<u>T</u>emporal data (DeepST). We leverage ST domain knowledge to design the architecture of DeepST, which is comprised of two components: spatio-temporal and global. The spatio-temporal component employs the framework of convolutional neural networks to simultaneously model spatial near and distant dependencies, and temporal closeness, period and trend. The global component is used to capture global factors, such as day of the week, weekday or weekend. Using DeepST, we build a real-time crowd flow forecasting system called UrbanFlow1. Experiment results on diverse ST datasets verify DeepST's ability to capture ST data's spatio-temporal properties, showing the advantages of DeepST beyond four baseline methods.

Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search This paper improves recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We, first, analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the sub-optimality of such a representation for matching descriptors and leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within the inverted file and are efficiently exploited for all images, even in the case of very large datasets. Experiments performed on a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short list of images, is complementary to our weak geometric consistency constraints and allows to further improve the accuracy.

Microsoft Coco: Common Objects In Context We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

Markov games as a framework for multi-agent reinforcement learning In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsis-tic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic.

Pors: proofs of retrievability for large files In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety. A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes. In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work. We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

On controller initialization in multivariable switching systems We consider a class of switched systems which consists of a linear MIMO and possibly unstable process in feedback interconnection with a multicontroller whose dynamics switch. It is shown how one can achieve significantly better transient performance by selecting the initial condition for every controller when it is inserted into the feedback loop. This initialization is obtained by performing the minimization of a quadratic cost function of the tracking error, controlled output, and control signal. We guarantee input-to-state stability of the closed-loop system when the average number of switches per unit of time is smaller than a specific value. If this is not the case then stability can still be achieved by adding a mild constraint to the optimization. We illustrate the use of our results in the control of a flexible beam actuated in torque. This system is unstable with two poles at the origin and contains several lightly damped modes, which can be easily excited by controller switching.

A competitive swarm optimizer for large scale optimization. In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position of each particle nor the global best position (or neighborhood best positions) is involved in updating the particles. Instead, a pairwise competition mechanism is introduced, where the particle that loses the competition will update its position by learning from the winner. To understand the search behavior of the proposed CSO, a theoretical proof of convergence is provided, together with empirical analysis of its exploration and exploitation abilities showing that the proposed CSO achieves a good balance between exploration and exploitation. Despite its algorithmic simplicity, our empirical results demonstrate that the proposed CSO exhibits a better overall performance than five state-of-the-art metaheuristic algorithms on a set of widely used large scale optimization problems and is able to effectively solve problems of dimensionality up to 5000.

A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Blockchain for the Internet of Vehicles: A Decentralized IoT Solution for Vehicles Communication Using Ethereum. The concept of smart cities has become prominent in modern metropolises due to the emergence of embedded and connected smart devices, systems, and technologies. They have enabled the connection of every "thing" to the Internet. Therefore, in the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in newly developed smart cities. The IoV has the potential to solve various traffic and road safety problems effectively in order to prevent fatal crashes. However, a particular challenge in the IoV, especially in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, is to ensure fast, secure transmission and accurate recording of the data. In order to overcome these challenges, this work is adapting Blockchain technology for real time application (RTA) to solve Vehicle-to-Everything (V2X) communications problems. Therefore, the main novelty of this paper is to develop a Blockchain-based IoT system in order to establish secure communication and create an entirely decentralized cloud computing platform. Moreover, the authors qualitatively tested the performance and resilience of the proposed system against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications such as security, centralization, and lack of privacy. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.

Reliable Computation Offloading for Edge-Computing-Enabled Software-Defined IoV Internet of Vehicles (IoV) has drawn great interest recent years. Various IoV applications have emerged for improving the safety, efficiency, and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving and emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this article, we integrate mobile-edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge-computing-aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with the reprocessing mechanism to EC-SDIoV. Since the optimization problem is nonconvex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

Energy Efficiency Resource Allocation For D2d Communication Network Based On Relay Selection In order to solve the problem of spectrum resource shortage and energy consumption, we put forward a new model that combines with D2D communication and energy harvesting technology: energy harvesting-aided D2D communication network under the cognitive radio (EHA-CRD), where the D2D users harvest energy from the base station and the D2D source communicate with D2D destination by D2D relays. Our goals are to investigate the maximization energy efficiency (EE) of the network by joint time allocation and relay selection while taking into the constraints of the signal-to-noise ratio of D2D and the rates of the Cellular users. During this process, the energy collection time and communication time are randomly allocated. The maximization problem of EE can be divided into two sub-problems: (1) relay selection problem; (2) time optimization problem. For the first sub-problem, we propose a weighted sum maximum algorithm to select the best relay. For the last sub-problem, the EE maximization problem is non-convex problem with time. Thus, by using fractional programming theory, we transform it into a standard convex optimization problem, and we propose the optimization iterative algorithm to solve the convex optimization problem for obtaining the optimal solution. And, the simulation results show that the proposed relay selection algorithm and time optimization algorithm are significantly improved compared with the existing algorithms.

Repeated Game Analysis for Cooperative MAC With Incentive Design for Wireless Networks. Cooperative communications offer appealing potentials to improve quality of service (QoS) for wireless networks. Many existing works on cooperative communications assume that participation in cooperative relaying is unconditional. In practice, however, due to resource consumption, it is vital to provide incentives for selfish cooperating peer nodes. In this paper, we analyze a cooperative medium a...

A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle The Internet of Vehicles (IoV) can obtain traffic information through a large number of data collected by sensors. However, the lack of data, abnormal data, and other low-quality problems have seriously restricted the development and application of the IoV. To solve the problem of missing data in a large-scale road network, the previous research achievements show that tensor decomposition method has the advantages in solving multi-dimensional data imputation problems, so we adopt this tensor mode to model traffic velocity data. A new method of data missing estimation with tensor heterogeneous ensemble learning based on FNN (Fuzzy Neural Network) named FNNTEL is proposed in this paper. The performance of this method is evaluated by our experiments and analysis. The proposed method is applied to be tested by the real data captured in Guangzhou and Tianjin of China respectively. A large number of experimental tests show that the performance of the new method is better than other commonly used technologies and different missing data generation models.

Occlusion-Aware Detection For Internet Of Vehicles In Urban Traffic Sensing Systems Vehicle detection is a fundamental challenge in urban traffic surveillance video. Due to the powerful representation ability of convolution neural network (CNN), CNN-based detection approaches have achieve incredible success on generic object detection. However, they can't deal well with vehicle occlusion in complex urban traffic scene. In this paper, we present a new occlusion-aware vehicle detection CNN framework, which is an effective and efficient framework for vehicle detection. First, we concatenate the low-level and high-level feature maps to capture more robust feature representation, then we fuse the local and global feature maps for handling vehicle occlusion, the context information is also been adopted in our framework. Extensive experiments demonstrate the competitive performance of our proposed framework. Our methods achieve better effect than primal Faster R-CNN in terms of accuracy on a new urban traffic surveillance dataset (UTSD) which contains a mass of occlusion vehicles and complex scenes.

Probabilistic encryption A new probabilistic model of data encryption is introduced. For this model, under suitable complexity assumptions, it is proved that extracting any information about the cleartext from the cyphertext is hard on the average for an adversary with polynomially bounded computational resources. The proof holds for any message space with any probability distribution. The first implementation of this model is presented. The security of this implementation is proved under the interactability assumptin of deciding Quadratic Residuosity modulo composite numbers whose factorization is unknown.

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the Internet of Things are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this article. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting and multicriteria decision making are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

Space-time super-resolution. We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple low-resolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By "temporal super-resolution," we mean recovering rapid dynamic events that occur faster than regular frame-rate. Such dynamic events are not visible (or else are observed incorrectly) in any of the input sequences, even if these are played in "slow-motion." The spatial and temporal dimensions are very different in nature, yet are interrelated. This leads to interesting visual trade-offs in time and space and to new video applications. These include: 1) treatment of spatial artifacts (e.g., motion-blur) by increasing the temporal resolution and 2) combination of input sequences of different space-time resolutions (e.g., NTSC, PAL, and even high quality still images) to generate a high quality video sequence. We further analyze and compare characteristics of temporal super-resolution to those of spatial super-resolution. These include: How many video cameras are needed to obtain increased resolution? What is the upper bound on resolution improvement via super-resolution? What is the temporal analogue to the spatial "ringing" effect?

The concept of flow in collaborative game-based learning Generally, high-school students have been characterized as bored and disengaged from the learning process. However, certain educational designs promote excitement and engagement. Game-based learning is assumed to be such a design. In this study, the concept of flow is used as a framework to investigate student engagement in the process of gaming and to explain effects on game performance and student learning outcome. Frequency 1550, a game about medieval Amsterdam merging digital and urban play spaces, has been examined as an exemplar of game-based learning. This 1-day game was played in teams by 216 students of three schools for secondary education in Amsterdam. Generally, these students show flow with their game activities, although they were distracted by solving problems in technology and navigation. Flow was shown to have an effect on their game performance, but not on their learning outcome. Distractive activities and being occupied with competition between teams did show an effect on the learning outcome of students: the fewer students were distracted from the game and the more they were engaged in group competition, the more students learned about the medieval history of Amsterdam. Consequences for the design of game-based learning in secondary education are discussed.

Segmentation-Based Image Copy-Move Forgery Detection Scheme In this paper, we propose a scheme to detect the copy-move forgery in an image, mainly by extracting the keypoints for comparison. The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to keypoint extraction. As a result, the copy-move regions can be detected by matching between these patches. The matching process consists of two stages. In the first stage, we find the suspicious pairs of patches that may contain copy-move forgery regions, and we roughly estimate an affine transform matrix. In the second stage, an Expectation-Maximization-based algorithm is designed to refine the estimated matrix and to confirm the existence of copy-move forgery. Experimental results prove the good performance of the proposed scheme via comparing it with the state-of-the-art schemes on the public databases.

Adaptive Fuzzy Control With Prescribed Performance for Block-Triangular-Structured Nonlinear Systems. In this paper, an adaptive fuzzy control method with prescribed performance is proposed for multi-input and multioutput block-triangular-structured nonlinear systems with immeasurable states. Fuzzy logic systems are adopted to identify the unknown nonlinear system functions. Adaptive fuzzy state observers are designed to solve the problem of unmeasured states, and a new observer-based output-feedb...

Design and Validation of a Cable-Driven Asymmetric Back Exosuit Lumbar spine injuries caused by repetitive lifting rank as the most prevalent workplace injury in the United States. While these injuries are caused by both symmetric and asymmetric lifting, asymmetric is often more damaging. Many back devices do not address asymmetry, so we present a new system called the Asymmetric Back Exosuit (ABX). The ABX addresses this important gap through unique design geometry and active cable-driven actuation. The suit allows the user to move in a wide range of lumbar trajectories while the “X” pattern cable routing allows variable assistance application for these trajectories. We also conducted a biomechanical analysis in OpenSim to map assistive cable force to effective lumbar torque assistance for a given trajectory, allowing for intuitive controller design in the lumbar joint space over the complex kinematic chain for varying lifting techniques. Human subject experiments illustrated that the ABX reduced lumbar erector spinae muscle activation during symmetric and asymmetric lifting by an average of 37.8% and 16.0%, respectively, compared to lifting without the exosuit. This result indicates the potential for our device to reduce lumbar injury risk.

Event-triggered sliding mode control of uncertain switched systems under denial-of-service attacks This study is concerned with the event-triggered sliding mode control problem for a class of cyber-physical switched systems, in which the Denial-of-Service (DoS) attacks may randomly occur according to the Bernoulli distribution. A key issue is how to design the output feedback sliding mode control (SMC) law for guaranteeing the dynamical performance of the closed-loop system under DoS attacks. To this end, an event-triggered mechanism is firstly introduced to reduce the communication load, under which the measurement signal is transmitted only when a certain triggering condition is satisfied. An usable output signal for the controller is constructed to compensate the effect of unmeasured states and DoS attacks. And then, a dynamic output feedback sliding mode controller is designed by means of the attack probability and the compensated output signals. Both the reachability and the mean-square exponential stability of sliding mode dynamics are investigated and the corresponding sufficient conditions are obtained. Finally, some numerical simulation results are provided.

Input-to-State Stabilizing Control under Denial-of-Service The issue of cyber-security has become ever more prevalent in the analysis and design of networked systems. In this paper, we analyze networked control systems in the presence of Denial-of-Service (DoS) attacks, namely attacks that prevent transmissions over the network. We characterize frequency and duration of the DoS attacks under which input-to-state stability (ISS) of the closed-loop system can be preserved. To achieve ISS, a suitable scheduling of the transmission times is determined. It is shown that the considered framework is flexible enough so as to allow the designer to choose from several implementation options that can be used for trading-off performance vs. communication resources. Examples are given to substantiate the analysis.

Survey on Recent Advances in Networked Control Systems. Networked control systems (NCSs) are systems whose control loops are closed through communication networks such that both control signals and feedback signals can be exchanged among system components (sensors, controllers, actuators, and so on). NCSs have a broad range of applications in areas such as industrial control and signal processing. This survey provides an overview on the theoretical dev...

Neural network-based event-triggered MFAC for nonlinear discrete-time processes. This paper is concerned with the event-triggered data-driven control problem for nonlinear discrete-time systems. An event-based data-driven model-free adaptive controller design algorithm together with constructing an adaptive event-trigger condition is developed. Different from the existing data-driven model-free adaptive control approach, an aperiodic neural network weight update law is introduced to estimate the controller parameters, and the event-trigger mechanism is activated only if the event-trigger error exceeds the threshold. Furthermore, by combining the equivalent-dynamic-linearization technique with the Lyapunov method, it is proved that both the closed-loop control system and the weight estimation error are ultimately bounded. Finally, two simulation examples are provided to demonstrate the effectiveness of the derived method.

Secure Consensus of Multiagent Systems With Input Saturation and Distributed Multiple DoS Attacks The secure consensus problem of multiagent systems with input saturation and denial-of-service (DoS) attacks constraints is an interesting research problem. To solve this problem, the assumptions that all the agents own the same saturation level and suffer the same DoS attacks from a single adversary are usually made. This brief removes the above assumptions and investigates the secure consensus problem of multiagent systems with different saturation levels and multiple DoS attacks. The studied multiagent systems have different layers, in which different saturation levels are studied for different layers. Moreover, the DoS attacks are launched from different adversaries and will cause different effects for different agents. To achieve the desired objective, two different consensus protocols for agents in different levels are first designed using the low-gain technique. Then, the investigated dynamic system under DoS attacks is modeled by a switched system by categorizing the DoS attacks into several types. The controller is proposed by solving parametric algebraic Riccati equation (ARE). Sufficient conditions for the DoS attack duration on each channel are derived.

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Massive MIMO for next generation wireless systems Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.

Deep Residual Learning for Image Recognition Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Communication theory of secrecy systems THE problems of cryptography and secrecy systems furnish an interesting application of communication theory.1 In this paper a theory of secrecy systems is developed. The approach is on a theoretical level and is intended to complement the treatment found in standard works on cryptography.2 There, a detailed study is made of the many standard types of codes and ciphers, and of the ways of breaking them. We will be more concerned with the general mathematical structure and properties of secrecy systems.

A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization In recent years, there has been a growing interest for the experimental analysis in the field of evolutionary algorithms. It is noticeable due to the existence of numerous papers which analyze and propose different types of problems, such as the basis for experimental comparisons of algorithms, proposals of different methodologies in comparison or proposals of use of different statistical techniques in algorithms’ comparison.In this paper, we focus our study on the use of statistical techniques in the analysis of evolutionary algorithms’ behaviour over optimization problems. A study about the required conditions for statistical analysis of the results is presented by using some models of evolutionary algorithms for real-coding optimization. This study is conducted in two ways: single-problem analysis and multiple-problem analysis. The results obtained state that a parametric statistical analysis could not be appropriate specially when we deal with multiple-problem results. In multiple-problem analysis, we propose the use of non-parametric statistical tests given that they are less restrictive than parametric ones and they can be used over small size samples of results. As a case study, we analyze the published results for the algorithms presented in the CEC’2005 Special Session on Real Parameter Optimization by using non-parametric test procedures.

Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion This article presents the results of video-based Human Robot Interaction (HRI) trials which investigated people's perceptions of different robot appearances and associated attention-seeking features and behaviors displayed by robots with different appearance and behaviors. The HRI trials studied the participants' preferences for various features of robot appearance and behavior, as well as their personality attributions towards the robots compared to their own personalities. Overall, participants tended to prefer robots with more human-like appearance and attributes. However, systematic individual differences in the dynamic appearance ratings are not consistent with a universal effect. Introverts and participants with lower emotional stability tended to prefer the mechanical looking appearance to a greater degree than other participants. It is also shown that it is possible to rate individual elements of a particular robot's behavior and then assess the contribution, or otherwise, of that element to the overall perception of the robot by people. Relating participants' dynamic appearance ratings of individual robots to independent static appearance ratings provided evidence that could be taken to support a portion of the left hand side of Mori's theoretically proposed `uncanny valley' diagram. Suggestions for future work are outlined.

Finite-approximation-error-based discrete-time iterative adaptive dynamic programming. In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite horizon discrete-time nonlinear systems with finite approximation errors. First, a new generalized value iteration algorithm of ADP is developed to make the iterative performance index function converge to the solution of the Hamilton-Jacobi-Bellman equation. The ...

An evolutionary programming approach for securing medical images using watermarking scheme in invariant discrete wavelet transformation. •The proposed watermarking scheme utilized improved discrete wavelet transformation (IDWT) to retrieve the invariant wavelet domain.•The entropy mechanism is used to identify the suitable region for insertion of watermark. This will improve the imperceptibility and robustness of the watermarking procedure.•The scaling factors such as PSNR and NC are considered for evaluation of the proposed method and the Particle Swarm Optimization is employed to optimize the scaling factors.

Attitudes Towards Social Robots In Education: Enthusiast, Practical, Troubled, Sceptic, And Mindfully Positive While social robots bring new opportunities for education, they also come with moral challenges. Therefore, there is a need for moral guidelines for the responsible implementation of these robots. When developing such guidelines, it is important to include different stakeholder perspectives. Existing (qualitative) studies regarding these perspectives however mainly focus on single stakeholders. In this exploratory study, we examine and compare the attitudes of multiple stakeholders on the use of social robots in primary education, using a novel questionnaire that covers various aspects of moral issues mentioned in earlier studies. Furthermore, we also group the stakeholders based on similarities in attitudes and examine which socio-demographic characteristics influence these attitude types. Based on the results, we identify five distinct attitude profiles and show that the probability of belonging to a specific profile is affected by such characteristics as stakeholder type, age, education and income. Our results also indicate that social robots have the potential to be implemented in education in a morally responsible way that takes into account the attitudes of various stakeholders, although there are multiple moral issues that need to be addressed first. Finally, we present seven (practical) implications for a responsible application of social robots in education following from our results. These implications provide valuable insights into how social robots should be implemented.

A Novel Mixed Control Approach for Fuzzy Systems via Membership Functions Online Learning Policy This article focuses on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{2}-\mathcal {L} _{\infty}/ \mathcal {H}_{\infty}$</tex-math></inline-formula> optimization control issue for a family of nonlinear plants by Takagi–Sugeno (T–S) fuzzy approach with actuator failure. First, considering unmeasurable system states, sufficient criteria for devising fuzzy imperfect premise matching dynamic output feedback controller to maintain asymptotic stability while guaranteeing a mixed performance for T–S fuzzy systems are provided. Therewith, in the light of feasible areas of dynamic output feedback controller membership functions (MFs), a new MFs online learning policy using gradient descent algorithm is proposed to learn the real-time values of MFs to acquire a better <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{2}-\mathcal {L}_{\infty}/ \mathcal {H}_{\infty}$</tex-math></inline-formula> control effect. Different from the traditional method using an imperfect premise matching scheme, under the proposed optimization algorithm, the trajectory of mixed performance index is lowered effectively. Afterward, a sufficient criterion is presented for assuring the convergence of the error of the cost function. Finally, the superiority of this online optimization learning policy is confirmed via simulations.

Further Study on Stabilization for Continuous-Time Takagi–Sugeno Fuzzy Systems With Time Delay In the recently published paper, a switching method has been proposed to deal with the time derivative of the membership functions and less conservative results can be obtained due to this method; however, this method is based on the assumption that the switching times are finite. In this article, this method is further studied and the average dwell-time (ADT) switching technique is applied to ens...

Robust Stability and Stabilization Conditions for Nonlinear Networked Control Systems With Network-Induced Delay via T&#x2013;S Fuzzy Model AbstractRobust stability and stabilization problems for a class of discrete-time nonlinear systems are studied in this article. The control signal is transmitted to the nonlinear systems through a lossy communication channel in which time-varying network-induced delay occurs. A Takagi–Sugeno (T–S) fuzzy model is used to represent the nonlinear systems. Using membership function boundary information, a novel membership-function-dependent (MFD) analysis approach is presented. Unlike most previous results, the proposed MFD analysis approach does not need to increase the number of slack variables to reduce conservatism. Combining auxiliary matrices and this approach, we propose some new fuzzy Lyapunov–Krasovskii functionals (FLKFs) that do not require the restrictions of positive definiteness imposed on Lyapunov matrices. Using the FLKF and MFD analysis approach, a sufficient condition in the form of linear matrix inequalities (LMIs) is developed to guarantee the stability of T–S fuzzy time-varying delay systems. Based on the results for stability, less conservative stabilization conditions are also derived. Finally, numerical examples show that less conservative results can be obtained by the proposed approach. The inverted pendulum system with state delay or network-induced delay is also studied to verify the effectiveness of the suggested approach.

Adaptive Fuzzy Observer-Based Fault Estimation for a Class of Nonlinear Stochastic Hybrid Systems This article studies the fault estimation problem for a class of continuous-time nonlinear Markovian jump systems with unmeasured states, unknown bounded sensor faults, and unknown nonlinearities simultaneously. In this article, a new adaptive fuzzy observer design scheme is developed, where the completely unknown nonlinear terms are approximated by adaptive fuzzy logic systems. By means of a nove...

Controller design for TS models using delayed nonquadratic Lyapunov functions. In the last few years, nonquadratic Lyapunov functions have been more and more frequently used in the analysis and controller design for Takagi-Sugeno fuzzy models. In this paper, we developed relaxed conditions for controller design using nonquadratic Lyapunov functions and delayed controllers and give a general framework for the use of such Lyapunov functions. The two controller design methods developed in this framework outperform and generalize current state-of-the-art methods. The proposed methods are extended to robust and H∞ control and α -sample variation.

Command Filter Based Adaptive Fuzzy Finite-Time Control for a Class of Uncertain Nonlinear Systems With Hysteresis This article addresses an adaptive fuzzy finite-time control for a class of uncertain strict-feedback nonlinear systems with backlashlike hysteresis and stochastic disturbances. At first, a novel criterion of semiglobally finite-time stability in probability (SGFSP) is established based on Lyapunov function method. Under the proposed stability criterion, an adaptive fuzzy finite-time control scheme is designed. In the design process of the controller, command filter technique is introduced to overcome the problems of “explosion of complexity” and “singularity” inhered in the traditional adaptive finite-time control based on the backstepping method. Meanwhile, via constructing the corresponding error compensating systems, the effect of errors generated by the command filters is reduced, such that the original systems have more better tracking performance. To cope with the influence of backlashlike hysteresis input, an auxiliary system is constructed, in which the output signal is applied to compensate the effect of the hysteresis. It is shown that the tracking error can converge to a small neighborhood of original in finite time, and the closed-loop system is SGFSP under the constructed controller. Finally, the effectiveness of the proposed control strategy is further verified by two simulation examples.

Adaptive actuator fault tolerant control for uncertain nonlinear systems with multiple actuators In this paper, a novel adaptive fault tolerant controller design is proposed for a class of nonlinear unknown systems with multiple actuators. The controller consists of an adaptive learning-based control law, a Nussbaum gain, and a switching function scheme. The adaptive control law is implemented by a two-layer neural network to accommodate the unknown system dynamics. Without the requirement of additional fault detection mechanism, the switching function is designed to automatically locate and turn off the unknown faulty actuators by observing a control performance index. The asymptotic stability of the system output in the presence of actuator failures is rigidly proved through standard Lyapunov approach, while the other signals of the closed-loop system are guaranteed to be bounded. The theoretical result is substantiated by simulation on a two-tank system.

Design of Stabilizers and Observers for a Class of Multivariable T-S Fuzzy Models on the Basis of New Interpolation Functions. An approach to design stabilizers and observers for a class of multiple-input multiple-output (MIMO) Takagi-Sugeno (T-S) fuzzy models is developed on the basis of local gains and the searching for a set of interpolation functions capable of properly combining the aforementioned local gains. As expected, the existence of such interpolation functions depends on the controllability and observability ...

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

Flocking in Fixed and Switching Networks This note analyzes the stability properties of a group of mobile agents that align their velocity vectors, and stabilize their inter-agent distances, using decentralized, nearest-neighbor interaction rules, exchanging information over networks that change arbitrarily (no dwell time between consecutive switches). These changes introduce discontinuities in the agent control laws. To accommodate for arbitrary switching in the topology of the network of agent interactions we employ nonsmooth analysis. The main result is that regardless of switching, convergence to a common velocity vector and stabilization of inter-agent distances is still guaranteed as long as the network remains connected at all times

Linear quadratic bumpless transfer A method for bumpless transfer using ideas from LQ theory is presented and shown to reduce to the Hanus conditioning scheme under certain conditions.

GASPAD: A General and Efficient mm-Wave Integrated Circuit Synthesis Method Based on Surrogate Model Assisted Evolutionary Algorithm The design and optimization (both sizing and layout) of mm-wave integrated circuits (ICs) have attracted much attention due to the growing demand in industry. However, available manual design and synthesis methods suffer from a high dependence on design experience, being inefficient or not general enough. To address this problem, a new method, called general mm-wave IC synthesis based on Gaussian process model assisted differential evolution (GASPAD), is proposed in this paper. A medium-scale computationally expensive constrained optimization problem must be solved for the targeted mm-wave IC design problem. Besides the basic techniques of using a global optimization algorithm to obtain highly optimized design solutions and using surrogate models to obtain a high efficiency, a surrogate model-aware search mechanism (SMAS) for tackling the several tens of design variables (medium scale) and a method to appropriately integrate constraint handling techniques into SMAS for tackling the multiple (high-) performance specifications are proposed. Experiments on two 60 GHz power amplifiers in a 65 nm CMOS technology and two mathematical benchmark problems are carried out. Comparisons with the state-of-art provide evidence of the important advantages of GASPAD in terms of solution quality and efficiency.

Deep Learning in Mobile and Wireless Networking: A Survey. The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Social Robots for (Second) Language Learning in (Migrant) Primary School Children Especially these days, innovation and support from technology to relieve pressure in education is highly urgent. This study tested the potential advantage of a social robot over a tablet in (second) language learning on performance, engagement, and enjoyment. Shortages in primary education call for new technology solutions. Previous studies combined robots with tablets, to compensate for robot’s limitations, however, this study applied direct human–robot interaction. Primary school children (N = 63, aged 4–6) participated in a 3-wave field experiment with story-telling exercises, either with a semi-autonomous robot (without tablet, using WOz) or a tablet. Results showed increased learning gains over time when training with a social robot, compared to the tablet. Children who trained with a robot were more engaged in the story-telling task and enjoyed it more. Robot’s behavioral style (social or neutral) hardly differed overall, however, seems to vary for high versus low educational abilities. While social robots need sophistication before being implemented in schools, our study shows the potential of social robots as tutors in (second) language learning.

Robust ear identification using sparse representation of local texture descriptors Automated personal identification using localized ear images has wide range of civilian and law-enforcement applications. This paper investigates a new approach for more accurate ear recognition and verification problem using the sparse representation of local gray-level orientations. We exploit the computational simplicity of localized Radon transform for the robust ear shape representation and also investigate the effectiveness of local curvature encoding using Hessian based feature representation. The ear representation problem is modeled as the sparse coding solution based on multi-orientation Radon transform dictionary whose solution is computed using the convex optimization approach. We also study the nonnegative formulation such problem, to address the limitations from the regularized optimization problem, in the sparse representation of localized ear features. The log-Gabor filter based approach and the localized Radon transform based feature representation has been used as baseline algorithm to ascertain the effectiveness of the proposed approach. We present experimental results from publically available UND and IITD ear databases which achieve significant improvement in the performance, both for the recognition and authentication problem, and confirm the usefulness of proposed approach for more accurate ear identification.

A brief review of the ear recognition process using deep neural networks. The process of precisely recognize people by ears has been getting major attention in recent years. It represents an important step in the biometric research, especially as a complement to face recognition systems which have difficult in real conditions. This is due to the great variation in shapes, variable lighting conditions, and the changing profile shape which is a planar representation of a complex object. An ear recognition system involving a convolutional neural networks (CNN) is proposed to identify a person given an input image. The proposed method matches the performance of other traditional approaches when analyzed against clean photographs. However, the F1 metric of the results shows improvements in specificity of the recognition. We also present a technique for improving the speed of a CNN applied to large input images through the optimization of the sliding window approach.

An ear biometric system based on artificial bees and the scale invariant feature transform. An ear biometric system based on artificial bees for ear image contrast enhancement is proposed.In the feature extraction stage, the scale invariant feature transform is used.The proposed approach was tested on three databases of ear biometrics: IIT Delhi, USTB 1 and USTB 2.The obtained results proved the superiority of the proposed in two out of three test databases. Ear recognition is a new biometric technology that competes with well-known biometric modalities such as fingerprint, face and iris. However, this modality suffers from common image acquisition problems, such as change in illumination, poor contrast, noise and pose variation. Using a 3D ear models reduce rotation, scale variation and translation-related problems, but they are computationally expensive. This paper presents a new architecture of ear biometrics that aims at solving the acquisition problems of 2D ear images. The proposed system uses a new ear image contrast enhancement approach based on the gray-level mapping technique, and uses an artificial bee colony (ABC) algorithm as an optimizer. This technique permits getting better-contrasted 2D ear images. In the feature extraction stage, the scale invariant feature transform (SIFT) is used. For the matching phase, the Euclidean distance is adopted. The proposed approach was tested on three reference ear image databases: IIT Delhi, USTB 1 and USTB 2, and compared with traditional ear image contrast enhancement approaches, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE). The obtained results show that the proposed approach outperforms traditional ear image contrast enhancement techniques, and increases the amount of detail in the ear image, and consequently improves the recognition rate.

On ear-based human identification in the mid-wave infrared spectrum In this paper the problem of human ear recognition in the Mid-wave infrared (MWIR) spectrum is studied in order to illustrate the advantages and limitations of the ear-based biometrics that can operate in day and night time environments. The main contributions of this work are two-fold: First, a dual-band database is assembled that consists of visible (baseline) and mid-wave IR left and right profile face images. Profile face images were collected using a high definition mid-wave IR camera that is capable of acquiring thermal imprints of human skin. Second, a fully automated, thermal imaging based, ear recognition system is proposed that is designed and developed to perform real-time human identification. The proposed system tests several feature extraction methods, namely: (i) intensity-based such as independent component analysis (ICA), principal component analysis (PCA), and linear discriminant analysis (LDA); (ii) shape-based such as scale invariant feature transform (SIFT); as well as (iii) texture-based such as local binary patterns (LBP), and local ternary patterns (LTP). Experimental results suggest that LTP (followed by LBP) yields the best performance (Rank1=80:68%) on manually segmented ears and (Rank1=68:18%) on ear images that are automatically detected and segmented. By fusing the matching scores obtained by LBP and LTP, the identification performance increases by about 5%. Although these results are promising, the outcomes of our study suggest that the design and development of automated ear-based recognition systems that can operate efficiently in the lower part of the passive IR spectrum are very challenging tasks.

Plastic surgery: a new dimension to face recognition Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. The contribution of this research is 1) preparing a face database of 900 individuals for plastic surgery, and 2) providing an analytical and experimental underpinning of the effect of plastic surgery on face recognition algorithms. The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance. Therefore, it is imperative to initiate a research effort so that future face recognition systems will be able to address this important problem.

3D ear recognition using local salience and principal manifold an emerging class of biometrics, human ear has drawn significant attention in recent years. In this paper, we propose a novel 3D ear shape matching and recognition system. First, we propose a novel method for computing saliency value of each point on 3D ear point clouds, which is based on the Gaussian-weighted average of the mean curvature and can be used to sort the keypoints accordingly. Then we propose the optimal selection of the salient key points using the Poisson Disk Sampling. Finally, we fit a surface to the neighborhood of each salient keypoint using the quadratic principal manifold method, establishing the local feature descriptor of each salient keypoint. The experimental results on ear shape matching show that, compared with other similar methods, the proposed system has higher approximation precision on shape feature detection and higher matching accuracy on the ear recognition.

Ear recognition using local binary patterns: A comparative experimental study. •A comparative study of ear recognition using local binary patterns variants is done.•A new texture operator is proposed and used as an ear feature descriptor.•Detailed analysis on Identification and verification is conducted separately.•An approximated recognition rate of 99% is achieved by some texture descriptors.•The study has significant insights and can benefit researchers in future works.

From template to image: reconstructing fingerprints from minutiae points. Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line Integral Convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint.

New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system. In this paper we describe the design of a fuzzy logic controller for the ball and beam system using a modified Ant Colony Optimization (ACO) method for optimizing the type of membership functions, the parameters of the membership functions and the fuzzy rules. This is achieved by applying a systematic and hierarchical optimization approach modifying the conventional ACO algorithm using an ant set partition strategy. The simulation results show that the proposed algorithm achieves better results than the classical ACO algorithm for the design of the fuzzy controller.

Social Perception and Steering for Online Avatars This paper presents work on a new platform for producing realistic group conversation dynamics in shared virtual environments. An avatar, representing users, should perceive the surrounding social environment just as humans would, and use the perceptual information for driving low level reactive behaviors. Unconscious reactions serve as evidence of life, and can also signal social availability and spatial awareness to others. These behaviors get lost when avatar locomotion requires explicit user control. For automating such behaviors we propose a steering layer in the avatars that manages a set of prioritized behaviors executed at different frequencies, which can be activated or deactivated and combined together. This approach gives us enough flexibility to model the group dynamics of social interactions as a set of social norms that activate relevant steering behaviors. A basic set of behaviors is described for conversations, some of which generate a social force field that makes the formation of conversation groups fluidly adapt to external and internal noise, through avatar repositioning and reorientations. The resulting social group behavior appears relatively robust, but perhaps more importantly, it starts to bring a new sense of relevance and continuity to the virtual bodies that often get separated from the ongoing conversation in the chat window.

Stabilization of switched continuous-time systems with all modes unstable via dwell time switching Stabilization of switched systems composed fully of unstable subsystems is one of the most challenging problems in the field of switched systems. In this brief paper, a sufficient condition ensuring the asymptotic stability of switched continuous-time systems with all modes unstable is proposed. The main idea is to exploit the stabilization property of switching behaviors to compensate the state divergence made by unstable modes. Then, by using a discretized Lyapunov function approach, a computable sufficient condition for switched linear systems is proposed in the framework of dwell time; it is shown that the time intervals between two successive switching instants are required to be confined by a pair of upper and lower bounds to guarantee the asymptotic stability. Based on derived results, an algorithm is proposed to compute the stability region of admissible dwell time. A numerical example is proposed to illustrate our approach.

Stable fuzzy logic control of a general class of chaotic systems This paper proposes a new approach to the stable design of fuzzy logic control systems that deal with a general class of chaotic processes. The stable design is carried out on the basis of a stability analysis theorem, which employs Lyapunov's direct method and the separate stability analysis of each rule in the fuzzy logic controller (FLC). The stability analysis theorem offers sufficient conditions for the stability of a general class of chaotic processes controlled by Takagi---Sugeno---Kang FLCs. The approach suggested in this paper is advantageous because inserting a new rule requires the fulfillment of only one of the conditions of the stability analysis theorem. Two case studies concerning the fuzzy logic control of representative chaotic systems that belong to the general class of chaotic systems are included in order to illustrate our stable design approach. A set of simulation results is given to validate the theoretical results.

A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

A Learning-Based Framework for Velocity Control in Autonomous Driving We present a framework for autonomous driving which can learn from human demonstrations, and we apply it to the longitudinal control of an autonomous car. Offline, we model car-following strategies from a set of example driving sequences. Online, the model is used to compute accelerations which replicate what a human driver would do in the same situation. This reference acceleration is tracked by a predictive controller which enforces a set of comfort and safety constraints before applying the final acceleration. The controller is designed to be robust to the uncertainty in the predicted motion of the preceding vehicle. In addition, we estimate the confidence of the driver model predictions and use it in the cost function of the predictive controller. As a result, we can handle cases where the training data used to learn the driver model does not provide sufficient information about how a human driver would handle the current driving situation. The approach is validated using a combination of simulations and experiments on our autonomous vehicle.

Analysing user physiological responses for affective video summarisation. Video summarisation techniques aim to abstract the most significant content from a video stream. This is typically achieved by processing low-level image, audio and text features which are still quite disparate from the high-level semantics that end users identify with (the ‘semantic gap’). Physiological responses are potentially rich indicators of memorable or emotionally engaging video content for a given user. Consequently, we investigate whether they may serve as a suitable basis for a video summarisation technique by analysing a range of user physiological response measures, specifically electro-dermal response (EDR), respiration amplitude (RA), respiration rate (RR), blood volume pulse (BVP) and heart rate (HR), in response to a range of video content in a variety of genres including horror, comedy, drama, sci-fi and action. We present an analysis framework for processing the user responses to specific sub-segments within a video stream based on percent rank value normalisation. The application of the analysis framework reveals that users respond significantly to the most entertaining video sub-segments in a range of content domains. Specifically, horror content seems to elicit significant EDR, RA, RR and BVP responses, and comedy content elicits comparatively lower levels of EDR, but does seem to elicit significant RA, RR, BVP and HR responses. Drama content seems to elicit less significant physiological responses in general, and both sci-fi and action content seem to elicit significant EDR responses. We discuss the implications this may have for future affective video summarisation approaches.

On the roles of eye gaze and head dynamics in predicting driver's intent to change lanes Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system (McCall et al., 2007) to determine the relative usefulness of each cue for determining intent. Various combinations of input data are presented to a discriminative classifier, which is trained to output a prediction of probable lane-change maneuver at a particular point in the future. Quantitative results from a naturalistic driving study are presented and show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane-change intent prediction. The addition of eye gaze does not improve performance as much as simpler head dynamics cues. The advantage of head data over eye data is shown to be statistically significant (p

Detection of Driver Fatigue Caused by Sleep Deprivation This paper aims to provide reliable indications of driver drowsiness based on the characteristics of driver-vehicle interaction. A test bed was built under a simulated driving environment, and a total of 12 subjects participated in two experiment sessions requiring different levels of sleep (partial sleep-deprivation versus no sleep-deprivation) before the experiment. The performance of the subjects was analyzed in a series of stimulus-response and routine driving tasks, which revealed the performance differences of drivers under different sleep-deprivation levels. The experiments further demonstrated that sleep deprivation had greater effect on rule-based than on skill-based cognitive functions: when drivers were sleep-deprived, their performance of responding to unexpected disturbances degraded, while they were robust enough to continue the routine driving tasks such as lane tracking, vehicle following, and lane changing. In addition, we presented both qualitative and quantitative guidelines for designing drowsy-driver detection systems in a probabilistic framework based on the paradigm of Bayesian networks. Temporal aspects of drowsiness and individual differences of subjects were addressed in the framework.

Online Prediction of Driver Distraction Based on Brain Activity Patterns This paper presents a new computational framework for early detection of driver distractions (map viewing) using brain activity measured by electroencephalographic (EEG) signals. Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver's needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.

A Forward Collision Warning Algorithm With Adaptation to Driver Behaviors. Significant effort has been made on designing user-acceptable driver assistance systems. To adapt to driver characteristics, this paper proposes a forward collision warning (FCW) algorithm that can adjust its warning thresholds in a real-time manner according to driver behavior changes, including both behavioral fluctuation and individual difference. This adaptive FCW algorithm overcomes the limit...

A CRNN module for hand pose estimation. •The input is no longer a single frame, but a sequence of several adjacent frames.•A CRNN module is proposed, which is basically the same as the standard RNN, except that it uses convolutional connection.•When the difference in the feature image of a certain layer is large, it is better to add CRNN / RNN after this layer.•Our method has the lowest error of output compared to the current state-of-the-art methods.

Deep convolutional neural network-based Bernoulli heatmap for head pose estimation Head pose estimation is a crucial problem for many tasks, such as driver attention, fatigue detection, and human behaviour analysis. It is well known that neural networks are better at handling classification problems than regression problems. It is an extremely nonlinear process to let the network output the angle value directly for optimization learning, and the weight constraint of the loss function will be relatively weak. This paper proposes a novel Bernoulli heatmap for head pose estimation from a single RGB image. Our method can achieve the positioning of the head area while estimating the angles of the head. The Bernoulli heatmap makes it possible to construct fully convolutional neural networks without fully connected layers and provides a new idea for the output form of head pose estimation. A deep convolutional neural network (CNN) structure with multiscale representations is adopted to maintain high-resolution information and low-resolution information in parallel. This kind of structure can maintain rich, high-resolution representations. In addition, channelwise fusion is adopted to make the fusion weights learnable instead of simple addition with equal weights. As a result, the estimation is spatially more precise and potentially more accurate. The effectiveness of the proposed method is empirically demonstrated by comparing it with other state-of-the-art methods on public datasets.

Reinforcement learning based data fusion method for multi-sensors In order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. Initially, the cubic B-spline interpolation is used to solve time alignment problems of multisource data. Then, the reinforcement learning based data fusion (RLBDF) method is proposed to obtain the fusion results. With the case that the priori knowledge of target is obtained, the fusion accuracy reinforcement is realized by the error between fused value and actual value. Furthermore, the Fisher information is instead used as the reward if the priori knowledge is unable to be obtained. Simulations results verify that the developed method is feasible and effective for the multi-sensors data fusion in air combat.

Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications FSA (artificial fish-swarm algorithm) is one of the best methods of optimization among the swarm intelligence algorithms. This algorithm is inspired by the collective movement of the fish and their various social behaviors. Based on a series of instinctive behaviors, the fish always try to maintain their colonies and accordingly demonstrate intelligent behaviors. Searching for food, immigration and dealing with dangers all happen in a social form and interactions between all fish in a group will result in an intelligent social behavior.This algorithm has many advantages including high convergence speed, flexibility, fault tolerance and high accuracy. This paper is a review of AFSA algorithm and describes the evolution of this algorithm along with all improvements, its combination with various methods as well as its applications. There are many optimization methods which have a affinity with this method and the result of this combination will improve the performance of this method. Its disadvantages include high time complexity, lack of balance between global and local search, in addition to lack of benefiting from the experiences of group members for the next movements.

Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning The literature on short-term traffic flow forecasting has undergone great development recently. Many works, describing a wide variety of different approaches, which very often share similar features and ideas, have been published. However, publications presenting new prediction algorithms usually employ different settings, data sets, and performance measurements, making it difficult to infer a clear picture of the advantages and limitations of each model. The aim of this paper is twofold. First, we review existing approaches to short-term traffic flow forecasting methods under the common view of probabilistic graphical models, presenting an extensive experimental comparison, which proposes a common baseline for their performance analysis and provides the infrastructure to operate on a publicly available data set. Second, we present two new support vector regression models, which are specifically devised to benefit from typical traffic flow seasonality and are shown to represent an interesting compromise between prediction accuracy and computational efficiency. The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods.

TSCA: A Temporal-Spatial Real-Time Charging Scheduling Algorithm for On-Demand Architecture in Wireless Rechargeable Sensor Networks. The collaborative charging issue in Wireless Rechargeable Sensor Networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles (WCVs) to sensors, providing a new paradigm to prolong network lifetime. Existing techniques on collaborative charging usually take the periodical and deterministic approach, but neglect influences of non-deterministic factors such as topological changes and node failures, making them unsuitable for large-scale WRSNs. In this paper, we develop a temporal-spatial charging scheduling algorithm, namely TSCA, for the on-demand charging architecture. We aim to minimize the number of dead nodes while maximizing energy efficiency to prolong network lifetime. First, after gathering charging requests, a WCV will compute a feasible movement solution. A basic path planning algorithm is then introduced to adjust the charging order for better efficiency. Furthermore, optimizations are made in a global level. Then, a node deletion algorithm is developed to remove low efficient charging nodes. Lastly, a node insertion algorithm is executed to avoid the death of abandoned nodes. Extensive simulations show that, compared with state-of-the-art charging scheduling algorithms, our scheme can achieve promising performance in charging throughput, charging efficiency, and other performance metrics.

A novel adaptive dynamic programming based on tracking error for nonlinear discrete-time systems In this paper, to eliminate the tracking error by using adaptive dynamic programming (ADP) algorithms, a novel formulation of the value function is presented for the optimal tracking problem (TP) of nonlinear discrete-time systems. Unlike existing ADP methods, this formulation introduces the control input into the tracking error, and ignores the quadratic form of the control input directly, which makes the boundedness and convergence of the value function independent of the discount factor. Based on the proposed value function, the optimal control policy can be deduced without considering the reference control input. Value iteration (VI) and policy iteration (PI) methods are applied to prove the optimality of the obtained control policy, and derived the monotonicity property and convergence of the iterative value function. Simulation examples realized with neural networks and the actor–critic structure are provided to verify the effectiveness of the proposed ADP algorithm.

Comprehensive survey of image steganography: Techniques, Evaluations, and trends in future research. Storing and communicating secret and/or private information has become part of our daily life whether it is for our employment or personal well-being. Therefore, secure storage and transmission of the secret information have received the undivided attention of many researchers. The techniques for hiding confidential data in inconspicuous digital media such as video, audio, and image are collectively termed as Steganography. Among various media types used, the popularity and availability of digital images are high and in this research work and hence, our focus is on implementing digital image steganography. The main challenge in designing a steganographic system is to maintain a fair trade-off between robustness, security, imperceptibility and higher bit embedding rate. This research article provides a thorough review of existing types of image steganography and the recent contributions in each category in multiple modalities. The article also provides a complete overview of image steganography including general operation, requirements, different aspects, different types and their performance evaluations. Different performance analysis measures for evaluating steganographic system are also discussed here. Moreover, we also discuss the strategy to select different cover media for different applications and a few state-of-the-art steganalysis systems.

Geometric attacks on image watermarking systems Synchronization errors can lead to significant performance loss in image watermarking methods, as the geometric attacks in the Stirmark benchmark software show. The authors describe the most common types of geometric attacks and survey proposed solutions.

Genetic Optimization Of Radial Basis Probabilistic Neural Networks This paper discusses using genetic algorithms (CA) to optimize the structure of radial basis probabilistic neural networks (RBPNN), including how to select hidden centers of the first hidden layer and to determine the controlling parameter of Gaussian kernel functions. In the process of constructing the genetic algorithm, a novel encoding method is proposed for optimizing the RBPNN structure. This encoding method can not only make the selected hidden centers sufficiently reflect the key distribution characteristic in the space of training samples set and reduce the hidden centers number as few as possible, but also simultaneously determine the optimum controlling parameters of Gaussian kernel functions matching the selected hidden centers. Additionally, we also constructively propose a new fitness function so as to make the designed RBPNN as simple as possible in the network structure in the case of not losing the network performance. Finally, we take the two benchmark problems of discriminating two-spiral problem and classifying the iris data, for example, to test and evaluate this designed GA. The experimental results illustrate that our designed CA can significantly reduce the required hidden centers number, compared with the recursive orthogonal least square algorithm (ROLSA) and the modified K-means algorithm (MKA). In particular, by means of statistical experiments it was proved that the optimized RBPNN by our designed GA, have still a better generalization performance with respect to the ones by the ROLSA and the MKA, in spite of the network scale having been greatly reduced. Additionally, our experimental results also demonstrate that our designed CA is also suitable for optimizing the radial basis function neural networks (RBFNN).

Current status and key issues in image steganography: A survey. Steganography and steganalysis are the prominent research fields in information hiding paradigm. Steganography is the science of invisible communication while steganalysis is the detection of steganography. Steganography means “covered writing” that hides the existence of the message itself. Digital steganography provides potential for private and secure communication that has become the necessity of most of the applications in today’s world. Various multimedia carriers such as audio, text, video, image can act as cover media to carry secret information. In this paper, we have focused only on image steganography. This article provides a review of fundamental concepts, evaluation measures and security aspects of steganography system, various spatial and transform domain embedding schemes. In addition, image quality metrics that can be used for evaluation of stego images and cover selection measures that provide additional security to embedding scheme are also highlighted. Current research trends and directions to improve on existing methods are suggested.

Hybrid local and global descriptor enhanced with colour information. Feature extraction is one of the most important steps in computer vision tasks such as object recognition, image retrieval and image classification. It describes an image by a set of descriptors where the best one gives a high quality description and a low computation. In this study, the authors propose a novel descriptor called histogram of local and global features using speeded up robust featur...

Dual hybrid medical watermarking using walsh-slantlet transform A hybrid robust lossless data hiding algorithm is proposed in this paper by using the Singular Value Decomposition (SVD) with Fast Walsh Transform (FWT) and Slantlet Transform (SLT) for image authentication. These transforms possess good energy compaction with distinct filtering, which leads to higher embedding capacity from 1.8 bit per pixel (bpp) up to 7.5bpp. In the proposed algorithm, Artificial Neural Network (ANN) is applied for region of interest (ROI) detection and two different watermarks are created. Embedding is done after applying FWH by changing the SVD coefficients and by changing the highest coefficients of SLT subbands. In dual hybrid embedding first watermark is the ROI and another watermark consists of three parts, i.e., patients’ personal details, unique biometric ID and the key for encryption. Comparison of the proposed algorithm is done with the existing watermarking techniques for analyzing the performance. Experiments are simulated on the proposed algorithm by casting numerous attacks for testing the visibility, robustness, security, authenticity, integrity and reversibility. The resultant outcome proves that the watermarked image has an improved imperceptibility with a high level of payload, low time complexity and high Peak Signal to Noise Ratio (PSNR) against the existing approaches.

Multiscale Transform-Based Secured Joint Efficient Medical Image Compression-Encryption Using Symmetric Key Cryptography And Ebcot Encoding Technique Due to the huge advancement in technology, digitizing the multimedia content like text, images and videos has become easier. Everyday huge amounts of multimedia content are shared through the social networks using internet. Sometimes this multimedia content can be hacked by the hackers. This will lead to the misuse of the data. On the other hand, the medical content needs high security and privacy. Motivated by this, joint secured medical image compression-encryption mechanisms are proposed in this paper using multiscale transforms and symmetric key encryption techniques. The multiscale transforms involved in this paper are wavelet transform, bandelet transform and curvelet transform. The encryption techniques involved in this paper are international data encryption algorithm (IDEA), Rivest Cipher (RC5) and Blowfish. The encoding technique used in this paper is embedded block coding with truncation (EBCOT). Experimental results are done for the proposed works and evaluated by using various parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Image Quality Index (IQI) and Structural Similarity Index (SSIM), Average Difference (AD), Normalized Cross-correlation (NK), Structural Content (SC), Maximum difference (MD), Laplacian Mean Squared Error (LMSE) and Normalized Absolute Error (NAE). It is justified that the proposed approaches in this paper yield good results.

A New Efficient Medical Image Cipher Based On Hybrid Chaotic Map And Dna Code In this paper, we propose a novel medical image encryption algorithm based on a hybrid model of deoxyribonucleic acid (DNA) masking, a Secure Hash Algorithm SHA-2 and a new hybrid chaotic map. Our study uses DNA sequences and operations and the chaotic hybrid map to strengthen the cryptosystem. The significant advantages of this approach consist in improving the information entropy which is the most important feature of randomness, resisting against various typical attacks and getting good experimental results. The theoretical analysis and experimental results show that the algorithm improves the encoding efficiency, enhances the security of the ciphertext, has a large key space and a high key sensitivity, and is able to resist against the statistical and exhaustive attacks.

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration. Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on ...

An effective implementation of the Lin–Kernighan traveling salesman heuristic This paper describes an implementation of the Lin–Kernighan heuristic, one of the most successful methods for generating optimal or near-optimal solutions for the symmetric traveling salesman problem (TSP). Computational tests show that the implementation is highly effective. It has found optimal solutions for all solved problem instances we have been able to obtain, including a 13,509-city problem (the largest non-trivial problem instance solved to optimality today).

Exoskeletons for human power augmentation The first load-bearing and energetically autonomous exoskeleton, called the Berkeley Lower Extremity Exoskeleton (BLEEX) walks at the average speed of two miles per hour while carrying 75 pounds of load. The project, funded in 2000 by the Defense Advanced Research Project Agency (DARPA) tackled four fundamental technologies: the exoskeleton architectural design, a control algorithm, a body LAN to host the control algorithm, and an on-board power unit to power the actuators, sensors and the computers. This article gives an overview of the BLEEX project.

Assist-As-Needed Training Paradigms For Robotic Rehabilitation Of Spinal Cord Injuries This paper introduces a new "assist-as-needed" (AAN) training paradigm for rehabilitation of spinal cord injuries via robotic training devices. In the pilot study reported in this paper, nine female adult Swiss-Webster mice were divided into three groups, each experiencing a different robotic training control strategy: a fixed training trajectory (Fixed Group, A), an AAN training method without interlimb coordination (Band Group, B), and an AAN training method with bilateral hindlimb coordination (Window Group, C). Fourteen days after complete transection at the mid-thoracic level, the mice were robotically trained to step in the presence of an acutely administered serotonin agonist, quipazine, for a period of six weeks. The mice that received AAN training (Groups B and C) show higher levels of recovery than Group A mice, as measured by the number, consistency, and periodicity of steps realized during testing sessions. Group C displays a higher incidence of alternating stepping than Group B. These results indicate that this training approach may be more effective than fixed trajectory paradigms in promoting robust post-injury stepping behavior. Furthermore, the constraint of interlimb coordination appears to be an important contribution to successful training.

An ID-Based Linearly Homomorphic Signature Scheme and Its Application in Blockchain. Identity-based cryptosystems mean that public keys can be directly derived from user identifiers, such as telephone numbers, email addresses, and social insurance number, and so on. So they can simplify key management procedures of certificate-based public key infrastructures and can be used to realize authentication in blockchain. Linearly homomorphic signature schemes allow to perform linear computations on authenticated data. And the correctness of the computation can be publicly verified. Although a series of homomorphic signature schemes have been designed recently, there are few homomorphic signature schemes designed in identity-based cryptography. In this paper, we construct a new ID-based linear homomorphic signature scheme, which avoids the shortcomings of the use of public-key certificates. The scheme is proved secure against existential forgery on adaptively chosen message and ID attack under the random oracle model. The ID-based linearly homomorphic signature schemes can be applied in e-business and cloud computing. Finally, we show how to apply it to realize authentication in blockchain.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Diagnosis and classification prediction model of pituitary tumor based on machine learning In order to improve the diagnosis and classification effect of pituitary tumors, this paper combines the current common machine learning methods and classification prediction methods to improve the traditional machine learning algorithms. Moreover, this paper analyzes the feature algorithm based on the feature extraction requirements of pituitary tumor pictures and compares a variety of commonly used algorithms to select a classification algorithm suitable for the model of this paper through comparison methods. In addition, this paper carries out the calculation of the prediction algorithm and verifies the algorithm according to the actual situation. Finally, based on the neural network algorithm, this paper designs and constructs the algorithm function module and combines the actual needs of pituitary tumors to build the model and verify the performance of the model. The research results show that the model system constructed in this paper meets the expected demand.

Research on enterprise knowledge service based on semantic reasoning and data fusion In the era of big data, the field of enterprise risk is facing considerable challenges brought by massive multisource heterogeneous information sources. In view of the proliferation of multisource and heterogeneous enterprise risk information, insufficient knowledge fusion capabilities, and the low level of intelligence in risk management, this article explores the application process of enterprise knowledge service models for rapid responses to risk incidents from the perspective of semantic reasoning and data fusion and clarifies the elements of the knowledge service model in the field of risk management. Based on risk data, risk decision making as the standard, risk events as the driving force, and knowledge graph analysis methods as the power, the risk domain knowledge service process is decomposed into three stages: prewarning, in-event response, and postevent summary. These stages are combined with the empirical knowledge of risk event handling to construct a three-level knowledge service model of risk domain knowledge acquisition-organization-application. This model introduces the semantic reasoning and data fusion method to express, organize, and integrate the knowledge needs of different stages of risk events; provide enterprise managers with risk management knowledge service solutions; and provide new growth points for the innovation of interdisciplinary knowledge service theory.

Efficiency evaluation research of a regional water system based on a game cross-efficiency model To solve the problem of regional water system evaluation, this paper proposes a system efficiency evaluation method based on the game cross-efficiency model and conducts an empirical analysis. First, autopoiesis is introduced as the theoretical basis. The characteristics of the authigenic system are combined with a regional water system, and the connotation and characteristics of the regional water system are defined. Second, based on the competitive relationship between regional water systems, the existing game crossover efficiency model is improved. A crossover efficiency model of other games is proposed to evaluate the efficiency of regional water systems. Then, the Pearl River Delta urban agglomeration is selected as the research object. The effects of four systematic evaluation methods based on the DEA method are compared horizontally to find the optimal system efficiency evaluation method. Finally, the characteristics of the regional water system in the Pearl River Delta are systematically analysed through the evaluation results, and the present situation of the regional water system is fully explained.

Exploring real-time fault detection of high-speed train traction motor based on machine learning and wavelet analysis The implementation of real-time fault detection technology for key components of high-speed train traction electromechanical system is of great significance for improving train motor reliability and reducing guarantee costs. It has become an inevitable trend. Machine learning is a new and powerful means of researching fault detection technology. Based on machine learning, this paper conducts real-time fault detection technology research on the electromechanical actuators of the key components of electromechanical systems. A model of the electromechanical actuator is established, and the mechanism, influence and fault injection method of the three faults of the electromechanical actuator motor shaft jamming, gear broken tooth, excessive ball screw clearance and two faults of internal leakage are analyzed. On this basis, a fault simulation model of the traction motor of a high-speed train was built to obtain simulation fault data. At the same time, based on wavelet packet decomposition and reconstruction, the fault simulation data of electromechanical actuators and hydraulic pumps are analyzed, the wavelet packet energy distribution is calculated, and time-domain statistics are combined to extract energy feature vectors that can reflect component fault characteristics. This paper proposes a fault diagnosis method for electromechanical actuators based on machine learning, designs, and improves the neural network learning algorithm and network parameters, and improves the classification effect of the neural network; proposes a fault diagnosis method based on the GA-SVM algorithm, using real values. The coded genetic algorithm improves the parameter optimization of the support vector machine, and improves the classification speed of the support vector machine. Finally, the effectiveness and superiority of the two fault diagnosis methods designed in this paper are verified on their respective objects.

Spatial–temporal grid clustering method based on frequent stay point recognition In order to identify geolocation of defaulter and extract travel information from trajectory data, spatial–temporal grid clustering method are adopted to analysis massive trajectory data. Firstly, the trajectory data are preprocessed, and the spacetime cluster method is applied to detect the travelers’ geolocation information based on the information the travel segments are extracted. Secondly, for the recognition of frequent stay point, we proposed the spatial–temporal grid clustering model with smooth trajectory division algorithm and which improve the efficiency of processing a large amount of trajectory data. Thirdly, we proposed the spatial–temporal grid clustering method based on frequent stay point recognition. The experiment results of stationary trajectory division indicate that the frequent stay point and frequent paths can be effectively excavated under the condition of small information loss. These results demonstrate convincingly the effectiveness of the proposed method.

Edge computing clone node recognition system based on machine learning Edge computing is an important cornerstone for the construction of 5G networks, but with the development of Internet technology, the computer nodes are extremely vulnerable in attacks, especially clone attacks, causing casualties. The principle of clonal node attack is that the attacker captures the legitimate nodes in the network and obtains all their legitimate information, copies several nodes with the same ID and key information, and puts these clonal nodes in different locations in the network to attack the edge computing devices, resulting in network paralysis. How to quickly and efficiently identify clone nodes and isolate them becomes the key to prevent clone node attacks and improve the security of edge computing. In order to improve the degree of protection of edge computing and identify clonal nodes more quickly and accurately, based on edge computing of machine learning, this paper uses case analysis method, the literature analysis method, and other methods to collect data from the database, and uses parallel algorithm to build a model of clonal node recognition. The results show that the edge computing based on machine learning can greatly improve the efficiency of clonal node recognition, the recognition speed is more than 30% faster than the traditional edge computing, and the recognition accuracy reaches 0.852, which is about 50% higher than the traditional recognition. The results show that the edge computing clonal node method based on machine learning can improve the detection success rate of clonal nodes and reduce the energy consumption and transmission overhead of nodes, which is of great significance to the detection of clonal nodes.

Accurate Self-Localization in RFID Tag Information Grids Using FIR Filtering Grid navigation spaces nested with the radio-frequency identification (RFID) tags are promising for industrial and other needs, because each tag can deliver information about a local two-dimensional or three-dimensional surrounding. The approach, however, requires high accuracy in vehicle self-localization. Otherwise, errors may lead to collisions; possibly even fatal. We propose a new extended finite impulse response (EFIR) filtering algorithm and show that it meets this need. The EFIR filter requires an optimal averaging interval, but does not involve the noise statistics which are often not well known to the engineer. It is more accurate than the extended Kalman filter (EKF) under real operation conditions and its iterative algorithm has the Kalman form. Better performance of the proposed EFIR filter is demonstrated based on extensive simulations in a comparison to EKF, which is widely used in RFID tag grids. We also show that errors in noise covariances may provoke divergence in EKF, whereas the EFIR filter remains stable and is thus more robust.

Evolutionary computation: comments on the history and current state Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete

Supporting social navigation on the World Wide Web This paper discusses a navigation behavior on Internet information services, in particular the World Wide Web, which is characterized by pointing out of information using various communication tools. We call this behavior social navigation as it is based on communication and interaction with other users, be that through email, or any other means of communication. Social navigation phenomena are quite common although most current tools (like Web browsers or email clients) offer very little support for it. We describe why social navigation is useful and how it can be better supported in future systems. We further describe two prototype systems that, although originally not designed explicitly as tools for social navigation, provide features that are typical for social navigation systems. One of these systems, the Juggler system, is a combination of a textual virtual environment and a Web client. The other system is a prototype of a Web- hotlist organizer, called Vortex. We use both systems to describe fundamental principles of social navigation systems.

Proofs of Storage from Homomorphic Identification Protocols Proofs of storage (PoS) are interactive protocols allowing a client to verify that a server faithfully stores a file. Previous work has shown that proofs of storage can be constructed from any homomorphic linear authenticator (HLA). The latter, roughly speaking, are signature/message authentication schemes where `tags' on multiple messages can be homomorphically combined to yield a `tag' on any linear combination of these messages. We provide a framework for building public-key HLAs from any identification protocol satisfying certain homomorphic properties. We then show how to turn any public-key HLA into a publicly-verifiable PoS with communication complexity independent of the file length and supporting an unbounded number of verifications. We illustrate the use of our transformations by applying them to a variant of an identification protocol by Shoup, thus obtaining the first unbounded-use PoS based on factoring (in the random oracle model).

Well-Solvable Special Cases of the Traveling Salesman Problem: A Survey. The traveling salesman problem (TSP) belongs to the most basic, most important, and most investigated problems in combinatorial optimization. Although it is an ${\cal NP}$-hard problem, many of its special cases can be solved efficiently in polynomial time. We survey these special cases with emphasis on the results that have been obtained during the decade 1985--1995. This survey complements an earlier survey from 1985 compiled by Gilmore, Lawler, and Shmoys [The Traveling Salesman Problem---A Guided Tour of Combinatorial Optimization, Wiley, Chichester, pp. 87--143].

A competitive swarm optimizer for large scale optimization. In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position of each particle nor the global best position (or neighborhood best positions) is involved in updating the particles. Instead, a pairwise competition mechanism is introduced, where the particle that loses the competition will update its position by learning from the winner. To understand the search behavior of the proposed CSO, a theoretical proof of convergence is provided, together with empirical analysis of its exploration and exploitation abilities showing that the proposed CSO achieves a good balance between exploration and exploitation. Despite its algorithmic simplicity, our empirical results demonstrate that the proposed CSO exhibits a better overall performance than five state-of-the-art metaheuristic algorithms on a set of widely used large scale optimization problems and is able to effectively solve problems of dimensionality up to 5000.

Adaptive Fuzzy Control With Prescribed Performance for Block-Triangular-Structured Nonlinear Systems. In this paper, an adaptive fuzzy control method with prescribed performance is proposed for multi-input and multioutput block-triangular-structured nonlinear systems with immeasurable states. Fuzzy logic systems are adopted to identify the unknown nonlinear system functions. Adaptive fuzzy state observers are designed to solve the problem of unmeasured states, and a new observer-based output-feedb...

Communication-Efficient Federated Learning Over MIMO Multiple Access Channels Communication efficiency is of importance for wireless federated learning systems. In this paper, we propose a communication-efficient strategy for federated learning over multiple-input multiple-output (MIMO) multiple access channels (MACs). The proposed strategy comprises two components. When sending a locally computed gradient, each device compresses a high dimensional local gradient to multiple lower-dimensional gradient vectors using block sparsification. When receiving a superposition of the compressed local gradients via a MIMO-MAC, a parameter server (PS) performs a joint MIMO detection and the sparse local-gradient recovery. Inspired by the turbo decoding principle, our joint detection-and-recovery algorithm accurately recovers the high-dimensional local gradients by iteratively exchanging their beliefs for MIMO detection and sparse local gradient recovery outputs. We then analyze the reconstruction error of the proposed algorithm and its impact on the convergence rate of federated learning. From simulations, our gradient compression and joint detection-and-recovery methods diminish the communication cost significantly while achieving identical classification accuracy for the case without any compression.

Smoothed Least-laxity-first Algorithm for EV Charging. We formulate EV charging as a feasibility problem that meets all EVs' energy demands before departure under charging rate constraints and total power constraint. We propose an online algorithm, the smoothed least-laxity-first (sLLF) algorithm, that decides on the current charging rates based on only the information up to the current time. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has significantly higher rate of generating feasible EV charging than several other common EV charging algorithms.

Robust Online Algorithms for Peak-Minimizing EV Charging Under Multistage Uncertainty. In this paper, we study how to utilize forecasts to design online electrical vehicle (EV) charging algorithms that can attain strong performance guarantees. We consider the scenario of an aggregator serving a large number of EVs together with its background load, using both its own renewable energy (for free) and the energy procured from the external grid. The goal of the aggregator is to minimize its peak procurement from the grid, subject to the constraint that each EV has to be fully charged before its deadline. Further, the aggregator can predict the future demand and the renewable energy supply with some levels of uncertainty. We show that such prediction can be very effective in reducing the competitive ratios of online control algorithms, and even allow online algorithms to achieve close-to-offline-optimal peak. Specifically, we first propose a 2-level increasing precision model (2-IPM), to model forecasts with different levels of accuracy. We then develop a powerful computational approach that can compute the optimal competitive ratio under 2-IPM over any online algorithm, and also online algorithms that can achieve the optimal competitive ratio. Simulation results show that, even with up to 20% day-ahead prediction errors, our online algorithms still achieve competitive ratios fairly close to 1, which are much better than the classic results in the literature with a competitive ratio of e. The second contribution of this paper is that we solve a dilemma for online algorithm design, e.g., an online algorithm with good competitive ratio may exhibit poor average-case performance. We propose a new Algorithm-Robustification procedure that can convert an online algorithm with good average-case performance to one with both the optimal competitive ratio and good average-case performance. We demonstrate via trace-based simulations the superior performance of the robustified version of a well-known heuristic algorithm based on model predictive control.

Optimal Scheduling for Electric Vehicle Charging With Discrete Charging Levels in Distribution Grid. To accommodate the increasing electric vehicle (EV) penetration in distribution grid, coordinated EV charging has been extensively studied in the literature. However, most of the existing works optimistically consider the EV charging rate as a continuous variable and implicitly ignore the capacity limitation in distribution transformers, which both have great impact on the efficiency and stability...

Definition and Evaluation of Model-Free Coordination of Electrical Vehicle Charging With Reinforcement Learning Initial DR studies mainly adopt model predictive control and thus require accurate models of the control problem (e.g., a customer behavior model), which are to a large extent uncertain for the EV scenario. Hence, model-free approaches, especially based on reinforcement learning (RL) are an attractive alternative. In this paper, we propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of EV charging stations. State-of-the-art algorithms either focus on a single EV, or perform the control of an aggregate of EVs in multiple steps (e.g., aggregate load decisions in one step, then a step translating the aggregate decision to individual connected EVs). On the contrary, we propose an RL approach to jointly control the whole set of EVs at once. We contribute a new MDP formulation, with a scalable state representation that is independent of the number of EV charging stations. Further, we use a batch reinforcement learning algorithm, i.e., an instance of fitted Q-iteration, to learn the optimal charging policy. We analyze its performance using simulation experiments based on a real-world EV charging data. More specifically, we (i) explore the various settings in training the RL policy (e.g., duration of the period with training data), (ii) compare its performance to an oracle all-knowing benchmark (which provides an upper bound for performance, relying on information that is not available or at least imperfect in practice), (iii) analyze performance over time, over the course of a full year to evaluate possible performance fluctuations (e.g, across different seasons), and (iv) demonstrate the generalization capacity of a learned control policy to larger sets of charging stations.

ACN-Data - Analysis and Applications of an Open EV Charging Dataset. We are releasing ACN-Data, a dynamic dataset of workplace EV charging which currently includes over 30,000 sessions with more added daily. In this paper we describe the dataset, as well as some interesting user behavior it exhibits. To demonstrate the usefulness of the dataset, we present three examples, learning and predicting user behavior using Gaussian mixture models, optimally sizing on-site solar generation for adaptive electric vehicle charging, and using workplace charging to smooth the net demand Duck Curve.

Hierarchical Electric Vehicle Charging Aggregator Strategy Using Dantzig-Wolfe Decomposition. This article focuses on reducing a charging cost for electric vehicles (EVs). A charging strategy is proposed to minimize the charging cost of EVs within the charging station constraints.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

Long short-term memory. Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

Toward Integrating Vehicular Clouds with IoT for Smart City Services Vehicular ad hoc networks, cloud computing, and the Internet of Things are among the emerging technology enablers offering a wide array of new application possibilities in smart urban spaces. These applications consist of smart building automation systems, healthcare monitoring systems, and intelligent and connected transportation, among others. The integration of IoT-based vehicular technologies will enrich services that are eventually going to ignite the proliferation of exciting and even more advanced technological marvels. However, depending on different requirements and design models for networking and architecture, such integration needs the development of newer communication architectures and frameworks. This work proposes a novel framework for architectural and communication design to effectively integrate vehicular networking clouds with IoT, referred to as VCoT, to materialize new applications that provision various IoT services through vehicular clouds. In this article, we particularly put emphasis on smart city applications deployed, operated, and controlled through LoRaWAN-based vehicular networks. LoraWAN, being a new technology, provides efficient and long-range communication possibilities. The article also discusses possible research issues in such an integration including data aggregation, security, privacy, data quality, and network coverage. These issues must be addressed in order to realize the VCoT paradigm deployment, and to provide insights for investors and key stakeholders in VCoT service provisioning. The article presents deep insights for different real-world application scenarios (i.e., smart homes, intelligent traffic light, and smart city) using VCoT for general control and automation along with their associated challenges. It also presents initial insights, through preliminary results, regarding data and resource management in IoT-based resource constrained environments through vehicular clouds.

Distributed multirobot localization In this paper, we present a new approach to the problem of simultaneously localizing a group of mobile robots capable of sensing one another. Each of the robots collects sensor data regarding its own motion and shares this information with the rest of the team during the update cycles. A single estimator, in the form of a Kalman filter, processes the available positioning information from all the members of the team and produces a pose estimate for every one of them. The equations for this centralized estimator can be written in a decentralized form, therefore allowing this single Kalman filter to be decomposed into a number of smaller communicating filters. Each of these filters processes the sensor data collected by its host robot. Exchange of information between the individual filters is necessary only when two robots detect each other and measure their relative pose. The resulting decentralized estimation schema, which we call collective localization, constitutes a unique means for fusing measurements collected from a variety of sensors with minimal communication and processing requirements. The distributed localization algorithm is applied to a group of three robots and the improvement in localization accuracy is presented. Finally, a comparison to the equivalent decentralized information filter is provided.

A simplified dual neural network for quadratic programming with its KWTA application. The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and the computational complexity of the neural network. The simplified dual neural network is shown to be globally convergent to the exact optimal solution. The complexity of the neural network architecture is reduced with the number of neurons equal to the number of inequality constraints. Its application to k-winners-take-all (KWTA) operation is discussed to demonstrate how to solve problems with this neural network.

NETWRAP: An NDN Based Real-TimeWireless Recharging Framework for Wireless Sensor Networks Using vehicles equipped with wireless energy transmission technology to recharge sensor nodes over the air is a game-changer for traditional wireless sensor networks. The recharging policy regarding when to recharge which sensor nodes critically impacts the network performance. So far only a few works have studied such recharging policy for the case of using a single vehicle. In this paper, we propose NETWRAP, an N DN based Real Time Wireless Rech arging Protocol for dynamic wireless recharging in sensor networks. The real-time recharging framework supports single or multiple mobile vehicles. Employing multiple mobile vehicles provides more scalability and robustness. To efficiently deliver sensor energy status information to vehicles in real-time, we leverage concepts and mechanisms from named data networking (NDN) and design energy monitoring and reporting protocols. We derive theoretical results on the energy neutral condition and the minimum number of mobile vehicles required for perpetual network operations. Then we study how to minimize the total traveling cost of vehicles while guaranteeing all the sensor nodes can be recharged before their batteries deplete. We formulate the recharge optimization problem into a Multiple Traveling Salesman Problem with Deadlines (m-TSP with Deadlines), which is NP-hard. To accommodate the dynamic nature of node energy conditions with low overhead, we present an algorithm that selects the node with the minimum weighted sum of traveling time and residual lifetime. Our scheme not only improves network scalability but also ensures the perpetual operation of networks. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed design. The results also validate the correctness of the theoretical analysis and show significant improvements that cut the number of nonfunctional nodes by half compared to the static scheme while maintaining the network overhead at the same level.

Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems. This paper addresses the finite-time tracking problem of nonlinear pure-feedback systems. Unlike the literature on traditional finite-time stabilization, in this paper the nonlinear system functions, including the bounding functions, are all totally unknown. Fuzzy logic systems are used to model those unknown functions. To present a finite-time control strategy, a criterion of semiglobal practical...

Convert Harm Into Benefit: A Coordination-Learning Based Dynamic Spectrum Anti-Jamming Approach This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern.

Federated Channel Learning for Intelligent Reflecting Surfaces with Fewer Pilot Signals Channel estimation is a critical task in intelligent reflecting surface (IRS)-assisted wireless systems due to the uncertainties imposed by environment dynamics and rapid changes in the IRS configuration. To deal with these uncertainties, deep learning (DL) approaches have been proposed. Previous works consider centralized learning (CL) approach for model training, which entails the collection of the whole training dataset from the users at the base station (BS), hence introducing huge transmission overhead for data collection. To address this challenge, this paper proposes a federated learning (FL) framework to jointly estimate both direct and cascaded channels in IRS-assisted wireless systems. We design a single convolutional neural network trained on the local datasets of the users without sending them to the BS. We show that the proposed FL-based channel estimation approach requires approximately 60% fewer pilot signals and it exhibits 12 times lower transmission overhead than CL, while maintaining satisfactory performance close to CL. In addition, it provides lower estimation error than the state-of-the-art DL-based schemes.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Elastic optical networking: a new dawn for the optical layer? Optical networks are undergoing significant changes, fueled by the exponential growth of traffic due to multimedia services and by the increased uncertainty in predicting the sources of this traffic due to the ever changing models of content providers over the Internet. The change has already begun: simple on-off modulation of signals, which was adequate for bit rates up to 10 Gb/s, has given way ...

Diverse Routing in Networks With Probabilistic Failures We develop diverse routing schemes for dealing with multiple, possibly correlated, failures. While disjoint path protection can effectively deal with isolated single link failures, recovering from multiple failures is not guaranteed. In particular, events such as natural disasters or intentional attacks can lead to multiple correlated failures, for which recovery mechanisms are not well understood. We take a probabilistic view of network failures where multiple failure events can occur simultaneously, and develop algorithms for finding diverse routes with minimum joint failure probability. Moreover, we develop a novel Probabilistic Shared Risk Link Group (PSRLG) framework for modeling correlated failures. In this context, we formulate the problem of finding two paths with minimum joint failure probability as an integer nonlinear program (INLP) and develop approximations and linear relaxations that can find nearly optimal solutions in most cases.

On the structure and complexity of the 2-connected Steiner network problem in the plane We consider the problem of finding a minimum Euclidean length graph 2-connecting a set of points in the plane. We show that the solution to this problem is an edge-disjoint union of full Steiner trees. This has three important corollaries. The first is a proof that the problem is NP-hard, even in the sense of finding a fully polynomial approximation scheme. The second is a complete description of the solutions for 2SNPP for rectangular arrays of lattice points. The third is a linear-time algorithm for constructing an optimal solution to 2SNPP given its topological description.

On Network Topology Augmentation for Global Connectivity under Regional Failures Several recent studies shed light on the vulnerability of networks against regional failures, which are failures of multiple nodes and links in a physical region due to a natural disaster. The paper defines a novel design framework, called Geometric Network Augmentation (GNA), which determines a set of node pairs and the new cable routes to be deployed between each of them to make the network always remain connected when a regional failure of a given size occurs. With the proposed GNA design framework, we provide mathematical analysis and efficient heuristic algorithms that are built on the latest computational geometry tools and combinatorial optimization techniques. Through extensive simulation, we demonstrate that augmentation with just a small number of new cable routes will achieve the desired resilience against all the considered regional failures.

Geographical route design of physical networks using earthquake risk information. In this article, we investigate the challenges in designing a communication network robust against earthquake-induced disasters. Due to the heterogeneity of local geology conditions, an earthquake may cause different devastating effects on network links at different locations. Therefore, the aim of this research was to develop a network design method that, based on actual seismic hazard informatio...

Solar superstorms: planning for an internet apocalypse ABSTRACTBlack swan events are hard-to-predict rare events that can significantly alter the course of our lives. The Internet has played a key role in helping us deal with the coronavirus pandemic, a recent black swan event. However, Internet researchers and operators are mostly blind to another black swan event that poses a direct threat to Internet infrastructure. In this paper, we investigate the impact of solar superstorms that can potentially cause large-scale Internet outages covering the entire globe and lasting several months. We discuss the challenges posed by such activity and currently available mitigation techniques. Using real-world datasets, we analyze the robustness of the current Internet infrastructure and show that submarine cables are at greater risk of failure compared to land cables. Moreover, the US has a higher risk for disconnection compared to Asia. Finally, we lay out steps for improving the Internet's resiliency.

A threshold of ln n for approximating set cover Given a collection F of subsets of S ={1,…,n}, setcover is the problem of selecting as few as possiblesubsets from F such that their union coversS,, and maxk-cover is the problem of selectingk subsets from F such that their union has maximum cardinality. Both these problems areNP-hard. We prove that (1 - o(1)) lnn is a threshold below which setcover cannot be approximated efficiently, unless NP has slightlysuperpolynomial time algorithms. This closes the gap (up to low-orderterms) between the ratio of approximation achievable by the greedyalogorithm (which is (1 - o(1)) lnn), and provious results of Lund and Yanakakis, that showed hardness ofapproximation within a ratio of log2n/2&sime;0.72 ln n. For maxk-cover, we show an approximationthreshold of (1 - 1/e)(up tolow-order terms), under assumption that P≠NP.

Magnetic MIMO: how to charge your phone in your pocket This paper bridges wireless communication with wireless power transfer. It shows that mobile phones can be charged remotely, while in the user's pocket by applying the concept of MIMO beamforming. However, unlike MIMO beamforming in communication systems which targets the radiated field, we transfer power by beamforming the non-radiated magnetic field and steering it toward the phone. We design MagMIMO, a new system for wireless charging of cell phones and portable devices. MagMIMO consumes as much power as existing solutions, yet it can charge a phone remotely without being removed from the user's pocket. Furthermore, the phone need not face the charging pad, and can charge independently of its orientation. We have built MagMIMO and demonstrated its ability to charge the iPhone and other smart phones, while in the user's pocket.

Fuzzy logic in control systems: fuzzy logic controller. I.

A General Equilibrium Model for Industries with Price and Service Competition This paper develops a stochastic general equilibrium inventory model for an oligopoly, in which all inventory constraint parameters are endogenously determined. We propose several systems of demand processes whose distributions are functions of all retailers' prices and all retailers' service levels. We proceed with the investigation of the equilibrium behavior of infinite-horizon models for industries facing this type of generalized competition, under demand uncertainty.We systematically consider the following three competition scenarios. (1) Price competition only: Here, we assume that the firms' service levels are exogenously chosen, but characterize how the price and inventory strategy equilibrium vary with the chosen service levels. (2) Simultaneous price and service-level competition: Here, each of the firms simultaneously chooses a service level and a combined price and inventory strategy. (3) Two-stage competition: The firms make their competitive choices sequentially. In a first stage, all firms simultaneously choose a service level; in a second stage, the firms simultaneously choose a combined pricing and inventory strategy with full knowledge of the service levels selected by all competitors. We show that in all of the above settings a Nash equilibrium of infinite-horizon stationary strategies exists and that it is of a simple structure, provided a Nash equilibrium exists in a so-called reduced game.We pay particular attention to the question of whether a firm can choose its service level on the basis of its own (input) characteristics (i.e., its cost parameters and demand function) only. We also investigate under which of the demand models a firm, under simultaneous competition, responds to a change in the exogenously specified characteristics of the various competitors by either: (i) adjusting its service level and price in the same direction, thereby compensating for price increases (decreases) by offering improved (inferior) service, or (ii) adjusting them in opposite directions, thereby simultaneously offering better or worse prices and service.

Rich Models for Steganalysis of Digital Images We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary $\\pm {\\hbox{1}}$ embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes.

Dynamic Management of Virtual Infrastructures Cloud infrastructures are becoming an appropriate solution to address the computational needs of scientific applications. However, the use of public or on-premises Infrastructure as a Service (IaaS) clouds requires users to have non-trivial system administration skills. Resource provisioning systems provide facilities to choose the most suitable Virtual Machine Images (VMI) and basic configuration of multiple instances and subnetworks. Other tasks such as the configuration of cluster services, computational frameworks or specific applications are not trivial on the cloud, and normally users have to manually select the VMI that best fits, including undesired additional services and software packages. This paper presents a set of components that ease the access and the usability of IaaS clouds by automating the VMI selection, deployment, configuration, software installation, monitoring and update of Virtual Appliances. It supports APIs from a large number of virtual platforms, making user applications cloud-agnostic. In addition it integrates a contextualization system to enable the installation and configuration of all the user required applications providing the user with a fully functional infrastructure. Therefore, golden VMIs and configuration recipes can be easily reused across different deployments. Moreover, the contextualization agent included in the framework supports horizontal (increase/decrease the number of resources) and vertical (increase/decrease resources within a running Virtual Machine) by properly reconfiguring the software installed, considering the configuration of the multiple resources running. This paves the way for automatic virtual infrastructure deployment, customization and elastic modification at runtime for IaaS clouds.

Surrogate-assisted hierarchical particle swarm optimization. Meta-heuristic algorithms, which require a large number of fitness evaluations before locating the global optimum, are often prevented from being applied to computationally expensive real-world problems where one fitness evaluation may take from minutes to hours, or even days. Although many surrogate-assisted meta-heuristic optimization algorithms have been proposed, most of them were developed for solving expensive problems up to 30 dimensions. In this paper, we propose a surrogate-assisted hierarchical particle swarm optimizer for high-dimensional problems consisting of a standard particle swarm optimization (PSO) algorithm and a social learning particle swarm optimization algorithm (SL-PSO), where the PSO and SL-PSO work together to explore and exploit the search space, and simultaneously enhance the global and local performance of the surrogate model. Our experimental results on seven benchmark functions of dimensions 30, 50 and 100 demonstrate that the proposed method is competitive compared with the state-of-the-art algorithms under a limited computational budget.

Attitudes Towards Social Robots In Education: Enthusiast, Practical, Troubled, Sceptic, And Mindfully Positive While social robots bring new opportunities for education, they also come with moral challenges. Therefore, there is a need for moral guidelines for the responsible implementation of these robots. When developing such guidelines, it is important to include different stakeholder perspectives. Existing (qualitative) studies regarding these perspectives however mainly focus on single stakeholders. In this exploratory study, we examine and compare the attitudes of multiple stakeholders on the use of social robots in primary education, using a novel questionnaire that covers various aspects of moral issues mentioned in earlier studies. Furthermore, we also group the stakeholders based on similarities in attitudes and examine which socio-demographic characteristics influence these attitude types. Based on the results, we identify five distinct attitude profiles and show that the probability of belonging to a specific profile is affected by such characteristics as stakeholder type, age, education and income. Our results also indicate that social robots have the potential to be implemented in education in a morally responsible way that takes into account the attitudes of various stakeholders, although there are multiple moral issues that need to be addressed first. Finally, we present seven (practical) implications for a responsible application of social robots in education following from our results. These implications provide valuable insights into how social robots should be implemented.

Energy-Efficient UAV enabled Data Collection via Wireless Charging: A Reinforcement Learning Approach In this article, we study the application of unmanned aerial vehicle (UAV) for data collection with wireless charging, which is crucial for providing seamless coverage and improving system performance in the next-generation wireless networks. To this end, we propose a reinforcement learning-based approach to plan the route of UAV to collect sensor data from sensor devices scattered in the physical...

Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach. A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of usersu0027 mobility information. The problem of joint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a three-step approach is proposed which is based on machine learning techniques to obtain both the position information of users and the trajectory design of UAVs. Firstly, a multi-agent Q-learning based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Secondly, in an effort to determine the mobility information of users based on a real dataset, their position data is collected from Twitter to describe the anonymous user-trajectories in the physical world. In the meantime, an echo state network (ESN) based prediction algorithm is proposed for predicting the future positions of users based on the real dataset. Thirdly, a multi-agent Q-learning based algorithm is conceived for predicting the position of UAVs in each time slot based on the movement of users. The algorithm is proved to be able to converge to an optimal state. In this algorithm, multiple UAVs act as agents to find optimal actions by interacting with their environment and learn from their mistakes. Numerical results are provided to demonstrate that as the size of the reservoir increases, the proposed ESN approach improves the prediction accuracy. Finally, we demonstrate that throughput gains of about $17%$ are achieved.

Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Cellular Networks. Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (...

A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems Recently, wireless technologies have been growing actively all around the world. In the context of wireless technology, fifth-generation (5G) technology has become a most challenging and interesting topic in wireless research. This article provides an overview of the Internet of Things (IoT) in 5G wireless systems. IoT in the 5G system will be a game changer in the future generation. It will open a door for new wireless architecture and smart services. Recent cellular network LTE (4G) will not be sufficient and efficient to meet the demands of multiple device connectivity and high data rate, more bandwidth, low-latency quality of service (QoS), and low interference. To address these challenges, we consider 5G as the most promising technology. We provide a detailed overview of challenges and vision of various communication industries in 5G IoT systems. The different layers in 5G IoT systems are discussed in detail. This article provides a comprehensive review on emerging and enabling technologies related to the 5G system that enables IoT. We consider the technology drivers for 5G wireless technology, such as 5G new radio (NR), multiple-input–multiple-output antenna with the beamformation technology, mm-wave commutation technology, heterogeneous networks (HetNets), the role of augmented reality (AR) in IoT, which are discussed in detail. We also provide a review on low-power wide-area networks (LPWANs), security challenges, and its control measure in the 5G IoT scenario. This article introduces the role of AR in the 5G IoT scenario. This article also discusses the research gaps and future directions. The focus is also on application areas of IoT in 5G systems. We, therefore, outline some of the important research directions in 5G IoT.

Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design. A novel framework is proposed for quality of experience (QoE)-driven deployment and dynamic movement of multiple unmanned aerial vehicles (UAVs). The problem of joint non-convex three-dimensional (3D) deployment and dynamic movement of the UAVs is formulated for maximizing the sum mean opinion score (MOS) of ground users, which is proved to be NP-hard. In the aim of solving this pertinent problem, a three-step approach is proposed for attaining 3D deployment and dynamic movement of multiple UAVs. Firstly, genetic algorithm based K-means (GAK-means) algorithm is utilized for obtaining the cell partition of the users. Secondly, Q-learning based deployment algorithm is proposed, in which each UAV acts as an agent, making their own decision for attaining 3D position by learning from trial and mistake. In contrast to conventional genetic algorithm based learning algorithms, the proposed algorithm is capable of training the direction selection strategy offline. Thirdly, Q-learning based movement algorithm is proposed in the scenario that the users are roaming. The proposed algorithm is capable of converging to an optimal state. Numerical results reveal that the proposed algorithms show a fast convergence rate after a small number of iterations. Additionally, the proposed Q-learning based deployment algorithm outperforms K-means algorithms and Iterative-GAKmean (IGK) algorithms with a low complexity.

Uplink Coverage and Capacity Analysis of mMTC in Ultra-Dense Networks In this paper, we investigate the uplink coverage and ergodic capacity of massive Machine-Type Communication (mMTC) considering an Ultra-Dense Network (UDN) environment. In MTC, devices equipped with sensing, computation, and communication capabilities connect to the Internet providing what is known as Internet-of-Things (IoT). A dense network would provide an all-in-one solution where scalable connectivity, high capacity, and uniform deep coverage are byproducts. To account for short link distances, the path loss is modeled as stretched exponential path loss (SEPL). Moreover, the fading is modeled as a general <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\alpha -\mu)$</tex-math></inline-formula> channel, where tractable and insightful results are derived for the Rayleigh fading special case. We consider the direct MTC access mode where mMTC nodes connect directly to the small cell. The analytical results disclose the impact of the system parameters and propagation environment parameters on the network performance. In particular, our results reveal that significant coverage enhancements and high uplink capacity are achievable at moderate cell densities, low transmission power, and moderate bandwidth. Moreover, the uplink network performance is independent of the maximum transmission power in the considered dense network scenario, allowing for longer battery lifetime of future IoT devices. The accuracy of the derived analytical results is assessed via extensive simulations.

UAV-enabled Data Collection for mMTC Networks: AEM Modeling and Energy-Efficient Trajectory Design Massive machine-type communications (mMTC) is a new key feature of 5G cellular and is expected to be further improved in future evolutions of the cellular standards. Data collection from machine-type communication devices (MTCDs), which can be achieved by various approaches, is important to operation of mMTC networks. This work studies data collection for mMTC networks enabled by unmanned aerial vehicle (UAV) stations moving in the air. Consider the limitation in battery lifetime at both the MTCDs and the UAV station, the UAV trajectory design problem is investigated from an energy efficiency perspective. In a generalized model where the target MTCDs are grouped into multiple clusters, the UAV station travels across the clusters and collect data from each cluster while hovering above the cluster. The corresponding MTCD clustering strategy, UAV hovering strategy and UAV flying strategy all have impacts on the energy consumption of the system, which results in a strongly coupled energy minimization problem that is difficult to solve. The sub-problems obtained through decomposition are decoupled in the proposed solution approach. Clustering of the MTCDs is done by a greedy learning clustering (GLC) algorithm. A novel modeling technique based on the idea of artificial energy map (AEM) is proposed to find the optimal hovering position within a cluster. The flying strategy that minimizes the energy consumption is equivalently transformed into a classic travelling salesman problem that is readily solved by the genetic algorithm (GA). Through alternating iterative optimization of the clustering and hovering strategies, the communication energy consumption and the UAV hovering energy consumption are monotonically decreasing until convergence.

Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular edge computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to l...

General Inner Approximation Algorithm For Non-Convex Mathematical Programs Inner approximation algorithms have had two major roles in the mathematical programming literature. Their first role was in the construction of algorithms for the decomposition of large-scale mathe...

The exploration/exploitation tradeoff in dynamic cellular genetic algorithms This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may give birth to a high number of algorithmic variants. We perform these changes in a methodological way by tuning the concept of ratio. Since the relationship (ratio) between the topology and the neighborhood shape defines the search selection pressure, we propose to analyze in depth the influence of this ratio on the exploration/exploitation tradeoff. As we will see, it is difficult to decide which ratio is best suited for a given problem. Therefore, we introduce a preprogrammed change of this ratio during the evolution as a possible additional improvement that removes the need of specifying a single ratio. A later refinement will lead us to the first adaptive dynamic kind of cellular models to our knowledge. We conclude that these dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy; we validate our results on a set of seven different problems of considerable complexity in order to better sustain our conclusions.

Parameter-Dependent LMIs in Robust Analysis: Characterization of Homogeneous Polynomially Parameter-Dependent Solutions Via LMI Relaxations This note investigates the robust stability of uncertain linear time-invariant systems in polytopic domains by means of parameter-dependent linear matrix inequality (PD-LMI) conditions, exploiting some algebraic properties provided by the uncertainty representation. A systematic procedure to construct a family of finite-dimensional LMI relaxations is provided. The robust stability is assessed by means of the existence of a Lyapunov function, more specifically, a homogeneous polynomially parameter-dependent Lyapunov (HPPDL) function of arbitrary degree. For a given degree , if an HPPDL solution exists, a sequence of relaxations based on real algebraic properties provides sufficient LMI conditions of increasing precision and constant number of decision variables for the existence of an HPPDL function which tend to the necessity. Alternatively, if an HPPDL solution of degree exists, a sequence of relaxations which increases the number of variables and the number of LMIs will provide an HPPDL solution of larger degree. The method proposed can be applied to determine homogeneous parameter-dependent matrix solutions to a wide variety of PD-LMIs by transforming the infinite-dimensional LMI problem described in terms of uncertain parameters belonging to the unit simplex in a sequence of finite-dimensional LMI conditions which converges to the necessary conditions for the existence of a homogeneous polynomially parameter-dependent solution of arbitrary degree. Illustrative examples show the efficacy of the proposed conditions when compared with other methods from the literature.

A Minimal Set Of Coordinates For Describing Humanoid Shoulder Motion The kinematics of the anatomical shoulder are analysed and modelled as a parallel mechanism similar to a Stewart platform. A new method is proposed to describe the shoulder kinematics with minimal coordinates and solve the indeterminacy. The minimal coordinates are defined from bony landmarks and the scapulothoracic kinematic constraints. Independent from one another, they uniquely characterise the shoulder motion. A humanoid mechanism is then proposed with identical kinematic properties. It is then shown how minimal coordinates can be obtained for this mechanism and how the coordinates simplify both the motion-planning task and trajectory-tracking control. Lastly, the coordinates are also shown to have an application in the field of biomechanics where they can be used to model the scapulohumeral rhythm.

Potential of Exoskeleton Technology to Assist Older Adults with Daily Living. Mobility impairments can prevent older adults from performing their daily activities which highly impacts a person's quality of life. Exoskeleton technology can assist older adults by providing additional support to compensate for age-related decline in muscle strength. To date little is known about the opinions and needs of older adults regarding exoskeletons as current research primarily focuses on the technical development of exoskeleton devices. Therefore, the aim of this paper is to inform the design of exoskeletons from a human-centered perspective. Interviews were conducted with seven older adults and six healthcare professionals. Results indicated that exoskeletons can be a valuable addition to existing mobility devices. Accepting the need for mobility aids was found to be challenging due to associated stigmas. Therefore, an exoskeleton with a discreet appearance was preferred. Ultimately, the willingness to use exoskeleton technology will depend on personal needs and preferences.

Convert Harm Into Benefit: A Coordination-Learning Based Dynamic Spectrum Anti-Jamming Approach This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern.

Survey on Network Virtualization Hypervisors for Software Defined Networking. Software defined networking (SDN) has emerged as a promising paradigm for making the control of communication networks flexible. SDN separates the data packet forwarding plane, i.e., the data plane, from the control plane and employs a central controller. Network virtualization allows the flexible sharing of physical networking resources by multiple users (tenants). Each tenant runs its own applic...

Minimum interference routing of bandwidth guaranteed tunnels with MPLS traffic engineering applications This paper presents new algorithms for dynamic routing of bandwidth guaranteed tunnels, where tunnel routing requests arrive one by one and there is no a priori knowledge regarding future requests. This problem is motivated by the service provider needs for fast deployment of bandwidth guaranteed services. Offline routing algorithms cannot be used since they require a priori knowledge of all tunnel requests that are to be rooted. Instead, on-line algorithms that handle requests arriving one by one and that satisfy as many potential future demands as possible are needed. The newly developed algorithms are on-line algorithms and are based on the idea that a newly routed tunnel must follow a route that does not “interfere too much” with a route that may he critical to satisfy a future demand. We show that this problem is NP-hard. We then develop path selection heuristics which are based on the idea of deferred loading of certain “critical” links. These critical links are identified by the algorithm as links that, if heavily loaded, would make it impossible to satisfy future demands between certain ingress-egress pairs. Like min-hop routing, the presented algorithm uses link-state information and some auxiliary capacity information for path selection. Unlike previous algorithms, the proposed algorithm exploits any available knowledge of the network ingress-egress points of potential future demands, even though the demands themselves are unknown. If all nodes are ingress-egress nodes, the algorithm can still be used, particularly to reduce the rejection rate of requests between a specified subset of important ingress-egress pairs. The algorithm performs well in comparison to previously proposed algorithms on several metrics like the number of rejected demands and successful rerouting of demands upon link failure

The set cover with pairs problem We consider a generalization of the set cover problem, in which elements are covered by pairs of objects, and we are required to find a minimum cost subset of objects that induces a collection of pairs covering all elements. Formally, let U be a ground set of elements and let ${\cal S}$ be a set of objects, where each object i has a non-negative cost wi. For every $\{ i, j \} \subseteq {\cal S}$, let ${\cal C}(i,j)$ be the collection of elements in U covered by the pair { i, j }. The set cover with pairs problem asks to find a subset $A \subseteq {\cal S}$ such that $\bigcup_{ \{ i, j \} \subseteq A } {\cal C}(i,j) = U$ and such that ∑i∈Awi is minimized. In addition to studying this general problem, we are also concerned with developing polynomial time approximation algorithms for interesting special cases. The problems we consider in this framework arise in the context of domination in metric spaces and separation of point sets.

Control Plane Latency With SDN Network Hypervisors: The Cost of Virtualization. Software defined networking (SDN) network hypervisors provide the functionalities needed for virtualizing software-defined networks. Hypervisors sit logically between the multiple virtual SDN networks (vSDNs), which reside on the underlying physical SDN network infrastructure, and the corresponding tenant (vSDN) controllers. Different SDN network hypervisor architectures have mainly been explored through proof-of-concept implementations. We fundamentally advance SDN network hypervisor research by conducting a model-based analysis of SDN hypervisor architectures. Specifically, we introduce mixed integer programming formulations for four different SDN network hypervisor architectures. Our model formulations can also optimize the placement of multi-controller switches in virtualized OpenFlow-enabled SDN networks. We employ our models to quantitatively examine the optimal placement of the hypervisor instances. We compare the control plane latencies of the different SDN hypervisor architectures and quantify the cost of virtualization, i.e., the latency overhead due to virtualizing SDN networks via hypervisors. For generalization, we quantify how the hypervisor architectures behave for different network topologies. Our model formulations and the insights drawn from our evaluations inform network operators about the trade-offs of the different hypervisor architectures and help choosing an architecture according to operator demands.

Flow Setup Latency in SDN Networks. In software-defined networking, the typical switch-controller cycle, from generating a network event notification at the controller until the flow rules are installed at the switches, is not an instantaneous activity. Our measurement results show that this has serious implications on the performance of flow setup procedure, specifically for larger networks: we observe that, even with software swit...

Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs AbstractContent Distribution and End-to-End Monitoring with Set Cover by PairsDigital content is housed at data centers located on nodes of a data network. Consumers of this content are also located on network nodes. Content flows from a data center to a consumer on a path defined by a routing protocol, such as open shortest path first. A pair of data centers is said to feasibly serve content to a consumer if there are disjoint paths from each data center to the consumer. In “Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs,” Johnson, Breslau, Diakonikolas, Duffield, Gu, Hajiaghayi, Karloff, Resende, and Sen study this problem when the goal is to minimize the number of required data centers to serve a set of consumers. They also study another facility location problem that arises in network traffic monitoring. Both problems are modeled as a set cover-by-pairs problem. The authors provide complexity results, a new lower-bounding integer programming formulation, and several heuristics. The lower bounds are easily computed with a commercial MIP solver and validate the claim of near-optimality of their heuristics.In this paper, we consider two special cases of the “cover-by-pairs” optimization problem that arises when we need to place facilities so that each customer is served by two facilities that reach it by disjoint shortest paths. These problems arise in a network traffic-monitoring scheme proposed by Breslau et al. and have potential applications to content distribution. The “set-disjoint” variant applies to networks that use the open shortest path first routing protocol, and the “path-disjoint” variant applies when multiprotocol label switching routing is enabled, making better solutions possible at the cost of greater operational expense. Although we can prove that no polynomial-time algorithm can guarantee good solutions for either version, we are able to provide heuristics that do very well in practice on instances with real-world network structure. Fast implementations of the heuristics, made possible by exploiting mathematical observations about the relationship between the network instances and the corresponding instances of the cover-by-pairs problem, allow us to perform an extensive experimental evaluation of the heuristics and what the solutions they produce tell us about the effectiveness of the proposed monitoring scheme. For the set-disjoint variant, we validate our claim of near-optimality via a new lower-bounding integer programming formulation. Although computing this lower bound requires solving the NP-hard hitting set problem and can underestimate the optimal value by a linear factor in the worst case, it can be computed quickly by CPLEX, and it equals the optimal solution value for all the instances in our extensive test bed.

Toward 6G Networks: Use Cases and Technologies Reliable data connectivity is vital for the ever increasingly intelligent, automated, and ubiquitous digital world. Mobile networks are the data highways and, in a fully connected, intelligent digital world, will need to connect everything, including people to vehicles, sensors, data, cloud resources, and even robotic agents. Fifth generation (5G) wireless networks, which are currently being deployed, offer significant advances beyond LTE, but may be unable to meet the full connectivity demands of the future digital society. Therefore, this article discusses technologies that will evolve wireless networks toward a sixth generation (6G) and which we consider as enablers for several potential 6G use cases. We provide a fullstack, system-level perspective on 6G scenarios and requirements, and select 6G technologies that can satisfy them either by improving the 5G design or by introducing completely new communication paradigms.

Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users...

Data-aggregation techniques in sensor networks: A survey First Page of the Article

An intelligent analyzer and understander of English The paper describes a working analysis and generation program for natural language, which handles paragraph length input. Its core is a system of preferential choice between deep semantic patterns, based on what we call “semantic density.” The system is contrasted:with syntax oriented linguistic approaches, and with theorem proving approaches to the understanding problem.

Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search. At the first level, the framework employs a data-parallel Gaussian process based global surrogate model to filter the evolutionary algorithm (EA) population of promising individuals. Subsequently, these potential individuals undergo a memetic search in the form of Lamarckian learning at the second level. The Lamarckian evolution involves a trust-region enabled gradient-based search strategy that employs radial basis function local surrogate models to accelerate convergence. Numerical results are presented on a series of benchmark test functions and on an aerodynamic shape design problem. The results obtained suggest that the proposed optimization framework converges to good designs on a limited computational budget. Furthermore, it is shown that the new algorithm gives significant savings in computational cost when compared to the traditional evolutionary algorithm and other surrogate assisted optimization frameworks

Fast and Accurate Estimation of RFID Tags Radio frequency identification (RFID) systems have been widely deployed for various applications such as object tracking, 3-D positioning, supply chain management, inventory control, and access control. This paper concerns the fundamental problem of estimating RFID tag population size, which is needed in many applications such as tag identification, warehouse monitoring, and privacy-sensitive RFID systems. In this paper, we propose a new scheme for estimating tag population size called Average Run-based Tag estimation (ART). The technique is based on the average run length of ones in the bit string received using the standardized framed slotted Aloha protocol. ART is significantly faster than prior schemes. For example, given a required confidence interval of 0.1% and a required reliability of 99.9%, ART is consistently 7 times faster than the fastest existing schemes (UPE and EZB) for any tag population size. Furthermore, ART's estimation time is provably independent of the tag population sizes. ART works with multiple readers with overlapping regions and can estimate sizes of arbitrarily large tag populations. ART is easy to deploy because it neither requires modification to tags nor to the communication protocol between tags and readers. ART only needs to be implemented on readers as a software module.

Gender Bias in Coreference Resolution. We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these Winogender schemas, we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.

Convert Harm Into Benefit: A Coordination-Learning Based Dynamic Spectrum Anti-Jamming Approach This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern.

What'S In A Name? An Online Survey On Gender Stereotyping Of Humanoid Social Robots This study investigated whether humanoid social robots are implicitly assigned a gender, which then influences evaluations and acceptance of the devices. To assess stereotyping, a naming task was used. Pictures of robots were presented in a mock marketing survey, and the participants were asked to provide a name for the device and rate them on a number of characteristics and select potential tasks for them. Forty participants filled out the web-based survey. The results showed overwhelming preferences for male names, which were more pronounced for older participants and for those with a more feminine self-image. Robots that were rated higher on agency and lower on communion attributes were more likely to be assigned technical tasks, and they were less likely to be accepted into participants' personal lives. Thus, technological artefacts are subject to stereotyping. These findings are discussed within the framework of feminist technoscience.

Effects of visual appearance on the attribution of applications in social robotics This paper investigates the influence of visual appearance of social robots on judgments about their potential applications. 183 participants rated the appropriateness of thirteen categories of applications for twelve social robots in an online study. The ratings were based on videos displaying the appearance of the robot combined with basic information about the robots' general functions. The results confirmed the hypothesis that the visual appearance of robots is a significant predictor for the estimation of applications in the eye of the beholder. Furthermore, the ratings showed an attractiveness bias: robots being judged as more attractive by the users also received more positive evaluations (i.e., ldquolikingrdquo).

Social Acceptance of Robots in Different Occupational Fields: A Systematic Literature Review. Robots today are working in both industrial and service sectors. Robots have evolved from one-function automatons to intelligent systems of versatile features, and the new generation of service robots are sharing same space and tasks with humans. The aim of this systematic literature review was to examine how the social acceptance of robots in different occupational fields has been studied and what kinds of attitudes the studies have discovered regarding robots as workers. The data were collected in October 2016 from four major bibliographic databases. Preliminary search results included 336 research articles from which 42 were selected to the final research through inclusion criteria. Of the studies, 69% concerned robots working in health and social services. Positive attitudes occurred more frequently in studies exposing participants to robots. Robots were considered appropriate for different work tasks. Telepresence robots were highly approved by health care staff. The criticism was directed to decreasing human contact and unnecessary deployment of new technology. Our results imply that attitudes toward robots are positive in many fields of work. Yet there is a need for validated measures and nationally representative data that would help us to further our understanding of social acceptance of robots in work.

The Naked Truth? When judging humans, (formal) clothes play a vital role for the attribution of trust, competence and sympathy. Most social robots, however, appear unclothed and not much is known whether and how clothes can influence how a robot is perceived. In an experiment, participants experienced either a formally dressed, an informally dressed or an undressed robot and rated their experience on different questionnaires. Inconsistent with our expectations, the data revealed no influence of robot clothing on the experience of the robot. Possible reasons and implications for further studies are discussed.

Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots This study emphasizes the need for standardized measurement tools for human robot interaction (HRI). If we are to make progress in this field then we must be able to compare the results from different studies. A literature review has been performed on the measurements of five key concepts in HRI: anthropomorphism, animacy, likeabil- ity, perceived intelligence, and perceived safety. The results have been distilled into five consistent questionnaires using semantic differential scales. We report reliability and valid- ity indicators based on several empirical studies that used these questionnaires. It is our hope that these questionnaires can be used by robot developers to monitor their progress. Psychologists are invited to further develop the question- naires by adding new concepts, and to conduct further vali- dations where it appears necessary.

Fuzzy logic in control systems: fuzzy logic controller. I.

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.

New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system. In this paper we describe the design of a fuzzy logic controller for the ball and beam system using a modified Ant Colony Optimization (ACO) method for optimizing the type of membership functions, the parameters of the membership functions and the fuzzy rules. This is achieved by applying a systematic and hierarchical optimization approach modifying the conventional ACO algorithm using an ant set partition strategy. The simulation results show that the proposed algorithm achieves better results than the classical ACO algorithm for the design of the fuzzy controller.

Image analogies This paper describes a new framework for processing images by example, called “image analogies.” The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a “filtered” version of the other, is presented as “training data”; and an application phase, in which the learned filter is applied to some new target image in order to create an “analogous” filtered result. Image analogies are based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of “image filter” effects, including traditional image filters, such as blurring or embossing; improved texture synthesis, in which some textures are synthesized with higher quality than by previous approaches; super-resolution, in which a higher-resolution image is inferred from a low-resolution source; texture transfer, in which images are “texturized” with some arbitrary source texture; artistic filters, in which various drawing and painting styles are synthesized based on scanned real-world examples; and texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface.

Communication in reactive multiagent robotic systems Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and effectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three different types of communication for three different tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can significantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as effective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.

Efficient and reliable low-power backscatter networks There is a long-standing vision of embedding backscatter nodes like RFIDs into everyday objects to build ultra-low power ubiquitous networks. A major problem that has challenged this vision is that backscatter communication is neither reliable nor efficient. Backscatter nodes cannot sense each other, and hence tend to suffer from colliding transmissions. Further, they are ineffective at adapting the bit rate to channel conditions, and thus miss opportunities to increase throughput, or transmit above capacity causing errors. This paper introduces a new approach to backscatter communication. The key idea is to treat all nodes as if they were a single virtual sender. One can then view collisions as a code across the bits transmitted by the nodes. By ensuring only a few nodes collide at any time, we make collisions act as a sparse code and decode them using a new customized compressive sensing algorithm. Further, we can make these collisions act as a rateless code to automatically adapt the bit rate to channel quality --i.e., nodes can keep colliding until the base station has collected enough collisions to decode. Results from a network of backscatter nodes communicating with a USRP backscatter base station demonstrate that the new design produces a 3.5× throughput gain, and due to its rateless code, reduces message loss rate in challenging scenarios from 50% to zero.

Internet of Things for Smart Cities The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

Inter-class sparsity based discriminative least square regression Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring the correlations among samples. The second one is that the used label matrix, i.e., zero–one label matrix is inappropriate for classification. To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), for multi-class classification. Different from other methods, the proposed method pursues that the transformed samples have a common sparsity structure in each class. For this goal, an inter-class sparsity constraint is introduced to the least square regression model such that the margins of samples from the same class can be greatly reduced while those of samples from different classes can be enlarged. In addition, an error term with row-sparsity constraint is introduced to relax the strict zero–one label matrix, which allows the method to be more flexible in learning the discriminative transformation matrix. These factors encourage the method to learn a more compact and discriminative transformation for regression and thus has the potential to perform better than other methods. Extensive experimental results show that the proposed method achieves the best performance in comparison with other methods for multi-class classification.

Hardware Circuits Design and Performance Evaluation of a Soft Lower Limb Exoskeleton Soft lower limb exoskeletons (LLEs) are wearable devices that have good potential in walking rehabilitation and augmentation. While a few studies focused on the structure design and assistance force optimization of the soft LLEs, rarely work has been conducted on the hardware circuits design. The main purpose of this work is to present a new soft LLE for walking efficiency improvement and introduce its hardware circuits design. A soft LLE for hip flexion assistance and a hardware circuits system with scalability were proposed. To assess the efficacy of the soft LLE, the experimental tests that evaluate the sensor data acquisition, force tracking performance, lower limb muscle activity and metabolic cost were conducted. The time error in the peak assistance force was just 1%. The reduction in the normalized root-mean-square EMG of the rectus femoris was 7.1%. The net metabolic cost in exoskeleton on condition was reduced by 7.8% relative to walking with no exoskeleton. The results show that the designed hardware circuits can be applied to the soft LLE and the soft LLE is able to improve walking efficiency of wearers.

A Hybrid Model for Short-Term Traffic Volume Prediction in Massive Transportation Systems The prediction of short-term volatile traffic becomes increasingly critical for efficient traffic engineering in intelligent transportation systems. Accurate forecast results can assist in traffic management and pedestrian route selection, which will help alleviate the huge congestion problem in the system. This paper presents a novel hybrid DTMGP model to accurately forecast the volume of passenger flows multi-step ahead with the comprehensive consideration of factors from temporal, origin-destination spatial, and frequency and self-similarity perspectives. We first apply discrete wavelet transform to decompose the traffic volume series into an appropriation component and several detailed components. Then we propose a more efficient tracking model to forecast the appropriation component and a novel Gaussian process model to forecast the detailed components. The forecasting performance is evaluated with real-time passenger flow data in Chongqing, China. Simulation results demonstrate that our hybrid model can achieve on average 20%–50% accuracy improvement, especially during rush hours.

Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. •The model of support vector regression with adaptive genetic algorithm and the seasonal mechanism is proposed.•Parameters selection and seasonal adjustment should be carefully selected.•We focus on latest and representative holiday daily data in China.•Two experiments are used to prove the effect of the model.•The AGASSVR is superior to AGA-SVR and BPNN.

Regression conformal prediction with random forests Regression conformal prediction produces prediction intervals that are valid, i.e., the probability of excluding the correct target value is bounded by a predefined confidence level. The most important criterion when comparing conformal regressors is efficiency; the prediction intervals should be as tight (informative) as possible. In this study, the use of random forests as the underlying model for regression conformal prediction is investigated and compared to existing state-of-the-art techniques, which are based on neural networks and k-nearest neighbors. In addition to their robust predictive performance, random forests allow for determining the size of the prediction intervals by using out-of-bag estimates instead of requiring a separate calibration set. An extensive empirical investigation, using 33 publicly available data sets, was undertaken to compare the use of random forests to existing state-of-the-art conformal predictors. The results show that the suggested approach, on almost all confidence levels and using both standard and normalized nonconformity functions, produced significantly more efficient conformal predictors than the existing alternatives.

Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network Bus arrival time prediction intends to improve the level of the services provided by transportation agencies. Intuitively, many stochastic factors affect the predictability of the arrival time, e.g., weather and local events. Moreover, the arrival time prediction for a current station is closely correlated with that of multiple passed stations. Motivated by the observations above, this paper propo...

Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data Traffic forecasting has recently attracted increasing interest due to the popularity of online navigation services, ridesharing and smart city projects. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information. To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Specifically, we propose an algorithm to acquire the upcoming traffic volume from an online navigation engine. Taking advantage of the piecewise-linear flow-density relationship, a novel transformer structure converts the upcoming volume into its equivalent in travel time. We combine this signal with the commonly-utilized travel-time signal, and then apply graph convolution to capture the spatial dependency. Particularly, we construct a compound adjacency matrix which reflects the innate traffic proximity. We conduct extensive experiments on real-world datasets. The results show that H-STGCN remarkably outperforms state-of-the-art methods in various metrics, especially for the prediction of non-recurring congestion.

Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting. Probabilistic time series forecasting involves estimating the distribution of future based on its history, which is essential for risk management in downstream decision-making. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets. We take the automatic relevance determination (ARD) view and devise a network to exploit the exogenous variables in addition to time series. In particular, our ARD network can incorporate the uncertainty of the exogenous variables and eventually helps identify useful exogenous variables and suppress those irrelevant for forecasting. The distribution of multi-step ahead forecasts are approximated by Monte Carlo simulation. We show in experiments that our model produces accurate and sharp probabilistic forecasts. The estimated uncertainty of our forecasting also realistically increases over time, in a spontaneous manner.

Transfer Knowledge between Cities The rapid urbanization has motivated extensive research on urban computing. It is critical for urban computing tasks to unlock the power of the diversity of data modalities generated by different sources in urban spaces, such as vehicles and humans. However, we are more likely to encounter the label scarcity problem and the data insufficiency problem when solving an urban computing task in a city where services and infrastructures are not ready or just built. In this paper, we propose a FLexible multimOdal tRAnsfer Learning (FLORAL) method to transfer knowledge from a city where there exist sufficient multimodal data and labels, to this kind of cities to fully alleviate the two problems. FLORAL learns semantically related dictionaries for multiple modalities from a source domain, and simultaneously transfers the dictionaries and labelled instances from the source into a target domain. We evaluate the proposed method with a case study of air quality prediction.

Space-time modeling of traffic flow. This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5min average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.

Model-based periodic event-triggered control for linear systems Periodic event-triggered control (PETC) is a control strategy that combines ideas from conventional periodic sampled-data control and event-triggered control. By communicating periodically sampled sensor and controller data only when needed to guarantee stability or performance properties, PETC is capable of reducing the number of transmissions significantly, while still retaining a satisfactory closed-loop behavior. In this paper, we will study observer-based controllers for linear systems and propose advanced event-triggering mechanisms (ETMs) that will reduce communication in both the sensor-to-controller channels and the controller-to-actuator channels. By exploiting model-based computations, the new classes of ETMs will outperform existing ETMs in the literature. To model and analyze the proposed classes of ETMs, we present two frameworks based on perturbed linear and piecewise linear systems, leading to conditions for global exponential stability and @?"2-gain performance of the resulting closed-loop systems in terms of linear matrix inequalities. The proposed analysis frameworks can be used to make tradeoffs between the network utilization on the one hand and the performance in terms of @?"2-gains on the other. In addition, we will show that the closed-loop performance realized by an observer-based controller, implemented in a conventional periodic time-triggered fashion, can be recovered arbitrarily closely by a PETC implementation. This provides a justification for emulation-based design. Next to centralized model-based ETMs, we will also provide a decentralized setup suitable for large-scale systems, where sensors and actuators are physically distributed over a wide area. The improvements realized by the proposed model-based ETMs will be demonstrated using numerical examples.

Affective social robots For human-robot interaction to proceed in a smooth, natural manner, robots must adhere to human social norms. One such human convention is the use of expressive moods and emotions as an integral part of social interaction. Such expressions are used to convey messages such as ''I'm happy to see you'' or ''I want to be comforted,'' and people's long-term relationships depend heavily on shared emotional experiences. Thus, we have developed an affective model for social robots. This generative model attempts to create natural, human-like affect and includes distinctions between immediate emotional responses, the overall mood of the robot, and long-term attitudes toward each visitor to the robot, with a focus on developing long-term human-robot relationships. This paper presents the general affect model as well as particular details of our implementation of the model on one robot, the Roboceptionist. In addition, we present findings from two studies that demonstrate the model's potential.

Rich Models for Steganalysis of Digital Images We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary $\\pm {\\hbox{1}}$ embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes.

Heterogeneous ensemble for feature drifts in data streams The nature of data streams requires classification algorithms to be real-time, efficient, and able to cope with high-dimensional data that are continuously arriving. It is a known fact that in high-dimensional datasets, not all features are critical for training a classifier. To improve the performance of data stream classification, we propose an algorithm called HEFT-Stream (H eterogeneous E nsemble with F eature drifT for Data Streams ) that incorporates feature selection into a heterogeneous ensemble to adapt to different types of concept drifts. As an example of the proposed framework, we first modify the FCBF [13] algorithm so that it dynamically update the relevant feature subsets for data streams. Next, a heterogeneous ensemble is constructed based on different online classifiers, including Online Naive Bayes and CVFDT [5]. Empirical results show that our ensemble classifier outperforms state-of-the-art ensemble classifiers (AWE [15] and OnlineBagging [21]) in terms of accuracy, speed, and scalability. The success of HEFT-Stream opens new research directions in understanding the relationship between feature selection techniques and ensemble learning to achieve better classification performance.

Orientation-aware RFID tracking with centimeter-level accuracy. RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, we in this paper propose OmniTrack, an orientation-aware RFID tracking approach. Our study discovers the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tag's orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeter-level location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Explainable Artificial Intelligence - Concepts, Applications, Research Challenges and Visions. The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasing the transparency of automatically learned prediction models, as for instance by deep or reinforcement learning. The second is aimed at anticipating the negative impact of opaque models with the desire to regulate or control impactful consequences of incorrect predictions, especially in sensitive areas like medicine and law. The formulation of methods to augment the construction of predictive models with domain knowledge can provide support for producing human understandable explanations for predictions. This runs in parallel with AI regulatory concerns, like the European Union General Data Protection Regulation, which sets standards for the production of explanations from automated or semi-automated decision making. Despite the fact that all this research activity is the growing acknowledgement that the topic of explainability is essential, it is important to recall that it is also among the oldest fields of computer science. In fact, early AI was re-traceable, interpretable, thus understandable by and explainable to humans. The goal of this research is to articulate the big picture ideas and their role in advancing the development of XAI systems, to acknowledge their historical roots, and to emphasise the biggest challenges to moving forward.

Factual and Counterfactual Explanations for Black Box Decision Making. The rise of sophisticated machine learning models has brought accurate but obscure decision systems, which hide their logic, thus undermining transparency, trust, and the adoption of artificial intelligence (AI) in socially sensitive and safety-critical contexts. We introduce a local rule-based explanation method, providing faithful explanations of the decision made by a black box classifier on a ...

Optimal classification trees. State-of-the-art decision tree methods apply heuristics recursively to create each split in isolation, which may not capture well the underlying characteristics of the dataset. The optimal decision tree problem attempts to resolve this by creating the entire decision tree at once to achieve global optimality. In the last 25 years, algorithmic advances in integer optimization coupled with hardware improvements have resulted in an astonishing 800 billion factor speedup in mixed-integer optimization (MIO). Motivated by this speedup, we present optimal classification trees, a novel formulation of the decision tree problem using modern MIO techniques that yields the optimal decision tree for axes-aligned splits. We also show the richness of this MIO formulation by adapting it to give optimal classification trees with hyperplanes that generates optimal decision trees with multivariate splits. Synthetic tests demonstrate that these methods recover the true decision tree more closely than heuristics, refuting the notion that optimal methods overfit the training data. We comprehensively benchmark these methods on a sample of 53 datasets from the UCI machine learning repository. We establish that these MIO methods are practically solvable on real-world datasets with sizes in the 1000s, and give average absolute improvements in out-of-sample accuracy over CART of 1---2 and 3---5% for the univariate and multivariate cases, respectively. Furthermore, we identify that optimal classification trees are likely to outperform CART by 1.2---1.3% in situations where the CART accuracy is high and we have sufficient training data, while the multivariate version outperforms CART by 4---7% when the CART accuracy or dimension of the dataset is low.

Bias in data-driven artificial intelligence systems - An introductory survey. Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Explanation in Artificial Intelligence: Insights from the Social Sciences. There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a ‘good’ explanation. There exist vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations to the explanation process. This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

JPEG Error Analysis and Its Applications to Digital Image Forensics JPEG is one of the most extensively used image formats. Understanding the inherent characteristics of JPEG may play a useful role in digital image forensics. In this paper, we introduce JPEG error analysis to the study of image forensics. The main errors of JPEG include quantization, rounding, and truncation errors. Through theoretically analyzing the effects of these errors on single and double JPEG compression, we have developed three novel schemes for image forensics including identifying whether a bitmap image has previously been JPEG compressed, estimating the quantization steps of a JPEG image, and detecting the quantization table of a JPEG image. Extensive experimental results show that our new methods significantly outperform existing techniques especially for the images of small sizes. We also show that the new method can reliably detect JPEG image blocks which are as small as 8 × 8 pixels and compressed with quality factors as high as 98. This performance is important for analyzing and locating small tampered regions within a composite image.

Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers An ad-hoc network is the cooperative engagement of a collection of Mobile Hosts without the required intervention of any centralized Access Point. In this paper we present an innovative design for the operation of such ad-hoc networks. The basic idea of the design is to operate each Mobile Host as a specialized router, which periodically advertises its view of the interconnection topology with other Mobile Hosts within the network. This amounts to a new sort of routing protocol. We have investigated modifications to the basic Bellman-Ford routing mechanisms, as specified by RIP [5], to make it suitable for a dynamic and self-starting network mechanism as is required by users wishing to utilize ad hoc networks. Our modifications address some of the previous objections to the use of Bellman-Ford, related to the poor looping properties of such algorithms in the face of broken links and the resulting time dependent nature of the interconnection topology describing the links between the Mobile Hosts. Finally, we describe the ways in which the basic network-layer routing can be modified to provide MAC-layer support for ad-hoc networks.

Efficient Signature Generation by Smart Cards We present a new public-key signature scheme and a corresponding authentication scheme that are based on discrete logarithms in a subgroup of units in Zp where p is a sufficiently large prime, e.g., p = 2512. A key idea is to use for the base of the discrete logarithm an integer a in Zp such that the order of a is a sufficiently large prime q, e.g., q = 2140. In this way we improve the ElGamal signature scheme in the speed of the procedures for the generation and the verification of signatures and also in the bit length of signatures. We present an efficient algorithm that preprocesses the exponentiation of a random residue modulo p.

Stabilizing a linear system by switching control with dwell time The use of networks in control systems to connect controllers and sensors/actuators has become common practice in many applications. This new technology has also posed a theoretical control problem of how to use the limited data rate of the network effectively. We consider a system where its sensor and actuator are connected by a finite data rate channel. A design method to stabilize a continuous-time, linear plant using a switching controller is proposed. In particular, to prevent the actuator from fast switching, or chattering, which can not only increase the necessary data rate but also damage the system, we employ a dwell-time switching scheme. It is shown that a systematic partition of the state-space enables us to reduce the complexity of the design problem

Empirical Modelling of Genetic Algorithms This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a graeco-latin square permits an initial study of a wide range of parameter settings. This is followed by fully crossed factorial experiments with narrower ranges, which allow detailed analysis by logistic regression. The empirical models derived can be used to determine optimal algorithm parameters and to shed light on interactions between the parameters and their relative importance. Re-fined models are produced, which are shown to be robust under extrapolation to up to triple the problem size.

Biologically-inspired soft exosuit. In this paper, we present the design and evaluation of a novel soft cable-driven exosuit that can apply forces to the body to assist walking. Unlike traditional exoskeletons which contain rigid framing elements, the soft exosuit is worn like clothing, yet can generate moments at the ankle and hip with magnitudes of 18% and 30% of those naturally generated by the body during walking, respectively. Our design uses geared motors to pull on Bowden cables connected to the suit near the ankle. The suit has the advantages over a traditional exoskeleton in that the wearer's joints are unconstrained by external rigid structures, and the worn part of the suit is extremely light, which minimizes the suit's unintentional interference with the body's natural biomechanics. However, a soft suit presents challenges related to actuation force transfer and control, since the body is compliant and cannot support large pressures comfortably. We discuss the design of the suit and actuation system, including principles by which soft suits can transfer force to the body effectively and the biological inspiration for the design. For a soft exosuit, an important design parameter is the combined effective stiffness of the suit and its interface to the wearer. We characterize the exosuit's effective stiffness, and present preliminary results from it generating assistive torques to a subject during walking. We envision such an exosuit having broad applicability for assisting healthy individuals as well as those with muscle weakness.

Inter-class sparsity based discriminative least square regression Least square regression is a very popular supervised classification method. However, two main issues greatly limit its performance. The first one is that it only focuses on fitting the input features to the corresponding output labels while ignoring the correlations among samples. The second one is that the used label matrix, i.e., zero–one label matrix is inappropriate for classification. To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), for multi-class classification. Different from other methods, the proposed method pursues that the transformed samples have a common sparsity structure in each class. For this goal, an inter-class sparsity constraint is introduced to the least square regression model such that the margins of samples from the same class can be greatly reduced while those of samples from different classes can be enlarged. In addition, an error term with row-sparsity constraint is introduced to relax the strict zero–one label matrix, which allows the method to be more flexible in learning the discriminative transformation matrix. These factors encourage the method to learn a more compact and discriminative transformation for regression and thus has the potential to perform better than other methods. Extensive experimental results show that the proposed method achieves the best performance in comparison with other methods for multi-class classification.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Human ear recognition based on local multi-scale LBP features with city-block distance The use of the ear as a biometric modality has emerged in recent years. It makes it possible to differentiate people thanks to its stability over time and to the richness of its characteristics such as texture, color and size. This paper proposes a novel approach to ear recognition based on a variant of the Local Binary Pattern descriptor called Multi-scale Local Binary Pattern (MLBP). MLBP is calculated locally, by dividing the image into several equal blocks, to extract the ear features which will be used in the matching process to make a decision by detecting the similarities between the feature vectors using City-Block distance (CTB). The proposed method is evaluated on three reference ear databases: IIT Delhi I, IIT Delhi II and USTB-1. The analysis of the results obtained have clearly shown the robustness and the stability of the proposed ear recognition method which is highly competitive, achieving an attractive recognition performances in terms of identification rate at rank-1 up to: 98.40% for IIT Delhi I, 98.64% for IIT Delhi II, and 98.33% for USTB-1.

Joint discriminative dimensionality reduction and dictionary learning for face recognition In linear representation based face recognition (FR), it is expected that a discriminative dictionary can be learned from the training samples so that the query sample can be better represented for classification. On the other hand, dimensionality reduction is also an important issue for FR. It cannot only reduce significantly the storage space of face images, but also enhance the discrimination of face feature. Existing methods mostly perform dimensionality reduction and dictionary learning separately, which may not fully exploit the discriminative information in the training samples. In this paper, we propose to learn jointly the projection matrix for dimensionality reduction and the discriminative dictionary for face representation. The joint learning makes the learned projection and dictionary better fit with each other so that a more effective face classification can be obtained. The proposed algorithm is evaluated on benchmark face databases in comparison with existing linear representation based methods, and the results show that the joint learning improves the FR rate, particularly when the number of training samples per class is small.

Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.

Feature and Rank Level Fusion for Privacy Preserved Multi-Biometric System AbstractPrivacy protection in biometric system is a newly emerging biometric technology that can provide the protection against various attacks by intruders. In this paper, the authors have presented a multi-level of random projection method based on face and ear biometric traits. Privacy preserved templates are used in the proposed system. The main idea behind the privacy preserve computation is the random projection algorithm. Multiple random projection matrixes are used to generate multiple templates for biometric authentication. Newly introduced random fusion method is used in the proposed system; therefore, proposed method can provide better template security, privacy and feature quality. Multiple randomly fused templates are used for recognition purpose and finally decision fusion is applied to generate the final classification result. The proposed method works in a similar way human cognition for face recognition works, furthermore it preserve privacy and multimodality of the system.

Ear recognition using local binary patterns: A comparative experimental study. •A comparative study of ear recognition using local binary patterns variants is done.•A new texture operator is proposed and used as an ear feature descriptor.•Detailed analysis on Identification and verification is conducted separately.•An approximated recognition rate of 99% is achieved by some texture descriptors.•The study has significant insights and can benefit researchers in future works.

Online Palmprint Identification Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.

Improved ear verification after surgery - An approach based on collaborative representation of locally competitive features. •Presents a comprehensive study for biometric verification performance of ears before and after surgery.•Extensive study on different type of ear-surgery is presented along with a new public ear database.•Presents a new feature extraction technique based on Topographic Locally Competitive Algorithm.•Demonstrates superior verification performance on both normal ear database and surgically modified ear database.•Discussion on computational complexity and state-of-art performance.

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems. The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30\%$</tex-math></inline-formula> compared with the existing upper confidence bound based learning algorithm.

Visual cryptography for general access structures A visual cryptography scheme for a set P of n participants is a method of encoding a secret image SI into n shadow images called shares, where each participant in P receives one share. Certain qualified subsets of participants can “visually” recover the secret image, but other, forbidden, sets of participants have no information (in an information-theoretic sense) on SI . A “visual” recovery for a set X ⊆ P consists of xeroxing the shares given to the participants in X onto transparencies, and then stacking them. The participants in a qualified set X will be able to see the secret image without any knowledge of cryptography and without performing any cryptographic computation. In this paper we propose two techniques for constructing visual cryptography schemes for general access structures. We analyze the structure of visual cryptography schemes and we prove bounds on the size of the shares distributed to the participants in the scheme. We provide a novel technique for realizing k out of n threshold visual cryptography schemes. Our construction for k out of n visual cryptography schemes is better with respect to pixel expansion than the one proposed by M. Naor and A. Shamir (Visual cryptography, in “Advances in Cryptology—Eurocrypt '94” CA. De Santis, Ed.), Lecture Notes in Computer Science, Vol. 950, pp. 1–12, Springer-Verlag, Berlin, 1995) and for the case of 2 out of n is the best possible. Finally, we consider graph-based access structures, i.e., access structures in which any qualified set of participants contains at least an edge of a given graph whose vertices represent the participants of the scheme.

Multi-stage genetic programming: A new strategy to nonlinear system modeling This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling nonlinear systems. The proposed strategy is based on incorporating the individual effect of predictor variables and the interactions among them to provide more accurate simulations. According to the MSGP strategy, an efficient formulation for a problem comprises different terms. In the first stage of the MSGP-based analysis, the output variable is formulated in terms of an influencing variable. Thereafter, the error between the actual and the predicted value is formulated in terms of a new variable. Finally, the interaction term is derived by formulating the difference between the actual values and the values predicted by the individually developed terms. The capabilities of MSGP are illustrated by applying it to the formulation of different complex engineering problems. The problems analyzed herein include the following: (i) simulation of pH neutralization process, (ii) prediction of surface roughness in end milling, and (iii) classification of soil liquefaction conditions. The validity of the proposed strategy is confirmed by applying the derived models to the parts of the experimental results that were not included in the analyses. Further, the external validation of the models is verified using several statistical criteria recommended by other researchers. The MSGP-based solutions are capable of effectively simulating the nonlinear behavior of the investigated systems. The results of MSGP are found to be more accurate than those of standard GP and artificial neural network-based models.

Collaborative Mobile Charging The limited battery capacity of sensor nodes has become one of the most critical impediments that stunt the deployment of wireless sensor networks (WSNs). Recent breakthroughs in wireless energy transfer and rechargeable lithium batteries provide a promising alternative to power WSNs: mobile vehicles/robots carrying high volume batteries serve as mobile chargers to periodically deliver energy to sensor nodes. In this paper, we consider how to schedule multiple mobile chargers to optimize energy usage effectiveness, such that every sensor will not run out of energy. We introduce a novel charging paradigm, collaborative mobile charging, where mobile chargers are allowed to intentionally transfer energy between themselves. To provide some intuitive insights into the problem structure, we first consider a scenario that satisfies three conditions, and propose a scheduling algorithm, PushWait, which is proven to be optimal and can cover a one-dimensional WSN of infinite length. Then, we remove the conditions one by one, investigating chargers' scheduling in a series of scenarios ranging from the most restricted one to a general 2D WSN. Through theoretical analysis and simulations, we demonstrate the advantages of the proposed algorithms in energy usage effectiveness and charging coverage.

A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

A Novel Low-Cost Wireless Footwear System for Monitoring Diabetic Foot Patients Diabetic foot is one of the main complications of diabetes with the characteristics of high incidence and difficulty in treatment. Diabetic patients with peripheral neuropathy may develop foot ulcers, and in severe cases amputations are required and some may even die. Plantar pressure can be used to assess the risk of developing diabetic foot, but the existing plantar pressure monitoring methods are not suitable for long-term monitoring in daily life. This study presents a novel low-cost shoe system for daily monitoring of plantar pressure in diabetics. It includes an insole with pressure sensor array, which can dynamically monitor the plantar pressure and display the changes of plantar pressure in real time in the mobile phone to provide early warning for patients with high risk of diabetic foot. As for the sensor, copper and carbon black were adopted as the electrode and conductive filler respectively, enabling a mass production with low price. It was soft and bendable, meeting the performance needs of daily plantar pressure monitoring. All devices were encapsulated in shoes, and the data was transmitted wirelessly through Bluetooth, which did not affect the user's walking. After using random forest for feature selection, five classifiers were used to classify the plantar pressure of healthy people, diabetic patients without peripheral neuropathy, and diabetic patients with peripheral neuropathy collected by this system. The experimental results showed that the accuracy of the random forest classifier was the highest, reaching 94.7%, which indicated that the system could be useful for daily plantar pressure monitoring of diabetic patients.

Fundamental Limits on Synchronizing Clocks Over Networks We characterize what is feasible concerning clock synchronization in wireline or wireless networks. We consider a net work of n nodes, equipped with affine clocks relative to a designated clock that exchange packets subject to link delays. Determining all unknown parameters, i.e., skews and offsets of all the clocks as well as the delays of all the communication links, is impossible. All nodal skews, as well as all round-trip delays between every pair of nodes, can be determined correctly. Also, every transmitting node can predict precisely the time indicated by the receiver's clock at which it receives the packet. However, the vector of unknown link delays and clock offsets can only be determined up to an (n - 1)-dimensional subspace, with each degree of freedom corresponding to the offset of one of the (n - 1) clocks. Invoking causality, that packets cannot be received before they are transmitted, the uncertainty set can be reduced to a polyhedron. We also investigate structured models for link delays as the sum of a transmitter-dependent delay, a receiver-dependent delay, and a known propagation delay, and identify conditions which permit a unique solution, and conditions under which the number of the residual degrees of freedom is independent of the network size. For receiver-receiver synchronization, where only receipt times are available, but no time-stamping is done by the sender, all nodal skews can still be determined, but delay differences between neighboring communication links with a common sender can only be characterized up to an affine transformation of the (n - 1) un known offsets. Moreover, causality does not help reduce the uncertainty set.

A Comparison of Reflective Photoplethysmography for Detection of Heart Rate, Blood Oxygen Saturation, and Respiration Rate at Various Anatomical Locations. Monitoring of vital signs is critical for patient triage and management. Principal assessments of patient conditions include respiratory rate heart/pulse rate and blood oxygen saturation. However, these assessments are usually carried out with multiple sensors placed in different body locations. The aim of this paper is to identify a single location on the human anatomy whereby a single 1 cm x 1 cm non-invasive sensor could simultaneously measure heart rate (HR), blood oxygen saturation (SpO(2)), and respiration rate (RR), at rest and while walking. To evaluate the best anatomical location, we analytically compared eight anatomical locations for photoplethysmography (PPG) sensors simultaneously acquired by a single microprocessor at rest and while walking, with a comparison to a commercial pulse oximeter and respiration rate ground truth. Our results show that the forehead produced the most accurate results for HR and SpO(2) both at rest and walking, however, it had poor RR results. The finger recorded similar results for HR and SpO(2), however, it had more accurate RR results. Overall, we found the finger to be the best location for measurement of all three parameters at rest; however, no site was identified as capable of measuring all parameters while walking.

A Decision Support System for real-time rescheduling of railways We present a Decision Support System (DSS) for real-time management of railway networks. The DSS employs a mathematical programming approach addressing traffic rescheduling under unexpected disturbances in a mixed-(single- and double-) tracked network. The DSS simulates the network behavior with the mathematical programming model based on the railway topology and constraints, rescheduling the timetable in real time, detecting and solving conflicts in the network. The DSS is applied to a real data set related to a large portion of a regional network in Southern Italy.

Train Rescheduling With Stochastic Recovery Time: A New Track-Backup Approach Train rescheduling is an important decision process in railway management. It aims to minimize the negative effects arising from the disturbances via real-time traffic management. Two main challenges are how to formulate the dynamic and complex rescheduling problem as an optimization model, and how to obtain a good solution within a short time limit. Focusing on the stochastic capacity recovery times of blocked tracks, we propose a new track-backup rescheduling (TBR) approach which optimally assigns each affected train a backup track, based on the estimation of recovery time, the original timetable, and track changing cost. Then, we formulate a mixed integer programming (MIP) model to obtain a conflict-free timetable which minimizes the delay cost and the expected track changing cost. A greedy algorithm is designed to reorder trains and reschedule the arrival and departure times, and then we use an MIP algorithm to solve the optimal track backup strategy. Based on the Beijing-Shanghai high-speed railway line, we conduct extensive experimental studies which show that the TBR approach can reduce the rescheduling cost by an average of 10.17% compared with traditional approaches. More important, the greedy-based algorithm is shown to be able to obtain good solutions (with an average error of only 2.85%) within 1.5 s, which implies the high potential of our approach in a real-time traffic management system where fast response is critical.

Fuzzy logic in control systems: fuzzy logic controller. I.

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.

New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system. In this paper we describe the design of a fuzzy logic controller for the ball and beam system using a modified Ant Colony Optimization (ACO) method for optimizing the type of membership functions, the parameters of the membership functions and the fuzzy rules. This is achieved by applying a systematic and hierarchical optimization approach modifying the conventional ACO algorithm using an ant set partition strategy. The simulation results show that the proposed algorithm achieves better results than the classical ACO algorithm for the design of the fuzzy controller.

Image analogies This paper describes a new framework for processing images by example, called “image analogies.” The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a “filtered” version of the other, is presented as “training data”; and an application phase, in which the learned filter is applied to some new target image in order to create an “analogous” filtered result. Image analogies are based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of “image filter” effects, including traditional image filters, such as blurring or embossing; improved texture synthesis, in which some textures are synthesized with higher quality than by previous approaches; super-resolution, in which a higher-resolution image is inferred from a low-resolution source; texture transfer, in which images are “texturized” with some arbitrary source texture; artistic filters, in which various drawing and painting styles are synthesized based on scanned real-world examples; and texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface.

Noninterference for a Practical DIFC-Based Operating System The Flume system is an implementation of decentralized information flow control (DIFC) at the operating system level. Prior work has shown Flume can be implemented as a practical extension to the Linux operating system, allowing real Web applications to achieve useful security guarantees. However, the question remains if the Flume system is actually secure. This paper compares Flume with other recent DIFC systems like Asbestos, arguing that the latter is inherently susceptible to certain wide-bandwidth covert channels, and proving their absence in Flume by means of a noninterference proof in the communicating sequential processes formalism.

Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art In the nearly six decades since researchers began to explore methods of creating them, exoskeletons have progressed from the stuff of science fiction to nearly commercialized products. While there are still many challenges associated with exoskeleton development that have yet to be perfected, the advances in the field have been enormous. In this paper, we review the history and discuss the state-of-the-art of lower limb exoskeletons and active orthoses. We provide a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature, and end with a discussion of the major advances that have been made and hurdles yet to be overcome.

Internet of Things for Smart Cities The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

Collective feature selection to identify crucial epistatic variants. In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

Social Robots for (Second) Language Learning in (Migrant) Primary School Children Especially these days, innovation and support from technology to relieve pressure in education is highly urgent. This study tested the potential advantage of a social robot over a tablet in (second) language learning on performance, engagement, and enjoyment. Shortages in primary education call for new technology solutions. Previous studies combined robots with tablets, to compensate for robot’s limitations, however, this study applied direct human–robot interaction. Primary school children (N = 63, aged 4–6) participated in a 3-wave field experiment with story-telling exercises, either with a semi-autonomous robot (without tablet, using WOz) or a tablet. Results showed increased learning gains over time when training with a social robot, compared to the tablet. Children who trained with a robot were more engaged in the story-telling task and enjoyed it more. Robot’s behavioral style (social or neutral) hardly differed overall, however, seems to vary for high versus low educational abilities. While social robots need sophistication before being implemented in schools, our study shows the potential of social robots as tutors in (second) language learning.

GMDH-based networks for intelligent intrusion detection. Network intrusion detection has been an area of rapid advancement in recent times. Similar advances in the field of intelligent computing have led to the introduction of several classification techniques for accurately identifying and differentiating network traffic into normal and anomalous. Group Method for Data Handling (GMDH) is one such supervised inductive learning approach for the synthesis of neural network models. Through this paper, we propose a GMDH-based technique for classifying network traffic into normal and anomalous. Two variants of the technique, namely, Monolithic and Ensemble-based, were tested on the KDD-99 dataset. The dataset was preprocessed and all features were ranked based on three feature ranking techniques, namely, Information Gain, Gain Ratio, and GMDH by itself. The results obtained proved that the proposed intrusion detection scheme yields high attack detection rates, nearly 98%, when compared with other intelligent classification techniques for network intrusion detection.

Mining network data for intrusion detection through combining SVMs with ant colony networks. In this paper, we introduce a new machine-learning-based data classification algorithm that is applied to network intrusion detection. The basic task is to classify network activities (in the network log as connection records) as normal or abnormal while minimizing misclassification. Although different classification models have been developed for network intrusion detection, each of them has its strengths and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM method with CSOACNs to take the advantages of both while avoiding their weaknesses. Our algorithm is implemented and evaluated using a standard benchmark KDD99 data set. Experiments show that CSVAC (Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both classification rate and run-time efficiency.

A survey of network anomaly detection techniques. Information and Communication Technology (ICT) has a great impact on social wellbeing, economic growth and national security in todays world. Generally, ICT includes computers, mobile communication devices and networks. ICT is also embraced by a group of people with malicious intent, also known as network intruders, cyber criminals, etc. Confronting these detrimental cyber activities is one of the international priorities and important research area. Anomaly detection is an important data analysis task which is useful for identifying the network intrusions. This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering. The paper also discusses research challenges with the datasets used for network intrusion detection.

Applications of Blockchain Technology beyond Cryptocurrency. Blockchain (BC), the technology behind the Bitcoin crypto-currency system, is considered to be both alluring and critical for ensuring enhanced security and (in some implementations, non-traceable) privacy for diverse applications in many other domains including in the Internet of Things (IoT) eco-system. Intensive research is currently being conducted in both academia and industry applying the Blockchain technology in multifarious applications. Proof-of-Work (PoW), a cryptographic puzzle, plays a vital role in ensuring BC security by maintaining a digital ledger of transactions, which is considered to be incorruptible. Furthermore, BC uses a changeable Public Key (PK) to record the usersu0027 identity, which provides an extra layer of privacy. Not only in cryptocurrency has the successful adoption of BC been implemented but also in multifaceted non-monetary systems such as in: distributed storage systems, proof-of-location, healthcare, decentralized voting and so forth. Recent research articles and projects/applications were surveyed to assess the implementation of BC for enhanced security, to identify associated challenges and to propose solutions for BC enabled enhanced security systems.

A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks. More and more network traffic data have brought great challenge to traditional intrusion detection system. The detection performance is tightly related to selected features and classifiers, but traditional feature selection algorithms and classification algorithms can't perform well in massive data environment. Also the raw traffic data are imbalanced, which has a serious impact on the classification results. In this paper, we propose a novel network intrusion detection model utilizing convolutional neural networks (CNNs). We use CNN to select traffic features from raw data set automatically, and we set the cost function weight coefficient of each class based on its numbers to solve the imbalanced data set problem. The model not only reduces the false alarm rate (FAR) but also improves the accuracy of the class with small numbers. To reduce the calculation cost further, we convert the raw traffic vector format into image format. We use the standard NSL-KDD data set to evaluate the performance of the proposed CNN model. The experimental results show that the accuracy, FAR, and calculation cost of the proposed model perform better than traditional standard algorithms. It is an effective and reliable solution for the intrusion detection of a massive network.

A Closer Look at Intrusion Detection System for Web Applications Intrusion Detection System (IDS) acts as a defensive tool to detect the security attacks on the web. IDS is a known methodology for detecting network-based attacks but is still immature in monitoring and identifying web-based application attacks. The objective of this research paper is to present a design methodology for efficient IDS with respect to web applications. In this paper, we present several specific aspects which make it challenging for an IDS to monitor and detect web attacks. The article also provides a comprehensive overview of the existing detection systems exclusively designed to observe web traffic. Furthermore, we identify various dimensions for comparing the IDS from different perspectives based on their design and functionalities. We also propose a conceptual framework of a web IDS with a prevention mechanism to offer systematic guidance for the implementation of the system. We compare its features with five existing detection systems, namely, AppSensor, PHPIDS, ModSecurity, Shadow Daemon, and AQTRONIX Web Knight. This paper will highly facilitate the interest groups with the cutting-edge information to understand the stronger and weaker sections of the domain and provide a firm foundation for developing an intelligent and efficient system.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Adam: A Method for Stochastic Optimization. We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT. The Internet of Things (IoT) is stepping out of its infancy into full maturity and establishing itself as a part of the future Internet. One of the technical challenges of having billions of devices deployed worldwide is the ability to manage them. Although access management technologies exist in IoT, they are based on centralized models which introduce a new variety of technical limitations to ma...

Multivariate Short-Term Traffic Flow Forecasting Using Time-Series Analysis Existing time-series models that are used for short-term traffic condition forecasting are mostly univariate in nature. Generally, the extension of existing univariate time-series models to a multivariate regime involves huge computational complexities. A different class of time-series models called structural time-series model (STM) (in its multivariate form) has been introduced in this paper to develop a parsimonious and computationally simple multivariate short-term traffic condition forecasting algorithm. The different components of a time-series data set such as trend, seasonal, cyclical, and calendar variations can separately be modeled in STM methodology. A case study at the Dublin, Ireland, city center with serious traffic congestion is performed to illustrate the forecasting strategy. The results indicate that the proposed forecasting algorithm is an effective approach in predicting real-time traffic flow at multiple junctions within an urban transport network.

State resetting for bumpless switching in supervisory control In this paper the realization and implementation of a multi-controller scheme made of a finite set of linear single-input-single-output controllers, possibly having different state dimensions, is studied. The supervisory control framework is considered, namely a minimal parameter dependent realization of the set of controllers such that all controllers share the same state space is used. A specific state resetting strategy based on the behavioral approach to system theory is developed in order to master the transient upon controller switching.

Adaptive dynamic programming and optimal control of nonlinear nonaffine systems. In this paper, a novel optimal control design scheme is proposed for continuous-time nonaffine nonlinear dynamic systems with unknown dynamics by adaptive dynamic programming (ADP). The proposed methodology iteratively updates the control policy online by using the state and input information without identifying the system dynamics. An ADP algorithm is developed, and can be applied to a general class of nonlinear control design problems. The convergence analysis for the designed control scheme is presented, along with rigorous stability analysis for the closed-loop system. The effectiveness of this new algorithm is illustrated by two simulation examples.

Finite-Time Adaptive Fuzzy Tracking Control Design for Nonlinear Systems. This paper addresses the finite-time tracking problem of nonlinear pure-feedback systems. Unlike the literature on traditional finite-time stabilization, in this paper the nonlinear system functions, including the bounding functions, are all totally unknown. Fuzzy logic systems are used to model those unknown functions. To present a finite-time control strategy, a criterion of semiglobal practical...

Energy harvesting algorithm considering max flow problem in wireless sensor networks. In Wireless Sensor Networks (WSNs), sensor nodes with poor energy always have bad effect on the data rate or max flow. These nodes are called bottleneck nodes. In this paper, in order to increase the max flow, we assume an energy harvesting WSNs environment to investigate the cooperation of multiple Mobile Chargers (MCs). MCs are mobile robots that use wireless charging technology to charge sensor nodes in WSNs. This means that in energy harvesting WSNs environments, sensor nodes can obtain energy replenishment by using MCs or collecting energy from nature by themselves. In our research, we use MCs to improve the energy of the sensor nodes by performing multiple rounds of unified scheduling, and finally achieve the purpose of increasing the max flow at sinks. Firstly, we model this problem as a Linear Programming (LP) to search the max flow in a round of charging scheduling and prove that the problem is NP-hard. In order to solve the problem, we propose a heuristic approach: deploying MCs in units of paths with the lowest energy node priority. To reduce the energy consumption of MCs and increase the charging efficiency, we also take the optimization of MCs’ moving distance into our consideration. Finally, we extend the method to multiple rounds of scheduling called BottleNeck. Simulation results show that Bottleneck performs well at increasing max flow.

Parallel Vision for Long-Tail Regularization: Initial Results From IVFC Autonomous Driving Testing Long-tail effect, characterized by highly frequent occurrence of normal scenarios and the scarce appearance of extreme “long-tail” scenarios, ubiquitously exists in the vision-related problems in the real-world applications. Though many computer vision methods to date have already achieved feasible performance for most of the normal scenarios, it is still challenging for existing vision systems to accurately perceive the long-tail scenarios. This deficiency largely hinders the practical application of computer vision systems, since long-tail problems may incur fatal consequences, such as traffic accidents, taking the vision systems of autonomous vehicles as an example. In this paper, we firstly propose a theoretical framework named Long-tail Regularization (LoTR), for analyzing and tackling the long-tail problems in the vision perception of autonomous driving. LoTR is able to regularize the scarcely occurred long-tail scenarios to be frequently encountered. Then we present a Parallel Vision Actualization System (PVAS), which consists of closed-loop optimization and virtual-real interaction, to search for challenging long-tail scenarios and produce large-scale long-tail driving scenarios for autonomous vehicles. In addition, we introduce how to perform PVAS in Intelligent Vehicle Future Challenge of China (IVFC), the most durable autonomous driving competition around the world. Results over the past decade demonstrate that PVAS can effectively guide the collection of long-tail data to diminish the cost in the real world, and thus promote the capability of vision systems to adapt to complex environments, alleviating the impact of long-tail effect.

Visual Human–Computer Interactions for Intelligent Vehicles and Intelligent Transportation Systems: The State of the Art and Future Directions Research on intelligent vehicles has been popular in the past decade. To fill the gap between automatic approaches and man-machine control systems, it is indispensable to integrate visual human-computer interactions (VHCIs) into intelligent vehicles systems. In this article, we review existing studies on VHCI in intelligent vehicles from three aspects: 1) visual intelligence; 2) decision making; and 3) macro deployment. We discuss how VHCI evolves in intelligent vehicles and how it enhances the capability of intelligent vehicles. We present several simulated scenarios and cases for future intelligent transportation system.

Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation. This article studies semantic segmentation using 3D LiDAR data. Popular deep learning methods applied for this task require a large number of manual annotations to train the parameters. We propose a new method that makes full use of the advantages of traditional methods and deep learning methods via incorporating human domain knowledge into the neural network model to reduce the demand for large n...

Parallel Point Clouds: Hybrid Point Cloud Generation And 3d Model Enhancement Via Virtual-Real Integration Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual-real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

Human-Machine Shared Driving: Challenges and Future Directions Distraction, misjudgement and driving mistakes can significantly affect a driver, resulting in an increased risk of accidents. There are diverse factors that can cause mistakes in driving such as unfamiliarity with the road, situation unawareness, fatigue, stress, and drowsiness. In emerging smart cars, sensing, actuation, advanced signal processing and machine learning are deployed to reduce the impact of driving errors by monitoring the state of the driver in real-time, detecting the mistakes, and deploying necessary actions to counteract them. Such strategies are collectively known as human-machine shared driving. Towards a better understanding of the developments taken place in this domain, as well as identifying gaps and trends in this discipline, a systematic review of the major studies and developments reported in the literature is conducted. The study is based on 155 papers of human-machine shared driving, selected through a thorough and comprehensive search of the literature. The review demonstrates that shared control approaches are mostly dependent on vehicle and environmental data obtained through various sensors. The majority of methods deploy active shared control by leveraging longitudinal and lateral dynamics. However, the precise recognition of driver’s intent and actions, accurate estimation of situation awareness, and modelling the trust between driver and automation are still major challenges preventing timely transition of control from the driver to machine or vice-versa, and resulting in fatal accidents. Major challenges in human-machine shared driving are identified and potential future directions of the work are explored.

Characterizing Driver Intention via Hierarchical Perception-Action Modeling. We seek a mechanism for the classification of the intentional behavior of a cognitive agent, specifically a driver, in terms of a psychological Perception-Action (P-A) model, such that the resulting system would be potentially suitable for use in intelligent driver assistance. P-A models of human intentionality assume that a cognitive agent&#39;s perceptual domain is learned in response to the outcome...

The Exploration of Autonomous Vehicle Driving Styles: Preferred Longitudinal, Lateral, and Vertical Accelerations. This paper describes a new approach in exploring preferred driving styles for autonomous vehicles through simulation of autonomous driving in real road conditions. A Wizard experiment with an equipped car was conducted to investigate the preferences of people with different driving styles, assertive and defensive, for three autonomous vehicle driving styles (defensive, assertive and light rail transit), inducing different acceleration forces, at three different road profiles. Subjective and objective measurements were collected. The results show that the defensive driving style was preferred and there were variations between participants related to their own driving style. The results indicate that the preferences of assertive drivers for the driving style of an autonomous vehicle may not match their own driving style. Yet, users of future autonomous vehicles should be able to indicate and adjust the driving behaviour of an autonomous vehicle to their own preferences in order to maximize comfort in travelling experience.

Federated Learning in Vehicular Networks: Opportunities and Solutions The emerging advances in personal devices and privacy concerns have given the rise to the concept of Federated Learning. Federated Learning proves its effectiveness and privacy preservation through collaborative local training and updating a shared machine learning model while protecting the individual data-sets. This article investigates a new type of vehicular network concept, namely a Federated Vehicular Network (FVN), which can be viewed as a robust distributed vehicular network. Compared to traditional vehicular networks, an FVN has centralized components and utilizes both DSRC and mmWave communication to achieve more scalable and stable performance. As a result, FVN can be used to support data-/computation-intensive applications such as distributed machine learning and Federated Learning. The article first outlines the enabling technologies of FVN. Then, we briefly discuss the high-level architecture of FVN and explain why such an architecture is adequate for Federated Learning. In addition, we use auxiliary Blockchain-based systems to facilitate transactions and mitigate malicious behaviors. Next, we discuss in detail one key component of FVN, a federated vehicular cloud (FVC), that is used for sharing data and models in FVN. In particular, we focus on the routing inside FVCs and present our solutions and preliminary evaluation results. Finally, we point out open problems and future research directions of this disruptive technology.

An Adaptive Dynamic Coding Method for Track Circuit in a High-Speed Railway This article proposes an adaptive dynamic coding method for track circuits in the Chinese Train Control System of high-speed railways. This method will improve the resolution of the train control system in tracking circuits occupied by trains and give the existing automatic block system the basic characteristics of a virtual block and moving block, including block boundary virtual, length changing...

A parallel vision approach to scene-specific pedestrian detection In recent years, with the development of computing power and deep learning algorithms, pedestrian detection has made great progress. Nevertheless, once a detection model trained on generic datasets (such as PASCAL VOC and MS COCO) is applied to a specific scene, its precision is limited by the distribution gap between the generic data and the specific scene data. It is difficult to train the model for a specific scene, due to the lack of labeled data from that scene. Even though we manage to get some labeled data from a specific scene, the changing environmental conditions make the pre-trained model perform bad. In light of these issues, we propose a parallel vision approach to scene-specific pedestrian detection. Given an object detection model, it is trained via two sequential stages: (1) the model is pre-trained on augmented-reality data, to address the lack of scene-specific training data; (2) the pre-trained model is incrementally optimized with newly synthesized data as the specific scene evolves over time. On publicly available datasets, our approach leads to higher precision than the models trained on generic data. To tackle the dynamically changing scene, we further evaluate our approach on the webcam data collected from Church Street Market Place, and the results are also encouraging.

A communication robot in a shopping mall This paper reports our development of a communication robot for use in a shopping mall to provide shopping information, offer route guidance, and build rapport. In the development, the major difficulties included sensing human behaviors, conversation in a noisy daily environment, and the needs of unexpected miscellaneous knowledge in the conversation. We chose a networkrobot system approach, where a single robot's poor sensing capability and knowledge are supplemented by ubiquitous sensors and a human operator. The developed robot system detects a person with floor sensors to initiate interaction, identifies individuals with radio-frequency identification (RFID) tags, gives shopping information while chatting, and provides route guidance with deictic gestures. The robotwas partially teleoperated to avoid the difficulty of speech recognition as well as to furnish a new kind of knowledge that only humans can flexibly provide. The information supplied by a human operator was later used to increase the robot's autonomy. For 25 days in a shopping mall, we conducted a field trial and gathered 2642 interactions. A total of 235 participants signed up to use RFID tags and, later, provided questionnaire responses. The questionnaire results are promising in terms of the visitors' perceived acceptability as well as the encouragement of their shopping activities. The results of the teleoperation analysis revealed that the amount of teleoperation gradually decreased, which is also promising.

Ubiquitous human upper-limb motion estimation using wearable sensors. Human motion capture technologies have been widely used in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health care, navigation, and so on. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors-on-chip, human motion capture using wearable microsensors has become an active research topic. Because of the agility in movement, upper-limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and propose a novel ubiquitous upper-limb motion estimation algorithm, which concentrates on modeling the relationship between upper-arm movement and forearm movement. A link structure with 5 degrees of freedom (DOF) is proposed to model the human upper-limb skeleton structure. Parameters are defined according to Denavit-Hartenberg convention, forward kinematics equations are derived, and an unscented Kalman filter is deployed to estimate the defined parameters. The experimental results have shown that the proposed upper-limb motion capture and analysis algorithm outperforms other fusion methods and provides accurate results in comparison to the BTS optical motion tracker.

Blockchain Based Data Integrity Service Framework for IoT Data It is a challenge to ensure data integrity for cloud-based Internet of Things (IoT) applications because of the inherently dynamic nature of IoT data. The available frameworks of data integrity verification with public auditability cannot avoid the Third Party Auditors (TPAs). However, in a dynamic environment, such as the IoT, the reliability of the TPA-based frameworks is far from being satisfactory. In this paper, we propose a blockchain-based framework for Data Integrity Service. Under such framework, a more reliable data integrity verification can be provided for both the Data Owners and the Data Consumers, without relying on any Third Party Auditor (TPA). In this paper, the relevant protocols and a subsequent prototype system, which is implemented to evaluate the feasibility of our proposals, are presented. The performance evaluation of the implemented prototype system is conducted, and the test results are discussed. The work lays a foundation for our future work on dynamic data integrity verification in a fully decentralized environment.

Object and Background Disentanglement for Unsupervised Cross-Domain Person Re-Identification Person re-identification (re-ID) is an important task in many application fields. While most previous works have conducted feature embedding under the supervision of a prior information, it is also true that data collection and annotation in real-world scenarios are very expensive. Moreover, although researchers have borrowed transferring knowledge to alleviate the dependence on labeling, the bias among various domains remains an open problem. Accordingly, motivated by the observation that reducing the style and background differences between domains can promote the generalization capability of the learning model, this paper proposes an unsupervised model, namely, person component decomposition and synthesis generative adversarial network (PCDS-GAN), to minimize the distribution gap among multiple person re-ID datasets. More specifically, we first disentangle the pedestrian image into foreground, background and style features, then use these features to synthesize person images with various backgrounds from the target domain. Finally, the synthesized images are used to train person re-ID models. Comprehensive experiments demonstrate that our model can effectively reduce the domain gap, and also outperforms state-of-the art methods on the Market-1501 and CUHK03 benchmarks.

Dynamic Authentication with Sensory Information for the Access Control Systems Access card authentication is critical and essential for many modern access control systems, which have been widely deployed in various government, commercial, and residential environments. However, due to the static identification information exchange among the access cards and access control clients, it is very challenging to fight against access control system breaches due to reasons such as loss, stolen or unauthorized duplications of the access cards. Although advanced biometric authentication methods such as fingerprint and iris identification can further identify the user who is requesting authorization, they incur high system costs and access privileges cannot be transferred among trusted users. In this work, we introduce a dynamic authentication with sensory information for the access control systems. By combining sensory information obtained from onboard sensors on the access cards as well as the original encoded identification information, we are able to effectively tackle the problems such as access card loss, stolen, and duplication. Our solution is backward-compatible with existing access control systems and significantly increases the key spaces for authentication. We theoretically demonstrate the potential key space increases with sensory information of different sensors and empirically demonstrate simple rotations can increase key space by more than 1,000,000 times with an authentication accuracy of 90 percent. We performed extensive simulations under various environment settings and implemented our design on WISP to experimentally verify the system performance.

RFID-based techniques for human-activity detection The iBracelet and the Wireless Identification and Sensing Platform promise the ability to infer human activity directly from sensor readings.

Providing DoS resistance for signature-based broadcast authentication in sensor networks Recent studies have demonstrated that it is feasible to perform public key cryptographic operations on resource-constrained sensor platforms. However, the significant energy consumption introduced by public key operations makes any public key-based protocol an easy target of Denial-of-Service (DoS) attacks. For example, if digital signature schemes such as ECDSA are used directly for broadcast authentication without further protection, an attacker can simply broadcast fake messages and force the receiving nodes to perform a huge number of unnecessary signature verifications, eventually exhausting their battery power. This paper shows how to mitigate such DoS attacks when digital signatures are used for broadcast authentication in sensor networks. Specifically, this paper first presents two filtering techniques, the group-based filter and the key chain-based filter, to handle the DoS attacks against signature verification. Both methods can significantly reduce the number of unnecessary signature verifications when a sensor node is under DoS attacks. This paper then combines these two filters and proposes a hybrid solution to further improve the performance.

On Resilience and Connectivity of Secure Wireless Sensor Networks Under Node Capture Attacks. Despite much research on probabilistic key predistribution schemes for wireless sensor networks over the past decade, few formal analyses exist that define schemes’ resilience to node-capture attacks precisely and under realistic conditions. In this paper, we analyze the resilience of the $q$ -composite key predistribution scheme, which mitigates the node capture vulnerability of the Eschenauer-Gligor scheme in the neighbor discovery phase. We derive scheme parameters to have a desired level of resiliency, and obtain optimal parameters that defend against different adversaries as much as possible. We also show that this scheme can be easily enhanced to achieve the same “perfect resilience” property as in the random pairwise key predistribution for attacks launched after neighbor discovery. Despite considerable attention to this scheme, much prior work explicitly or implicitly uses an incorrect computation for the probability of link compromise under node-capture attacks and ignores the real-world transmission constraints of sensor nodes. Moreover, we derive the critical network parameters to ensure connectivity in both the absence and presence of node-capture attacks. We also investigate node replication attacks by analyzing the adversary’s optimal strategy.

Radiation Constrained Wireless Charger Placement Wireless Power Transfer has become a commercially viable technology to charge devices because of the convenience of no power wiring and the reliability of continuous power supply. This paper concerns the fundamental issue of wireless charger placement with electromagnetic radiation (EMR) safety. Although there are a few wireless charging schemes consider EMR safety, none of them addresses the charger placement issue. In this paper, we propose PESA, a wireless charger Placement scheme that guarantees EMR SAfety for every location on the plane. First, we discretize the whole charging area and formulate the problem into the Multidimensional 0/1 Knapsack (MDK) problem. Second, we propose a fast approximation algorithm to the MDK problem. Third, we propose a near optimal scheme to improve speed by double partitioning the area. We prove that the output of our algorithm is better than $(1-\epsilon)$ of the optimal solution to PESA with a smaller EMR threshold $(1-\epsilon /2)R_{t}$ and a larger EMR coverage radius $(1+\epsilon /2)D$ . We conducted both simulations and field experiments to evaluate the performance of our scheme. Our experimental results show that in terms of charging utility, our algorithm outperforms the comparison algorithms.

WiPhone: Smartphone-based Respiration Monitoring Using Ambient Reflected WiFi Signals AbstractRecent years have witnessed a trend of monitoring human respiration using Channel State Information (CSI) retrieved from commodity WiFi devices. Existing approaches essentially leverage signal propagation in a Line-of-Sight (LoS) setting to achieve good performance. However, in real-life environments, LoS can be easily blocked by furniture, home appliances and walls. This paper presents a novel smartphone-based system named WiPhone, aiming to robustly monitor human respiration in NLoS settings. Since a smartphone is usually carried around by one subject, leveraging directly-reflected CSI signals in LoS becomes infeasible. WiPhone exploits ambient reflected CSI signals in a Non-Line-of-Sight (NLoS) setting to quantify the relationship between CSI signals reflected from the environment and a subject's chest displacement. In this way, WiPhone successfully turns ambient reflected signals which have been previously considered "destructive" into beneficial sensing capability. CSI signals obtained from smartphone are usually very noisy and may scatter over different sub-carriers. We propose a density-based preprocessing method to extract useful CSI amplitude patterns for effective respiration monitoring. We conduct extensive experiments with 8 subjects in a real home environment. WiPhone achieves a respiration rate error of 0.31 bpm (breaths per minute) on average in a range of NLoS settings.

Prolonging Sensor Network Lifetime Through Wireless Charging The emerging wireless charging technology is a promising alternative to address the power constraint problem in sensor networks. Comparing to existing approaches, this technology can replenish energy in a more controllable manner and does not require accurate location of or physical alignment to sensor nodes. However, little work has been reported on designing and implementing a wireless charging system for sensor networks. In this paper, we design such a system, build a proof-of-concept prototype, conduct experiments on the prototype to evaluate its feasibility and performance in small-scale networks, and conduct extensive simulations to study its performance in large-scale networks. Experimental and simulation results demonstrate that the proposed system can utilize the wireless charging technology effectively to prolong the network lifetime through delivering energy by a robot to where it is needed. The effects of various configuration and design parameters have also been studied, which may serve as useful guidelines in actual deployment of the proposed system in practice.

A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks Wireless rechargeable sensor networks are widely used in many fields. However, the limited battery capacity of sensor nodes hinders its development. With the help of wireless energy transfer technology, employing a mobile charger to charge sensor nodes wirelessly has become a promising technology for prolonging the lifetime of wireless sensor networks. Considering that the energy consumption rate varies significantly among sensors, we need a better way to model the charging demand of each sensor, such that the sensors are able to be charged multiple times in one charging tour. Therefore, time window is used to represent charging demand. In order to allow the mobile charger to respond to these charging demands in time and transfer more energy to the sensors, we introduce a new metric: charging reward. This new metric enables us to measure the quality of sensor charging. And then, we study the problem of how to schedule the mobile charger to replenish the energy supply of sensors, such that the sum of charging rewards collected by mobile charger on its charging tour is maximized. The sum of the collected charging reward is subject to the energy capacity constraint on the mobile charger and the charging time windows of all sensor nodes. We first prove that this problem is NP-hard. Due to the complexity of the problem, then deep reinforcement learning technique is exploited to obtain the moving path for mobile charger. Finally, experimental simulations are conducted to evaluate the performance of the proposed charging algorithm, and the results show that the proposed scheme is very promising.

Data-aggregation techniques in sensor networks: A survey First Page of the Article

Federated Learning: Challenges, Methods, and Future Directions Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data analysis. In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.

AnnotatEd: A social navigation and annotation service for web-based educational resources Web page annotation and adaptive navigation support are two active, but independent research directions focused on the same goal: expanding the functionality of the Web as a hypertext system. The goal of the AnnotatEd system presented in this paper has been to integrate annotation and adaptive navigation support into a single value-added service where the components can reinforce each other and create new unique attributes. This paper describes the implementation of AnnotatEd from early prototypes to the current version, which has been explored in several contexts. We summarize some lessons we learned during the development process and which defined the current functionality of the system. We also present the results of several classroom studies of the system. These results demonstrate the importance of the browsing-based information access supported by AnnotatEd and the value of both the annotation and navigation support functionalities offered by the system.

Multimodal Feature-Based Surface Material Classification. When a tool is tapped on or dragged over an object surface, vibrations are induced in the tool, which can be captured using acceleration sensors. The tool-surface interaction additionally creates audible sound waves, which can be recorded using microphones. Features extracted from camera images provide additional information about the surfaces. We present an approach for tool-mediated surface clas...

Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic texture remains a challenging problem. In this paper, we show that it is possible to recover textures faithful to semantic classes. In particular, we only need to modulate features of a few intermediate layers in a single network conditioned on semantic segmentation probability maps. This is made possible through a novel Spatial Feature Transform (SFT) layer that generates affine transformation parameters for spatial-wise feature modulation. SFT layers can be trained end-to-end together with the SR network using the same loss function. During testing, it accepts an input image of arbitrary size and generates a high-resolution image with just a single forward pass conditioned on the categorical priors. Our final results show that an SR network equipped with SFT can generate more realistic and visually pleasing textures in comparison to state-of-the-art SRGAN [27] and EnhanceNet [38].

A Muscle Synergy-Driven ANFIS Approach to Predict Continuous Knee Joint Movement Continuous motion prediction plays a significant role in realizing seamless control of robotic exoskeletons and orthoses. Explicitly modeling the relationship between coordinated muscle activations from surface electromyography (sEMG) and human limb movements provides a new path of sEMG-based human–machine interface. Instead of the numeric features from individual channels, we propose a muscle synergy-driven adaptive network-based fuzzy inference system (ANFIS) approach to predict continuous knee joint movements, in which muscle synergy reflects the motor control information to coordinate muscle activations for performing movements. Four human subjects participated in the experiment while walking at five types of speed: 2.0 km/h, 2.5 km/h, 3.0 km/h, 3.5 km/h, and 4.0 km/h. The study finds that the acquired muscle synergies associate the muscle activations with human joint movements in a low-dimensional space and have been further utilized for predicting knee joint angles. The proposed approach outperformed commonly used numeric features from individual sEMG channels with an average correlation coefficient of 0.92 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \pm $</tex-math></inline-formula> 0.05. Results suggest that the correlation between muscle activations and knee joint movements is captured by the muscle synergy-driven ANFIS model and can be utilized for the estimation of continuous joint angles.

An objective visual security assessment for cipher-images based on local entropy In recent years, many practical algorithms have been put forward for images and videos encryption. Security analysis on these encryption algorithms focuses research on cryptographic security, and few work relate to visual security. Visual security means that the encrypted video content is unintelligible to human vision. The higher visual security the encryption algorithm can provide, the less information an attacker from the cipher-images to obtain, the greater the difficulty of attack is. Therefore, visual security assessment for cipher-images is a very important indicator in security evaluation of visual media. So far, systematic research on visual security assessment for cipher-images is far from enough. Moreover, there are no practical objective indicators or evaluation methods on visual security have been proposed at present. According to the changes on image information entropy between cipher-images and original images, we present a visual security assessment algorithm based on local entropy. The experiments result shows that the scheme can provide an efficient objective assessment which is match up to subjective assessment, and is also suitable for security assessment of other selective encryption algorithms.

Probabilistic encryption A new probabilistic model of data encryption is introduced. For this model, under suitable complexity assumptions, it is proved that extracting any information about the cleartext from the cyphertext is hard on the average for an adversary with polynomially bounded computational resources. The proof holds for any message space with any probability distribution. The first implementation of this model is presented. The security of this implementation is proved under the interactability assumptin of deciding Quadratic Residuosity modulo composite numbers whose factorization is unknown.

Secure and privacy preserving keyword searching for cloud storage services Cloud storage services enable users to remotely access data in a cloud anytime and anywhere, using any device, in a pay-as-you-go manner. Moving data into a cloud offers great convenience to users since they do not have to care about the large capital investment in both the deployment and management of the hardware infrastructures. However, allowing a cloud service provider (CSP), whose purpose is mainly for making a profit, to take the custody of sensitive data, raises underlying security and privacy issues. To keep user data confidential against an untrusted CSP, a natural way is to apply cryptographic approaches, by disclosing the data decryption key only to authorized users. However, when a user wants to retrieve files containing certain keywords using a thin client, the adopted encryption system should not only support keyword searching over encrypted data, but also provide high performance. In this paper, we investigate the characteristics of cloud storage services and propose a secure and privacy preserving keyword searching (SPKS) scheme, which allows the CSP to participate in the decipherment, and to return only files containing certain keywords specified by the users, so as to reduce both the computational and communication overhead in decryption for users, on the condition of preserving user data privacy and user querying privacy. Performance analysis shows that the SPKS scheme is applicable to a cloud environment.

CryptCloud<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="ning-ieq1-2791538.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula>: Secure and Expressive Data Access Control for Cloud Storage Secure cloud storage, which is an emerging cloud service, is designed to protect the confidentiality of outsourced data but also to provide flexible data access for cloud users whose data is out of physical control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is regarded as one of the most promising techniques that may be leveraged to secure the guarantee of the service. However, the use of CP-ABE may yield an inevitable security breach which is known as the misuse of access credential (i.e., decryption rights), due to the intrinsic “all-or-nothing” decryption feature of CP-ABE. In this paper, we investigate the two main cases of access credential misuse: one is on the semi-trusted authority side, and the other is on the side of cloud user. To mitigate the misuse, we propose the first accountable authority and revocable CP-ABE based cloud storage system with white-box traceability and auditing, referred to as CryptCloud±. We also present the security analysis and further demonstrate the utility of our system via experiments.

Efficient Encrypted Images Filtering and Transform Coding With Walsh-Hadamard Transform and Parallelization. Since homomorphic encryption operations have high computational complexity, image applications based on homomorphic encryption are often time consuming, which makes them impractical. In this paper, we study efficient encrypted image applications with the encrypted domain Walsh-Hadamard transform (WHT) and parallel algorithms. We first present methods to implement real and complex WHTs in the encry...

Image Feature Extraction in Encrypted Domain With Privacy-Preserving SIFT Privacy has received considerable attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario where the server is resource-abundant, and is capable of finishing the designated tasks. It is envisioned that secure media applications with privacy preservation will be treated seriously. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to target the importance of privacy-preserving SIFT (PPSIFT) and to address the problem of secure SIFT feature extraction and representation in the encrypted domain. As all of the operations in SIFT must be moved to the encrypted domain, we propose a privacy-preserving realization of the SIFT method based on homomorphic encryption. We show through the security analysis based on the discrete logarithm problem and RSA that PPSIFT is secure against ciphertext only attack and known plaintext attack. Experimental results obtained from different case studies demonstrate that the proposed homomorphic encryption-based privacy-preserving SIFT performs comparably to the original SIFT and that our method is useful in SIFT-based privacy-preserving applications.

Distinctive Image Features from Scale-Invariant Keypoints This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

An introduction to ROC analysis Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.

Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions The ever-increasing number of resource-constrained machine-type communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as enhanced mobile broadband (eMBB), massive machine type communications (mMTCs), and ultra-reliable and low latency communications (URLLCs), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include quality of service (QoS) provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead, and radio access network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy random access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and narrowband IoT (NB-IoT). Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions toward addressing RAN congestion problem, and then identify potential advantages, challenges, and use cases for the applications of emerging machine learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning approach in the mMTC scenario along with the recent advances toward enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions.

Priced Oblivious Transfer: How to Sell Digital Goods We consider the question of protecting the privacy of customers buying digital goods. More specifically, our goal is to allow a buyer to purchase digital goods from a vendor without letting the vendor learn what, and to the extent possible also when and how much, it is buying. We propose solutions which allow the buyer, after making an initial deposit, to engage in an unlimited number of priced oblivious-transfer protocols, satisfying the following requirements: As long as the buyer's balance contains sufficient funds, it will successfully retrieve the selected item and its balance will be debited by the item's price. However, the buyer should be unable to retrieve an item whose cost exceeds its remaining balance. The vendor should learn nothing except what must inevitably be learned, namely, the amount of interaction and the initial deposit amount (which imply upper bounds on the quantity and total price of all information obtained by the buyer). In particular, the vendor should be unable to learn what the buyer's current balance is or when it actually runs out of its funds. The technical tools we develop, in the process of solving this problem, seem to be of independent interest. In particular, we present the first one-round (two-pass) protocol for oblivious transfer that does not rely on the random oracle model (a very similar protocol was independently proposed by Naor and Pinkas [21]). This protocol is a special case of a more general "conditional disclosure" methodology, which extends a previous approach from [11] and adapts it to the 2-party setting.

Minimum acceleration criterion with constraints implies bang-bang control as an underlying principle for optimal trajectories of arm reaching movements. Rapid arm-reaching movements serve as an excellent test bed for any theory about trajectory formation. How are these movements planned? A minimum acceleration criterion has been examined in the past, and the solution obtained, based on the Euler-Poisson equation, failed to predict that the hand would begin and end the movement at rest (i.e., with zero acceleration). Therefore, this criterion was rejected in favor of the minimum jerk, which was proved to be successful in describing many features of human movements. This letter follows an alternative approach and solves the minimum acceleration problem with constraints using Pontryagin's minimum principle. We use the minimum principle to obtain minimum acceleration trajectories and use the jerk as a control signal. In order to find a solution that does not include nonphysiological impulse functions, constraints on the maximum and minimum jerk values are assumed. The analytical solution provides a three-phase piecewise constant jerk signal (bang-bang control) where the magnitude of the jerk and the two switching times depend on the magnitude of the maximum and minimum available jerk values. This result fits the observed trajectories of reaching movements and takes into account both the extrinsic coordinates and the muscle limitations in a single framework. The minimum acceleration with constraints principle is discussed as a unifying approach for many observations about the neural control of movements.

Online Prediction of Driver Distraction Based on Brain Activity Patterns This paper presents a new computational framework for early detection of driver distractions (map viewing) using brain activity measured by electroencephalographic (EEG) signals. Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver's needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.

A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

Energy harvesting algorithm considering max flow problem in wireless sensor networks. In Wireless Sensor Networks (WSNs), sensor nodes with poor energy always have bad effect on the data rate or max flow. These nodes are called bottleneck nodes. In this paper, in order to increase the max flow, we assume an energy harvesting WSNs environment to investigate the cooperation of multiple Mobile Chargers (MCs). MCs are mobile robots that use wireless charging technology to charge sensor nodes in WSNs. This means that in energy harvesting WSNs environments, sensor nodes can obtain energy replenishment by using MCs or collecting energy from nature by themselves. In our research, we use MCs to improve the energy of the sensor nodes by performing multiple rounds of unified scheduling, and finally achieve the purpose of increasing the max flow at sinks. Firstly, we model this problem as a Linear Programming (LP) to search the max flow in a round of charging scheduling and prove that the problem is NP-hard. In order to solve the problem, we propose a heuristic approach: deploying MCs in units of paths with the lowest energy node priority. To reduce the energy consumption of MCs and increase the charging efficiency, we also take the optimization of MCs’ moving distance into our consideration. Finally, we extend the method to multiple rounds of scheduling called BottleNeck. Simulation results show that Bottleneck performs well at increasing max flow.

Fault-Tolerant Formation Control for Heterogeneous Vehicles Via Reinforcement Learning This article focuses on the formation control problem of multiple heterogeneous vehicles in air–ground coordination under communication link faults and actuator faults. To address communication link faults among heterogeneous vehicles, a distributed observer is first designed using the air and ground local vehicle information and its convergence is discussed. Using the outputs of the distributed observer and the states of the vehicles, fault-tolerant controller policies are designed for the team of heterogeneous vehicles via reinforcement learning but without knowledge of the vehicle dynamics. Simulation results are included to demonstrate the effectiveness of the proposed fault-tolerant controller.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

MakeltTalk: speaker-aware talking-head animation AbstractWe present a method that generates expressive talking-head videos from a single facial image with audio as the only input. In contrast to previous attempts to learn direct mappings from audio to raw pixels for creating talking faces, our method first disentangles the content and speaker information in the input audio signal. The audio content robustly controls the motion of lips and nearby facial regions, while the speaker information determines the specifics of facial expressions and the rest of the talking-head dynamics. Another key component of our method is the prediction of facial landmarks reflecting the speaker-aware dynamics. Based on this intermediate representation, our method works with many portrait images in a single unified framework, including artistic paintings, sketches, 2D cartoon characters, Japanese mangas, and stylized caricatures. In addition, our method generalizes well for faces and characters that were not observed during training. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating generated talking-heads of significantly higher quality compared to prior state-of-the-art methods.

Space-time super-resolution. We propose a method for constructing a video sequence of high space-time resolution by combining information from multiple low-resolution video sequences of the same dynamic scene. Super-resolution is performed simultaneously in time and in space. By "temporal super-resolution," we mean recovering rapid dynamic events that occur faster than regular frame-rate. Such dynamic events are not visible (or else are observed incorrectly) in any of the input sequences, even if these are played in "slow-motion." The spatial and temporal dimensions are very different in nature, yet are interrelated. This leads to interesting visual trade-offs in time and space and to new video applications. These include: 1) treatment of spatial artifacts (e.g., motion-blur) by increasing the temporal resolution and 2) combination of input sequences of different space-time resolutions (e.g., NTSC, PAL, and even high quality still images) to generate a high quality video sequence. We further analyze and compare characteristics of temporal super-resolution to those of spatial super-resolution. These include: How many video cameras are needed to obtain increased resolution? What is the upper bound on resolution improvement via super-resolution? What is the temporal analogue to the spatial "ringing" effect?

Transient attributes for high-level understanding and editing of outdoor scenes We live in a dynamic visual world where the appearance of scenes changes dramatically from hour to hour or season to season. In this work we study \"transient scene attributes\" -- high level properties which affect scene appearance, such as \"snow\", \"autumn\", \"dusk\", \"fog\". We define 40 transient attributes and use crowdsourcing to annotate thousands of images from 101 webcams. We use this \"transient attribute database\" to train regressors that can predict the presence of attributes in novel images. We demonstrate a photo organization method based on predicted attributes. Finally we propose a high-level image editing method which allows a user to adjust the attributes of a scene, e.g. change a scene to be \"snowy\" or \"sunset\". To support attribute manipulation we introduce a novel appearance transfer technique which is simple and fast yet competitive with the state-of-the-art. We show that we can convincingly modify many transient attributes in outdoor scenes.

Semantic Understanding of Scenes through the ADE20K Dataset. Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. Totally there are 25k images of the complex everyday scenes containing a variety of objects in their natural spatial context. On average there are 19.5 instances and 10.5 object classes per image. Based on ADE20K, we construct benchmarks for scene parsing and instance segmentation. We provide baseline performances on both of the benchmarks and re-implement state-of-the-art models for open source. We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance. We show that the networks trained on ADE20K are able to segment a wide variety of scenes and objects.

Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures. This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also perform text-to-video generation which, to the best of our knowledge, makes it the first approach of its kind. It combines a Variational Autoencoder(VAE) with a Recurrent Attention Mechanism in a novel manner to create a temporally dependent sequence of frames that are gradually formed over time. The recurrent attention mechanism in Sync-DRAW attends to each individual frame of the video in sychronization, while the VAE learns a latent distribution for the entire video at the global level. Our experiments with Bouncing MNIST, KTH and UCF-101 suggest that Sync-DRAW is efficient in learning the spatial and temporal information of the videos and generates frames with high structural integrity, and can generate videos from simple captions on these datasets.

Dynamic Facial Expression Generation on Hilbert Hypersphere With Conditional Wasserstein Generative Adversarial Nets In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded as points on a hypersphere. By proposing a conditional version of manifold-valued Wasserstein generative adversarial network (GAN) for motion generation on the hypersphere, w...

Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images Various combinations of cameras enrich computational photography, among which reference-based superresolution (RefSR) plays a critical role in multiscale imaging systems. However, existing RefSR approaches fail to accomplish high-fidelity super-resolution under a large resolution gap, e.g., 8x upscaling, due to the lower consideration of the underlying scene structure. In this paper, we aim to solve the RefSR problem in actual multiscale camera systems inspired by multiplane image (MPI) representation. Specifically, we propose Cross-MPI, an end-to-end RefSR network composed of a novel plane-aware attention-based MPI mechanism, a multiscale guided upsampling module as well as a super-resolution (SR) synthesis and fusion module. Instead of using a direct and exhaustive matching between the cross-scale stereo, the proposed plane-aware attention mechanism fully utilizes the concealed scene structure for efficient attention-based correspondence searching. Further combined with a gentle coarse-to-fine guided upsampling strategy, the proposed Cross-MPI can achieve a robust and accurate detail transmission. Experimental results on both digitally synthesized and optical zoom cross-scale data show that the Cross-MPI framework can achieve superior performance against the existing RefSR methods and is a real fit for actual multiscale camera systems even with large-scale differences.

End-To-End Time-Lapse Video Synthesis From A Single Outdoor Image Time-lapse videos usually contain visually appealing content but are often difficult and costly to create. In this paper, we present an end-to-end solution to synthesize a time-lapse video from a single outdoor image using deep neural networks. Our key idea is to train a conditional generative adversarial network based on existing datasets of time-lapse videos and image sequences. We propose a multi-frame joint conditional generation framework to effectively learn the correlation between the illumination change of an outdoor scene and the time of the day. We further present a multi-domain training scheme for robust training of our generative models from two datasets with different distributions and missing timestamp labels. Compared to alternative time-lapse video synthesis algorithms, our method uses the timestamp as the control variable and does not require a reference video to guide the synthesis of the final output. We conduct ablation studies to validate our algorithm and compare with state-of-the-art techniques both qualitatively and quantitatively.

Sequence to Sequence Learning with Neural Networks. Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.

A General Equilibrium Model for Industries with Price and Service Competition This paper develops a stochastic general equilibrium inventory model for an oligopoly, in which all inventory constraint parameters are endogenously determined. We propose several systems of demand processes whose distributions are functions of all retailers' prices and all retailers' service levels. We proceed with the investigation of the equilibrium behavior of infinite-horizon models for industries facing this type of generalized competition, under demand uncertainty.We systematically consider the following three competition scenarios. (1) Price competition only: Here, we assume that the firms' service levels are exogenously chosen, but characterize how the price and inventory strategy equilibrium vary with the chosen service levels. (2) Simultaneous price and service-level competition: Here, each of the firms simultaneously chooses a service level and a combined price and inventory strategy. (3) Two-stage competition: The firms make their competitive choices sequentially. In a first stage, all firms simultaneously choose a service level; in a second stage, the firms simultaneously choose a combined pricing and inventory strategy with full knowledge of the service levels selected by all competitors. We show that in all of the above settings a Nash equilibrium of infinite-horizon stationary strategies exists and that it is of a simple structure, provided a Nash equilibrium exists in a so-called reduced game.We pay particular attention to the question of whether a firm can choose its service level on the basis of its own (input) characteristics (i.e., its cost parameters and demand function) only. We also investigate under which of the demand models a firm, under simultaneous competition, responds to a change in the exogenously specified characteristics of the various competitors by either: (i) adjusting its service level and price in the same direction, thereby compensating for price increases (decreases) by offering improved (inferior) service, or (ii) adjusting them in opposite directions, thereby simultaneously offering better or worse prices and service.

Mobile cloud computing: A survey Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. In this paper, we provide an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues. We conclude the paper with a critical analysis of challenges that have not yet been fully met, and highlight directions for future work.

Eye-vergence visual servoing enhancing Lyapunov-stable trackability Visual servoing methods for hand---eye configuration are vulnerable for hand's dynamical oscillation, since nonlinear dynamical effects of whole manipulator stand against the stable tracking ability (trackability). Our proposal to solve this problem is that the controller for visual servoing of the hand and the one for eye-vergence should be separated independently based on decoupling each other, where the trackability is verified by Lyapunov analysis. Then the effectiveness of the decoupled hand and eye-vergence visual servoing method is evaluated through simulations incorporated with actual dynamics of 7-DoF robot with additional 3-DoF for eye-vergence mechanism by amplitude and phase frequency analysis.

An improved E-DRM scheme for mobile environments. With the rapid development of information science and network technology, Internet has become an important platform for the dissemination of digital content, which can be easily copied and distributed through the Internet. Although convenience is increased, it causes significant damage to authors of digital content. Digital rights management system (DRM system) is an access control system that is designed to protect digital content and ensure illegal users from maliciously spreading digital content. Enterprise Digital Rights Management system (E-DRM system) is a DRM system that prevents unauthorized users from stealing the enterprise's confidential data. User authentication is the most important method to ensure digital rights management. In order to verify the validity of user, the biometrics-based authentication protocol is widely used due to the biological characteristics of each user are unique. By using biometric identification, it can ensure the correctness of user identity. In addition, due to the popularity of mobile device and Internet, user can access digital content and network information at anytime and anywhere. Recently, Mishra et al. proposed an anonymous and secure biometric-based enterprise digital rights management system for mobile environment. Although biometrics-based authentication is used to prevent users from being forged, the anonymity of users and the preservation of digital content are not ensured in their proposed system. Therefore, in this paper, we will propose a more efficient and secure biometric-based enterprise digital rights management system with user anonymity for mobile environments.

Intention-detection strategies for upper limb exosuits: model-based myoelectric vs dynamic-based control The cognitive human-robot interaction between an exosuit and its wearer plays a key role in determining both the biomechanical effects of the device on movements and its perceived effectiveness. There is a lack of evidence, however, on the comparative performance of different control methods, implemented on the same device. Here, we compare two different control approaches on the same robotic suit: a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a dynamic-based control that provides support against gravity using an inverse dynamic model. Tested on a cohort of four healthy participants, assistance from the exosuit results in a marked reduction in the effort of muscles working against gravity with both control approaches (peak reduction of 68.6±18.8%, for the dynamic arm model and 62.4±25.1% for the myoprocessor), when compared to an unpowered condition. Neither of the two controllers had an affect on the performance of their users in a joint-angle tracking task (peak errors of 15.4° and 16.4° for the dynamic arm model and myoprocessor, respectively, compared to 13.1o in the unpowered condition). However, our results highlight the remarkable adaptability of the myoprocessor to seamlessly adapt to changing external dynamics.

Training in VR: A Preliminary Study on Learning Assembly/Disassembly Sequences. This paper presents our ongoing work for operators training exploiting an immersive Mixed Reality system. Users, immersed in a Virtual Environment, can be trained in assembling or disassembling complex mechanical machineries. Taking input from current industry-level procedures, the training consists in guided step-by-step operations in order to teach the operators how to assemble, disassemble and maintain a certain machine. In our system the interaction is performed in a natural way: the user can see his own real hands, by means of a 3D camera placed on the HMD, and use them to grab and move the machine pieces in order to perform the training task. We believe that seeing your own hands during manipulative tasks present fundamental advantages over mediated techniques. In this paper we describe the system architecture and present our strategy as well as the results of a pilot test aiming at a preliminary evaluation of the system.

Supporting Precise Manual-handling Task using Visuo-haptic Interaction. Precise manual handling skills are necessary to create art and to paint models. However, these skills are difficult to learn. Some research has approached this issue using mechanical devices. However, mechanical systems have high costs and limit the user's degree-of-freedom. In our research, we propose a system using visuo-haptics to support accurate work without using any mechanical devices. We considered the principle that when a visuo-haptic force is generated on a user's hand in the opposite direction of a target path, the user moves her/his hand to the right direction reflexively to repel the force. Based on this idea, we created a system that can modify users' hand movement by showing a dummy hand using a mixed reality display, which supports precise manual-handling tasks. To demonstrate this, we performed experiments conducted with a video see-through system that uses a head mounted display (HMD). The results showed that an expansion of the deviation between the target route and the actual hand position improved accuracy up to 50%. We also saw a tendency for a lager expansion to give the most improvement in quality, but slow down working speed at the same time. According to experimental results, we find that a gain of about 2.5 gives an ideal balance between the working precision and the drawing speed.

A Case Study on Virtual Reality American Football Training. We present a study of American football training through the use of virtual reality. We developed a proprietary training software SIDEKIQ designed for professional training of student athletes in an immersive virtual reality environment, where trainees experience the football gameplays created by their coaches on desktop PCs, Oculus Rift or even CAVE-like facility. A user evaluation was conducted to quantify the effectiveness of the VR training over a 3-day training session. The result showed on average 30% overall improvement in the scores collected from the assessment.

Educational virtual environments: A ten-year review of empirical research (1999-2009) This study is a ten-year critical review of empirical research on the educational applications of Virtual Reality (VR). Results show that although the majority of the 53 reviewed articles refer to science and mathematics, researchers from social sciences also seem to appreciate the educational value of VR and incorporate their learning goals in Educational Virtual Environments (EVEs). Although VR supports multisensory interaction channels, visual representations predominate. Few are the studies that incorporate intuitive interactivity, indicating a research trend in this direction. Few are the settings that use immersive EVEs reporting positive results on users' attitudes and learning outcomes, indicating that there is a need for further research on the capabilities of such systems. Features of VR that contribute to learning such as first order experiences, natural semantics, size, transduction, reification, autonomy and presence are exploited according to the educational context and content. Presence seems to play an important role in learning and it is a subject needing further and intensive studies. Constructivism seems to be the theoretical model the majority of the EVEs are based on. The studies present real world, authentic tasks that enable context and content dependent knowledge construction. They also provide multiple representations of reality by representing the natural complexity of the world. Findings show that collaboration and social negotiation are not only limited to the participants of an EVE, but exist between participants and avatars, offering a new dimension to computer assisted learning. Little can yet be concluded regarding the retention of the knowledge acquired in EVEs. Longitudinal studies are necessary, and we believe that the main outcome of this study is the future research perspectives it brings to light.

A Model for Understanding How Virtual Reality Aids Complex Conceptual Learning Designers and evaluators of immersive virtual reality systems have many ideas concerning how virtual reality can facilitate learning. However, we have little information concerning which of virtual reality's features provide the most leverage for enhancing understanding or how to customize those affordances for different learning environments. In part, this reflects the truly complex nature of learning. Features of a learning environment do not act in isolation; other factors such as the concepts or skills to be learned, individual characteristics, the learning experience, and the interaction experience all play a role in shaping the learning process and its outcomes. Through Project Science Space, we have been trying to identify, use, and evaluate immersive virtual reality's affordances as a means to facilitate the mastery of complex, abstract concepts. In doing so, we are beginning to understand the interplay between virtual reality's features and other important factors in shaping the learning process and learning outcomes for this type of material. In this paper, we present a general model that describes how we think these factors work together and discuss some of the lessons we are learning about virtual reality's affordances in the context of this model for complex conceptual learning.

Labels and event processes in the Asbestos operating system Asbestos, a new operating system, provides novel labeling and isolation mechanisms that help contain the effects of exploitable software flaws. Applications can express a wide range of policies with Asbestos's kernel-enforced labels, including controls on interprocess communication and system-wide information flow. A new event process abstraction defines lightweight, isolated contexts within a single process, allowing one process to act on behalf of multiple users while preventing it from leaking any single user's data to others. A Web server demonstration application uses these primitives to isolate private user data. Since the untrusted workers that respond to client requests are constrained by labels, exploited workers cannot directly expose user data except as allowed by application policy. The server application requires 1.4 memory pages per user for up to 145,000 users and achieves connection rates similar to Apache, demonstrating that additional security can come at an acceptable cost.

Probabilistic encryption A new probabilistic model of data encryption is introduced. For this model, under suitable complexity assumptions, it is proved that extracting any information about the cleartext from the cyphertext is hard on the average for an adversary with polynomially bounded computational resources. The proof holds for any message space with any probability distribution. The first implementation of this model is presented. The security of this implementation is proved under the interactability assumptin of deciding Quadratic Residuosity modulo composite numbers whose factorization is unknown.

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions The ever-increasing number of resource-constrained machine-type communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as enhanced mobile broadband (eMBB), massive machine type communications (mMTCs), and ultra-reliable and low latency communications (URLLCs), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include quality of service (QoS) provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead, and radio access network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy random access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and narrowband IoT (NB-IoT). Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions toward addressing RAN congestion problem, and then identify potential advantages, challenges, and use cases for the applications of emerging machine learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning approach in the mMTC scenario along with the recent advances toward enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions.

Scalable and efficient provable data possession. Storage outsourcing is a rising trend which prompts a number of interesting security issues, many of which have been extensively investigated in the past. However, Provable Data Possession (PDP) is a topic that has only recently appeared in the research literature. The main issue is how to frequently, efficiently and securely verify that a storage server is faithfully storing its client's (potentially very large) outsourced data. The storage server is assumed to be untrusted in terms of both security and reliability. (In other words, it might maliciously or accidentally erase hosted data; it might also relegate it to slow or off-line storage.) The problem is exacerbated by the client being a small computing device with limited resources. Prior work has addressed this problem using either public key cryptography or requiring the client to outsource its data in encrypted form. In this paper, we construct a highly efficient and provably secure PDP technique based entirely on symmetric key cryptography, while not requiring any bulk encryption. Also, in contrast with its predecessors, our PDP technique allows outsourcing of dynamic data, i.e, it efficiently supports operations, such as block modification, deletion and append.

Empirical Modelling of Genetic Algorithms This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a graeco-latin square permits an initial study of a wide range of parameter settings. This is followed by fully crossed factorial experiments with narrower ranges, which allow detailed analysis by logistic regression. The empirical models derived can be used to determine optimal algorithm parameters and to shed light on interactions between the parameters and their relative importance. Re-fined models are produced, which are shown to be robust under extrapolation to up to triple the problem size.

Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks Because of the complicity of consensus control of nonlinear multiagent systems in state time-delay, most of previous works focused only on linear systems with input time-delay. An adaptive neural network (NN) consensus control method for a class of nonlinear multiagent systems with state time-delay is proposed in this paper. The approximation property of radial basis function neural networks (RBFNNs) is used to neutralize the uncertain nonlinear dynamics in agents. An appropriate Lyapunov-Krasovskii functional, which is obtained from the derivative of an appropriate Lyapunov function, is used to compensate the uncertainties of unknown time delays. It is proved that our proposed approach guarantees the convergence on the basis of Lyapunov stability theory. The simulation results of a nonlinear multiagent time-delay system and a multiple collaborative manipulators system show the effectiveness of the proposed consensus control algorithm.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Survey on traffic prediction in smart cities. The rapid development in machine learning and in the emergence of new data sources makes it possible to examine and predict traffic conditions in smart cities more accurately than ever. This can help to optimize the design and management of transport services in a future automated city. In this paper, we provide a detailed presentation of the traffic prediction methods for such intelligent cities, also giving an overview of the existing data sources and prediction models.

Network-wide Crowd Flow Prediction of Sydney Trains via Customized Online Non-negative Matrix Factorization. Crowd Flow Prediction (CFP) is one major challenge in the intelligent transportation systems of the Sydney Trains Network. However, most advanced CFP methods only focus on entrance and exit flows at the major stations or a few subway lines, neglecting Crowd Flow Distribution (CFD) forecasting problem across the entire city network. CFD prediction plays an irreplaceable role in metro management as a tool that can help authorities plan route schedules and avoid congestion. In this paper, we propose three online non-negative matrix factorization (ONMF) models. ONMF-AO incorporates an Average Optimization strategy that adapts to stable passenger flows. ONMF-MR captures the Most Recent trends to achieve better performance when sudden changes in crowd flow occur. The Hybrid model, ONMF-H, integrates both ONMF-AO and ONMF-MR to exploit the strengths of each model in different scenarios and enhance the models' applicability to real-world situations. Given a series of CFD snapshots, both models learn the latent attributes of the train stations and, therefore, are able to capture transition patterns from one timestamp to the next by combining historic guidance. Intensive experiments on a large-scale, real-world dataset containing transactional data demonstrate the superiority of our ONMF models.

Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling Ride-hailing applications are becoming more and more popular for providing drivers and passengers with convenient ride services, especially in metropolises like Beijing or New York. To obtain the passengers' mobility patterns, the online platforms of ride services need to predict the number of passenger demands from one region to another in advance. We formulate this problem as an Origin-Destination Matrix Prediction (ODMP) problem. Though this problem is essential to large-scale providers of ride services for helping them make decisions and some providers have already put it forward in public, existing studies have not solved this problem well. One of the main reasons is that the ODMP problem is more challenging than the common demand prediction. Besides the number of demands in a region, it also requires the model to predict the destinations of them. In addition, data sparsity is a severe issue. To solve the problem effectively, we propose a unified model, Grid-Embedding based Multi-task Learning (GEML) which consists of two components focusing on spatial and temporal information respectively. The Grid-Embedding part is designed to model the spatial mobility patterns of passengers and neighboring relationships of different areas, the pre-weighted aggregator of which aims to sense the sparsity and range of data. The Multi-task Learning framework focuses on modeling temporal attributes and capturing several objectives of the ODMP problem. The evaluation of our model is conducted on real operational datasets from UCAR and Didi. The experimental results demonstrate the superiority of our GEML against the state-of-the-art approaches.

Regression conformal prediction with random forests Regression conformal prediction produces prediction intervals that are valid, i.e., the probability of excluding the correct target value is bounded by a predefined confidence level. The most important criterion when comparing conformal regressors is efficiency; the prediction intervals should be as tight (informative) as possible. In this study, the use of random forests as the underlying model for regression conformal prediction is investigated and compared to existing state-of-the-art techniques, which are based on neural networks and k-nearest neighbors. In addition to their robust predictive performance, random forests allow for determining the size of the prediction intervals by using out-of-bag estimates instead of requiring a separate calibration set. An extensive empirical investigation, using 33 publicly available data sets, was undertaken to compare the use of random forests to existing state-of-the-art conformal predictors. The results show that the suggested approach, on almost all confidence levels and using both standard and normalized nonconformity functions, produced significantly more efficient conformal predictors than the existing alternatives.

Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction Predicting urban flow is essential for city risk assessment and traffic management, which profoundly impacts people's lives and property. Recently, some deep learning models, focusing on capturing spatio-temporal (ST) correlations between urban regions, have been proposed to predict urban flows. However, these models overlook latent region functions that impact ST correlations greatly. Thus, it is necessary to have a framework to assist these deep models in tackling the region function issue. However, it is very challenging because of two problems: 1) how to make deep models predict flows taking into consideration latent region functions; 2) how to make the framework generalize to a variety of deep models. To tackle these challenges, we propose a novel framework that employs matrix factorization for spatio-temporal neural networks (MF-STN), capable of enhancing the state-of-the-art deep ST models. MF-STN consists of two components: 1) a ST feature learner, which obtains features of ST correlations from all regions by the corresponding sub-networks in the existing deep models; and 2) a region-specific predictor, which leverages the learned ST features to make region-specific predictions. In particular, matrix factorization is employed on the neural networks, namely, decomposing the region-specific parameters of the predictor into learnable matrices, i.e., region embedding matrices and parameter embedding matrices, to model latent region functions and correlations among regions. Extensive experiments were conducted on two real-world datasets, illustrating that MF-STN can significantly improve the performance of some representative ST models while preserving model complexity.

DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events Event crowd management has been a significant research topic with high social impact. When some big events happen such as an earthquake, typhoon, and national festival, crowd management becomes the first priority for governments (e.g. police) and public service operators (e.g. subway/bus operator) to protect people's safety or maintain the operation of public infrastructures. However, under such event situations, human behavior will become very different from daily routines, which makes prediction of crowd dynamics at big events become highly challenging, especially at a citywide level. Therefore in this study, we aim to extract the deep trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations. Motivated by these, we build an online system called DeepUrbanEvent which can iteratively take citywide crowd dynamics from the current one hour as input and report the prediction results for the next one hour as output. A novel deep learning architecture built with recurrent neural networks is designed to effectively model these highly-complex sequential data in an analogous manner to video prediction tasks. Experimental results demonstrate the superior performance of our proposed methodology to the existing approaches. Lastly, we apply our prototype system to multiple big real-world events and show that it is highly deployable as an online crowd management system.

GSTNet - Global Spatial-Temporal Network for Traffic Flow Prediction.

Graph WaveNet for Deep Spatial-Temporal Graph Modeling. Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph structure (relation) does not necessarily reflect the true dependency and genuine relation may be missing due to the incomplete connections in the data. Furthermore, existing methods are ineffective to capture the temporal trends as the RNNs or CNNs employed in these methods cannot capture long-range temporal sequences. To overcome these limitations, we propose in this paper a novel graph neural network architecture, Graph WaveNet, for spatial-temporal graph modeling. By developing a novel adaptive dependency matrix and learn it through node embedding, our model can precisely capture the hidden spatial dependency in the data. With a stacked dilated 1D convolution component whose receptive field grows exponentially as the number of layers increases, Graph WaveNet is able to handle very long sequences. These two components are integrated seamlessly in a unified framework and the whole framework is learned in an end-to-end manner. Experimental results on two public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm.

Footprints: history-rich tools for information foraging Inspired by Hill and Hollans original work [7], we have beendeveloping a theory of interaction history and building tools toapply this theory to navigation in a complex information space. Wehave built a series of tools - map, paths, annota- tions andsignposts - based on a physical-world navigation metaphor. Thesetools have been in use for over a year. Our user study involved acontrolled browse task and showed that users were able to get thesame amount of work done with significantly less effort.

Poisson Arrivals See Time Averages In many stochastic models, particularly in queueing theory, Poisson arrivals both observe see a stochastic process and interact with it. In particular cases and/or under restrictive assumptions it ...

Tour Planning for Mobile Data-Gathering Mechanisms in Wireless Sensor Networks In this paper, we propose a new data-gathering mechanism for large-scale wireless sensor networks by introducing mobility into the network. A mobile data collector, for convenience called an M-collector in this paper, could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, working like a mobile base station and gathering data while moving through the field. An M-collector starts the data-gathering tour periodically from the static data sink, polls each sensor while traversing its transmission range, then directly collects data from the sensor in single-hop communications, and finally transports the data to the static sink. Since data packets are directly gathered without relays and collisions, the lifetime of sensors is expected to be prolonged. In this paper, we mainly focus on the problem of minimizing the length of each data-gathering tour and refer to this as the single-hop data-gathering problem (SHDGP). We first formalize the SHDGP into a mixed-integer program and then present a heuristic tour-planning algorithm for the case where a single M-collector is employed. For the applications with strict distance/time constraints, we consider utilizing multiple M-collectors and propose a data-gathering algorithm where multiple M-collectors traverse through several shorter subtours concurrently to satisfy the distance/time constraints. Our single-hop mobile data-gathering scheme can improve the scalability and balance the energy consumption among sensors. It can be used in both connected and disconnected networks. Simulation results demonstrate that the proposed data-gathering algorithm can greatly shorten the moving distance of the collectors compared with the covering line approximation algorithm and is close to the optimal algorithm for small networks. In addition, the proposed data-gathering scheme can significantly prolong the network lifetime compared with a network with static data sink or a network in which the mobile collector c- n only move along straight lines.

Addictive links: engaging students through adaptive navigation support and open social student modeling Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. Over the last 8 years we have explored a lesser known effect of adaptive annotation -- its ability to significantly increase student engagement in working with non-mandatory educational content. In the presence of adaptive link annotation, students tend to access significantly more learning content; they stay with it longer, return to it more often and explore a wider variety of learning resources. This talk will present an overview of our exploration of the addictive links effect in many course-long studies, which we ran in several domains (C, SQL and Java programming), for several types of learning content (quizzes, problems, interactive examples). The first part of the talk will review our exploration of a more traditional knowledge-based personalization approach and the second part will focus on more recent studies of social navigation and open social student modeling.

AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction. •Treating protein structure prediction (PSP) problem as a multi-objective optimization problem is designed.•A multi-objective evolutionary algorithm which reuses past search experience to enhance search capacity is proposed.•A novel method to measure the similarity between two proteins’ conformations in genotype space is introduced.•A complete test on 25 proteins is carried out to verify the effectiveness of reusing strategy.

Hardware Circuits Design and Performance Evaluation of a Soft Lower Limb Exoskeleton Soft lower limb exoskeletons (LLEs) are wearable devices that have good potential in walking rehabilitation and augmentation. While a few studies focused on the structure design and assistance force optimization of the soft LLEs, rarely work has been conducted on the hardware circuits design. The main purpose of this work is to present a new soft LLE for walking efficiency improvement and introduce its hardware circuits design. A soft LLE for hip flexion assistance and a hardware circuits system with scalability were proposed. To assess the efficacy of the soft LLE, the experimental tests that evaluate the sensor data acquisition, force tracking performance, lower limb muscle activity and metabolic cost were conducted. The time error in the peak assistance force was just 1%. The reduction in the normalized root-mean-square EMG of the rectus femoris was 7.1%. The net metabolic cost in exoskeleton on condition was reduced by 7.8% relative to walking with no exoskeleton. The results show that the designed hardware circuits can be applied to the soft LLE and the soft LLE is able to improve walking efficiency of wearers.

iMOS: Enabling VoIP QoS Monitoring at Intermediate Nodes in an OpenFlow SDN The growing popularity of outsourced enterprise VoIP services poses a significant quality assurance issue for service providers. VoIP traffic is very sensitive to network impairments and maintaining high QoS across multiple domains can be challenging. We propose to use SDN and our implementation of intermediate VoIP call quality measurement to provide an advanced VoIP monitoring service. Our solution can automatically detect and locate quality issues for VoIP traffic.

A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification The identification of network applications through observation of associated packet traffic flows is vital to the areas of network management and surveillance. Currently popular methods such as port number and payload-based identification exhibit a number of shortfalls. An alternative is to use machine learning (ML) techniques and identify network applications based on per-flow statistics, derived from payload-independent features such as packet length and inter-arrival time distributions. The performance impact of feature set reduction, using Consistency-based and Correlation-based feature selection, is demonstrated on Naïve Bayes, C4.5, Bayesian Network and Naïve Bayes Tree algorithms. We then show that it is useful to differentiate algorithms based on computational performance rather than classification accuracy alone, as although classification accuracy between the algorithms is similar, computational performance can differ significantly.

A Support Vector Machine Based Approach for Forecasting of Network Weather Services We present forecasting related results using a recently introduced technique called Support Vector Machines (SVM) for measurements of processing, memory, disk space, communication latency and bandwidth derived from Network Weather Services (NWS). We then compare the performance of support vector machines with the forecasting techniques existing in network weather services using a set of metrics like mean absolute error, mean square error among others. The models are used to make predictions for several future time steps as against the present network weather services method of just the immediate future time step. The number of future time steps for which the prediction is done is referred to as the depth of prediction set. The support vector machines forecasts are found to be more accurate and outperform the existing methods. The performance improvement using support vector machines becomes more pronounced as the depth of the prediction set increases. The data gathered is from a production environment (i.e., non-experimental).

End-to-end quality adaptation scheme based on QoE prediction for video streaming service in LTE networks How to measure the user's feeling about mobile video service and to improve the quality of experience (QoE), has become a concern of network operators and service providers. In this paper, we first investigate the QoE evaluation method for video streaming over Long-Term Evolution (LTE) networks, and propose an end-to-end video quality prediction model based on the gradient boosting machine. In the proposed QoE prediction model, cross-layer parameters extracted from the network layer, the application layer, video content and user equipment are taken into account. Validation results show that our proposed model outperforms ITU-T G.1070 model with a smaller root mean squared error and a higher Pearson correlation coefficient. Second, a window-based bit rate adaptation scheme, which is implemented in the video streaming server, is proposed to improve the quality of video streaming service in LTE networks. In the proposed scheme, the encoding bit rate is adjusted according to two control parameters, the value of predicted QoE and the feedback congestion state of the network. Simulation results show that our proposed end-to-end quality adaptation scheme efficiently improves user-perceived quality compared to the scenarios with fixed bit rates.

Estimating Video Streaming QoE in the 5G Architecture Using Machine Learning Compared to earlier mobile network generations, the 5G system architecture has been significantly enhanced by the introduction of network analytics functionalities and ex- tended capabilities of interacting with third party Application Functions (AFs). Combining these capabilities, new features for Quality of Experience (QoE) estimation can be designed and introduced in next generation networks. It is, however, unclear how 5G networks can collect monitoring data and application metrics, how they correlate to each other, and which techniques can be used in 5G systems for QoE estimation. This paper studies the feasibility of Machine Learning (ML) techniques for QoE estimation and evaluates their performance for a mobile video streaming use-case. A simulator has been implemented with OMNeT++ for generating traces to (i) examine the relevance of features generated from 5G monitoring data and (ii) to study the QoE estimation accuracy (iii) for a variable number of used features.

Distinctive Image Features from Scale-Invariant Keypoints This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.

A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot Central idea is to avoid or slow down full convergence through the dynamic variation of parameters.Performance of different ACO variants was observed to choose one as the basis to the proposed approach.Convergence fuzzy controller with the objective of maintaining diversity to avoid premature convergence was created. Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.

Online Palmprint Identification Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.

Communication in reactive multiagent robotic systems Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and effectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three different types of communication for three different tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can significantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as effective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.

Efficient and reliable low-power backscatter networks There is a long-standing vision of embedding backscatter nodes like RFIDs into everyday objects to build ultra-low power ubiquitous networks. A major problem that has challenged this vision is that backscatter communication is neither reliable nor efficient. Backscatter nodes cannot sense each other, and hence tend to suffer from colliding transmissions. Further, they are ineffective at adapting the bit rate to channel conditions, and thus miss opportunities to increase throughput, or transmit above capacity causing errors. This paper introduces a new approach to backscatter communication. The key idea is to treat all nodes as if they were a single virtual sender. One can then view collisions as a code across the bits transmitted by the nodes. By ensuring only a few nodes collide at any time, we make collisions act as a sparse code and decode them using a new customized compressive sensing algorithm. Further, we can make these collisions act as a rateless code to automatically adapt the bit rate to channel quality --i.e., nodes can keep colliding until the base station has collected enough collisions to decode. Results from a network of backscatter nodes communicating with a USRP backscatter base station demonstrate that the new design produces a 3.5× throughput gain, and due to its rateless code, reduces message loss rate in challenging scenarios from 50% to zero.

Internet of Things for Smart Cities The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

Collective feature selection to identify crucial epistatic variants. In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

Ethical Considerations Of Applying Robots In Kindergarten Settings: Towards An Approach From A Macroperspective In child-robot interaction (cHRI) research, many studies pursue the goal to develop interactive systems that can be applied in everyday settings. For early education, increasingly, the setting of a kindergarten is targeted. However, when cHRI and research are brought into a kindergarten, a range of ethical and related procedural aspects have to be considered and dealt with. While ethical models elaborated within other human-robot interaction settings, e.g., assisted living contexts, can provide some important indicators for relevant issues, we argue that it is important to start developing a systematic approach to identify and tackle those ethical issues which rise with cHRI in kindergarten settings on a more global level and address the impact of the technology from a macroperspective beyond the effects on the individual. Based on our experience in conducting studies with children in general and pedagogical considerations on the role of the institution of kindergarten in specific, in this paper, we enfold some relevant aspects that have barely been addressed in an explicit way in current cHRI research. Four areas are analyzed and key ethical issues are identified in each area: (1) the institutional setting of a kindergarten, (2) children as a vulnerable group, (3) the caregivers' role, and (4) pedagogical concepts. With our considerations, we aim at (i) broadening the methodology of the current studies within the area of cHRI, (ii) revalidate it based on our comprehensive empirical experience with research in kindergarten settings, both laboratory and real-world contexts, and (iii) provide a framework for the development of a more systematic approach to address the ethical issues in cHRI research within kindergarten settings.

Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends Transportation systems operate in a domain that is anything but simple. Many exhibit both spatial and temporal characteristics, at varying scales, under varying conditions brought on by external sources such as social events, holidays, and the weather. Yet, modeling the interplay of factors, devising generalized representations, and subsequently using them to solve a particular problem can be a challenging task. These situations represent only a fraction of the difficulties faced by modern intelligent transportation systems (ITS). In this paper, we present a survey that highlights the role modeling techniques within the realm of deep learning have played within ITS. We focus on how practitioners have formulated problems to address these various challenges, and outline both architectural and problem-specific considerations used to develop solutions. We hope this survey can help to serve as a bridge between the machine learning and transportation communities, shedding light on new domains and considerations in the future.

A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems. Bus service is the most important function of public transportation. Besides the major goal of carrying passengers around, providing a comfortable travel experience for passengers is also a key business consideration. To provide a comfortable travel experience, effective bus scheduling is essential. Traditional approaches are based on fixed timetables. The wide adoptions of smart card fare collect...

Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction Due to the widespread applications in real-world scenarios, metro ridership prediction is a crucial but challenging task in intelligent transportation systems. However, conventional methods either ignore the topological information of metro systems or directly learn on physical topology, and cannot fully explore the patterns of ridership evolution. To address this problem, we model a metro system ...

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.

Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precision. Therefore, we propose a deep learning architecture combining the residual network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) (called “ResLSTM”) to forecast short-term passenger fl...

Data-Driven Intelligent Transportation Systems: A Survey For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

On the security of public key protocols Recently the use of public key encryption to provide secure network communication has received considerable attention. Such public key systems are usually effective against passive eavesdroppers, who merely tap the lines and try to decipher the message. It has been pointed out, however, that an improperly designed protocol could be vulnerable to an active saboteur, one who may impersonate another user or alter the message being transmitted. Several models are formulated in which the security of protocols can be discussed precisely. Algorithms and characterizations that can be used to determine protocol security in these models are given.

ImageNet Classification with Deep Convolutional Neural Networks. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

Markov games as a framework for multi-agent reinforcement learning In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsis-tic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic.

Scalable and efficient provable data possession. Storage outsourcing is a rising trend which prompts a number of interesting security issues, many of which have been extensively investigated in the past. However, Provable Data Possession (PDP) is a topic that has only recently appeared in the research literature. The main issue is how to frequently, efficiently and securely verify that a storage server is faithfully storing its client's (potentially very large) outsourced data. The storage server is assumed to be untrusted in terms of both security and reliability. (In other words, it might maliciously or accidentally erase hosted data; it might also relegate it to slow or off-line storage.) The problem is exacerbated by the client being a small computing device with limited resources. Prior work has addressed this problem using either public key cryptography or requiring the client to outsource its data in encrypted form. In this paper, we construct a highly efficient and provably secure PDP technique based entirely on symmetric key cryptography, while not requiring any bulk encryption. Also, in contrast with its predecessors, our PDP technique allows outsourcing of dynamic data, i.e, it efficiently supports operations, such as block modification, deletion and append.

On controller initialization in multivariable switching systems We consider a class of switched systems which consists of a linear MIMO and possibly unstable process in feedback interconnection with a multicontroller whose dynamics switch. It is shown how one can achieve significantly better transient performance by selecting the initial condition for every controller when it is inserted into the feedback loop. This initialization is obtained by performing the minimization of a quadratic cost function of the tracking error, controlled output, and control signal. We guarantee input-to-state stability of the closed-loop system when the average number of switches per unit of time is smaller than a specific value. If this is not the case then stability can still be achieved by adding a mild constraint to the optimization. We illustrate the use of our results in the control of a flexible beam actuated in torque. This system is unstable with two poles at the origin and contains several lightly damped modes, which can be easily excited by controller switching.

A competitive swarm optimizer for large scale optimization. In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position of each particle nor the global best position (or neighborhood best positions) is involved in updating the particles. Instead, a pairwise competition mechanism is introduced, where the particle that loses the competition will update its position by learning from the winner. To understand the search behavior of the proposed CSO, a theoretical proof of convergence is provided, together with empirical analysis of its exploration and exploitation abilities showing that the proposed CSO achieves a good balance between exploration and exploitation. Despite its algorithmic simplicity, our empirical results demonstrate that the proposed CSO exhibits a better overall performance than five state-of-the-art metaheuristic algorithms on a set of widely used large scale optimization problems and is able to effectively solve problems of dimensionality up to 5000.

A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

A Selective Biogeography-Based Optimizer Considering Resource Allocation for Large-Scale Global Optimization AbstractBiogeography-based optimization (BBO), a recent proposed metaheuristic algorithm, has been successfully applied to many optimization problems due to its simplicity and efficiency. However, BBO is sensitive to the curse of dimensionality; its performance degrades rapidly as the dimensionality of the search space increases. In this paper, a selective migration operator is proposed to scale up the performance of BBO and we name it selective BBO (SBBO). The differential migration operator is selected heuristically to explore the global area as far as possible whist the normal distributed migration operator is chosen to exploit the local area. By the means of heuristic selection, an appropriate migration operator can be used to search the global optimum efficiently. Moreover, the strategy of cooperative coevolution (CC) is adopted to solve large-scale global optimization problems (LSOPs). To deal with subgroup imbalance contribution to the whole solution in the context of CC, a more efficient computing resource allocation is proposed. Extensive experiments are conducted on the CEC 2010 benchmark suite for large-scale global optimization, and the results show the effectiveness and efficiency of SBBO compared with BBO variants and other representative algorithms for LSOPs. Also, the results confirm that the proposed computing resource allocation is vital to the large-scale optimization within the limited computation budget.

Multiobjective Optimization Models for Locating Vehicle Inspection Stations Subject to Stochastic Demand, Varying Velocity and Regional Constraints Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.

Intrinsic dimension estimation: Advances and open problems. •The paper reviews state-of-the-art of the methods of Intrinsic Dimension (ID) Estimation.•The paper defines the properties that an ideal ID estimator should have.•The paper reviews, under the above mentioned framework, the major ID estimation methods underlining their advances and the open problems.

Alignment-Supervised Bidimensional Attention-Based Recursive Autoencoders for Bilingual Phrase Representation. Exploiting semantic interactions between the source and target linguistic items at different levels of granularity is crucial for generating compact vector representations for bilingual phrases. To achieve this, we propose alignment-supervised bidimensional attention-based recursive autoencoders (ABattRAE) in this paper. ABattRAE first individually employs two recursive autoencoders to recover hierarchical tree structures of bilingual phrase, and treats the subphrase covered by each node on the tree as a linguistic item. Unlike previous methods, ABattRAE introduces a bidimensional attention network to measure the semantic matching degree between linguistic items of different languages, which enables our model to integrate information from all nodes by dynamically assigning varying weights to their corresponding embeddings. To ensure the accuracy of the generated attention weights in the attention network, ABattRAE incorporates word alignments as supervision signals to guide the learning procedure. Using the general stochastic gradient descent algorithm, we train our model in an end-to-end fashion, where the semantic similarity of translation equivalents is maximized while the semantic similarity of nontranslation pairs is minimized. Finally, we incorporate a semantic feature based on the learned bilingual phrase representations into a machine translation system for better translation selection. Experimental results on NIST Chinese–English and WMT English–German test sets show that our model achieves substantial improvements of up to 2.86 and 1.09 BLEU points over the baseline, respectively. Extensive in-depth analyses demonstrate the superiority of our model in learning bilingual phrase embeddings.

Surrogate-Assisted Evolutionary Framework for Data-Driven Dynamic Optimization Recently, dynamic optimization has received much attention from the swarm and evolutionary computation community. However, few studies have investigated data-driven evolutionary dynamic optimization, and most algorithms for evolutionary dynamic optimization are based on analytical mathematical functions. In this paper, we investigate data-driven evolutionary dynamic optimization. First, we develop a surrogate-assisted evolutionary framework for solving data-driven dynamic optimization problems (DD-DOPs). Second, we employ a benchmark based on the typical dynamic optimization problems set in order to verify the performance of the proposed framework. The experimental results demonstrate that the proposed framework is effective for solving DD-DOPs.

Biobjective Task Scheduling for Distributed Green Data Centers The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms. Note to Practitioners-This work aims to maximize the profit and minimize the task loss for DGDCs powered by renewable energy and smart grid by jointly determining the split of tasks among multiple ISPs. Existing task scheduling algorithms fail to jointly consider and optimize the profit of DGDC providers and QoS of tasks. Therefore, they fail to intelligently schedule tasks of heterogeneous applications and allocate infrastructure resources within their response time bounds. In this work, a new method that tackles drawbacks of existing algorithms is proposed. It is achieved by adopting the proposed SBDE algorithm that solves a multiobjective optimization problem. Simulation experiments demonstrate that compared with three typical task scheduling approaches, it increases profit and decreases task loss. It can be readily and easily integrated and implemented in real-life industrial DGDCs. The future work needs to investigate the real-time green energy prediction with historical data and further combine prediction and task scheduling together to achieve greener and even net-zero-energy data centers.

A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems. Integrating data-driven surrogate models and simulation models of different accuracies (or fidelities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple fidelities in global optimization is a major challenge. To address it, the two major contributions of this paper include: (1) development of a new multi-fidelity surrogate-model-based optimization framework, which substantially improves reliability and efficiency of optimization compared to many existing methods, and (2) development of a data mining method to address the discrepancy between the low- and high-fidelity simulation models. A new efficient global optimization method is then proposed, referred to as multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution. Its advantages are verified by mathematical benchmark problems and a real-world antenna design automation problem. Crown Copyright (c) 2015 Published by Elsevier B.V. All rights reserved.

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems. Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a...

New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system. In this paper we describe the design of a fuzzy logic controller for the ball and beam system using a modified Ant Colony Optimization (ACO) method for optimizing the type of membership functions, the parameters of the membership functions and the fuzzy rules. This is achieved by applying a systematic and hierarchical optimization approach modifying the conventional ACO algorithm using an ant set partition strategy. The simulation results show that the proposed algorithm achieves better results than the classical ACO algorithm for the design of the fuzzy controller.

Social navigation support in a course recommendation system The volume of course-related information available to students is rapidly increasing. This abundance of information has created the need to help students find, organize, and use resources that match their individual goals, interests, and current knowledge. Our system, CourseAgent, presented in this paper, is an adaptive community-based hypermedia system, which provides social navigation course recommendations based on students’ assessment of course relevance to their career goals. CourseAgent obtains students’ explicit feedback as part of their natural interactivity with the system. This work presents our approach to eliciting explicit student feedback and then evaluates this approach.

A Certificateless Authenticated Key Agreement Protocol for Digital Rights Management System.

Device self-calibration in location systems using signal strength histograms Received signal strength RSS fingerprinting is an attractive solution for indoor positioning using Wireless Local Area Network WLAN due to the wide availability of WLAN access points and the ease of monitoring RSS measurements on WLAN-enabled mobile devices. Fingerprinting systems rely on a radiomap collected using a reference device inside the localisation area; however, a major limitation is that the quality of the location information can be degraded if the user carries a different device. This is because diverse devices tend to report the RSS values very differently for a variety of reasons. To ensure compatibility with the existing radiomap, we propose a self-calibration method that attains a good mapping between the reference and user devices using RSS histograms. We do so by relating the RSS histogram of the reference device, which is deduced from the radiomap, and the RSS histogram of the user device, which is updated concurrently with positioning. Unlike other approaches, our calibration method does not require any user intervention, e.g. collecting calibration data using the new device prior to positioning. Experimental results with five smartphones in a real indoor environment demonstrate the effectiveness of the proposed method and indicate that it is more robust to device diversity compared with other calibration methods in the literature.

Substituting Motion Effects with Vibrotactile Effects for 4D Experiences. In this paper, we present two methods to substitute motion effects using vibrotactile effects in order to improve the 4D experiences of viewers. This work was motivated by the needs of more affordable 4D systems for individual users. Our sensory substitution algorithms convert motion commands to vibrotactile commands to a grid display that uses multiple actuators. While one method is based on the fundamental principle of vestibular feedback, the other method makes use of intuitive visually-based mapping from motion to vibrotactile stimulation. We carried out a user study and could confirm the effectiveness of our substitution methods in improving 4D experiences. To our knowledge, this is the first study that investigated the feasibility of replacing motion effects using much simpler and less expensive vibrotactile effects.

Learning Feature Recovery Transformer for Occluded Person Re-Identification One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e., the interference of noise during feature matching and the loss of pedestrian information brought by the occlusions. In this paper, we propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously, which mainly consists of visibility graph matching and feature recovery transformer. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its k-nearest neighbors in the gallery to recover the complete features. Extensive experiments across different person Re-ID datasets, including occluded, partial and holistic datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly outperforms state-of-the-art results by at least 6.2% Rank- 1 accuracy and 7.2% mAP scores on the challenging Occluded-Duke dataset.

Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost Ear detection from a profile face image is an important step in many applications including biometric recognition. But accurate and rapid detection of the ear for real-time applications is a challenging task, particularly in the presence of occlusions. In this work, a cascaded AdaBoost based ear detection approach is proposed. In an experiment with a test set of 203 profile face images, all the ears were accurately detected by the proposed detector with a very low (5 x 10-6) false positive rate. It is also very fast and relatively robust to the presence of occlusions and degradation of the ear images (e.g. motion blur). The detection process is fully automatic and does not require any manual intervention.

Plastic surgery: a new dimension to face recognition Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. The contribution of this research is 1) preparing a face database of 900 individuals for plastic surgery, and 2) providing an analytical and experimental underpinning of the effect of plastic surgery on face recognition algorithms. The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance. Therefore, it is imperative to initiate a research effort so that future face recognition systems will be able to address this important problem.

One-class support vector machines: an application in machine fault detection and classification Fast incipient machine fault diagnosis is becoming one of the key requirements for economical and optimal process operation management. Artificial neural networks have been used to detect machine faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for machine fault detection and classification in electro-mechanical machinery from vibration measurements using one-class support vector machines (SVMs). In order to evaluate one-class SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

Deep learning human actions from video via sparse filtering and locally competitive algorithms Physiological and psychophysical evidence suggest that early visual cortex compresses the visual input on the basis of spatial and orientation-tuned filters. Recent computational advances have suggested that these neural response characteristics may reflect a `sparse coding' architecture--in which a small number of neurons need to be active for any given image--yielding critical structure latent in natural scenes. Here we present a novel neural network architecture combining a sparse filter model and locally competitive algorithms (LCAs), and demonstrate the network's ability to classify human actions from video. Sparse filtering is an unsupervised feature learning algorithm designed to optimize the sparsity of the feature distribution directly without having the need to model the data distribution. LCAs are defined by a system of differential equations where the initial conditions define an optimization problem and the dynamics converge to a sparse decomposition of the input vector. We applied this architecture to train a classifier on categories of motion in human action videos. Inputs to the network were small 3D patches taken from frame differences in the videos. Dictionaries were derived for each action class and then activation levels for each dictionary were assessed during reconstruction of a novel test patch. Overall, classification accuracy was at ¿ 97 %. We discuss how this sparse filtering approach provides a natural framework for multi-sensory and multimodal data processing including RGB video, RGBD video, hyper-spectral video, and stereo audio/video streams.

A Skin-Color and Template Based Technique for Automatic Ear Detection This paper proposes an efficient skin-color and template based technique for automatic ear detection in a side face image. The technique first separates skin regions from non skin regions and then searches for the ear within skin regions. Ear detection process involves three major steps. First, Skin Segmentation to eliminate all non-skin pixels from the image, second Ear Localization to perform ear detection using template matching approach, and third Ear Verification to validate the ear detection using the Zernike moments based shape descriptor. To handle the detection of ears of various shapes and sizes, an ear template is created considering the ears of various shapes (triangular, round, oval and rectangular) and resized automatically to a size suitable for the detection. Proposed technique is tested on the IIT Kanpur ear database consisting of 150 side face images and gives 94% accuracy.

On shape-mediated enrolment in ear biometrics Ears are a new biometric with major advantage in that they appear to maintain their shape with increased age. Any automatic biometric system needs enrolment to extract the target area from the background. In ear biometrics the inputs are often human head profile images. Furthermore ear biometrics is concerned with the effects of partial occlusion mostly caused by hair and earrings. We propose an ear enrolment algorithm based on finding the elliptical shape of the ear using a Hough Transform (HT) accruing tolerance to noise and occlusion. Robustness is improved further by enforcing some prior knowledge. We assess our enrolment on two face profile datasets; as well as synthetic occlusion.

Non-negative dictionary based sparse representation classification for ear recognition with occlusion. By introducing an identity occlusion dictionary to encode the occluded part on the source image, sparse representation based classification has shown good performance on ear recognition under partial occlusion. However, large number of atoms of the conventional occlusion dictionary brings expensive computational load to the SRC model solving. In this paper, we propose a non-negative dictionary based sparse representation and classification scheme for ear recognition. The non-negative dictionary includes the Gabor features dictionary extracted from the ear images, and non-negative occlusion dictionary learned from the identity occlusion dictionary. A test sample with occlusion can be sparsely represented over the Gabor feature dictionary and the occlusion dictionary. The sparse coding coefficients are noted with non-negativity and much more sparsity, and the non-negative dictionary has shown increasing discrimination ability. Experimental results on the USTB ear database show that the proposed method performs better than existing ear recognition methods under partial occlusion based on SRC.

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66% [33]). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%, [26]) on this dataset.

Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optimal performance obtained by the offline algorithm.

Statistical tools for digital forensics A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.

A Model for Understanding How Virtual Reality Aids Complex Conceptual Learning Designers and evaluators of immersive virtual reality systems have many ideas concerning how virtual reality can facilitate learning. However, we have little information concerning which of virtual reality's features provide the most leverage for enhancing understanding or how to customize those affordances for different learning environments. In part, this reflects the truly complex nature of learning. Features of a learning environment do not act in isolation; other factors such as the concepts or skills to be learned, individual characteristics, the learning experience, and the interaction experience all play a role in shaping the learning process and its outcomes. Through Project Science Space, we have been trying to identify, use, and evaluate immersive virtual reality's affordances as a means to facilitate the mastery of complex, abstract concepts. In doing so, we are beginning to understand the interplay between virtual reality's features and other important factors in shaping the learning process and learning outcomes for this type of material. In this paper, we present a general model that describes how we think these factors work together and discuss some of the lessons we are learning about virtual reality's affordances in the context of this model for complex conceptual learning.

Wireless Networks with RF Energy Harvesting: A Contemporary Survey Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to power the next generation wireless networks. As this emerging technology enables proactive energy replenishment of wireless devices, it is advantageous in supporting applications with quality of service (QoS) requirements. In this paper, we present a comprehensive literature review on the research progresses in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs including system architecture, RF energy harvesting techniques and existing applications. Then, we present the background in circuit design as well as the state-of-the-art circuitry implementations, and review the communication protocols specially designed for RF-EHNs. We also explore various key design issues in the development of RFEHNs according to the network types, i.e., single-hop networks, multi-antenna networks, relay networks, and cognitive radio networks. Finally, we envision some open research directions.

Collective feature selection to identify crucial epistatic variants. In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

A Rendezvous-Based Approach Enabling Energy-Efficient Sensory Data Collection with Mobile Sinks A large class of Wireless Sensor Networks (WSN) applications involve a set of isolated urban areas (e.g., urban parks or building blocks) covered by sensor nodes (SNs) monitoring environmental parameters. Mobile sinks (MSs) mounted upon urban vehicles with fixed trajectories (e.g., buses) provide the ideal infrastructure to effectively retrieve sensory data from such isolated WSN fields. Existing approaches involve either single-hop transfer of data from SNs that lie within the MS's range or heavy involvement of network periphery nodes in data retrieval, processing, buffering, and delivering tasks. These nodes run the risk of rapid energy exhaustion resulting in loss of network connectivity and decreased network lifetime. Our proposed protocol aims at minimizing the overall network overhead and energy expenditure associated with the multihop data retrieval process while also ensuring balanced energy consumption among SNs and prolonged network lifetime. This is achieved through building cluster structures consisted of member nodes that route their measured data to their assigned cluster head (CH). CHs perform data filtering upon raw data exploiting potential spatial-temporal data redundancy and forward the filtered information to appropriate end nodes with sufficient residual energy, located in proximity to the MS's trajectory. Simulation results confirm the effectiveness of our approach against as well as its performance gain over alternative methods.

Using mobile data collectors to improve network lifetime of wireless sensor networks with reliability constraints In this paper, we focus on maximizing network lifetime of a Wireless Sensor Network (WSN) using mobile Data Collectors (DCs) without compromising on the reliability requirements. We consider a heterogeneous WSN which consists of a large number of sensor nodes, a few DCs, and a static Base Station (BS). The sensor nodes are static and are deployed uniformly in the terrain. The DCs have locomotion capabilities and their movement can be controlled. Each sensor node periodically sends sensed event packets to its nearest DC. The DCs aggregate the event packets received from the sensor nodes and send these aggregate event packets to the static BS. We address the following problem: the DCs should send the aggregate event packets to the BS with a given reliability while avoiding the hotspot regions such that the network lifetime is improved. Reliability is achieved by sending each aggregate event packet via multiple paths to the BS. The network lifetime is maximized by moving the DCs in such a way that the forwarding load is distributed among the sensor nodes. We propose both centralized and distributed approaches for finding a movement strategy of the DCs. We show via simulations that the proposed approaches achieve the required reliability and also maximize the network lifetime compared to the existing approaches.

An Energy-Balanced Heuristic for Mobile Sink Scheduling in Hybrid WSNs. Wireless sensor networks (WSNs) are integrated as a pillar of collaborative Internet of Things (IoT) technologies for the creation of pervasive smart environments. Generally, IoT end nodes (or WSN sensors) can be mobile or static. In this kind of hybrid WSNs, mobile sinks move to predetermined sink locations to gather data sensed by static sensors. Scheduling mobile sinks energy-efficiently while ...

An energy-efficient path determination strategy for mobile data collectors in wireless sensor network. In wireless sensor networks, introduction of mobility has been considered to be a good strategy to greatly reduce the energy dissipation of the static sensor nodes. This task is achieved by considering the path in which the mobile data collectors move to collect data from the sensors. In this work a data gathering approach is proposed in which some mobile collectors visit only certain sojourn points (SPs) or data collection points in place of all sensor nodes. The mobile collectors start out on their journey after gathering information about the network from the sink, gather data from the sensors and transfer the data to the sink. To address this problem, an algorithm named Mobile Collector Path Planning (MCPP) is proposed. MCPP schema is validated via computer simulation considering both obstacle free and obstacle-resisting network and based on metrics like energy consumption by the static sensor nodes and network life time. The simulation results show a reduction of about 12% in energy consumption and 15% improvement in network lifetime as compared with existing algorithms.

Adaptive Range-based Target Localization Using Diffusion Gauss-Newton Method in Industrial Environments In a noisy manufacturing environment, range-based target localization for wireless sensor networks (WSNs) experiences various variations in range measurements, thus causing the unsatisfactory results. Starting from an empirical observation on the existing various industrial noise distribution, in this paper, a diffusion Gauss–Newton (GN) algorithm with cooperation strategy is proposed for solving target localization non linear-least-squares problem in a WSN. The proposed algorithm has an equalization effect on the unbalance noise distribution over the network by aggregating the global estimates into local GN update via diffusion strategy. When facing a hostile industrial environment where the ambient noise is heterogeneous or has the sudden changes across partial nodes, the significant performance degradation is produced by diffusion GN. To solve the problem, we propose further an improved version of diffusion GN, which is adaptive to sudden changes on noisy range measurements. Instead of using a static combiner, the new algorithm leverages the evolutionary game theory to assign a time-varying weight for the estimate from each neighboring node based on the individual range error. Consequently, the good estimates from the neighbors with high SNR have a larger weight in the combiner than the bad estimates caused by the low SNR or high noise. We also propose a simple but effective energy-accuracy tradeoff scheme by using a sigmoidal utility function. Some simulation examples show that the standard diffusion GN is effective in stationary noise and its improved version provides the adaptation to changing noisy environment. The effectiveness of energy–accuracy tradeoff is also validated.

Maximizing Data Collection Throughput on a Path in Energy Harvesting Sensor Networks Using a Mobile Sink. In energy harvesting wireless sensor networks (EH-WSNs), maximizing the data collection throughput is one of the most challenging issues. In this paper, we consider the problem of data collection on a pre-specified path using a mobile sink which has a fixed-mobility pattern. As a generalization of the previous works, we propose an optimization model for the problem which incorporates the effective and heterogeneous duration of sensors' transmission in each time slot. To improve the network throughput, a simple condition is proposed which determines the maximum number of available time slots to each sensor node. Accordingly, the proposed condition specifies the constant velocity of the mobile sink. The NP-Hardness of the problem under the proposed condition is proved and an online centralized algorithm with less complexity is designed to handle the problem. Its complexity is in polynomial order and is easily scalable to the networks with large number of sensor nodes. Furthermore, we address the effect of increase in time slot period on the total amount of collected data which has not been yet exploited well. Finally, through extensive simulations on different set of deployed nodes, we observe that the proposed algorithm significantly increases the network throughput when the travelled distance by sink per time slot is reduced down to the adjusted point.

A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots Autonomous mobile robots navigating in changing and dynamic unstructured environments like the outdoor environments need to cope with large amounts of uncertainties that are inherent of natural environments. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present a novel reactive control architecture for autonomous mobile robots that is based on type-2 FLC to implement the basic navigation behaviors and the coordination between these behaviors to produce a type-2 hierarchical FLC. In our experiments, we implemented this type-2 architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2-based control system dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1-based control system while achieving a significant rule reduction compared to the type-1 system.

Evaluating the On-Demand Mobile Charging in Wireless Sensor Networks Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. Different from energy harvesting systems, the utilization of mobile energy chargers is able to provide more reliable energy supply than the dynamic energy harvested from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work we analyze the On-Demand Mobile Charging (DMC) problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present two examples on determining the essential system parameters such as the optimal remaining energy level for individual sensor nodes to send out their recharging requests and the minimal energy capacity required for the mobile charger. Through extensive simulation with real-world system settings, we verify that our analytical results match the simulation results well and the system designs based on our analysis are effective.

Modeling air-to-ground path loss for low altitude platforms in urban environments The reliable prediction of coverage footprint resulting from an airborne wireless radio base station, is at utmost importance, when it comes to the new emerging applications of air-to-ground wireless services. These applications include the rapid recovery of damaged terrestrial wireless infrastructure due to a natural disaster, as well as the fulfillment of sudden wireless traffic overload in certain spots due to massive movement of crowds. In this paper, we propose a statistical propagation model for predicting the air-to-ground path loss between a low altitude platform and a terrestrial terminal. The prediction is based on the urban environment properties, and is dependent on the elevation angle between the terminal and the platform. The model shows that air-to-ground path loss is following two main propagation groups, characterized by two different path loss profiles. In this paper we illustrate the methodology of which the model was deduced, as well as we present the different path loss profiles including the occurrence probability of each.

Reaching Agreement in the Presence of Faults The problem addressed here concerns a set of isolated processors, some unknown subset of which may be faulty, that communicate only by means of two-party messages. Each nonfaulty processor has a private value of information that must be communicated to each other nonfaulty processor. Nonfaulty processors always communicate honestly, whereas faulty processors may lie. The problem is to devise an algorithm in which processors communicate their own values and relay values received from others that allows each nonfaulty processor to infer a value for each other processor. The value inferred for a nonfaulty processor must be that processor's private value, and the value inferred for a faulty one must be consistent with the corresponding value inferred by each other nonfaulty processor.It is shown that the problem is solvable for, and only for, n ≥ 3m + 1, where m is the number of faulty processors and n is the total number. It is also shown that if faulty processors can refuse to pass on information but cannot falsely relay information, the problem is solvable for arbitrary n ≥ m ≥ 0. This weaker assumption can be approximated in practice using cryptographic methods.

Student Models that Invite the Learner In: The SMILI:() Open Learner Modelling Framework In recent years, the learner models of some adaptive learning environments have been opened to the learners they represent. However, as yet there is no standard way of describing and analysing these 'open learner models'. This is, in part, due to the variety of issues that can be important or relevant in any particular learner model. The lack of a framework to discuss open learner models poses several difficulties: there is no systematic way to analyse and describe the open learner models of any one system; there is no systematic way to compare the features of open learner models in different systems; and the designers of each new adaptive learning system must repeatedly tread the same path of studying the many diverse uses and approaches of open learner modelling so that they might determine how to make use of open learner modelling in their system. We believe this is a serious barrier to the effective use of open learner models. This paper presents such a framework, and gives examples of its use to describe and compare adaptive educational systems.

Trajectory control of biomimetic robots for demonstrating human arm movements This study describes the trajectory control of biomimetic robots by developing human arm trajectory planning. First, the minimum jerk trajectory of the joint angles is produced analytically, and the trajectory of the elbow joint angle is modified by a time-adjustment of the joint motion of the elbow relative to the shoulder. Next, experiments were conducted in which gyro sensors were utilized, and the trajectories observed were compared with those which had been produced. The results showed that the proposed trajectory control is an advantageous scheme for demonstrating human arm movements.

A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

Hardware Circuits Design and Performance Evaluation of a Soft Lower Limb Exoskeleton Soft lower limb exoskeletons (LLEs) are wearable devices that have good potential in walking rehabilitation and augmentation. While a few studies focused on the structure design and assistance force optimization of the soft LLEs, rarely work has been conducted on the hardware circuits design. The main purpose of this work is to present a new soft LLE for walking efficiency improvement and introduce its hardware circuits design. A soft LLE for hip flexion assistance and a hardware circuits system with scalability were proposed. To assess the efficacy of the soft LLE, the experimental tests that evaluate the sensor data acquisition, force tracking performance, lower limb muscle activity and metabolic cost were conducted. The time error in the peak assistance force was just 1%. The reduction in the normalized root-mean-square EMG of the rectus femoris was 7.1%. The net metabolic cost in exoskeleton on condition was reduced by 7.8% relative to walking with no exoskeleton. The results show that the designed hardware circuits can be applied to the soft LLE and the soft LLE is able to improve walking efficiency of wearers.

Robust Design of Federated Learning for Edge-Intelligent Networks Mass data traffics, low-latency wireless services and advanced artificial intelligence (AI) technologies have driven the emergence of a new paradigm for wireless networks, namely edge-intelligent networks, which are more efficient and flexible than traditional cloud-intelligent networks. Considering users’ privacy, model sharing-based federated learning (FL) that migrates model parameters but not private data from edge devices to a central cloud is particularly attractive for edge-intelligent networks. Due to multiple rounds of iterative updating of high-dimensional model parameters between base station (BS) and edge devices, the communication reliability is a critical issue of FL for edge-intelligent networks. We reveal the impacts of the errors generated during model broadcast and model aggregation via wireless channels caused by channel fading, interference and noise on the accuracy of FL, especially when there exists channel uncertainty. To alleviate the impacts, we propose a robust FL algorithm for edge-intelligent networks with channel uncertainty, which is formulated as a worst-case optimization problem with joint device selection and transceiver design. Finally, simulation results validate the robustness and effectiveness of the proposed algorithm.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

A review on the design and optimization of interval type-2 fuzzy controllers A review of the methods used in the design of interval type-2 fuzzy controllers has been considered in this work. The fundamental focus of the work is based on the basic reasons for optimizing type-2 fuzzy controllers for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy controllers for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy controllers. We also mention alternative approaches to designing type-2 fuzzy controllers without optimization techniques. We also provide a comparison of the different optimization methods for the case of designing type-2 fuzzy controllers.

Mobile cloud computing: A survey Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. In this paper, we provide an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues. We conclude the paper with a critical analysis of challenges that have not yet been fully met, and highlight directions for future work.

Harmony search algorithm for solving Sudoku Harmony search (HS) algorithm was applied to solving Sudoku puzzle. The HS is an evolutionary algorithm which mimics musicians' behaviors such as random play, memory-based play, and pitch-adjusted play when they perform improvisation. Sudoku puzzles in this study were formulated as an optimization problem with number-uniqueness penalties. HS could successfully solve the optimization problem after 285 function evaluations, taking 9 seconds. Also, sensitivity analysis of HS parameters was performed to obtain a better idea of algorithm parameter values.

DEC: dynamically evolving clustering and its application to structure identification of evolving fuzzy models. Identification of models from input-output data essentially requires estimation of appropriate cluster centers. In this paper, a new online evolving clustering approach for streaming data is proposed. Unlike other approaches that consider either the data density or distance from existing cluster centers, this approach uses cluster weight and distance before generating new clusters. To capture the dynamics of the data stream, the cluster weight is defined in both data and time space in such a way that it decays exponentially with time. It also applies concepts from computational geometry to determine the neighborhood information while forming clusters. A distinction is made between core and noncore clusters to effectively identify the real outliers. The approach efficiently estimates cluster centers upon which evolving Takagi-Sugeno models are developed. The experimental results with developed models show that the proposed approach attains results at par or better than existing approaches and significantly reduces the computational overhead, which makes it suitable for real-time applications.

An Easily Understandable Grey Wolf Optimizer and Its Application to Fuzzy Controller Tuning. This paper proposes an easily understandable Grey Wolf Optimizer (GWO) applied to the optimal tuning of the parameters of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). GWO is employed for solving optimization problems focused on the minimization of discrete-time objective functions defined as the weighted sum of the absolute value of the control error and of the squared output sensitivity function, and the vector variable consists of the tuning parameters of the T-S PI-FCs. Since the sensitivity functions are introduced with respect to the parametric variations of the process, solving these optimization problems is important as it leads to fuzzy control systems with a reduced process parametric sensitivity obtained by a GWO-based fuzzy controller tuning approach. GWO algorithms applied with this regard are formulated in easily understandable terms for both vector and scalar operations, and discussions on stability, convergence, and parameter settings are offered. The controlled processes referred to in the course of this paper belong to a family of nonlinear servo systems, which are modeled by second order dynamics plus a saturation and dead zone static nonlinearity. Experimental results concerning the angular position control of a laboratory servo system are included for validating the proposed method.

Stability Analysis and Estimation of Domain of Attraction for Positive Polynomial Fuzzy Systems With Input Saturation AbstractIn this paper, the stability and positivity of positive polynomial fuzzy model based (PPFMB) control system are investigated, in which the positive polynomial fuzzy model and positive polynomial fuzzy controller are allowed to have different premise membership functions from each other. These mismatched premise membership functions can increase the flexibility of controller design; however, it will lead to the conservative results when the stability is analyzed based on the Lyapunov stability theory. To relax the positivity/stability conditions, the improved Taylor-series-membership-functions-dependent (ITSMFD) method is introduced by introducing the sample points information of Taylor-series approximate membership functions, local error information and boundary information of substate space of premise variables into the stability/positivity conditions. Meanwhile, the ITSMFD method is extended to the PPFMB control system with input saturation to relax the estimation of domain of attraction. Finally, simulation examples are presented to verify the feasibility of this method.

A robust fuzzy adaptive law for evolving control systems. In this paper an adaptive law with leakage is presented. This law can be used in the consequent part of Takagi–Sugeno-based control. The approach enables easy implementation in the control systems with evolving antecedent part. This combination results in a high-performance and robust control of nonlinear and slowly varying systems. It is shown in the paper that the proposed adaptive law is a natural way to cope with the parasitic dynamics. The boundedness of estimated parameters, the tracking error and all the signals in the system is guaranteed if the leakage parameter σ′ is large enough. This means that the proposed adaptive law ensures the global stability of the system. A simulation example is given that illustrates the proposed approach.

A survey on industrial applications of fuzzy control Fuzzy control has long been applied to industry with several important theoretical results and successful results. Originally introduced as model-free control design approach, model-based fuzzy control has gained widespread significance in the past decade. This paper presents a survey on recent developments of analysis and design of fuzzy control systems focused on industrial applications reported after 2000.

Swarm Intelligence: From Natural to Artificial Systems

Adaptive Federated Learning in Resource Constrained Edge Computing Systems Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a cen...

Distributed wireless communication system: a new architecture for future public wireless access The distributed wireless communication system (DWCS) is a new architecture for a wireless access system with distributed antennas, distributed processors, and distributed control. With distributed antennas, the system capacity can be expanded through dense frequency reuse, and the transmission power can be greatly decreased. With distributed processors control, the system works like a software or network radio, so different standards can coexist, and the system capacity can be increased by coprocessing of signals to and from multiple antennas.

A lightweight soft exosuit for gait assistance In this paper we present a soft lower-extremity robotic exosuit intended to augment normal muscle function in healthy individuals. Compared to previous exoskeletons, the device is ultra-lightweight, resulting in low mechanical impedance and inertia. The exosuit has custom McKibben style pneumatic actuators that can assist the hip, knee and ankle. The actuators attach to the exosuit through a network of soft, inextensible webbing triangulated to attachment points utilizing a novel approach we call the virtual anchor technique. This approach is designed to transfer forces to locations on the body that can best accept load. Pneumatic actuation was chosen for this initial prototype because the McKibben actuators are soft and can be easily driven by an off-board compressor. The exosuit itself (human interface and actuators) had a mass of 3500 g and with peripherals (excluding air supply) is 7144 g. In order to examine the exosuit's performance, a pilot study with one subject was performed which investigated the effect of the ankle plantar-flexion timing on the wearer's hip, knee and ankle joint kinematics and metabolic power when walking. Wearing the suit in a passive unpowered mode had little effect on hip, knee and ankle joint kinematics as compared to baseline walking when not wearing the suit. Engaging the actuators at the ankles at 30% of the gait cycle for 250 ms altered joint kinematics the least and also minimized metabolic power. The subject's average metabolic power was 386.7 W, almost identical to the average power when wearing no suit (381.8 W), and substantially less than walking with the unpowered suit (430.6 W). This preliminary work demonstrates that the exosuit can comfortably transmit joint torques to the user while not restricting mobility and that with further optimization, has the potential to reduce the wearer's metabolic cost during walking.

Differentially private Naive Bayes learning over multiple data sources. For meeting diverse requirements of data analysis, the machine learning classifier has been provided as a tool to evaluate data in many applications. Due to privacy concerns of preventing disclosing sensitive information, data owners often suppress their data for an untrusted trainer to train a classifier. Some existing work proposed privacy-preserving solutions for learning algorithms, which allow a trainer to build a classifier over the data from a single owner. However, they cannot be directly used in the multi-owner setting where each owner is not totally trusted for each other. In this paper, we propose a novel privacy-preserving Naive Bayes learning scheme with multiple data sources. The proposed scheme enables a trainer to train a Naive Bayes classifier over the dataset provided jointly by different data owners, without the help of a trusted curator. The training result can achieve ϵ-differential privacy while the training will not break the privacy of each owner. We implement the prototype of the scheme and conduct corresponding experiment.

Attitudes Towards Social Robots In Education: Enthusiast, Practical, Troubled, Sceptic, And Mindfully Positive While social robots bring new opportunities for education, they also come with moral challenges. Therefore, there is a need for moral guidelines for the responsible implementation of these robots. When developing such guidelines, it is important to include different stakeholder perspectives. Existing (qualitative) studies regarding these perspectives however mainly focus on single stakeholders. In this exploratory study, we examine and compare the attitudes of multiple stakeholders on the use of social robots in primary education, using a novel questionnaire that covers various aspects of moral issues mentioned in earlier studies. Furthermore, we also group the stakeholders based on similarities in attitudes and examine which socio-demographic characteristics influence these attitude types. Based on the results, we identify five distinct attitude profiles and show that the probability of belonging to a specific profile is affected by such characteristics as stakeholder type, age, education and income. Our results also indicate that social robots have the potential to be implemented in education in a morally responsible way that takes into account the attitudes of various stakeholders, although there are multiple moral issues that need to be addressed first. Finally, we present seven (practical) implications for a responsible application of social robots in education following from our results. These implications provide valuable insights into how social robots should be implemented.

Optimal Planning Of Pev Charging Station With Single Output Multiple Cables Charging Spots Coordinated charging can alter the profile of plug-in electric vehicle charging load and reduce the required amount of charging spots by encouraging customers to use charging spots at off-peak hours. Therefore, real-time coordinated charging should be considered at the planning stage. To enhance charging station's utilization and save corresponding investment costs by incorporating coordinated charging, a new charging spot model, namely single output multiple cables charging spot (SOMC spot), is designed in this paper. A two-stage stochastic programming model is developed for planning a public parking lot charging station equipped with SOMC spots. The first stage of the programming model is planning of SOMC spots and its objective is to obtain an optimal configuration of the charging station to minimize the station's equivalent annual costs, including investment and operation costs. The second stage of the programming model involves a probabilistic simulation procedure, in which coordinated charging is simulated, so that the influence of coordinated charging on the planning is considered. A case study of a residential parking lot charging station verifies the effectiveness of the proposed planning model. And the proposed coordinated charging for SOMC spots shows great potential in saving equivalent annual costs for providing charging services.

Optimizing the Deployment of Electric Vehicle Charging Stations Using Pervasive Mobility Data. With the recent advances in battery technology and the resulting decrease in the charging times, public charging stations are becoming a viable option for Electric Vehicle (EV) drivers. Concurrently, emergence and the wide-spread use of location-tracking devices in mobile phones and wearable devices has paved the way to track individual-level human movements to an unprecedented spatial and temporal grain. Motivated by these developments, we propose a novel methodology to perform data-driven optimization of EV charging station locations. We formulate the problem as a discrete optimization problem on a geographical grid, with the objective of covering the entire demand region while minimizing a measure of drivers’ total excess driving distance to reach charging stations, the related energy overhead, and the number of charging stations. Since optimally solving the problem is computationally infeasible, we present computationally efficient solutions based on the genetic algorithm. We then apply the proposed methodology to optimize EV charging stations layout in the city of Boston, starting from Call Detail Records (CDR) of one million users over the span of 4 months. The results show that the genetic algorithm provides solutions that significantly reduce drivers’ excess driving distance to charging stations, energy overhead, and the number of charging stations required compared to both a locally-optimized feasible solution and the current charging station deployment in the Boston metro area. We further investigate the robustness of the proposed methodology and show that building upon well-known regularity of aggregate human mobility patterns, the layout computed for demands based on the single day movements preserves its advantage also in later days and months. When collectively considered, the results presented in this paper indicate the potential of data-driven approaches for optimally placing public charging facilities at urban scale.

Optimized Charging Scheduling with Single Mobile Charger for Wireless Rechargeable Sensor Networks. Due to the rapid development of wireless charging technology, the recharging issue in wireless rechargeable sensor network (WRSN) has been a popular research problem in the past few years. The weakness of previous work is that charging route planning is not reasonable. In this work, a dynamic optimal scheduling scheme aiming to maximize the vacation time ratio of a single mobile changer for WRSN is proposed. In the proposed scheme, the wireless sensor network is divided into several sub-networks according to the initial topology of deployed sensor networks. After comprehensive analysis of energy states, working state and constraints for different sensor nodes in WRSN, we transform the optimized charging path problem of the whole network into the local optimization problem of the sub networks. The optimized charging path with respect to dynamic network topology in each sub-network is obtained by solving an optimization problem, and the lifetime of the deployed wireless sensor network can be prolonged. Simulation results show that the proposed scheme has good and reliable performance for a small wireless rechargeable sensor network.

Toward Distributed Battery Switch Based Electro-Mobility Using Publish/Subscribe System. With the growing popularization of Electric Vehicle (EVs), Electro-mobility (in terms of where to charge EV) has become an increasingly important research problem in smart cities. One of the major concerns is the anxiety of EVs, as drivers may suffer from discomfort due to long charging time. In this paper, we leverage the battery switch technology to provide an even faster charging than plug-in c...

Design and Planning of a Multiple-Charger Multiple-Port Charging System for PEV Charging Station. Investment of charging facilities is facing deficit problems in many countries at the initial development stage of plug-in electric vehicles (PEVs). In this paper, we study the charging facility planning problem faced by a PEV charging station investor who aims to serve PEV customers with random behaviors and demands (but follow a series of predicted distributions) with lower economic costs of bot...

Time-Efficient Target Tags Information Collection in Large-Scale RFID Systems By integrating the micro-sensor on RFID tags to obtain the environment information, the sensor-augmented RFID system greatly supports the applications that are sensitive to environment. To quickly collect the information from all tags, many researchers dedicate on well arranging tag replying orders to avoid the signal collisions. Compared to from all tags, collecting information from a part of tag...

A survey on ear biometrics Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non-contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion, earprint forensics, ear symmetry, ear classification, and ear individuality. This article provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers.

DeepFace: Closing the Gap to Human-Level Performance in Face Verification In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

Markov games as a framework for multi-agent reinforcement learning In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsis-tic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic.

Pors: proofs of retrievability for large files In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety. A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes. In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work. We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

On controller initialization in multivariable switching systems We consider a class of switched systems which consists of a linear MIMO and possibly unstable process in feedback interconnection with a multicontroller whose dynamics switch. It is shown how one can achieve significantly better transient performance by selecting the initial condition for every controller when it is inserted into the feedback loop. This initialization is obtained by performing the minimization of a quadratic cost function of the tracking error, controlled output, and control signal. We guarantee input-to-state stability of the closed-loop system when the average number of switches per unit of time is smaller than a specific value. If this is not the case then stability can still be achieved by adding a mild constraint to the optimization. We illustrate the use of our results in the control of a flexible beam actuated in torque. This system is unstable with two poles at the origin and contains several lightly damped modes, which can be easily excited by controller switching.

Completely Pinpointing the Missing RFID Tags in a Time-Efficient Way Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45% of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time.

An indoor localization solution using Bluetooth RSSI and multiple sensors on a smartphone. In this paper, we propose an indoor positioning system using a Bluetooth receiver, an accelerometer, a magnetic field sensor, and a barometer on a smartphone. The Bluetooth receiver is used to estimate distances from beacons. The accelerometer and magnetic field sensor are used to trace the movement of moving people in the given space. The horizontal location of the person is determined by received signal strength indications (RSSIs) and the traced movement. The barometer is used to measure the vertical position where a person is located. By combining RSSIs, the traced movement, and the vertical position, the proposed system estimates the indoor position of moving people. In experiments, the proposed approach showed excellent performance in localization with an overall error of 4.8%.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Event-Triggered <inline-formula> <tex-math notation="LaTeX">$H_\infty$ </tex-math></inline-formula> State Estimation of 2-DOF Quarter-Car Suspension Systems With Nonhomogeneous Markov Switching In this paper, the event-triggered H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> state estimation problem is investigated for a two-degree-of-freedom quarter-car suspension system operated over a switching-channel network environment. First, the channel-switching is governed by a nonhomogeneous Markov chain whose probability transition matrix is time-varying. Then, a Markov jump linear system model is adopted to represent the overall networked system in view of the event-triggered communication scheme, signal quantization and random packet losses on account of the limited network bandwidth. By virtue of the Lyapunov functional and linear matrix inequality method, the event-triggered H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> state estimation problem is transformed into an optimization problem that switching-channel-dependent estimators are designed such that the estimation error system is exponentially stable in the mean square sense and achieves a desired performance level. Finally, a simulation example is used to demonstrate the validity of proposed design method.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Impact of Immersive Virtual Reality Content Using 360-Degree Videos in Undergraduate Education This article investigated the impact of immersive virtual reality (VR) content, using 360-degree videos, in undergraduate education. To improve the delivery and reality of 360-degree VR content, we filmed the video in the third person so that the viewers could feel like they were in the environment where the lecture was conducted. To verify the educational effects, 33 university students participated in our experiment. We conducted pretest learning, using 360-degree videos, and posttest learning via conventional 2-D videos for statistical analysis. A paired <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -test was used to compare the means of the pretest and the posttest. In addition, learning via 360-degree videos was assessed for its effectiveness through questionnaires consisting of five measurement elements—engagement, immersion, motivation, cognitive benefits, and perceived learning effectiveness—and comparing them to the existing 2-D video method, based on e-learning. From the results, we confirmed that the teaching material delivered through 360-degree VR content allows students to be more focused, immersed, and interested than 2-D learning modes. Furthermore, the high scores of cognitive and perceived learning elements imply that VR-based 360-degree educational content can encourage more active participation than traditional lectures and can improve the ability to analyze and organize study lessons.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Ethical Considerations Of Applying Robots In Kindergarten Settings: Towards An Approach From A Macroperspective In child-robot interaction (cHRI) research, many studies pursue the goal to develop interactive systems that can be applied in everyday settings. For early education, increasingly, the setting of a kindergarten is targeted. However, when cHRI and research are brought into a kindergarten, a range of ethical and related procedural aspects have to be considered and dealt with. While ethical models elaborated within other human-robot interaction settings, e.g., assisted living contexts, can provide some important indicators for relevant issues, we argue that it is important to start developing a systematic approach to identify and tackle those ethical issues which rise with cHRI in kindergarten settings on a more global level and address the impact of the technology from a macroperspective beyond the effects on the individual. Based on our experience in conducting studies with children in general and pedagogical considerations on the role of the institution of kindergarten in specific, in this paper, we enfold some relevant aspects that have barely been addressed in an explicit way in current cHRI research. Four areas are analyzed and key ethical issues are identified in each area: (1) the institutional setting of a kindergarten, (2) children as a vulnerable group, (3) the caregivers' role, and (4) pedagogical concepts. With our considerations, we aim at (i) broadening the methodology of the current studies within the area of cHRI, (ii) revalidate it based on our comprehensive empirical experience with research in kindergarten settings, both laboratory and real-world contexts, and (iii) provide a framework for the development of a more systematic approach to address the ethical issues in cHRI research within kindergarten settings.

Event-Triggered Communication and Annular Finite-Time <italic>H</italic>∞ Filtering for Networked Switched Systems Event-triggered communication mechanism (ETCM) provides an efficient way to reduce unwanted network traffic. This article studies the co-design of an ETCM and an annular finite-time (AFT) H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter for networked switched systems (NSSs). First, the AFT definition and ETCM are presented. Second, a set of mode-dependent average dwell-time (MADT) switching rules is given. By resorting to a delay-dependent Lyapunov functional approach, some feasible AFT H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filters are designed. Third, it is proved that the filtering error system (FES) has a good performance in attenuating the external disturbances. Finally, the feasibility of the developed method is verified via simulation.

Li-function activated ZNN with finite-time convergence applied to redundant-manipulator kinematic control via time-varying Jacobian matrix pseudoinversion. This paper presents and investigates the application of Zhang neural network (ZNN) activated by Li function to kinematic control of redundant robot manipulators via time-varying Jacobian matrix pseudoinversion. That is, by using Li activation function and by computing the time-varying pseudoinverse of the Jacobian matrix (of the robot manipulator), the resultant ZNN model is applied to redundant-manipulator kinematic control. Note that there are nine novelties and differences of ZNN from the conventional gradient neural network in the research methodology. More importantly, such a Li-function activated ZNN (LFAZNN) model has the property of finite-time convergence (showing its feasibility to redundant-manipulator kinematic control). Simulation results based on a four-link planar robot manipulator and a PA10 robot manipulator further demonstrate the effectiveness of the presented LFAZNN model, as well as show the LFAZNN application prospect.

Kinematic model to control the end-effector of a continuum robot for multi-axis processing This paper presents a novel kinematic approach for controlling the end-effector of a continuum robot for in-situ repair/inspection in restricted and hazardous environments. Forward and inverse kinematic (IK) models have been developed to control the last segment of the continuum robot for performing multi-axis processing tasks using the last six Degrees of Freedom (DoF). The forward kinematics (FK) is proposed using a combination of Euler angle representation and homogeneous matrices. Due to the redundancy of the system, different constraints are proposed to solve the IK for different cases; therefore, the IK model is solved for bending and direction angles between (-pi/2 to + pi/2) radians. In addition, a novel method to calculate the Jacobian matrix is proposed for this type of hyper-redundant kinematics. The error between the results calculated using the proposed Jacobian algorithm and using the partial derivative equations of the FK map (with respect to linear and angular velocity) is evaluated. The error between the two models is found to be insignificant, thus, the Jacobian is validated as a method of calculating the IK for six DoF.

Optimization-Based Inverse Model of Soft Robots With Contact Handling. This letter presents a physically based algorithm to interactively simulate and control the motion of soft robots interacting with their environment. We use the finite-element method to simulate the nonlinear deformation of the soft structure, its actuators, and surroundings and propose a control method relying on a quadratic optimization to find the inverse of the model. The novelty of this work ...

A Finite-Time Convergent and Noise-Rejection Recurrent Neural Network and Its Discretization for Dynamic Nonlinear Equations Solving. The so-called zeroing neural network (ZNN) is an effective recurrent neural network for solving dynamic problems including the dynamic nonlinear equations. There exist numerous unperturbed ZNN models that can converge to the theoretical solution of solvable nonlinear equations in infinity long or finite time. However, when these ZNN models are perturbed by external disturbances, the convergence pe...

An overview of calibration technology of industrial robots With the continuous improvement of automation, industrial robots have become an indispensable part of automated production lines. They widely used in a number of industrial production activities, such as spraying, welding, handling, etc., and have a great role in these sectors. Recently, the robotic technology is developing towards high precision, high intelligence. Robot calibration technology has a great significance to improve the accuracy of robot. However, it has much work to be done in the identification of robot parameters. The parameter identification work of existing serial and parallel robots is introduced. On the one hand, it summarizes the methods for parameter calibration and discusses their advantages and disadvantages. On the other hand, the application of parameter identification is introduced. This overview has a great reference value for robot manufacturers to choose proper identification method, points further research areas for researchers. Finally, this paper analyzes the existing problems in robot calibration, which may be worth researching in the future.

Robotic Intra-Operative Ultrasound: Virtual Environments and Parallel Systems Robotic intra-operative ultrasound has the potential to improve the conventional practice of diagnosis and procedure guidance that are currently performed manually. Working towards automatic or semi-automatic ultrasound, being able to define ultrasound views and the corresponding probe poses via intelligent approaches become crucial. Based on the concept of parallel system which incorporates the ingredients of artificial systems, computational experiments, and parallel execution, this paper utilized a recent developed robotic trans-esophageal ultrasound system as the study object to explore the method for developing the corresponding virtual environments and present the potential applications of such systems. The proposed virtual system includes the use of 3D slicer as the main workspace and graphic user interface (GUI), Matlab engine to provide robotic control algorithms and customized functions, and PLUS (Public software Library for UltraSound imaging research) toolkit to generate simulated ultrasound images. Detailed implementation methods were presented and the proposed features of the system were explained. Based on this virtual system, example uses and case studies were presented to demonstrate its capabilities when used together with the physical TEE robot. This includes standard view definition and customized view optimization for pre-planning and navigation, as well as robotic control algorithm evaluations to facilitate real-time automatic probe pose adjustments. To conclude, the proposed virtual system would be a powerful tool to facilitate the further developments and clinical uses of the robotic intra-operative ultrasound systems.

Relaxed Real-Time Scheduling Stabilization of Discrete-Time Takagi-Sugeno Fuzzy Systems via An Alterable-Weights-Based Ranking Switching Mechanism. The problem of relaxed real-time scheduling stabilization of nonlinear systems in the Takagi-Sugeno fuzzy model form is studied by proposing a new alterable-weights-based ranking switching mechanism. Thanks to the proposed alterable-weights-based ranking switching mechanism, a new fuzzy switching controller is developed with a set of activated modes that are adjusted by the real-time joint distrib...

A tutorial on support vector regression In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.

Inspecting and Visualizing Distributed Bayesian Student Models Bayesian Belief Networks provide a principled, mathematically sound, and logically rational mechanism to represent student models. The belief net backbone structure proposed by Reye [14,15] offers a practical way to represent and update Bayesian student models describing both cognitive and social aspects of the learner. Considering students as active participants in the modelling process, this paper explores visualization and inspectability issues of Bayesian student modelling. This paper also presents ViSMod an integrated tool to visualize and inspect distributed Bayesian student models.

A Certificateless Authenticated Key Agreement Protocol for Digital Rights Management System.

Haptic feedback for enhancing realism of walking simulations. In this paper, we describe several experiments whose goal is to evaluate the role of plantar vibrotactile feedback in enhancing the realism of walking experiences in multimodal virtual environments. To achieve this goal we built an interactive and a noninteractive multimodal feedback system. While during the use of the interactive system subjects physically walked, during the use of the noninteractive system the locomotion was simulated while subjects were sitting on a chair. In both the configurations subjects were exposed to auditory and audio-visual stimuli presented with and without the haptic feedback. Results of the experiments provide a clear preference toward the simulations enhanced with haptic feedback showing that the haptic channel can lead to more realistic experiences in both interactive and noninteractive configurations. The majority of subjects clearly appreciated the added feedback. However, some subjects found the added feedback unpleasant. This might be due, on one hand, to the limits of the haptic simulation and, on the other hand, to the different individual desire to be involved in the simulations. Our findings can be applied to the context of physical navigation in multimodal virtual environments as well as to enhance the user experience of watching a movie or playing a video game.

TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation. Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data. This requires a system that understands usersu0027 questions and converts them to SQL queries automatically. In this paper we present a novel approach, TypeSQL, which views this problem as a slot filling task. Additionally, TypeSQL utilizes type information to better understand rare entities and numbers in natural language questions. We test this idea on the WikiSQL dataset and outperform the prior state-of-the-art by 5.5% in much less time. We also show that accessing the content of databases can significantly improve the performance when usersu0027 queries are not well-formed. TypeSQL gets 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model.

Energy harvesting algorithm considering max flow problem in wireless sensor networks. In Wireless Sensor Networks (WSNs), sensor nodes with poor energy always have bad effect on the data rate or max flow. These nodes are called bottleneck nodes. In this paper, in order to increase the max flow, we assume an energy harvesting WSNs environment to investigate the cooperation of multiple Mobile Chargers (MCs). MCs are mobile robots that use wireless charging technology to charge sensor nodes in WSNs. This means that in energy harvesting WSNs environments, sensor nodes can obtain energy replenishment by using MCs or collecting energy from nature by themselves. In our research, we use MCs to improve the energy of the sensor nodes by performing multiple rounds of unified scheduling, and finally achieve the purpose of increasing the max flow at sinks. Firstly, we model this problem as a Linear Programming (LP) to search the max flow in a round of charging scheduling and prove that the problem is NP-hard. In order to solve the problem, we propose a heuristic approach: deploying MCs in units of paths with the lowest energy node priority. To reduce the energy consumption of MCs and increase the charging efficiency, we also take the optimization of MCs’ moving distance into our consideration. Finally, we extend the method to multiple rounds of scheduling called BottleNeck. Simulation results show that Bottleneck performs well at increasing max flow.

Chaotic multi-verse optimizer-based feature selection The multi-verse optimizer (MVO) is a new evolutionary algorithm inspired by the concepts of multi-verse theory namely, the white/black holes, which represents the interaction between the universes. However, the MVO has some drawbacks, like any other evolutionary algorithms, such as slow convergence and getting stuck in local optima (maximum or minimum). This paper provides a novel chaotic MVO algorithm (CMVO) to avoid these drawbacks, where chaotic maps are used to improve the performance of MVO algorithm. The CMVO algorithm is applied to solve the feature selection problem, in which five benchmark datasets are used to evaluate the performance of CMVO algorithm. The results of CMVO is compared with standard MVO and two other swarm algorithms. The experimental results show that logistic chaotic map is the best chaotic map that increases the performance of MVO, and also the MVO is better than other swarm algorithms.

Tabu search based multi-watermarks embedding algorithm with multiple description coding Digital watermarking is a useful solution for digital rights management systems, and it has been a popular research topic in the last decade. Most watermarking related literature focuses on how to resist deliberate attacks by applying benchmarks to watermarked media that assess the effectiveness of the watermarking algorithm. Only a few papers have concentrated on the error-resilient transmission of watermarked media. In this paper, we propose an innovative algorithm for vector quantization (VQ) based image watermarking, which is suitable for error-resilient transmission over noisy channels. By incorporating watermarking with multiple description coding (MDC), the scheme we propose to embed multiple watermarks can effectively overcome channel impairments while retaining the capability for copyright and ownership protection. In addition, we employ an optimization technique, called tabu search, to optimize both the watermarked image quality and the robustness of the extracted watermarks. We have obtained promising simulation results that demonstrate the utility and practicality of our algorithm. (C) 2011 Elsevier Inc. All rights reserved.

On Deployment of Wireless Sensors on 3-D Terrains to Maximize Sensing Coverage by Utilizing Cat Swarm Optimization With Wavelet Transform. In this paper, a deterministic sensor deployment method based on wavelet transform (WT) is proposed. It aims to maximize the quality of coverage of a wireless sensor network while deploying a minimum number of sensors on a 3-D surface. For this purpose, a probabilistic sensing model and Bresenham's line of sight algorithm are utilized. The WT is realized by an adaptive thresholding approach for the generation of the initial population. Another novel aspect of the paper is that the method followed utilizes a Cat Swarm Optimization (CSO) algorithm, which mimics the behavior of cats. We have modified the CSO algorithm so that it can be used for sensor deployment problems on 3-D terrains. The performance of the proposed algorithm is compared with the Delaunay Triangulation and Genetic Algorithm based methods. The results reveal that CSO based sensor deployment which utilizes the wavelet transform method is a powerful and successful method for sensor deployment on 3-D terrains.

FPGA-Based Parallel Metaheuristic PSO Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation This paper presents a field-programmable gate array (FPGA)-based parallel metaheuristic particle swarm optimization algorithm (PPSO) and its application to global path planning for autonomous robot navigating in structured environments with obstacles. This PPSO consists of three parallel PSOs along with a communication operator in one FPGA chip. The parallel computing architecture takes advantages of maintaining better population diversity and inhibiting premature convergence in comparison with conventional PSOs. The collision-free discontinuous path generated from the PPSO planner is then smoothed using the cubic B-spline and system-on-a-programmable-chip (SoPC) technology. Experimental results are conducted to show the merit of the proposed FPGA-based PPSO path planner and smoother for global path planning of autonomous mobile robot navigation.

EHCR-FCM: Energy Efficient Hierarchical Clustering and Routing using Fuzzy C-Means for Wireless Sensor Networks Wireless Sensor Network (WSN) is a part of Internet of Things (IoT), and has been used for sensing and collecting the important information from the surrounding environment. Energy consumption in this process is the most important issue, which primarily depends on the clustering technique and packet routing strategy. In this paper, we propose an Energy efficient Hierarchical Clustering and Routing using Fuzzy C-Means (EHCR-FCM) which works on three-layer structure, and depends upon the centroid of the clusters and grids, relative Euclidean distances and residual energy of the nodes. This technique is useful for the optimal usage of energy by employing grid and cluster formation in a dynamic manner and energy-efficient routing. The fitness value of the nodes have been used in this proposed work to decide that whether it may work as the Grid Head (GH) or Cluster Head (CH). The packet routing strategy of all the GHs depend upon the relative Euclidean distances among them, and also on their residual energy. In addition to this, we have also performed the energy consumption analysis, and found that our proposed approach is more energy efficient, better in terms of the number of cluster formation, network lifetime, and it also provides better coverage.

Hybrid Bird Swarm Optimized Quasi Affine Algorithm Based Node Location in Wireless Sensor Networks Wireless sensor networks (WSN) with the Internet of Things (IoT) play a vital key concept while performing the information transmission process. The WSN with IoT has been effectively utilized in different research contents such as network protocol selection, topology control, node deployment, location technology and network security, etc. Among that, node location is one of the crucial problems that need to be resolved to improve communication. The node location is directly influencing the network performance, lifetime and data sense. Therefore, this paper introduces the Bird Swarm Optimized Quasi-Affine Evolutionary Algorithm (BSOQAEA) to fix the node location problem in sensor networks. The proposed algorithm analyzes the node location, and incorporates the dynamic shrinking space process is to save time. The introduced evolutionary algorithm optimizes the node centroid location performed according to the received signal strength indications (RSSI). The created efficiency in the system is determined using high node location accuracy, minimum distance error, and location error.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Adam: A Method for Stochastic Optimization. We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

Untangling Blockchain: A Data Processing View of Blockchain Systems. Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values, and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core ...

Multivariate Short-Term Traffic Flow Forecasting Using Time-Series Analysis Existing time-series models that are used for short-term traffic condition forecasting are mostly univariate in nature. Generally, the extension of existing univariate time-series models to a multivariate regime involves huge computational complexities. A different class of time-series models called structural time-series model (STM) (in its multivariate form) has been introduced in this paper to develop a parsimonious and computationally simple multivariate short-term traffic condition forecasting algorithm. The different components of a time-series data set such as trend, seasonal, cyclical, and calendar variations can separately be modeled in STM methodology. A case study at the Dublin, Ireland, city center with serious traffic congestion is performed to illustrate the forecasting strategy. The results indicate that the proposed forecasting algorithm is an effective approach in predicting real-time traffic flow at multiple junctions within an urban transport network.

A novel full structure optimization algorithm for radial basis probabilistic neural networks. In this paper, a novel full structure optimization algorithm for radial basis probabilistic neural networks (RBPNN) is proposed. Firstly, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to heuristically select the initial hidden layer centers of the RBPNN, and then the recursive orthogonal least square (ROLS) algorithm combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. Finally, the effectiveness and efficiency of our proposed algorithm are evaluated through a plant species identification task involving 50 plant species.

G2-type SRMPC scheme for synchronous manipulation of two redundant robot arms. In this paper, to remedy the joint-angle drift phenomenon for manipulation of two redundant robot arms, a novel scheme for simultaneous repetitive motion planning and control (SRMPC) at the joint-acceleration level is proposed, which consists of two subschemes. To do so, the performance index of each SRMPC subscheme is derived and designed by employing the gradient dynamics twice, of which a convergence theorem and its proof are presented. In addition, for improving the accuracy of the motion planning and control, position error, and velocity, error feedbacks are incorporated into the forward kinematics equation and analyzed via Zhang neural-dynamics method. Then the two subschemes are simultaneously reformulated as two quadratic programs (QPs), which are finally unified into one QP problem. Furthermore, a piecewise-linear projection equation-based neural network (PLPENN) is used to solve the unified QP problem, which can handle the strictly convex QP problem in an inverse-free manner. More importantly, via such a unified QP formulation and the corresponding PLPENN solver, the synchronism of two redundant robot arms is guaranteed. Finally, two given tasks are fulfilled by 2 three-link and 2 five-link planar robot arms, respectively. Computer-simulation results validate the efficacy and accuracy of the SRMPC scheme and the corresponding PLPENN solver for synchronous manipulation of two redundant robot arms.

Adaptive Fuzzy Control With Prescribed Performance for Block-Triangular-Structured Nonlinear Systems. In this paper, an adaptive fuzzy control method with prescribed performance is proposed for multi-input and multioutput block-triangular-structured nonlinear systems with immeasurable states. Fuzzy logic systems are adopted to identify the unknown nonlinear system functions. Adaptive fuzzy state observers are designed to solve the problem of unmeasured states, and a new observer-based output-feedb...

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Edge User Allocation with Dynamic Quality of Service. In edge computing, edge servers are placed in close proximity to end-users. App vendors can deploy their services on edge servers to reduce network latency experienced by their app users. The edge user allocation (EUA) problem challenges service providers with the objective to maximize the number of allocated app users with hired computing resources on edge servers while ensuring their fixed quality of service (QoS), e.g., the amount of computing resources allocated to an app user. In this paper, we take a step forward to consider dynamic QoS levels for app users, which generalizes but further complicates the EUA problem, turning it into a dynamic QoS EUA problem. This enables flexible levels of quality of experience (QoE) for app users. We propose an optimal approach for finding a solution that maximizes app users' overall QoE. We also propose a heuristic approach for quickly finding sub-optimal solutions to large-scale instances of the dynamic QoS EUA problem. Experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approaches against a baseline approach and the state of the art.

A Game-Theoretical Approach for User Allocation in Edge Computing Environment Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently.

QoE-Aware Bandwidth Allocation for Video Traffic Using Sigmoidal Programming. The problem of bandwidth allocation in networks is traditionally solved using distributed rate allocation algorithms under the general framework of network utility maximization (NUM). Despite many advances in solving the computationally intensive flow assignment problem in NUM, the common but unrealistic assumption of concavity of utility functions undermines the performance of existing systems in...

Enhancing Video Rate Adaptation With Mobile Edge Computing and Caching in Software-Defined Mobile Networks. Recent advances in software-defined mobile networks (SDMNs), in-network caching, and mobile edge computing (MEC) can have significant effects on video services in next generation mobile networks. In this paper, we jointly consider SDMNs, in-network caching, and MEC to enhance the video service in next generation mobile networks. We use a new video experience evaluation standard called U-video mean...

QoE-Driven Cache Management for HTTP Adaptive Bit Rate Streaming Over Wireless Networks In this paper, we investigate the problem of optimal content cache management for HTTP adaptive bit rate (ABR) streaming over wireless networks. Specifically, in the media cloud, each content is transcoded into a set of media files with diverse playback rates, and appropriate files will be dynamically chosen in response to channel conditions and screen forms. Our design objective is to maximize the quality of experience (QoE) of an individual content for the end users, under a limited storage budget. Deriving a logarithmic QoE model from our experimental results, we formulate the individual content cache management for HTTP ABR streaming over wireless network as a constrained convex optimization problem. We adopt a two-step process to solve the snapshot problem. First, using the Lagrange multiplier method, we obtain the numerical solution of the set of playback rates for a fixed number of cache copies and characterize the optimal solution analytically. Our investigation reveals a fundamental phase change in the optimal solution as the number of cached files increases. Second, we develop three alternative search algorithms to find the optimal number of cached files, and compare their scalability under average and worst complexity metrics. Our numerical results suggest that, under optimal cache schemes, the maximum QoE measurement, i.e., mean-opinion-score (MOS), is a concave function of the allowable storage size. Our cache management can provide high expected QoE with low complexity, shedding light on the design of HTTP ABR streaming services over wireless networks.

Green Resource Allocation Based on Deep Reinforcement Learning in Content-Centric IoT In the era of information, the green services of content-centric IoT are expected to offer users the better satisfaction of Quality of Experience (QoE) than that in a conventional IoT. Nevertheless, the network traffic and new demands from IoT users increase along with the promising of the content-centric computing system. Therefore, the satisfaction of QoE will become the major challenge in the content-centric computing system for IoT users. In this article, to enhance the satisfaction of QoE, we propose QoE models to evaluate the qualities of the IoT concerning both network and users. The value of QoE does not only refer to the network cost, but also the Mean Opinion Score (MOS) of users. Therefore, our models could capture the influence factors from network cost and services for IoT users based on IoT conditions. Specially, we mainly focus on the issues of cache allocation and transmission rate. Under this content-centric IoT, aiming to allocate the cache capacity among content-centric computing nodes and handle the transmission rates under a constrained total network cost and MOS for the whole IoT, we devote our efforts to the following two aspects. First, we formulate the QoE as a green resource allocation problem under the different transmission rate to acquire the best QoE. Then, in the basis of the node centrality, we will propose a suboptimal dynamic approach, which is suitable for IoT with content delivery frequently. Furthermore, we present a green resource allocation algorithm based on Deep Reinforcement Learning (DRL) to improve accuracy of QoE adaptively. Simulation results reveal that our proposals could achieve high QoE performance for content-centric IoT.

Latency-Aware Application Module Management for Fog Computing Environments. The fog computing paradigm has drawn significant research interest as it focuses on bringing cloud-based services closer to Internet of Things (IoT) users in an efficient and timely manner. Most of the physical devices in the fog computing environment, commonly named fog nodes, are geographically distributed, resource constrained, and heterogeneous. To fully leverage the capabilities of the fog nodes, large-scale applications that are decomposed into interdependent Application Modules can be deployed in an orderly way over the nodes based on their latency sensitivity. In this article, we propose a latency-aware Application Module management policy for the fog environment that meets the diverse service delivery latency and amount of data signals to be processed in per unit of time for different applications. The policy aims to ensure applications’ Quality of Service (QoS) in satisfying service delivery deadlines and to optimize resource usage in the fog environment. We model and evaluate our proposed policy in an iFogSim-simulated fog environment. Results of the simulation studies demonstrate significant improvement in performance over alternative latency-aware strategies.

Accurate Self-Localization in RFID Tag Information Grids Using FIR Filtering Grid navigation spaces nested with the radio-frequency identification (RFID) tags are promising for industrial and other needs, because each tag can deliver information about a local two-dimensional or three-dimensional surrounding. The approach, however, requires high accuracy in vehicle self-localization. Otherwise, errors may lead to collisions; possibly even fatal. We propose a new extended finite impulse response (EFIR) filtering algorithm and show that it meets this need. The EFIR filter requires an optimal averaging interval, but does not involve the noise statistics which are often not well known to the engineer. It is more accurate than the extended Kalman filter (EKF) under real operation conditions and its iterative algorithm has the Kalman form. Better performance of the proposed EFIR filter is demonstrated based on extensive simulations in a comparison to EKF, which is widely used in RFID tag grids. We also show that errors in noise covariances may provoke divergence in EKF, whereas the EFIR filter remains stable and is thus more robust.

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advantages such as preserving data privacy, FL still has challenges of heterogeneity across UEs' data and physical resources. To address these challenges, we first propose FEDL, a FL algorithm which can handle heterogeneous UE data without further assumptions except strongly convex and smooth loss functions. We provide a convergence rate characterizing the trade-off between local computation rounds of each UE to update its local model and global communication rounds to update the FL global model. We then employ FEDL in wireless networks as a resource allocation optimization problem that captures the trade-off between FEDL convergence wall clock time and energy consumption of UEs with heterogeneous computing and power resources. Even though the wireless resource allocation problem of FEDL is non-convex, we exploit this problem's structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights into problem design. Finally, we empirically evaluate the convergence of FEDL with PyTorch experiments, and provide extensive numerical results for the wireless resource allocation sub-problems. Experimental results show that FEDL outperforms the vanilla FedAvg algorithm in terms of convergence rate and test accuracy in various settings.

A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots Autonomous mobile robots navigating in changing and dynamic unstructured environments like the outdoor environments need to cope with large amounts of uncertainties that are inherent of natural environments. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present a novel reactive control architecture for autonomous mobile robots that is based on type-2 FLC to implement the basic navigation behaviors and the coordination between these behaviors to produce a type-2 hierarchical FLC. In our experiments, we implemented this type-2 architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2-based control system dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1-based control system while achieving a significant rule reduction compared to the type-1 system.

IntrospectiveViews: an interface for scrutinizing semantic user models User models are a key component for user-adaptive systems They represent information about users such as interests, expertise, goals, traits, etc This information is used to achieve various adaptation effects, e.g., recommending relevant documents or products To ensure acceptance by users, these models need to be scrutable, i.e., users must be able to view and alter them to understand and if necessary correct the assumptions the system makes about the user However, in most existing systems, this goal is not met In this paper, we introduce IntrospectiveViews, an interface that enables the user to view and edit her user model Furthermore, we present the results of a formative evaluation that show the importance users give in general to different aspects of scrutable user models and also substantiate our claim that IntrospectiveViews is an appropriate realization of an interface to such models.

Cost-Effective Authentic and Anonymous Data Sharing with Forward Security Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to reauthenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Multiple switching-time-dependent discretized Lyapunov functions/functionals methods for stability analysis of switched time-delay stochastic systems. This paper presents novel approaches for stability analysis of switched linear time-delay stochastic systems under dwell time constraint. Instead of using comparison principle, piecewise switching-time-dependent discretized Lyapunov functions/functionals are introduced to analyze the stability of switched stochastic systems with constant or time-varying delays. These Lyapunov functions/functionals are decreasing during the dwell time and non-increasing at switching instants, which lead to two mode-dependent dwell-time-based delay-independent stability criteria for the switched systems without restricting the stability of the subsystems. Comparison and numerical examples are provided to show the efficiency of the proposed results.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done via centralized learning (CL), where the whole training dataset is collected from the users at the base station (BS). This approach introduces huge communication overhead for data collection. In this paper, to address this challenge, we propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We develop FL-based channel estimation schemes for both conventional and RIS (intelligent reflecting surface) assisted massive MIMO (multiple-input multiple-output) systems, where a single CNN is trained for two different datasets for both scenarios. We evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower overhead than CL, while maintaining satisfactory performance close to CL. Furthermore, the proposed architecture exhibits lower estimation error than the state-of-the-art ML-based schemes.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors. Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

Ethical Considerations Of Applying Robots In Kindergarten Settings: Towards An Approach From A Macroperspective In child-robot interaction (cHRI) research, many studies pursue the goal to develop interactive systems that can be applied in everyday settings. For early education, increasingly, the setting of a kindergarten is targeted. However, when cHRI and research are brought into a kindergarten, a range of ethical and related procedural aspects have to be considered and dealt with. While ethical models elaborated within other human-robot interaction settings, e.g., assisted living contexts, can provide some important indicators for relevant issues, we argue that it is important to start developing a systematic approach to identify and tackle those ethical issues which rise with cHRI in kindergarten settings on a more global level and address the impact of the technology from a macroperspective beyond the effects on the individual. Based on our experience in conducting studies with children in general and pedagogical considerations on the role of the institution of kindergarten in specific, in this paper, we enfold some relevant aspects that have barely been addressed in an explicit way in current cHRI research. Four areas are analyzed and key ethical issues are identified in each area: (1) the institutional setting of a kindergarten, (2) children as a vulnerable group, (3) the caregivers' role, and (4) pedagogical concepts. With our considerations, we aim at (i) broadening the methodology of the current studies within the area of cHRI, (ii) revalidate it based on our comprehensive empirical experience with research in kindergarten settings, both laboratory and real-world contexts, and (iii) provide a framework for the development of a more systematic approach to address the ethical issues in cHRI research within kindergarten settings.

Patch-Based Discriminative Feature Learning For Unsupervised Person Re-Identification While discriminative local features have been shown effective in solving the person re-identification problem, they are limited to be trained on fully pairwise labelled data which is expensive to obtain. In this work, we overcome this problem by proposing a patch-based unsupervised learning framework in order to learn discriminative feature from patches instead of the whole images. The patch-based learning leverages similarity between patches to learn a discriminative model. Specifically, we develop a PatchNet to select patches from the feature map and learn discriminative features for these patches. To provide effective guidance for the PatchNet to learn discriminative patch feature on unlabeled datasets, we propose an unsupervised patch-based discriminative feature learning loss. In addition, we design an image-level feature learning loss to leverage all the patch features of the same image to serve as an image-level guidance for the PatchNet. Extensive experiments validate the superiority of our method for unsupervised person re-id. Our code is available at https://github.com/QizeYang/PAUL.

Omni-Scale Feature Learning For Person Re-Identification As an instance-level recognition problem, person reidentification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales. We callse features of both homogeneous and heterogeneous scales omni-scale features. In this paper, a novel deep ReID CNN is designed, termed Omni-Scale Network (OSNet), for omni-scale feature learning. This is achieved by designing a residual block composed of multiple convolutional feature streams, each detecting features at a certain scale. Importantly, a novel unified aggregation gate is introduced to dynamically fuse multiscale features with input-dependent channel-wise weights. To efficiently learn spatial-channel correlations and avoid overfitting, the building block uses both pointwise and depthwise convolutions. By stacking such blocks layer-by-layer, our OSNet is extremely lightweight and can be trained from scratch on existing ReID benchmarks. Despite its small model size, our OSNet achieves state-of-the-art performance on six person-ReID datasets. Code and models are available at: https://github.com/ KaiyangZhou/deep-person-reid.

Differentiable Learning-to-Normalize via Switchable Normalization. We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different operations for different normalization layers of a deep neural network (DNN). SN switches among three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch, by learning their importance weights in an end-to-end manner. SN has several good properties. First, it adapts to various network architectures and tasks (see Fig.1). Second, it is robust to a wide range of batch sizes, maintaining high performance when small minibatch is presented (e.g. 2 images/GPU). Third, SN treats all channels as a group, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging problems, such as image classification in ImageNet, object detection and segmentation in COCO, artistic image stylization, and neural architecture search. We hope SN will help ease the usages and understand the effects of normalization techniques in deep learning. The code of SN will be made available in this https URL.

Generalizing Across Domains via Cross-Gradient Training. We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD jointly trains a label and a domain classifier on examples perturbed by loss gradients of each other’s objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and (2) data augmentation is a more stable and accurate method than domain adversarial training.

Vrstc: Occlusion-Free Video Person Re-Identification Video person re-identification (re-ID) plays an important role in surveillance video analysis. However, the performance of video re-ID degenerates severely under partial occlusion. In this paper, we propose a novel network, called Spatio-Temporal Completion network (STCnet), to explicitly handle partial occlusion problem. Different from most previous works that discard the occluded frames, STCnet can recover the appearance of the occluded parts. For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame. For another, the temporal patterns of pedestrian sequence provide important clues to generate the contents of occluded parts. With the spatio-temporal information, STCnet can recover the appearance for the occluded parts, which could be leveraged with those unoccluded parts for more accurate video re-ID. By combining a re-ID network with STCnet, a video re-ID framework robust to partial occlusion (VRSTC) is proposed. Experiments on three challenging video re-ID databases demonstrate that the proposed approach outperforms the state-of-the-arts.

Perceive Where to Focus: Learning Visibility-Aware Part-Level Features for Partial Person Re-Identification This paper considers a realistic problem in person re-identification (re-ID) task, i.e., partial re-ID. Under partial re-ID scenario, the images may contain a partial observation of a pedestrian. If we directly compare a partial pedestrian image with a holistic one, the extreme spatial misalignment significantly compromises the discriminative ability of the learned representation. We propose a Visibility-aware Part Model (VPM) for partial re-ID, which learns to perceive the visibility of regions through self-supervision. The visibility awareness allows VPM to extract region-level features and compare two images with focus on their shared regions (which are visible on both images). VPM gains two-fold benefit toward higher accuracy for partial re-ID. On the one hand, compared with learning a global feature, VPM learns region-level features and thus benefits from fine-grained information. On the other hand, with visibility awareness, VPM is capable to estimate the shared regions between two images and thus suppresses the spatial misalignment. Experimental results confirm that our method significantly improves the learned feature representation and the achieved accuracy is on par with the state of the art.

Learning Discriminative Features with Multiple Granularities for Person Re-Identification. The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we obtain a top result of Rank-1/mAP=96.6%/94.2% with this method after re-ranking.

Microsoft Coco: Common Objects In Context We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

Digital games in the classroom? A contextual approach to teachers' adoption intention of digital games in formal education Interest in using digital games for formal education has steadily increased in the past decades. When it comes to actual use, however, the uptake of games in the classroom remains limited. Using a contextual approach, the possible influence of factors on a school (N=60) and teacher (N=409) level are analyzed. Findings indicate that there is no effect of factors on the school level whereas on a teacher level, a model is tested, explaining 68% of the variance in behavioral intention, in which curriculum-relatedness and previous experience function as crucial determinants of the adoption intention. These findings add to previous research on adoption determinants related to digital games in formal education. Furthermore, they provide insight into the relations between different adoption determinants and their association with behavioral intention.

Response time in man-computer conversational transactions The literature concerning man-computer transactions abounds in controversy about the limits of "system response time" to a user's command or inquiry at a terminal. Two major semantic issues prohibit resolving this controversy. One issue centers around the question of "Response time to what?" The implication is that different human purposes and actions will have different acceptable or useful response times.

Stabilization of switched continuous-time systems with all modes unstable via dwell time switching Stabilization of switched systems composed fully of unstable subsystems is one of the most challenging problems in the field of switched systems. In this brief paper, a sufficient condition ensuring the asymptotic stability of switched continuous-time systems with all modes unstable is proposed. The main idea is to exploit the stabilization property of switching behaviors to compensate the state divergence made by unstable modes. Then, by using a discretized Lyapunov function approach, a computable sufficient condition for switched linear systems is proposed in the framework of dwell time; it is shown that the time intervals between two successive switching instants are required to be confined by a pair of upper and lower bounds to guarantee the asymptotic stability. Based on derived results, an algorithm is proposed to compute the stability region of admissible dwell time. A numerical example is proposed to illustrate our approach.

A novel data hiding for color images based on pixel value difference and modulus function This paper proposes a novel data hiding method using pixel-value difference and modulus function for color image with the large embedding capacity(hiding 810757 bits in a 512 512 host image at least) and a high-visual-quality of the cover image. The proposed method has fully taken into account the correlation of the R, G and B plane of a color image. The amount of information embedded the R plane and the B plane determined by the difference of the corresponding pixel value between the G plane and the median of G pixel value in each pixel block. Furthermore, two sophisticated pixel value adjustment processes are provided to maintain the division consistency and to solve underflow and overflow problems. The most importance is that the secret data are completely extracted through the mathematical theoretical proof.

OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Using redundancy in FDDI networks The connection management (CMT) of the fiber distributed data interface (FDDI) standard supports redundancy through a number of different network configurations. This redundancy may be used to increase the reliability of the network. These network configurations include using dual attachment stations in the dual ring, invoking the hold policy, using concentrator trees and using dual homing in the concentrator tree. The reliability of these configurations is examined and compared with network configurations without redundancy. The results show that the dual ring is able to withstand single faults, but not multiple faults. Other configuration options must be used to withstand multiple faults. The hold policy does not significantly increase the fault tolerance of FDDI. There are a number of useful configuration options in FDDI that allow redundancy. Depending upon the required level of robustness, FDDI configurations are available to meet the requirements

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer's electricity bill whilst minimizing the daily volume of curtailed energy, and therefore considering the user's comfort level. To avoid shifting a large portion of consumer demand toward the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer's demand goes beyond a prescribed power threshold. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller in minimizing the household's daily electricity bill while {preserving} comfort level, as well as preventing creation of new least-price peaks.

Scalable and Privacy-Preserving Data Sharing Based on Blockchain. With the development of network technology and cloud computing, data sharing is becoming increasingly popular, and many scholars have conducted in-depth research to promote its flourish. As the scale of data sharing expands, its privacy protection has become a hot issue in research. Moreover, in data sharing, the data is usually maintained in multiple parties, which brings new challenges to protect the privacy of these multi-party data. In this paper, we propose a trusted data sharing scheme using blockchain. We use blockchain to prevent the shared data from being tampered, and use the Paillier cryptosystem to realize the confidentiality of the shared data. In the proposed scheme, the shared data can be traded, and the transaction information is protected by using the (p, t)-threshold Paillier cryptosystem. We conduct experiments in cloud storage scenarios and the experimental results demonstrate the efficiency and effectiveness of the proposed scheme.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Joint Optimization of Edge Computing Architectures and Radio Access Networks. Virtualized radio access network (vRAN) architectures and multiple-access edge computing (MEC) systems constitute two key solutions for the emerging Tactile Internet applications and the increasing mobile data traffic. Their efficient deployment, however, requires a careful design tailored to the available network resources and user demand. In this paper, we propose a novel modeling approach and a...

Towards a flexible functional split for cloud-RAN networks Very dense deployments of small cells are one of the key enablers to tackle the ever-growing demand on mobile bandwidth. In such deployments, centralization of RAN functions on cloud resources is envisioned to overcome severe inter-cell interference and to keep costs acceptable. However, RAN back-haul constraints need to be considered when designing the functional split between RAN front-ends and centralized equipment. In this paper we analyse constraints and outline applications of flexible RAN centralization.

Towards a Cost Optimal Design for a 5G Mobile Core Network Based on SDN and NFV. With the rapid growth of user traffic, service innovation, and the persistent necessity to reduce costs, today&#39;s mobile operators are faced with several challenges. In networking, two concepts have emerged aiming at cost reduction, increase of network scalability and deployment flexibility, namely Network Functions Virtualization (NFV) and Software Defined Networking (SDN). NFV mitigates the depen...

Enabling Dynamically Centralized RAN Architectures in 5G and Beyond In order to deliver the high data rates promised for 5G networks, mobile base stations need to be deployed in dense layouts. This results in increased inter-cell interference, which can be mitigated by leveraging centralized architectures in radio access networks. Nonetheless, centralizing all the processing requires prohibitively high link capacities for the fronthaul network connecting centraliz...

Flex5G: Flexible Functional Split in 5G Networks. 5G networks are expected to support various applications with diverse requirements in terms of latency, data rates, and traffic volume. Cloud-RAN(C-RAN) and densely deployed small cells are two of the tools at disposal of mobile network operators to cope with such challenges. In order to mitigate the fronthaul requirements imposed by the C-RAN architecture, several functional splits, each characte...

Completely derandomized self-adaptation in evolution strategies. This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigorously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is observed. On moderately mis-scaled functions a speed up factor of three to ten can be expected.

Hiding Traces of Resampling in Digital Images Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques.

Fog computing and its role in the internet of things Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).

Efficient Signature Generation by Smart Cards We present a new public-key signature scheme and a corresponding authentication scheme that are based on discrete logarithms in a subgroup of units in Zp where p is a sufficiently large prime, e.g., p = 2512. A key idea is to use for the base of the discrete logarithm an integer a in Zp such that the order of a is a sufficiently large prime q, e.g., q = 2140. In this way we improve the ElGamal signature scheme in the speed of the procedures for the generation and the verification of signatures and also in the bit length of signatures. We present an efficient algorithm that preprocesses the exponentiation of a random residue modulo p.

Stabilizing a linear system by switching control with dwell time The use of networks in control systems to connect controllers and sensors/actuators has become common practice in many applications. This new technology has also posed a theoretical control problem of how to use the limited data rate of the network effectively. We consider a system where its sensor and actuator are connected by a finite data rate channel. A design method to stabilize a continuous-time, linear plant using a switching controller is proposed. In particular, to prevent the actuator from fast switching, or chattering, which can not only increase the necessary data rate but also damage the system, we employ a dwell-time switching scheme. It is shown that a systematic partition of the state-space enables us to reduce the complexity of the design problem

A Web-Based Tool For Control Engineering Teaching In this article a new tool for control engineering teaching is presented. The tool was implemented using Java applets and is freely accessible through Web. It allows the analysis and simulation of linear control systems and was created to complement the theoretical lectures in basic control engineering courses. The article is not only centered in the description of the tool but also in the methodology to use it and its evaluation in an electrical engineering degree. Two practical problems are included in the manuscript to illustrate the use of the main functions implemented. The developed web-based tool can be accessed through the link http://www.controlweb.cyc.ull.es. (C) 2006 Wiley Periodicals, Inc.

Beamforming for MISO Interference Channels with QoS and RF Energy Transfer We consider a multiuser multiple-input single-output interference channel where the receivers are characterized by both quality-of-service (QoS) and radio-frequency (RF) energy harvesting (EH) constraints. We consider the power splitting RF-EH technique where each receiver divides the received signal into two parts a) for information decoding and b) for battery charging. The minimum required power that supports both the QoS and the RF-EH constraints is formulated as an optimization problem that incorporates the transmitted power and the beamforming design at each transmitter as well as the power splitting ratio at each receiver. We consider both the cases of fixed beamforming and when the beamforming design is incorporated into the optimization problem. For fixed beamforming we study three standard beamforming schemes, the zero-forcing (ZF), the regularized zero-forcing (RZF) and the maximum ratio transmission (MRT); a hybrid scheme, MRT-ZF, comprised of a linear combination of MRT and ZF beamforming is also examined. The optimal solution for ZF beamforming is derived in closed-form, while optimization algorithms based on second-order cone programming are developed for MRT, RZF and MRT-ZF beamforming to solve the problem. In addition, the joint-optimization of beamforming and power allocation is studied using semidefinite programming (SDP) with the aid of rank relaxation.

An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks. Existing studies on wireless sensor networks (WSNs) have revealed that the limited battery capacity of sensor nodes (SNs) hinders their perpetual operation. Recent findings in the domain of wireless energy transfer (WET) have attracted a lot of attention of academia and industry to cater the lack of energy in the WSNs. The main idea of WET is to restore the energy of SNs using one or more wireless mobile chargers (MCs), which leads to a new paradigm of wireless rechargeable sensor networks (WRSNs). The determination of an optimal order of charging the SNs (i.e., charging schedule) in an on-demand WRSN is a well-known NP-hard problem. Moreover, care must be taken while designing the charging schedule of an MC as requesting SNs introduce both spatial and temporal constraints. In this paper, we first present a Linear Programming (LP) formulation for the problem of scheduling an MC and then propose an efficient solution based on gravitational search algorithm (GSA). Our method is presented with a novel agent representation scheme and an efficient fitness function. We perform extensive simulations on the proposed scheme to demonstrate its effectiveness over two state-of-the-art algorithms, namely first come first serve (FCFS) and nearest job next with preemption (NJNP). The simulation results reveal that the proposed scheme outperforms both the existing algorithms in terms of charging latency. The virtue of our scheme is also proved by the well-known statistical test, analysis of variance (ANOVA), followed by post hoc analysis.

Attitudes Towards Social Robots In Education: Enthusiast, Practical, Troubled, Sceptic, And Mindfully Positive While social robots bring new opportunities for education, they also come with moral challenges. Therefore, there is a need for moral guidelines for the responsible implementation of these robots. When developing such guidelines, it is important to include different stakeholder perspectives. Existing (qualitative) studies regarding these perspectives however mainly focus on single stakeholders. In this exploratory study, we examine and compare the attitudes of multiple stakeholders on the use of social robots in primary education, using a novel questionnaire that covers various aspects of moral issues mentioned in earlier studies. Furthermore, we also group the stakeholders based on similarities in attitudes and examine which socio-demographic characteristics influence these attitude types. Based on the results, we identify five distinct attitude profiles and show that the probability of belonging to a specific profile is affected by such characteristics as stakeholder type, age, education and income. Our results also indicate that social robots have the potential to be implemented in education in a morally responsible way that takes into account the attitudes of various stakeholders, although there are multiple moral issues that need to be addressed first. Finally, we present seven (practical) implications for a responsible application of social robots in education following from our results. These implications provide valuable insights into how social robots should be implemented.

Effects of robotic knee exoskeleton on human energy expenditure. A number of studies discuss the design and control of various exoskeleton mechanisms, yet relatively few address the effect on the energy expenditure of the user. In this paper, we discuss the effect of a performance augmenting exoskeleton on the metabolic cost of an able-bodied user/pilot during periodic squatting. We investigated whether an exoskeleton device will significantly reduce the metabolic cost and what is the influence of the chosen device control strategy. By measuring oxygen consumption, minute ventilation, heart rate, blood oxygenation, and muscle EMG during 5-min squatting series, at one squat every 2 s, we show the effects of using a prototype robotic knee exoskeleton under three different noninvasive control approaches: gravity compensation approach, position-based approach, and a novel oscillator-based approach. The latter proposes a novel control that ensures synchronization of the device and the user. Statistically significant decrease in physiological responses can be observed when using the robotic knee exoskeleton under gravity compensation and oscillator-based control. On the other hand, the effects of position-based control were not significant in all parameters although all approaches significantly reduced the energy expenditure during squatting.

Wearable soft sensing suit for human gait measurement Wearable robots based on soft materials will augment mobility and performance of the host without restricting natural kinematics. Such wearable robots will need soft sensors to monitor the movement of the wearer and robot outside the lab. Until now wearable soft sensors have not demonstrated significant mechanical robustness nor been systematically characterized for human motion studies of walking and running. Here, we present the design and systematic characterization of a soft sensing suit for monitoring hip, knee, and ankle sagittal plane joint angles. We used hyper-elastic strain sensors based on microchannels of liquid metal embedded within elastomer, but refined their design with the use of discretized stiffness gradients to improve mechanical durability. We found that these robust sensors could stretch up to 396% of their original lengths, would restrict the wearer by less than 0.17% of any given joint's torque, had gauge factor sensitivities of greater than 2.2, and exhibited less than 2% change in electromechanical specifications through 1500 cycles of loading-unloading. We also evaluated the accuracy and variability of the soft sensing suit by comparing it with joint angle data obtained through optical motion capture. The sensing suit had root mean square (RMS) errors of less than 5° for a walking speed of 0.89 m/s and reached a maximum RMS error of 15° for a running speed of 2.7 m/s. Despite the deviation of absolute measure, the relative repeatability of the sensing suit's joint angle measurements were statistically equivalent to that of optical motion capture at all speeds. We anticipate that wearable soft sensing will also have applications beyond wearable robotics, such as in medical diagnostics and in human-computer interaction.

Development of an orthosis for walking assistance using pneumatic artificial muscle: a quantitative assessment of the effect of assistance. In recent years, there is an increase in the number of people that require support during walking as a result of a decrease in the leg muscle strength accompanying aging. An important index for evaluating walking ability is step length. A key cause for a decrease in step length is the loss of muscle strength in the legs. Many researchers have designed and developed orthoses for walking assistance. In this study, we advanced the design of an orthosis for walking assistance that assists the forward swing of the leg to increase step length. We employed a pneumatic artificial muscle as the actuator so that flexible assistance with low rigidity can be achieved. To evaluate the performance of the system, we measured the effect of assistance quantitatively. In this study, we constructed a prototype of the orthosis and measure EMG and step length on fitting it to a healthy subject so as to determine the effect of assistance, noting the increase in the obtained step length. Although there was an increase in EMG stemming from the need to maintain body balance during the stance phase, we observed that the EMG of the sartorius muscle, which helps swing the leg forward, decreased, and the strength of the semitendinosus muscle, which restrains the leg against over-assistance, did not increase but decreased. Our experiments showed that the assistance force provided by the developed orthosis is not adequate for the intended task, and the development of a mechanism that provides appropriate assistance is required in the future.

Ergonomics of exoskeletons: Objective performance metrics In this paper it is shown how variation of the kinematic structure of an exoskeleton and variation of its fixation strength on the human limb influences objective task performance metrics, such as interface load, tracking error and voluntary range of motion in a signal tracking experiment.

Biologically-inspired soft exosuit. In this paper, we present the design and evaluation of a novel soft cable-driven exosuit that can apply forces to the body to assist walking. Unlike traditional exoskeletons which contain rigid framing elements, the soft exosuit is worn like clothing, yet can generate moments at the ankle and hip with magnitudes of 18% and 30% of those naturally generated by the body during walking, respectively. Our design uses geared motors to pull on Bowden cables connected to the suit near the ankle. The suit has the advantages over a traditional exoskeleton in that the wearer's joints are unconstrained by external rigid structures, and the worn part of the suit is extremely light, which minimizes the suit's unintentional interference with the body's natural biomechanics. However, a soft suit presents challenges related to actuation force transfer and control, since the body is compliant and cannot support large pressures comfortably. We discuss the design of the suit and actuation system, including principles by which soft suits can transfer force to the body effectively and the biological inspiration for the design. For a soft exosuit, an important design parameter is the combined effective stiffness of the suit and its interface to the wearer. We characterize the exosuit's effective stiffness, and present preliminary results from it generating assistive torques to a subject during walking. We envision such an exosuit having broad applicability for assisting healthy individuals as well as those with muscle weakness.

Design analysis of a passive weight-support lower-extremity-exoskeleton with compliant knee-joint This paper presents the design concept of a weight-support lower-extremity-exoskeleton (LEE) with a compliant joint to relieve compressive load in the knee. Along with a leg dynamic model and a knee bio-joint model, a compliant exoskeleton knee-joint has been designed using topology optimization and experimentally evaluated. Results suggest that the gait-based design of a LEE can be divided into two parts in terms of knee angles; compliant coupling and body-weight support. The concept feasibility and dynamic models of the passive LEE design have been experimentally validated with measured plantar forces. Both simulation and experimental results agree with data in-vivo confirming the effectiveness of the LEE in supporting human body-weight during walking, and also provide a basis for computing the internal knee forces as a percentage of bodyweight.

Eyes are faster than hands: A soft wearable robot learns user intention from the egocentric view. To perceive user intentions for wearable robots, we present a learning-based intention detection methodology using a first-person-view camera.

Soft Robotic Suits: State of the Art, Core Technologies, and Open Challenges Wearable robots are undergoing a disruptive transition, from the rigid machines that populated the science-fiction world in the early 1980s to lightweight robotic apparel, hardly distinguishable from our daily clothes. In less than a decade of development, soft robotic suits have achieved important results in human motor assistance and augmentation. In this article, we start by giving a definition of soft robotic suits and proposing a taxonomy to classify existing systems. We then critically review the modes of actuation, the physical human–robot interface and the intention-detection strategies of state-of-the-art soft robotic suits, highlighting the advantages and limitations of different approaches. Finally, we discuss the impact of this new technology on human movements, for both augmenting human function and supporting motor impairments, and identify areas that are in need of further development.

Evolutionary computation: comments on the history and current state Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete

Amazon mechanical turk: Gold mine or coal mine?

Rich Models for Steganalysis of Digital Images We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous approaches, we make the model assembly a part of the training process driven by samples drawn from the corresponding cover- and stego-sources. Ensemble classifiers are used to assemble the model as well as the final steganalyzer due to their low computational complexity and ability to efficiently work with high-dimensional feature spaces and large training sets. We demonstrate the proposed framework on three steganographic algorithms designed to hide messages in images represented in the spatial domain: HUGO, edge-adaptive algorithm by Luo , and optimally coded ternary $\\pm {\\hbox{1}}$ embedding. For each algorithm, we apply a simple submodel-selection technique to increase the detection accuracy per model dimensionality and show how the detection saturates with increasing complexity of the rich model. By observing the differences between how different submodels engage in detection, an interesting interplay between the embedding and detection is revealed. Steganalysis built around rich image models combined with ensemble classifiers is a promising direction towards automatizing steganalysis for a wide spectrum of steganographic schemes.

A novel data hiding for color images based on pixel value difference and modulus function This paper proposes a novel data hiding method using pixel-value difference and modulus function for color image with the large embedding capacity(hiding 810757 bits in a 512 512 host image at least) and a high-visual-quality of the cover image. The proposed method has fully taken into account the correlation of the R, G and B plane of a color image. The amount of information embedded the R plane and the B plane determined by the difference of the corresponding pixel value between the G plane and the median of G pixel value in each pixel block. Furthermore, two sophisticated pixel value adjustment processes are provided to maintain the division consistency and to solve underflow and overflow problems. The most importance is that the secret data are completely extracted through the mathematical theoretical proof.

Adaptive fuzzy tracking control for switched uncertain strict-feedback nonlinear systems. •Adaptive tracking control for switched strict-feedback nonlinear systems is proposed.•The generalized fuzzy hyperbolic model is used to approximate nonlinear functions.•The designed controller has fewer design parameters comparing with existing methods.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Wireless Power Transfer Protocols in Sensor Networks: Experiments and Simulations. Rapid technological advances in the domain of Wireless Power Transfer pave the way for novel methods for power management in systems of wireless devices, and recent research works have already started considering algorithmic solutions for tackling emerging problems. In this paper, we investigate the problem of efficient and balanced Wireless Power Transfer in Wireless Sensor Networks. We employ wireless chargers that replenish the energy of network nodes. We propose two protocols that configure the activity of the chargers. One protocol performs wireless charging focused on the charging efficiency, while the other aims at proper balance of the chargers' residual energy. We conduct detailed experiments using real devices and we validate the experimental results via larger scale simulations. We observe that, in both the experimental evaluation and the evaluation through detailed simulations, both protocols achieve their main goals. The Charging Oriented protocol achieves good charging efficiency throughout the experiment, while the Energy Balancing protocol achieves a uniform distribution of energy within the chargers.

Adaptive SSO based node selection for partial charging in wireless sensor network Wireless chargers provide dynamic range of power to wireless sensor network (WSN). These chargers may come under the Wireless Rechargeable Sensor Networks (WRSNs) category. The WRSNs are flexible in nature and this is further improved by introducing the mobile chargers (MCs) in WRSNs. MCs perform wireless energy transfer in WSN to replenish the energy minimized nodes. WRSNs offer controllable and predictable energy replacement to maximize the network lifetime. The availability of a single MC fails to maximize the lifetime of whole network. Therefore, multiple MCs are used in this partial charging approach to maximize the lifetime of whole network. To accomplish this partial charging process, initially the whole network is clustered by k-means clustering algorithm. Then, a deer hunting optimization (DHO) algorithm is introduced to select the Cluster Head (CH) for the selected clusters. Through the selected CH, the data transmission is performed between the source node and sink node. Next, the nodes that lost their energy during data transmission is identified using a metaheuristic algorithm namely adaptive social ski-driver optimization (ASSO). Based on the optimal result, the path is planned by multiple MCs to perform partial charging. While compared with single MC, the multiple MCs may face some coordination problems. To avoid this, the multiple MCs are co-ordinate on a distance basis, so that the complex partial charging can be achieved effectively. Partial charging improves the robustness and scalability of the entire network. Experimental results shown by the proposed approach has significantly outperforms the existing methods in terms of efficiency, network lifetime, delay, and complexity. The overall survival rate achieved by the proposed algorithm is 98% for 400 nodes, which is better than other algorithms. This is due to the introduction of an optimization for optimal node selection, whose performance has directly induced an influence in overall survival rate.

A state-of-the-art survey on wireless rechargeable sensor networks: perspectives and challenges Wireless rechargeable sensor network (WRSN) is an emerging technology that has risen intending to enhance network lifetime of the conventional wireless sensor networks (WSNs). WRSNs play a major role in achieving the durability of data collection, improving charging efficiency, enhancing network lifetime as well as better use of the network in worst conditions or low cost. In this paper, we have come up with a detailed overview of the developing wireless rechargeable networks where sensor nodes take advantage of wireless power transfer techniques and serve the network for a longer period. Moreover, this paper provides an overview and brief description of different papers related to WRSN from the last decade. Following a brief introduction, we briefly defined a few basic terms, and classified the charging schemes based on charging cycles, scheduling schemes, charging range, charging approaches, and number of mobile chargers. Furthermore, we discussed joint optimization techniques in WRSNs, unmanned aerial vehicle aided WRSNs and security threats in networks. Finally, we summarized the whole survey in tables and ended it by discussing the future direction and concluding remarks.

Charging Path Optimization For Wireless Rechargeable Sensor Network In wireless rechargeable sensor networks(WRSNs), charging path planning becomes more and more important. In this paper, a charging path planning model based on high-dimensional multi-objective optimization is proposed, which takes life cycle, distance, energy consumption and charging time into consideration. At the same time, an improved algorithm is proposed to improve the crossover mode and diversity of the reference-point-based many-objective evolutionary algorithm following non-dominated sorting genetic algorithm(NSGA)&NSGA-II framework(we call it NSGA-III) for charging path planning. In the end, the validity of the charging process and the rationality of the charging path are verified by experimental comparison.

Multi-Mc Charging Schedule Algorithm With Time Windows In Wireless Rechargeable Sensor Networks The limited lifespan of the traditional Wireless Sensor Networks (WSNs) has always restricted the broad application and development of WSNs. The current studies have shown that the wireless power transmission technology can effectively prolong the lifetime of WSNs. In most present studies on charging schedules, the sensor nodes will be charged once they have energy consumption, which will cause higher cost and lower networks utility. It is assumed in this paper that the sensor nodes in Wireless Rechargeable Sensor Networks (WRSNs) will be charged only after its energy is lower than a certain value. Each node has a charging time window and is charged within its respective time window. In large-scale wireless sensor networks, single mobile charger (MC) is difficult to ensure that all sensor nodes work properly. Therefore, it is propoesd in this paper that the multiple MCs which are used to replenish energy for the sensor nodes. When the average energy of all the sensor nodes falls below the upper energy threshold, each MC begins to charge the sensor nodes. The genetic algorithm has a great advantage in solving optimization problems. However, it could easily lead to inadequate search. Therefore, the genetic algorithm is improved by 2-opt strategy. And then multi-MC charging schedule algorithm with time windows based on genetic algorithm is proposed and simulated. The simulation results show that the algorithm designed in this paper can timely replenish energy for each sensor node and minimize the total charging cost.

Multi-Node Wireless Energy Charging in Sensor Networks Wireless energy transfer based on magnetic resonant coupling is a promising technology to replenish energy to a wireless sensor network (WSN). However, charging sensor nodes one at a time poses a serious scalability problem. Recent advances in magnetic resonant coupling show that multiple nodes can be charged at the same time. In this paper, we exploit this multi-node wireless energy transfer technology and investigate whether it is a scalable technology to address energy issues in a WSN. We consider a wireless charging vehicle (WCV) periodically traveling inside a WSN and charging sensor nodes wirelessly. Based on charging range of the WCV, we propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal cells. We pursue a formal optimization framework by jointly optimizing traveling path, flow routing, and charging time. By employing discretization and a novel Reformulation-Linearization Technique (RLT), we develop a provably near-optimal solution for any desired level of accuracy. Through numerical results, we demonstrate that our solution can indeed address the charging scalability problem in a WSN.

Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search This paper improves recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We, first, analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the sub-optimality of such a representation for matching descriptors and leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within the inverted file and are efficiently exploited for all images, even in the case of very large datasets. Experiments performed on a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short list of images, is complementary to our weak geometric consistency constraints and allows to further improve the accuracy.

Microsoft Coco: Common Objects In Context We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

The Whale Optimization Algorithm. The Whale Optimization Algorithm inspired by humpback whales is proposed.The WOA algorithm is benchmarked on 29 well-known test functions.The results on the unimodal functions show the superior exploitation of WOA.The exploration ability of WOA is confirmed by the results on multimodal functions.The results on structural design problems confirm the performance of WOA in practice. This paper proposes a novel nature-inspired meta-heuristic optimization algorithm, called Whale Optimization Algorithm (WOA), which mimics the social behavior of humpback whales. The algorithm is inspired by the bubble-net hunting strategy. WOA is tested with 29 mathematical optimization problems and 6 structural design problems. Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods. The source codes of the WOA algorithm are publicly available at http://www.alimirjalili.com/WOA.html

Collaborative privacy management The landscape of the World Wide Web with all its versatile services heavily relies on the disclosure of private user information. Unfortunately, the growing amount of personal data collected by service providers poses a significant privacy threat for Internet users. Targeting growing privacy concerns of users, privacy-enhancing technologies emerged. One goal of these technologies is the provision of tools that facilitate a more informative decision about personal data disclosures. A famous PET representative is the PRIME project that aims for a holistic privacy-enhancing identity management system. However, approaches like the PRIME privacy architecture require service providers to change their server infrastructure and add specific privacy-enhancing components. In the near future, service providers are not expected to alter internal processes. Addressing the dependency on service providers, this paper introduces a user-centric privacy architecture that enables the provider-independent protection of personal data. A central component of the proposed privacy infrastructure is an online privacy community, which facilitates the open exchange of privacy-related information about service providers. We characterize the benefits and the potentials of our proposed solution and evaluate a prototypical implementation.

Cognitive Cars: A New Frontier for ADAS Research This paper provides a survey of recent works on cognitive cars with a focus on driver-oriented intelligent vehicle motion control. The main objective here is to clarify the goals and guidelines for future development in the area of advanced driver-assistance systems (ADASs). Two major research directions are investigated and discussed in detail: 1) stimuli–decisions–actions, which focuses on the driver side, and 2) perception enhancement–action-suggestion–function-delegation, which emphasizes the ADAS side. This paper addresses the important achievements and major difficulties of each direction and discusses how to combine the two directions into a single integrated system to obtain safety and comfort while driving. Other related topics, including driver training and infrastructure design, are also studied.

Online Prediction of Driver Distraction Based on Brain Activity Patterns This paper presents a new computational framework for early detection of driver distractions (map viewing) using brain activity measured by electroencephalographic (EEG) signals. Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver's needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.

An indoor localization solution using Bluetooth RSSI and multiple sensors on a smartphone. In this paper, we propose an indoor positioning system using a Bluetooth receiver, an accelerometer, a magnetic field sensor, and a barometer on a smartphone. The Bluetooth receiver is used to estimate distances from beacons. The accelerometer and magnetic field sensor are used to trace the movement of moving people in the given space. The horizontal location of the person is determined by received signal strength indications (RSSIs) and the traced movement. The barometer is used to measure the vertical position where a person is located. By combining RSSIs, the traced movement, and the vertical position, the proposed system estimates the indoor position of moving people. In experiments, the proposed approach showed excellent performance in localization with an overall error of 4.8%.

Convert Harm Into Benefit: A Coordination-Learning Based Dynamic Spectrum Anti-Jamming Approach This paper mainly investigates the multi-user anti-jamming spectrum access problem. Using the idea of “converting harm into benefit,” the malicious jamming signals projected by the enemy are utilized by the users as the coordination signals to guide spectrum coordination. An “internal coordination-external confrontation” multi-user anti-jamming access game model is constructed, and the existence of Nash equilibrium (NE) as well as correlated equilibrium (CE) is demonstrated. A coordination-learning based anti-jamming spectrum access algorithm (CLASA) is designed to achieve the CE of the game. Simulation results show the convergence, and effectiveness of the proposed CLASA algorithm, and indicate that our approach can help users confront the malicious jammer, and coordinate internal spectrum access simultaneously without information exchange. Last but not least, the fairness of the proposed approach under different jamming attack patterns is analyzed, which illustrates that this approach provides fair anti-jamming spectrum access opportunities under complicated jamming pattern.

Beyond 5G: Leveraging Cell Free TDD Massive MIMO Using Cascaded Deep Learning This letter deals with the calibration of Time Division Duplexing (TDD) reciprocity in an Orthogonal Frequency Division Multiplexing (OFDM) based Cell Free Massive MIMO system where the responses of the (Radio Frequency) RF chains render the end to end channel non-reciprocal, even though the physical wireless channel is reciprocal. We further address the non-availability of the uplink channel estimates at locations other than pilot subcarriers and propose a single-shot solution to estimate the downlink channel at all subcarriers from the uplink channel at selected pilot subcarriers. We propose a cascade of two Deep Neural Networks (DNN) to achieve the objective. The proposed method is easily scalable and removes the need for relative reciprocity calibration based on the cooperation of antennas, which usually introduces dependency in Cell Free Massive MIMO systems.

Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm Wireless communications are envisioned to bring about dramatic changes in the future, with a variety of emerging applications, such as virtual reality, Internet of Things, and so on, becoming a reality. However, these compelling applications have imposed many new challenges, including unknown channel models, low-latency requirement in large-scale super-dense networks, and so on. The amazing success of deep learning in various fields, particularly in computer science, has recently stimulated increasing interest in applying it to address those challenges. Hence, in this review, a pair of dominant methodologies of using DL for wireless communications are investigated. The first one is DL-based architecture design, which breaks the classical model-based block design rule of wireless communications in the past decades. The second one is DL-based algorithm design, which will be illustrated by several examples in a series of typical techniques conceived for 5G and beyond. Their principles, key features, and performance gains will be discussed. Open problems and future research opportunities will also be pointed out, highlighting the interplay between DL and wireless communications. We expect that this review can stimulate more novel ideas and exciting contributions for intelligent wireless communications.

Cell-Free Massive MIMO for 6G Wireless Communication Networks The recently commercialized fifth-generation (5G) wireless networks have achieved many improvements, including air interface enhancement, spectrum expansion, and network intensification by several key technologies, such as massive multiple-input multiple-output (MIMO), millimeter-wave communications, and ultra-dense networking. Despite the deployment of 5G commercial systems, wireless communicatio...

Unsupervised Machine Learning in 6G Networks -State-of-the-art and Future Trends Wireless communication systems play a very crucial role for business, commercial, health and safety applications. With the commercial deployment of fifth generation (5G), academic and industrial research focuses on the sixth generation (6G) of wireless communication systems. Artificial Intelligence (AI) and especially Machine Learning (ML), will be a key component of 6G systems. Here, we present an up-to-date review of future 6G wireless systems and the role of unsupervised ML techniques in them.

Deep Reinforcement Learning for Energy-Efficient Beamforming Design in Cell-Free Networks Cell-free network is considered as a promising architecture for satisfying more demands of future wireless networks, where distributed access points coordinate with an edge cloud processor to jointly provide service to a smaller number of user equipments in a compact area. In this paper, the problem of uplink beamforming design is investigated for maximizing the long-term energy efficiency (EE) wi...

Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combine-quantize-and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing large-scale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

Sequence to Sequence Learning with Neural Networks. Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.

Chimp optimization algorithm. •A novel optimizer called Chimp Optimization Algorithm (ChOA) is proposed.•ChOA is inspired by individual intelligence and sexual motivation of chimps.•ChOA alleviates the problems of slow convergence rate and trapping in local optima.•The four main steps of Chimp hunting are implemented.

A bayesian network approach to traffic flow forecasting A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data

A simplified dual neural network for quadratic programming with its KWTA application. The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and the computational complexity of the neural network. The simplified dual neural network is shown to be globally convergent to the exact optimal solution. The complexity of the neural network architecture is reduced with the number of neurons equal to the number of inequality constraints. Its application to k-winners-take-all (KWTA) operation is discussed to demonstrate how to solve problems with this neural network.

NETWRAP: An NDN Based Real-TimeWireless Recharging Framework for Wireless Sensor Networks Using vehicles equipped with wireless energy transmission technology to recharge sensor nodes over the air is a game-changer for traditional wireless sensor networks. The recharging policy regarding when to recharge which sensor nodes critically impacts the network performance. So far only a few works have studied such recharging policy for the case of using a single vehicle. In this paper, we propose NETWRAP, an N DN based Real Time Wireless Rech arging Protocol for dynamic wireless recharging in sensor networks. The real-time recharging framework supports single or multiple mobile vehicles. Employing multiple mobile vehicles provides more scalability and robustness. To efficiently deliver sensor energy status information to vehicles in real-time, we leverage concepts and mechanisms from named data networking (NDN) and design energy monitoring and reporting protocols. We derive theoretical results on the energy neutral condition and the minimum number of mobile vehicles required for perpetual network operations. Then we study how to minimize the total traveling cost of vehicles while guaranteeing all the sensor nodes can be recharged before their batteries deplete. We formulate the recharge optimization problem into a Multiple Traveling Salesman Problem with Deadlines (m-TSP with Deadlines), which is NP-hard. To accommodate the dynamic nature of node energy conditions with low overhead, we present an algorithm that selects the node with the minimum weighted sum of traveling time and residual lifetime. Our scheme not only improves network scalability but also ensures the perpetual operation of networks. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed design. The results also validate the correctness of the theoretical analysis and show significant improvements that cut the number of nonfunctional nodes by half compared to the static scheme while maintaining the network overhead at the same level.

OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.

Myoelectric or Force Control? A Comparative Study on a Soft Arm Exosuit The intention-detection strategy used to drive an exosuit is fundamental to evaluate the effectiveness and acceptability of the device. Yet, current literature on wearable soft robotics lacks evidence on the comparative performance of different control approaches for online intention-detection. In the present work, we compare two different and complementary controllers on a wearable robotic suit, previously formulated and tested by our group; a model-based myoelectric control ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myoprocessor</i> ), which estimates the joint torque from the activation of target muscles, and a force control that estimates human torques using an inverse dynamics model ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic arm</i> ). We test them on a cohort of healthy participants performing tasks replicating functional activities of daily living involving a wide range of dynamic movements. Our results suggest that both controllers are robust and effective in detecting human–motor interaction, and show comparable performance for augmenting muscular activity. In particular, the biceps brachii activity was reduced by up to 74% under the assistance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic arm</i> and up to 47% under the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myoprocessor</i> , compared to a no-suit condition. However, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myoprocessor</i> outperformed the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic arm</i> in promptness and assistance during movements that involve high dynamics. The exosuit work normalized with respect to the overall work was <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$68.84 \pm 3.81\%$</tex-math></inline-formula> when it was ran by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myoprocessor</i> , compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$45.29 \pm 7.71\%$</tex-math></inline-formula> during the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic arm</i> condition. The reliability and accuracy of motor intention detection strategies in wearable device is paramount for both the efficacy and acceptability of this technology. In this article, we offer a detailed analysis of the two most widely used control approaches, trying to highlight their intrinsic structural differences and to discuss their different and complementary performance.

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between the words receptionist and female, while maintaining desired associations such as between the words queen and female. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.

NLTK: the natural language toolkit The Natural Language Toolkit is a suite of program modules, data sets, tutorials and exercises, covering symbolic and statistical natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past three years, NLTK has become popular in teaching and research. We describe the toolkit and report on its current state of development.

WP:clubhouse?: an exploration of Wikipedia's gender imbalance Wikipedia has rapidly become an invaluable destination for millions of information-seeking users. However, media reports suggest an important challenge: only a small fraction of Wikipedia's legion of volunteer editors are female. In the current work, we present a scientific exploration of the gender imbalance in the English Wikipedia's population of editors. We look at the nature of the imbalance itself, its effects on the quality of the encyclopedia, and several conflict-related factors that may be contributing to the gender gap. Our findings confirm the presence of a large gender gap among editors and a corresponding gender-oriented disparity in the content of Wikipedia's articles. Further, we find evidence hinting at a culture that may be resistant to female participation.

Understanding Back-Translation at Scale. An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences. We find that in all but resource poor settings back-translations obtained via sampling or noised beam outputs are most effective. Our analysis shows that sampling or noisy synthetic data gives a much stronger training signal than data generated by beam or greedy search. We also compare how synthetic data compares to genuine bitext and study various domain effects. Finally, we scale to hundreds of millions of monolingual sentences and achieve a new state of the art of 35 BLEU on the WMTu002714 English-German test set.

Language (Technology) is Power: A Critical Survey of "Bias" in NLP We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.

Identifying and Reducing Gender Bias in Word-Level Language Models. Many text corpora exhibit socially problematic biases, which can be propagated or amplified in the models trained on such data. For example, doctor cooccurs more frequently with male pronouns than female pronouns. In this study we (i) propose a metric to measure gender bias; (ii) measure bias in a text corpus and the text generated from a recurrent neural network language model trained on the text corpus; (iii) propose a regularization loss term for the language model that minimizes the projection of encoder-trained embeddings onto an embedding subspace that encodes gender; (iv) finally, evaluate efficacy of our proposed method on reducing gender bias. We find this regularization method to be effective in reducing gender bias up to an optimal weight assigned to the loss term, beyond which the model becomes unstable as the perplexity increases. We replicate this study on three training corpora---Penn Treebank, WikiText-2, and CNN/Daily Mail---resulting in similar conclusions.

Controlling Linguistic Style Aspects in Neural Language Generation. Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controlling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts. We demonstrate the approach on the movie reviews domain and show that it is successful in generating coherent sentences corresponding to the required linguistic style and content.

Explanation in Artificial Intelligence: Insights from the Social Sciences. There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a ‘good’ explanation. There exist vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations to the explanation process. This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

A robust image authentication method distinguishing JPEG compression from malicious manipulation Image authentication verifies the originality of an image by detecting malicious manipulations. Its goal is different from that of image watermarking, which embeds into the image a signature surviving most manipulations. Most existing methods for image authentication treat all types of manipulation equally (i.e., as unacceptable). However, some practical applications demand techniques that can distinguish acceptable manipulations (e.g., compression) from malicious ones. In this paper, we present an effective technique for image authentication which can prevent malicious manipulations but allow JPEG lossy compression. The authentication signature is based on the invariance of the relationships between discrete cosine transform (DCT) coefficients at the same position in separate blocks of an image. These relationships are preserved when DCT coefficients are quantized in JPEG compression. Our proposed method can distinguish malicious manipulations from JPEG lossy compression regardless of the compression ratio or the number of compression iterations. We describe adaptive methods with probabilistic guarantee to handle distortions introduced by various acceptable manipulations such as integer rounding, image filtering, image enhancement, or scaling-recaling. We also present theoretical and experimental results to demonstrate the effectiveness of the technique

Multistep-Ahead time series prediction Multistep-ahead prediction is the task of predicting a sequence of values in a time series. A typical approach, known as multi-stage prediction, is to apply a predictive model step-by-step and use the predicted value of the current time step to determine its value in the next time step. This paper examines two alternative approaches known as independent value prediction and parameter prediction. The first approach builds a separate model for each prediction step using the values observed in the past. The second approach fits a parametric function to the time series and builds models to predict the parameters of the function. We perform a comparative study on the three approaches using multiple linear regression, recurrent neural networks, and a hybrid of hidden Markov model with multiple linear regression. The advantages and disadvantages of each approach are analyzed in terms of their error accumulation, smoothness of prediction, and learning difficulty.

Micro aerial vehicle networks: an experimental analysis of challenges and opportunities The need for aerial networks is growing with the recent advance of micro aerial vehicles, which enable a wide range of civilian applications. Our experimental analysis shows that wireless connectivity among MAVs is challenged by the mobility and heterogeneity of the nodes, lightweight antenna design, body blockage, constrained embedded resources, and limited battery power. However, the movement and location of MAVs are known and may be controlled to establish wireless links with the best transmission opportunities in time and space. This special ecosystem undoubtedly requires a rethinking of wireless communications and calls for novel networking approaches. Supported by empirical results, we identify important research questions, and introduce potential solutions and directions for investigation.

Multiple switching-time-dependent discretized Lyapunov functions/functionals methods for stability analysis of switched time-delay stochastic systems. This paper presents novel approaches for stability analysis of switched linear time-delay stochastic systems under dwell time constraint. Instead of using comparison principle, piecewise switching-time-dependent discretized Lyapunov functions/functionals are introduced to analyze the stability of switched stochastic systems with constant or time-varying delays. These Lyapunov functions/functionals are decreasing during the dwell time and non-increasing at switching instants, which lead to two mode-dependent dwell-time-based delay-independent stability criteria for the switched systems without restricting the stability of the subsystems. Comparison and numerical examples are provided to show the efficiency of the proposed results.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

The impact of knowledge complementarities on supply chain performance through knowledge exchange We develop the measurement of knowledge complementarities (KC) in supply chain.We provide empirical evidence about the effects of KC on knowledge exchange.Our results confirm that knowledge exchange affects supply chain performance. The extent of knowledge complementarities (KC) is an important theoretical and practical issue in inter-firm relationships. However, extant research on KC is not clear about what constitutes KC and how the benefits of KC are realized. Further, few empirical studies have examined the impact of KC on inter-firm performance. The purpose of this study is to identify the dimensions of KC and to empirically examine the relationships among KC, inter-firm knowledge exchange, and supply chain performance. We have used data collected from 70 matched pairs of buyer and supplier in a procurement dyad to test a proposed model. In both sample sets, the results show that the relationship between knowledge exchange and supply chain performance was positive and significant. We also found positive relationships between knowledge exchange and inter-organizational relationship characteristics such as inter-organizational trust and inter-organizational information systems integration. While the path from KC to knowledge exchange was positive and significant in the buyer sample, it was not significant in the supplier sample.

The Sybil Attack Large-scale peer-to-peer systems facesecurity threats from faulty or hostile remotecomputing elements. To resist these threats, manysuch systems employ redundancy. However, if asingle faulty entity can present multiple identities,it can control a substantial fraction of the system,thereby undermining this redundancy. Oneapproach to preventing these &quot;Sybil attacks&quot; is tohave a trusted agency certify identities. Thispaper shows that, without a logically centralizedauthority, Sybil...

BLEU: a method for automatic evaluation of machine translation Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.

Computational thinking Summary form only given. My vision for the 21st century, Computational Thinking, will be a fundamental skill used by everyone in the world. To reading, writing, and arithmetic, we should add computational thinking to every child's analytical ability. Computational thinking involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to computer science. Thinking like a computer scientist means more than being able to program a computer. It requires the ability to abstract and thus to think at multiple levels of abstraction. In this talk I will give many examples of computational thinking, argue that it has already influenced other disciplines, and promote the idea that teaching computational thinking can not only inspire future generations to enter the field of computer science but benefit people in all fields.

Fuzzy logic in control systems: fuzzy logic controller. I.

Switching between stabilizing controllers This paper deals with the problem of switching between several linear time-invariant (LTI) controllers—all of them capable of stabilizing a speci4c LTI process—in such a way that the stability of the closed-loop system is guaranteed for any switching sequence. We show that it is possible to 4nd realizations for any given family of controller transfer matrices so that the closed-loop system remains stable, no matter how we switch among the controller. The motivation for this problem is the control of complex systems where con8icting requirements make a single LTI controller unsuitable. ? 2002 Published by Elsevier Science Ltd.

Tabu Search - Part I

Bidirectional recurrent neural networks In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction. Structure and training procedure of the proposed network are explained. In regression and classification experiments on artificial data, the proposed structure gives better results than other approaches. For real data, classification experiments for phonemes from the TIMIT database show the same tendency. In the second part of this paper, it is shown how the proposed bidirectional structure can be easily modified to allow efficient estimation of the conditional posterior probability of complete symbol sequences without making any explicit assumption about the shape of the distribution. For this part, experiments on real data are reported

An intensive survey of fair non-repudiation protocols With the phenomenal growth of the Internet and open networks in general, security services, such as non-repudiation, become crucial to many applications. Non-repudiation services must ensure that when Alice sends some information to Bob over a network, neither Alice nor Bob can deny having participated in a part or the whole of this communication. Therefore a fair non-repudiation protocol has to generate non-repudiation of origin evidences intended to Bob, and non-repudiation of receipt evidences destined to Alice. In this paper, we clearly define the properties a fair non-repudiation protocol must respect, and give a survey of the most important non-repudiation protocols without and with trusted third party (TTP). For the later ones we discuss the evolution of the TTP's involvement and, between others, describe the most recent protocol using a transparent TTP. We also discuss some ad-hoc problems related to the management of non-repudiation evidences.

Dynamic movement and positioning of embodied agents in multiparty conversations For embodied agents to engage in realistic multiparty conversation, they must stand in appropriate places with respect to other agents and the environment. When these factors change, such as an agent joining the conversation, the agents must dynamically move to a new location and/or orientation to accommodate. This paper presents an algorithm for simulating movement of agents based on observed human behavior using techniques developed for pedestrian movement in crowd simulations. We extend a previous group conversation simulation to include an agent motion algorithm. We examine several test cases and show how the simulation generates results that mirror real-life conversation settings.

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

Lane-level traffic estimations using microscopic traffic variables This paper proposes a novel inference method to estimate lane-level traffic flow, time occupancy and vehicle inter-arrival time on road segments where local information could not be measured and assessed directly. The main contributions of the proposed method are 1) the ability to perform lane-level estimations of traffic flow, time occupancy and vehicle inter-arrival time and 2) the ability to adapt to different traffic regimes by assessing only microscopic traffic variables. We propose a modified Kriging estimation model which explicitly takes into account both spatial and temporal variability. Performance evaluations are conducted using real-world data under different traffic regimes and it is shown that the proposed method outperforms a Kalman filter-based approach.

Scalable and Privacy-Preserving Data Sharing Based on Blockchain. With the development of network technology and cloud computing, data sharing is becoming increasingly popular, and many scholars have conducted in-depth research to promote its flourish. As the scale of data sharing expands, its privacy protection has become a hot issue in research. Moreover, in data sharing, the data is usually maintained in multiple parties, which brings new challenges to protect the privacy of these multi-party data. In this paper, we propose a trusted data sharing scheme using blockchain. We use blockchain to prevent the shared data from being tampered, and use the Paillier cryptosystem to realize the confidentiality of the shared data. In the proposed scheme, the shared data can be traded, and the transaction information is protected by using the (p, t)-threshold Paillier cryptosystem. We conduct experiments in cloud storage scenarios and the experimental results demonstrate the efficiency and effectiveness of the proposed scheme.

Robot tutor and pupils’ educational ability: Teaching the times tables Research shows promising results of educational robots in language and STEM tasks. In language, more research is available, occasionally in view of individual differences in pupils’ educational ability levels, and learning seems to improve with more expressive robot behaviors. In STEM, variations in robots’ behaviors have been examined with inconclusive results and never while systematically investigating how differences in educational abilities match with different robot behaviors. We applied an autonomously tutoring robot (without tablet, partly WOz) in a 2 × 2 experiment of social vs. neutral behavior in above-average vs. below-average schoolchildren (N = 86; age 8–10 years) while rehearsing the multiplication tables on a one-to-one basis. The standard school test showed that on average, pupils significantly improved their performance even after 3 occasions of 5-min exercises. Beyond-average pupils profited most from a robot tutor, whereas those below average in multiplication benefited more from a robot that showed neutral rather than more social behavior.

Open Multi-Access Network Platform with Dynamic Task Offloading and Intelligent Resource Monitoring We constructed an open multi-access network platform using open source hardware and software. The open multi-access network platform is characterized by flexible utilization of network functions, integral management and control of wired and wireless access networks, zero-touch provisioning, intelligent resource monitoring, and dynamic task offloading. We also propose application-driven dynamic task offloading that utilizes intelligent resource monitoring to ensure effective task processing in edge and cloud servers. For this purpose, we developed a mobile application and server applications for the open multi-access network platform. To investigate the feasibility and availability of our developed platform, we experimentally and analytically evaluated the effectiveness of application-driven dynamic task offloading and intelligent resource monitoring. The experimental results demonstrate that application-driven dynamic task offloading could reduce real-time task response time and traffic over metro and core networks.