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Automatic source scanner identification using 1D convolutional neural network In this digital world, digitized documents can be considered original or a piece of evidence; checking the authenticity of any suspicious image has become an unavoidable concern to preserve the trust in its legitimacy. However, identifying the source of a digital image without any prior embedded information is a very challenging task. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN) model to solve the source scanner identification (SSI) problem blindly. Unlike traditional methods based on handcrafted features, the proposed framework can dynamically learn and extract scanner device-specific features. This work, comprised of the 1D-CNN and a support vector machine (SVM) as a classifier, was trained on nine scanners of different brands and models. The experimental result shows that our model achieves 98.15% accuracy on full images and overall accuracy of 93.13% on segments from test images, outperforming other state-of-art approaches. Our model also proves to be able to distinguish between scanners of the same model. Furthermore, the SVM classifier improved the 1D-CNN accuracy by approximately 3% compared to its original configuration.

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 "Sybil attacks" 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.

Wireless Charging Technologies: Fundamentals, Standards, and Network Applications Wireless charging is a technology of transmitting power through an air gap to electrical devices for the purpose of energy replenishment. The recent progress in wireless charging techniques and development of commercial products have provided a promising alternative way to address the energy bottleneck of conventionally portable battery-powered devices. However, the incorporation of wireless charg...

Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review. Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

An RFID-Based Closed-Loop Wireless Power Transmission System for Biomedical Applications This brief presents a standalone closed-loop wireless power transmission system that is built around a commercial off-the-shelf (COTS) radio-frequency identification (RFID) reader (TRF7960) operating at 13.56 MHz. It can be used for inductively powering implantable biomedical devices in a closed loop. Any changes in the distance and misalignment between transmitter and receiver coils in near-field wireless power transmission can cause a significant change in the received power, which can cause either a malfunction or excessive heat dissipation. RFID circuits are often used in an open loop. However, their back telemetry capability can be utilized to stabilize the received voltage on the implant. Our measurements showed that the delivered power to the transponder was maintained at 11.2 mW over a range of 0.5 to 2 cm, while the transmitter power consumption changed from 78 mW to 1.1 W. The closed-loop system can also oppose voltage variations as a result of sudden changes in the load current.

Tag-based cooperative data gathering and energy recharging in wide area RFID sensor networks The Wireless Identification and Sensing Platform (WISP) conjugates the identification potential of the RFID technology and the sensing and computing capability of the wireless sensors. Practical issues, such as the need of periodically recharging WISPs, challenge the effective deployment of large-scale RFID sensor networks (RSNs) consisting of RFID readers and WISP nodes. In this view, the paper proposes cooperative solutions to energize the WISP devices in a wide-area sensing network while reducing the data collection delay. The main novelty is the fact that both data transmissions and energy transfer are based on the RFID technology only: RFID mobile readers gather data from the WISP devices, wirelessly recharge them, and mutually cooperate to reduce the data delivery delay to the sink. Communication between mobile readers relies on two proposed solutions: a tag-based relay scheme, where RFID tags are exploited to temporarily store sensed data at pre-determined contact points between the readers; and a tag-based data channel scheme, where the WISPs are used as a virtual communication channel for real time data transfer between the readers. Both solutions require: (i) clustering the WISP nodes; (ii) dimensioning the number of required RFID mobile readers; (iii) planning the tour of the readers under the energy and time constraints of the nodes. A simulative analysis demonstrates the effectiveness of the proposed solutions when compared to non-cooperative approaches. Differently from classic schemes in the literature, the solutions proposed in this paper better cope with scalability issues, which is of utmost importance for wide area networks.

Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks. The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.

Hybrid charging scheduling schemes for three-dimensional underwater wireless rechargeable sensor networks. •We are the first to study charging issue for 3D underwater rechargeable sensor networks.•We develop a series of charging algorithms for enhancing energy efficiency.•Our schemes can save energy, save time, and ensure effective utilization of resources.

Energy-Efficient Massive MIMO SWIPT-Enabled Systems This study focuses on the downlink (DL) transmission of massive multiple-input-multiple-output (MIMO) with the support of simultaneous wireless information and power transfer (SWIPT) system based on a power-splitting (PS) scheme. To reach the green design target in the concept of wireless communication networks, this paper maximizes the system’s energy efficiency (EE) using joint system-level optimization. First, the closed-form expressions of the harvested energy and achievable data rate are first derived. Then, we formulated the EE maximization problem by jointly optimizing user equipment (UE) pilot transmission time allocation, the received PS ratios, a base station (BS) transmit power allocation, and a number of BS antennas while considering the minimum data rate as user’s quality-of-service (QoS) requirement and the maximum BS power transmission constraint. However, the formulated EE maximization problem is non-convex and non-linear which is difficult to solve. Hence, we propose an efficient low-complex alternative optimization (LCAO) algorithm to solve the non-convex problem iteratively with an acceptable computational complexity level. Simulation results are provided to evaluate the derived closed-form expressions and validate the proposed LCAO algorithm’s efficiency. The optimality, convergence, and complexity of the LCAO algorithm are analytically investigated. Results indicate that the proposed LCAO algorithm outperforms the equal power allocation (EPA) scheme by 15% better EE gain and 4% better than the max-min fairness scheme when the BS power transmission capacity is higher than 3 (Watt).

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

Toward an Intelligent Edge: Wireless Communication Meets Machine Learning. The recent revival of AI is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interest in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Acco...

Synonymous Paraphrasing Using WordNet and Internet We propose a method of synonymous paraphrasing of a text based on WordNet synonymy data and Internet statistics of stable word combinations (collocations). Given a text, we look for words or expressions in it for which WordNet provides synonyms, and substitute them with such synonyms only if the latter form valid collocations with the surrounding words according to the statistics gathered from Internet. We present two important applications of such synonymous paraphrasing: (1) style-checking and correction: automatic evaluation and computer-aided improvement of writing style with regard to various aspects (increasing vs. decreasing synonymous variation, conformistic vs. individualistic selection of synonyms, etc.) and (2) steganography: hiding of additional information in the text by special selection of synonyms. A basic interactive algorithm of style improvement is outlined and an example of its application to editing of newswire text fragment in English is traced. Algorithms of style evaluation and information hiding are also proposed.

Effective management of medical information through a novel blind watermarking technique. Medical Data Management (MDM) domain consists of various issues of medical information like authentication, security, privacy, retrieval and storage etc. Medical Image Watermarking (MIW) techniques have recently emerged as a leading technology to solve the problems associated with MDM. This paper proposes a blind, Contourlet Transform (CNT) based MIW scheme, robust to high JPEG and JPEG2000 compression and simultaneously capable of addressing a range of MDM issues like medical information security, content authentication, safe archiving and controlled access retrieval etc. It also provides a way for effective data communication along with automated medical personnel teaching. The original medical image is first decomposed by CNT. The Low pass subband is used to embed the watermark in such a way that enables the proposed method to extract the embedded watermark in a blind manner. Inverse CNT is then applied to get the watermarked image. Extensive experiments were carried out and the performance of the proposed scheme is evaluated through both subjective and quantitative measures. The experimental results and comparisons, confirm the effectiveness and efficiency of the proposed technique in the MDM paradigm.

Open Set Domain Adaptation When the training and the test data belong to different domains, the accuracy of an object classifier is significantly reduced. Therefore, several algorithms have been proposed in the last years to diminish the so called domain shift between datasets. However, all available evaluation protocols for domain adaptation describe a closed set recognition task, where both domains, namely source and target, contain exactly the same object classes. In this work, we also explore the field of domain adaptation in open sets, which is a more realistic scenario where only a few categories of interest are shared between source and target data. Therefore, we propose a method that fits in both closed and open set scenarios. The approach learns a mapping from the source to the target domain by jointly solving an assignment problem that labels those target instances that potentially belong to the categories of interest present in the source dataset. A thorough evaluation shows that our approach outperforms the state-of-the-art.

A General Framework for Unmet Demand Prediction in On-Demand Transport Services Emerging on-demand transport services, such as Uber and GoGoVan, usually face the dilemma of demand supply imbalance, meaning that the spatial distributions of orders and drivers are imbalanced. Due to such imbalance, much supply resource is wasted while a considerable amount of order demand cannot be met in time. To address this dilemma, knowing the unmet demand in the near future is of high importance for service providers because they can dispatch their vehicles in advance to alleviate the impending demand supply imbalance, we develop a general framework for predicting the unmet demand in future time slots. Under this framework, we first evaluate the predictability of unmet demand in on-demand transport services and find that unmet demand is highly predictable. Then, we extract both static and dynamic urban features relevant to unmet demand from data sets in multiple domains. Finally, multiple prediction models are trained to predict unmet demand by using the extracted features. As demonstrated via experiments, the proposed framework can predict unmet demand in on-demand transport services effectively and flexibly.

Speech emotion recognition using deep 1D & 2D CNN LSTM networks. We aimed at learning deep emotion features to recognize speech emotion. Two convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D CNN LSTM network and one 2D CNN LSTM network, were constructed to learn local and global emotion-related features from speech and log-mel spectrogram respectively. The two networks have the similar architecture, both consisting of four local feature learning blocks (LFLBs) and one long short-term memory (LSTM) layer. LFLB, which mainly contains one convolutional layer and one max-pooling layer, is built for learning local correlations along with extracting hierarchical correlations. LSTM layer is adopted to learn long-term dependencies from the learned local features. The designed networks, combinations of the convolutional neural network (CNN) and LSTM, can take advantage of the strengths of both networks and overcome the shortcomings of them, and are evaluated on two benchmark databases. The experimental results show that the designed networks achieve excellent performance on the task of recognizing speech emotion, especially the 2D CNN LSTM network outperforms the traditional approaches, Deep Belief Network (DBN) and CNN on the selected databases. The 2D CNN LSTM network achieves recognition accuracies of 95.33% and 95.89% on Berlin EmoDB of speaker-dependent and speaker-independent experiments respectively, which compare favourably to the accuracy of 91.6% and 92.9% obtained by traditional approaches; and also yields recognition accuracies of 89.16% and 52.14% on IEMOCAP database of speaker-dependent and speaker-independent experiments, which are much higher than the accuracy of 73.78% and 40.02% obtained by DBN and CNN.

The redundant discrete wavelet transform and additive noise The behavior under additive noise of the redundant discrete wavelet transform (RDWT), which is a frame expansion that is essentially an undecimated discrete wavelet transform, is studied. Known prior results in the form of inequalities bound distortion energy in the original signal domain from additive noise in frame-expansion coefficients. In this letter, a precise relationship between RDWT-domai...

Deep learning for source camera identification on mobile devices. •The design of an efficient CNN architecture for the SCI problem on mobile devices.•The evaluation of different CNN configurations.•The usage of a unique dataset (MICHE-I) of images taken from several mobile devices.•A 98.1% of accuracy on model detection.•A 91.1% of accuracy on sensor detection.

Smooth filtering identification based on convolutional neural networks The increasing prevalence of digital technology brings great convenience to human life, while also shows us the problems and challenges. Relying on easy-to-use image editing tools, some malicious manipulations, such as image forgery, have already threatened the authenticity of information, especially the electronic evidence in the crimes. As a result, digital forensics attracts more and more attention of researchers. Since some general post-operations, like widely used smooth filtering, can affect the reliability of forensic methods in various ways, it is also significant to detect them. Furthermore, the determination of detailed filtering parameters assists to recover the tampering history of an image. To deal with this problem, we propose a new approach based on convolutional neural networks (CNNs). Through adding a transform layer, obtained distinguishable frequency-domain features are put into a conventional CNN model, to identify the template parameters of various types of spatial smooth filtering operations, such as average, Gaussian and median filtering. Experimental results on a composite database show that putting the images directly into the conventional CNN model without transformation can not work well, and our method achieves better performance than some other applicable related methods, especially in the scenarios of small size and JPEG compression.

Real-time detecting one specific tampering operation in multiple operator chains Currently, many forensic techniques have been developed to determine the processing history of given multimedia contents. However, because of the interaction among tampering operations, there are still fundamental limits on the determination of tampering order and type. Up to now, a few works consider the cases where multiple operation types are involved in. In these cases, we not only need to consider the interplay of operation order, but also should quantify the detectability of one specific operation. In this paper, we propose an efficient information theoretical framework to solve this problem. Specially, we analyze the operation detection problem from the perspective of set partitioning and detection theory. Then, under certain detectors, we present the information framework to contrast the detected hypotheses and true hypotheses. Some constraint criterions are designed to improve the detection performance of an operation. In addition, Maximum-Likelihood Estimation (MLE) is used to obtain the best detector. Finally, a multiple chain set is examined in this paper, where three efficient detection methods have been proposed and the effectiveness of our framework has been demonstrated by simulations.

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.

Theory of Mind for a Humanoid Robot If we are to build human-like robots that can interact naturally with people, our robots must know not only about the properties of objects but also the properties of animate agents in the world. One of the fundamental social skills for humans is the attribution of beliefs, goals, and desires to other people. This set of skills has often been called a “theory of mind.” This paper presents the theories of Leslie (1994) and Baron-Cohen (1995) on the development of theory of mind in human children and discusses the potential application of both of these theories to building robots with similar capabilities. Initial implementation details and basic skills (such as finding faces and eyes and distinguishing animate from inanimate stimuli) are introduced. I further speculate on the usefulness of a robotic implementation in evaluating and comparing these two models.

Gravity-Balancing Leg Orthosis and Its Performance Evaluation In this paper, we propose a device to assist persons with hemiparesis to walk by reducing or eliminating the effects of gravity. The design of the device includes the following features: 1) it is passive, i.e., it does not include motors or actuators, but is only composed of links and springs; 2) it is safe and has a simple patient-machine interface to accommodate variability in geometry and inertia of the subjects. A number of methods have been proposed in the literature to gravity-balance a machine. Here, we use a hybrid method to achieve gravity balancing of a human leg over its range of motion. In the hybrid method, a mechanism is used to first locate the center of mass of the human limb and the orthosis. Springs are then added so that the system is gravity-balanced in every configuration. For a quantitative evaluation of the performance of the device, electromyographic (EMG) data of the key muscles, involved in the motion of the leg, were collected and analyzed. Further experiments involving leg-raising and walking tasks were performed, where data from encoders and force-torque sensors were used to compute joint torques. These experiments were performed on five healthy subjects and a stroke patient. The results showed that the EMG activity from the rectus femoris and hamstring muscles with the device was reduced by 75%, during static hip and knee flexion, respectively. For leg-raising tasks, the average torque for static positioning was reduced by 66.8% at the hip joint and 47.3% at the knee joint; however, if we include the transient portion of the leg-raising task, the average torque at the hip was reduced by 61.3%, and at the knee was increased by 2.7% at the knee joints. In the walking experiment, there was a positive impact on the range of movement at the hip and knee joints, especially for the stroke patient: the range of movement increased by 45% at the hip joint and by 85% at the knee joint. We believe that this orthosis can be potentially used to desig- - n rehabilitation protocols for patients with stroke

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.

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.

Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges. Fog computing is an emerging paradigm that extends computation, communication, and storage facilities toward the edge of a network. Compared to traditional cloud computing, fog computing can support delay-sensitive service requests from end-users (EUs) with reduced energy consumption and low traffic congestion. Basically, fog networks are viewed as offloading to core computation and storage. Fog n...

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 ...

On Theoretical Modeling of Sensor Cloud: A Paradigm Shift From Wireless Sensor Network. This paper focuses on the theoretical modeling of sensor cloud, which is one of the first attempts in this direction. We endeavor to theoretically characterize virtualization, which is a fundamental mechanism for operations within the sensor-cloud architecture. Existing related research works on sensor cloud have primarily focused on the ideology and the challenges that wireless sensor network (WS...

Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications Due to various challenging issues such as, computational complexity and more delay in cloud computing, edge computing has overtaken the conventional process by efficiently and fairly allocating the resources i.e., power and battery lifetime in Internet of things (IoT)-based industrial applications. In the meantime, intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT-based devices in industries. But the challenging issue in these power hungry, short battery lifetime, and delay-intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial datasets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques, for example, predictive transmission power control (TPC) and baseline. Thus, this paper proposes 1) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices; 2) a system-level battery model by evaluating the energy dissipation in IoT devices; and 3) a data reliability model for edge AI-based IoT devices over hybrid TPC and duty-cycle network. Two important cases, for instance, static (i.e., product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental testbed reveals that the proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (∼0.95) by appropriately tuning duty cycle and TPC unlike conventional methods.

Towards Big data processing in IoT: Path Planning and Resource Management of UAV Base Stations in Mobile-Edge Computing System. Heavy data load and wide cover range have always been crucial problems for big data processing in Internet of Things (IoT). Recently, mobile-edge computing (MEC) and unmanned aerial vehicle base stations (UAV-BSs) have emerged as promising techniques in IoT. In this article, we propose a three-layer online data processing network based on the MEC technique. On the bottom layer, raw data are generated by distributed sensors with local information. Upon them, UAV-BSs are deployed as moving MEC servers, which collect data and conduct initial steps of data processing. On top of them, a center cloud receives processed results and conducts further evaluation. For online processing requirements, the edge nodes should stabilize delay to ensure data freshness. Furthermore, limited onboard energy poses constraints to edge processing capability. In this article, we propose an online edge processing scheduling algorithm based on Lyapunov optimization. In cases of low data rate, it tends to reduce edge processor frequency for saving energy. In the presence of a high data rate, it will smartly allocate bandwidth for edge data offloading. Meanwhile, hovering UAV-BSs bring a large and flexible service coverage, which results in a path planning issue. In this article, we also consider this problem and apply deep reinforcement learning to develop an online path planning algorithm. Taking observations of around environment as an input, a CNN network is trained to predict action rewards. By simulations, we validate its effectiveness in enhancing service coverage. The result will contribute to big data processing in future IoT.

A Survey On Sensor-Cloud: Architecture, Applications, And Approaches Nowadays, wireless sensor network (WSN) applications have been used in several important areas, such as healthcare, military, critical infrastructure monitoring, environment monitoring, and manufacturing. However, due to the limitations of WSNs in terms of memory, energy, computation, communication, and scalability, efficient management of the large number of WSNs data in these areas is an important issue to deal with. There is a need for a powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the WSN data as well as analysis (online and offline) of the processed information under context using inherently complex models to extract events of interest. In this scenario, cloud computing is becoming a promising technology to provide a flexible stack of massive computing, storage, and software services in a scalable and virtualized manner at low cost. Therefore, in recent years, Sensor-Cloud infrastructure is becoming popular that can provide an open, flexible, and reconfigurable platform for several monitoring and controlling applications. In this paper, we present a comprehensive study of representative works on Sensor-Cloud infrastructure, which will provide general readers an overview of the Sensor-Cloud platform including its definition, architecture, and applications. The research challenges, existing solutions, and approaches as well as future research directions are also discussed in this paper.

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.

Wireless Charging Technologies: Fundamentals, Standards, and Network Applications Wireless charging is a technology of transmitting power through an air gap to electrical devices for the purpose of energy replenishment. The recent progress in wireless charging techniques and development of commercial products have provided a promising alternative way to address the energy bottleneck of conventionally portable battery-powered devices. However, the incorporation of wireless charg...

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.

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.

Prediction, Detection, and Correction of Faraday Rotation in Full-Polarimetric L-Band SAR Data With the synthetic aperture radar (SAR) sensor PALSAR onboard the Advanced Land Observing Satellite, a new full-polarimetric spaceborne L-band SAR instrument has been launched into orbit. At L-band, Faraday rotation (FR) can reach significant values, degrading the quality of the received SAR data. One-way rotations exceeding 25 deg are likely to happen during the lifetime of PALSAR, which will significantly reduce the accuracy of geophysical parameter recovery if uncorrected. Therefore, the estimation and correction of FR effects is a prerequisite for data quality and continuity. In this paper, methods for estimating FR are presented and analyzed. The first unambiguous detection of FR in SAR data is presented. A set of real data examples indicates the quality and sensitivity of FR estimation from PALSAR data, allowing the measurement of FR with high precision in areas where such measurements were previously inaccessible. In examples, we present the detection of kilometer-scale ionospheric disturbances, a spatial scale that is not detectable by ground-based GPS measurements. An FR prediction method is presented and validated. Approaches to correct for the estimated FR effects are applied, and their effectiveness is tested on real data.

Software-Defined Networking: A Comprehensive Survey The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this - ew paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms - with a focus on aspects such as resiliency, scalability, performance, security, and dependability - as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

Design of robust fuzzy fault detection filter for polynomial fuzzy systems with new finite frequency specifications This paper investigates the problem of fault detection filter design for discrete-time polynomial fuzzy systems with faults and unknown disturbances. The frequency ranges of the faults and the disturbances are assumed to be known beforehand and to reside in low, middle or high frequency ranges. Thus, the proposed filter is designed in the finite frequency range to overcome the conservatism generated by those designed in the full frequency domain. Being of polynomial fuzzy structure, the proposed filter combines the H−/H∞ performances in order to ensure the best robustness to the disturbance and the best sensitivity to the fault. Design conditions are derived in Sum Of Squares formulations that can be easily solved via available software tools. Two illustrative examples are introduced to demonstrate the effectiveness of the proposed method and a comparative study with LMI method is also provided.

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 Capacity Assessment of Distributed Generation in Unbalanced Distribution Networks Incorporating ANM Techniques. To settle a large-scale integration of renewable distributed generations (DGs), it requires to assess the maximal DG hosting capacity of active distribution networks (ADNs). For fully exploiting the ability of ADNs to accommodate DG, this paper proposes a robust comprehensive DG capacity assessment method considering three-phase power flow modelling and active network management (ANM) techniques. ...

Effective Utilization of Available PEV Battery Capacity for Mitigation of Solar PV Impact and Grid Support With Integrated V2G Functionality. Utilizing battery storage devices in plug-in electric vehicles (PEVs) for grid support using vehicle-to-grid (V2G) concept is gaining popularity. With appropriate control strategies, the PEV batteries and associated power electronics can be exploited for solar photovoltaic (PV) impact mitigation and grid support. However, as the PEV batteries have limited capacity and the capacity usage is also co...

Managing Demand for Plug-in Electric Vehicles in Unbalanced LV Systems With Photovoltaics. Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids,...

Electric Vehicle Charge-Discharge Management for Utilization of Photovoltaic by Coordination between Home and Grid Energy Management Systems This paper proposes an electric vehicle (EV) charge-discharge management framework for the effective utilization of photovoltaic (PV) output through coordination based on information exchange between home energy management system (HEMS) and grid energy management system (GEMS). In our proposed framework, the HEMS determines an EV charge discharge plan for reducing the residential operation cost an...

Optimal Number of Electric Vehicles for Existing Networks Considering Economic and Emission Dispatch The future transportation network is expected to be dominated by electric vehicles (EVs). With an increase in grid-connected vehicles, the power grid needs to increase its generation capacity to meet their charging demand. Thus, from a grid perspective, it is essential to find the maximum number of EVs that the existing infrastructure can handle. This research proposes a new framework to identify the fleet size capability of a power network in both uncontrolled and vehicle-to-grid (V2G) modes of operation. The proposed model is modeled as an optimization problem and tested on a system that is formed by combining IEEE 33-bus radial distribution system and a ten-unit generation system. Simulation results show that the current system can handle a fixed number of EVs when they operate independently. Participation of EVs in a V2G market significantly enhances the fleet size handling capability of the existing infrastructure, which is approximately four times the present capacity. Furthermore, a relation between the fleet size of EVs and operating points of plants is extracted, which is very beneficial for system planners. The result reveals that the time span requirements for power infrastructure upgradation can be increased by twofold if the V2G market is applied in the network.

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

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.

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.

5G Virtualized Multi-access Edge Computing Platform for IoT Applications. The next generation of fifth generation (5G) network, which is implemented using Virtualized Multi-access Edge Computing (vMEC), Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies, is a flexible and resilient network that supports various Internet of Things (IoT) devices. While NFV provides flexibility by allowing network functions to be dynamically deployed and inter-connected, vMEC provides intelligence at the edge of the mobile network reduces latency and increases the available capacity. With the diverse development of networking applications, the proposed vMEC use of Container-based Virtualization Technology (CVT) as gateway with IoT devices for flow control mechanism in scheduling and analysis methods will effectively increase the application Quality of Service (QoS). In this work, the proposed IoT gateway is analyzed. The combined effect of simultaneously deploying Virtual Network Functions (VNFs) and vMEC applications on a single network infrastructure, and critically in effecting exhibits low latency, high bandwidth and agility that will be able to connect large scale of devices. The proposed platform efficiently exploiting resources from edge computing and cloud computing, and takes IoT applications that adapt to network conditions to degrade an average 30% of end to end network latency.

A Hierarchical Architecture Using Biased Min-Consensus for USV Path Planning This paper proposes a hierarchical architecture using the biased min-consensus (BMC) method, to solve the path planning problem of unmanned surface vessel (USV). We take the fixed-point monitoring mission as an example, where a series of intermediate monitoring points should be visited once by USV. The whole framework incorporates the low-level layer planning the standard path between any two intermediate points, and the high-level fashion determining their visiting sequence. First, the optimal standard path in terms of voyage time and risk measure is planned by the BMC protocol, given that the corresponding graph is constructed with node state and edge weight. The USV will avoid obstacles or keep a certain distance safely, and arrive at the target point quickly. It is proven theoretically that the state of the graph will converge to be stable after finite iterations, i.e., the optimal solution can be found by BMC with low calculation complexity. Second, by incorporating the constraint of intermediate points, their visiting sequence is optimized by BMC again with the reconstruction of a new virtual graph based on the former planned results. The extensive simulation results in various scenarios also validate the feasibility and effectiveness of our method for autonomous navigation.

Containment control for second-order multi-agent systems with time-varying delays. This paper considers the containment control problem for second-order multi-agent systems with time-varying delays. Both the containment control problem with multiple stationary leaders and the problem with multiple dynamic leaders are investigated. Sufficient conditions on the communication digraph, the feedback gains, and the allowed upper bound of the delays to ensure containment control are given. In the case that the leaders are stationary, the Lyapunov–Razumikhin function method is used. In the case that the leaders are dynamic, the Lyapunov–Krasovskii functional method and the linear matrix inequality (LMI) method are jointly used. A novel discretized Lyapunov functional method is introduced to utilize the upper bound of the derivative of the delays no matter how large it is, which leads to a better result on the allowed upper bound of the delays to ensure containment control. Finally, numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results.

Distributed Containment Control for Multiple Unknown Second-Order Nonlinear Systems With Application to Networked Lagrangian Systems. In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors' velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

Fully distributed containment control of high-order multi-agent systems with nonlinear dynamics. In this paper, distributed containment control problems for high-order multi-agent systems with nonlinear dynamics are investigated under directed communication topology. The states of the leaders are only available to a subset of the followers and the inputs of the leaders are possibly nonzero and time varying. Distributed adaptive nonlinear protocol is proposed based only on the relative state information, under which the states of the followers converge to the dynamic convex hull spanned by those of the leaders. As the special case with only one dynamic leader, leader–follower consensus problem is also solved with the proposed protocol. The adaptive protocol here is independent of the eigenvalues of the Laplacian matrix, which means the protocol can be implemented by each agent in a fully distributed fashion. A simulation example is provided to illustrate the theoretical results.

Output Containment Control of Linear Heterogeneous Multi-Agent Systems Using Internal Model Principle. This paper studies the output containment control of linear heterogeneous multi-agent systems, where the system dynamics and even the state dimensions can generally be different. Since the states can have different dimensions, standard results from state containment control do not apply. Therefore, the control objective is to guarantee the convergence of the output of each follower to the dynamic ...

Finite-Time Consensus Tracking Neural Network FTC of Multi-Agent Systems The finite-time consensus fault-tolerant control (FTC) tracking problem is studied for the nonlinear multi-agent systems (MASs) in the nonstrict feedback form. The MASs are subject to unknown symmetric output dead zones, actuator bias and gain faults, and unknown control coefficients. According to the properties of the neural network (NN), the unstructured uncertainties problem is solved. The Nussbaum function is used to address the output dead zones and unknown control directions problems. By introducing an arbitrarily small positive number, the “singularity” problem caused by combining the finite-time control and backstepping design is solved. According to the backstepping design and Lyapunov stability theory, a finite-time adaptive NN FTC controller is obtained, which guarantees that the tracking error converges to a small neighborhood of zero in a finite time, and all signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed method is illustrated via a physical example.

Observer-based Adaptive Fuzzy Containment Control for Multiple Uncertain Nonlinear Systems The adaptive fuzzy containment control problem is addressed for multiple uncertain nonlinear strict-feedback systems with immeasurable states and multiple leaders under directed communication graphs. By utilizing fuzzy logic systems to model the followers’ dynamics, a distributed fuzzy state observer is designed for the state estimation using only the relative position information. Then, an observer-based containment control scheme is constructed by the adaptive fuzzy control technique as well as the command filter. The filtering error loop is introduced to compensate the error arising from the command filter. The proposed adaptive fuzzy containment control scheme guarantees that all followers are driven into the dynamic convex hull spanned by the leaders with a bounded containment error, if there exists at least one of the leaders who has a directed path to the follower. Simulation results are given to illustrate the control performance of the proposed containment control method.

Designing Fully Distributed Consensus Protocols for Linear Multi-Agent Systems With Directed Graphs This technical note addresses the distributed consensus protocol design problem for multi-agent systems with general linear dynamics and directed communication graphs. Existing works usually design consensus protocols using the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph, which however is global information. In this technical note, based on only the agent dynamics and the relative states of neighboring agents, a distributed adaptive consensus protocol is designed to achieve leader-follower consensus in the presence of a leader with a zero input for any communication graph containing a directed spanning tree with the leader as the root node. The proposed adaptive protocol is independent of any global information of the communication graph and thereby is fully distributed. Extensions to the case with multiple leaders are further studied.

Interval type-2 fuzzy logic for dynamic parameter adaptation in a modified gravitational search algorithm. In this paper a method for dynamically adjusting parameters in meta-heuristics based on interval type-2 fuzzy logic is proposed. Nowadays meta-heuristic algorithms have become a powerful choice in solving complex optimization problems. The gravitational search algorithm (GSA) based on the Newton laws of gravity and acceleration can be used to solve optimization problems achieving good results, however like in other optimization algorithms a critical issue is an appropriate adjustment of its parameters depending on the type of problem. In this paper the main contribution is a proposed method aimed at dynamic parameter adjustment in GSA with the help of type-2 fuzzy logic. Simulation results on benchmark problems show the advantages of the proposed approach.

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.

Non-interactive verifiable computing: outsourcing computation to untrusted workers We introduce and formalize the notion of Verifiable Computation, which enables a computationally weak client to "outsource" the computation of a function F on various dynamically-chosen inputs x1, ...,xk to one or more workers. The workers return the result of the function evaluation, e.g., yi = F(xi), as well as a proof that the computation of F was carried out correctly on the given value xi. The primary constraint is that the verification of the proof should require substantially less computational effort than computing F(i) from scratch. We present a protocol that allows the worker to return a computationally-sound, non-interactive proof that can be verified in O(mċpoly(λ)) time, where m is the bit-length of the output of F, and λ is a security parameter. The protocol requires a one-time pre-processing stage by the client which takes O(|C|ċpoly(λ)) time, where C is the smallest known Boolean circuit computing F. Unlike previous work in this area, our scheme also provides (at no additional cost) input and output privacy for the client, meaning that the workers do not learn any information about the xi or yi values.

A web-based e-learning system for increasing study efficiency by stimulating learner's motivation Due to the opportunities provided by the Internet, more and more people are taking advantage of distance learning courses and during the last few years enormous research efforts have been dedicated to the development of distance learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is about 30%. One of the reasons is the low study desire when the learner studies the learning materials. In this research, we propose an interactive Web-based e-learning system. The purpose of our system is to increase the e-learning completion rate by stimulating learner's motivation. The proposed system has three subsystems: the learning subsystem, learner support subsystem, and teacher support subsystem. The learning subsystem improves the learner's study desire. The learner support subsystem supports the learner during the study, and the teacher support subsystem supports the teacher to get the learner's study state. To evaluate the proposed system, we developed several experiments and surveys. By using new features such as: display of learner's study history, change of interface color, encourage function, ranking function, self-determination of the study materials, and grouping of learners, the proposed system can increase the learning efficiency.

Robust DT-CWT Watermarking for DIBR 3D Images The popularity of 3D content is on the rise since it provides an immersive experience to viewers. In this situation, depth-image-based rendering (DIBR) has taken on an important role in 3D technology due to its low bandwidth cost and ease of depth configuration. Noting that the viewer could record provided center view or synthesized views for illegal distribution, it is clear that copyright protection must be taken into account for the DIBR 3D content, including the possibility that one single view could be illegally distributed as 2D content. In this paper, we propose a robust watermarking scheme for DIBR 3D images by quantization on dual-tree complex wavelet transform (DT-CWT) coefficients with consideration of imperceptibility. To make the proposed scheme robust to DIBR process, two characteristics of DT-CWT are employed: approximate shift invariance and directional selectivity. We select certain coefficient sub-blocks and group the coefficient rows based on the properties of DIBR. On the extraction side, the threshold is carefully chosen with a low false positive rate. The simulation results show that the embedded watermark is stably extracted from the center view and the synthesized left and right views. In addition, even if the synthesized left and right views are distorted by general attacks, the watermark is successfully extracted. Furthermore, the proposed scheme is robust to pre-processing of the depth image and baseline adjusting, which are common processing on the DIBR system for better quality of 3D views.

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.

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.

Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey. Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterizat...

A predictive controller for autonomous vehicle path tracking This paper presents a model predictive controller (MPC) structure for solving the path-tracking problem of terrestrial autonomous vehicles. To achieve the desired performance during high-speed driving, the controller architecture considers both the kinematic and the dynamic control in a cascade structure. Our study contains a comparative study between two kinematic linear predictive control strategies: The first strategy is based on the successive linearization concept, and the other strategy combines a local reference frame with an approaching path strategy. Our goal is to search for the strategy that best comprises the performance and hardware-cost criteria. For the dynamic controller, a decentralized predictive controller based on a linearized model of the vehicle is used. Practical experiments obtained using an autonomous "Mini-Baja" vehicle equipped with an embedded computing system are presented. These results confirm that the proposed MPC structure is the solution that better matches the target criteria.

An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Intelligent Transportation Systems The visibility of images of outdoor road scenes will generally become degraded when captured during inclement weather conditions. Drivers often turn on the headlights of their vehicles and streetlights are often activated, resulting in localized light sources in images capturing road scenes in these conditions. Additionally, sandstorms are also weather events that are commonly encountered when driving in some regions. In sandstorms, atmospheric sand has a propensity to irregularly absorb specific portions of a spectrum, thereby causing color-shift problems in the captured image. Traditional state-of-the-art restoration techniques are unable to effectively cope with these hazy road images that feature localized light sources or color-shift problems. In response, we present a novel and effective haze removal approach to remedy problems caused by localized light sources and color shifts, which thereby achieves superior restoration results for single hazy images. The performance of the proposed method has been proven through quantitative and qualitative evaluations. Experimental results demonstrate that the proposed haze removal technique can more effectively recover scene radiance while demanding fewer computational costs than traditional state-of-the-art haze removal techniques.

Automatic Detection and Classification of Road Lane Markings Using Onboard Vehicular Cameras This paper presents a new approach for road lane classification using an onboard camera. Initially, lane boundaries are detected using a linear–parabolic lane model, and an automatic on-the-fly camera calibration procedure is applied. Then, an adaptive smoothing scheme is applied to reduce noise while keeping close edges separated, and pairs of local maxima–minima of the gradient are used as cues to identify lane markings. Finally, a Bayesian classifier based on mixtures of Gaussians is applied to classify the lane markings present at each frame of a video sequence as dashed, solid, dashed solid, solid dashed, or double solid. Experimental results indicate an overall accuracy of over 96% using a variety of video sequences acquired with different devices and resolutions.

Pedestrian Tracking Using Online Boosted Random Ferns Learning in Far-Infrared Imagery for Safe Driving at Night. Pedestrian-vehicle accidents that occur at night are a major social problem worldwide. Advanced driver assistance systems that are equipped with cameras have been designed to automatically prevent such accidents. Among the various types of cameras used in such systems, far-infrared (FIR) cameras are favorable because they are invariant to illumination changes. Therefore, this paper focuses on a pedestrian nighttime tracking system with an FIR camera that is able to discern thermal energy and is mounted on the forward roof part of a vehicle. Since the temperature difference between the pedestrian and background depends on the season and the weather, we therefore propose two models to detect pedestrians according to the season and the weather, which are determined using Weber–Fechner's law. For tracking pedestrians, we perform real-time online learning to track pedestrians using boosted random ferns and update the trackers at each frame. In particular, we link detection responses to trajectories based on similarities in position, size, and appearance. There is no standard data set for evaluating the tracking performance using an FIR camera; thus, we created the Keimyung University tracking data set (KMUTD) by combining the KMU sudden pedestrian crossing (SPC) data set [21] for summer nights with additional tracking data for winter nights. The KMUTD contains video sequences involving a moving camera, moving pedestrians, sudden shape deformations, unexpected motion changes, and partial or full occlusions between pedestrians at night. The proposed algorithm is successfully applied to various pedestrian video sequences of the KMUTD; specifically, the proposed algorithm yields more accurate tracking performance than other existing methods.

Effects of Different Alcohol Dosages on Steering Behavior in Curve Driving. Objective: The aim of this article is to explore the detailed characteristics of steering behavior in curve driving at different alcohol dosages. Background: Improper operation of the steering wheel is a contributing factor to increased crash risks on curves. Method: The experiments were conducted using a driving simulator. Twenty-five licensed drivers were recruited to perform the experiments at the four different breath alcohol concentration (BrAC) levels. The steering angle (SA), steering speed (SS), steering reversal rate (SRR), and peak-to-peak value of the steering angle (PP) were used to characterize the steering behavior. The vehicle's speed and the number of lane exceedances per kilometer were also used to examine the driving performance. Results: The SSs on the 200 m (chi(2)(3) = 20.67, p < .001), 500 m (chi(2)(3) = 22.42, p < .001), and 800 m (chi(2)(3) = 22.86, p < .001) radius curves were significantly faster for drivers under the influence of alcohol compared with those given a placebo. There were significant effects of alcohol on the SRR and PP on the 200 m, 500 m, and 800 m radius curves. Conclusion: For all of the curves, the SS, SRR, and PP had a tendency to increase as the BrAC increased. The large PP at a high BrAC, accompanied by the high speed, SS, and SRR, resulted in a high probability of lane exceedance. The use of measures of SS, SRR, and PP aided in the improvement of the accuracy of the intoxication detection for the different types of curves. Application: The most important application is to provide guidance for detecting alcohol-impaired-driving.

Risky Driver Recognition Based on Vehicle Speed Time Series. Risky driving is a major cause of traffic accidents. In this paper, we propose a new method that recognizes risky driving behaviors purely based on vehicle speed time series. This method first retrieves the important distribution pattern of the sampled positive speed-change (value and duration) tuples for individual drivers within different speed ranges. Then, it identifies the risky drivers based...

Driver Fatigue Detection Systems: A Review Driver fatigue has been attributed to traffic accidents; therefore, fatigue-related traffic accidents have a higher fatality rate and cause more damage to the surroundings compared with accidents where the drivers are alert. Recently, many automobile companies have installed driver assistance technologies in vehicles for driver assistance. Third party companies are also manufacturing fatigue detection devices; however, much research is still required for improvement. In the field of driver fatigue detection, continuous research is being performed and several articles propose promising results in constrained environments, still much progress is required. This paper presents state-of-the-art review of recent advancement in the field of driver fatigue detection. Methods are categorized into five groups, i.e., subjective reporting, driver biological features, driver physical features, vehicular features while driving, and hybrid features depending on the features used for driver fatigue detection. Various approaches have been compared for fatigue detection, and areas open for improvements are deduced.

The ApolloScape Dataset for Autonomous Driving Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate environmental perception is expected to be a key enabling technical piece. However, providing a large scale dataset for the design and evaluation of scene parsing algorithms, in particular for outdoor scenes, has been difficult. The per-pixel labelling process is prohibitively expensive, limiting the scale of existing ones. In this paper, we present a large-scale open dataset, ApolloScape, that consists of RGB videos and corresponding dense 3D point clouds. Comparing with existing datasets, our dataset has the following unique properties. The first is its scale, our initial release contains over 140K images - each with its per-pixel semantic mask, up to 1M is scheduled. The second is its complexity. Captured in various traffic conditions, the number of moving objects averages from tens to over one hundred (Figure 1). And the third is the 3D attribute, each image is tagged with high-accuracy pose information at cm accuracy and the static background point cloud has mm relative accuracy. We are able to label these many images by an interactive and efficient labelling pipeline that utilizes the high-quality 3D point cloud. Moreover, our dataset also contains different lane markings based on the lane colors and styles. We expect our new dataset can deeply benefit various autonomous driving related applications that include but not limited to 2D/3D scene understanding, localization, transfer learning, and driving simulation.

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.

Touch Is Everywhere: Floor Surfaces as Ambient Haptic Interfaces Floor surfaces are notable for the diverse roles that they play in our negotiation of everyday environments. Haptic communication via floor surfaces could enhance or enable many computer-supported activities that involve movement on foot. In this paper, we discuss potential applications of such interfaces in everyday environments and present a haptically augmented floor component through which several interaction methods are being evaluated. We describe two approaches to the design of structured vibrotactile signals for this device. The first is centered on a musical phrase metaphor, as employed in prior work on tactile display. The second is based upon the synthesis of rhythmic patterns of virtual physical impact transients. We report on an experiment in which participants were able to identify communication units that were constructed from these signals and displayed via a floor interface at well above chance levels. The results support the feasibility of tactile information display via such interfaces and provide further indications as to how to effectively design vibrotactile signals for them.

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...

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

Sustainable and Efficient Data Collection from WSNs to Cloud. The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a b...

LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT The data generated by millions of sensors in the industrial Internet of Things (IIoT) are extremely dynamic, heterogeneous, and large scale and pose great challenges on the real-time analysis and decision making for anomaly detection in the IIoT. In this article, we propose a long short-term memory (LSTM)-Gauss-NBayes method, which is a synergy of the long short-term memory neural network (LSTM-NN) and the Gaussian Bayes model for outlier detection in the IIoT. In a nutshell, the LSTM-NN builds a model on normal time series. It detects outliers by utilizing the predictive error for the Gaussian Naive Bayes model. Our method exploits advantages of both LSTM and Gaussian Naive Bayes models, which not only has strong prediction capability of LSTM for future time point data, but also achieves an excellent classification performance of the Gaussian Naive Bayes model through the predictive error. We evaluate our approaches on three real-life datasets that involve both long-term and short-term time dependence. Empirical studies demonstrate that our proposed techniques outperform the best-known competitors, which is a preferable choice for detecting anomalies.

Squeezed Convolutional Variational AutoEncoder for unsupervised anomaly detection in edge device industrial Internet of Things In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference times are reduced while similar levels of performance is maintained.

Time Series Anomaly Detection for Trustworthy Services in Cloud Computing Systems As a powerful architecture for large-scale computation, cloud computing has revolutionized the way that computing infrastructure is abstracted and utilized. Coupled with the challenges caused by Big Data, the rocketing development of cloud computing boosts the complexity of system management and maintenance, resulting in weakened trustworthiness of cloud services. To cope with this problem, a comp...

Deep Learning Based Anomaly Detection in Water Distribution Systems Water distribution system (WDS) is one of the most essential infrastructures all over the world. However, incidents such as natural disasters, accidents and intentional damages are endangering the safety of drinking water. With the advance of sensor technologies, different kinds of sensors are being deployed to monitor operative and quality indicators such as flow rate, pH, turbidity, the amount of chlorine dioxide etc. This brings the possibility to detect anomalies in real time based on the data collected from the sensors and different kinds of methods have been applied to tackle this task such as the traditional machine learning methods (e.g. logistic regression, support vector machine, random forest). Recently, researchers tried to apply the deep learning methods (e.g. RNN, CNN) for WDS anomaly detection but the results are worse than that of the traditional machine learning methods. In this paper, by taking into account the characteristics of the WDS monitoring data, we integrate sequence-to-point learning and data balancing with the deep learning model Long Short-term Memory (LSTM) for the task of anomaly detection in WDSs. With a public data set, we show that by choosing an appropriate input length and balance the training data our approach achieves better F1 score than the state-of-the-art method in the literature.

Context-Aware Learning for Anomaly Detection with Imbalanced Log Data Logs are used to record runtime states and significant events for a software system. They are widely used for anomaly detection. Logs produced by most of the real-world systems show clear characteristics of imbalanced data because the number of samples in different classes varies sharply. The distribution of imbalanced data makes the anomaly classifier bias toward the majority class, so it is diff...

The Dangers of Post-hoc Interpretability - Unjustified Counterfactual Explanations. Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model. However, they create the risk of having explanations that are a result of some artifacts learned by the model instead of actual knowledge from the data. This paper focuses on the case of counterfactual explanations and asks whether the generated instances can be justified, i.e. continuously connected to some ground-truth data. We evaluate the risk of generating unjustified counterfactual examples by investigating the local neighborhoods of instances whose predictions are to be explained and show that this risk is quite high for several datasets. Furthermore, we show that most state of the art approaches do not differentiate justified from unjustified counterfactual examples, leading to less useful explanations.

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

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.

Modeling taxi driver anticipatory behavior. As part of a wider behavioral agent-based model that simulates taxi drivers' dynamic passenger-finding behavior under uncertainty, we present a model of strategic behavior of taxi drivers in anticipation of substantial time varying demand at locations such as airports and major train stations. The model assumes that, considering a particular decision horizon, a taxi driver decides to transfer to such a destination based on a reward function. The dynamic uncertainty of demand is captured by a time dependent pick-up probability, which is a cumulative distribution function of waiting time. The model allows for information learning by which taxi drivers update their beliefs from past experiences. A simulation on a real road network, applied to test the model, indicates that the formulated model dynamically improves passenger-finding strategies at the airport. Taxi drivers learn when to transfer to the airport in anticipation of the time-varying demand at the airport to minimize their waiting time.

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.

A Hybrid Integrated Deep Learning Model For The Prediction Of Citywide Spatio-Temporal Flow Volumes The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.

Knowledge harvesting in the big-data era The proliferation of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web and text sources have enabled the automatic construction of very large knowledge bases. Endeavors of this kind include projects such as DBpedia, Freebase, KnowItAll, ReadTheWeb, and YAGO. These projects provide automatically constructed knowledge bases of facts about named entities, their semantic classes, and their mutual relationships. They contain millions of entities and hundreds of millions of facts about them. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, semantic search for entities and relations in Web and enterprise data, and entity-oriented analytics over unstructured contents. Prominent examples of how knowledge bases can be harnessed include the Google Knowledge Graph and the IBM Watson question answering system. This tutorial presents state-of-the-art methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications. Particular emphasis will be on the twofold role of knowledge bases for big-data analytics: using scalable distributed algorithms for harvesting knowledge from Web and text sources, and leveraging entity-centric knowledge for deeper interpretation of and better intelligence with Big Data.

Reservoir computing approaches to recurrent neural network training Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir computing, greatly facilitated the practical application of RNNs and outperformed classical fully trained RNNs in many tasks. It has lately become a vivid research field with numerous extensions of the basic idea, including reservoir adaptation, thus broadening the initial paradigm to using different methods for training the reservoir and the readout. This review systematically surveys both current ways of generating/adapting the reservoirs and training different types of readouts. It offers a natural conceptual classification of the techniques, which transcends boundaries of the current “brand-names” of reservoir methods, and thus aims to help in unifying the field and providing the reader with a detailed “map” of it.

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.

Dest-ResNet: A Deep Spatiotemporal Residual Network for Hotspot Traffic Speed Prediction. With the ever-increasing urbanization process, the traffic jam has become a common problem in the metropolises around the world, making the traffic speed prediction a crucial and fundamental task. This task is difficult due to the dynamic and intrinsic complexity of the traffic environment in urban cities, yet the emergence of crowd map query data sheds new light on it. In general, a burst of crowd map queries for the same destination in a short duration (called "hotspot'') could lead to traffic congestion. For example, queries of the Capital Gym burst on weekend evenings lead to traffic jams around the gym. However, unleashing the power of crowd map queries is challenging due to the innate spatiotemporal characteristics of the crowd queries. To bridge the gap, this paper firstly discovers hotspots underlying crowd map queries. These discovered hotspots address the spatiotemporal variations. Then Dest-ResNet (Deep spatiotemporal Residual Network) is proposed for hotspot traffic speed prediction. Dest-ResNet is a sequence learning framework that jointly deals with two sequences in different modalities, i.e., the traffic speed sequence and the query sequence. The main idea of Dest-ResNet is to learn to explain and amend the errors caused when the unimodal information is applied individually. In this way, Dest-ResNet addresses the temporal causal correlation between queries and the traffic speed. As a result, Dest-ResNet shows a 30% relative boost over the state-of-the-art methods on real-world datasets from Baidu Map.

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks. Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

A survey on machine learning for data fusion. •We sum up a group of main challenges that data fusion might face.•We propose a thorough list of requirements to evaluate data fusion methods.•We review the literature of data fusion based on machine learning.•We comment on how a machine learning method can ameliorate fusion performance.•We present significant open issues and valuable future research directions.

Discovering spatio-temporal causal interactions in traffic data streams The detection of outliers in spatio-temporal traffic data is an important research problem in the data mining and knowledge discovery community. However to the best of our knowledge, the discovery of relationships, especially causal interactions, among detected traffic outliers has not been investigated before. In this paper we propose algorithms which construct outlier causality trees based on temporal and spatial properties of detected outliers. Frequent substructures of these causality trees reveal not only recurring interactions among spatio-temporal outliers, but potential flaws in the design of existing traffic networks. The effectiveness and strength of our algorithms are validated by experiments on a very large volume of real taxi trajectories in an urban road network.

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.

Adaptive Navigation Support Adaptive navigation support is a specific group of technologies that support user navigation in hyperspace, by adapting to the goals, preferences and knowledge of the individual user. These technologies, originally developed in the field of adaptive hypermedia, are becoming increasingly important in several adaptive Web applications, ranging from Web-based adaptive hypermedia to adaptive virtual reality. This chapter provides a brief introduction to adaptive navigation support, reviews major adaptive navigation support technologies and mechanisms, and illustrates these with a range of examples.

Learning to Predict Driver Route and Destination Intent For many people, driving is a routine activity where people drive to the same destinations using the same routes on a regular basis. Many drivers, for example, will drive to and from work along a small set of routes, at about the same time every day of the working week. Similarly, although a person may shop on different days or at different times, they will often visit the same grocery store(s). In this paper, we present a novel approach to predicting driver intent that exploits the predictable nature of everyday driving. Our approach predicts a driver's intended route and destination through the use of a probabilistic model learned from observation of their driving habits. We show that by using a low-cost GPS sensor and a map database, it is possible to build a hidden Markov model (HMM) of the routes and destinations used by the driver. Furthermore, we show that this model can be used to make accurate predictions of the driver's destination and route through on-line observation of their GPS position during the trip. We present a thorough evaluation of our approach using a corpus of almost a month of real, everyday driving. Our results demonstrate the effectiveness of the approach, achieving approximately 98% accuracy in most cases. Such high performance suggests that the method can be harnessed for improved safety monitoring, route planning taking into account traffic density, and better trip duration prediction

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.

Massive MIMO Antenna Selection: Switching Architectures, Capacity Bounds, and Optimal Antenna Selection Algorithms. Antenna selection is a multiple-input multiple-output (MIMO) technology, which uses radio frequency (RF) switches to select a good subset of antennas. Antenna selection can alleviate the requirement on the number of RF transceivers, thus being attractive for massive MIMO systems. In massive MIMO antenna selection systems, RF switching architectures need to be carefully considered. In this paper, w...

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.

Who is Afraid of the Humanoid? Investigating Cultural Differences in the Acceptance of Robots

What People Assume About Humanoid And Animal-Type Robots: Cross-Cultural Analysis Between Japan, Korea, And The United States To broadly explore the rationale behind more socially acceptable robot design and to investigate the psychological aspects of social acceptance of robotics, a cross-cultural research instrument, the Robot Assumptions Questionnaire (RAQ) was administered to the university students in Japan, Korea, and the United States, focusing on five factors relating to humanoid and animal-type robots: relative autonomy, social relationship with humans, emotional aspects, roles assumed, and images held. As a result, it was found that (1) Students in Japan, Korea, and the United States tend to assume that humanoid robots perform concrete tasks in society, and that animal-type robots play a pet- or toy-like role; (2) Japanese students tend to more strongly assume that humanoid robots have somewhat human characteristics and that their roles are related to social activities including communication, than do the Korean and the US students; (3) Korean students tend to have more negative attitudes toward the social influences of robots, in particular, humanoid robots, than do the Japanese students, while more strongly assuming that robots' roles are related to medical fields than do the Japanese students, and (4) Students in the USA tend to have both more positive and more negative images of robots than do Japanese students, while more weakly assuming robots as blasphemous of nature than do Japanese and Korean students. In addition, the paper discusses some engineering implications of these research results.

Cross-cultural study on human-robot greeting interaction: acceptance and discomfort by Egyptians and Japanese.

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.

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.

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 Tutorial On Visual Servo Control This article provides a tutorial introduction to visual servo control of robotic manipulators, Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework, We begin by reviewing the prerequisite topics from robotics and computer vision, including a brief review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process, We then present a taxonomy of visual servo control systems, The two major classes of systems, position-based and image-based systems, are then discussed in detail, Since any visual servo system must be capable of tracking image features in a sequence of images, we also include an overview of feature-based and correlation-based methods for tracking, We conclude the tutorial with a number of observations on the current directions of the research field of visual servo control.

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 ...

Precomputing Oblivious Transfer Alice and Bob are too untrusting of computer scientists to let their privacy depend on unproven assumptions such as the existence of one-way functions. Firm believers in Schrödinger and Heisenberg, they might accept a quantum OT device, but IBM’s prototype is not yet portable. Instead, as part of their prenuptial agreement, they decide to visit IBM and perform some OT’s in advance, so that any later divorces, coin-flipping or other important interactions can be done more conveniently, without needing expensive third parties. Unfortunately, OT can’t be done in advance in a direct way, because even though Bob might not know what bit Alice will later send (even if she first sends a random bit and later corrects it, for example), he would already know which bit or bits he will receive. We address the problem of precomputing oblivious transfer and show that OT can be precomputed at a cost of Θ(κ) prior transfers (a tight bound). In contrast, we show that variants of OT, such as one-out-of-two OT, can be precomputed using only one prior transfer. Finally, we show that all variants can be reduced to a single precomputed one-out-of-two oblivious transfer.

From structure-from-motion point clouds to fast location recognition Efficient view registration with respect to a given 3D re- construction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating im- ages from large photo collections. We present a fast loca- tion recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we pro- pose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the compu- tation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize mod- ern graphics processing units to deliver real-time perfor- mance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collec- tions to the corresponding landmarks.

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.

Local Load Redistribution Attacks in Power Systems With Incomplete Network Information Power grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. Recent studies have shown that an attacker can inject pre-determined false data into smart meters such that it can pass the residue test of conventional state estimator. However, the calculation of the false data vector relies on the network (topology and parameter) information of the entire grid. In practice, it is impossible for an attacker to obtain all network information of a power grid. Unfortunately, this does not make power systems immune to false data injection attacks. In this paper, we propose a local load redistribution attacking model based on incomplete network information and show that an attacker only needs to obtain the network information of the local attacking region to inject false data into smart meters in the local region without being detected by the state estimator. Simulations on the modified IEEE 14-bus system demonstrate the correctness and effectiveness of the proposed model. The results of this paper reveal the mechanism of local false data injection attacks and highlight the importance and complexity of defending power systems against false data injection attacks.

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.

Mobile data gathering and energy harvesting in rechargeable wireless sensor networks. In this paper, we study the joint data gathering and energy harvesting (JoDGE) problem in rechargeable wireless sensor networks (RWSNs) with a mobile sink. In RWSNs, the sensor nodes are equipped with RF circuit to harvest energy from a mobile sink that moves along a pre-defined path, and at the same time, transmit gathered sensor data to the sink. Given that the consumed and harvested energy at a sensor node is proportional and inversely proportional to the square of transmission distance, a far-relay approach is proposed to select the sensor nodes closer to the path to assist the data transmission of the farther sensor nodes. Under the far-relay approach, we formulate a network utility maximization problem (NUM), and propose an optimal scheduling scheme (Opt-JoDGE), which jointly considers the power allocation, relay selection and time slot scheduling policies. By employing the Lyapunov drift theory, a near optimal buffer-battery-aware adaptive scheduling (NO-BBA) scheme is further proposed, in which the run-time status of the data buffer and battery are utilized. Extensive simulation experiments validate the feasibility and performance of JoDGE and NO-BBA. The results show that the performance of NO-BBA is close to that of Opt-JoDGE, especially when a certain delay is tolerable.

Mobility in wireless sensor networks - Survey and proposal. Targeting an increasing number of potential application domains, wireless sensor networks (WSN) have been the subject of intense research, in an attempt to optimize their performance while guaranteeing reliability in highly demanding scenarios. However, hardware constraints have limited their application, and real deployments have demonstrated that WSNs have difficulties in coping with complex communication tasks – such as mobility – in addition to application-related tasks. Mobility support in WSNs is crucial for a very high percentage of application scenarios and, most notably, for the Internet of Things. It is, thus, important to know the existing solutions for mobility in WSNs, identifying their main characteristics and limitations. With this in mind, we firstly present a survey of models for mobility support in WSNs. We then present the Network of Proxies (NoP) assisted mobility proposal, which relieves resource-constrained WSN nodes from the heavy procedures inherent to mobility management. The presented proposal was implemented and evaluated in a real platform, demonstrating not only its advantages over conventional solutions, but also its very good performance in the simultaneous handling of several mobile nodes, leading to high handoff success rate and low handoff time.

Tag-based cooperative data gathering and energy recharging in wide area RFID sensor networks The Wireless Identification and Sensing Platform (WISP) conjugates the identification potential of the RFID technology and the sensing and computing capability of the wireless sensors. Practical issues, such as the need of periodically recharging WISPs, challenge the effective deployment of large-scale RFID sensor networks (RSNs) consisting of RFID readers and WISP nodes. In this view, the paper proposes cooperative solutions to energize the WISP devices in a wide-area sensing network while reducing the data collection delay. The main novelty is the fact that both data transmissions and energy transfer are based on the RFID technology only: RFID mobile readers gather data from the WISP devices, wirelessly recharge them, and mutually cooperate to reduce the data delivery delay to the sink. Communication between mobile readers relies on two proposed solutions: a tag-based relay scheme, where RFID tags are exploited to temporarily store sensed data at pre-determined contact points between the readers; and a tag-based data channel scheme, where the WISPs are used as a virtual communication channel for real time data transfer between the readers. Both solutions require: (i) clustering the WISP nodes; (ii) dimensioning the number of required RFID mobile readers; (iii) planning the tour of the readers under the energy and time constraints of the nodes. A simulative analysis demonstrates the effectiveness of the proposed solutions when compared to non-cooperative approaches. Differently from classic schemes in the literature, the solutions proposed in this paper better cope with scalability issues, which is of utmost importance for wide area networks.

Improving charging capacity for wireless sensor networks by deploying one mobile vehicle with multiple removable chargers. Wireless energy transfer is a promising technology to prolong the lifetime of wireless sensor networks (WSNs), by employing charging vehicles to replenish energy to lifetime-critical sensors. Existing studies on sensor charging assumed that one or multiple charging vehicles being deployed. Such an assumption may have its limitation for a real sensor network. On one hand, it usually is insufficient to employ just one vehicle to charge many sensors in a large-scale sensor network due to the limited charging capacity of the vehicle or energy expirations of some sensors prior to the arrival of the charging vehicle. On the other hand, although the employment of multiple vehicles can significantly improve the charging capability, it is too costly in terms of the initial investment and maintenance costs on these vehicles. In this paper, we propose a novel charging model that a charging vehicle can carry multiple low-cost removable chargers and each charger is powered by a portable high-volume battery. When there are energy-critical sensors to be charged, the vehicle can carry the chargers to charge multiple sensors simultaneously, by placing one portable charger in the vicinity of one sensor. Under this novel charging model, we study the scheduling problem of the charging vehicle so that both the dead duration of sensors and the total travel distance of the mobile vehicle per tour are minimized. Since this problem is NP-hard, we instead propose a (3+ϵ)-approximation algorithm if the residual lifetime of each sensor can be ignored; otherwise, we devise a novel heuristic algorithm, where ϵ is a given constant with 0 < ϵ ≤ 1. Finally, we evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the performance of the proposed algorithms are very promising.

Speed control of mobile chargers serving wireless rechargeable networks. Wireless rechargeable networks have attracted increasing research attention in recent years. For charging service, a mobile charger is often employed to move across the network and charge all network nodes. To reduce the charging completion time, most existing works have used the “move-then-charge” model where the charger first moves to specific spots and then starts charging nodes nearby. As a result, these works often aim to reduce the moving delay or charging delay at the spots. However, the charging opportunity on the move is largely overlooked because the charger can charge network nodes while moving, which as we analyze in this paper, has the potential to greatly reduce the charging completion time. The major challenge to exploit the charging opportunity is the setting of the moving speed of the charger. When the charger moves slow, the charging delay will be reduced (more energy will be charged during the movement) but the moving delay will increase. To deal with this challenge, we formulate the problem of delay minimization as a Traveling Salesman Problem with Speed Variations (TSP-SV) which jointly considers both charging and moving delay. We further solve the problem using linear programming to generate (1) the moving path of the charger, (2) the moving speed variations on the path and (3) the stay time at each charging spot. We also discuss possible ways to reduce the calculation complexity. Extensive simulation experiments are conducted to study the delay performance under various scenarios. The results demonstrate that our proposed method achieves much less completion time compared to the state-of-the-art work.

A Prediction-Based Charging Policy and Interference Mitigation Approach in the Wireless Powered Internet of Things The Internet of Things (IoT) technology has recently drawn more attention due to its ability to achieve the interconnections of massive physic devices. However, how to provide a reliable power supply to energy-constrained devices and improve the energy efficiency in the wireless powered IoT (WP-IoT) is a twofold challenge. In this paper, we develop a novel wireless power transmission (WPT) system, where an unmanned aerial vehicle (UAV) equipped with radio frequency energy transmitter charges the IoT devices. A machine learning framework of echo state networks together with an improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -means clustering algorithm is used to predict the energy consumption and cluster all the sensor nodes at the next period, thus automatically determining the charging strategy. The energy obtained from the UAV by WPT supports the IoT devices to communicate with each other. In order to improve the energy efficiency of the WP-IoT system, the interference mitigation problem is modeled as a mean field game, where an optimal power control policy is presented to adapt and analyze the large number of sensor nodes randomly deployed in WP-IoT. The numerical results verify that our proposed dynamic charging policy effectively reduces the data packet loss rate, and that the optimal power control policy greatly mitigates the interference, and improve the energy efficiency of the whole network.

Design of Self-sustainable Wireless Sensor Networks with Energy Harvesting and Wireless Charging AbstractEnergy provisioning plays a key role in the sustainable operations of Wireless Sensor Networks (WSNs). Recent efforts deploy multi-source energy harvesting sensors to utilize ambient energy. Meanwhile, wireless charging is a reliable energy source not affected by spatial-temporal ambient dynamics. This article integrates multiple energy provisioning strategies and adaptive adjustment to accomplish self-sustainability under complex weather conditions. We design and optimize a three-tier framework with the first two tiers focusing on the planning problems of sensors with various types and distributed energy storage powered by environmental energy. Then we schedule the Mobile Chargers (MC) between different charging activities and propose an efficient, 4-factor approximation algorithm. Finally, we adaptively adjust the algorithms to capture real-time energy profiles and jointly optimize those correlated modules. Our extensive simulations demonstrate significant improvement of network lifetime (\(\)), increase of harvested energy (15%), reduction of network cost (30%), and the charging capability of MC by 100%.

Double warning thresholds for preemptive charging scheduling in Wireless Rechargeable Sensor Networks. Wireless power transfer technique provides new alternatives for solving the limited power capacity problem for ubiquitous mobile wireless devices, and makes wireless rechargeable sensor networks (WRSNs) promising. However, mainly due to the underestimate of unbalanced influences of spatial and temporal constraints posed by charging requests, traditional scheduling strategies achieve rather low charging request throughput and success rate, posing as a major bottleneck for further improvement. In this paper, we propose a Double Warning thresholds with Double Preemption (DWDP) charging scheme, in which double warning thresholds are used when residual energy levels of sensor nodes fall below certain thresholds. By introducing specific comparison rules, warning thresholds can be used to adjust charging priorities of different sensors, warn the upcoming recharge deadlines, as well as support preemptive scheduling. Then DWDP is extended to where multiple Wireless Charging Vehicles (WCVs) are employed, and a Collaborative Charging DWDP, namely CCDWDP is proposed. Finally, we conduct extensive simulations to manifest the advantages of DWDP as well as CCDWDP. Simulation results reveal that DWDP can achieve better performance in guaranteeing the successful scheduling of the high-priority task and improving stability of the system. CCDWDP outperforms in terms of high charging throughput and short charging delay.

Joint Charging Tour Planning and Depot Positioning for Wireless Sensor Networks Using Mobile Chargers. Recent breakthrough in wireless energy transfer technology has enabled wireless sensor networks (WSNs) to operate with zero-downtime through the use of mobile energy chargers (MCs), that periodically replenish the energy supply of the sensor nodes. Due to the limited battery capacity of the MCs, a significant number of MCs and charging depots are required to guarantee perpetual operations in large...

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.

Output-feedback stochastic nonlinear stabilization The authors present the first result on global output-feedback stabilization (in probability) for stochastic nonlinear continuous-time systems. The class of systems that they consider is a stochastic counterpart of the broadest class of deterministic systems for which globally stabilizing controllers are currently available. Their controllers are “inverse optimal” and possess an infinite gain margin. A reader of the paper needs no prior familiarity with techniques of stochastic control

Development of Recurrent Neural Network Considering Temporal-Spatial Input Dynamics for Freeway Travel Time Modeling AbstractAbstract:ï źThe artificial neural network ANN is one advance approach to freeway travel time prediction. Various studies using different inputs have come to no consensus on the effects of input selections. In addition, very little discussion has been made on the temporal-spatial aspect of the ANN travel time prediction process. In this study, we employ an ANN ensemble technique to analyze the effects of various input settings on the ANN prediction performances. Volume, occupancy, and speed are used as inputs to predict travel times. The predictions are then compared against the travel times collected from the toll collection system in Houston. The results show speed or occupancy measured at the segment of interest may be used as sole input to produce acceptable predictions, but all three variables together tend to yield the best prediction results. The inclusion of inputs from both upstream and downstream segments is statistically better than using only the inputs from current segment. It also appears that the magnitude of prevailing segment travel time can be used as a guideline to set up temporal input delays for better prediction accuracies. The evaluation of spatiotemporal input interactions reveals that past information on downstream and current segments is useful in improving prediction accuracy whereas past inputs from the upstream location do not provide as much constructive information. Finally, a variant of the state-space model SSNN, namely time-delayed state-space neural network TDSSNN, is proposed and compared against other popular ANN models. The comparison shows that the TDSSNN outperforms other networks and remains very comparable with the SSNN. Future research is needed to analyze TDSSNN's ability in corridor prediction settings.

Fixed-Time Consensus Tracking for Multiagent Systems With High-Order Integrator Dynamics. This paper addresses the fixed-time leader-follower consensus problem for high-order integrator multiagent systems subject to matched external disturbances. A new cascade control structure, based on a fixed-time distributed observer, is developed to achieve the fixed-time consensus tracking control. A simulation example is included to show the efficacy and the performance of the proposed control structure with respect to different initial conditions.

Stochastic QoE-aware optimization of multisource multimedia content delivery for mobile cloud The increasing popularity of mobile video streaming in wireless networks has stimulated growing demands for efficient video streaming services. However, due to the time-varying throughput and user mobility, it is still difficult to provide high quality video services for mobile users. Our proposed optimization method considers key factors such as video quality, bitrate level, and quality variations to enhance quality of experience over wireless networks. The mobile network and device parameters are estimated in order to deliver the best quality video for the mobile user. We develop a rate adaptation algorithm using Lyapunov optimization for multi-source multimedia content delivery to minimize the video rate switches and provide higher video quality. The multi-source manager algorithm is developed to select the best stream based on the path quality for each path. The node joining and cluster head election mechanism update the node information. As the proposed approach selects the optimal path, it also achieves fairness and stability among clients. The quality of experience feature metrics like bitrate level, rebuffering events, and bitrate switch frequency are employed to assess video quality. We also employ objective video quality assessment methods like VQM, MS-SSIM, and SSIMplus for video quality measurement closer to human visual assessment. Numerical results show the effectiveness of the proposed method as compared to the existing state-of-the-art methods in providing quality of experience and bandwidth utilization.

Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition. Occlusion over ear surfaces results in performance degradation of ear registration and recognition systems. In this paper, we propose an occlusion-resistant three-dimensional (3D) ear recognition system consisting of four primary components: (1) an ear detection component, (2) a local feature extraction and matching component, (3) a holistic matching component, and (4) a decision-level fusion algorithm. The ear detection component is implemented based on faster region-based convolutional neural networks. In the local feature extraction and matching component, a symmetric space-centered 3D shape descriptor based on the surface patch histogram of indexed shapes (SPHIS) is used to generate a set of keypoints and a feature vector for each keypoint. Then, a two-step noncooperative game theory (NGT)-based method is proposed. The proposed symmetric game-based method is effectively applied to determine a set of keypoints that satisfy the rigid constraints from initial keypoint correspondences. In the holistic matching component, a proposed variant of breed surface voxelization is used to calculate the holistic registration error. Finally, the decision-level fusion algorithm is applied to generate the final match scores. Evaluation results from experiments conducted show that the proposed method produces competitive results for partial occlusion on a dataset consisting of natural and random occlusion.

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.

Learning pairwise SVM on hierarchical deep features for ear recognition. Convolutional neural networks (CNNs)-based deep features have been demonstrated with remarkable performance in various vision tasks, such as image classification and face verification. Compared with the hand-crafted descriptors, deep features exhibit more powerful representation ability. Typically, higher layer features contain more semantic information, while lower layer features can provide more...

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.

Distributed and Dynamic Service Placement in Pervasive Edge Computing Networks The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects.

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.

Energy-Aware Task Offloading and Resource Allocation for Time-Sensitive Services in Mobile Edge Computing Systems Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is tak...

Dynamic Deployment and Cost-Sensitive Provisioning for Elastic Mobile Cloud Services. As mobile customers gradually occupying the largest share of cloud service users, the effective and cost-sensitive provisioning of mobile cloud services quickly becomes a main theme in cloud computing. The key issues involved are much more than just enabling mobile users to access remote cloud resources through wireless networks. The resource limited and intermittent disconnection problems of mobile environments have intrinsic conflict with the continuous connection assumption of the cloud service usage patterns. We advocate that seamless service provisioning in mobile cloud can only be achieved with full exploitation of all available resources around mobile users. An elastic framework is proposed to automatically and dynamically deploy cloud services on data center, base stations, client units, even peer devices. The best deployment location is dynamically determined based on a context-aware and cost-sensitive evaluation model. To facilitate easy adoption of the proposed framework, a service development model and associated semi-automatic tools are provided such that cloud service developers can easily convert a service for execution on different platforms without porting. Prototype implementation and evaluation on the Google Cloud and Android platforms demonstrate that our mechanism can successfully maintain seamless services with very low overhead.

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency-related issues, its deployment engenders new challenges. In this article, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing-enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

A Cooperative Resource Allocation Model For Iot Applications In Mobile Edge Computing With the advancement in the development of the Internet of Things (IoT) technology, as well as the industrial IoT, various applications and services are benefiting from this emerging technology such as smart healthcare systems, virtual realities applications, connected and autonomous vehicles, to name a few. However, IoT devices are known for being limited computation capacities which is crucial to the device's availability time. Traditional approaches used to offload the applications to the cloud to ease the burden on the end user's devices, however, greater latency and network traffic issues still persist. Mobile Edge Computing (MEC) technology has emerged to address these issues and enhance the survivability of cloud infrastructure. While a lot of attempts have been made to manage an efficient process of applications offload, many of which either focus on the allocation of computational or communication protocols without considering a cooperative solution. In addition, a single-user scenario was considered. Therefore, we study multi-user IoT applications offloading for a MEC system, which cooperatively considers to allocate both the resources of computation and communication. The proposed system focuses on minimizing the weighted overhead of local IoT devices, and minimize the offload measured by the delay and energy consumption. The mathematical formulation is a typical mixed integer nonlinear programming (MINP), and this is an NP-hard problem. We obtain the solution to the objective function by splitting the objective problem into three sub-problems. Extensive set of evaluations have been performed so as to get the evaluation of the proposed model. The collected results indicate that offloading decisions, energy consumption, latency, and the impact of the number of IoT devices have shown superior improvement over traditional models.

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.

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.

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.

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.

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.

Finite-Time Input-to-State Stability and Applications to Finite-Time Control Design This paper extends the well-known concept, Sontag's input-to-state stability (ISS), to finite-time control problems. In other words, a new concept, finite-time input-to-state stability (FTISS), is proposed and then is applied to both the analysis of finite-time stability and the design of finite-time stabilizing feedback laws of control systems. With finite-time stability, nonsmoothness has to be considered, and serious technical challenges arise in the design of finite-time controllers and the stability analysis of the closed-loop system. It is found that FTISS plays an important role as the conventional ISS in the context of asymptotic stability analysis and smooth feedback stabilization. Moreover, a robust adaptive controller is proposed to handle nonlinear systems with parametric and dynamic uncertainties by virtue of FTISS and related arguments.

Distributed Containment Control for Multiple Unknown Second-Order Nonlinear Systems With Application to Networked Lagrangian Systems. In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors' velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

Fully distributed containment control of high-order multi-agent systems with nonlinear dynamics. In this paper, distributed containment control problems for high-order multi-agent systems with nonlinear dynamics are investigated under directed communication topology. The states of the leaders are only available to a subset of the followers and the inputs of the leaders are possibly nonzero and time varying. Distributed adaptive nonlinear protocol is proposed based only on the relative state information, under which the states of the followers converge to the dynamic convex hull spanned by those of the leaders. As the special case with only one dynamic leader, leader–follower consensus problem is also solved with the proposed protocol. The adaptive protocol here is independent of the eigenvalues of the Laplacian matrix, which means the protocol can be implemented by each agent in a fully distributed fashion. A simulation example is provided to illustrate the theoretical results.

Output Containment Control of Linear Heterogeneous Multi-Agent Systems Using Internal Model Principle. This paper studies the output containment control of linear heterogeneous multi-agent systems, where the system dynamics and even the state dimensions can generally be different. Since the states can have different dimensions, standard results from state containment control do not apply. Therefore, the control objective is to guarantee the convergence of the output of each follower to the dynamic ...

Finite-Time Consensus Tracking Neural Network FTC of Multi-Agent Systems The finite-time consensus fault-tolerant control (FTC) tracking problem is studied for the nonlinear multi-agent systems (MASs) in the nonstrict feedback form. The MASs are subject to unknown symmetric output dead zones, actuator bias and gain faults, and unknown control coefficients. According to the properties of the neural network (NN), the unstructured uncertainties problem is solved. The Nussbaum function is used to address the output dead zones and unknown control directions problems. By introducing an arbitrarily small positive number, the “singularity” problem caused by combining the finite-time control and backstepping design is solved. According to the backstepping design and Lyapunov stability theory, a finite-time adaptive NN FTC controller is obtained, which guarantees that the tracking error converges to a small neighborhood of zero in a finite time, and all signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed method is illustrated via a physical example.

Observer-based Adaptive Fuzzy Containment Control for Multiple Uncertain Nonlinear Systems The adaptive fuzzy containment control problem is addressed for multiple uncertain nonlinear strict-feedback systems with immeasurable states and multiple leaders under directed communication graphs. By utilizing fuzzy logic systems to model the followers’ dynamics, a distributed fuzzy state observer is designed for the state estimation using only the relative position information. Then, an observer-based containment control scheme is constructed by the adaptive fuzzy control technique as well as the command filter. The filtering error loop is introduced to compensate the error arising from the command filter. The proposed adaptive fuzzy containment control scheme guarantees that all followers are driven into the dynamic convex hull spanned by the leaders with a bounded containment error, if there exists at least one of the leaders who has a directed path to the follower. Simulation results are given to illustrate the control performance of the proposed containment control method.

Containment control of heterogeneous linear multi-agent systems In this note, we study the containment control problem of heterogeneous linear multi-agent systems based on output regulation framework. Motivated by leader-follower output regulation problems, the leaders are assumed to be exosystems. In controller design approach for each follower, we utilize a distributed dynamic state feedback control scheme. To achieve the objective of this work, we modify the conventional output regulation error in such a way that it can handle more than one leader, and we also introduce a dynamic compensator. Our work is based on a new formulation for containment error that guarantees the convergence of all follower agents to the dynamic convex hull spanned by the leaders, and also enables us to use output regulation techniques with some modifications to solve the containment problem. Finally, a numerical example is given to illustrate the validity of theoretical results.

Fuzzy controllers: synthesis and equivalences It has been proved that fuzzy controllers are capable of approximating any real continuous control function on a compact set to arbitrary accuracy. In particular, any given linear control can be achieved with a fuzzy controller for a given accuracy. The aim of this paper is to show how to automatically build this fuzzy controller. The proposed design methodology is detailed for the synthesis of a Sugeno or Mamdani type fuzzy controller precisely equivalent to a given PI controller. The main idea is to equate the output of the fuzzy controller with the output of the PI controller at some particular input values, called modal values. The rule base and the distribution of the membership functions can thus be deduced. The analytic expression of the output of the generated fuzzy controller is then established. For Sugeno-type fuzzy controllers, precise equivalence is directly obtained. For Mamdani-type fuzzy controllers, the defuzzification strategy and the inference operators have to be correctly chosen to provide linear interpolation between modal values. The usual inference operators satisfying the linearity requirement when using the center of gravity defuzzification method are proposed

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures–independence, normality, and homoscedasticity–yields to nonparametric ones the task of performing a rigorous comparison among algorithms.

Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns This paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform" are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in true problems of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis.

A Convex Characterization Of Gain-Scheduled H-Infinity Controllers An important class of linear time-varying systems consists of plants where the state-space matrices are fixed functions of some time-varying physical parameters theta. Small gain techniques can be applied to such systems to derive robust time-invariant controllers. Yet, this approach is often overly conservative when the parameters theta undergo large variations during system operation, In general, higher performance can be achieved by control laws that incorporate available measurements of theta and therefore ''adjust'' to the current plant dynamics. This paper discusses extensions of H-infinity, synthesis techniques to allow for controller dependence on time-varying but measured parameters. When this dependence is linear fractional, the existence of such gain-scheduled H-infinity, controllers is fully characterized in terms of linear matrix inequalities. The underlying synthesis problem is therefore a convex program for which efficient optimization techniques are available. The formalism and derivation techniques developed here apply to both the continuous- and discrete-time problems. Existence conditions for robust time-invariant controllers are recovered as a special case, and extensions to gain-scheduling in the face of parametric uncertainty are discussed. In particular, simple heuristics are proposed to compute such controllers.

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.

Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications. Given the escalating population across the globe, it has become paramount to construct smart cities, aiming for improving the management of urban flows relying on efficient information and communication technologies (ICT). Vehicular sensing networks (VSNs) play a critical role in maintaining the efficient operation of smart cities. Naturally, there are numerous challenges to be solved before the w...

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.

GenVibe - Exploration of Interactive Generation of Personal Vibrotactile Patterns.

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.

VibViz: Organizing, visualizing and navigating vibration libraries With haptics now common in consumer devices, diversity in tactile perception and aesthetic preferences confound haptic designers. End-user customization out of example sets is an obvious solution, but haptic collections are notoriously difficult to explore. This work addresses the provision of easy and highly navigable access to large, diverse sets of vibrotactile stimuli, on the premise that multiple access pathways facilitate discovery and engagement. We propose and examine five disparate organization schemes (taxonomies), describe how we created a 120-item library with diverse functional and affective characteristics, and present VibViz, an interactive tool for end-user library navigation and our own investigation of how different taxonomies can assist navigation. An exploratory user study with and of VibViz suggests that most users gravitate towards an organization based on sensory and emotional terms, but also exposes rich variations in their navigation patterns and insights into the basis of effective haptic library navigation.

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...

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.

Implementing Vehicle Routing Algorithms

GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.

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.

A novel Lyapunov function for a non-weighted L2 gain of asynchronously switched linear systems. In this paper, a novel Lyapunov function is proposed to study switched linear systems with a switching delay between activation of system modes and activation of candidate controller modes. The novelty consists in continuity of the Lyapunov function at the switching instants and discontinuity when the system modes and controller modes are matched. This structure is exploited to construct a time-varying Lyapunov function that is non-increasing at time instants of discontinuity. Stability criteria based on the novel Lyapunov function are developed to guarantee global asymptotic stability in the noiseless case. Most importantly, when exogenous disturbances are considered, the proposed Lyapunov function can be used to guarantee a finite non-weightedL2 gain for asynchronously switched systems, for which Lyapunov functions proposed in literature are inconclusive. A numerical example illustrates the effectiveness of the proposed method.

Generalized dilations and numerically solving discrete-time homogeneous optimization problems We introduce generalized dilations, a broader class of operators than that of dilations, and consider homogeneity with respect to this new class of dilations. For discrete-time systems that are asymptotically controllable and homogeneous (with degree zero) we propose a method to numerically approximate any homogeneous value function (solution to an infinite horizon optimization problem) to arbitrary accuracy. We also show that the method can be used to generate an offline computed stabilizing feedback law.

Delay-independent stability of homogeneous systems. A class of nonlinear systems with homogeneous right-hand sides and time-varying delay is studied. It is assumed that the trivial solution of a system is asymptotically stable when delay is equal to zero. By the usage of the Lyapunov direct method and the Razumikhin approach, it is proved that the asymptotic stability of the zero solution of the system is preserved for an arbitrary continuous nonnegative and bounded delay. The conditions of stability of time-delay systems by homogeneous approximation are obtained. Furthermore, it is shown that the presented approaches permit to derive delay-independent stability conditions for some types of nonlinear systems with distributed delay. Two examples of nonlinear oscillatory systems are given to demonstrate the effectiveness of our results.

Exponential Stability Of Homogeneous Impulsive Positive Delay Systems Of Degree One This paper investigates the global exponential stability of homogeneous impulsive positive delay systems of degree one. By using the max-separable Lyapunov functions, a sufficient criterion is obtained for exponential stability of continuous-time homogeneous impulsive positive delay systems of degree one. We also provide the corresponding counterpart for discrete-time homogeneous impulsive positive delay systems of degree one. Our results show that a stable impulse-free system can keep its original stability property under certain destabilising impulsive perturbations. It should be noted that it's the first time that the exponential stability results for homogeneous impulsive positive delay systems of degree one are given. Numerical examples are provided to demonstrate the effectiveness of the derived results.

Global Stabilization of a Class of Stochastic Nonlinear Time-Delay Systems With SISS Inverse Dynamics This article studies the adaptive state feedback stabilization problem of stochastic nonlinear systems with many uncertainties, such as parametric uncertainties, time-varying delay, unknown powers, and stochastic inverse dynamics. By virtue of the adding-a-power-integrator technique, an adaptive control scheme together with a novel state feedback control law is proposed to deal with these unknowns. Compared with the existing literature, both of adaptive dynamics, higher and lower powers are introduced into the construction of our controller. Based on some ideas of Lyapunov function, we analyze the global stabilization of the whole system. Finally, a simulation example is given to show the effectiveness of our theoretical results.

Finite-Time Stability of Homogeneous Impulsive Positive Systems of Degree One This paper investigates the finite-time stability (FTS) of a special class of hybrid systems, namely homogeneous impulsive positive systems of degree one. By using max-separable Lyapunov functions together with average impulsive interval method, a sufficient FTS criterion is obtained for homogeneous impulsive positive systems of degree one. It should be noted that it’s the first time that the FTS result for homogeneous impulsive positive systems of degree one is given. Finally, some numerical examples are provided to demonstrate the effectiveness of the presented theoretical results.

Adaptive tracking control for switched stochastic nonlinear systems with unknown actuator dead-zone This paper is concerned with the problem of adaptive tracking control for a class of switched stochastic nonlinear systems in nonstrict-feedback form with unknown nonsymmetric actuator dead-zone and arbitrary switchings. A variable separation approach is used to overcome the difficulty in dealing with the nonstrict-feedback structure. Furthermore, by combining radial basis function neural networks' universal approximation ability and adaptive backstepping technique with common stochastic Lyapunov function method, an adaptive control algorithm is proposed for the considered system. It is shown that the target signal can be almost surely tracked by the system output, and the tracking error is semi-globally uniformly ultimately bounded in 4 t h moment. Finally, the simulation studies for a ship maneuvering system are presented to show the effectiveness of the proposed approach.

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.

Multi-Hop Cooperative Computation Offloading for Industrial IoT–Edge–Cloud Computing Environments The concept of the industrial Internet of things (IIoT) is being widely applied to service provisioning in many domains, including smart healthcare, intelligent transportation, autopilot, and the smart grid. However, because of the IIoT devices’ limited onboard resources, supporting resource-intensive applications, such as 3D sensing, navigation, AI processing, and big-data analytics, remains a challenging task. In this paper, we study the multi-hop computation-offloading problem for the IIoT–edge–cloud computing model and adopt a game-theoretic approach to achieving Quality of service (QoS)-aware computation offloading in a distributed manner. First, we study the computation-offloading and communication-routing problems with the goal of minimizing each task's computation time and energy consumption, formulating the joint problem as a potential game in which the IIoT devices determine their computation-offloading strategies. Second, we apply a free–bound mechanism that can ensure a finite improvement path to a Nash equilibrium. Third, we propose a multi-hop cooperative-messaging mechanism and develop two QoS-aware distributed algorithms that can achieve the Nash equilibrium. Our simulation results show that our algorithms offer a stable performance gain for IIoT in various scenarios and scale well as the device size increases.

Automated Synthesis of Data Paths in Digital Systems This paper presents a unifying procedure, called Facet, for the automated synthesis of data paths at the register-transfer level. The procedure minimizes the number of storage elements, data operators, and interconnection units. A design generator named Emerald, based on Facet, was developed and implemented to facilitate extensive experiments with the methodology. The input to the design generator is a behavioral description which is viewed as a code sequence. Emerald provides mechanisms for interactively manipulating the code sequence. Different forms of the code sequence are mapped into data paths of different cost and speed. Data paths for the behavioral descriptions of the AM2910, the AM2901, and the IBM System/370 were produced and analyzed. Designs for the AM2910 and the AM2901 are compared with commercial designs. Overall, the total number of gates required for Emerald's designs is about 15 percent more than the commercial designs. The design space spanned by the behavioral specification of the AM2901 is extensively explored.

Development of a UAV-LiDAR System with Application to Forest Inventory We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m(2)) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points per m(2), to 0.15 m when the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.

A review on interval type-2 fuzzy logic applications in intelligent control. A review of the applications of interval type-2 fuzzy logic in intelligent control has been considered in this paper. The fundamental focus of the paper is based on the basic reasons for using 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.

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...

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.

Concept and Prototype Design of an Underwater Soft Exoskeleton In this paper, we proposed the concept of underwater soft exoskeleton which could provide assistance to swimming motions. With this motivation, we designed the first version prototype called Powered Swimsuit to assist the wearer in fin swimming. The actuator system was embedded inside of a portable water-proof cabin that was fabricated on the back of the swimsuit. The assistive force was applied on the bottom of the fins via soft cables. During the propelling period of the stroke cycle, the cables pulled the ankle joints to provide assistance to plantarflexions. After implementation of the prototype, we carried out initial experiment to test the proposed soft exoskeleton. The subject was asked to swim with frog kicks under two assistance levels. Several kinematic parameters including the tilt angles, accelerations and angular velocities of the lower extremities were recorded simultaneously. With higher assistance, larger absolute tilt angles and accelerations, larger signal profile changes in angular velocities were found within the sagittal plane of the measured foot. The preliminary results showed the feasibility of the proposed Powered Swimsuit in underwater motion assistance.

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.

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.

Adaptive impedance control of a robotic orthosis for gait rehabilitation. Intervention of robotic devices in the field of physical gait therapy can help in providing repetitive, systematic, and economically viable training sessions. Interactive or assist-as-needed (AAN) gait training encourages patient voluntary participation in the robotic gait training process which may aid in rapid motor function recovery. In this paper, a lightweight robotic gait training orthosis w...

Nonparametric Bayesian Prior Inducing Deep Network for Automatic Detection of Cognitive Status Pilots’ brain fatigue status recognition faces two important issues. They are how to extract brain cognitive features and how to identify these fatigue characteristics. In this article, a gamma deep belief network is proposed to extract multilayer deep representations of high-dimensional cognitive data. The Dirichlet distributed connection weight vector is upsampled layer by layer in each iteratio...

Fatigue Detection of Pilots’ Brain Through Brains Cognitive Map and Multilayer Latent Incremental Learning Model This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots’ cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.

Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties. This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover...

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.

Vision meets robotics: The KITTI dataset We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

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.

GameFlow: a model for evaluating player enjoyment in games Although player enjoyment is central to computer games, there is currently no accepted model of player enjoyment in games. There are many heuristics in the literature, based on elements such as the game interface, mechanics, gameplay, and narrative. However, there is a need to integrate these heuristics into a validated model that can be used to design, evaluate, and understand enjoyment in games. We have drawn together the various heuristics into a concise model of enjoyment in games that is structured by flow. Flow, a widely accepted model of enjoyment, includes eight elements that, we found, encompass the various heuristics from the literature. Our new model, GameFlow, consists of eight elements -- concentration, challenge, skills, control, clear goals, feedback, immersion, and social interaction. Each element includes a set of criteria for achieving enjoyment in games. An initial investigation and validation of the GameFlow model was carried out by conducting expert reviews of two real-time strategy games, one high-rating and one low-rating, using the GameFlow criteria. The result was a deeper understanding of enjoyment in real-time strategy games and the identification of the strengths and weaknesses of the GameFlow model as an evaluation tool. The GameFlow criteria were able to successfully distinguish between the high-rated and low-rated games and identify why one succeeded and the other failed. We concluded that the GameFlow model can be used in its current form to review games; further work will provide tools for designing and evaluating enjoyment in games.

Adapting visual category models to new domains Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to nonimage data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.

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.

GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.

5G Virtualized Multi-access Edge Computing Platform for IoT Applications. The next generation of fifth generation (5G) network, which is implemented using Virtualized Multi-access Edge Computing (vMEC), Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies, is a flexible and resilient network that supports various Internet of Things (IoT) devices. While NFV provides flexibility by allowing network functions to be dynamically deployed and inter-connected, vMEC provides intelligence at the edge of the mobile network reduces latency and increases the available capacity. With the diverse development of networking applications, the proposed vMEC use of Container-based Virtualization Technology (CVT) as gateway with IoT devices for flow control mechanism in scheduling and analysis methods will effectively increase the application Quality of Service (QoS). In this work, the proposed IoT gateway is analyzed. The combined effect of simultaneously deploying Virtual Network Functions (VNFs) and vMEC applications on a single network infrastructure, and critically in effecting exhibits low latency, high bandwidth and agility that will be able to connect large scale of devices. The proposed platform efficiently exploiting resources from edge computing and cloud computing, and takes IoT applications that adapt to network conditions to degrade an average 30% of end to end network latency.

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.

Gendered voice and robot entities: Perceptions and reactions of male and female subjects There is recent evidence that males and females view robots differently, from the way robots are conceptualized, to the way humans respond when they interact with them. In this paper, we further explore gender-based differences in human-robot interaction. Moreover, we provide the first available evidence for sex-related differences in reactions to gendered synthetic voices that are either disembodied or phys- ically embodied within a robot. Results indicate that physical embodiment and perceived entity gender may interact with hu- man sex-related characteristics and pre-experimental attitudes in determining how people respond to artificial entities.

A Mass-Produced Sociable Humanoid Robot: Pepper: The First Machine of Its Kind. As robotics technology evolves, we believe that personal social robots will be one of the next big expansions in the robotics sector. Based on the accelerated advances in this multidisciplinary domain and the growing number of use cases, we can posit that robots will play key roles in everyday life and will soon coexist with us, leading all people to a smarter, safer, healthier, and happier existe...

Gender Ambiguous, not Genderless - Designing Gender in Voice User Interfaces (VUIs) with Sensitivity.

She's Electric - The Influence of Body Proportions on Perceived Gender of Robots across Cultures. The assignment of gender to robots is a debatable topic. Subtle aspects related to gender, in a robot's appearance, may create biased expectations of the robot's abilities and influence user acceptance. The present research is a cross-cultural study involving more than 150 participants to investigate the perception of gender in robot design by manipulating body proportions. We are focusing specifically on the contrast between two extremely different cultures: Peruvian and Japanese. From the survey based on stimuli varying in the proportion between chest, waist, and hips, the results indicate the importance of chest-to-hip ratio and waist-to-hip ratio in the attribution of gender to robots.

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.

Wireless sensor networks: a survey This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.

Constrained Kalman filtering for indoor localization of transport vehicles using floor-installed HF RFID transponders Localization of transport vehicles is an important issue for many intralogistics applications. The paper presents an inexpensive solution for indoor localization of vehicles. Global localization is realized by detection of RFID transponders, which are integrated in the floor. The paper presents a novel algorithm for fusing RFID readings with odometry using Constraint Kalman filtering. The paper presents experimental results with a Mecanum based omnidirectional vehicle on a NaviFloor® installation, which includes passive HF RFID transponders. The experiments show that the proposed Constraint Kalman filter provides a similar localization accuracy compared to a Particle filter but with much lower computational expense.

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 ...

Precomputing Oblivious Transfer Alice and Bob are too untrusting of computer scientists to let their privacy depend on unproven assumptions such as the existence of one-way functions. Firm believers in Schrödinger and Heisenberg, they might accept a quantum OT device, but IBM’s prototype is not yet portable. Instead, as part of their prenuptial agreement, they decide to visit IBM and perform some OT’s in advance, so that any later divorces, coin-flipping or other important interactions can be done more conveniently, without needing expensive third parties. Unfortunately, OT can’t be done in advance in a direct way, because even though Bob might not know what bit Alice will later send (even if she first sends a random bit and later corrects it, for example), he would already know which bit or bits he will receive. We address the problem of precomputing oblivious transfer and show that OT can be precomputed at a cost of Θ(κ) prior transfers (a tight bound). In contrast, we show that variants of OT, such as one-out-of-two OT, can be precomputed using only one prior transfer. Finally, we show that all variants can be reduced to a single precomputed one-out-of-two oblivious transfer.

From structure-from-motion point clouds to fast location recognition Efficient view registration with respect to a given 3D re- construction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating im- ages from large photo collections. We present a fast loca- tion recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we pro- pose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the compu- tation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize mod- ern graphics processing units to deliver real-time perfor- mance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collec- tions to the corresponding landmarks.

A three-network architecture for on-line learning and optimization based on adaptive dynamic programming In this paper, we propose a novel adaptive dynamic programming (ADP) architecture with three networks, an action network, a critic network, and a reference network, to develop internal goal-representation for online learning and optimization. Unlike the traditional ADP design normally with an action network and a critic network, our approach integrates the third network, a reference network, into the actor-critic design framework to automatically and adaptively build an internal reinforcement signal to facilitate learning and optimization overtime to accomplish goals. We present the detailed design architecture and its associated learning algorithm to explain how effective learning and optimization can be achieved in this new ADP architecture. Furthermore, we test the performance of our architecture both on the cart-pole balancing task and the triple-link inverted pendulum balancing task, which are the popular benchmarks in the community to demonstrate its learning and control performance over time.

A Model Predictive Control Approach to Microgrid Operation Optimization. Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then, the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach.

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.

Retail and Wholesale Electricity Pricing Considering Electric Vehicle Mobility We consider the operation of multiple charging network operators (CNOs), each serving a population of electric vehicle (EV) drivers who subscribe to them for charging services. The CNOs set retail prices for the charging stations they operate, which effectively control the electricity demand and queues formed at their charging stations. The stations’ electricity demand also ties the CNOs’ retail optimization problem to their position in the wholesale market of electricity, creating a coupling between the electric transportation systems and the bulk power grid, which is managed by an independent system operator (ISO). In this paper, we provide retail pricing mechanisms and iterative wholesale market-clearing algorithms that together maximize social welfare in this coupled system. Specifically, our algorithm would enable the CNO to reduce its electricity costs and the customers’ travel delays (including charging station queueing times) and the ISO to reduce societal electricity costs by tapping into the EVs’ flexibility as mobile electric loads. Our theoretical analysis relies on the application of congestion pricing techniques to a capacitated user equilibrium problem on an energy-and-time expanded transportation network model. This expanded network allows us to track the travel costs of individual EVs, including their limited battery charge, the cost of waiting in queues at public charging stations, as well as the temporally variant nature of travel times.

Computational difficulties of bilevel linear programming We show, using small examples, that two algorithms previously published for the Bilevel Linear Programming problem BLP may fail to find the optimal solution and thus must be considered to be heuris...

Competitive charging station pricing for plug-in electric vehicles This paper considers the problem of charging station pricing and station selection of plug-in electric vehicles (PEVs). Every PEV needs to select a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions, in an attempt to maximize its profit. To obtain insights of such a highly coupled system, we consider a one-dimensional system with two charging stations and Poisson arriving PEVs. We propose a multi-leader-multi-follower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in Stage I, and the PEVs (followers) make their charging station selections in Stage II. We show that there always exists a unique charging station selection equilibrium in Stage II, and such equilibrium depends on the price difference between the charging stations. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in Stage I. Unfortunately, it is hard to compute the pricing equilibrium in closed form. To overcome this challenge, we develop a low-complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of our Stackelberg game with no information exchange.

Placement of EV Charging Stations - Balancing Benefits Among Multiple Entities. This paper studies the problem of multistage placement of electric vehicle (EV) charging stations with incremental EV penetration rates. A nested logit model is employed to analyze the charging preference of the individual consumer (EV owner) and predict the aggregated charging demand at the charging stations. The EV charging industry is modeled as an oligopoly where the entire market is dominated...

An Analysis of Price Competition in Heterogeneous Electric Vehicle Charging Stations In this paper, we investigate the price competition among electric vehicle charging stations (EVCSs) with renewable power generators (RPGs). Both a large-sized EVCS (L-EVCS) and small-sized EVCSs (S-EVCSs), which have different capacities, are considered. Moreover, the price elasticity of electric vehicles (EVs), the effect of the distance between an EV and the EVCSs, and the impact of the number ...

Deployment of the Electric Vehicle Charging Station Considering Existing Competitors The problem of optimal planning of plug-in electric vehicle (PEV) charging stations is studied. Different from many other works, we assume the station investor to be a new private entrant into the market who intends to maximize its own profit in a competitive environment. A modified Huff gravity-based model is adopted to describe the probabilistic patronizing behaviors of PEV drivers. Accordingly, a bi-level optimization model is formulated to decide not only the optimal site and size of the new charging station, but also the retail charging prices in the future operation stage. Based on the specific characteristics of the problem, the operation level sub-problem is reformulated to a convex programming and an efficient solution algorithm is designed for the overall bi-level optimization. Numerical examples of different scales demonstrate the effectiveness of the proposed modeling and computation methods, as well as the importance of considering the competitive effects when planning the charging station.

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.

An online mechanism for multi-unit demand and its application to plug-in hybrid electric vehicle charging We develop an online mechanism for the allocation of an expiring resource to a dynamic agent population. Each agent has a non-increasing marginal valuation function for the resource, and an upper limit on the number of units that can be allocated in any period. We propose two versions on a truthful allocation mechanism. Each modifies the decisions of a greedy online assignment algorithm by sometimes cancelling an allocation of resources. One version makes this modification immediately upon an allocation decision while a second waits until the point at which an agent departs the market. Adopting a prior-free framework, we show that the second approach has better worst-case allocative efficiency and is more scalable. On the other hand, the first approach (with immediate cancellation) may be easier in practice because it does not need to reclaim units previously allocated. We consider an application to recharging plug-in hybrid electric vehicles (PHEVs). Using data from a real-world trial of PHEVs in the UK, we demonstrate higher system performance than a fixed price system, performance comparable with a standard, but non-truthful scheduling heuristic, and the ability to support 50% more vehicles at the same fuel cost than a simple randomized policy.

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...

Multi-column Deep Neural Networks for Image Classification Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.

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.

Maximizing the Lifetime of Wireless Sensor Networks with Mobile Sink in Delay-Tolerant Applications This paper proposes a framework to maximize the lifetime of the wireless sensor networks (WSNs) by using a mobile sink when the underlying applications tolerate delayed information delivery to the sink. Within a prescribed delay tolerance level, each node does not need to send the data immediately as it becomes available. Instead, the node can store the data temporarily and transmit it when the mobile sink is at the most favorable location for achieving the longest WSN lifetime. To find the best solution within the proposed framework, we formulate optimization problems that maximize the lifetime of the WSN subject to the delay bound constraints, node energy constraints, and flow conservation constraints. We conduct extensive computational experiments on the optimization problems and find that the lifetime can be increased significantly as compared to not only the stationary sink model but also more traditional mobile sink models. We also show that the delay tolerance level does not affect the maximum lifetime of the WSN.

Reliable and Fast Hand-Offs in Low-Power Wireless Networks Hand-off (or hand-over), the process where mobile nodes select the best access point available to transfer data, has been well studied in wireless networks. The performance of a hand-off process depends on the specific characteristics of the wireless links. In the case of low-power wireless networks, hand-off decisions must be carefully taken by considering the unique properties of inexpensive low-power radios. This paper addresses the design, implementation and evaluation of smart-HOP, a hand-off mechanism tailored for low-power wireless networks. This work has three main contributions. First, it formulates the hard hand-off process for low-power networks (such as typical wireless sensor networks - WSNs) with a probabilistic model, to investigate the impact of the most relevant channel parameters through an analytical approach. Second, it confirms the probabilistic model through simulation and further elaborates on the impact of several hand-off parameters. Third, it fine-tunes the most relevant hand-off parameters via an extended set of experiments, in a realistic experimental scenario. The evaluation shows that smart-HOP performs well in the transitional region while achieving more than 98 percent relative delivery ratio and hand-off delays in the order of a few tens of a milliseconds.

An efficient medium access control protocol for WSN-UAV. Recent advances in Unmanned Aerial Vehicle (UAV) technologies have enhanced Wireless Sensor Networks (WSNs) by offering a UAV as a mobile data gathering node. These systems are called WSN-UAV that are well-suited for remote monitoring and emergency applications. Since previous Medium Access Control (MAC) protocols proposed in WSNs are not appropriate in the presence of a UAV, few researches have proposed new MAC protocols to meet WSN-UAV requirements. MAC protocols of WSN-UAV should be extremely efficient and fair due to the time-limited presence of the UAV in the neighborhood of each sensor. However, issues such as high throughput in dense networks, fairness among sensors, and efficiency have not been resolved yet in a satisfactory manner. Moreover, previous works lack analytical evaluation of their protocols. In this paper, we present a novel MAC protocol in WSN-UAV, called Advanced Prioritized MAC (AP-MAC), that can provide high throughput, fairness, and efficiency, especially in dense networks. We also analytically evaluate AP-MAC using a 3-dimensional Markov chain and validate its correctness using simulation. Simulation results under various scenarios confirm that AP-MAC can approximately improve throughput and fairness up to 20% and 25%, respectively, leading to higher efficiency compared with previous work in WSN-UAV systems such as Prioritized Frame Selection (PFS).

Wireless Powered Sensor Networks for Internet of Things: Maximum Throughput and Optimal Power Allocation. This paper investigates a wireless powered sensor network, where multiple sensor nodes are deployed to monitor a certain external environment. A multiantenna power station (PS) provides the power to these sensor nodes during wireless energy transfer phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center during wireless i...

A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction. Estimation of end-to-end network traffic plays an important role in traffic engineering and network planning. The direct measurement of a network&#39;s traffic matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct traffic matrix remains a great challenge. This paper studies end-to-end network traffic reconstruction in large-scale networks...

Edgf: Empirical Dataset Generation Framework For Wireless Sensor Networks In wireless sensor networks (WSNs), simulation practices, system models, algorithms, and protocols have been published worldwide based on the assumption of randomness. The applied statistics used for randomness in WSNs are broad, e.g., random deployment, activity tracking, packet generation, etc. Even though authors' adequate formal and informal information and pledge validation of the proposal became challenging, the minuscule information alteration in implementation and validation can reflect the enormous effect on eventual results. In this proposal, we show how the results are affected by the generalized assumption made on randomness. In sensor node deployment, ambiguity arises due to node error-value (epsilon), and its upper bound in the relative position is estimated to understand the delicacy of diminutives changes. Besides, the effect of uniformity in the traffic and participation of scheduling position of nodes is also generalized. We propose an algorithm to generate the unified dataset for the general and some specific applications system models in WSNs. The results produced by our algorithm reflect the pseudo-randomness and can efficiently regenerate through seed value for validation.

Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks This paper presents and analyzes a three-tier architecture for collecting sensor data in sparse sensor networks. Our approach exploits the presence of mobile entities (called MULEs) present in the environment. When in close range, MULEs pick up data from the sensors, buffer it, and deliver it to wired access points. This can lead to substantial power savings at the sensors as they only have to transmit over a short-range. This paper focuses on a simple analytical model for understanding performance as system parameters are scaled. Our model assumes a two-dimensional random walk for mobility and incorporates key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points), latency and the required buffer capacities on the sensors and the MULEs. The modeling and simulation results can be used for further analysis and provide certain guidelines for deployment of such systems.

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.

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.

Tabu Search - Part I

A vision-based formation control framework We describe a framework for cooperative control of a group of nonholonomic mobile robots that allows us to build complex systems from simple controllers and estimators. The resultant modular approach is attractive because of the potential for reusability. Our approach to composition also guarantees stability and convergence in a wide range of tasks. There are two key features in our approach: 1) a paradigm for switching between simple decentralized controllers that allows for changes in formation; 2) the use of information from a single type of sensor, an omnidirectional camera, for all our controllers. We describe estimators that abstract the sensory information at different levels, enabling both decentralized and centralized cooperative control. Our results include numerical simulations and experiments using a testbed consisting of three nonholonomic robots.

Cost of not splitting in routing: characterization and estimation This paper studies the performance difference of joint routing and congestion control when either single-path routes or multipath routes are used. Our performance metric is the total utility achieved by jointly optimizing transmission rates using congestion control and paths using source routing. In general, this performance difference is strictly positive and hard to determine--in fact an NP-hard problem. To better estimate this performance gap, we develop analytical bounds to this "cost of not splitting" in routing. We prove that the number of paths needed for optimal multipath routing differs from that of optimal single-path routing by no more than the number of links in the network. We provide a general bound on the performance loss, which is independent of the number of source-destination pairs when the latter is larger than the number of links in a network. We also propose a vertex projection method and combine it with a greedy branch-and-bound algorithm to provide progressively tighter bounds on the performance loss. Numerical examples are used to show the effectiveness of our approximation technique and estimation algorithms.

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.

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.

Channel Knowledge Map for Environment-Aware Communications: EM Algorithm for Map Construction Channel knowledge map (CKM) is an emerging technique to enable environment-aware wireless communications, in which databases with location-specific channel knowledge are used to facilitate or even obviate real-time channel state information acquisition. One fundamental problem for CKM-enabled communication is how to efficiently construct the CKM based on finite measurement data points at limited user locations. Towards this end, this paper proposes a novel map construction method based on the expectation maximization (EM) algorithm, by utilizing the available measurement data, jointly with the expert knowledge of well-established statistic channel models. The key idea is to partition the available data points into different groups, where each group shares the same modelling parameter values to be determined. We show that determining the modelling parameter values can be formulated as a maximum likelihood estimation problem with latent variables, which is then efficiently solved by the classic EM algorithm. Compared to the pure data-driven methods such as the nearest neighbor based interpolation, the proposed method is more efficient since only a small number of modelling parameters need to be determined and stored. Furthermore, the proposed method is extended for constructing a specific type of CKM, namely, the channel gain map (CGM), where closed-form expressions are derived for the E-step and M-step of the EM algorithm. Numerical results are provided to show the effectiveness of the proposed map construction method as compared to the benchmark curve fitting method with one single model.

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.

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

A brief overview of machine learning methods for short-term traffic forecasting and future directions. Short-term traffic forecasting is a vital part of intelligent transportation systems. Recently, the combination of unprecedented data availability and the repaid development of machine learning techniques have brought on immense advancement in this field. In this paper, we aim to provide a brief overview of machine learning approaches for short-term traffic forecasting to facilitate research in related fields. We first introduce traffic forecasting and the challenges, and then introduce different approaches for modeling the temporal and/or spatial dependencies. Finally, we discuss several important directions for the future research.

Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction. Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of the destination passengers. We believe it is suboptimal to preallocate the taxi into each region-based solely on the taxi origin demand. In this paper, we present a challenging and worth-exploring task, called taxi origin-destination demand prediction, which aims at predicting the taxi demand between all-region pairs in a future time interval. Its main challenges come from how to effectively capture the diverse contextual information to learn the demand patterns. We address this problem with a novel contextualized spatial–temporal network (CSTN), which consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC), and global correlation context (GCC), respectively. First, an LSC module utilizes two convolution neural networks to learn the local spatial dependencies of taxi, demand respectively, from the origin view and the destination view. Second, a TEC module incorporates the local spatial features of taxi demand and the meteorological information to a Convolutional Long Short-term Memory Network (ConvLSTM) for the analysis of taxi demand evolution. Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs. The extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of our CSTN over other compared methods for the taxi origin-destination demand prediction.

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.

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.

Vehicle Speed Prediction Using a Markov Chain With Speed Constraints Modern vehicles are designed to improve fuel consumption while satisfying emissions regulations. As a result, powertrains are becoming increasingly complex and changing rapidly. Optimal control based on the future vehicle speed is one way to address these changes. In this approach, accurate prediction of velocity is closely related to the performance of optimal control. However, there exists uncer...

Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction. Accurate traffic forecast is a challenging problem due to the large-scale problem size, as well as the complex and dynamic nature of spatio-temporal dependency of traffic flow. Most existing graph-based CNNs attempt to capture the static relations while largely neglecting the dynamics underlying sequential data. In this paper, we present dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatio-temporal structures and predict future traffic from historical traffic flow. In particular, DST-GCNN is a two stream network. In the flow prediction stream, we present a novel graph-based spatio-temporal convolutional layer to extract features from a graph representation of traffic flow. Then several such layers are stacked together to predict future traffic over time. Meanwhile, the proximity relations between nodes in the graph are often time variant as the traffic condition changes over time. To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream. Experiments on real traffic datasets demonstrate that the proposed model achieves competitive performances compared with the other state-of-the-art methods.

Predicting Taxi–Passenger Demand Using Streaming Data Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering knowledge, which, in return, delivers information for real-time decision making. Intelligent transportation systems for taxi dispatching and for finding time-saving routes are already exploring these sensing data. This paper introduces a novel methodology for predicting the spatial distribution of taxi–passengers for a short-term time horizon using streaming data. First, the information was aggregated into a histogram time series. Then, three time-series forecasting techniques were combined to originate a prediction. Experimental tests were conducted using the online data that are transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide effective insight into the spatiotemporal distribution of taxi–passenger demand for a 30-min horizon.

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.

Non-interactive verifiable computing: outsourcing computation to untrusted workers We introduce and formalize the notion of Verifiable Computation, which enables a computationally weak client to "outsource" the computation of a function F on various dynamically-chosen inputs x1, ...,xk to one or more workers. The workers return the result of the function evaluation, e.g., yi = F(xi), as well as a proof that the computation of F was carried out correctly on the given value xi. The primary constraint is that the verification of the proof should require substantially less computational effort than computing F(i) from scratch. We present a protocol that allows the worker to return a computationally-sound, non-interactive proof that can be verified in O(mċpoly(λ)) time, where m is the bit-length of the output of F, and λ is a security parameter. The protocol requires a one-time pre-processing stage by the client which takes O(|C|ċpoly(λ)) time, where C is the smallest known Boolean circuit computing F. Unlike previous work in this area, our scheme also provides (at no additional cost) input and output privacy for the client, meaning that the workers do not learn any information about the xi or yi values.

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

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.

SmartVeh: Secure and Efficient Message Access Control and Authentication for Vehicular Cloud Computing. With the growing number of vehicles and popularity of various services in vehicular cloud computing (VCC), message exchanging among vehicles under traffic conditions and in emergency situations is one of the most pressing demands, and has attracted significant attention. However, it is an important challenge to authenticate the legitimate sources of broadcast messages and achieve fine-grained message access control. In this work, we propose SmartVeh, a secure and efficient message access control and authentication scheme in VCC. A hierarchical, attribute-based encryption technique is utilized to achieve fine-grained and flexible message sharing, which ensures that vehicles whose persistent or dynamic attributes satisfy the access policies can access the broadcast message with equipped on-board units (OBUs). Message authentication is enforced by integrating an attribute-based signature, which achieves message authentication and maintains the anonymity of the vehicles. In order to reduce the computations of the OBUs in the vehicles, we outsource the heavy computations of encryption, decryption and signing to a cloud server and road-side units. The theoretical analysis and simulation results reveal that our secure and efficient scheme is suitable for VCC.

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 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.

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...

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.

Robust watermarking method in DFT domain for effective management of medical imaging In general, management of medical data is achieved by several issues of medical information such as authentication, security, integrity, privacy, among others. Because medical images and their related electronic patient record (EPR) data are stored separately; the probability of corruption of this information or their detachment from the corresponding EPR data could be very high. Losing data from the corresponding medical image may lead to a wrong diagnostic. Digital watermarking has recently emerged as a suitable solution to solve some of the problems associated with the management of medical images. This paper proposes a robust watermarking method for medical images to avoid their detachment from the corresponding EPR data in which the watermark is embedded using the digital imaging and communications in medicine standard metadata together with cryptographic techniques. In order to provide a high robustness of the watermark while preserving at the same time a high quality of the watermarked images, the generated watermark is embedded into the magnitude of the middle frequencies of the discrete Fourier transform of the original medical image. During the detection process, the watermark data bits are recovered and detected using the bit correct rate criterion. Extensive experiments were carried out, and the performance of the proposed method is evaluated in terms of imperceptibility, payload, robustness and detachment detection. Quantitative evaluation of the watermarked images is performed by using three of the more common metrics: the peak signal-to-noise ratio, structural similarity index and visual information fidelity. Experimental results show the watermark robustness against several of the more aggressive geometric and signal processing distortions. The receiver operating characteristics curves also show the desirable detachment detection performance of the proposed method. A comparison with the previously reported methods with similar purposes respect to the proposed method is also provided.

Robust Watermarking Scheme Applied to Radiological Medical Images We present a watermarking scheme that combines data compression and encryption in application to radiological medical images. In this approach we combine the image moment theory and image homogeneity in order to recover the watermark after a geometrical distortion. Image quality is measured with metrics used in image processing, such as PSNR and MSE.

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.

Security Enhancement Of Medical Imaging Via Imperceptible And Robust Watermarking In this letter we present an imperceptible and robust watermarking algorithm that uses a cryptographic hash function in the authentication application of digital medical imaging. In the proposed scheme we combine discrete Fourier transform (DFT) and local image masking to detect the watermark after a geometrical distortion and improve its imperceptibility. The image quality is measured by metrics currently used in digital image processing, such as VSNR, SSIM and PSNR.

Digital image ownership authentication via camouflaged unseen-visible watermarking. In recent years, end users can easily capture digital images using several devices, such as smartphones, mobile devices and digital imaging cameras, allowing such images to be easily copied, manipulated, transmitted or format converted without any restrictions. This fact suggests the necessity to develop digital tools, such as digital watermarking, to solve the issues associated with copyright protection and ownership authentication of digital images. To claim the ownership of a digital image, we propose a camouflaged, unseen-visible watermarking technique based on luminance and texture properties in conjunction with an image enhancement criterion. The proposed method has some advantages over invisible and visible watermarking methodologies in terms of readability and imperceptibility of the watermark, respectively. The experimental results demonstrate that the proposed scheme is effective and applicable for digital images on a variety of topics, including natural scenes and man-made objects, both indoors and outdoors. A comparison with previously reported methods based on unseen-visible watermarking techniques is also provided.

A blind image watermarking scheme combining spatial domain and frequency domain In order to realize the copyright protection of color image effectively, combining the advantages of spatial-domain watermarking scheme and frequency-domain one, a blind color image watermarking scheme with high performance in the spatial domain is proposed in the paper. The presented scheme does not require real discrete cosine transform (DCT) and discrete Hartley transform (DHT), but only uses the different quantization steps to complete the embedding and blind extracting of color watermark in the spatial domain according to the unique characteristic of direct current (DC) components of DCT and DHT. The contributions of this paper include the following: (1) This scheme combined the strengths of watermarking scheme in the spatial domain and frequency domain, which has fast speed and strong robustness; (2) the scheme makes full use of the energy aggregation characteristics of image block, and the invisibility of the watermarking scheme has greatly improved; and (3) different quantization steps are chosen to embed and extract watermark in different layers, which reduce the modification range of pixel value effectively. The experimental results show that compared with the existing schemes, the proposed watermarking scheme has higher performance, such as better invisibility, stronger robustness and shorter execution time.

Information hiding in medical images: a robust medical image watermarking system for E-healthcare Abstract Electronic transmission of the medical images is one of the primary requirements in a typical Electronic-Healthcare (E-Healthcare) system. However this transmission could be liable to hackers who may modify the whole medical image or only a part of it during transit. To guarantee the integrity of a medical image, digital watermarking is being used. This paper presents two different watermarking algorithms for medical images in transform domain. In first technique, a digital watermark and Electronic Patients Record (EPR) have been embedded in both regions; Region of Interest (ROI) and Region of Non-Interest (RONI). In second technique, Region of Interest (ROI) is kept untouched for tele-diagnosis purpose and Region of Non-Interest (RONI) is used to hide the digital watermark and EPR. In either algorithm 8 × 8 block based Discrete Cosine Transform (DCT) has been used. In each 8 × 8 block two DCT coefficients are selected and their magnitudes are compared for embedding the watermark/EPR. The selected coefficients are modified by using a threshold for embedding bit a ‘0’ or bit ‘1’ of the watermark/EPR. The proposed techniques have been found robust not only to singular attacks but also to hybrid attacks. Comparison results viz-a - viz payload and robustness show that the proposed techniques perform better than some existing state of art techniques. As such the proposed algorithms could be useful for e-healthcare systems.

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.

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.

Reciprocal N-body Collision Avoidance In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace. In our formulation, each robot acts fully in- dependently, and does not communicate with other robots. Based on the definition of velocity obstacles (5), we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear program. We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute collision-free actions for all of them in only a few millisec- onds. To the best of our knowledge, this method is the first that can guarantee local collision-free motion for a large number of robots in a cluttered workspace.

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.

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.

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.

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.

Specifying Safety of Autonomous Vehicles in Signal Temporal Logic We develop a set of contracts for autonomous control software that ensures that if all traffic participants follow the contracts, the overall traffic system will be collision-free. We express our contracts in Signal Temporal Logic (STL), a lightweight specification language that enables V &V methodologies. We demonstrate how the specification can be used for evaluation of the performance of autonomy software, and We provide preliminary evidence that our contracts are not excessively conservative, i.e., they are not more restrictive than existing guidelines for safe driving by humans.

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...

Secured telemedicine of medical imaging based on dual robust watermarking Medical information management has progressed in the last few years because of the advances in information technologies. Nowadays, it is possible to share medical images among specialists geographically distant to interpret, discuss, and get improved diagnostics. However, any alteration of transmitted image metadata may lead to issues related to information security, such as detachment and authentication. Detachment refers to link the data of an electronic patient record to an incorrect medical image, while authentication aims to identify the image source. These security problems are critical as they may cause the loss of sensitive data or wrong medical diagnoses. Digital watermarking is an emerging technique that faces these security problems as it allows to embed the metadata directly into the medical image. This paper proposes a hybrid and robust watermarking technique to prevent detachment and authenticate medical images. The quantization index modulation algorithm under dither modulation in conjunction with forwarding error correction is used to embed relevant metadata as a robust-imperceptible watermarking to avoid detachment. The visible-imperceptible watermarking paradigm, whose use is an innovation in medical images, is applied to insert a second watermark in the spatial domain to perform authentication. The experimental results show the contribution of the proposed scheme and its efficiency regarding robustness and imperceptibility.

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.

Age of information performance of multiaccess strategies with packet management We consider a system consisting of N source nodes communicating with a common receiver. Each source node has a buffer of infinite capacity to store incoming bursty traffic in the form of status updates transmitted in packets, which should maintain the status information at the receiver fresh. Packets waiting for transmission can be discarded to avoid wasting network resources for the transmission ...

A Low-Complexity Analytical Modeling for Cross-Layer Adaptive Error Protection in Video Over WLAN We find a low-complicity and accurate model to solve the problem of optimizing MAC-layer transmission of real-time video over wireless local area networks (WLANs) using cross-layer techniques. The objective in this problem is to obtain the optimal MAC retry limit in order to minimize the total packet loss rate. First, the accuracy of Fluid and M/M/1/K analytical models is examined. Then we derive a closed-form expression for service time in WLAN MAC transmission, and will use this in mathematical formulation of our optimization problem based on M/G/1 model. Subsequently we introduce an approximate and simple formula for MAC-layer service time, which leads to the M/M/1 model. Compared with M/G/1, we particularly show that our M/M/1-based model provides a low-complexity and yet quite accurate means for analyzing MAC transmission process in WLAN. Using our M/M/1 model-based analysis, we derive closed-form formulas for the packet overflow drop rate and optimum retry-limit. These closed-form expressions can be effectively invoked for analyzing adaptive retry-limit algorithms. Simulation results (network simulator-2) will verify the accuracy of our analytical models.

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 ...

Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE. In cellular massive machine-type communications, a device can transmit directly to the BS or through an aggregator (intermediate node). While direct device-BS communication has recently been the focus of 5G/3GPP research and standardization efforts, the use of aggregators remains a less explored topic. In this article we analyze the deployment scenarios in which aggregators can perform cellular ac...

Reliable Transmission of Short Packets through Queues and Noisy Channels under Latency and Peak-Age Violation Guarantees. This paper investigates the probability that the delay and the peak-age of information exceed a desired threshold in a point-to-point communication system with short information packets. The packets are generated according to a stationary memoryless Bernoulli process, placed in a single-server queue and then transmitted over a wireless channel. A variable-length stop-feedback coding scheme—a general strategy that encompasses simple automatic repetition request (ARQ) and more sophisticated hybrid ARQ techniques as special cases—is used by the transmitter to convey the information packets to the receiver. By leveraging finite-blocklength results, the delay violation and the peak-age violation probabilities are characterized without resorting to approximations based on larg-deviation theory as in previous literature. Numerical results illuminate the dependence of delay and peak-age violation probability on system parameters such as the frame size and the undetected error probability, and on the chosen packet-management policy. The guidelines provided by our analysis are particularly useful for the design of low-latency ultra-reliable communication systems.

Age of information in a decentralized network of parallel queues with routing and packets losses The paper deals with age of information (AoI) in a network of multiple sources and parallel queues with buffering capabilities, preemption in service and losses in served packets. The queues do not communicate between each other and the packets are dispatched through the queues according to a predefined probabilistic routing. By making use of the stochastic hybrid system (SHS) method, we provide a...

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.

Theory and Applications of Robust Optimization In this paper we survey the primary research, both theoretical and applied, in the area of robust optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multistage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.

Revenue-optimal task scheduling and resource management for IoT batch jobs in mobile edge computing With the growing prevalence of Internet of Things (IoT) devices and technology, a burgeoning computing paradigm namely mobile edge computing (MEC) is delicately proposed and designed to accommodate the application requirements of IoT scenario. In this paper, we focus on the problems of dynamic task scheduling and resource management in MEC environment, with the specific objective of achieving the optimal revenue earned by edge service providers. While the majority of task scheduling and resource management algorithms are formulated by an integer programming (IP) problem and solved in a dispreferred NP-hard manner, we innovatively investigate the problem structure and identify a favorable property namely totally unimodular constraints. The totally unimodular property further helps to design an equivalent linear programming (LP) problem which can be efficiently and elegantly solved at polynomial computational complexity. In order to evaluate our proposed approach, we conduct simulations based on real-life IoT dataset to verify the effectiveness and efficiency of our approach.

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?

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.

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 robust medical image watermarking against salt and pepper noise for brain MRI images. 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. During the transmission of medical images between hospitals or specialists through the network, the main priority is to protect a patient's documents against any act of tampering by unauthorised individuals. Because of this, there is a need for medical image authentication scheme to enable proper diagnosis on patient. In addition, medical images are also susceptible to salt and pepper impulse noise through the transmission in communication channels. This noise may also be intentionally used by the invaders to corrupt the embedded watermarks inside the medical images. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this work addresses the issue of designing a new watermarking method that can withstand high density of salt and pepper noise for brain MRI images. For this purpose, combination of a spatial domain watermarking method, channel coding and noise filtering schemes are used. The region of non-interest (RONI) of MRI images from five different databases are used as embedding area and electronic patient record (EPR) is considered as embedded data. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER).

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.

A Survey on Deep Learning in Medical Image Analysis. •A summary of all deep learning algorithms used in medical image analysis is given.•The most successful algorithms for key image analysis tasks are identified.•300 papers applying deep learning to different applications have been summarized.

Performance of Massive MIMO Uplink with Zero-Forcing receivers under Delayed Channels. In this paper, we analyze the performance of the uplink communication of massive multicell multiple-input multiple-output (MIMO) systems under the effects of pilot contamination and delayed channels because of terminal mobility. The base stations (BSs) estimate the channels through the uplink training and then use zero-forcing (ZF) processing to decode the transmit signals from the users. The prob...

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]. Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP t...

Massive MIMO Detection Techniques: A Survey Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.

UAV-Assisted Communication Efficient Federated Learning in the Era of the Artificial Intelligence of Things Artificial Intelligence (AI) based models are increasingly deployed in the Internet of Things (IoT), paving the evolution of the IoT into the AI of things (AIoT). Currently, the predominant approach for AI model training is cloud-centric and involves the sharing of data with external parties. To preserve privacy while enabling collaborative model training across distributed IoT devices, the machin...

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.

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.

Learning Traffic as Images: A Deep Convolution Neural Network for Large-scale Transportation Network Speed Prediction. This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

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.

Traveling Salesman Problems with Profits Traveling salesman problems with profits (TSPs with profits) are a generalization of the traveling salesman problem (TSP), where it is not necessary to visit all vertices. A profit is associated with each vertex. The overall goal is the simultaneous optimization of the collected profit and the travel costs. These two optimization criteria appear either in the objective function or as a constraint. In this paper, a classification of TSPs with profits is proposed, and the existing literature is surveyed. Different classes of applications, modeling approaches, and exact or heuristic solution techniques are identified and compared. Conclusions emphasize the interest of this class of problems, with respect to applications as well as theoretical results.

Pattern Extraction Methods for Ear Biometrics - A Survey The Human Ear is a new class of relatively stable Biometrics that has drawn researcher's attention recently. Human ear is a perfect data for passive person identification, which can be applied to provide security in the public places. In this article, we overview the proposed Pattern extraction algorithms from 2D and 3D Ear images.

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.

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].

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.

An Interaction-aware Evaluation Method for Highly Automated Vehicles It is important to build a rigorous verification and validation (V&amp;V) process to evaluate the safety of highly automated vehicles (HAVs) before their wide deployment on public roads. In this paper, we propose an interaction-aware framework for HAV safety evaluation which is suitable for some highly-interactive driving scenarios including highway merging, roundabout entering, etc. Contrary to e...

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.

Specifying Safety of Autonomous Vehicles in Signal Temporal Logic We develop a set of contracts for autonomous control software that ensures that if all traffic participants follow the contracts, the overall traffic system will be collision-free. We express our contracts in Signal Temporal Logic (STL), a lightweight specification language that enables V &V methodologies. We demonstrate how the specification can be used for evaluation of the performance of autonomy software, and We provide preliminary evidence that our contracts are not excessively conservative, i.e., they are not more restrictive than existing guidelines for safe driving by humans.

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...

A Hybrid Transforms-Based Robust Video Zero-Watermarking Algorithm for Resisting High Efficiency Video Coding Compression With the rampancy of pirated videos, video watermarking for copyright protection has become a widely researched topic. In this paper, zero-watermarking is applied to videos for the first time to resist high efficiency video coding compression, which can improve the robustness of the watermarking algorithm and ensure the videos' quality. A robust video zero-watermarking algorithm based on the discrete wavelet transform, the all phase biorthogonal transform, and singular value decomposition is proposed. Utilizing the properties of hybrid transforms, robust features can be extracted from videos, and robust zero-watermarks can be constructed. Experimental results demonstrate that the proposed algorithm has strong robustness to high efficiency video coding compression attacks with different quantization parameters. In addition, the algorithm can also resist common image processing attacks, geometric attacks, frame-based attacks, and hybrid attacks. Compared with existing video watermarking algorithms, the proposed algorithm can more accurately and completely reconstruct watermark images.

Geometrically resilient color image zero-watermarking algorithm based on quaternion Exponent moments. •A robust color image zero-watermarking against geometric attacks is proposed.•Rotation invariance, scale invariance and stability of QEMs are discussed in detail.•The relationship between left- and right-side QEMs is analyzed.•Comparison experiments with the comparative algorithms are conducted in two aspects.•The proposed algorithm has better performance than the comparative algorithms.

On (k, n)*-visual cryptography scheme Let P = {1, 2, . . . , n} be a set of elements called participants. In this paper we construct a visual cryptography scheme (VCS) for the strong access structure specified by the set Γ0 of all minimal qualified sets, where $${\Gamma_0=\{S: S\subseteq P, 1\in S}$$ and |S| = k}. Any VCS for this strong access structure is called a (k, n)*-VCS. We also obtain bounds for the optimal pixel expansion and optimal relative contrast for a (k, n)*-VCS.

Fast computation of Jacobi-Fourier moments for invariant image recognition The Jacobi-Fourier moments (JFMs) provide a wide class of orthogonal rotation invariant moments (ORIMs) which are useful for many image processing, pattern recognition and computer vision applications. They, however, suffer from high time complexity and numerical instability at high orders of moment. In this paper, a fast method based on the recursive computation of radial kernel function of JFMs is proposed which not only reduces time complexity but also improves their numerical stability. Fast recursive method for the computation of Jacobi-Fourier moments is proposed.The proposed method not only reduces time complexity but also improves numerical stability of moments.Better image reconstruction is achieved with lower reconstruction error.Proposed method is useful for many image processing, pattern recognition and computer vision applications.

Image analysis by generalized Chebyshev–Fourier and generalized pseudo-Jacobi–Fourier moments In this paper, we present two new sets, named the generalized Chebyshev–Fourier radial polynomials and the generalized pseudo Jacobi–Fourier radial polynomials, which are orthogonal over the unit circle. These generalized radial polynomials are then scaled to define two new types of continuous orthogonal moments, which are invariant to rotation. The classical Chebyshev–Fourier and pseudo Jacobi–Fourier moments are the particular cases of the proposed moments with parameter α=0. The relationships among the proposed two generalized radial polynomials and Jacobi polynomials, shift Jacobi polynomials, and the hypergeometric functions are derived in detail, and some interesting properties are discussed. Two recursive methods are developed for computing radial polynomials so that it is possible to improve computation speed and to avoid numerical instability. Simulation results are provided to validate the proposed moment functions and to compare their performance with previous works.

A kernel-based method for fast and accurate computation of PHT in polar coordinates A novel kernel-based method is proposed for fast, highly accurate and numerically stable computations of polar harmonic transforms (PHT) in polar coordinates. Euler formula is used to derive a novel trigonometric formula where the later one is used in the kernel generation. The simplified radial and angular kernels are used in efficient computation PHTs. The proposed method removes the numerical approximation errors involved in conventional methods and provides highly accurate PHTs coefficients which results in highly improved image reconstruction capabilities. Numerical experiments are performed where the results are compared with those of the recent existing methods. In addition to the tremendous reduction in computational times, the obtained results of the proposed method clearly show a significant improvement in rotational invariance.

Quaternion polar harmonic Fourier moments for color images. •Quaternion polar harmonic Fourier moments (QPHFM) is proposed.•Complex Chebyshev-Fourier moments (CHFM) is extended to quaternion QCHFM.•Comparison experiments between QPHFM and QZM, QPZM, QOFMM, QCHFM and QRHFM are conducted.•QPHFM performs superbly in image reconstruction and invariant object recognition.•The importance of phase information of QPHFM in image reconstruction are discussed.

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.

A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing. With the increasing importance of images in people’s daily life, content-based image retrieval (CBIR) has been widely studied. Compared with text documents, images consume much more storage space. Hence, its maintenance is considered to be a typical example for cloud storage outsourcing. For privacy-preserving purposes, sensitive images, such as medical and personal images, need to be encrypted before outsourcing, which makes the CBIR technologies in plaintext domain to be unusable. In this paper, we propose a scheme that supports CBIR over encrypted images without leaking the sensitive information to the cloud server. First, feature vectors are extracted to represent the corresponding images. After that, the pre-filter tables are constructed by locality-sensitive hashing to increase search efficiency. Moreover, the feature vectors are protected by the secure kNN algorithm, and image pixels are encrypted by a standard stream cipher. In addition, considering the case that the authorized query users may illegally copy and distribute the retrieved images to someone unauthorized, we propose a watermark-based protocol to deter such illegal distributions. In our watermark-based protocol, a unique watermark is directly embedded into the encrypted images by the cloud server before images are sent to the query user. Hence, when image copy is found, the unlawful query user who distributed the image can be traced by the watermark extraction. The security analysis and the experiments show the security and efficiency of the proposed scheme.

Reciprocal N-body Collision Avoidance In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace. In our formulation, each robot acts fully in- dependently, and does not communicate with other robots. Based on the definition of velocity obstacles (5), we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear program. We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute collision-free actions for all of them in only a few millisec- onds. To the best of our knowledge, this method is the first that can guarantee local collision-free motion for a large number of robots in a cluttered workspace.

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.

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.

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.

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.

RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments RGB-D cameras (such as the Microsoft Kinect) are novel sensing systems that capture RGB images along with per-pixel depth information. In this paper we investigate how such cameras can be used for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. We present RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment. Visual and depth information are also combined for view-based loop-closure detection, followed by pose optimization to achieve globally consistent maps. We evaluate RGB-D Mapping on two large indoor environments, and show that it effectively combines the visual and shape information available from RGB-D cameras.

A survey of information-centric networking. The information-centric networking (ICN) concept is a significant common approach of several future Internet research activities. The approach leverages in-network caching, multiparty communication through replication, and interaction models decoupling senders and receivers. The goal is to provide a network infrastructure service that is better suited to today¿s use (in particular. content distrib...

Hierarchical Semantic Hashing: Visual Localization from Buildings on Maps In this paper we present a vision-based method for instant global localization from a given aerial image. The approach mimics how humans localize themselves on maps using spatial layout of semantic elements on the map. Unlike other matching and localization methods that use visual appearance or feature matching, our method relies on robust and consistently detectable semantic elements that are invariant to illumination, temporal variations and occlusions. We use the buildings on the map and on the given aerial query image as our semantic elements. Spatial relations between these elements are efficiently stored and queried under a hierarchical semantic version of the Geometric Hashing algorithm that is inherently rotation and scale invariant. We also present a method to obtain building locations from a given query image using image classification and processing techniques. Overall this approach provides fast and robust localization over large areas. We show our experimental results for localizing satellite image tiles from a 16.5 km sq dense city map with over 7,000 buildings.

On the Feasibility of Attribute-Based Encryption on Internet of Things Devices. The Internet of Things (IoT) is emerging with the pace of technology evolution, connecting people and things through the Internet. IoT devices enable large-scale data collection and sharing for a wide range of applications. However, it is challenging to securely manage interconnected IoT devices because the collected data could contain sensitive personal information. The authors believe that attribute-based encryption (ABE) could be an effective cryptographic tool for secure management of IoT devices. However, little research has addressed ABE's actual feasibility in the IoT thus far. This article investigates such feasibility considering well-known IoT platforms--specifically, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero. A thorough evaluation confirms that adopting ABE in the IoT is indeed feasible.

Attribute-based encryption for fine-grained access control of encrypted data As more sensitive data is shared and stored by third-party sites on the Internet, there will be a need to encrypt data stored at these sites. One drawback of encrypting data, is that it can be selectively shared only at a coarse-grained level (i.e., giving another party your private key). We develop a new cryptosystem for fine-grained sharing of encrypted data that we call Key-Policy Attribute-Based Encryption (KP-ABE). In our cryptosystem, ciphertexts are labeled with sets of attributes and private keys are associated with access structures that control which ciphertexts a user is able to decrypt. We demonstrate the applicability of our construction to sharing of audit-log information and broadcast encryption. Our construction supports delegation of private keys which subsumesHierarchical Identity-Based Encryption (HIBE).

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.

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.

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.

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.

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.

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.

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.

QoE-DEER: A QoE-Aware Decentralized Resource Allocation Scheme for Edge Computing With the increasing prevalence of online services mounted on IoT devices, edge computing gains significant momentum over conventional cloud-centric architecture. Edge servers are geographically deployed in a distributed manner nearby IoT devices, which not only frees online services from the high hardware requirement but also sharply reduces network latency experienced by IoT users. Recent works have extensively studied the problem of edge resource management and request scheduling to achieve high Quality of Service (QoS) with low latency, but there has been little focus on Quality of Experience (QoE) that an edge resource allocation scheme brings about. In this article, we study the Edge Resource Allocation (ERA) problem across multiple service requests with the objective of overall QoE maximization, which has non-polynomial computational complexity. To attack the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {NP}$ </tex-math></inline-formula> -hardness of solving the ERA problem, we adopt a game-theoretic approach to formulate the ERA problem as a potential game ERAGame which admits a Nash Equilibrium (NE). Then, we novelly present a decentralized algorithm namely QoE-DEER to find an NE solution which equivalently maximizes the overall QoE as the ERA problem. Finally, the performance and convergence of our algorithm is evaluated both theoretically and experimentally, which indicates its significant advantages over the state-of-the-art approaches.

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.

Drivers Learn City-Scale Intra-Daily Dynamic Equilibrium Understanding driver behavior in on-demand mobility services is crucial for designing efficient and sustainable transport models. Drivers’ delivery strategy is well understood, but their search strategy and learning process still lack an empirically validated model. Here we provide a game-theoretic model of driver search strategy and learning dynamics, interpret the collective outcome in a thermodynamic framework, and verify its various implications empirically. We capture driver search strategies in a multi-market oligopoly model, which has a unique Nash equilibrium and is globally asymptotically stable. The equilibrium can therefore be obtained via heuristic learning rules where drivers pursue the incentive gradient or simply imitate others. To help understand city-scale phenomena, we offer a macroscopic view with the laws of thermodynamics. With 870 million trips of over 50k drivers in New York City, we show that the equilibrium well explains the spatiotemporal patterns of driver search behavior, and estimate an empirical constitutive relation. We find that new drivers learn the equilibrium within a year, and those who stay longer learn better. The collective response to new competition is also as predicted. Among empirical studies of driver strategy in on-demand services, our work examines the longest period, the most trips, and is the largest for taxi industry.

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.

Hamilton Paths In Grid Graphs

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.

Network virtualization for disaster resilience of cloud services Today's businesses and consumer applications are becoming increasingly dependent on cloud solutions, making them vulnerable to service outages that can result in a loss of communication or access to business-critical services and data. Are we really prepared for such failure scenarios? Given that failures can occur on both the network and data center sides, is it possible to have efficient end-to-end recovery? The answer is mostly negative due to the separate operation of these domains. This article offers a solution to this problem based on network virtualization, and discusses the necessary architecture and algorithm details. It also answers the question of whether it is better to provide resilience in the virtual or physical layer from a cost effectiveness and failure coverage perspective.

A new approach to solving the multiple traveling salesperson problem using genetic algorithms The multiple traveling salesperson problem (MTSP) involves scheduling m>1 salespersons to visit a set of n>m locations so that each location is visited exactly once while minimizing the total (or maximum) distance traveled by the salespersons. The MTSP is similar to the notoriously difficult traveling salesperson problem (TSP) with the added complication that each location may be visited by any one of the salespersons. Previous studies investigated solving the MTSP with genetic algorithms (GAs) using standard TSP chromosomes and operators. This paper proposes a new GA chromosome and related operators for the MTSP and compares the theoretical properties and computational performance of the proposed technique to previous work. Computational testing shows the new approach results in a smaller search space and, in many cases, produces better solutions than previous techniques.

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

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.

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.

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

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.

GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.

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.

Assessment in and of serious games: an overview There is a consensus that serious games have a significant potential as a tool for instruction. However, their effectiveness in terms of learning outcomes is still understudied mainly due to the complexity involved in assessing intangible measures. A systematic approach--based on established principles and guidelines--is necessary to enhance the design of serious games, and many studies lack a rigorous assessment. An important aspect in the evaluation of serious games, like other educational tools, is user performance assessment. This is an important area of exploration because serious games are intended to evaluate the learning progress as well as the outcomes. This also emphasizes the importance of providing appropriate feedback to the player. Moreover, performance assessment enables adaptivity and personalization to meet individual needs in various aspects, such as learning styles, information provision rates, feedback, and so forth. This paper first reviews related literature regarding the educational effectiveness of serious games. It then discusses how to assess the learning impact of serious games and methods for competence and skill assessment. Finally, it suggests two major directions for future research: characterization of the player's activity and better integration of assessment in games.

Motivations for Play in Online Games. An empirical model of player motivations in online games provides the foundation to understand and assess how players differ from one another and how motivations of play relate to age, gender, usage patterns, and in-game behaviors. In the current study, a factor analytic approach was used to create an empirical model of player motivations. The analysis revealed 10 motivation subcomponents that grouped into three overarching components (achievement, social, and immersion). Relationships between motivations and demographic variables (age, gender, and usage patterns) are also presented.

Acceptance of game-based learning by secondary school teachers The adoption and the effectiveness of game-based learning depend largely on the acceptance by classroom teachers, as they can be considered the true change agents of the schools. Therefore, we need to understand teachers' perceptions and beliefs that underlie their decision-making processes. The present study focuses on the factors that influence the acceptance of commercial video games as learning tools in the classroom. A model for describing the acceptance and predicting the uptake of commercial games by secondary school teachers is suggested. Based on data gathered from 505 teachers, the model is tested and evaluated. The results are then linked to previous research in the domains of technology acceptance and game-based learning. Highlights¿ We examine 505 secondary school teachers' acceptance of game-based learning. ¿ We propose, test and evaluate a model for understanding and predicting acceptance. ¿ Teacher beliefs about the use of commercial games appear to be rather complex. ¿ The proposed model explains 57% of the variance in teachers' behavioral intention. ¿ Complexity and experience do not affect behavioral intention in the model.

Is game-based learning better in flow experience and various types of cognitive load than non-game-based learning? Perspective from multimedia and media richness. The study examined differences on flow experiences and different kinds of cognitive loads (intrinsic, extraneous, and germane cognitive loads) between game-based learning and non-game-based learning groups. Participants were students of two classes taking a general education course, named Life and Technology, in a university. There were a total of 103 participants in the experiment: 50 students in one class (experimental group) used game-based learning materials; 53 students in the other class (control group) used non-game-based learning materials (webpage-based learning material). The results revealed that the game-based learning group significantly created more flow experiences than the non-game-based learning group (p

Techniques To Motivate Learner Improvement In Game-Based Assessment Learner motivation to self-improve is a crucial effectiveness factor in all modes and settings of learning. Game-based learning was long used for attracting and maintaining students' interest especially in small ages, deploying means such as scoring, timing, scores of peers (i.e., hall of fame), etc. These techniques can provide recognition for high-scoring players, while also developing a sense of safe "distance" in the impersonal electronic environment for low-scoring players. In addition, constructive feedback on mistakes a player makes can contribute to avoiding similar mistakes in the future, thus achieving better performance in the game, while constructing valuable new knowledge when a knowledge gap is detected. This paper investigates an integrated approach to designing, implementing, and using an adaptive game for assessing and gradually improving multiplication skills. Student motivation is fostered by incorporating the Open Learner Model approach, which exposes part of the underlying user model to the students in a graphically simplified manner that is easily perceivable and offers a clear picture of student performance. In addition, the Open Learner Model is expanded with visualizations of social comparison information, where students can access the progress of anonymous peers and summative class scores for improving self-reflection and fostering self-regulated learning. This paper also presents the feedback received by the preliminary testing of the game and discusses the effect of assessing multiplication skills of primary school pupils using the adaptive game-based approach on increasing pupil motivation to self-improve.

GameFlow: a model for evaluating player enjoyment in games Although player enjoyment is central to computer games, there is currently no accepted model of player enjoyment in games. There are many heuristics in the literature, based on elements such as the game interface, mechanics, gameplay, and narrative. However, there is a need to integrate these heuristics into a validated model that can be used to design, evaluate, and understand enjoyment in games. We have drawn together the various heuristics into a concise model of enjoyment in games that is structured by flow. Flow, a widely accepted model of enjoyment, includes eight elements that, we found, encompass the various heuristics from the literature. Our new model, GameFlow, consists of eight elements -- concentration, challenge, skills, control, clear goals, feedback, immersion, and social interaction. Each element includes a set of criteria for achieving enjoyment in games. An initial investigation and validation of the GameFlow model was carried out by conducting expert reviews of two real-time strategy games, one high-rating and one low-rating, using the GameFlow criteria. The result was a deeper understanding of enjoyment in real-time strategy games and the identification of the strengths and weaknesses of the GameFlow model as an evaluation tool. The GameFlow criteria were able to successfully distinguish between the high-rated and low-rated games and identify why one succeeded and the other failed. We concluded that the GameFlow model can be used in its current form to review games; further work will provide tools for designing and evaluating enjoyment in games.

Wireless sensor networks: a survey This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed.

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.

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

2 Algorithms For Constructing A Delaunay Triangulation This paper provides a unified discussion of the Delaunay triangulation. Its geometric properties are reviewed and several applications are discussed. Two algorithms are presented for constructing the triangulation over a planar set ofN points. The first algorithm uses a divide-and-conquer approach. It runs inO(N logN) time, which is asymptotically optimal. The second algorithm is iterative and requiresO(N2) time in the worst case. However, its average case performance is comparable to that of the first algorithm.

MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer Wireless power transfer (WPT) is a promising new solution to provide convenient and perpetual energy supplies to wireless networks. In practice, WPT is implementable by various technologies such as inductive coupling, magnetic resonate coupling, and electromagnetic (EM) radiation, for short-/mid-/long-range applications, respectively. In this paper, we consider the EM or radio signal enabled WPT in particular. Since radio signals can carry energy as well as information at the same time, a unified study on simultaneous wireless information and power transfer (SWIPT) is pursued. Specifically, this paper studies a multiple-input multiple-output (MIMO) wireless broadcast system consisting of three nodes, where one receiver harvests energy and another receiver decodes information separately from the signals sent by a common transmitter, and all the transmitter and receivers may be equipped with multiple antennas. Two scenarios are examined, in which the information receiver and energy receiver are separated and see different MIMO channels from the transmitter, or co-located and see the identical MIMO channel from the transmitter. For the case of separated receivers, we derive the optimal transmission strategy to achieve different tradeoffs for maximal information rate versus energy transfer, which are characterized by the boundary of a so-called rate-energy (R-E) region. For the case of co-located receivers, we show an outer bound for the achievable R-E region due to the potential limitation that practical energy harvesting receivers are not yet able to decode information directly. Under this constraint, we investigate two practical designs for the co-located receiver case, namely time switching and power splitting, and characterize their achievable R-E regions in comparison to the outer bound.

GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.

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.

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.

Convexity of the cost functional in an optimal control problem for a class of positive switched systems. This paper deals with the optimal control of a class of positive switched systems. The main feature of this class is that switching alters only the diagonal entries of the dynamic matrix. The control input is represented by the switching signal itself and the optimal control problem is that of minimizing a positive linear combination of the final state variable. First, the switched system is embedded in the class of bilinear systems with control variables living in a simplex, for each time point. The main result is that the cost is convex with respect to the control variables. This ensures that any Pontryagin solution is optimal. Algorithms to find the optimal solution are then presented and an example, taken from a simplified model for HIV mutation mitigation is discussed.

Input-to-state stability of switched systems and switching adaptive control In this paper we prove that a switched nonlinear system has several useful input-to-state stable (ISS)-type properties under average dwell-time switching signals if each constituent dynamical system is ISS. This extends available results for switched linear systems. We apply our result to stabilization of uncertain nonlinear systems via switching supervisory control, and show that the plant states can be kept bounded in the presence of bounded disturbances when the candidate controllers provide ISS properties with respect to the estimation errors. Detailed illustrative examples are included.

Discrete-Time Switched Linear Systems State Feedback Design With Application to Networked Control This technical note addresses the state feedback switched control design problem for discrete-time switched linear systems. More specifically, the control goal is to jointly design a set of state feedback gains and a state dependent switching function, ensuring H2 and H∞ guaranteed performance. The conditions are based on Lyapunov or Riccati-Metzler inequalities, which allow the derivation of simpler alternative conditions that are expressed as LMIs whenever a scalar variable is fixed. The theoretical results are well adapted to deal with the self-triggered control design problem, where the switching rule is responsible for the scheduling of multiple sampling periods, to be considered in the communication channel in order to improve performance. This method is compared to others from the literature. Examples show the validity of the proposed technique in both contexts, switched and networked control systems.

Robust Model-Based Fault Diagnosis for PEM Fuel Cell Air-Feed System. In this paper, the design of a nonlinear observer-based fault diagnosis approach for polymer electrolyte membrane (PEM) fuel cell air-feed systems is presented, taking into account a fault scenario of sudden air leak in the air supply manifold. Based on a simplified nonlinear model proposed in the literature, a modified super-twisting (ST) sliding mode algorithm is employed to the observer design....

A simple approach for switched control design with control bumps limitation. By its own nature, control of switched systems in general leads to expressive discontinuities at switching times. Hence, this class of dynamic systems needs additional care as far as implementation constraints such as for instance control amplitude limitation is concerned. This paper aims at providing numerically tractable conditions to be incorporated in the control design procedure in order to reduce control bumps. The switching strategy and continuous control laws are jointly determined as well as an H∞ guaranteed cost is minimized. Due to its theoretical and practical importance, special attention is given to the dynamic output feedback control design problem. The results are illustrated by means of examples borrowed from the literature which are also used for comparisons that put in evidence the efficiency of the proposed strategy.

Stability and Stabilization of Switched Linear Systems With Mode-Dependent Average Dwell Time. In this paper, the stability and stabilization problems for a class of switched linear systems with mode-dependent average dwell time (MDADT) are investigated in both continuous-time and discrete-time contexts. The proposed switching law is more applicable in practice than the average dwell time (ADT) switching in which each mode in the underlying system has its own ADT. The stability criteria for switched systems with MDADT in nonlinear setting are firstly derived, by which the conditions for stability and stabilization for linear systems are also presented. A numerical example is given to show the validity and potential of the developed techniques. © 2011 IEEE.

Research on gain scheduling Gain scheduling for nonlinear controller design is described in terms of general features of the approach and in terms of early examples of applications in flight control and automotive engine control. Then recent research is discussed, emphasizing work on linearization-based scheduling and work on linear parameter-varying approaches. (C) 2000 Elsevier Science Ltd. All rights reserved.

A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems. Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the "Single Network Adaptive Critic (SNAC)" is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems.

Semantic Image Synthesis With Spatially-Adaptive Normalization We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers. We show that this is suboptimal as the normalization layers tend to "wash away" semantic information. To address the issue, we propose using the input layout. for modulating the activations in normalization layers through a spatially-adaptive,learned transformation. Experiments on several challenging datasets demonstrate the advantage of the proposed method over existing approaches, regarding both visual fidelity and align-ment with input layouts. Finally, our model allows user control over both semantic and style as synthesizing images.

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.

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...

Adaptive dynamic surface control of a class of nonlinear systems with unknown direction control gains and input saturation. In this paper, adaptive neural network based dynamic surface control (DSC) is developed for a class of nonlinear strict-feedback systems with unknown direction control gains and input saturation. A Gaussian error function based saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Based on backstepping combined with DSC, adaptive radial basis function neural network control is developed to guarantee that all the signals in the closed-loop system are globally bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results demonstrate the effectiveness of the proposed approach and the good performance is guaranteed even though both the saturation constraints and the wrong control direction are occurred.

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.

Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education. In Wireless Sensor Networks (WSN) of mobile education (such as mobile learning), in order to keep a better and lower energy consumption, reduce the energy hole and prolong the network life cycle, we propose a novel unequal clustering routing protocol considering energy balancing based on network partition & distance (UCNPD, which means Unequal Clustering based on Network Partition & Distance) for WSN in this paper. In the design model of this protocol, we know that all the network node data reaches the base station (BS) through the nodes near the BS, and the nodes in this area will use more energy, so we define a ring area using the BS as the center to form a circle, then we partition the network area based on the distance from node to the BS. These parts of nodes are to build connection with the BS, and the others follow the optimized clustering routing service protocol which uses a timing mechanism to elect the cluster head. It reduces the energy consumption of cluster reconstruction. Furthermore, we build unequal clusters by setting different competitive radius, which is helpful for balancing the network energy consumption. For the selection of message route, we considered all the energy of cluster head, the distances to BS and the degrees of node to reduce and balance the energy consumption. Simulation results demonstrate that the protocol can efficiently decrease the speed of the nodes death, prolong the network lifetime, and balance the energy dissipation of all nodes.

High delivery rate position-based routing algorithms for 3D ad hoc networks Position-based routing algorithms use the geographic position of the nodes in a network to make the forwarding decisions. Recent research in this field primarily addresses such routing algorithms in two dimensional (2D) space. However, in real applications, nodes may be distributed in three dimensional (3D) environments. In this paper, we propose several randomized position-based routing algorithms and their combination with restricted directional flooding-based algorithms for routing in 3D environments. The first group of algorithms AB3D are extensions of previous randomized routing algorithms from 2D space to 3D space. The second group ABLAR chooses m neighbors according to a space-partition heuristic and forwards the message to all these nodes. The third group T-ABLAR-T uses progress-based routing until a local minimum is reached. The algorithm then switches to ABLAR for one step after which the algorithm switches back to the progress-based algorithm again. The fourth group AB3D-ABLAR uses an algorithm from the AB3D group until a threshold is passed in terms of number of hops. The algorithm then switches to an ABLAR algorithm. The algorithms are evaluated and compared with current routing algorithms. The simulation results on unit disk graphs (UDG) show a significant improvement in delivery rate (up to 99%) and a large reduction of the traffic.

Three-Dimensional Position-Based Adaptive Real-Time Routing Protocol for wireless sensor networks Devices for wireless sensor networks (WSN) are limited by power, and thus, routing protocols should be designed with this constraint in mind. WSNs are used in three-dimensional (3D) scenarios such as the surface of sea or lands with different levels of height. This paper presents and evaluates the Three-Dimensional Position-Based Adaptive Real-Time Routing Protocol (3DPBARP) as a novel, real-time, position-based and energy-efficient routing protocol for WSNs. 3DPBARP is a lightweight protocol that reduces the number of nodes which receive the radio frequency (RF) signal using a novel parent forwarding region (PFR) algorithm. 3DPBARP as a Geographical Routing Protocol (GRP) reduces the number of forwarding nodes and thus the traffic and packet collision in the network. A series of performance evaluations through MATLAB and Omnet++ simulations show significant improvements in network performance parameters and total energy consumption over the 3D Position-Based Routing Protocol (3DPBRP) and Directed Flooding Routing Protocol (DFRP).

A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs). The last decade has seen a growing interest in the use of Unmanned Aerial Vehicles (UAVs) for various applications and services. UAVs, or drones as referred to, have shown to be efficient in completing complex tasks when organized as ad hoc connected groups, thus forming a Flying Ad hoc Network (FANET). Although similar to Mobile Ad hoc Network (MANET) and Vehicular Ad hoc Network (VANET), FANETs have their own characteristics. One of the main difference is the fact that UAVs in general, but particularly when organized are FANETs, are mission-based, and their mobility models are often dictated by the purpose of their mission and the nature of the task they plan to accomplish. Therefore, routing protocols for FANETs should take into consideration the nature of the applications and services that the UAVs are deployed for, and factor in the mobility models. However, designing routing protocols for FANETs is not an easy task given the highly dynamic topology of FANETs and the flying constraints they are subjected to. Compared to topology-based routing, position-based routing demonstrated high efficiency and resilience to handle the high mobility of FANET nodes. To this end, in this paper, we propose a comprehensive survey of position-based routing protocols for FANETs with their various categories. We propose a classification and a taxonomy of these protocols, including a detailed description of the routing schemes used in each category. We propose a comparative study based on various criteria, and discuss the advantages and weaknesses of each protocol. Furthermore, new challenges for future research are presented, which introduce a new kind of coordination between UAVs and existing VANETs on the ground. The originality of this survey is that it complements the existing surveys on the same theme by providing more details on some aspects that have been addressed only ostensibly by other surveys in the literature.

Adaptive Communication Protocols in Flying Ad Hoc Network. The flying ad hoc network (FANET) is a new paradigm of wireless communication that governs the autonomous movement of UAVs and supports UAV-to-UAV communication. A FANET can provide an effective real-time communication solution for the multiple UAV systems considering each flying UAV as a router. However, existing mobile ad hoc protocols cannot meet the needs of FANETs due to high-speed mobility a...

3D Transformative Routing for UAV Swarming Networks: A Skeleton-Guided, GPS-Free Approach A challenging issue for a three-dimensional (3D) unmanned aerial vehicle (UAV) network is addressed in this paper - how do we efficiently establish and maintain one or multiple routes among swarm regions (i.e., groups of UAVs), during the dynamic swarming process? Inspired by the human nervous system which can efficiently send brain signals to any tissue, we propose a 3D transformative routing (3D...

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 Comparative Study of Distributed Learning Environments on Learning Outcomes Advances in information and communication technologies have fueled rapid growth in the popularity of technology-supported distributed learning (DL). Many educational institutions, both academic and corporate, have undertaken initiatives that leverage the myriad of available DL technologies. Despite their rapid growth in popularity, however, alternative technologies for DL are seldom systematically evaluated for learning efficacy. Considering the increasing range of information and communication technologies available for the development of DL environments, we believe it is paramount for studies to compare the relative learning outcomes of various technologies.In this research, we employed a quasi-experimental field study approach to investigate the relative learning effectiveness of two collaborative DL environments in the context of an executive development program. We also adopted a framework of hierarchical characteristics of group support system (GSS) technologies, outlined by DeSanctis and Gallupe (1987), as the basis for characterizing the two DL environments.One DL environment employed a simple e-mail and listserv capability while the other used a sophisticated GSS (herein referred to as Beta system). Interestingly, the learning outcome of the e-mail environment was higher than the learning outcome of the more sophisticated GSS environment. The post-hoc analysis of the electronic messages indicated that the students in groups using the e-mail system exchanged a higher percentage of messages related to the learning task. The Beta system users exchanged a higher level of technology sense-making messages. No significant difference was observed in the students' satisfaction with the learning process under the two DL environments.

A Framework of Joint Mobile Energy Replenishment and Data Gathering in Wireless Rechargeable Sensor Networks Recent years have witnessed the rapid development and proliferation of techniques on improving energy efficiency for wireless sensor networks. Although these techniques can relieve the energy constraint on wireless sensors to some extent, the lifetime of wireless sensor networks is still limited by sensor batteries. Recent studies have shown that energy rechargeable sensors have the potential to provide perpetual network operations by capturing renewable energy from external environments. However, the low output of energy capturing devices can only provide intermittent recharging opportunities to support low-rate data services due to spatial-temporal, geographical or environmental factors. To provide steady and high recharging rates and achieve energy efficient data gathering from sensors, in this paper, we propose to utilize mobility for joint energy replenishment and data gathering. In particular, a multi-functional mobile entity, called SenCarin this paper, is employed, which serves not only as a mobile data collector that roams over the field to gather data via short-range communication but also as an energy transporter that charges static sensors on its migration tour via wireless energy transmissions. Taking advantages of SenCar's controlled mobility, we focus on the joint optimization of effective energy charging and high-performance data collections. We first study this problem in general networks with random topologies. We give a two-step approach for the joint design. In the first step, the locations of a subset of sensors are periodically selected as anchor points, where the SenCar will sequentially visit to charge the sensors at these locations and gather data from nearby sensors in a multi-hop fashion. To achieve a desirable balance between energy replenishment amount and data gathering latency, we provide a selection algorithm to search for a maximum number of anchor points where sensors hold the least battery energy, and meanwhile by visiting them, - he tour length of the SenCar is no more than a threshold. In the second step, we consider data gathering performance when the SenCar migrates among these anchor points. We formulate the problem into a network utility maximization problem and propose a distributed algorithm to adjust data rates at which sensors send buffered data to the SenCar, link scheduling and flow routing so as to adapt to the up-to-date energy replenishing status of sensors. Besides general networks, we also study a special scenario where sensors are regularly deployed. For this case we can provide a simplified solution of lower complexity by exploiting the symmetry of the topology. Finally, we validate the effectiveness of our approaches by extensive numerical results, which show that our solutions can achieve perpetual network operations and provide high network utility.

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.

Higher Order Tensor Decomposition For Proportional Myoelectric Control Based On Muscle Synergies Muscle synergies have recently been utilised in myoelectric control systems. Thus far, all proposed synergy-based systems rely on matrix factorisation methods. However, this is limited in terms of task-dimensionality. Here, the potential application of higher-order tensor decomposition as a framework for proportional myoelectric control is demonstrated. A novel constrained Tucker decomposition (consTD) technique of synergy extraction is proposed for synergy-based myoelectric control model and compared with state-of-the-art matrix factorisation models. The extracted synergies were used to estimate control signals for the wrist?s Degree of Freedom (DoF) through direct projection. The consTD model was able to estimate the control signals for each DoF by utilising all data in one 3rd-order tensor. This is contrast with matrix factorisation models where data are segmented for each DoF and then the synergies often have to be realigned. Moreover, the consTD method offers more information by providing additional shared synergies, unlike matrix factorisation methods. The extracted control signals were fed to a ridge regression to estimate the wrist's kinematics based on real glove data. The Coefficient of Determination (R-2) for the reconstructed wrist position showed that the proposed consTD was higher than matrix factorisation methods. In sum, this study provides the first proof of concept for the use of higher-order tensor decomposition in proportional myoelectric control and it highlights the potential of tensors to provide an objective and direct approach to identify synergies.

Lossless image hierarchical recovery based on POB number system. •Tampered images can be recovered losslessly.•All tampered blocks can be detected using the two-level comparison.•Two kinds of refinements are proposed to eliminate error-detected blocks.

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.

Integrating Encryption and Marking for Remote Sensing Image Based on Orthogonal Decomposition For the special characters, remote sensing image has higher requirements not only in the security but also in the management; it requires not only the active encryption during storage and transmission for preventing information leakage but also the marking technology to prevent illegal usage as well as copyright protection or even source tracing. Therefore, this paper proposes to integrate encryption and marking technology by the independence and fusion of orthogonal decomposition for the comprehensive security protection of remote sensing image. Under the proposed scheme, encryption and marking technology can achieve the operation independence and content mergence; moreover, there is no special requirement in selecting encryption and marking algorithms. It makes up the shortage of recent integration of encryption and watermarking based on spatial scrambling in applicability and security. According to the experimental results, integration of encryption and marking technology based on orthogonal decomposition satisfies the common constraints of encryption, and marking technology, furthermore, has little impact on remote sensing image data characters and later applications.

Separable reversible data hiding in encrypted images via adaptive embedding strategy with block selection. •An adaptive, separable reversible data hiding scheme in encrypted image is proposed.•Analogues stream-cipher and block permutation are used to encrypt original image.•Classification and selection for encrypted blocks are conducted during embedding.•An accurate prediction strategy was employed to achieve perfect image recovery.•Our scheme has better rate-distortion performance than some state-of-the-art schemes.

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.

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 ...

On Theoretical Modeling of Sensor Cloud: A Paradigm Shift From Wireless Sensor Network. This paper focuses on the theoretical modeling of sensor cloud, which is one of the first attempts in this direction. We endeavor to theoretically characterize virtualization, which is a fundamental mechanism for operations within the sensor-cloud architecture. Existing related research works on sensor cloud have primarily focused on the ideology and the challenges that wireless sensor network (WS...

Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications Due to various challenging issues such as, computational complexity and more delay in cloud computing, edge computing has overtaken the conventional process by efficiently and fairly allocating the resources i.e., power and battery lifetime in Internet of things (IoT)-based industrial applications. In the meantime, intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT-based devices in industries. But the challenging issue in these power hungry, short battery lifetime, and delay-intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial datasets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques, for example, predictive transmission power control (TPC) and baseline. Thus, this paper proposes 1) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices; 2) a system-level battery model by evaluating the energy dissipation in IoT devices; and 3) a data reliability model for edge AI-based IoT devices over hybrid TPC and duty-cycle network. Two important cases, for instance, static (i.e., product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental testbed reveals that the proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (∼0.95) by appropriately tuning duty cycle and TPC unlike conventional methods.

Towards Big data processing in IoT: Path Planning and Resource Management of UAV Base Stations in Mobile-Edge Computing System. Heavy data load and wide cover range have always been crucial problems for big data processing in Internet of Things (IoT). Recently, mobile-edge computing (MEC) and unmanned aerial vehicle base stations (UAV-BSs) have emerged as promising techniques in IoT. In this article, we propose a three-layer online data processing network based on the MEC technique. On the bottom layer, raw data are generated by distributed sensors with local information. Upon them, UAV-BSs are deployed as moving MEC servers, which collect data and conduct initial steps of data processing. On top of them, a center cloud receives processed results and conducts further evaluation. For online processing requirements, the edge nodes should stabilize delay to ensure data freshness. Furthermore, limited onboard energy poses constraints to edge processing capability. In this article, we propose an online edge processing scheduling algorithm based on Lyapunov optimization. In cases of low data rate, it tends to reduce edge processor frequency for saving energy. In the presence of a high data rate, it will smartly allocate bandwidth for edge data offloading. Meanwhile, hovering UAV-BSs bring a large and flexible service coverage, which results in a path planning issue. In this article, we also consider this problem and apply deep reinforcement learning to develop an online path planning algorithm. Taking observations of around environment as an input, a CNN network is trained to predict action rewards. By simulations, we validate its effectiveness in enhancing service coverage. The result will contribute to big data processing in future IoT.

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.

Data MULEs: modeling and analysis of a three-tier architecture for sparse sensor networks This paper presents and analyzes a three-tier architecture for collecting sensor data in sparse sensor networks. Our approach exploits the presence of mobile entities (called MULEs) present in the environment. When in close range, MULEs pick up data from the sensors, buffer it, and deliver it to wired access points. This can lead to substantial power savings at the sensors as they only have to transmit over a short-range. This paper focuses on a simple analytical model for understanding performance as system parameters are scaled. Our model assumes a two-dimensional random walk for mobility and incorporates key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points), latency and the required buffer capacities on the sensors and the MULEs. The modeling and simulation results can be used for further analysis and provide certain guidelines for deployment of such systems.

A Novel Framework of Multi-Hop Wireless Charging for Sensor Networks Using Resonant Repeaters. Wireless charging has provided a convenient alternative to renew nodes’ energy in wireless sensor networks. Due to physical limitations, previous works have only considered recharging a single node at a time, which has limited efficiency and scalability. Recent advances on multi-hop wireless charging is gaining momentum and provides fundamental support to address this problem. However, existing single-node charging designs do not consider and cannot take advantage of such opportunities. In this paper, we propose a new framework to enable multi-hop wireless charging using resonant repeaters. First, we present a realistic model that accounts for detailed physical factors to calculate charging efficiencies. Second, to achieve balance between energy efficiency and data latency, we propose a hybrid data gathering strategy that combines static and mobile data gathering to overcome their respective drawbacks and provide theoretical analysis. Then, we formulate multi-hop recharge schedule into a bi-objective NP-hard optimization problem. We propose a two-step approximation algorithm that first finds the minimum charging cost and then calculates the charging vehicles’ moving costs with bounded approximation ratios. Finally, upon discovering more room to reduce the total system cost, we develop a post-optimization algorithm that iteratively adds more stopping locations for charging vehicles to further improve the results while ensuring none of the nodes will deplete battery energy. Our extensive simulations show that the proposed algorithms can handle dynamic energy demands effectively, and can cover at least three times of nodes and reduce service interruption time by an order of magnitude compared to the single-node charging scheme.

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.

A sub-constant error-probability low-degree test, and a sub-constant error-probability PCP characterization of NP

Image saliency: From intrinsic to extrinsic context We propose a novel framework for automatic saliency estimation in natural images. We consider saliency to be an anomaly with respect to a given context that can be global or local. In the case of global context, we estimate saliency in the whole image relative to a large dictionary of images. Unlike in some prior methods, this dictionary is not annotated, i.e., saliency is assumed unknown. In the case of local context, we partition the image into patches and estimate saliency in each patch relative to a large dictionary of un-annotated patches from the rest of the image. We propose a unified framework that applies to both cases in three steps. First, given an input (image or patch) we extract k nearest neighbors from the dictionary. Then, we geometrically warp each neighbor to match the input. Finally, we derive the saliency map from the mean absolute error between the input and all its warped neighbors. This algorithm is not only easy to implement but also outperforms state-of-the-art methods.

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.

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.

Incorporating non-linear tire dynamics into a convex approach to shared steering control Active safety systems enabled by steer-by-wire technology can share control with a driver, augmenting the driver's steering commands to avoid collisions and prevent loss of control. The extent to which this can be done is limited by the controller's ability to anticipate dangerous scenarios in order to appropriately intervene and steer the vehicle to safety. However, the non-linear nature of tire dynamics poses a challenge in predicting and modifying vehicle behavior in real-time. In this paper, online successive linearizations of the future planned vehicle trajectory approximates these non-linear dynamics in a real-time, model predictive controller that shares control with a human driver. Simulation results of aggressive maneuvers demonstrate the usefulness of this approach as well as illustrate interesting interactions between the sometimes competing objectives of vehicle stability and collision avoidance.

A predictive controller for autonomous vehicle path tracking This paper presents a model predictive controller (MPC) structure for solving the path-tracking problem of terrestrial autonomous vehicles. To achieve the desired performance during high-speed driving, the controller architecture considers both the kinematic and the dynamic control in a cascade structure. Our study contains a comparative study between two kinematic linear predictive control strategies: The first strategy is based on the successive linearization concept, and the other strategy combines a local reference frame with an approaching path strategy. Our goal is to search for the strategy that best comprises the performance and hardware-cost criteria. For the dynamic controller, a decentralized predictive controller based on a linearized model of the vehicle is used. Practical experiments obtained using an autonomous "Mini-Baja" vehicle equipped with an embedded computing system are presented. These results confirm that the proposed MPC structure is the solution that better matches the target criteria.

An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Intelligent Transportation Systems The visibility of images of outdoor road scenes will generally become degraded when captured during inclement weather conditions. Drivers often turn on the headlights of their vehicles and streetlights are often activated, resulting in localized light sources in images capturing road scenes in these conditions. Additionally, sandstorms are also weather events that are commonly encountered when driving in some regions. In sandstorms, atmospheric sand has a propensity to irregularly absorb specific portions of a spectrum, thereby causing color-shift problems in the captured image. Traditional state-of-the-art restoration techniques are unable to effectively cope with these hazy road images that feature localized light sources or color-shift problems. In response, we present a novel and effective haze removal approach to remedy problems caused by localized light sources and color shifts, which thereby achieves superior restoration results for single hazy images. The performance of the proposed method has been proven through quantitative and qualitative evaluations. Experimental results demonstrate that the proposed haze removal technique can more effectively recover scene radiance while demanding fewer computational costs than traditional state-of-the-art haze removal techniques.

Automatic Detection and Classification of Road Lane Markings Using Onboard Vehicular Cameras This paper presents a new approach for road lane classification using an onboard camera. Initially, lane boundaries are detected using a linear–parabolic lane model, and an automatic on-the-fly camera calibration procedure is applied. Then, an adaptive smoothing scheme is applied to reduce noise while keeping close edges separated, and pairs of local maxima–minima of the gradient are used as cues to identify lane markings. Finally, a Bayesian classifier based on mixtures of Gaussians is applied to classify the lane markings present at each frame of a video sequence as dashed, solid, dashed solid, solid dashed, or double solid. Experimental results indicate an overall accuracy of over 96% using a variety of video sequences acquired with different devices and resolutions.

Pedestrian Tracking Using Online Boosted Random Ferns Learning in Far-Infrared Imagery for Safe Driving at Night. Pedestrian-vehicle accidents that occur at night are a major social problem worldwide. Advanced driver assistance systems that are equipped with cameras have been designed to automatically prevent such accidents. Among the various types of cameras used in such systems, far-infrared (FIR) cameras are favorable because they are invariant to illumination changes. Therefore, this paper focuses on a pedestrian nighttime tracking system with an FIR camera that is able to discern thermal energy and is mounted on the forward roof part of a vehicle. Since the temperature difference between the pedestrian and background depends on the season and the weather, we therefore propose two models to detect pedestrians according to the season and the weather, which are determined using Weber–Fechner's law. For tracking pedestrians, we perform real-time online learning to track pedestrians using boosted random ferns and update the trackers at each frame. In particular, we link detection responses to trajectories based on similarities in position, size, and appearance. There is no standard data set for evaluating the tracking performance using an FIR camera; thus, we created the Keimyung University tracking data set (KMUTD) by combining the KMU sudden pedestrian crossing (SPC) data set [21] for summer nights with additional tracking data for winter nights. The KMUTD contains video sequences involving a moving camera, moving pedestrians, sudden shape deformations, unexpected motion changes, and partial or full occlusions between pedestrians at night. The proposed algorithm is successfully applied to various pedestrian video sequences of the KMUTD; specifically, the proposed algorithm yields more accurate tracking performance than other existing methods.

Effects of Different Alcohol Dosages on Steering Behavior in Curve Driving. Objective: The aim of this article is to explore the detailed characteristics of steering behavior in curve driving at different alcohol dosages. Background: Improper operation of the steering wheel is a contributing factor to increased crash risks on curves. Method: The experiments were conducted using a driving simulator. Twenty-five licensed drivers were recruited to perform the experiments at the four different breath alcohol concentration (BrAC) levels. The steering angle (SA), steering speed (SS), steering reversal rate (SRR), and peak-to-peak value of the steering angle (PP) were used to characterize the steering behavior. The vehicle's speed and the number of lane exceedances per kilometer were also used to examine the driving performance. Results: The SSs on the 200 m (chi(2)(3) = 20.67, p < .001), 500 m (chi(2)(3) = 22.42, p < .001), and 800 m (chi(2)(3) = 22.86, p < .001) radius curves were significantly faster for drivers under the influence of alcohol compared with those given a placebo. There were significant effects of alcohol on the SRR and PP on the 200 m, 500 m, and 800 m radius curves. Conclusion: For all of the curves, the SS, SRR, and PP had a tendency to increase as the BrAC increased. The large PP at a high BrAC, accompanied by the high speed, SS, and SRR, resulted in a high probability of lane exceedance. The use of measures of SS, SRR, and PP aided in the improvement of the accuracy of the intoxication detection for the different types of curves. Application: The most important application is to provide guidance for detecting alcohol-impaired-driving.

Control Authority Transfer Method for Automated-to-Manual Driving Via a Shared Authority Mode. Although automation-initiated and driver-initiated control transfers from automated to manual driving may yield unstable steering activity even when the drivers are focused on the road environment ahead, there are few studies on the development of control transfer methods at an operational level after a request to intervene. Therefore, we propose a shared authority mode connecting the automated an...

Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics. To improve vehicle path-following performance and to reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for the steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error, and steering angle as the inputs and a driver&#39;s expected steering ratio...

Parallel End-to-End Autonomous Mining: An IoT-Oriented Approach. This article proposes a new solution for end-to-end autonomous mining operations: Internet of Things (IoT)-based parallel mining, consisting of the concept definition, the solution given, and the concrete realization. The proposed parallel mining is inspired by the artificial societies (A) for modeling, computational experiments (C) for analysis, and parallel execution (P) for control (ACP) approa...

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

Robust State-Dependent Switching of Linear Systems With Dwell Time. A state-dependent switching law that obeys a dwell time constraint and guarantees the stability of a switched linear system is designed. Sufficient conditions are obtained for the stability of the switched systems when the switching law is applied in presence of polytopic type parameter uncertainty. A Lyapunov function, in quadratic form, is assigned to each subsystem such that it is non-increasing at the switching instants. During the dwell time, this function varies piecewise linearly in time. After the dwell, the system switches if the switching results in a decrease in the value of the LF. The method proposed is also applicable to robust stabilization via state-feedback. It is further extended to guarantee a bound on the L 2 -gain of the switching system; it is also used in deriving state-feedback control law that robustly achieves a prescribed L 2 -gain bound.

A parameter set division and switching gain-scheduling controllers design method for time-varying plants. This paper presents a new technique to design switching gain-scheduling controllers for plants with measurable time-varying parameters. By dividing the parameter set into a sufficient number of subsets, and by designing a robust controller to each subset, the designed switching gain-scheduling controllers achieve a desired L2-gain performance for each subset, while ensuring stability whenever a controller switching occurs due to the crossing of the time-varying parameters between any two adjacent subsets. Based on integral quadratic constraints theory and Lyapunov stability theory, a switching gain-scheduling controllers design problem amounts to solving optimization problems. Each optimization problem is to be solved by a combination of the bisection search and the numerical nonsmooth optimization method. The main advantage of the proposed technique is that the division of the parameter region is determined automatically, without any prespecified parameter set division which is required in most of previously developed switching gain-scheduling controllers design methods. A numerical example illustrates the validity of the proposed technique.

Fast Map Matching, An Algorithm Integrating Hidden Markov Model With Precomputation Wide deployment of global positioning system (GPS) sensors has generated a large amount of data with numerous applications in transportation research. Due to the observation error, a map matching (MM) process is commonly performed to infer a path on a road network from a noisy GPS trajectory. The increasing data volume calls for the design of efficient and scalable MM algorithms. This article presents fast map matching (FMM), an algorithm integrating hidden Markov model with precomputation, and provides an open-source implementation. An upper bounded origin-destination table is precomputed to store all pairs of shortest paths within a certain length in the road network. As a benefit, repeated routing queries known as the bottleneck of MM are replaced with hash table search. Additionally, several degenerate cases and a problem of reverse movement are identified and addressed in FMM. Experiments on a large collection of real-world taxi trip trajectories demonstrate that FMM has achieved a considerable single-processor MM speed of 25,000-45,000 points/second varying with the output mode. Investigation on the running time of different steps in FMM reveals that after precomputation is employed, the new bottleneck is located in candidate search, and more specifically, the projection of a GPS point to the polyline of a road edge. Reverse movement in the result is also effectively reduced by applying a penalty.

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.

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.

Interior point methods 25 years later. Interior point methods for optimization have been around for more than 25 years now. Their presence has shaken up the field of optimization. Interior point methods for linear and (convex) quadratic programming display several features which make them particularly attractive for very large scale optimization. Among the most impressive of them are their low-degree polynomial worst-case complexity and an unrivalled ability to deliver optimal solutions in an almost constant number of iterations which depends very little, if at all, on the problem dimension. Interior point methods are competitive when dealing with small problems of dimensions below one million constraints and variables and are beyond competition when applied to large problems of dimensions going into millions of constraints and variables. In this survey we will discuss several issues related to interior point methods including the proof of the worst-case complexity result, the reasons for their amazingly fast practical convergence and the features responsible for their ability to solve very large problems. The ever-growing sizes of optimization problems impose new requirements on optimization methods and software. In the final part of this paper we will therefore address a redesign of interior point methods to allow them to work in a matrix-free regime and to make them well-suited to solving even larger problems. (C) 2011 Elsevier B.V. All rights reserved.

Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues. In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization. GAs are well known for their remarkable generality and versatility and have been applied in a wide variety of settings in wireless networks. In this paper, we provide a comprehensive survey of the applications of GAs in wireless networks. We provide both an exposition of common GA models and configuration and provide a broad-ranging survey of GA techniques in wireless networks. We also point out open research issues and define potential future work. While various surveys on GAs exist in the literature, our paper is the first paper, to the best of our knowledge, which focuses on their application in wireless networks.

Fundamentals of the Downlink Green Coverage and Energy Efficiency in Heterogeneous Networks. This paper studies the proposed green (energy-efficient) coverage probability, link and network energy efficiencies in the downlink of a heterogeneous cellular network (HetNet) consisting of $K$ independent Poisson point processes of base stations (BSs). The important statistical properties of the universal (general) cell association functions are first studied, and the cell load statistics for power-law cell association functions, which can characterize the accurate void cell probability of a BS in every tier, are also derived. A simple and feasible green channel-aware cell association (GCA) scheme is proposed and the green coverage probability is also proposed for any particular cell association scheme, such as the maximum received power association (MRPA) and the nearest BS association (NBA) schemes. Then, the link and network energy efficiencies are proposed to characterize the mean spectrum efficiency per unit power consumption for a BS and the mean area spectrum efficiency for the HetNet, respectively. All the tight bounds on the green coverage probability, link, and network energy efficiencies for the GCA, MRPA, and NBA schemes are found. They are theoretically shown to pose the fundamental maximum limits on the link and network energy efficiencies achieved by any other cell association schemes, and such a fact is validated by numerical results as well.

Multiple UAVs as Relays: Multi-Hop Single Link Versus Multiple Dual-Hop Links. Unmanned aerial vehicles (UAVs) have found many important applications in communications. They can serve as either aerial base stations or mobile relays to improve the quality of services. In this paper, we study the use of multiple UAVs in relaying. Considering two typical uses of multiple UAVs as relays that form either a single multi-hop link or multiple dual-hop links, we first optimize the pl...

Unmanned Aerial Vehicle-Aided Communications: Joint Transmit Power and Trajectory Optimization. This letter investigates the transmit power and trajectory optimization problem for unmanned aerial vehicle (UAV)-aided networks. Different from majority of the existing studies with fixed communication infrastructure, a dynamic scenario is considered where a flying UAV provides wireless services for multiple ground nodes simultaneously. To fully exploit the controllable channel variations provide...

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.

Optimal Scheduling for Quality of Monitoring in Wireless Rechargeable Sensor Networks Wireless Rechargeable Sensor Network (WRSN) is an emerging technology to address the energy constraint in sensor networks. The protocol design in WRSN is extremely challenging due to the complicated interactions between rechargeable sensor nodes and readers, capable of mobility and functioning as energy distributors and data collectors. In this paper, we for the first time investigate the optimal scheduling problem in WRSN for stochastic event capture, i.e., how to jointly mobilize the readers for energy distribution and schedule sensor nodes for optimal quality of monitoring (QoM). We analyze the QoM for three application scenarios: i) the reader travels at a fixed speed to recharge sensor nodes and sensor nodes consume the collected energy in an aggressive way, ii) the reader stops to recharge sensor nodes for a predefined time during its periodic traveling and sensor nodes deplete energy aggressively, iii) the reader stops to recharge sensor nodes but sensor nodes can adopt optimal duty cycle scheduling for maximal QoM. We provide analytical results for achieving the optimal QoM under arbitrary parameter settings. Extensive simulation results are offered to demonstrate the correctness and effectiveness of our results.

Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number M of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame) . To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-M regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system.

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.

Movie2Comics: Towards a Lively Video Content Presentation a type of artwork, comics is prevalent and popular around the world. However, despite the availability of assistive software and tools, the creation of comics is still a labor-intensive and time-consuming process. This paper proposes a scheme that is able to automatically turn a movie clip to comics. Two principles are followed in the scheme: 1) optimizing the information preservation of the movie; and 2) generating outputs following the rules and the styles of comics. The scheme mainly contains three components: script-face mapping, descriptive picture extraction, and cartoonization. The script-face mapping utilizes face tracking and recognition techniques to accomplish the mapping between characters' faces and their scripts. The descriptive picture extraction then generates a sequence of frames for presentation. Finally, the cartoonization is accomplished via three steps: panel scaling, stylization, and comics layout design. Experiments are conducted on a set of movie clips and the results have demonstrated the usefulness and the effectiveness of the scheme.

Soft Wearable Motion Sensing Suit For Lower Limb Biomechanics Measurements Motion sensing has played an important role in the study of human biomechanics as well as the entertainment industry. Although existing technologies, such as optical or inertial based motion capture systems, have relatively high accuracy in detecting body motions, they still have inherent limitations with regards to mobility and wearability. In this paper, we present a soft motion sensing suit for measuring lower extremity joint motion. The sensing suit prototype includes a pair of elastic tights and three hyperelastic strain sensors. The strain sensors are made of silicone elastomer with embedded microchannels filled with conductive liquid. To form a sensing suit, these sensors are attached at the hip, knee, and ankle areas to measure the joint angles in the sagittal plane. The prototype motion sensing suit has significant potential as an autonomous system that can be worn by individuals during many activities outside the laboratory, from running to rock climbing. In this study we characterize the hyperelastic sensors in isolation to determine their mechanical and electrical responses to strain, and then demonstrate the sensing capability of the integrated suit in comparison with a ground truth optical motion capture system. Using simple calibration techniques, we can accurately track joint angles and gait phase. Our efforts result in a calculated trade off: with a maximum error less than 8%, the sensing suit does not track joints as accurately as optical motion capture, but its wearability means that it is not constrained to use only in a lab.

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.

A Hierarchical Architecture Using Biased Min-Consensus for USV Path Planning This paper proposes a hierarchical architecture using the biased min-consensus (BMC) method, to solve the path planning problem of unmanned surface vessel (USV). We take the fixed-point monitoring mission as an example, where a series of intermediate monitoring points should be visited once by USV. The whole framework incorporates the low-level layer planning the standard path between any two intermediate points, and the high-level fashion determining their visiting sequence. First, the optimal standard path in terms of voyage time and risk measure is planned by the BMC protocol, given that the corresponding graph is constructed with node state and edge weight. The USV will avoid obstacles or keep a certain distance safely, and arrive at the target point quickly. It is proven theoretically that the state of the graph will converge to be stable after finite iterations, i.e., the optimal solution can be found by BMC with low calculation complexity. Second, by incorporating the constraint of intermediate points, their visiting sequence is optimized by BMC again with the reconstruction of a new virtual graph based on the former planned results. The extensive simulation results in various scenarios also validate the feasibility and effectiveness of our method for autonomous navigation.

Event-Triggered Adaptive Control for a Class of Uncertain Nonlinear Systems. In this technical note, the problem of event-trigger based adaptive control for a class of uncertain nonlinear systems is considered. The nonlinearities of the system are not required to be globally Lipschitz. Since the system contains unknown parameters, it is a difficult task to check the assumption of the input-to-state stability (ISS) with respect to the measurement errors, which is required in most existing literature. To solve this problem, we design both the adaptive controller and the triggering event at the same time such that the ISS assumption is no longer needed. In addition to presenting new design methodologies based on the fixed threshold strategy and relative threshold strategy, we also propose a new strategy named the switching threshold strategy. It is shown that the proposed control schemes guarantee that all the closed-loop signals are globally bounded and the tracking/stabilization error exponentially converges towards a compact set which is adjustable.

Fuzzy Adaptive Tracking Control of Wheeled Mobile Robots With State-Dependent Kinematic and Dynamic Disturbances Unlike most works based on pure nonholonomic constraint, this paper proposes a fuzzy adaptive tracking control method for wheeled mobile robots, where unknown slippage occurs and violates the nonholononomic constraint in the form of state-dependent kinematic and dynamic disturbances. These disturbances degrade tracking performance significantly and, therefore, should be compensated. To this end, the kinematics with state-dependent disturbances are rigorously derived based on the general form of slippage in the mobile robots, and fuzzy adaptive observers together with parameter adaptation laws are designed to estimate the state-dependent disturbances in both kinematics and dynamics. Because of the modular structure of the proposed method, it can be easily combined with the previous controllers based on the model with the pure nonholonomic constraint, such that the combination of the fuzzy adaptive observers with the previously proposed backstepping-like feedback linearization controller can guarantee the trajectory tracking errors to be globally ultimately bounded, even when the nonholonomic constraint is violated, and their ultimate bounds can be adjusted appropriately for various types of trajectories in the presence of large initial tracking errors and disturbances. Both the stability analysis and simulation results are provided to validate the proposed controller.

Leader-following consensus in second-order multi-agent systems with input time delay: An event-triggered sampling approach. This paper analytically investigates an event-triggered leader-following consensus in second-order multi-agent systems with time delay in the control input. Each agent׳s update of control input is driven by properly defined event, which depends on the measurement error, the states of its neighboring agents at their individual time instants, and an exponential decay function. Necessary and sufficient conditions are presented to ensure a leader-following consensus. Moreover, the control is updated only when the event-triggered condition is satisfied, which significantly decreases the number of communication among nodes, avoided effectively the continuous communication of the information channel among agents and excluded the Zeno-behavior of triggering time sequences. A numerical simulation example is given to illustrate the theoretical results.

Adaptive neural control for a class of stochastic nonlinear systems by backstepping approach. This paper addresses adaptive neural control for a class of stochastic nonlinear systems which are not in strict-feedback form. Based on the structural characteristics of radial basis function (RBF) neural networks (NNs), a backstepping design approach is extended from stochastic strict-feedback systems to a class of more general stochastic nonlinear systems. In the control design procedure, RBF NNs are used to approximate unknown nonlinear functions and the backstepping technique is utilized to construct the desired controller. The proposed adaptive neural controller guarantees that all the closed-loop signals are bounded and the tracking error converges to a sufficiently small neighborhood of the origin. Two simulation examples are used to illustrate the effectiveness of the proposed approach.

Adaptive Neural Quantized Control for a Class of MIMO Switched Nonlinear Systems With Asymmetric Actuator Dead-Zone. This paper concentrates on the adaptive state-feedback quantized control problem for a class of multiple-input-multiple-output (MIMO) switched nonlinear systems with unknown asymmetric actuator dead-zone. In this study, we employ different quantizers for different subsystem inputs. The main challenge of this study is to deal with the coupling between the quantizers and the dead-zone nonlinearities...

Control of nonlinear systems under dynamic constraints: A unified barrier function-based approach. Although there are fruitful results on adaptive control of constrained parametric/nonparametric strict-feedback nonlinear systems, most of them are contingent upon “feasibility conditions”, and/or are only applicable to constant and symmetric constraints. In this work, we present a robust adaptive control solution free from “feasibility conditions” and capable of accommodating much more general dynamic constraints. In our design, instead of employing the commonly used piecewise Barrier Lyapunov Function (BLF), we build a unified barrier function upon the constrained states, with which we convert the original constrained nonlinear system into an equivalent “non-constrained” one. Then by stabilizing the “unconstrained” system, the asymmetric state constraints imposed dynamically are handled gracefully. By blending a new coordinate transformation into the backstepping design, we develop a control strategy completely obviating the “feasibility conditions” for the system. It is worth noting that the requirement on the constraints to be obeyed herein is much less restrictive as compared to those imposed in most existing methods, rendering the resultant control less demanding in design and more user-friendly in implementation. Both theoretical analysis and numerical simulation verify the effectiveness and benefits of the proposed method.

Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN...

Distributed Event-Triggered Control for Multi-Agent Systems Event-driven strategies for multi-agent systems are motivated by the future use of embedded microprocessors with limited resources that will gather information and actuate the individual agent controller updates. The controller updates considered here are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state, and are applied to a first order agreement problem. A centralized formulation is considered first and then its distributed counterpart, in which agents require knowledge only of their neighbors' states for the controller implementation. The results are then extended to a self-triggered setup, where each agent computes its next update time at the previous one, without having to keep track of the state error that triggers the actuation between two consecutive update instants. The results are illustrated through simulation examples.

On the Generalized Local Stability and Local Stabilization Conditions for Discrete-Time Takagi–Sugeno Fuzzy Systems In this paper, local stability and local stabilization problems are considered for discrete-time nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy systems. Improved methods to assess the local stability, design locally stabilizing control laws, and estimate the domain of attraction are developed in terms of single-parameter minimization problems subject to linear matrix inequality (LMI) constraints. The improvement is achieved by applying a convergent LMI relaxation technique to prove positivity of homogeneous polynomial parameter-dependent matrices of arbitrary degree with variables in the simplex. We also take advantage of the recently developed parameter variation modeling technique to deal with the case that the variation rate of the membership functions of the T-S fuzzy systems is bounded and less than one. Finally, several illustrative examples demonstrate the validity and efficiency of the proposed method.

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.

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.

Very High Force Hydraulic McKibben Artificial Muscle with a p-Phenylene-2, 6-benzobisoxazole Cord Sleeve Small and lightweight actuators that generate high force and high energy are strongly required for realizing powerful robots and tools. By applying ultra-high-strength p-phenylene-2,6-benzobisoxazole fiber sleeves to McKibben artificial muscles, new hydraulic artificial muscles have been developed. While conventional McKibben muscles are driven by a maximum pneumatic pressure of 0.7 MPa, the newly developed muscles are driven by a maximum water hydraulic of pressure of 4 MPa, resulting in very high force capability. This paper presents the materials and structure of the new artificial muscle and the experimental results. The developed muscles are evaluated by four parameters - force density per volume (FDV), force density per mass (FDM), energy density per volume (EDV) and energy density per mass (EDM) - for comparisons with other conventional linear actuators. The prototype artificial muscle, which is 40 mm in diameter and 700 mm in length, can achieve a maximum contracting force of 28 kN, FDV of 32.3 x 10(-3) N/mm(3), FDM of 9.44 x 10(3) N/kg, EDV of 2600 x 10(-3) J/mm(3) and EDM of 762 x 10(3) J/kg. These values are 1.7 to 33 times larger than those of the typical conventional actuators. As the result, a high force artificial muscle of 40 mm in diameter that generates 28-kN contracting force has been developed successfully. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2010

Stochastic Dynamic Pricing for EV Charging Stations With Renewable Integration and Energy Storage. This paper studies the problem of stochastic dynamic pricing and energy management policy for electric vehicle (EV) charging service providers. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with multiple uncertainties-charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity pric...

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.

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.

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.

Sensing pressure distribution on a lower-limb exoskeleton physical human-machine interface. A sensory apparatus to monitor pressure distribution on the physical human-robot interface of lower-limb exoskeletons is presented. We propose a distributed measure of the interaction pressure over the whole contact area between the user and the machine as an alternative measurement method of human-robot interaction. To obtain this measure, an array of newly-developed soft silicone pressure sensors is inserted between the limb and the mechanical interface that connects the robot to the user, in direct contact with the wearer's skin. Compared to state-of-the-art measures, the advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for the assessment of safety and comfort of human-robot interaction. This paper presents the new sensor and its characterization, and the development of an interaction measurement apparatus, which is applied to a lower-limb rehabilitation robot. The system is calibrated, and an example its use during a prototypical gait training task is presented.

A soft wearable robotic device for active knee motions using flat pneumatic artificial muscles We present the design of a soft wearable robotic device composed of elastomeric artificial muscle actuators and soft fabric sleeves, for active assistance of knee motions. A key feature of the device is the two-dimensional design of the elastomer muscles that not only allows the compactness of the device, but also significantly simplifies the manufacturing process. In addition, the fabric sleeves make the device lightweight and easily wearable. The elastomer muscles were characterized and demonstrated an initial contraction force of 38N and maximum contraction of 18mm with 104kPa input pressure, approximately. Four elastomer muscles were employed for assisted knee extension and flexion. The robotic device was tested on a 3D printed leg model with an articulated knee joint. Experiments were conducted to examine the relation between systematic change in air pressure and knee extension-flexion. The results showed maximum extension and flexion angles of 95° and 37°, respectively. However, these angles are highly dependent on underlying leg mechanics and positions. The device was also able to generate maximum extension and flexion forces of 3.5N and 7N, respectively.

Robotic Artificial Muscles: Current Progress and Future Perspectives Robotic artificial muscles are a subset of artificial muscles that are capable of producing biologically inspired motions useful for robot systems, i.e., large power-to-weight ratios, inherent compliance, and large range of motions. These actuators, ranging from shape memory alloys to dielectric elastomers, are increasingly popular for biomimetic robots as they may operate without using complex linkage designs or other cumbersome mechanisms. Recent achievements in fabrication, modeling, and control methods have significantly contributed to their potential utilization in a wide range of applications. However, no survey paper has gone into depth regarding considerations pertaining to their selection, design, and usage in generating biomimetic motions. In this paper, we discuss important characteristics and considerations in the selection, design, and implementation of various prominent and unique robotic artificial muscles for biomimetic robots, and provide perspectives on next-generation muscle-powered robots.

Development of muscle suit for upper limb We have been developing a "muscle suit" that provides muscular support to the paralyzed or those otherwise unable to move unaided, as well as to manual workers. The muscle suit is a garment without a metal frame and uses a McKibben actuator driven by compressed air. Because actuators are sewn into the garment, no metal frame is needed, making the muscle suit very light and cheap. With the muscle suit, the patient can willfully control his or her movement. The muscle suit is very helpful for both muscular and emotional support. We propose an armor-type muscle suit in order to overcome issues of a prototype system and then show how abduction motion, which we believe, is the most difficult motion for the upper body, is realized.

Power Assist System HAL-3 for Gait Disorder Person We have developed the power assistive suit, HAL (Hybrid Assistive Leg) which provide the self-walking aid for gait disorder persons or aged persons. In this paper, We introduce HAL-3 system, improving HAL-1,2 systems which had developed previously. EMG signal was used as the input information of power assist controller. We propose a calibration method to identify parameters which relates the EMG to joint torque by using HAL-3. We could obtain suitable torque estimated by EMG and realize an apparatus that enables power to be used for walking and standing up according to the intention of the operator.

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.

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...

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.

Human Shoulder Modeling Including Scapulo-Thoracic Constraint And Joint Sinus Cones In virtual human modeling, the shoulder is usually composed of clavicular, scapular and arm segments related by rotational joints. Although the model is improved, the realistic animation of the shoulder is hardly achieved. This is due to the fact that it is difficult to coordinate the simultaneous motion of the shoulder components in a consistent way. Also, the common use of independent one-degree of freedom (DOF) joint hierarchies does not properly render the 3-D accessibility space of real joints. On the basis of former biomechanical investigations, we propose here an extended shoulder model including scapulo-thoracic constraint and joint sinus cones. As a demonstration, the model is applied, using inverse kinematics, to the animation of a 3-D anatomic muscled skeleton model. (C) 2000 Elsevier Science Ltd. All rights reserved.

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.

Transmit or Discard: Optimizing Data Freshness in Networked Embedded Systems with Energy Harvesting Sources This paper explores how to optimize the freshness of real-time data in energy harvesting based networked embedded systems. We introduce the concept of Age of Information (AoI) to quantitatively measure the data freshness and present a comprehensive analysis on the average AoI of the real-time data with stochastic update arrival and energy replenishment rates. Both an optimal offline solution and an effective online solution are designed to judiciously select a subset of the real-time data updates and determine their corresponding transmission times to optimize the average AoI subject to energy constraints. Our extensive experiments have validated the effectiveness of the proposed solutions, and showed that these two methods can significantly improve the average AoI by 47.2% comparing to the state-of-the-art solutions for low energy replenishment rate.

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.

Fast, Fair, and Efficient Flows in Networks We study the problem of minimizing the maximum latency of flows in networks with congestion. We show that this problem is NP-hard, even when all arc latency functions are linear and there is a single source and sink. Still, an optimal flow and an equilibrium flow share a desirable property in this situation: All flow-carrying paths have the same length, i.e., these solutions are “fair,” which is in general not true for optimal flows in networks with nonlinear latency functions. In addition, the maximum latency of the Nash equilibrium, which can be computed efficiently, is within a constant factor of that of an optimal solution. That is, the so-called price of anarchy is bounded. In contrast, we present a family of instances with multiple sources and a single sink for which the price of anarchy is unbounded, even in networks with linear latencies. Furthermore, we show that an s-t-flow that is optimal with respect to the average latency objective is near-optimal for the maximum latency objective, and it is close to being fair. Conversely, the average latency of a flow minimizing the maximum latency is also within a constant factor of that of a flow minimizing the average latency.

Cancel-and-tighten algorithm for quickest flow problems. Given a directed graph with a capacity and a transit time for each arc and with single source and single sink nodes, the quickest flow problem is to find the minimum time horizon to send a given amount of flow from the source to the sink. This is one of the fundamental dynamic flow problems. Parametric search is one of the basic approaches to solving the problem. Recently, Lin and Jaillet (SODA, 2015) proposed an algorithm whose time complexity is the same as that of the minimum cost flow algorithm. Their algorithm employs a cost scaling technique, and its time complexity is weakly polynomial time. In this article, we modify their algorithm by adopting a technique to construct a strongly polynomial time algorithm for solving the minimum cost flow problem. The proposed algorithm runs in O(nm(2)(log n)(2)) time, where n and m are the numbers of nodes and arcs, respectively. (C) 2016 Wiley Periodicals, Inc.

On the Age of Information in a CSMA Environment In this paper, we investigate a network where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> links contend for the channel using the well-known carrier sense multiple access scheme. By leveraging the notion of stochastic hybrid systems, we find: 1) a closed-form expression of the average age when links generate packets at will 2) an upperbound of the average age when packets arrive stochastically to each link. This upperbound is shown to be generally tight, and to be equal to the average age in certain scenarios. Armed with these expressions, we formulate the problem of minimizing the average age by calibrating the back-off time of each link. Interestingly, we show that the minimum average age is achieved for the same back-off time in both the sampling and stochastic arrivals scenarios. Then, by analyzing its structure, we convert the formulated optimization problem to an equivalent convex problem that we find its optimal solution. Insights on the interaction between links and numerical implementations of the optimized Carrier Sense Multiple Access (CSMA) scheme in an IEEE 802.11 environment are presented. Next, to further improve the performance of the optimized CSMA scheme, we propose a modification to it by giving each link the freedom to transition to SLEEP mode. The proposed approach provides a way to reduce the burden on the channel when possible. This leads, as will be shown in the paper, to an improvement in the performance of the network. Simulations results are then laid out to highlight the performance gain offered by our approach in comparison to the optimized standard CSMA scheme.

Age-Minimal Transmission for Energy Harvesting Sensors With Finite Batteries: Online Policies An energy-harvesting sensor node that is sending status updates to a destination is considered. The sensor is equipped with a battery of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">finite</italic> size to save its incoming energy, and consumes one unit of energy per status update transmission, which is delivered to the destination instantly over an error-free channel. The setting is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</italic> in which the harvested energy is revealed to the sensor causally over time after it arrives, and the goal is to design status update transmission times (policy) such that the long term average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">age of information</italic> (AoI) is minimized. The AoI is defined as the time elapsed since the latest update has reached at the destination. Two energy arrival models are considered: a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random battery recharge</italic> (RBR) model, and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incremental battery recharge</italic> (IBR) model. In both models, energy arrives according to a Poisson process with unit rate, with values that completely fill up the battery in the RBR model, and with values that fill up the battery incrementally in a unit-by-unit fashion in the IBR model. The key approach to characterizing the optimal status update policy for both models is showing the optimality of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">renewal policies</italic> , in which the inter-update times follow a renewal process in a certain manner that depends on the energy arrival model and the battery size. It is then shown that the optimal renewal policy has an energy-dependent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">threshold</italic> structure, in which the sensor sends a status update only if the AoI grows above a certain threshold that depends on the energy available in its battery. For both the random and the incremental battery recharge models, the optimal energy-dependent thresholds are characterized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicitly</italic> , i.e., in closed-form, in terms of the optimal long term average AoI. It is also shown that the optimal thresholds are monotonically decreasing in the energy available in the battery, and that the smallest threshold, which comes in effect when the battery is full, is equal to the optimal long term average AoI.

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.

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.

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.

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.

Fed-IIoT: A Robust Federated Malware Detection Architecture in Industrial IoT The sheer volume of industrial Internet of Things (IIoT) malware is one of the most serious security threats in today&#39;s interconnected world, with new types of advanced persistent threats and advanced forms of obfuscations. This article presents a robust federated learning based architecture called Fed-IIoT for detecting Android malware applications in IIoT. Fed-IIoT consists of <...

The graph Voronoi diagram with applications The Voronoi diagram is a famous structure of computational geometry. We show that there is a straightforward equivalent in graph theory which can be efficiently computed. In particular, we give two algorithms for the computation of graph Voronoi diagrams, prove a lower bound on the problem, and identify cases where the algorithms presented are optimal. The space requirement of a graph Voronoi diagram is modest, since it needs no more space than does the graph itself. The investigation of graph Voronoi diagrams is motivated by many applications and problems on networks that can be easily solved with their help. This includes the computation of nearest facilities, all nearest neighbors and closest pairs, some kind of collision free moving, and anticenters and closest points. (C) 2000 John Wiley & Sons, Inc.

Least squares one-class support vector machine In this paper, we reformulate a standard one-class SVM (support vector machine) and derive a least squares version of the method, which we call LS (least squares) one-class SVM. The LS one-class SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. One can use the distance to the hyperplane as a proximity measure to determine which objects resemble training objects better than others. This differs from the standard one-class SVMs that detect which objects resemble training objects. We demonstrate the performance of the LS one-class SVM on relevance ranking with positive examples, and also present the comparison with traditional methods including the standard one-class SVM. The experimental results indicate the efficacy of the LS one-class SVM.

Modulation Classification Based on Signal Constellation Diagrams and Deep Learning. Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and appl...

Machine Learning for the Detection and Identification of Internet of Things Devices: A Survey The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT devices has exposed the IoT to untold risks with severe consequences. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. Conventional approaches use cryptographic mechanisms to a...

Uncertainty Theory Based Reliability-Centric Cyber-Physical System Design Cyber-physical systems (CPSs) are built from, and depend upon, the seamless integration of software and hardware components. The most important challenge in CPS design and verification is to design CPS to be reliable in a variety of uncertainties, i.e., unanticipated and rapidly evolving environments and disturbances. The costs, delays and reliability of the designed CPS are highly dependent on software-hardware partitioning in the design. The key challenges in partitioning CPSs is that it is difficult to formalize reliability characterization in the same way as the uncertain cost and time delay. In this paper, we propose a new CPS design paradigm for reliability assurance while coping with uncertainty. To be specific, we develop an uncertain programming model for partitioning based on the uncertainty theory, to support the assured reliability. The uncertainty effect of the cost and delay time of components to be implemented can be modeled by the uncertainty variables with uncertainty distributions, and the reliability characterization is recursively derived. We convert the uncertain programming model and customize an improved heuristic to solve the converted model. Experiment results on some benchmarks and random graphs show that the uncertain method produces the design with higher reliability. Besides, in order to demonstrate the effectiveness of our model for in coping with uncertainty in design stage, we apply this uncertain framework and existing deterministic models in the design process of a sub-system that is used in real world subway control. The system implemented based on the uncertain model works better than the result of deterministic models. The proposed design paradigm has the potential to be generalized to the design of CPSs for greater assurances of safety and security under a variety of uncertainties

A Survey on TOA Based Wireless Localization and NLOS Mitigation Techniques Localization of a wireless device using the time-of-arrivals (TOAs) from different base stations has been studied extensively in the literature. Numerous localization algorithms with different accuracies, computational complexities, a-priori knowledge requirements, and different levels of robustness against non-line-of-sight (NLOS) bias effects also have been reported. However, to our best knowledge, a detailed unified survey of different localization and NLOS mitigation algorithms is not available in the literature. This paper aims to give a comprehensive review of these different TOA-based localization algorithms and their technical challenges, and to point out possible future research directions. Firstly, fundamental lower bounds and some practical estimators that achieve close to these bounds are summarized for line-of-sight (LOS) scenarios. Then, after giving the fundamental lower bounds for NLOS systems, different NLOS mitigation techniques are classified and summarized. Simulation results are also provided in order to compare the performance of various techniques. Finally, a table that summarizes the key characteristics of the investigated techniques is provided to conclude the paper.

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.

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.

Firefly algorithm, stochastic test functions and design optimisation Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimisation problems. In this paper, we show how to use the recently developed firefly algorithm to solve non-linear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in the literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.

Development of a UAV-LiDAR System with Application to Forest Inventory We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m(2)) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points per m(2), to 0.15 m when the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.

Solving the data sparsity problem in destination prediction Destination prediction is an essential task for many emerging location-based applications such as recommending sightseeing places and targeted advertising according to destinations. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, almost all the existing techniques use various kinds of extra information such as road network, proprietary travel planner, statistics requested from government, and personal driving habits. Such extra information, in most circumstances, is unavailable or very costly to obtain. Thereby we approach the task of destination prediction by using only historical trajectory dataset. However, this approach encounters the \"data sparsity problem\", i.e., the available historical trajectories are far from enough to cover all possible query trajectories, which considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis (SubSyn) to address the data sparsity problem. SubSyn first decomposes historical trajectories into sub-trajectories comprising two adjacent locations, and then connects the sub-trajectories into \"synthesised\" trajectories. This process effectively expands the historical trajectory dataset to contain much more trajectories. Experiments based on real datasets show that SubSyn can predict destinations for up to ten times more query trajectories than a baseline prediction algorithm. Furthermore, the running time of the SubSyn-training algorithm is almost negligible for a large set of 1.9 million trajectories, and the SubSyn-prediction algorithm runs over two orders of magnitude faster than the baseline prediction algorithm constantly.

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.

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.

The Evolution of Sink Mobility Management in Wireless Sensor Networks: A Survey Sink mobility has long been recognized as an efficient method of improving system performance in wireless sensor networks (WSNs), e.g. relieving traffic burden from a specific set of nodes. Though tremendous research efforts have been devoted to this topic during the last decades, yet little attention has been paid for the research summarization and guidance. This paper aims to fill in the blank and presents an up-to-date survey on the sink mobility issue. Its main contribution is to review mobility management schemes from an evolutionary point of view. The related schemes have been divided into four categories: uncontrollable mobility (UMM), pathrestricted mobility (PRM), location-restricted mobility (LRM) and unrestricted mobility (URM). Several representative solutions are described following the proposed taxonomy. To help readers comprehend the development flow within the category, the relationship among different solutions is outlined, with detailed descriptions as well as in-depth analysis. In this way, besides some potential extensions based on current research, we are able to identify several open issues that receive little attention or remain unexplored so far.

Mobility in wireless sensor networks - Survey and proposal. Targeting an increasing number of potential application domains, wireless sensor networks (WSN) have been the subject of intense research, in an attempt to optimize their performance while guaranteeing reliability in highly demanding scenarios. However, hardware constraints have limited their application, and real deployments have demonstrated that WSNs have difficulties in coping with complex communication tasks – such as mobility – in addition to application-related tasks. Mobility support in WSNs is crucial for a very high percentage of application scenarios and, most notably, for the Internet of Things. It is, thus, important to know the existing solutions for mobility in WSNs, identifying their main characteristics and limitations. With this in mind, we firstly present a survey of models for mobility support in WSNs. We then present the Network of Proxies (NoP) assisted mobility proposal, which relieves resource-constrained WSN nodes from the heavy procedures inherent to mobility management. The presented proposal was implemented and evaluated in a real platform, demonstrating not only its advantages over conventional solutions, but also its very good performance in the simultaneous handling of several mobile nodes, leading to high handoff success rate and low handoff time.

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 ...

On Theoretical Modeling of Sensor Cloud: A Paradigm Shift From Wireless Sensor Network. This paper focuses on the theoretical modeling of sensor cloud, which is one of the first attempts in this direction. We endeavor to theoretically characterize virtualization, which is a fundamental mechanism for operations within the sensor-cloud architecture. Existing related research works on sensor cloud have primarily focused on the ideology and the challenges that wireless sensor network (WS...

Collaborative mobile charging policy for perpetual operation in large-scale wireless rechargeable sensor networks. In this paper, we present a mobile charging policy for perpetual operation of large-scale wireless rechargeable sensor networks (WRSNs). In these networks, dedicated mobile chargers (MCs) move throughout the network and supply energy for power-limited sensors. The MCs not only charge the sensors but also charge each other. We develop a hop-based mobile charging policy (HMCP) minimizing the number of required MCs. The HMCP considers both the sensors’ unbalanced energy consumption rate and the MCs’ limited energy capacity. The minimum number of MCs is is formulated as an integer programming problem. We first verify the existence of an optimal solution, and later design an algorithm to obtain the optimal solution. Based on HMCP, we propose HMCP+ for the case that only one MC can recharge sensors in each region. HMCP+ plans MCs’ paths to decrease mobile energy consumption by MCs. Finally, performance of the proposed polices is validated through the simulation results.

Near optimal bounded route association for drone-enabled rechargeable WSNs. This paper considers the multi-drone wireless charging scheme in large scale wireless sensor networks, where sensors can be charged by the charging drone with wireless energy transfer. As existing studies rarely focus on the route association issue with limited energy capacity, we consider this fundamental issue and study how to optimize the route association to maximize the overall charging coverage utility, when charging routes and associated nodes should be jointly selected. We first formulate a bounded route association problem which is proven to be NP-hard. Then we cast it as maximizing a monotone submodular function subject to matroid constraints and devise an efficient and accessible algorithm with a 13α approximation ratio, where α is the bound of Fully Polynomial-Time Approximation Scheme (FPTAS) solving a knapsack problem. Extensive numerical evaluations and trace-driven evaluations have been carried out to validate our theoretical effect, and the results show that our algorithm has near-optimal performance covering at most 85.3% and 70.5% of the surrogate-optimal solution achieved by CPLEX toolbox, respectively.

Latency-Aware Path Planning for Disconnected Sensor Networks With Mobile Sinks Data collection with mobile elements can greatly improve the load balance degree and accordingly prolong the longevity for wireless sensor networks (WSNs). In this pattern, a mobile sink generally traverses the sensing field periodically and collect data from multiple Anchor Points (APs) which constitute a traveling tour. However, due to long-distance traveling, this easily causes large latency of data delivery. In this paper, we propose a path planning strategy of mobile data collection, called the Dual Approximation of Anchor Points (DAAP), which aims to achieve full connectivity for partitioned WSNs and construct a shorter path. DAAP is novel in two aspects. On the one hand, it is especially designed for disconnected WSNs where sensor nodes are scattered in multiple isolated segments. On the other hand, it has the least calculational complexity compared with other existing works. DAAP is formulated as a location approximation problem and then solved by a greedy location selection mechanism, which follows two corresponding principles. On the one hand, the APs of periphery segments must be as near the network center as possible. On the other hand, the APs of other isolated segments must be as close to the current path as possible. Finally, experimental results confirm that DAAP outperforms existing works in delay-tough applications.

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.

A Mobile Platform for Wireless Charging and Data Collection in Sensor Networks Wireless energy transfer (WET) is a new technology that can be used to charge the batteries of sensor nodes without wires. Although wireless, WET does require a charging station to be brought to within reasonable range of a sensor node so that a good energy transfer efficiency can be achieved. On the other hand, it has been well recognized that data collection with a mobile base station has significant advantages over a static one. Given that a mobile platform is required for WET, a natural approach is to employ the same mobile platform to carry the base station for data collection. In this paper, we study the interesting problem of co-locating a wireless charger (for WET) and a mobile base station on the same mobile platform – the wireless charging vehicle (WCV). The WCV travels along a preplanned path inside the sensor network. Our goal is to minimize energy consumption of the entire system while ensuring that (i) each sensor node is charged in time so that it will never run out of energy, and (ii) all data collected from the sensor nodes are relayed to the mobile base station. We develop a mathematical model for this problem (OPT-t), which is timedependent. Instead of solving OPT-t directly, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. Our results offer a solution on how to use a single mobile platform to address both WET and data collection in sensor networks.

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 ...

Distributed finite-time attitude containment control for multiple rigid bodies Distributed finite-time attitude containment control for multiple rigid bodies is addressed in this paper. When there exist multiple stationary leaders, we propose a model-independent control law to guarantee that the attitudes of the followers converge to the stationary convex hull formed by those of the leaders in finite time by using both the one-hop and two-hop neighbors’ information. We also discuss the special case of a single stationary leader and propose a control law using only the one-hop neighbors’ information to guarantee cooperative attitude regulation in finite time. When there exist multiple dynamic leaders, a distributed sliding-mode estimator and a non-singular sliding surface were given to guarantee that the attitudes and angular velocities of the followers converge, respectively, to the dynamic convex hull formed by those of the leaders in finite time. We also explicitly show the finite settling time.

Very High Force Hydraulic McKibben Artificial Muscle with a p-Phenylene-2, 6-benzobisoxazole Cord Sleeve Small and lightweight actuators that generate high force and high energy are strongly required for realizing powerful robots and tools. By applying ultra-high-strength p-phenylene-2,6-benzobisoxazole fiber sleeves to McKibben artificial muscles, new hydraulic artificial muscles have been developed. While conventional McKibben muscles are driven by a maximum pneumatic pressure of 0.7 MPa, the newly developed muscles are driven by a maximum water hydraulic of pressure of 4 MPa, resulting in very high force capability. This paper presents the materials and structure of the new artificial muscle and the experimental results. The developed muscles are evaluated by four parameters - force density per volume (FDV), force density per mass (FDM), energy density per volume (EDV) and energy density per mass (EDM) - for comparisons with other conventional linear actuators. The prototype artificial muscle, which is 40 mm in diameter and 700 mm in length, can achieve a maximum contracting force of 28 kN, FDV of 32.3 x 10(-3) N/mm(3), FDM of 9.44 x 10(3) N/kg, EDV of 2600 x 10(-3) J/mm(3) and EDM of 762 x 10(3) J/kg. These values are 1.7 to 33 times larger than those of the typical conventional actuators. As the result, a high force artificial muscle of 40 mm in diameter that generates 28-kN contracting force has been developed successfully. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2010

Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks. Predicting passenger pickup/dropoff demands based on historical mobility trips has been of great importance towards better vehicle distribution for the emerging mobility-on-demand (MOD) services. Prior works focused on predicting next-step passenger demands at selected locations or hotspots. However, we argue that multi-step citywide passenger demands encapsulate both time-varying demand trends and global statuses, and hence are more beneficial to avoiding demand-service mismatching and developing effective vehicle distribution/scheduling strategies. In this paper, we propose an end-to-end deep neural network solution to the prediction task. We employ the encoder-decoder framework based on convolutional and ConvLSTM units to identify complex features that capture spatiotemporal influences and pickup-dropoff interactions on citywide passenger demands. A novel attention model is incorporated to emphasize the effects of latent citywide mobility regularities. We evaluate our proposed method using real-word mobility trips (taxis and bikes) and the experimental results show that our method achieves higher prediction accuracy than the adaptations of the state-of-the-art approaches.

Design and Experimental Validation of a Distributed Cooperative Transportation Scheme Leveraging explicit communication and cooperation of multiple robots brings about multiple advantages in the solution of tasks with autonomous robotic agents. For this reason, to the end of transporting polygonal objects with a group of mobile robots, the aim of this article is to develop a fully distributed decision-making and control scheme that lets the robots cooperate as equals, without any k...

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.

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.

DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems. While Deep Neural Networks (DNNs) have established the fundamentals of image-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To address the safety issues in autonomous driving systems, a recent set of testing techniques have been designed to automatically generate artificial driving scenes to enrich test suite, e.g., generating new input images transformed from the original ones. However, these techniques are insufficient due to two limitations: first, many such synthetic images often lack diversity of driving scenes, and hence compromise the resulting efficacy and reliability. Second, for machine-learning-based systems, a mismatch between training and application domain can dramatically degrade system accuracy, such that it is necessary to validate inputs for improving system robustness. In this paper, we propose DeepRoad, an unsupervised DNN-based framework for automatically testing the consistency of DNN-based autonomous driving systems and online validation. First, DeepRoad automatically synthesizes large amounts of diverse driving scenes without using image transformation rules (e.g. scale, shear and rotation). In particular, DeepRoad is able to produce driving scenes with various weather conditions (including those with rather extreme conditions) by applying Generative Adversarial Networks (GANs) along with the corresponding real-world weather scenes. Second, DeepRoad utilizes metamorphic testing techniques to check the consistency of such systems using synthetic images. Third, DeepRoad validates input images for DNN-based systems by measuring the distance of the input and training images using their VGGNet features. We implement DeepRoad to test three well-recognized DNN-based autonomous driving systems in Udacity self-driving car challenge. The experimental results demonstrate that DeepRoad can detect thousands of inconsistent behaviors for these systems, and effectively validate input images to potentially enhance the system robustness as well.

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...

Enhanced Deep Residual Networks for Single Image Super-Resolution. Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge [26].

Multi-scale Single Image Dehazing Using Perceptual Pyramid Deep Network Haze adversely degrades quality of an image thereby affecting its aesthetic appeal and visibility in outdoor scenes. Single image dehazing is particularly challenging due to its ill-posed nature. Most existing work, including the recent convolutional neural network (CNN) based methods, rely on the classical mathematical formulation where the hazy image is modeled as the superposition of attenuated scene radiance and the atmospheric light. In this work, we explore CNNs to directly learn a non-linear function between hazy images and the corresponding clear images. We present a multi-scale image dehazing method using Perceptual Pyramid Deep Network based on the recently popular dense blocks and residual blocks. The proposed method involves an encoder-decoder structure with a pyramid pooling module in the decoder to incorporate contextual information of the scene while decoding. The network is learned by minimizing the mean squared error and perceptual losses. Multi-scale patches are used during training and inference process to further improve the performance. Experiments on the recently released NTIRE2018-Dehazing dataset demonstrates the superior performance of the proposed method over recent state-of-the-art approaches. Additionally, the proposed method is ranked among top-3 methods in terms of quantitative performance in the recently conducted NTIRE2018-Dehazing challenge. Code can be found at https://github.com/hezhangsprinter/NTIRE-2018-Dehazing-Challenge.

Enhanced Pix2pix Dehazing Network In this paper we reduce the image dehazing problem to an image-to-image translationproblem, and propose Enhanced Pix2pix Dehazing Network (EPDN), which generates a haze-free image without relying on the physical scattering model. EPDN is embedded by a generative adversarial network, which isfollowed by a well-designed enhancer Inspiredby visualperception global-first[5] theory,the discriminatorguides the generatorto create apseudo realistic image on a coarse scale, while the enhancerfollowing the generator is required to produce a realistic dehazing image on the fine scale. The enhancer contains two enhancing blocks based on the receptivefield model, which reinforces the dehazing effect in both color and details. The embedded GAN is jointly trainedwith the enhancer Extensive experiment results on synthetic datasets and real-world datasets show that the proposed EPDN is superior to the state-ofthe-art methods in terms of PSNR, SSIM, PI, and subjective visual 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.

Deep Cross-Modal Audio-Visual Generation. Cross-modal audio-visual perception has been a long-lasting topic in psychology and neurology, and various studies have discovered strong correlations in human perception of auditory and visual stimuli. Despite work on computational multimodal modeling, the problem of cross-modal audio-visual generation has not been systematically studied in the literature. In this paper, we make the first attempt to solve this cross-modal generation problem leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks to achieve cross-modal audio-visual generation of musical performances. We explore different encoding methods for audio and visual signals, and work on two scenarios: instrument-oriented generation and pose-oriented generation. Being the first to explore this new problem, we compose two new datasets with pairs of images and sounds of musical performances of different instruments. Our experiments using both classification and human evaluation demonstrate that our model has the ability to generate one modality, i.e., audio/visual, from the other modality, i.e., visual/audio, to a good extent. Our experiments on various design choices along with the datasets will facilitate future research in this new problem space.

Distillhash: Unsupervised Deep Hashing By Distilling Data Pairs Due to the high storage and search efficiency, hashing has become prevalent for large-scale similarity search. Particularly, deep hashing methods have greatly improved the search performance under supervised scenarios. In contrast, unsupervised deep hashing models can hardly achieve satisfactory performance due to the lack of reliable supervisory similarity signals. To address this issue, we propose a novel deep unsupervised hashing model, dubbed DistillHash, which can learn a distilled data set consisted of data pairs, which have confidence similarity signals. Specifically, we investigate the relationship between the initial noisy similarity signals learned from local structures and the semantic similarity labels assigned by a Bayes optimal classifier. We show that under a mild assumption, some data pairs, of which labels are consistent with those assigned by the Bayes optimal classifier, can be potentially distilled. Inspired by this fact, we design a simple yet effective strategy to distill data pairs automatically and further adopt a Bayesian learning framework to learn hash functions from the distilled data set. Extensive experimental results on three widely used benchmark datasets show that the proposed DistillHash consistently accomplishes the state-of-the-art search performance.

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...

Dual Attention Network For Scene Segmentation In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture contexts by multi-scale feature fusion, we propose a Dual Attention Network (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the feature at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data.(1).

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.

Energy-Aware Task Offloading and Resource Allocation for Time-Sensitive Services in Mobile Edge Computing Systems Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is tak...

Dynamic surface control for a class of nonlinear systems A method is proposed for designing controllers with arbitrarily small tracking error for uncertain, mismatched nonlinear systems in the strict feedback form. This method is another "synthetic input technique," similar to backstepping and multiple surface control methods, but with an important addition, /spl tau/-1 low pass filters are included in the design where /spl tau/ is the relative degree of the output to be controlled. It is shown that these low pass filters allow a design where the model is not differentiated, thus ending the complexity arising due to the "explosion of terms" that has made other methods difficult to implement in practice. The backstepping approach, while suffering from the problem of "explosion of terms" guarantees boundedness of tracking errors globally; however, the proposed approach, while being simpler to implement, can only guarantee boundedness of tracking error semiglobally, when the nonlinearities in the system are non-Lipschitz.

Parallel Multi-Block ADMM with o(1/k) Convergence This paper introduces a parallel and distributed algorithm for solving the following minimization problem with linear constraints: $$\\begin{aligned} \\text {minimize} ~~&f_1(\\mathbf{x}_1) + \\cdots + f_N(\\mathbf{x}_N)\\\\ \\text {subject to}~~&A_1 \\mathbf{x}_1 ~+ \\cdots + A_N\\mathbf{x}_N =c,\\\\&\\mathbf{x}_1\\in {\\mathcal {X}}_1,~\\ldots , ~\\mathbf{x}_N\\in {\\mathcal {X}}_N, \\end{aligned}$$minimizef1(x1)+ź+fN(xN)subject toA1x1+ź+ANxN=c,x1źX1,ź,xNźXN,where $$N \\ge 2$$Nź2, $$f_i$$fi are convex functions, $$A_i$$Ai are matrices, and $${\\mathcal {X}}_i$$Xi are feasible sets for variable $$\\mathbf{x}_i$$xi. Our algorithm extends the alternating direction method of multipliers (ADMM) and decomposes the original problem into N smaller subproblems and solves them in parallel at each iteration. This paper shows that the classic ADMM can be extended to the N-block Jacobi fashion and preserve convergence in the following two cases: (i) matrices $$A_i$$Ai are mutually near-orthogonal and have full column-rank, or (ii) proximal terms are added to the N subproblems (but without any assumption on matrices $$A_i$$Ai). In the latter case, certain proximal terms can let the subproblem be solved in more flexible and efficient ways. We show that $$\\Vert {\\mathbf {x}}^{k+1} - {\\mathbf {x}}^k\\Vert _M^2$$źxk+1-xkźM2 converges at a rate of o(1 / k) where M is a symmetric positive semi-definte matrix. Since the parameters used in the convergence analysis are conservative, we introduce a strategy for automatically tuning the parameters to substantially accelerate our algorithm in practice. We implemented our algorithm (for the case ii above) on Amazon EC2 and tested it on basis pursuit problems with 300 GB of distributed data. This is the first time that successfully solving a compressive sensing problem of such a large scale is reported.

Wireless Powered Sensor Networks for Internet of Things: Maximum Throughput and Optimal Power Allocation. This paper investigates a wireless powered sensor network, where multiple sensor nodes are deployed to monitor a certain external environment. A multiantenna power station (PS) provides the power to these sensor nodes during wireless energy transfer phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center during wireless i...

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 new grouping genetic algorithm approach to the multiple traveling salesperson problem The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m 1 salespersons and n m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.

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.

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.

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].

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion. This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model ...

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.

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.

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.

Multi-column Deep Neural Networks for Image Classification Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.

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.

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.

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.

Roadmap-Based Path Planning - Using the Voronoi Diagram for a Clearance-Based Shortest Path Path planning still remains one of the core problems in modern robotic applications, such as the design of autonomous vehicles and perceptive systems. The basic path-planning problem is concerned with finding a good-quality path from a source point to a destination point that does not result in collision with any obstacles. In this article, we chose the roadmap approach and utilized the Voronoi diagram to obtain a path that is a close approximation of the shortest path satisfying the required clearance value set by the user. The advantage of the proposed technique versus alternative path-planning methods is in its simplicity, versatility, and efficiency.

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.

Development of a UAV-LiDAR System with Application to Forest Inventory We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m(2)) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points per m(2), to 0.15 m when the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.

Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm. In this investigation a new optimization algorithm named as interactive search algorithm (ISA) is presented. This method is developed through modifying and hybridizing the affirmative features of recently developed integrated particle swarm optimization (iPSO) algorithm with the pairwise knowledge sharing mechanism of the teaching and learning based optimization (TLBO) method. Proposed ISA provides two different navigation schemes as Tracking and Interacting. Each agent based on its tendency factor can pick one of these two schemes for searching the domain. Additionally, ISA utilizes an improved fly-back technique to handle problem constraints. The proposed method is tested on a set of mathematical and structural optimization benchmark problems with discrete and continuous variables. Numerical results indicate that the new algorithm is competitive with other well-stablished metaheuristic algorithms.

A parallel compact cuckoo search algorithm for three-dimensional path planning The three-dimensional (3D) path planning of unmanned robots focuses on avoiding collisions with obstacles and finding an optimized path to the target location in a complex three-dimensional environment. An improved cuckoo search algorithm based on compact and parallel techniques for three-dimensional path planning problems is proposed. This paper implements the compact cuckoo search algorithm, and then, a new parallel communication strategy is proposed. The compact scheme can effectively save the memory of the unmanned robot. The parallel scheme can increase the accuracy and achieve faster convergence. The proposed algorithm is tested on several selected functions and three-dimensional path planning. Results compared with other methods show that the proposed algorithm can provide more competitive results and achieve more efficient execution.

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.

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.

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.

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.

Reservoir computing approaches to recurrent neural network training Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir computing, greatly facilitated the practical application of RNNs and outperformed classical fully trained RNNs in many tasks. It has lately become a vivid research field with numerous extensions of the basic idea, including reservoir adaptation, thus broadening the initial paradigm to using different methods for training the reservoir and the readout. This review systematically surveys both current ways of generating/adapting the reservoirs and training different types of readouts. It offers a natural conceptual classification of the techniques, which transcends boundaries of the current “brand-names” of reservoir methods, and thus aims to help in unifying the field and providing the reader with a detailed “map” of it.

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.

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.

Quaternion polar harmonic Fourier moments for color images. •Quaternion polar harmonic Fourier moments (QPHFM) is proposed.•Complex Chebyshev-Fourier moments (CHFM) is extended to quaternion QCHFM.•Comparison experiments between QPHFM and QZM, QPZM, QOFMM, QCHFM and QRHFM are conducted.•QPHFM performs superbly in image reconstruction and invariant object recognition.•The importance of phase information of QPHFM in image reconstruction are discussed.

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.

Fixed-Time Adaptive Fuzzy Containment Dynamic Surface Control for Nonlinear Multiagent Systems This article investigates the fixed-time adaptive fuzzy containment control problem for nonlinear multiagent systems under the directed communication topologies. The controlled systems have the unknown internal dynamics and mismatched disturbances, and fuzzy logic systems are utilized to identify the unknown internal dynamics. The mismatched disturbances and approximate errors are reconstructed via a disturbance observer. Then, by introducing an adding power integral method, a fixed-time adaptive fuzzy containment DSC scheme is developed to deal with the problem of “computation complexity.” The presented containment control method can not only guarantee that the controlled system is semiglobal practical fixed-time stable, but also avoid the “singular problem” in fixed-time backstepping recursive control technology. Finally, an application of marine surface vehicle is provided to verify the effectiveness of the presented fixed-time fuzzy containment control 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.

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.

A global averaging method for dynamic time warping, with applications to clustering Mining sequential data is an old topic that has been revived in the last decade, due to the increasing availability of sequential datasets. Most works in this field are centred on the definition and use of a distance (or, at least, a similarity measure) between sequences of elements. A measure called dynamic time warping (DTW) seems to be currently the most relevant for a large panel of applications. This article is about the use of DTW in data mining algorithms, and focuses on the computation of an average of a set of sequences. Averaging is an essential tool for the analysis of data. For example, the K-means clustering algorithm repeatedly computes such an average, and needs to provide a description of the clusters it forms. Averaging is here a crucial step, which must be sound in order to make algorithms work accurately. When dealing with sequences, especially when sequences are compared with DTW, averaging is not a trivial task. Starting with existing techniques developed around DTW, the article suggests an analysis framework to classify averaging techniques. It then proceeds to study the two major questions lifted by the framework. First, we develop a global technique for averaging a set of sequences. This technique is original in that it avoids using iterative pairwise averaging. It is thus insensitive to ordering effects. Second, we describe a new strategy to reduce the length of the resulting average sequence. This has a favourable impact on performance, but also on the relevance of the results. Both aspects are evaluated on standard datasets, and the evaluation shows that they compare favourably with existing methods. The article ends by describing the use of averaging in clustering. The last section also introduces a new application domain, namely the analysis of satellite image time series, where data mining techniques provide an original approach.

Touch Is Everywhere: Floor Surfaces as Ambient Haptic Interfaces Floor surfaces are notable for the diverse roles that they play in our negotiation of everyday environments. Haptic communication via floor surfaces could enhance or enable many computer-supported activities that involve movement on foot. In this paper, we discuss potential applications of such interfaces in everyday environments and present a haptically augmented floor component through which several interaction methods are being evaluated. We describe two approaches to the design of structured vibrotactile signals for this device. The first is centered on a musical phrase metaphor, as employed in prior work on tactile display. The second is based upon the synthesis of rhythmic patterns of virtual physical impact transients. We report on an experiment in which participants were able to identify communication units that were constructed from these signals and displayed via a floor interface at well above chance levels. The results support the feasibility of tactile information display via such interfaces and provide further indications as to how to effectively design vibrotactile signals for them.

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.

Ambiotherm: Enhancing Sense of Presence in Virtual Reality by Simulating Real-World Environmental Conditions. In this paper, we present and evaluate Ambiotherm, a wearable accessory for Head Mounted Displays (HMD) that provides thermal and wind stimuli to simulate real-world environmental conditions, such as ambient temperatures and wind conditions, to enhance the sense of presence in Virtual Reality (VR). Ambiotherm consists of a Ambient Temperature Module that is attached to the user's neck, a Wind Simulation Module focused towards the user's face, and a Control Module utilizing Bluetooth communication. We demonstrate Ambiotherm with two VR environments, a hot desert, and a snowy mountain, to showcase the different types of simulated environmental conditions. We conduct several studies to 1) address design factors of the system and 2) evaluate Ambiotherm's effect on factors related to a user's sense of presence. Our findings show that the addition of wind and thermal stimuli significantly improves sensory and realism factors, contributing towards an enhanced sense of presence when compared to traditional VR experiences.

Adding Proprioceptive Feedback to Virtual Reality Experiences Using Galvanic Vestibular Stimulation. We present a small and lightweight wearable device that enhances virtual reality experiences and reduces cybersickness by means of galvanic vestibular stimulation (GVS). GVS is a specific way to elicit vestibular reflexes that has been used for over a century to study the function of the vestibular system. In addition to GVS, we support physiological sensing by connecting heart rate, electrodermal activity and other sensors to our wearable device using a plug and play mechanism. An accompanying Android app communicates with the device over Bluetooth (BLE) for transmitting the GVS stimulus to the user through electrodes attached behind the ears. Our system supports multiple categories of virtual reality applications with different types of virtual motion such as driving, navigating by flying, teleporting, or riding. We present a user study in which participants (N = 20) experienced significantly lower cybersickness when using our device and rated experiences with GVS-induced haptic feedback as significantly more immersive than a no-GVS baseline.

Improvement of olfactory display using solenoid valves The research on olfactory sense in virtual reality has gradually expanded even though the technology is still premature. We have developed an olfactory display composed of multiple solenoid valves. In the present study, an extended olfactory display, where 32 component odors can be blended in any recipe, is described; the previous version has only 8 odor components. The size was unchanged even though the number of odor components was four times larger than that in the previous display. The complexity of blending was greatly reduced because of algorithm improvement. The blending method and the fundamental experiment using a QCM (quartz crystal microbalance) sensor are described here

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.

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.

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.

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.

A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks. Data collection through mobile sink (MS) in wireless sensor networks (WSNs) is an effective solution to the hot-spot or sink-hole problem caused by multi-hop routing using the static sink. Rendezvous point (RP) based MS path design is a common and popular technique used in this regard. However, design of the optimal path is a well-known NP-hard problem. Therefore, an evolutionary approach like multi-objective particle swarm optimization (MOPSO) can prove to be a very promising and reasonable approach to solve the same. In this paper, we first present a Linear Programming formulation for the stated problem and then, propose an MOPSO-based algorithm to design an energy efficient trajectory for the MS. The algorithm is presented with an efficient particle encoding scheme and derivation of a proficient multi-objective fitness function. We use Pareto dominance in MOPSO for obtaining both local and global best guides for each particle. We carry out rigorous simulation experiments on the proposed algorithm and compare the results with two existing algorithms namely, tree cluster based data gathering algorithm (TCBDGA) and energy aware sink relocation (EASR). The results demonstrate that the proposed algorithm performs better than both of them in terms of various performance metrics. The results are also validated through the statistical test, analysis of variance (ANOVA) and its least significant difference (LSD) post hoc analysis.

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.

Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably lose the edge information, which contains spatial features of vessels while performing down-sampling, leading to the limited segmentation performance of fine blood vessels. Furthermore, the existing methods ignore the dynamic topological correlations among feature maps in the deep learning framework, resulting in the inefficient capture of the channel characterization. To address these limitations, we propose a novel dual encoder-based dynamic-channel graph convolutional network with edge enhancement (DE-DCGCN-EE) for retinal vessel segmentation. Specifically, we first design an edge detection-based dual encoder to preserve the edge of vessels in down-sampling. Secondly, we investigate a dynamic-channel graph convolutional network to map the image channels to the topological space and synthesize the features of each channel on the topological map, which solves the limitation of insufficient channel information utilization. Finally, we study an edge enhancement block, aiming to fuse the edge and spatial features in the dual encoder, which is beneficial to improve the accuracy of fine blood vessel segmentation. Competitive experimental results on five retinal image datasets validate the efficacy of the proposed DE-DCGCN-EE, which achieves more remarkable segmentation results against the other state-of-the-art methods, indicating its potential clinical application.

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.

Application of motor algebra to the analysis of human arm movements Motor algebra, a 4D degenerate geometric algebra, offers a rigorous yet simple representation of the 3D velocity of a rigid body. Using this representation, we study 3D extended arm pointing and reaching movements. We analyze the choice of arm orientation about the vector connecting the shoulder and the wrist, in cases for which this orientation is not prescribed by the task. Our findings show that the changes in this orientation throughout the movement were very small, possibly indicating an underlying motion planning strategy. We additionally examine the decomposition of movements into submovements and reconstruct the motion by assuming superposition of the velocity profiles of the underlying submovements by analyzing both the translational and rotational components of the 3D spatial velocity. This movement decomposition method reveals a larger number of submovement than is found using previously applied submovement extraction methods that are based only on the analysis of the hand tangential velocity. The reconstructed velocity profiles and final orientations are relatively close to the actual values, indicating that single-axis submovements may be the basic building blocks underlying 3D movement construction.

Parametrization and Range of Motion of the Ball-and-Socket Joint The ball-and-socket joint model is used to represent articulations with three rotational degrees of free- dom (DOF), such as the human shoulder and the hip. The goal of this paper is to discuss two related prob- lems: the parametrization and the definition of realistic joint boundaries for ball-and-socket joints. Doing this accurately is difficult, yet important for motion generators (such as inverse kinematics and dynamics engines) and for motion manipulators (such as motion retargeting), since the resulting motions should satisfy the anatomic constraints. The difficulty mainly comes from the complex nature of 3D orientations and of human articulations. The underlying question of parametrization must be addressed before realis- tic and meaningful boundaries can be defined over the set of 3D orientations. In this paper, we review and compare several known methods, and advocate the use of the swing-and-twist parametrization, that parti- tions an arbitrary orientation into two meaningful components. The related problem of induced twist is discussed. Finally, we review some joint boundaries representations based on this decomposition, and show an example.

Positional kinematics of humanoid arms We present the positional abilities of a humanoid manipulator based on an improved kinematical model of the human arm. This was synthesized from electro-optical measurements of healthy female and male subjects. The model possesses three joints: inner shoulder joint, outer shoulder joint and elbow joint. The first functions as the human sternoclavicular joint, the second functions as the human glenohumeral joint, and the last replicates the human humeroulnar rotation. There are three links included, the forearm and the upper arm link which are of a constant length, and the shoulder link which is expandable. Mathematical interrelations between the joint coordinates are also taken into consideration. We determined the reachability of a humanoid arm, treated its orienting redundancy in the shoulder complex and the positional redundancy in the shoulder-elbow complexes, and discussed optimum configurations in executing different tasks. The results are important for the design and control of humanoid robots, in medicine and sports.

Design of a Bio-Inspired Wearable Exoskeleton for Applications in Robotics In this paper we explain the methodology we adopted to design the kinematics structure of a multi-contact points haptic interface. We based our concept on the analysis of the human arm anatomy and kinematics with the intend to synthesize a system that will be able to interface with the human limb in a very natural way. We proposed a simplified kinematic model of the human arm using a notation coming from the robotics field. To find out the best kinematics architecture we employed real movement data, measured from a human subject, and integrated them with the kinematic model of the exoskeleton, this allow us to test the system before its construction and to formalize specific requirements. We also implemented and tested a first passive version of the shoulder joint.

Reliability and precision of 3D wireless measurement of scapular kinematics. To direct interventions aimed at improving scapular position and motion in shoulder pathologies, a clinically feasible, objective, sensitive and reliable assessment of scapular dyskinesis is needed. The aim of this study is to evaluate the intra- and inter-observer reliability and the precision of 3D scapula kinematics measurement using wireless sensors of an inertial and magnetic measurement system (IMMS). Scapular kinematics during humerus anteflexion and abduction of 20 subjects without shoulder pathologies were measured twice by two observers at two different days, using IMMS. Similar movement patterns and corresponding high intraclass correlation coefficients were found within (intra) and between (inter) observers, especially for scapular retraction/protraction (0.65–0.85) and medio/lateral rotation (0.56–0.91). Lowest reliability and highest difference in range of motion were observed for anterior/posterior tilt. Medio/lateral rotation and anterior/posterior tilt showed a high precision, with standard error of measurement being mostly below 5°. The inter-observer measurements of retraction/protraction showed lowest precision, reflected in systematic differences. This is caused by an offset in anatomical calibration of the sensors. IMMS enables easy and objective measurement of 3D scapula kinematics. Further research in a patient population should focus on clinical feasibility and validity for measurement of scapular dyskinesis. This would include the application of a scapula locator to enhance anatomical calibration.

Analytical Inverse Kinematics Solver for Anthropomorphic 7-DOF Redundant Manipulators with Human-Like Configuration Constraints. It is a common belief that service robots shall move in a human-like manner to enable natural and convenient interaction with a human user or collaborator. In particular, this applies to anthropomorphic 7-DOF redundant robot manipulators that have a shoulder-elbow-wrist configuration. On the kinematic level, human-like movement then can be realized by means of selecting a redundancy resolution for the inverse kinematics (IK), which realizes human-like movement through respective nullspace preferences. In this paper, key positions are introduced and defined as Cartesian positions of the manipulator's elbow and wrist joints. The key positions are used as constraints on the inverse kinematics in addition to orientation constraints at the end-effector, such that the inverse kinematics can be calculated through an efficient analytical scheme and realizes human-like configurations. To obtain suitable key positions, a correspondence method named wrist-elbow-in-line is derived to map key positions of human demonstrations to the real robot for obtaining a valid analytical inverse kinematics solution. A human demonstration tracking experiment is conducted to evaluate the end-effector accuracy and human-likeness of the generated motion for a 7-DOF Kuka-LWR arm. The results are compared to a similar correspondance method that emphasizes only the wrist postion and show that the subtle differences between the two different correspondence methods may lead to significant performance differences. Furthermore, the wrist-elbow-in-line method is validated as more stable in practical application and extended for obstacle avoidance.

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 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.

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.

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 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.

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...

Doa-Gan: Dual-Order Attentive Generative Adversarial Network For Image Copy-Move Forgery Detection And Localization Images can be manipulated for nefarious purposes to hide content or to duplicate certain objects through copy-move operations. Discovering a well-crafted copy-move forgery in images can be very challenging for both humans and machines; for example, an object on a uniform background can be replaced by an image patch of the same background. In this paper, we propose a Generative Adversarial Network with a dual-order attention model to detect and localize copy-move forgeries. In the generator, the first-order attention is designed to capture copy-move location information, and the second-order attention exploits more discriminative features for the patch co-occurrence. Both attention maps are extracted from the affinity matrix and are used to fuse location-aware and co-occurrence features for the final detection and localization branches of the network. The discriminator network is designed to further ensure more accurate localization results. To the best of our knowledge, we are the first to propose such a network architecture with the 1st-order attention mechanism from the affinity matrix. We have performed extensive experimental validation and our state-of-the-art results strongly demonstrate the efficacy of the proposed approach.

Movie2Comics: Towards a Lively Video Content Presentation a type of artwork, comics is prevalent and popular around the world. However, despite the availability of assistive software and tools, the creation of comics is still a labor-intensive and time-consuming process. This paper proposes a scheme that is able to automatically turn a movie clip to comics. Two principles are followed in the scheme: 1) optimizing the information preservation of the movie; and 2) generating outputs following the rules and the styles of comics. The scheme mainly contains three components: script-face mapping, descriptive picture extraction, and cartoonization. The script-face mapping utilizes face tracking and recognition techniques to accomplish the mapping between characters' faces and their scripts. The descriptive picture extraction then generates a sequence of frames for presentation. Finally, the cartoonization is accomplished via three steps: panel scaling, stylization, and comics layout design. Experiments are conducted on a set of movie clips and the results have demonstrated the usefulness and the effectiveness of the scheme.

View-Based Discriminative Probabilistic Modeling for 3D Object Retrieval and Recognition In view-based 3D object retrieval and recognition, each object is described by multiple views. A central problem is how to estimate the distance between two objects. Most conventional methods integrate the distances of view pairs across two objects as an estimation of their distance. In this paper, we propose a discriminative probabilistic object modeling approach. It builds probabilistic models for each object based on the distribution of its views, and the distance between two objects is defined as the upper bound of the Kullback–Leibler divergence of the corresponding probabilistic models. 3D object retrieval and recognition is accomplished based on the distance measures. We first learn models for each object by the adaptation from a set of global models with a maximum likelihood principle. A further adaption step is then performed to enhance the discriminative ability of the models. We conduct experiments on the ETH 3D object dataset, the National Taiwan University 3D model dataset, and the Princeton Shape Benchmark. We compare our approach with different methods, and experimental results demonstrate the superiority of our approach.

DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting Most existing CNN-based methods for crowd counting always suffer from large scale variation in objects of interest, leading to density maps of low quality. In this paper, we propose a novel deep model called Dilated-Attention-Deformable ConvNet (DADNet), which consists of two schemes: multi-scale dilated attention and deformable convolutional DME (Density Map Estimation). The proposed model explores a scale-aware attention fusion with various dilation rates to capture different visual granularities of crowd regions of interest, and utilizes deformable convolutions to generate a high-quality density map. There are two merits as follows: (1) varying dilation rates can effectively identify discriminative regions by enlarging the receptive fields of convolutional kernels upon surrounding region cues, and (2) deformable CNN operations promote the accuracy of object localization in the density map by augmenting the spatial object location sampling with adaptive offsets and scalars. DADNet not only excels at capturing rich spatial context of salient and tiny regions of interest simultaneously, but also keeps a robustness to background noises, such as partially occluded objects. Extensive experiments on benchmark datasets verify that DADNet achieves the state-of-the-art performance. Visualization results of the multi-scale attention maps further validate the remarkable interpretability achieved by our solution.

Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the data. Missing data imputation has been a long-standing research topic and critical application for real-world intelligent transportation systems. A widely applied imputation method is low-rank matrix/tensor completion; however, the low-rank assumption only preserves the global structure while ignores the strong local consistency in spatiotemporal data. In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing temporal variation as a new regularization term into the completion of a third-order (sensor x time of day x day) tensor. The third-order tensor structure allows us to better capture the global consistency of traffic data, such as the inherent seasonality and day-to-day similarity. To achieve local consistency, we design the temporal variation by imposing an autoregressive model for each time series with coefficients as learnable parameters. Different from previous spatial and temporal regularization schemes, the minimization of temporal variation can better characterize temporal generative mechanisms beyond local smoothness, allowing us to deal with more challenging scenarios such as ``blackout'' missing. To solve the optimization problem in LATC, we introduce an alternating minimization scheme that estimates the low-rank tensor and autoregressive coefficients iteratively. We conduct extensive numerical experiments on several real-world traffic data sets, and our results demonstrate the effectiveness of LATC in diverse missing scenarios.

A deep learning approach to patch-based image inpainting forensics. Although image inpainting is now an effective image editing technique, limited work has been done for inpainting forensics. The main drawbacks of the conventional inpainting forensics methods lie in the difficulties on inpainting feature extraction and the very high computational cost. In this paper, we propose a novel approach based on a convolutional neural network (CNN) to detect patch-based inpainting operation. Specifically, the CNN is built following the encoder–decoder network structure, which allows us to predict the inpainting probability for each pixel in an image. To guide the CNN to automatically learn the inpainting features, a label matrix is generated for the CNN training by assigning a class label for each pixel of an image, and the designed weighted cross-entropy serves as the loss function. They further help to strongly supervise the CNN to capture the manipulation information rather than the image content features. By the established CNN, inpainting forensics does not need to consider feature extraction and classifier design, and use any postprocessing as in conventional forensics methods. They are combined into the unique framework and optimized simultaneously. Experimental results show that the proposed method achieves superior performance in terms of true positive rate, false positive rate and the running time, as compared with state-of-the-art methods for inpainting forensics, and is very robust against JPEG compression and scaling manipulations.

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 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.

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.

A Comparative Study of Distributed Learning Environments on Learning Outcomes Advances in information and communication technologies have fueled rapid growth in the popularity of technology-supported distributed learning (DL). Many educational institutions, both academic and corporate, have undertaken initiatives that leverage the myriad of available DL technologies. Despite their rapid growth in popularity, however, alternative technologies for DL are seldom systematically evaluated for learning efficacy. Considering the increasing range of information and communication technologies available for the development of DL environments, we believe it is paramount for studies to compare the relative learning outcomes of various technologies.In this research, we employed a quasi-experimental field study approach to investigate the relative learning effectiveness of two collaborative DL environments in the context of an executive development program. We also adopted a framework of hierarchical characteristics of group support system (GSS) technologies, outlined by DeSanctis and Gallupe (1987), as the basis for characterizing the two DL environments.One DL environment employed a simple e-mail and listserv capability while the other used a sophisticated GSS (herein referred to as Beta system). Interestingly, the learning outcome of the e-mail environment was higher than the learning outcome of the more sophisticated GSS environment. The post-hoc analysis of the electronic messages indicated that the students in groups using the e-mail system exchanged a higher percentage of messages related to the learning task. The Beta system users exchanged a higher level of technology sense-making messages. No significant difference was observed in the students' satisfaction with the learning process under the two DL environments.

A Framework of Joint Mobile Energy Replenishment and Data Gathering in Wireless Rechargeable Sensor Networks Recent years have witnessed the rapid development and proliferation of techniques on improving energy efficiency for wireless sensor networks. Although these techniques can relieve the energy constraint on wireless sensors to some extent, the lifetime of wireless sensor networks is still limited by sensor batteries. Recent studies have shown that energy rechargeable sensors have the potential to provide perpetual network operations by capturing renewable energy from external environments. However, the low output of energy capturing devices can only provide intermittent recharging opportunities to support low-rate data services due to spatial-temporal, geographical or environmental factors. To provide steady and high recharging rates and achieve energy efficient data gathering from sensors, in this paper, we propose to utilize mobility for joint energy replenishment and data gathering. In particular, a multi-functional mobile entity, called SenCarin this paper, is employed, which serves not only as a mobile data collector that roams over the field to gather data via short-range communication but also as an energy transporter that charges static sensors on its migration tour via wireless energy transmissions. Taking advantages of SenCar's controlled mobility, we focus on the joint optimization of effective energy charging and high-performance data collections. We first study this problem in general networks with random topologies. We give a two-step approach for the joint design. In the first step, the locations of a subset of sensors are periodically selected as anchor points, where the SenCar will sequentially visit to charge the sensors at these locations and gather data from nearby sensors in a multi-hop fashion. To achieve a desirable balance between energy replenishment amount and data gathering latency, we provide a selection algorithm to search for a maximum number of anchor points where sensors hold the least battery energy, and meanwhile by visiting them, - he tour length of the SenCar is no more than a threshold. In the second step, we consider data gathering performance when the SenCar migrates among these anchor points. We formulate the problem into a network utility maximization problem and propose a distributed algorithm to adjust data rates at which sensors send buffered data to the SenCar, link scheduling and flow routing so as to adapt to the up-to-date energy replenishing status of sensors. Besides general networks, we also study a special scenario where sensors are regularly deployed. For this case we can provide a simplified solution of lower complexity by exploiting the symmetry of the topology. Finally, we validate the effectiveness of our approaches by extensive numerical results, which show that our solutions can achieve perpetual network operations and provide high network utility.

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.

Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning In Federated edge learning (FEEL), energy-constrained devices at the network edge consume significant energy when training and uploading their local machine learning models, leading to a decrease in their lifetime. This work proposes novel solutions for energy-efficient FEEL by jointly considering local training data, available computation, and communications resources, and deadline constraints of FEEL rounds to reduce energy consumption. This paper considers a system model where the edge server is equipped with multiple antennas employing beamforming techniques to communicate with the local users through orthogonal channels. Specifically, we consider a problem that aims to find the optimal user's resources, including the fine-grained selection of relevant training samples, bandwidth, transmission power, beamforming weights, and processing speed with the goal of minimizing the total energy consumption given a deadline constraint on the communication rounds of FEEL. Then, we devise tractable solutions by first proposing a novel fine-grained training algorithm that excludes less relevant training samples and effectively chooses only the samples that improve the model's performance. After that, we derive closed-form solutions, followed by a Golden-Section-based iterative algorithm to find the optimal computation and communication resources that minimize energy consumption. Experiments using MNIST and CIFAR-10 datasets demonstrate that our proposed algorithms considerably outperform the state-of-the-art solutions as energy consumption decreases by 79% for MNIST and 73% for CIFAR-10 datasets.

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.

ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents. While AV development today involves verification, validation, and testing, end-to-end assessment of AV systems under accidental faults in realistic driving scenarios has been largely unexplored. This paper presents DriveFI, a machine learning-based fault injection engine, which can mine situations and faults that maximally impact AV safety, as demonstrated on two industry-grade AV technology stacks (from NVIDIA and Baidu). For example, DriveFI found 561 safety-critical faults in less than 4 hours. In comparison, random injection experiments executed over several weeks could not find any safety-critical faults.

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...

Real-Time PEV Charging/Discharging Coordination in Smart Distribution Systems. This paper proposes a novel online coordination method for the charging of plug-in electric vehicles (PEVs) in smart distribution networks. The goal of the proposed method is to optimally charge the PEVs in order to maximize the PEV owners&#39; satisfaction and to minimize system operating costs without violating power system constraints. Unlike the solutions reported in the literature, the proposed c...

Competitive on-line scheduling with level of service Motivated by an application in thinwire visualization, we study an abstract on-line scheduling problem where the size of each requested service can be scaled down by the scheduler. Thus, our problem embodies a notion of "Level of Service" that is increasingly important in multimedia applications. We give two schedulers FirstFit and EndFit based on two simple heuristics, and generalize them into a class of greedy schedulers. We show that both FirstFit and EndFit are 2-competitive, and any greedy scheduler is 3-competitive. These bounds are shown to be tight.

Coordinated Charging of Electric Vehicles for Congestion Prevention in the Distribution Grid Distributed energy resources (DERs), like electric vehicles (EVs), can offer valuable services to power systems, such as enabling renewable energy to the electricity producer and providing ancillary services to the system operator. However, these new DERs may challenge the distribution grid due to insufficient capacity in peak hours. This paper aims to coordinate the valuable services and operation constraints of three actors: the EV owner, the Fleet operator (FO) and the Distribution system operator (DSO), considering the individual EV owner's driving requirement, the charging cost of EV and thermal limits of cables and transformers in the proposed market framework. Firstly, a theoretical market framework is described. Within this framework, FOs who represent their customer's (EV owners) interests will centrally guarantee the EV owners' driving requirements and procure the energy for their vehicles with lower cost. The congestion problem will be solved by a coordination between DSO and FOs through a distribution grid capacity market scheme. Then, a mathematical formulation of the market scheme is presented. Further, some case studies are shown to illustrate the effectiveness of the proposed solutions.

Auc2Charge: An Online Auction Framework for Eectric Vehicle Park-and-Charge The increasing market share of electric vehicles (EVs) makes large-scale charging stations indispensable infrastructure for integrating EVs into the future smart grid. Thus their operation modes have drawn great attention from researchers. One promising mode called park-and-charge was recently proposed. It allows people to park their EVs at a parking lot, where EVs can get charged during the parking time. This mode has been experimented and demonstrated in small scale. However, the missing of an efficient market mechanism is an important gap preventing its large-scale deployment. Existing pricing policies, e.g., pay-by-use and flat-rate pricing, would jeopardize the efficiency of electricity allocation and the corresponding social welfare in the park-and-charge mode, and thus are inapplicable. To find an efficient mechanism, this paper explores the feasibility and benefits of utilizing auction mechanism in the EV park-and-charge mode. The auction allows EV users to submit and update bids on their charging demand to the charging station, which makes corresponding electricity allocation and pricing decisions. To this end, we propose Auc2Charge, an online auction framework. Auc2Charge is truthful and individual rational. Running in polynomial time, it provides an efficient electricity allocation for EV users with a close-form approximation ratio on system social welfare. Through both theoretical analysis and numerical simulation, we demonstrate the efficacy of Auc2Charge in terms of social welfare and user satisfaction.

Decentralized Plug-in Electric Vehicle Charging Selection Algorithm in Power Systems This paper uses a charging selection concept for plug-in electric vehicles (PEVs) to maximize user convenience levels while meeting predefined circuit-level demand limits. The optimal PEV-charging selection problem requires an exhaustive search for all possible combinations of PEVs in a power system, which cannot be solved for the practical number of PEVs. Inspired by the efficiency of the convex relaxation optimization tool in finding close-to-optimal results in huge search spaces, this paper proposes the application of the convex relaxation optimization method to solve the PEV-charging selection problem. Compared with the results of the uncontrolled case, the simulated results indicate that the proposed PEV-charging selection algorithm only slightly reduces user convenience levels, but significantly mitigates the impact of the PEV-charging on the power system. We also develop a distributed optimization algorithm to solve the PEV-charging selection problem in a decentralized manner, i.e., the binary charging decisions (charged or not charged) are made locally by each vehicle. Using the proposed distributed optimization algorithm, each vehicle is only required to report its power demand rather than report several of its private user state information, mitigating the security problems inherent in such problem. The proposed decentralized algorithm only requires low-speed communication capability, making it suitable for real-time implementation.

Online Coordinated Charging Decision Algorithm for Electric Vehicles Without Future Information The large-scale integration of plug-in electric vehicles (PEVs) to the power grid spurs the need for efficient charging coordination mechanisms. It can be shown that the optimal charging schedule smooths out the energy consumption over time so as to minimize the total energy cost. In practice, however, it is hard to smooth out the energy consumption perfectly, because the future PEV charging demand is unknown at the moment when the charging rate of an existing PEV needs to be determined. In this paper, we propose an online coordinated charging decision (ORCHARD) algorithm, which minimizes the energy cost without knowing the future information. Through rigorous proof, we show that ORCHARD is strictly feasible in the sense that it guarantees to fulfill all charging demands before due time. Meanwhile, it achieves the best known competitive ratio of 2.39. By exploiting the problem structure, we propose a novel reduced-complexity algorithm to replace the standard convex optimization techniques used in ORCHARD. Through extensive simulations, we show that the average performance gap between ORCHARD and the offline optimal solution, which utilizes the complete future information, is as small as 6.5%. By setting a proper speeding factor, the average performance gap can be further reduced to 5%.

Random Forests Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, &ast;&ast;&ast;, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

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.

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.

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.

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 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.

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.

Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity Smart home appliances can take command and act intelligently, making them suitable for implementing optimization techniques. Artificial intelligence (AI) based control of these smart devices enables demand-side management (DSM) of electricity consumption. By integrating human feedback and activity in the decision process, this work proposes a deep Reinforcement Learning (RL) method for managing smart devices to optimize electricity cost and comfort residents. Our contributions are twofold. Firstly, we incorporate human feedback in the objective function of our DSM technique that we name Home Energy Recommendation System (HERS). Secondly, we include human activity data in the RL state definition to enhance the energy optimization performance. We perform comprehensive experimental analyses to compare the proposed deep RL approach with existing approaches that lack the aforementioned critical decision-making features. The proposed model is robust to varying resident activities and preferences and applicable to a broad spectrum of homes with different resident profiles.

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.

A Novel Efficient Remote Data Possession Checking Protocol in Cloud Storage. As an important application in cloud computing, cloud storage offers user scalable, flexible, and high-quality data storage and computation services. A growing number of data owners choose to outsource data files to the cloud. Because cloud storage servers are not fully trustworthy, data owners need dependable means to check the possession for their files outsourced to remote cloud servers. To add...

Witness indistinguishable and witness hiding protocols

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).

Publicly Verifiable Computation of Polynomials Over Outsourced Data With Multiple Sources. Among all types of computations, the polynomial function evaluation is a fundamental, yet an important one due to its wide usage in the engineering and scientific problems. In this paper, we investigate publicly verifiable outsourced computation for polynomial evaluation with the support of multiple data sources. Our proposed verification scheme is universally applicable to all types of polynomial...

Betrayal, Distrust, and Rationality: Smart Counter-Collusion Contracts for Verifiable Cloud Computing. Cloud computing has become an irreversible trend. Together comes the pressing need for verifiability, to assure the client the correctness of computation outsourced to the cloud. Existing verifiable computation techniques all have a high overhead, thus if being deployed in the clouds, would render cloud computing more expensive than the on-premises counterpart. To achieve verifiability at a reasonable cost, we leverage game theory and propose a smart contract based solution. In a nutshell, a client lets two clouds compute the same task, and uses smart contracts to stimulate tension, betrayal and distrust between the clouds, so that rational clouds will not collude and cheat. In the absence of collusion, verification of correctness can be done easily by crosschecking the results from the two clouds. We provide a formal analysis of the games induced by the contracts, and prove that the contracts will be effective under certain reasonable assumptions. By resorting to game theory and smart contracts, we are able to avoid heavy cryptographic protocols. The client only needs to pay two clouds to compute in the clear, and a small transaction fee to use the smart contracts. We also conducted a feasibility study that involves implementing the contracts in Solidity and running them on the official Ethereum network.

Cloud-aided lightweight certificateless authentication protocol with anonymity for wireless body area networks. With the development of cloud computing and wireless body area networks (WBANs), wearable equipments are able to become new intelligent terminals to provide services for users, which plays an important role to improve the human health-care service. However, The traditional WBANs devices have limited computing and storage capabilities. These restrictions limit the services that WBANs can provide to users. Thus the concept of Cloud-aided WBANs has been proposed to enhance the capabilities of WBANs. In addition, due to the openness of the cloud computing environment, the protection of the user's physiological information and privacy remains a major concern. In previous authentication protocols, few of them can protect the user's private information in insecure channel. In this paper, we propose a cloud-aided lightweight certificateless authentication protocol with anonymity for wireless body area networks. Our protocol ensures that no one can obtain user's real identity except for the network manager in the registration phase. Moreover, in the authentication phase, the network manager cannot know the user's real identity. Note that, through the security analysis, we can conclude that our protocol can provide stronger security protection of private information than most of existing schemes in insecure channel.

Blockchain based efficient and robust fair payment for outsourcing services in cloud computing. •A blockchain based fair payment framework BCPay for cloud services is proposed.•An all-or-nothing checking-proof protocol is introduced in BCPay.•BCPay enjoys soundness and robust fairness without relying on any third-party.•BCPay is very efficient in terms of the number of transactions and computation cost.•We present the applications of BCPay in PDP and outsourcing computation.

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 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.

Grey Wolf Optimizer. This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.

Development of a UAV-LiDAR System with Application to Forest Inventory We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m(2)) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points per m(2), to 0.15 m when the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.

A review on interval type-2 fuzzy logic applications in intelligent control. A review of the applications of interval type-2 fuzzy logic in intelligent control has been considered in this paper. The fundamental focus of the paper is based on the basic reasons for using 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.

Neural network adaptive tracking control for a class of uncertain switched nonlinear systems. •Study the method of the tracking control of the switched uncertain nonlinear systems under arbitrary switching signal controller.•A multilayer neural network adaptive controller with multilayer weight norm adaptive estimation is been designed.•The adaptive law is expand from calculation the second layer weight of neural network to both of the two layers weight.•The controller proposed improve the tracking error performance of the closed-loop system greatly.

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.

FISS GAN: A Generative Adversarial Network for Foggy Image Semantic Segmentation Because pixel values of foggy images are irregularly higher than those of images captured in normal weather (clear images), it is difficult to extract and express their texture. No method has previously been developed to directly explore the relationship between foggy images and semantic segmentation images. We investigated this relationship and propose a generative adversarial network (GAN) for f...

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.

Intelligence Testing for Autonomous Vehicles: A New Approach. In this paper, we study how to test the intelligence of an autonomous vehicle. Comprehensive testing is crucial to both vehicle manufactories and customers. Existing testing approaches can be categorized into two kinds: scenario-based testing and functionality-based testing. We first discuss the shortcomings of these two kinds of approaches, and then propose a new testing framework to combine the ...

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...

A Review Of Computer Graphics Approaches To Urban Modeling From A Machine Learning Perspective Urban modeling facilitates the generation of virtual environments for various scenarios about cities. It requires expertise and consideration, and therefore consumes massive time and computation resources. Nevertheless, related tasks sometimes result in dissatisfaction or even failure. These challenges have received significant attention from researchers in the area of computer graphics. Meanwhile, the burgeoning development of artificial intelligence motivates people to exploit machine learning, and hence improves the conventional solutions. In this paper, we present a review of approaches to urban modeling in computer graphics using machine learning in the literature published between 2010 and 2019. This serves as an overview of the current state of research on urban modeling from a machine learning perspective.

Dynamic Trajectory Planning for Vehicle Autonomous Driving Trajectory planning is one of the key and challenging tasks in autonomous driving. This paper proposes a novel method that dynamically plans trajectories, with the aim to achieve quick and safe reaction to the changing driving environment and optimal balance between vehicle performance and driving comfort. With the proposed method, such complex maneuvers can be decomposed into two sub-maneuvers, i.e., lane change and lane keeping, or their combinations, such that the trajectory planning is generalized and simplified, mainly based on lane change maneuvers. A two fold optimization-based method is proposed for stationary trajectory planning as well as dynamic trajectory planning in the presence of a dynamic traffic environment. Simulation is conducted to demonstrate the efficiency and effectiveness of the proposed method.

DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems. While Deep Neural Networks (DNNs) have established the fundamentals of image-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To address the safety issues in autonomous driving systems, a recent set of testing techniques have been designed to automatically generate artificial driving scenes to enrich test suite, e.g., generating new input images transformed from the original ones. However, these techniques are insufficient due to two limitations: first, many such synthetic images often lack diversity of driving scenes, and hence compromise the resulting efficacy and reliability. Second, for machine-learning-based systems, a mismatch between training and application domain can dramatically degrade system accuracy, such that it is necessary to validate inputs for improving system robustness. In this paper, we propose DeepRoad, an unsupervised DNN-based framework for automatically testing the consistency of DNN-based autonomous driving systems and online validation. First, DeepRoad automatically synthesizes large amounts of diverse driving scenes without using image transformation rules (e.g. scale, shear and rotation). In particular, DeepRoad is able to produce driving scenes with various weather conditions (including those with rather extreme conditions) by applying Generative Adversarial Networks (GANs) along with the corresponding real-world weather scenes. Second, DeepRoad utilizes metamorphic testing techniques to check the consistency of such systems using synthetic images. Third, DeepRoad validates input images for DNN-based systems by measuring the distance of the input and training images using their VGGNet features. We implement DeepRoad to test three well-recognized DNN-based autonomous driving systems in Udacity self-driving car challenge. The experimental results demonstrate that DeepRoad can detect thousands of inconsistent behaviors for these systems, and effectively validate input images to potentially enhance the system robustness as well.

Testing Scenario Library Generation for Connected and Automated Vehicles: An Adaptive Framework How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this article, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate for the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a newly designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study is investigated and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.

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.

Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. Fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions. Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy. Based on the fuzzy basis function representations, an orthogonal least-squares (OLS) learning algorithm is developed for designing fuzzy systems based on given input-output pairs; then, the OLS algorithm is used to select significant fuzzy basis functions which are used to construct the final fuzzy system. The fuzzy basis function expansion is used to approximate a controller for the nonlinear ball and beam system, and the simulation results show that the control performance is improved by incorporating some common-sense fuzzy control rules.

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations.

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.

Progressive Image Enhancement under Aesthetic Guidance. Most existing image enhancement methods function like a black box, which cannot clearly reveal the procedure behind each image enhancement operation. To overcome this limitation, in this paper, we design a progressive image enhancement framework, which generates an expected "good" retouched image with a group of self-interpretable image filters under the guidance of an aesthetic assessment model. The introduced aesthetic network effectively alleviates the shortage of paired training samples by providing extra supervision, and eliminate the bias caused by human subjective preferences. The self-interpretable image filters designed in our image enhancement framework, make the overall image enhancing procedure easy-to-understand. Extensive experiments demonstrate the effectiveness of our proposed framework.

Enhancing fault tolerance in vehicular ad-hoc networks using artificial bee colony algorithm-based spanning trees Providing efficient unicast communication is a crucial challenge in Vehicular Ad-hoc Networks (VANETs). Road-Side Unit (RSU) guarantees unicast communication by constructing the spanning tree among vehicles. Recent papers proposed artificial intelligence-based algorithms for constructing a group of spanning trees in VANETs to deal with the failure of nodes and fast-moving vehicles. The algorithms consider the Euclidean distance between vehicles as a weight function. In such approaches, it is possible for a common non-leaf vehicle in all obtained spanning trees to become unavailable; the spanning trees of the VANETs become paralyzed. To address this challenge, in this paper, a two-phase near-optimal spanning tree contraction in the RSU that is named Fault Tolerance near-optimal Spanning Trees (FTST) is proposed. In the FTST, first, the Multi-objective Artificial Bee Colony (MABC) algorithm is used to construct a spanning tree for the input VANET’s graph with the near-minimum weight and the maximum number of leaves. Then, the second phase of the FTST tries to construct a near-minimum spanning tree with the maximum number of leaves so that the first step spanning tree’s non-leaves can leave off. Implementation results demonstrate the FTST will be suitable for VANET’s applications by improving the fault tolerance of the network and reducing the injected traffic into it.

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.

MU-GAN: Facial Attribute Editing based on Multi-attention Mechanism Facial attribute editing has mainly two objectives: 1) translating image from a source domain to a target one, and 2) only changing the facial regions related to a target attribute and preserving the attribute-excluding details. In this work, we propose a multi-attention U-Net-based generative adversarial network (MU-GAN). First, we replace a classic convolutional encoder-decoder with a symmetric ...

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...

ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation Recognizing and locating objects by algorithms are essential and challenging issues for Intelligent Transportation Systems. However, the increasing demand for much labeled data hinders the further application of deep learning-based object detection. One of the optimal solutions is to train the target model with an existing dataset and then adapt it to new scenes, namely Unsupervised Domain Adaptation (UDA). However, most of existing methods at the pixel level mainly focus on adapting the model from source domain to target domain and ignore the essence of UDA to learn domain-invariant feature learning. Meanwhile, almost all methods at the feature level ignore to make conditional distributions matched for UDA while conducting feature alignment between source and target domain. Considering these problems, this paper proposes the ParaUDA, a novel framework of learning invariant representations for UDA in two aspects: pixel level and feature level. At the pixel level, we adopt CycleGAN to conduct domain transfer and convert the problem of original unsupervised domain adaptation to supervised domain adaptation. At the feature level, we adopt an adversarial adaption model to learn domain-invariant representation by aligning the distributions of domains between different image pairs with same mixture distributions. We evaluate our proposed framework in different scenes, from synthetic scenes to real scenes, from normal weather to challenging weather, and from scenes across cameras. The results of all the above experiments show that ParaUDA is effective and robust for adapting object detection models from source scenes to target scenes.

China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program In this article, we introduce the Intelligent Vehicles Future Challenge of China (IVFC), which has lasted 12 years. Some key features of the tests and a few interesting findings of IVFC are selected and presented. Through the IVFCs held between 2009 and 2020, we gradually established a set of theories, methods, and tools to collect tests? data and efficiently evaluate the performance of autonomous vehicles so that we could learn how to improve both the autonomous vehicles and the testing system itself.

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 comparative study of texture measures with classification based on featured distributions This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented

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.

Node Reclamation and Replacement for Long-Lived Sensor Networks When deployed for long-term tasks, the energy required to support sensor nodes' activities is far more than the energy that can be preloaded in their batteries. No matter how the battery energy is conserved, once the energy is used up, the network life terminates. Therefore, guaranteeing long-term energy supply has persisted as a big challenge. To address this problem, we propose a node reclamation and replacement (NRR) strategy, with which a mobile robot or human labor called mobile repairman (MR) periodically traverses the sensor network, reclaims nodes with low or no power supply, replaces them with fully charged ones, and brings the reclaimed nodes back to an energy station for recharging. To effectively and efficiently realize the strategy, we develop an adaptive rendezvous-based two-tier scheduling scheme (ARTS) to schedule the replacement/reclamation activities of the MR and the duty cycles of nodes. Extensive simulations have been conducted to verify the effectiveness and efficiency of the ARTS scheme.

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.

Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications. Given the escalating population across the globe, it has become paramount to construct smart cities, aiming for improving the management of urban flows relying on efficient information and communication technologies (ICT). Vehicular sensing networks (VSNs) play a critical role in maintaining the efficient operation of smart cities. Naturally, there are numerous challenges to be solved before the w...

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...

A Reinforcement Learning Approach to Autonomous Decision Making of Intelligent Vehicles on Highways Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic transportation environments. In this paper, a reinforcement learning approach with value function approximation and feature learning is proposed for autonomous decision making of intelligent vehicles on highways. In the proposed approach, the sequential decision making problem for lane changing and overtaking is modeled as a Markov decision process with multiple goals, including safety, speediness, smoothness, etc. In order to learn optimized policies for autonomous decision-making, a multiobjective approximate policy iteration (MO-API) algorithm is presented. The features for value function approximation are learned in a data-driven way, where sparse kernel-based features or manifold-based features can be constructed based on data samples. Compared with previous RL algorithms such as multiobjective Q-learning, the MO-API approach uses data-driven feature representation for value and policy approximation so that better learning efficiency can be achieved. A highway simulation environment using a 14 degree-of-freedom vehicle dynamics model was established to generate training data and test the performance of different decision-making methods for intelligent vehicles on highways. The results illustrate the advantages of the proposed MO-API method under different traffic conditions. Furthermore, we also tested the learned decision policy on a real autonomous vehicle to implement overtaking decision and control under normal traffic on highways. The experimental results also demonstrate the effectiveness of the proposed method.

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/.

VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies. Recently, the end-to-end driving method has emerged, which performs well and generalizes to new environments by directly learning from expert-provided data. However, many...

Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.

ParaUDA: Invariant Feature Learning With Auxiliary Synthetic Samples for Unsupervised Domain Adaptation Recognizing and locating objects by algorithms are essential and challenging issues for Intelligent Transportation Systems. However, the increasing demand for much labeled data hinders the further application of deep learning-based object detection. One of the optimal solutions is to train the target model with an existing dataset and then adapt it to new scenes, namely Unsupervised Domain Adaptation (UDA). However, most of existing methods at the pixel level mainly focus on adapting the model from source domain to target domain and ignore the essence of UDA to learn domain-invariant feature learning. Meanwhile, almost all methods at the feature level ignore to make conditional distributions matched for UDA while conducting feature alignment between source and target domain. Considering these problems, this paper proposes the ParaUDA, a novel framework of learning invariant representations for UDA in two aspects: pixel level and feature level. At the pixel level, we adopt CycleGAN to conduct domain transfer and convert the problem of original unsupervised domain adaptation to supervised domain adaptation. At the feature level, we adopt an adversarial adaption model to learn domain-invariant representation by aligning the distributions of domains between different image pairs with same mixture distributions. We evaluate our proposed framework in different scenes, from synthetic scenes to real scenes, from normal weather to challenging weather, and from scenes across cameras. The results of all the above experiments show that ParaUDA is effective and robust for adapting object detection models from source scenes to target scenes.

China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program In this article, we introduce the Intelligent Vehicles Future Challenge of China (IVFC), which has lasted 12 years. Some key features of the tests and a few interesting findings of IVFC are selected and presented. Through the IVFCs held between 2009 and 2020, we gradually established a set of theories, methods, and tools to collect tests? data and efficiently evaluate the performance of autonomous vehicles so that we could learn how to improve both the autonomous vehicles and the testing system itself.

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 comparative study of texture measures with classification based on featured distributions This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented

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.

Node Reclamation and Replacement for Long-Lived Sensor Networks When deployed for long-term tasks, the energy required to support sensor nodes' activities is far more than the energy that can be preloaded in their batteries. No matter how the battery energy is conserved, once the energy is used up, the network life terminates. Therefore, guaranteeing long-term energy supply has persisted as a big challenge. To address this problem, we propose a node reclamation and replacement (NRR) strategy, with which a mobile robot or human labor called mobile repairman (MR) periodically traverses the sensor network, reclaims nodes with low or no power supply, replaces them with fully charged ones, and brings the reclaimed nodes back to an energy station for recharging. To effectively and efficiently realize the strategy, we develop an adaptive rendezvous-based two-tier scheduling scheme (ARTS) to schedule the replacement/reclamation activities of the MR and the duty cycles of nodes. Extensive simulations have been conducted to verify the effectiveness and efficiency of the ARTS scheme.

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.

Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications. Given the escalating population across the globe, it has become paramount to construct smart cities, aiming for improving the management of urban flows relying on efficient information and communication technologies (ICT). Vehicular sensing networks (VSNs) play a critical role in maintaining the efficient operation of smart cities. Naturally, there are numerous challenges to be solved before the w...

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...

Local2Global: Unsupervised multi-view deep graph representation learning with Nearest Neighbor Constraint Multi-view feature fusion is a vital phase for multi-view representation learning. Recently, most Graph Auto-Encoders (GAEs) and their variants focus on multi-view learning. However, most of them ignore deep representation fusion of features of each multi-view. Furthermore, there are scarcely unsupervised constraints guiding to enhance the graph representation capability in training process. In this paper, we propose a novel unsupervised Multi-view Deep Graph Representation Learning (MDGRL) framework on multi-view data which is based on the Graph Auto-Encoders (GAEs) for local feature leaning, a feature fusion module for producing global representation and a valid variant of Variational Graph Auto-Encoder (VGAE) for global deep graph representation learning. To fuse Nearest Neighbor Constraint (NNC) between the maximal degree nodes which represents the most close joining node and their adjacent nodes into VGAE, we propose a new Nearest Neighbor Constraint Variational Graph Auto-Encoder (NNC-VGAE) to enhance the global deep graph representation capability for multi-view data. In the training process of NNC-VGAE, NNC makes the adjacent nodes gradually close to the maximal degree node. Hence, the proposed MDGRL has excellent deep graph representation capability for multi-view data. Experiments on eight non-medical benchmark multi-view data sets and four medical data sets confirm the effectiveness of our MDGRL compared with other state-of-the-art methods for unsupervised clustering.

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.

Cloud-aided lightweight certificateless authentication protocol with anonymity for wireless body area networks. With the development of cloud computing and wireless body area networks (WBANs), wearable equipments are able to become new intelligent terminals to provide services for users, which plays an important role to improve the human health-care service. However, The traditional WBANs devices have limited computing and storage capabilities. These restrictions limit the services that WBANs can provide to users. Thus the concept of Cloud-aided WBANs has been proposed to enhance the capabilities of WBANs. In addition, due to the openness of the cloud computing environment, the protection of the user's physiological information and privacy remains a major concern. In previous authentication protocols, few of them can protect the user's private information in insecure channel. In this paper, we propose a cloud-aided lightweight certificateless authentication protocol with anonymity for wireless body area networks. Our protocol ensures that no one can obtain user's real identity except for the network manager in the registration phase. Moreover, in the authentication phase, the network manager cannot know the user's real identity. Note that, through the security analysis, we can conclude that our protocol can provide stronger security protection of private information than most of existing schemes in insecure channel.

Dynamic Fully Homomorphic encryption-based Merkle Tree for lightweight streaming authenticated data structures. Fully Homomorphic encryption-based Merkle Tree (FHMT) is a novel technique for streaming authenticated data structures (SADS) to achieve the streaming verifiable computation. By leveraging the computing capability of fully homomorphic encryption, FHMT shifts almost all of the computation tasks to the server, reaching nearly no overhead for the client. Therefore, FHMT is an important technique to construct a more efficient lightweight ADS for resource-limited clients. But the typical FHMT cannot support the dynamic scenario very well because it cannot expend freely since its height is fixed. We now present our fully dynamic FHMT construction, which is a construction that is able to authenticate an unbounded number of data elements and improves upon the state-of-the-art in terms of computational overhead. We divided the algorithms of the DFHMT with the following phases: initialization, insertion, tree expansion, query and verification. The DFHMT removes the drawbacks of the static FHMT. In the initialization phase, it is not required for the scale of the tree to be determined, and the scale of the tree can be adaptively expanded during the data-appending phase. This feature is more suitable for streaming data environments. We analyzed the security of the DFHMT, and point out that DFHMT has the same security with FHMT. The storage, communication and computation overhead of DFHMT is also analyzed, the results show that the client uses simple numerical multiplications and additions to replace hash operations, which reduces the computational burden of the client; the length of the authentication path in DFHMT is shorter than FHMT, which reduces storage and communication overhead. The performance of DFHMT was compared with other construction techniques of SADS via some tests, the results show that DFHMT strikes the performance balance between the client and server, which has some performance advantage for lightweight devices.

Witness indistinguishable and witness hiding protocols

Identity-based chameleon hashing and signatures without key exposure The notion of chameleon hash function without key exposure plays an important role in designing secure chameleon signatures. However, all of the existing key-exposure free chameleon hash schemes are presented in the setting of certificate-based systems. In 2004, Ateniese and de Medeiros questioned whether there is an efficient construction for identity-based chameleon hashing without key exposure. In this paper, we propose the first identity-based chameleon hash scheme without key exposure based on the three-trapdoor mechanism, which provides an affirmative answer to the open problem. Moreover, we use the proposed chameleon hash scheme to design an identity-based chameleon signature scheme, which achieves all the desired security properties.

A Covert Channel Over VoLTE via Adjusting Silence Periods. Covert channels represent unforeseen communication methods that exploit authorized overt communication as the carrier medium for covert messages. Covert channels can be a secure and effective means of transmitting confidential information hidden in overt traffic. For covert timing channel, the covert message is usually modulated into inter-packet delays (IPDs) of legitimate traffic, which is not suitable for voice over LTE (VoLTE) since the IPDs of VoLTE traffic are fixed to lose the possibility of being modulated. For this reason, we propose a covert channel via adjusting silence periods, which modulates covert message by the postponing or extending silence periods in VoLTE traffic. To keep the robustness, we employ the Gray code to encode the covert message to reduce the impact of packet loss. Moreover, the proposed covert channel enables the tradeoff between the robustness and voice quality which is an important performance indicator for VoLTE. The experiment results show that the proposed covert channel is undetectable by statistical tests and outperforms the other covert channels based on IPDs in terms of robustness.

A Novel Efficient Remote Data Possession Checking Protocol in Cloud Storage. As an important application in cloud computing, cloud storage offers user scalable, flexible, and high-quality data storage and computation services. A growing number of data owners choose to outsource data files to the cloud. Because cloud storage servers are not fully trustworthy, data owners need dependable means to check the possession for their files outsourced to remote cloud servers. To add...

How to Use Bitcoin to Design Fair Protocols. We study a model of fairness in secure computation in which an adversarial party that aborts on receiving output is forced to pay a mutually predefined monetary penalty. We then show how the Bitcoin network can be used to achieve the above notion of fairness in the twoparty as well as the multiparty setting (with a dishonest majority). In particular, we propose new ideal functionalities and protocols for fair secure computation and fair lottery in this model. One of our main contributions is the definition of an ideal primitive, which we call F-CR(star) (CR stands for "claim-or-refund"), that formalizes and abstracts the exact properties we require from the Bitcoin network to achieve our goals. Naturally, this abstraction allows us to design fair protocols in a hybrid model in which parties have access to the F-CR(star) functionality, and is otherwise independent of the Bitcoin ecosystem. We also show an efficient realization of F-CR(star) that requires only two Bitcoin transactions to be made on the network. Our constructions also enjoy high efficiency. In a multiparty setting, our protocols only require a constant number of calls to F-CR(star) per party on top of a standard multiparty secure computation protocol. Our fair multiparty lottery protocol improves over previous solutions which required a quadratic number of Bitcoin transactions.

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

QoE-Driven Edge Caching in Vehicle Networks Based on Deep Reinforcement Learning The Internet of vehicles (IoV) is a large information interaction network that collects information on vehicles, roads and pedestrians. One of the important uses of vehicle networks is to meet the entertainment needs of driving users through communication between vehicles and roadside units (RSUs). Due to the limited storage space of RSUs, determining the content cached in each RSU is a key challenge. With the development of 5G and video editing technology, short video systems have become increasingly popular. Current widely used cache update methods, such as partial file precaching and content popularity- and user interest-based determination, are inefficient for such systems. To solve this problem, this paper proposes a QoE-driven edge caching method for the IoV based on deep reinforcement learning. First, a class-based user interest model is established. Compared with the traditional file popularity- and user interest distribution-based cache update methods, the proposed method is more suitable for systems with a large number of small files. Second, a quality of experience (QoE)-driven RSU cache model is established based on the proposed class-based user interest model. Third, a deep reinforcement learning method is designed to address the QoE-driven RSU cache update issue effectively. The experimental results verify the effectiveness of the proposed algorithm.

Reciprocal N-body Collision Avoidance In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace. In our formulation, each robot acts fully in- dependently, and does not communicate with other robots. Based on the definition of velocity obstacles (5), we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear program. We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute collision-free actions for all of them in only a few millisec- onds. To the best of our knowledge, this method is the first that can guarantee local collision-free motion for a large number of robots in a cluttered workspace.

End-user programming architecture facilitates the uptake of robots in social therapies. This paper proposes an architecture that makes programming of robot behavior of an arbitrary complexity possible for end-users and shows the technical solutions in a way that is easy to understand and generalize to different situations. It aims to facilitate the uptake and actual use of robot technologies in therapies for training social skills to autistic children. However, the framework is easy to generalize for an arbitrary human–robot interaction application, where users with no technical background need to program robots, i.e. in various assistive robotics applications. We identified the main needs of end-user programming of robots as a basic prerequisite for the uptake of robots in assistive applications. These are reusability, modularity, affordances for natural interaction and the ease of use. After reviewing the shortcomings of the existing architectures, we developed an initial architecture according to these principles and embedded it in a robot platform. Further, we used a co-creation process to develop and concretize the architecture to facilitate solutions and create affordances for robot specialists and therapists. Several pilot tests showed that different user groups, including therapists with general computer skills and adolescents with autism could make simple training or general behavioral scenarios within 1 h, by connecting existing behavioral blocks and by typing textual robot commands for fine-tuning the behaviors. In addition, this paper explains the basic concepts behind the TiViPE based robot control platform, and gives guidelines for choosing the robot programming tool and designing end-user platforms for robots.

Fast computation of Jacobi-Fourier moments for invariant image recognition The Jacobi-Fourier moments (JFMs) provide a wide class of orthogonal rotation invariant moments (ORIMs) which are useful for many image processing, pattern recognition and computer vision applications. They, however, suffer from high time complexity and numerical instability at high orders of moment. In this paper, a fast method based on the recursive computation of radial kernel function of JFMs is proposed which not only reduces time complexity but also improves their numerical stability. Fast recursive method for the computation of Jacobi-Fourier moments is proposed.The proposed method not only reduces time complexity but also improves numerical stability of moments.Better image reconstruction is achieved with lower reconstruction error.Proposed method is useful for many image processing, pattern recognition and computer vision applications.

Neural network adaptive tracking control for a class of uncertain switched nonlinear systems. •Study the method of the tracking control of the switched uncertain nonlinear systems under arbitrary switching signal controller.•A multilayer neural network adaptive controller with multilayer weight norm adaptive estimation is been designed.•The adaptive law is expand from calculation the second layer weight of neural network to both of the two layers weight.•The controller proposed improve the tracking error performance of the closed-loop system greatly.

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 New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models Taking advantage of their inherent dexterity, robotic arms are competent in completing many tasks efficiently. As a result of the modeling complexity and kinematic uncertainty of robotic arms, model-free control paradigm has been proposed and investigated extensively. However, robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying. In this paper, we first propose a new kind of zeroing neural network (ZNN), i.e., integration-enhanced noise-tolerant ZNN (IENT-ZNN) with integration-enhanced noise-tolerant capability. Then, a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms, which improves the performance of robotic arms with the disturbance of noise, without knowing the structural parameters of the robotic arms. The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis. Finally, simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

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.

Early DoS/DDoS Detection Method using Short-term Statistics Early detection methods are required to prevent the DoS / DDoS attacks. The detection methods using the entropy have been classified into the long-term entropy based on the observation of more than 10,000 packets and the short-term entropy that of less than 10,000 packets. The long-term entropy have less fluctuation leading to easy detection of anomaly accesses using the threshold, while having the defects in detection at the early attacking stage and of difficulty to trace the short term attacks. In this paper, we propose and evaluate the DoS/DDoS detection method based on the short-term entropy focusing on the early detection. Firstly, the pre-experiment extracted the effective window width; 50 for DDoS and 500 for slow DoS attacks. Secondly, we showed that classifying the type of attacks can be made possible using the distribution of the average and standard deviation of the entropy. In addition, we generated the pseudo attacking packets under a normal condition to calculate the entropy and carry out a test of significance. When the number of attacking packets is equal to the number of arriving packets, the high detection results with False-negative = 5% was extracted, and the effectiveness of the proposed method was shown.

Adaptive Clustering with Feature Ranking for DDoS Attacks Detection Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second, the Modified Global K-means algorithm (MGKM) is used as the basic incremental clustering algorithm to identify the cluster structure of the target data. Third, the linear correlation coefficient is used for feature ranking. Lastly, the feature ranking result is used to inform and recalculate the clusters. This adaptive process can make worthwhile adjustments to the working feature vector according to different patterns of DDoS attacks, and can improve the quality of the clusters and the effectiveness of the clustering algorithm. The experimental results demonstrate that our method is effective and adaptive in detecting the separate phases of DDoS attacks.

The role of KL divergence in anomaly detection We study the role of Kullback-Leibler divergence in the framework of anomaly detection, where its abilities as a statistic underlying detection have never been investigated in depth. We give an in-principle analysis of network attack detection, showing explicitly attacks may be masked at minimal cost through 'camouflage'. We illustrate on both synthetic distributions and ones taken from real traffic.

An Anomaly Detection Model Based on One-Class SVM to Detect Network Intrusions. Intrusion detection occupies a decision position in solving the network security problems. Support Vector Machines (SVMs) are one of the widely used intrusion detection techniques. However, the commonly used two-class SVM algorithms are facing difficulties of constructing the training dataset. That is because in many real application scenarios, normal connection records are easy to be obtained, but attack records are not so. We propose an anomaly detection model based on One-class SVM to detect network intrusions. The one-class SVM adopts only normal network connection records as the training dataset. But after being trained, it is able to recognize normal from various attacks. This just meets the requirements of the anomaly detection. Experimental results on KDDCUP99 dataset show that compared to Probabilistic Neural Network (PNN) and C-SVM, our anomaly detection model based on One-class SVM achieves higher detection rates and yields average better performance in terms of precision, recall and F-value.

Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN : An Experimental Approach. The expected advent of the Internet of Things (IoT) has triggered a large demand of embedded devices, which envisions the autonomous interaction of sensors and actuators while offering all sort of smart services. However, these IoT devices are limited in computation, storage, and network capacity, which makes them easy to hack and compromise. To achieve secure development of IoT, it is necessary to engineer scalable security solutions optimized for the IoT ecosystem. To this end, Software Defined Networking (SDN) is a promising paradigm that serves as a pillar in the fifth generation of mobile systems (5G) that could help to detect and mitigate Denial of Service (DoS) and Distributed DoS (DDoS) threats. In this work, we propose to experimentally evaluate an entropy-based solution to detect and mitigate DoS and DDoS attacks in IoT scenarios using a stateful SDN data plane. The obtained results demonstrate for the first time the effectiveness of this technique targeting real IoT data traffic.

Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of mu s in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language.

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.

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.

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].

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.

Neural network adaptive tracking control for a class of uncertain switched nonlinear systems. •Study the method of the tracking control of the switched uncertain nonlinear systems under arbitrary switching signal controller.•A multilayer neural network adaptive controller with multilayer weight norm adaptive estimation is been designed.•The adaptive law is expand from calculation the second layer weight of neural network to both of the two layers weight.•The controller proposed improve the tracking error performance of the closed-loop system greatly.

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.

Two-Dimensional Polar Harmonic Transforms for Invariant Image Representation This paper introduces a set of 2D transforms, based on a set of orthogonal projection bases, to generate a set of features which are invariant to rotation. We call these transforms Polar Harmonic Transforms (PHTs). Unlike the well-known Zernike and pseudo-Zernike moments, the kernel computation of PHTs is extremely simple and has no numerical stability issue whatsoever. This implies that PHTs encompass the orthogonality and invariance advantages of Zernike and pseudo-Zernike moments, but are free from their inherent limitations. This also means that PHTs are well suited for application where maximal discriminant information is needed. Furthermore, PHTs make available a large set of features for further feature selection in the process of seeking for the best discriminative or representative features for a particular application.

Errata and comments on "Generic orthogonal moments: Jacobi-Fourier moments for invariant image description". Ping et al. [Z. Ping, H. Ren, J. Zou, Y. Sheng, and W. Bo, Generic orthogonal moments: Jacobi–Fourier moments for invariant image description, Pattern Recognition 40 (4) (2007) 1245–1254] made a landmark contribution to the theory of two-dimensional orthogonal moments confined to the unit disk by unifying the radial kernels of existing polynomial-based circular orthogonal moments under the roof of shifted Jacobi polynomials. However, the work contains some errata that result mainly from the confusion between the two slightly different definitions of shifted Jacobi polynomials in the literature. Taking into account the great importance and the high impact of the work in the pattern recognition community, this paper points out the confusing points, corrects the errors, and gives some other relevant comments. The corrections developed in this paper are illustrated by some experimental evidence.

Generic orthogonal moments: Jacobi-Fourier moments for invariant image description A multi-distorted invariant orthogonal moments, Jacobi-Fourier Moments (JFM), were proposed. The integral kernel of the moments was composed of radial Jacobi polynomial and angular Fourier complex componential factor. The variation of two parameters in Jacobi polynomial, @a and @b, can form various types of orthogonal moments: Legendre-Fourier Moments (@a=1,@b=1); Chebyshev-Fourier Moments (@a=2,@b=32); Orthogonal Fourier-Mellin Moments (@a=2,@b=2); Zernike Moments and pseudo-Zernike Moments, and so on. Therefore, Jacobi-Fourier Moments are generic expressions of orthogonal moments formed by a radial orthogonal polynomial and angular Fourier complex component factor, providing a common mathematical tool for performance analysis of the orthogonal moments. In the paper, Jacobi-Fourier Moments were calculated for a deterministic image, and the original image was reconstructed with the moments. The relationship between Jacobi-Fourier Moments and other orthogonal moments was studied. Theoretical analysis and experimental investigation were conducted in terms of the description performance and noise sensibility of the JFM.

Quaternion Zernike moments and their invariants for color image analysis and object recognition Moments and moment invariants have become a powerful tool in pattern recognition and image analysis. Conventional methods to deal with color images are based on RGB decomposition or graying, which may lose some significant color information. In this paper, by using the algebra of quaternions, we introduce the quaternion Zernike moments (QZMs) to deal with the color images in a holistic manner. It is shown that the QZMs can be obtained from the conventional Zernike moments of each channel. We also provide the theoretical framework to construct a set of combined invariants with respect to rotation, scaling and translation (RST) transformation. Experimental results are provided to illustrate the efficiency of the proposed descriptors.

Combined invariants to similarity transformation and to blur using orthogonal Zernike moments. The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better.

Lossless medical image watermarking method based on significant difference of cellular automata transform coefficient. Conventional medical image watermarking techniques focus on improving invisibility and robustness of the watermarking mechanism to prevent medical disputes. This paper proposes a medical image watermarking algorithm based on the significant difference of cellular automata transform (CAT) for copyright protection. The medical image is firstly subsampled into four subimages, and two images are randomly chosen to obtain two low-frequency bandwidths using CAT. Coefficients within a low-frequency bandwidth are important information in an image. Hence, the difference between two low-frequency bandwidths is used as an important feature in the medical image. From these important features, watermarks and cover images can be used to generate an ownership share image (OSI) used for verifying the medical image. Besides appearing like cover images, the OSI will also be able to register with a third party. When a suspected medical image requires verification, the important features from the suspected medical image are first extracted. The master share image (MSI) can be generated from the important features from the suspected medical image. Lastly, the OSI and MSI can be combined to extract the watermark to verify the suspected medical image. The advantage of our proposed method is that the medical image does not require alteration to protect the copyright of the medical image. This means that while the image is protected, medical disputes will be unlikely and the appearance of the registered OSI will carry significant data to make management more convenient. Lastly, the proposed method has the features of having better security, invisibility, and robustness. Moreover, experimental results have demonstrated that our method results in good performance.

Information hiding in medical images: a robust medical image watermarking system for E-healthcare Abstract Electronic transmission of the medical images is one of the primary requirements in a typical Electronic-Healthcare (E-Healthcare) system. However this transmission could be liable to hackers who may modify the whole medical image or only a part of it during transit. To guarantee the integrity of a medical image, digital watermarking is being used. This paper presents two different watermarking algorithms for medical images in transform domain. In first technique, a digital watermark and Electronic Patients Record (EPR) have been embedded in both regions; Region of Interest (ROI) and Region of Non-Interest (RONI). In second technique, Region of Interest (ROI) is kept untouched for tele-diagnosis purpose and Region of Non-Interest (RONI) is used to hide the digital watermark and EPR. In either algorithm 8 × 8 block based Discrete Cosine Transform (DCT) has been used. In each 8 × 8 block two DCT coefficients are selected and their magnitudes are compared for embedding the watermark/EPR. The selected coefficients are modified by using a threshold for embedding bit a ‘0’ or bit ‘1’ of the watermark/EPR. The proposed techniques have been found robust not only to singular attacks but also to hybrid attacks. Comparison results viz-a - viz payload and robustness show that the proposed techniques perform better than some existing state of art techniques. As such the proposed algorithms could be useful for e-healthcare systems.

Mantra-Net: Manipulation Tracing Network For Detection And Localization Of Image Forgeries With Anomalous Features To fight against real-life image forgery, which commonly involves different types and combined manipulations, we propose a unified deep neural architecture called ManTra-Net. Unlike many existing solutions, ManTra-Net is an end-to-end network that performs both detection and localization without extra preprocessing and postprocessing. ManTra-Net is a fully convolutional network and handles images of arbitrary sizes and many known forgery types such splicing, copy-move, removal, enhancement, and even unknown types. This paper has three salient contributions. We design a simple yet effective self-supervised learning task to learn robust image manipulation traces from classifying 385 image manipulation types. Further, we formulate the forgery localization problem as a local anomaly detection problem, design a Z-score feature to capture local anomaly, and propose a novel long short-term memory solution to assess local anomalies. Finally, we carefully conduct ablation experiments to systematically optimize the proposed network design. Our extensive experimental results demonstrate the generalizability, robustness and superiority of ManTra-Net, not only in single types of manipulations/forgeries, but also in their complicated combinations.

Learning A Discriminative Null Space For Person Re-Identification Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training images. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discriminative power. In this work, we propose to overcome the SSS problem in re-id distance metric learning by matching people in a discriminative null space of the training data. In this null space, images of the same person are collapsed into a single point thus minimising the within-class scatter to the extreme and maximising the relative between-class separation simultaneously. Importantly, it has a fixed dimension, a closed-form solution and is very efficient to compute. Extensive experiments carried out on five person re-identification benchmarks including VIPeR, PRID2011, CUHK01, CUHK03 and Market1501 show that such a simple approach beats the state-of-the-art alternatives, often by a big margin.

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.

Dynamic priority protocols for packet voice Since the reconstruction of continuous speech from voice packets is complicated by the variable delays of the packets through the network, a dynamic priority protocol is proposed to minimize the variability of packet delays. The protocol allows the priority of a packet to vary with time. After a discussion of the concept of dynamic priorities, two examples of dynamic priorities are studied through queueing analysis and simulations. Optimal properties of the oldest customer first (OCF) and earliest deadline first (EDF) disciplines are proven, suggesting that they may be theoretically effective in reducing the variability of packet delays. Simulation results of the OCF discipline indicate that the OCF discipline is most effective under conditions of long routes and heavy traffic, i.e., the conditions when delay variability is most likely to be significant. Under OCF, the delays of packets along long routes are improved at the expense of packets along short routes. It is noted that more complex and realistic simulations, including simulations of the EDF discipline, are needed

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.

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.

Toward Encrypted Video Tampering Detection and Localization Based on POB Number System Over Cloud. The unlimited growth in the amount of multimedia content has shifted the global infrastructure to the cloud-based multimedia hosting. However, the high probability of security breaches of the content is increasing demand for secure solutions toward this end. One such feasible solution is to encrypt the content to unreadable form before outsourcing to the cloud-based servers. In this paper, the med...

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.

Integrating Encryption and Marking for Remote Sensing Image Based on Orthogonal Decomposition For the special characters, remote sensing image has higher requirements not only in the security but also in the management; it requires not only the active encryption during storage and transmission for preventing information leakage but also the marking technology to prevent illegal usage as well as copyright protection or even source tracing. Therefore, this paper proposes to integrate encryption and marking technology by the independence and fusion of orthogonal decomposition for the comprehensive security protection of remote sensing image. Under the proposed scheme, encryption and marking technology can achieve the operation independence and content mergence; moreover, there is no special requirement in selecting encryption and marking algorithms. It makes up the shortage of recent integration of encryption and watermarking based on spatial scrambling in applicability and security. According to the experimental results, integration of encryption and marking technology based on orthogonal decomposition satisfies the common constraints of encryption, and marking technology, furthermore, has little impact on remote sensing image data characters and later applications.

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.

Research on Emergency Vehicle Routing Planning Based on Short-Term Traffic Flow Prediction. Aiming at the problem of traffic emergency vehicles routing planning caused by road traffic jam and road damage, analyzes the spatio-temporal characteristics of traffic data and the real-time congestion of traffic network, constructs the weights of traffic indicators of different routes by fuzzy rough set, built a weighted prediction model based on these weights, propose a guided local search algorithm with variable penalty function. A new penalty utility function is added based on the traditional guided local search algorithm. When the traditional guided local search algorithm falls into the local optimum, the penalty strategy is transformed and searched again to expand the neighborhood of the search. By comparing with traditional guided local search algorithm and other algorithm in different examples, the validity and stability of this algorithm in the route planning of emergency vehicles are verified, which provides a theoretical basis for the emergency logistics vehicle route planning.

Estimation of prediction error by using K-fold cross-validation Estimation of prediction accuracy is important when our aim is prediction. The training error is an easy estimate of prediction error, but it has a downward bias. On the other hand, K-fold cross-validation has an upward bias. The upward bias may be negligible in leave-one-out cross-validation, but it sometimes cannot be neglected in 5-fold or 10-fold cross-validation, which are favored from a computational standpoint. Since the training error has a downward bias and K-fold cross-validation has an upward bias, there will be an appropriate estimate in a family that connects the two estimates. In this paper, we investigate two families that connect the training error and K-fold cross-validation.

Traffic Flow Forecasting for Urban Work Zones None of numerous existing traffic flow forecasting models focus on work zones. Work zone events create conditions that are different from both normal operating conditions and incident conditions. In this paper, four models were developed for forecasting traffic flow for planned work zone events. The four models are random forest, regression tree, multilayer feedforward neural network, and nonparametric regression. Both long-term and short-term traffic flow forecasting applications were investigated. Long-term forecast involves forecasting 24 h in advance using historical traffic data, and short-term forecasts involves forecasting 1 h and 45, 30, and 15 min in advance using real-time temporal and spatial traffic data. Models were evaluated using data from work zone events on two types of roadways, a freeway, i.e., I-270, and a signalized arterial, i.e., MO-141, in St. Louis, MO, USA. The results showed that the random forest model yielded the most accurate long-term and short-term work zone traffic flow forecasts. For freeway data, the most influential variables were the latest interval's look-back traffic flows at the upstream, downstream, and current locations. For arterial data, the most influential variables were the traffic flows from the three look-back intervals at the current location only.

Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model. With the emerging electric vehicle (EV) and fast charging technologies, EV load forecasting has become a concern for planners and operators of EV charging stations (CSs). Due to the nonstationary feature of the traffic flow (TF) and the erratic nature of the charging procedures, EV charging load is difficult to accurately forecast. In this article, TF is first predicted using a deep-learning-based...

A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation The video-based separation of foreground (FG) and background (BG) has been widely studied due to its vital role in many applications, including intelligent transportation and video surveillance. Most of the existing algorithms are based on traditional computer vision techniques that perform pixel-level processing assuming that FG and BG possess distinct visual characteristics. Recently, state-of-the-art solutions exploit deep learning models targeted originally for image classification. Major drawbacks of such a strategy are the lacking delineation of FG regions due to missing temporal information as they segment the FG based on a single frame object detection strategy. To grapple with this issue, we excogitate a 3D convolutional neural network (3D CNN) with long short-term memory (LSTM) pipelines that harness seminal ideas, viz., fully convolutional networking, 3D transpose convolution, and residual feature flows. Thence, an FG-BG segmenter is implemented in an encoder-decoder fashion and trained on representative FG-BG segments. The model devises a strategy called double encoding and slow decoding, which fuses the learned spatio-temporal cues with appropriate feature maps both in the down-sampling and up-sampling paths for achieving well generalized FG object representation. Finally, from the Sigmoid confidence map generated by the 3D CNN-LSTM model, the FG is identified automatically by using Nobuyuki Otsu’s method and an empirical global threshold. The analysis of experimental results via standard quantitative metrics on 16 benchmark datasets including both indoor and outdoor scenes validates that the proposed 3D CNN-LSTM achieves competitive performance in terms of figure of merit evaluated against prior and state-of-the-art methods. Besides, a failure analysis is conducted on 20 video sequences from the DAVIS 2016 dataset.

Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach. Transportation systems might be heavily affected by factors such as accidents and weather. Specifically, inclement weather conditions may have a drastic impact on travel time and traffic flow. This study has two objectives: first, to investigate a correlation between weather parameters and traffic flow and, second, to improve traffic flow prediction by proposing a novel holistic architecture. It i...

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].

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.

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%.

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.

Constrained Kalman filtering for indoor localization of transport vehicles using floor-installed HF RFID transponders Localization of transport vehicles is an important issue for many intralogistics applications. The paper presents an inexpensive solution for indoor localization of vehicles. Global localization is realized by detection of RFID transponders, which are integrated in the floor. The paper presents a novel algorithm for fusing RFID readings with odometry using Constraint Kalman filtering. The paper presents experimental results with a Mecanum based omnidirectional vehicle on a NaviFloor® installation, which includes passive HF RFID transponders. The experiments show that the proposed Constraint Kalman filter provides a similar localization accuracy compared to a Particle filter but with much lower computational expense.

Problem of dynamic change of tags location in anticollision RFID systems Presently the necessity of building anticollision RFID systems with dynamic location change of tags appear more often. Such solutions are used in identification of moving cars, trains (automatic identification of vehicles – AVI processes) as well as moving parts and elements in industry, commerce, science and medicine (internet of things). In the paper there were presented operation stages in the RFID anticollision system necessary to communicate with groups of tags entering and leaving read/write device interrogation zone and communication phases in conditions of dynamic location change of tags. The mentioned aspects influence RFID system reliability, which is characterized by the efficiency coefficient and the identification probability of objects in specific interrogation zone. The communication conditions of correct operation of multiple RFID system are crucial for efficient exchange of data with all tags during their dynamic location changes. Presented problem will be the base to specify new application tag parameters (such as maximum speed of tag motion) and synthesis of interrogation zone required for concrete anticollision RFID applications with dynamic location change of tags.

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.

Exploring a Surface Using RFID Grid and Group of Mobile Robots. The paper deals with discovering a surface covered with a grid of RFID transponders using a group of robots and a master control unit. The robots move across the surface, read data from the transponders and send it to the master. The master collects the data, analyze it to create a map and sends commands to the robots. This way optimization of robot movements is possible to speed up the discovery. Two types of RFID grid have been considered: square- and triangle-based. A laboratory prototype has been created with class 2.0 robots and the master unit running CPDev SFC program under Windows IoT.

A standalone RFID Indoor Positioning System Using Passive Tags Indoor positioning systems (IPSs) locate objects in closed structures such as office buildings, hospitals, stores, factories, and warehouses, where Global Positioning System devices generally do not work. Most available systems apply wireless concepts, optical tracking, and/or ultrasound. This paper presents a standalone IPS using radio frequency identification (RFID) technology. The concept is ba...

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.

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.

Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices. Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost, which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

Parameter tuning for configuring and analyzing evolutionary algorithms In this paper we present a conceptual framework for parameter tuning, provide a survey of tuning methods, and discuss related methodological issues. The framework is based on a three-tier hierarchy of a problem, an evolutionary algorithm (EA), and a tuner. Furthermore, we distinguish problem instances, parameters, and EA performance measures as major factors, and discuss how tuning can be directed to algorithm performance and/or robustness. For the survey part we establish different taxonomies to categorize tuning methods and review existing work. Finally, we elaborate on how tuning can improve methodology by facilitating well-funded experimental comparisons and algorithm analysis.

Cyber warfare: steganography vs. steganalysis For every clever method and tool being developed to hide information in multimedia data, an equal number of clever methods and tools are being developed to detect and reveal its secrets.

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.

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

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 driving styles for autonomous vehicles from demonstration It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations, others might prefer a more relaxed style. Typically, a large number of parameters such as acceleration profiles, distances to other cars, speed during lane changes, etc., characterize a human driver's style. Manual tuning of these parameters may be a tedious and error-prone task. Therefore, we propose a learning from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers.

Theoretical and experimental investigation of driver noncooperative-game steering control behavior This paper investigates two noncooperative-game strategies which may be used to represent a human driver's steering control behavior in response to vehicle automated steering intervention. The first strategy, namely the Nash strategy is derived based on the assumption that a Nash equilibrium is reached in a noncooperative game of vehicle path-following control involving a driver and a vehicle automated steering controller. The second one, namely the Stackelberg strategy is derived based on the assumption that a Stackelberg equilibrium is reached in a similar context. A simulation study is performed to study the differences between the two proposed noncooperative-game strategies. An experiment using a fixed-base driving simulator is carried out to measure six test driver's steering behavior in response to vehicle automated steering intervention. The Nash strategy is then fitted to measured driver steering wheel angles following a model identification procedure. Control weight parameters involved in the Nash strategy are identified. It is found that the proposed Nash strategy with the identified control weights is capable of representing the trend of measured driver steering behavior and vehicle lateral responses. It is also found that the proposed Nash strategy is superior to the classic driver steering control strategy which has widely been used for modeling driver steering control over the past. A discussion on improving automated steering control using the gained knowledge of driver noncooperative-game steering control behavior was made.

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.

Speech emotion recognition approaches in human computer interaction Speech Emotion Recognition (SER) represents one of the emerging fields in human-computer interaction. Quality of the human-computer interface that mimics human speech emotions relies heavily on the types of features used and also on the classifier employed for recognition. The main purpose of this paper is to present a wide range of features employed for speech emotion recognition and the acoustic characteristics of those features. Also in this paper, we analyze the performance in terms of some important parameters such as: precision, recall, F-measure and recognition rate of the features using two of the commonly used emotional speech databases namely Berlin emotional database and Danish emotional database. Emotional speech recognition is being applied in modern human-computer interfaces and the overview of 10 interesting applications is also presented in this paper to illustrate the importance of this technique.

Camera-based drowsiness reference for driver state classification under real driving conditions Experts assume that accidents caused by drowsiness are significantly under-reported in police crash investigations (1-3%). They estimate that about 24-33% of the severe accidents are related to drowsiness. In order to develop warning systems that detect reduced vigilance based on the driving behavior, a reliable and accurate drowsiness reference is needed. Studies have shown that measures of the driver's eyes are capable to detect drowsiness under simulator or experiment conditions. In this study, the performance of the latest eye tracking based in-vehicle fatigue prediction measures are evaluated. These measures are assessed statistically and by a classification method based on a large dataset of 90 hours of real road drives. The results show that eye-tracking drowsiness detection works well for some drivers as long as the blinks detection works properly. Even with some proposed improvements, however, there are still problems with bad light conditions and for persons wearing glasses. As a summary, the camera based sleepiness measures provide a valuable contribution for a drowsiness reference, but are not reliable enough to be the only reference.

Fully Automated Driving: Impact of Trust and Practice on Manual Control Recovery. Objective: An experiment was performed in a driving simulator to investigate the impacts of practice, trust, and interaction on manual control recovery (MCR) when employing fully automated driving (FAD). Background: To increase the use of partially or highly automated driving efficiency and to improve safety, some studies have addressed trust in driving automation and training, but few studies have focused on FAD. FAD is an autonomous system that has full control of a vehicle without any need for intervention by the driver. Method: A total of 69 drivers with a valid license practiced with FAD. They were distributed evenly across two conditions: simple practice and elaborate practice. Results: When examining emergency MCR, a correlation was found between trust and reaction time in the simple practice group (i.e., higher trust meant a longer reaction time), but not in the elaborate practice group. This result indicated that to mitigate the negative impact of overtrust on reaction time, more appropriate practice may be needed. Conclusions: Drivers should be trained in how the automated device works so as to improve MCR performance in case of an emergency. Application: The practice format used in this study could be used for the first interaction with an FAD car when acquiring such a vehicle.

Visual-Manual Distraction Detection Using Driving Performance Indicators With Naturalistic Driving Data. This paper investigates the problem of driver distraction detection using driving performance indicators from onboard kinematic measurements. First, naturalistic driving data from the integrated vehicle-based safety system program are processed, and cabin camera data are manually inspected to determine the driver&#39;s state (i.e., distracted or attentive). Second, existing driving performance metrics...

Pre-Training With Asynchronous Supervised Learning For Reinforcement Learning Based Autonomous Driving Rule-based autonomous driving systems may suffer from increased complexity with large-scale intercoupled rules, so many researchers are exploring learning-based approaches. Reinforcement learning (RL) has been applied in designing autonomous driving systems because of its outstanding performance on a wide variety of sequential control problems. However, poor initial performance is a major challenge to the practical implementation of an RL-based autonomous driving system. RL training requires extensive training data before the model achieves reasonable performance, making an RL-based model inapplicable in a real-world setting, particularly when data are expensive. We propose an asynchronous supervised learning (ASL) method for the RL-based end-to-end autonomous driving model to address the problem of poor initial performance before training this RL-based model in real-world settings. Specifically, prior knowledge is introduced in the ASL pre-training stage by asynchronously executing multiple supervised learning processes in parallel, on multiple driving demonstration data sets. After pre-training, the model is deployed on a real vehicle to be further trained by RL to adapt to the real environment and continuously break the performance limit. The presented pre-training method is evaluated on the race car simulator, TORCS (The Open Racing Car Simulator), to verify that it can be sufficiently reliable in improving the initial performance and convergence speed of an end-to-end autonomous driving model in the RL training stage. In addition, a real-vehicle verification system is built to verify the feasibility of the proposed pre-training method in a real-vehicle deployment. Simulations results show that using some demonstrations during a supervised pre-training stage allows significant improvements in initial performance and convergence speed in the RL training stage.

DEAP: A Database for Emotion Analysis ;Using Physiological Signals We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.

Big Data Analytics in Intelligent Transportation Systems: A Survey. Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data a...

Provable data possession at untrusted stores We introduce a model for provable data possession (PDP) that allows a client that has stored data at an untrusted server to verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking supports large data sets in widely-distributed storage system. We present two provably-secure PDP schemes that are more efficient than previous solutions, even when compared with schemes that achieve weaker guarantees. In particular, the overhead at the server is low (or even constant), as opposed to linear in the size of the data. Experiments using our implementation verify the practicality of PDP and reveal that the performance of PDP is bounded by disk I/O and not by cryptographic computation.

Infrastructure as a Service and Cloud Technologies To choose the most appropriate cloud-computing model for your organization, you must analyze your IT infrastructure, usage, and needs. To help with this, this article describes cloud computing's current status.

Measurement of the Ionospheric Scintillation Parameter $C_{k}L$ From SAR Images of Clutter. Space-based synthetic aperture radar (SAR) can be affected by the ionosphere, particularly at L-band and below. A technique is described that exploits the reduction in SAR image contrast to measure the strength of ionospheric turbulence parameter CkL. The theory describing the effect of the ionosphere on the SAR point spread function (PSF) and the consequent effect on clutter is reviewed and exten...

Downlink Power Control for Cell-Free Massive MIMO With Deep Reinforcement Learning Recently, model-free power control approaches have been developed to achieve the near-optimal performance of cell-free (CF) massive multiple-input multiple-output (MIMO) with affordable computational complexity. In particular, deep reinforcement learning (DRL) is one of such promising techniques for realizing effective power control. In this paper, we propose a model-free method adopting the deep deterministic policy gradient algorithm (DDPG) with feedforward neural networks (NNs) to solve the downlink max-min power control problem in CF massive MIMO systems. Our result shows that compared with the conventional convex optimization algorithm, the proposed DDPG method can effectively strike a performance-complexity trade-off obtaining 1,000 times faster implementation speed and approximately the same achievable user rate as the optimal solution produced by conventional numerical convex optimization solvers, thereby offering effective power control implementations for large-scale systems. Finally, we extend the DDPG algorithm to both the max-sum and the max-product power control problems, while achieving better performance than that achieved by the conventional deep learning algorithm.

A survey on optimization metaheuristics Metaheuristics are widely recognized as efficient approaches for many hard optimization problems. This paper provides a survey of some of the main metaheuristics. It outlines the components and concepts that are used in various metaheuristics in order to analyze their similarities and differences. The classification adopted in this paper differentiates between single solution based metaheuristics and population based metaheuristics. The literature survey is accompanied by the presentation of references for further details, including applications. Recent trends are also briefly discussed.

CBSO: a memetic brain storm optimization with chaotic local search. Brain storm optimization (BSO) is a newly proposed optimization algorithm inspired by human being brainstorming process. After its appearance, much attention has been paid on and many attempts to improve its performance have been made. The search ability of BSO has been enhanced, but it still suffers from sticking into stagnation during exploitation phase. This paper proposes a novel method which incorporates BSO with chaotic local search (CLS) with the purpose of alleviating this situation. Chaos has properties of randomicity and ergodicity. These properties ensure CLS can explore every state of the search space if the search time duration is long enough. The incorporation of CLS can make BSO break the stagnation and keep the population’s diversity simultaneously, thus realizing a better balance between exploration and exploitation. Twelve chaotic maps are randomly selected for increasing the diversity of the search mechanism. Experimental and statistical results based on 25 benchmark functions demonstrate the superiority of the proposed method.

MO4: A Many-Objective Evolutionary Algorithm for Protein Structure Prediction Protein structure prediction (PSP) problems are a major biocomputing challenge, owing to its scientific intrinsic that assists researchers to understand the relationship between amino acid sequences and protein structures, and to study the function of proteins. Although computational resources increased substantially over the last decade, a complete solution to PSP problems by computational methods has not yet been obtained. Using only one energy function is insufficient to characterize proteins because of their complexity. Diverse protein energy functions and evolutionary computation algorithms have been extensively studied to assist in the prediction of protein structures in different ways. Such algorithms are able to provide a better protein with less computational resources requirement than deep learning methods. For the first time, this study proposes a many-objective PSP (MaOPSP) problem with four types of objectives to alleviate the impact of imprecise energy functions for predicting protein structures. A many-objective evolutionary algorithm (MaOEA) is utilized to solve MaOPSP. The proposed method is compared with existing methods by examining 34 proteins. An analysis of the objectives demonstrates that our generated conformations are more reasonable than those generated by single/multiobjective optimization methods. Experimental results indicate that solving a PSP problem as an MaOPSP problem with four objectives yields better PSPs, in terms of both accuracy and efficiency. The source code of the proposed method can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://toyamaailab.github.io/sourcedata.html</uri> .

Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation This paper presents a comprehensive learning particle swarm optimization algorithm with enhanced exploration and exploitation, named as “heterogeneous comprehensive learning particle swarm optimization” (HCLPSO). In this algorithm, the swarm population is divided into two subpopulations. Each subpopulation is assigned to focus solely on either exploration or exploitation. Comprehensive learning (CL) strategy is used to generate the exemplars for both subpopulations. In the exploration-subpopulation, the exemplars are generated by using personal best experiences of the particles in the exploration-subpopulation itself. In the exploitation-subpopulation, the personal best experiences of the entire swarm population are used to generate the exemplars. As the exploration-subpopulation does not learn from any particles in the exploitation-subpopulation, the diversity in the exploration-subpopulation can be retained even if the exploitation-subpopulation converges prematurely. The heterogeneous comprehensive learning particle swarm optimization algorithm is tested on shifted and rotated benchmark problems and compared with other recent particle swarm optimization algorithms to demonstrate superior performance of the proposed algorithm over other particle swarm optimization variants.

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.

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.

Chaotic Local Search-Based Differential Evolution Algorithms for Optimization JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.

Autoencoding Evolutionary Search With Learning Across Heterogeneous Problems. To enhance the search performance of evolutionary algorithms, reusing knowledge captured from past optimization experiences along the search process has been proposed in the literature, and demonstrated much promise. In the literature, there are generally three types of approaches for reusing knowledge from past search experiences, namely exact storage and reuse of past solutions, the reuse of mod...

Making Sensor Networks Immortal: An Energy-Renewal Approach With Wireless Power Transfer Wireless sensor networks are constrained by limited battery energy. Thus, finite network lifetime is widely regarded as a fundamental performance bottleneck. Recent breakthrough in the area of wireless power transfer offers the potential of removing this performance bottleneck, i.e., allowing a sensor network to remain operational forever. In this paper, we investigate the operation of a sensor network under this new enabling energy transfer technology. We consider the scenario of a mobile charging vehicle periodically traveling inside the sensor network and charging each sensor node's battery wirelessly. We introduce the concept of renewable energy cycle and offer both necessary and sufficient conditions. We study an optimization problem, with the objective of maximizing the ratio of the wireless charging vehicle (WCV)'s vacation time over the cycle time. For this problem, we prove that the optimal traveling path for the WCV is the shortest Hamiltonian cycle and provide a number of important properties. Subsequently, we develop a near-optimal solution by a piecewise linear approximation technique and prove its performance guarantee.

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.

Multiresolution Spatial and Temporal Coding in a Wireless Sensor Network for Long-Term Monitoring Applications In many WSN (wireless sensor network) applications, such as [1], [2], [3], the targets are to provide long-term monitoring of environments. In such applications, energy is a primary concern because sensor nodes have to regularly report data to the sink and need to continuously work for a very long time so that users may periodically request a rough overview of the monitored environment. On the other hand, users may occasionally query more in-depth data of certain areas to analyze abnormal events. These requirements motivate us to propose a multiresolution compression and query (MRCQ) framework to support in-network data compression and data storage in WSNs from both space and time domains. Our MRCQ framework can organize sensor nodes hierarchically and establish multiresolution summaries of sensing data inside the network, through spatial and temporal compressions. In the space domain, only lower resolution summaries are sent to the sink; the other higher resolution summaries are stored in the network and can be obtained via queries. In the time domain, historical data stored in sensor nodes exhibit a finer resolution for more recent data, and a coarser resolution for older data. Our methods consider the hardware limitations of sensor nodes. So, the result is expected to save sensors' energy significantly, and thus, can support long-term monitoring WSN applications. A prototyping system is developed to verify its feasibility. Simulation results also show the efficiency of MRCQ compared to existing work.

Joint Optimization of Source Precoding and Relay Beamforming in Wireless MIMO Relay Networks. This paper considers joint linear processing at multi-antenna sources and one multiple-input multiple-output (MIMO) relay station for both one-way and two-way relay-assisted wireless communications. The one-way relaying is applicable in the scenario of downlink transmission by a multi-antenna base station to multiple single-antenna users with the help of one MIMO relay. In such a scenario, the objective of join linear processing is to maximize the information throughput to users. The design problem is equivalently formulated as the maximization of the worst signal-to-interference-plus-noise ratio (SINR) among all users subject to various transmission power constraints. Such a program of nonconvex objective minimization under nonconvex constraints is transformed to a canonical d.c. (difference of convex functions/sets) program of d.c. function optimization under convex constraints through nonconvex duality with zero duality gap. An efficient iterative algorithm is then applied to solve this canonical d.c program. For the scenario of using one MIMO relay to assist two sources exchanging their information in two-way relying manner, the joint linear processing aims at either minimizing the maximum mean square error (MSE) or maximizing the total information throughput of the two sources. By applying tractable optimization for the linear minimum MSE estimator and d.c. programming, an iterative algorithm is developed to solve these two optimization problems. Extensive simulation results demonstrate that the proposed methods substantially outperform previously-known joint optimization methods.

Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations. In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks.

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.

Distributed Containment Control for Multiple Unknown Second-Order Nonlinear Systems With Application to Networked Lagrangian Systems. In this paper, we consider the distributed containment control problem for multiagent systems with unknown nonlinear dynamics. More specifically, we focus on multiple second-order nonlinear systems and networked Lagrangian systems. We first study the distributed containment control problem for multiple second-order nonlinear systems with multiple dynamic leaders in the presence of unknown nonlinearities and external disturbances under a general directed graph that characterizes the interaction among the leaders and the followers. A distributed adaptive control algorithm with an adaptive gain design based on the approximation capability of neural networks is proposed. We present a necessary and sufficient condition on the directed graph such that the containment error can be reduced as small as desired. As a byproduct, the leaderless consensus problem is solved with asymptotical convergence. Because relative velocity measurements between neighbors are generally more difficult to obtain than relative position measurements, we then propose a distributed containment control algorithm without using neighbors' velocity information. A two-step Lyapunov-based method is used to study the convergence of the closed-loop system. Next, we apply the ideas to deal with the containment control problem for networked unknown Lagrangian systems under a general directed graph. All the proposed algorithms are distributed and can be implemented using only local measurements in the absence of communication. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

Fully distributed containment control of high-order multi-agent systems with nonlinear dynamics. In this paper, distributed containment control problems for high-order multi-agent systems with nonlinear dynamics are investigated under directed communication topology. The states of the leaders are only available to a subset of the followers and the inputs of the leaders are possibly nonzero and time varying. Distributed adaptive nonlinear protocol is proposed based only on the relative state information, under which the states of the followers converge to the dynamic convex hull spanned by those of the leaders. As the special case with only one dynamic leader, leader–follower consensus problem is also solved with the proposed protocol. The adaptive protocol here is independent of the eigenvalues of the Laplacian matrix, which means the protocol can be implemented by each agent in a fully distributed fashion. A simulation example is provided to illustrate the theoretical results.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets. A new fuzzy inference Petri net (FIPN) method is proposed for modeling and control of hybrid human-machine systems.A multi-model approach is developed for operator functional state (OFS) prediction using EEG data.Multiple fuzzy models are represented in a unified framework of FIPN.The simulation results verified the feasibility and effectiveness of the proposed FIPN method. Background and objectiveIn human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. MethodsPetri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. ResultsExperiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). ConclusionThe simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller.

Sliding Mode Control for a Class of Nonlinear Multi-agent System With Time Delay and Uncertainties. In this paper, a type of multi-agent system is adopted in the practical project with time delay, uncertainties, and linear feedback. In addition, sliding mode control is used to ensure the robust stability of the system since it is insensitive to parameter change and interference. For the system in three different conditions, namely fixed structure, every agent being only influenced by single time...

Circular formation flight control for unmanned aerial vehicles with directed network and external disturbance This paper proposes a new distributed formation flight protocol for unmanned aerial vehicles ( UAVs ) to perform coordinated circular tracking around a set of circles on a target sphere. Different from the previous results limited in bidirectional networks and disturbance-free motions, this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound. Distinguishing from the design of a common Lyapunov function for bidirectional cases, we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as input. Then the whole control system is regarded as a cascade connection of these two subsystems, which is proved to be stable by input-to-state stability ( ISS ) theory. For the purpose of encountering the external disturbance, the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere ( say, the circular tracking control algorithm ) with the help of the switching function. Meanwhile, the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere ( say, the formation keeping control law ) for formation keeping. The validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.

Reinforcement Learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system •A combination of Deep Q-Learning algorithm and metaheuristic GSA is offered.•GSA initializes the weights and the biases of the neural networks.•A comparison with classical random, metaheuristic PSO and GWO is carried out.•The validation is done on real-time nonlinear servo system position control.•The drawbacks of randomly initialized neural networks are mitigated.

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.

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.

Mobile Edge Computing: A Survey. Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple appli...

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...

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).

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.

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.

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.

Vibrotactile Display: Perception, Technology, and Applications This paper reviews the technology and applications of vibrotactile display, an effective information transfer modality for the emerging area of haptic media. Our emphasis is on summarizing foundational knowledge in this area and providing implementation guidelines for application designers who do not yet have a background in haptics. Specifically, we explain the relevant human vibrotactile perceptual capabilities, detail the main types of commercial vibrotactile actuators, and describe how to build both monolithic and localized vibrotactile displays. We then identify exemplary vibrotactile display systems in application areas ranging from the presentation of physical object properties to broadcasting vibrotactile media content.

Piecewise linear mapping functions for image registration A new approach to determination of mapping functions for registration of digital images is presented. Given the coordinates of corresponding control points in two images of the same scene, first the images are divided into triangular regions by triangulating the control points. Then a linear mapping function is obtained by registering each pair of corresponding triangular regions in the images. The overall mapping function is then obtained by piecing together the linear mapping functions.

Investigating 4d Movie Audiences' Emotional Responses To Motion Effects And Empathy Designing 4D effects corresponding to audiences' emotional responses is important because 4D effects are critical components of 4D movies that provide rich emotional experiences. The recent increasing popularity of 4D content has led to the development of motion effect technology, which involves motions of the chair according to the scene. However, it is difficult to find studies that systematically investigated the influence of motion effects on audiences' emotional responses. This study investigated the emotional responses of a 4D movie audience to motion effects according to their level of empathy. Participants (mean: 25.0 years, standard deviation: 5.0) with varying levels of empathy watched movie clips provided with or without single pitch motion effects. The degree that the motion effect and empathy affected the elicited emotions differed depending on the emotion type. For example, participants with high empathy reported stronger intensity of fear when short and weak motion effects were exhibited than when there was no motion effect. Distinct motion effect design guidelines that can be adopted to enhance audiences' emotional experiences were proposed. The findings can be referred to when investigating the emotional responses of 4D movie audiences.

Absolute and Differential Thresholds of Motion Effects in Cardinal Directions ABSTRACTIn this paper, we report both absolute and differential thresholds for motion in the six cardinal directions as comprehensively as possible. As with general 4D motion effects, we used sinusoidal motions with low intensity and large frequency as stimuli. Hence, we could also compare the effectiveness of motion types in delivering motion effects. We found that the thresholds for the z-axis (up-down) were higher than those for the x-axis (front-back) and y-axis (left-right) in both kinds of thresholds and that the type of motion significantly affected both thresholds. Further, between differential thresholds and reference intensities, we found a strong linear relationship for roll, yaw and, surge. Compared to them, a relatively weak linear relationship was observed for the rest of the motion types. Our results can be useful for generating motion effects for 4D contents while considering the human sensitivity to motion feedback.

Real-time perception-level translation from audio signals to vibrotactile effects In this paper, we propose a real-time perception-level audio-to-vibrotactile translation algorithm. Unlike previous signal-level conversion methods, our algorithm considers only perceptual characteristics, such as loudness and roughness, of audio and tactile stimuli. This perception-level approach allows for designing intuitive and explicit conversion models with clear understandings of their perceptual consequences. Our current implementation is tailored to accurate detection of special sound effects to provide well-synchronized audio-tactile feedback in immersive applications. We also assessed the performance of our translation algorithm in terms of the detection rate of special sound effects, computational performance, and user preference. All the experimental results supported that our algorithm works well as intended with better performance than the signal-level conversion methods, especially for games. Our algorithm can be easily realized in current products, including mobile devices, gaming devices, and 4D home theater systems, for richer user experience.

Camera Space Synthesis of Motion Effects Emphasizing a Moving Object in 4D films Four-dimensional (4D) films, which provide special physical effects to the audience with audiovisual stimuli, are gaining more popularity and acceptance. One of the most frequent 4D effects is the object-based motion effect, which refers to the vestibular stimulus generated by a motion chair to emphasize a moving object of interest, e.g., the flying iron man, displayed on the screen. In this paper...

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.

An online mechanism for multi-unit demand and its application to plug-in hybrid electric vehicle charging We develop an online mechanism for the allocation of an expiring resource to a dynamic agent population. Each agent has a non-increasing marginal valuation function for the resource, and an upper limit on the number of units that can be allocated in any period. We propose two versions on a truthful allocation mechanism. Each modifies the decisions of a greedy online assignment algorithm by sometimes cancelling an allocation of resources. One version makes this modification immediately upon an allocation decision while a second waits until the point at which an agent departs the market. Adopting a prior-free framework, we show that the second approach has better worst-case allocative efficiency and is more scalable. On the other hand, the first approach (with immediate cancellation) may be easier in practice because it does not need to reclaim units previously allocated. We consider an application to recharging plug-in hybrid electric vehicles (PHEVs). Using data from a real-world trial of PHEVs in the UK, we demonstrate higher system performance than a fixed price system, performance comparable with a standard, but non-truthful scheduling heuristic, and the ability to support 50% more vehicles at the same fuel cost than a simple randomized policy.

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...

Multi-column Deep Neural Networks for Image Classification Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.

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.

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 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.

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.

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...

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.

Integrating Encryption and Marking for Remote Sensing Image Based on Orthogonal Decomposition For the special characters, remote sensing image has higher requirements not only in the security but also in the management; it requires not only the active encryption during storage and transmission for preventing information leakage but also the marking technology to prevent illegal usage as well as copyright protection or even source tracing. Therefore, this paper proposes to integrate encryption and marking technology by the independence and fusion of orthogonal decomposition for the comprehensive security protection of remote sensing image. Under the proposed scheme, encryption and marking technology can achieve the operation independence and content mergence; moreover, there is no special requirement in selecting encryption and marking algorithms. It makes up the shortage of recent integration of encryption and watermarking based on spatial scrambling in applicability and security. According to the experimental results, integration of encryption and marking technology based on orthogonal decomposition satisfies the common constraints of encryption, and marking technology, furthermore, has little impact on remote sensing image data characters and later applications.

Separable reversible data hiding in encrypted images via adaptive embedding strategy with block selection. •An adaptive, separable reversible data hiding scheme in encrypted image is proposed.•Analogues stream-cipher and block permutation are used to encrypt original image.•Classification and selection for encrypted blocks are conducted during embedding.•An accurate prediction strategy was employed to achieve perfect image recovery.•Our scheme has better rate-distortion performance than some state-of-the-art schemes.

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.

Methods for Flexible Management of Blockchain-Based Cryptocurrencies in Electricity Markets and Smart Grids The growing trend in the use of blockchain-based cryptocurrencies in modern communities provides several advantages, but also imposes several challenges to energy markets and power systems, in general. This paper aims at providing recommendations for efficient use of digital cryptocurrencies in today's and future smart power systems, in order to face the challenging aspects of this new technology. In this paper, existing issues and challenges of smart grids in the presence of blockchain-based cryptocurrencies are presented and some innovative approaches for efficient integration and management of blockchain-based cryptocurrencies in smart grids are proposed. Also some recommendations are given for improving the smart grids performance in the presence of digital cryptocurrencies and some future research directions are highlighted.

Local Load Redistribution Attacks in Power Systems With Incomplete Network Information Power grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. Recent studies have shown that an attacker can inject pre-determined false data into smart meters such that it can pass the residue test of conventional state estimator. However, the calculation of the false data vector relies on the network (topology and parameter) information of the entire grid. In practice, it is impossible for an attacker to obtain all network information of a power grid. Unfortunately, this does not make power systems immune to false data injection attacks. In this paper, we propose a local load redistribution attacking model based on incomplete network information and show that an attacker only needs to obtain the network information of the local attacking region to inject false data into smart meters in the local region without being detected by the state estimator. Simulations on the modified IEEE 14-bus system demonstrate the correctness and effectiveness of the proposed model. The results of this paper reveal the mechanism of local false data injection attacks and highlight the importance and complexity of defending power systems against false data injection attacks.