Accelerating CNN inference on FPGAs: A Survey

Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classi cation and segmentation. The large amount of processing required by CNNs calls for dedicated and tailored hardware support methods. Moreover, CNN workloads have a streaming nature, well suited to recon gurable hardware architectures such as FPGAs. The amount and diversity of research on the subject of CNN FPGA acceleration within the last 3 years demonstrates the tremendous industrial and academic interest. This paper presents a state-of-the-art of CNN inference accelerators over FPGAs. The computational workloads, their parallelism and the involved memory accesses are analyzed. At the level of neurons, optimizations of the convolutional and fully connected layers are explained and the performances of the di erent methods compared. At the network level, approximate computing and datapath optimization methods are covered and state-of-the-art approaches compared. The methods and tools investigated in this survey represent the recent trends in FPGA CNN inference accelerators and will fuel the future advances on e cient hardware deep learning.


Modular joint design for performance enhanced humanoid robot LOLA

The paper presents the performance enhanced humanoid robot LOLA which is currently being manufactured. The goal of the project is the realization of a fast, human-like walking motion. The robot is characterized by its lightweight construction, a modular, multi-sensory joint design with brushless motors and an electronics architecture using decentralized joint controllers. The fusion of motor, gear and sensors into a highly integrated, mechatronic joint module has several advantages for the whole system, including high power density, good dynamic performance and reliability. Additional degrees of freedom are introduced in elbow, waist and toes. Linear actuators are employed for the knee joints for a better mass distribution in the legs


Localized forgery detection in hyperspectral document images

Hyperspectral imaging is emerging as a promising technology to discover patterns that are otherwise hard to identify with regular cameras. Recent research has shown the potential of hyperspectral image analysis to automatically distinguish visually similar inks. However, a major limitation of prior work is that automatic distinction only works when the number of inks to be distinguished is known a priori and their relative proportions in the inspected image are roughly equal. This research work aims at addressing these two problems. We show how anomaly detection combined with unsupervised clustering can be used to handle cases where the proportions of pixels belonging to the two inks are highly unbalanced. We have performed experiments on the publicly available UWA Hyperspectral Documents dataset. Our results show that INFLO anomaly detection algorithm is able to best distinguish inks for highly unbalanced ink proportions.


HYDRA: hybrid design for remote attestation (using a formally verified microkernel)

Remote Attestation (RA) allows a trusted entity (verifier) to securely measure internal state of a remote untrusted hardware platform (prover). RA can be used to establish a static or dynamic root of trust in embedded and cyber-physical systems. It can also be used as a building block for other security services and primitives, such as software updates and patches, verifiable deletion and memory resetting. There are three major types of RA designs: hardware-based, software-based, and hybrid, each with its own set of benefits and drawbacks.
 This paper presents the first hybrid RA design - called HYDRA - that builds upon formally verified software components that ensure memory isolation and protection, as well as enforce access control to memory and other resources. HYDRA obtains these properties by using the formally verified seL4 microkernel. (Until now, this was only attainable with purely hardware-based designs.) Using seL4 imposes fewer hardware requirements on the underlying microprocessor. Also, building upon a formally verified software component increases confidence in security of the overall design of HYDRA and its implementation. We instantiate HYDRA on two commodity hardware platforms and assess the performance and overhead of performing RA on such platforms via experimentation; we show that HYDRA can attest 10MB of memory in less than 250msec when using a Speck-based cryptographic checksum.


A Novel Boil-Off Gas Re-Liquefaction Using a Spray Recondenser for Liquefied Natural-Gas Bunkering Operations

This study presents the design of a novel boil-off gas (BOG) re-liquefaction technology using a BOG recondenser system. The BOG recondenser system targets the liquefied natural gas (LNG) bunkering operation, in which the BOG phase transition occurs in a pressure vessel instead of a heat exchanger. The BOG that is generated during LNG bunkering operation is characterized as an intermittent flow with various peak loads. The system was designed to temporarily store the transient BOG inflow, condense it with subcooled LNG and store the condensed liquid. The superiority of the system was verified by comparing it with the most extensively employed conventional re-liquefaction system in terms of consumption energy and via an exergy analysis. Static simulations were conducted for three compositions; the results indicated that the proposed system provided 0 to 6.9% higher efficiencies. The exergy analysis indicates that the useful work of the conventional system is 24.9%, and the useful work of the proposed system is 26.0%. Process dynamic simulations of six cases were also performed to verify the behaviour of the BOG recondenser system. The results show that the pressure of the holdup in the recondenser vessel increased during the BOG inflow mode and decreased during the initialization mode. The maximum pressure of one of the bunkering cases was 3.45 bar. The system encountered a challenge during repetitive operations due to overpressurizing of the BOG recondenser vessel.


The compressive response of carbon fiber composite pyramidal truss sandwich cores Dedicated to Professor

Pyramidal truss sandwich cores with relative densities 4q in the range 1 – 10 % have been made from carbon fiber reinforced polymer laminates using a snap-fitting method. The measured quasi-static uniaxial compressive strength increased with increasing 4q from 1 to 11 MPa over the relative density range investigated here. A robust face-sheet/ truss joint design was developed to suppress truss– face sheet node fracture. Core failure then occurred by either (i) Euler buckling (4q < 2%) or (ii) delamination failure (4q > 2%) of the struts. Micro-buckling failure of the struts was not observed in the experiments reported here. Analytical models for the collapse of the composite cores by Euler bucking, delamination failure and micro-buckling of the struts have been developed. Good agreement between the measurements and predictions based on the Euler buckling and delamination failure of the struts is obtained. The micro-buckling analysis indicates this mechanism of failure is not activated until delamination is suppressed. The measurements and predictions reported here indicate that composite cellular materials with a pyramidal micro-structure reside in a gap in the strength versus density material property space, providing new opportunities for lightweight, high strength structural design.


Unsupervized Image Clustering With SIFT-Based Soft-Matching Affinity Propagation

It is known that affinity propagation can perform exemplar-based unsupervised image clustering by taking as input similarities between pairs of images and producing a set of exemplars that best represent the images, and then assigning each nonexemplar image to its most appropriate exemplar. However, the clustering performance of affinity propagation is largely limited by the adopted similarity between any pair of images. As the scale invariant feature transform (SIFT) has been widely employed to extract image features, the nonmetric similarity between any pair of images was proposed by “hard” matching of SIFT features (e.g., counting the number of matching SIFT features). In this letter, we notice, however, that the decision of hard matching of SIFT features is binary, which is not necessary for deriving similarities. Hence, we propose a novel measure of similarities by replacing hard matching with the so-called soft matching. Experimental examples show that significant performance gains can be achieved by the resulting affinity propagation algorithm.


Data quality is a critical issue during the implementation of an enterprise resource planning (ERP) system. Data quality problems can have a significant impact on an organisation’s information system. Therefore, it is essential to understand data quality issues to ensure success in implementing ERP systems. This paper uses SAP as an example of an ERP system and describes a study, which explores data quality problems with existing systems, and identifies critical success factors that impact data quality. The study resulted in the development of a framework for understanding data quality issues in implementing an ERP, and application of this framework in a case study in two large Australian organisations. The findings of the study suggest that the importance of data quality needs to be widely understood in implementing an ERP, as well as providing recommendations that may be useful to practitioners. Computerised databases continue to proliferate and as organisations become increasingly dependent upon their databases to support business process and decision making, the number of errors in stored data and the organisational impact of these errors are likely to increase (Klein, 1998). Information research has demonstrated that inaccurate and incomplete data may adversely affect the competitive success of an organisation (Redman, 1992). Poor quality information can have significant social and business impacts (Strong et al., 1997). For example, NBC News reported, `̀ dead people still eat!’’ It was found that, because of outdated information in government databases, food stamps continued to be sent to recipients long after they died. Fraud from food stamps cost US taxpayers billions of dollars. Business and industry often have similar DQ problems. For example, a financial company absorbed a net loss totalling more than $250 million when interest rates changed dramatically, all because the company database was lacking in quality and simple updates (Huang et al., 1999). In order to ensure DQ in information systems, it is important to understand the underlying factors that influence DQ. There have been some studies of critical success factors in information systems and quality management, such as total quality management (TQM) and just-in-time (JIT). Some of the DQ literature has also addressed the critical points and steps for DQ. Table I shows the related research efforts and reflects issues or elements of critical success factors for DQ. ERP-SAP ERP systems use relational database technology to integrate the various elements of an organisation’s information systems. They provide a number of separate, but integrated modules. The use of an ERP avoids the costs of maintaining many separate `̀ legacy’’ systems and overcomes the problems associated with interfacing different systems. It is quite expensive to implement an ERP system, requiring multimillion-dollar budgets and large project teams. Despite the expense, such systems are becoming very widely used in the world’s largest companies (Scapens et al., 1998). SAP is one of the well-known ERP packages on the market, with strengths in finance and accounting. SAP is an integrated business system, which evolved from a concept first developed by five former IBM systems engineers in 1972. It is a software package designed to enable businesses to effectively and efficiently run a variety of business processes within a single integrated system. SAP stands for systems, applications and products in data processing. It is produced by SAP AG, based in Walldorf, Germany, which employs more than 22,000 people in more than 50 countries. SAP AG is the third-largest software company in the world. SAP software is deployed at more than 22,000 business installations in more than 100 countries and is currently used by companies of all sizes, including more than half of the world’s 500 top companies (SAP AG Corporate Overview, 2000). Therefore, SAP is an excellent system to study in an effort to evaluate ERP environments. Table I Summary of literature review identifying factors influencing (data) quality Fa ctor Sa raph et a l. (19 89) E ng lish (1999) Firth (19 96) W ang (1 998) H uang e t a l. (1 999) Segev (1996 ) Z hu and M eredith (19 95) O rr (1998) Cu sh ing (1974) Yu and N eter (19 73) Fie lds et a l. (19 86) N ich ols (19 87) Bo w en (199 3) Tra in in g * * * * * To p m anage m ent supp ort * * * * * * O rganisation al stru cture (com m unicatio n) * * M anag e chang e * E m ployee/p erson nel re la tions * * * D Q contro l Interna l contro l * Input cont ro l * So urce: From Xu, 200 0 [ 48 ] Hongjiang Xu, Jeretta Horn Nord, Noel Brown and G. Daryl Nord Data quality issues in implementing an ERP Industrial Management & Data Systems 102/1 [2002] 47±58


Real-Time Billboard Substitution in a Video Stream

We present a system that accepts as input a continuous stream of TV broadcast images from sports events. The system automatically detects a predetermined billboard in the scene, and replaces it with a user defined pattern, with no cooperation from the billboards or camera operators. The replacement is performed seamlessly so that a viewer should not be able to detect the substitution. This allows the targeting of advertising to the appropriate audience, which is especially useful for international events. This requires several modules using state of the art computer graphics, image processing and computer vision technology. The system relies on modular design, and on a pipeline architecture, in which the search and track modules propagate their results in symbolic form throughout the pipeline buffer, and the replacement is performed at the exit of the pipe only, therefore relying on accumulated information. Also, images are processed only once. This allows the system to make replacement decisions based on complete sequences, thus avoiding mid-sequence on screen billboard changes. We present the algorithms and the overall system architecture, and discuss further applications of the technology.


A Comprehensive Study of StaQC for Deep Code Summarization

Learning the mapping between natural language (NL) and programming language, such as retrieving or generating code snippets based on NL queries and annotating code snippets using NL, has been explored by lots of research works [2, 19, 21]. At the core of these works are machine learning and deep learning models, which usually demand for large datasets of <NL, code> pairs for training. This paper describes an experimental study of StaQC [50], a large-scale and high-quality dataset of <NL, code> pairs in Python and SQL domain, systematically mined from the Stack Overflow forum (SO). We compare StaQC with two other popular datasets mined from SO on the code summarization task, showing that StaQC helps achieve substantially better results, improving the current state-of-the-art model by an order of 8% ∼ 9% in BLEU metric.


Finding Groups in Data: An Introduction to Cluster Analysis

Description: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.


A Cluster Separation Measure

A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.


K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality

The K-means algorithm is a commonly used technique in cluster analysis. In this paper, several questions about the algorithm are addressed. The clustering problem is first cast as a nonconvex mathematical program. Then, a rigorous proof of the finite convergence of the K-means-type algorithm is given for any metric. It is shown that under certain conditions the algorithm may fail to converge to a local minimum, and that it converges under differentiability conditions to a Kuhn-Tucker point. Finally, a method for obtaining a local-minimum solution is given.


Optimization of Control Parameters for Genetic Algorithms

The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency. A class of adaptive search procedures called genetic algorithms (GA) has been used to optimize a wide variety of complex systems. GA's are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems. The results are validated on an image registration problem. GA's are shown to be effective for both levels of the systems optimization problem.


Performance Optimization of a High Current Dual Active Bridge with a Wide Operating Voltage Range

The main aim of this paper is to improve the performance of high current dual active bridge converters when operated over a wide voltage range. A typical application is for fuel cell vehicles where a bi-directional interface between a 12V battery and a high voltage DC bus is required. The battery side voltage ranges from 11V to 16V while the fuel cell is operated between 220V and 447V and the required power is typically 1kW. Careful analysis shows that the high currents on the battery side cause significant design issues in order to obtain a high efficiency. The standard phase shift modulation method can result in high conduction and switching losses. This paper proposes a combined triangular and trapezoidal modulation method to reduce losses over the wide operating range. Approximately, a 2% improvement in efficiency can be expected. An experimental system is used to verify the improved performance of the dual active bridge using the proposed advanced modulation method.


Segmentation of color image using adaptive thresholding and masking with watershed algorithm

The individualization of an object from a digital image is a common problem in the field of image processing. We propose a modified version of the watershed algorithm for image segmentation. We have presented an adaptive masking and a threshoding mechanism over each color channel to overcome over segmentation problem, before combining the segmentation from each channel into the final one. Our proposed method ensures accuracy and quality of the 10 different kinds of color images. The experimental results are obtained using image quality assessment (IQA) metrics such as PSNR, MSE, PSNRRGB and Color Image Quality Measure (CQM) based on reversible YUV color transformation. Consequently, the proposed modified watershed approach can enhance the image segmentation performance. Similarly, it is worth noticing that our proposed MWS approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application. According to the visual and quantitative verification, the proposed algorithm is performing better than three other algorithms.


Disease recognition in Mango crop using modified rotational kernel transform features

Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT based scheme is used to calculate the directional features and histograms for plant parts like leaf and fruit of Mango crop. These histograms and the directional feature set with use of artificial neural network lead to a better recognition technique of Anthracnose disease which occurs in form of black spots on fruits and leaves of mango plant. The results obtained using proposed MRKT directional feature set have shows the better results with accuracy up to 98%.


A Taxonomy of Attacks on the DNP3 Protocol

Distributed Network Protocol (DNP3) is the predominant SCADA protocol in the energy sector – more than 75% of North American electric utilities currently use DNP3 for industrial control applications. This paper presents a taxonomy of attacks on the protocol. The attacks are classified based on targets (control center, outstation devices and network/communication paths) and threat categories (interception, interruption, modification and fabrication). To facilitate risk analysis and mitigation strategies, the attacks are associated with the specific DNP3 protocol layers they exploit. Also, the operational impact of the attacks is categorized in terms of three key SCADA objectives: process confidentiality, process awareness and process control. The attack taxonomy clarifies the nature and scope of the threats to DNP3 systems, and can provide insights into the relative costs and benefits of implementing mitigation strategies.


A Roadmap for the Value-Loading Problem

We analyze the value-loading problem. This is the problem of robustly encoding moral values into an AI agent interacting with a complex environment. Like many before, we argue that this is both a major concern and an extremely challenging problem. Solving it will likely require years, if not decades, of multidisciplinary work by teams of top scientists and experts. Given how uncertain the timeline of human-level AI research is, we thus argue that a pragmatic partial solution should be designed as


Adaptive Kalman Filtering Methods for Low-Cost GPS/INS Localization for Autonomous Vehicles

For autonomous vehicles, navigation systems must be accurate enough to provide lane-level localization. Highaccuracy sensors are available but not cost-effective for production use. Although prone to significant error in poor circumstances, even low-cost GPS systems are able to correct Inertial Navigation Systems (INS) to limit the effects of dead reckoning error over short periods between sufficiently accurate GPS updates. Kalman filters (KF) are a standard approach for GPS/INS integration, but require careful tuning in order to achieve quality results. This creates a motivation for a KF which is able to adapt to different sensors and circumstances on its own. Typically for adaptive filters, either the process (Q) or measurement (R) noise covariance matrix is adapted, and the other is fixed to values estimated a priori. We show that by adapting Q based on the state-correction sequence and R based on GPS receiver-reported standard deviation, our filter reduces GPS root-mean-squared error by 23% in comparison to raw GPS, with 15% from only adapting R.


User simulation for spoken dialogue systems: learning and evaluation

We propose the “advanced” n-grams as a new technique for simulating user behaviour in spoken dialogue systems, and we compare it with two methods used in our prior work, i.e. linear feature combination and “normal” n-grams. All methods operate on the intention level and can incorporate speech recognition and understanding errors. In the linear feature combination model user actions (lists of〈 speech act, task 〉 pairs) are selected, based on features of the current dialogue state which encodes the whole history of the dialogue. The user simulation based on “normal” n-grams treats a dialogue as a sequence of lists of 〈 speech act, task 〉 pairs. Here the length of the history considered is restricted by the order of the n-gram. The “advanced” n-grams are a variation of the normal ngrams, where user actions are conditioned not only on speech acts and tasks but also on the current status of the tasks, i.e. whether the information needed by the application (in our case flight booking) has been provided and confirmed by the user. This captures elements of goal-directed user behaviour. All models were trained and evaluated on the C OMMUNICATOR corpus, to which we added annotations for user actions and dialogue context. We then evaluate how closely the synthetic responses resemble the real user responses by comparing the user response generated by each user simulation model in a given dialogue context (taken from the annotated corpus) with the actual user response. We propose the expected accuracy, expected precision, and expected recall evaluation metrics as opposed to standard precision and recall used in prior work. We also discuss why they are more appropriate metrics for evaluating user simulation models compared to their standard counterparts. The advanced n-grams produce higher scores than the normal n-grams for small values of n, which proves their strength when little amount of data is available to train larger ngrams. The linear model produces the best expected accuracy but with respect to expected precision and expected recall it is outperformed by the large n-grams even though it is trained using more information. As a task-based evaluation, we also run each of the user simulation models against a system policy trained on the same corpus. Here the linear feature combination model outperforms the other methods and the advanced n-grams outperform the normal ngrams for all values of n, which again shows their potential. We also calculate the perplexity of the different user models.


Correlated Nonideal Effects of Dark and Light I–V Characteristics in a-Si/c-Si Heterojunction Solar Cells

a-Si/c-Si (amorphous Silcon/crystalline Silicon) heterojunction solar cells exhibit several distinctive dark and light I-V nonideal features. The dark I-V of these cells exhibits unusually high ideality factors at low forward-bias and the occurrence of a “knee” at medium forward-bias. Nonidealities under illumination, such as the failure of superposition and the occurrence of an “S-type” curve, are also reported in these cells. However, the origin of these nonidealities and how the dark I-V nonidealities manifest themselves under illumination, and vice versa, have not been clearly and consistently explained in the current literature. In this study, a numerical framework is used to interpret the origin of the dark I-V nonidealities, and a novel simulation technique is developed to separate the photo-current and the contact injection current components of the light I-V. Using this technique, the voltage dependence of photo-current is studied to explain the failure of the superposition principle and the origin of the S-type light I-V characteristics. The analysis provides a number of insights into the correlations between the dark I-V and the light I-V. Finally, using the experimental results from this study and from the current literature, it is shown that these nonideal effects indeed affect the dark I-V and the light I-V in a predictable manner.


Power Control in Ad-Hoc Networks: Theory, Architecture, Algorithm and Implementation of the COMPOW Protocol

We present a new protocol for power control in ad hoc networks. We describe the issues in conceptualizing the power control problem, and provide an architecturally simple as well as theoretically well founded solution. The solution is shown to simultaneously satisfy the three objectives of maximizing the traffic carrying capacity of the entire network, extending battery life through providing low power routes, and reducing the contention at the MAC layer. Further, the protocol has the plug and play feature that it can be employed in conjunction with any routing protocol that pro-actively maintains a routing table. The protocol, called COMPOW, has been implemented in the Linux kernel and we describe the software architecture and implementation details.


Supervised Matrix Factorization for Cross-Modality Hashing

Matrix factorization has been recently utilized for the task of multi-modal hashing for cross-modality visual search, where basis functions are learned to map data from different modalities to the same Hamming embedding. In this paper, we propose a novel cross-modality hashing algorithm termed Supervised Matrix Factorization Hashing (SMFH) which tackles the multi-modal hashing problem with a collective non-matrix factorization across the different modalities. In particular, SMFH employs a well-designed binary code learning algorithm to preserve the similarities among multi-modal original features through a graph regularization. At the same time, semantic labels, when available, are incorporated into the learning procedure. We conjecture that all these would facilitate to preserve the most relevant information during the binary quantization process, and hence improve the retrieval accuracy. We demonstrate the superior performance of SMFH on three cross-modality visual search benchmarks, i.e., the PASCAL-Sentence, Wiki, and NUS-WIDE, with quantitative comparison to various stateof-the-art methods [Kumar and Udupa, 2011; Rastegari et al., 2013; Zhang and Li, 2014; Ding et al., 2014].


Proposing a new design approach for M-learning applications

As Information and Communication Technologies (ICT) are developing frequently, new learning approaches have been introduced to facilitate teaching and learning processes. Mobile devices are growing in popularity and there has been an increasing interest in their potential as innovative tools for learning. Mobile learning or shortly (M-learning) is a new learning approach intending to use mobile devices such as laptops, smart phones and personal digital assistant (PDA) in learning process in any place and at any time. M-learning application needs to be designed in a way that considers the special features and constraints of mobile devices such as screen size, available storage, processor speed and battery life. This paper explores the existing design approaches for M-learning and highlights their main limitations. As a step forward in this direction, this paper also proposes a new design approach for M-learning applications focusing mainly on three main aspects: learner, learning content and technology. The approach consists of three phases: starting dimensions (SD) phase, M-learning Development (MLD) phase and Learning Content Design (LCD) phase. Finally, this paper provides a case study scenario to demonstrate the feasibility of the proposed approach.


A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks

Localization is one of the key technologies in wireless sensor networks (WSNs), since it provides fundamental support for many location-aware protocols and applications. Constraints on cost and power consumption make it infeasible to equip each sensor node in the network with a global position system (GPS) unit, especially for large-scale WSNs. A promising method to localize unknown nodes is to use mobile anchor nodes (MANs), which are equipped with GPS units moving among unknown nodes and periodically broadcasting their current locations to help nearby unknown nodes with localization. A considerable body of research has addressed the mobile anchor node assisted localization (MANAL) problem. However, to the best of our knowledge, no updated surveys on MAAL reflecting recent advances in the field have been presented in the past few years. This survey presents a review of the most successful MANAL algorithms, focusing on the achievements made in the past decade, and aims to become a starting point for researchers who are initiating their endeavors in MANAL research field. In addition, we seek to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful advances in this research field.


Coarse-to-Fine Question Answering for Long Documents

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-ofthe-art models. While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences. Inspired by how people first skim the document, identify relevant parts, and carefully read these parts to produce an answer, we combine a coarse, fast model for selecting relevant sentences and a more expensive RNN for producing the answer from those sentences. We treat sentence selection as a latent variable trained jointly from the answer only using reinforcement learning. Experiments demonstrate the state of the art performance on a challenging subset of the WIKIREADING dataset (Hewlett et al., 2016) and on a new dataset, while speeding up the model by 3.5x-6.7x.


Direct Causality Detection via the Transfer Entropy Approach

The detection of direct causality, as opposed to indirect causality, is an important and challenging problem in root cause and hazard propagation analysis. Several methods provide effective solutions to this problem when linear relationships between variables are involved. For nonlinear relationships, currently only overall causality analysis can be conducted, but direct causality cannot be identified for such processes. In this paper, we describe a direct causality detection approach suitable for both linear and nonlinear connections. Based on an extension of the transfer entropy approach, a direct transfer entropy (DTE) concept is proposed to detect whether there is a direct information flow pathway from one variable to another. Especially, a differential direct transfer entropy concept is defined for continuous random variables, and a normalization method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. The effectiveness of the proposed method is illustrated by several examples, including one experimental case study and one industrial case study.


From Adjective Glosses to Attribute Concepts: Learning Different Aspects That an Adjective Can Describe

Adjectives are one of the major contributors to conveying subjective meaning to sentences. Understanding the semantics of adjectives is essential for understanding natural language sentences. In this paper we propose a novel approach for learning different properties that an adjective can describe, corresponding to the ‘attribute concepts’, which is not currently available in existing linguistic resources. We accomplish this by reading adjective glosses and bootstrapping attribute concepts from a seed of adjectives. We show that we can learn new attribute concepts using adjective glosses of WordNet adjectives with high accuracy as compared with human annotation on a test set.


Smart home garden irrigation system using Raspberry Pi

Irrigation system is a method of allowing water to drip slowly to the roots of plants, either onto the soil surface or directly onto the root zone, through solenoid valve. However, it is found that the market price of the system is expensive for small area coverage. Thus, this paper proposes a design for smart home garden irrigation system that implements ready-to-use, energy-efficient, and cost effective devices. Raspberry Pi, which is implemented in this system is integrated with multi-sensors such as soil moisture sensors, ultrasonic sensors, and light sensors. This proposed system managed to reduce cost, minimize waste water, and reduce physical human interface. In this paper, the relay is utilized to control the switching of solenoid valve. The system also managed to measure moisture of the soil and control the solenoid valve according to human's requirements. It is conducted with Graphical User Interface (GUI) using Android application to activate watering activity. Email notification is also sent to the home user for alert purposes either for normal or critical operations. An experimental setup has been tested and it is proven that the system can intelligently control and monitor the soil moisture levels in the experiment field.


Achieving lightweight trustworthy traceability

Despite the fact that traceability is a required element of almost all safety-critical software development processes, the trace data is often incomplete, inaccurate, redundant, conflicting, and outdated. As a result, it is neither trusted nor trustworthy. In this vision paper we propose a philosophical change in the traceability landscape which transforms traceability from a heavy-weight process producing untrusted trace links, to a light-weight results-oriented trustworthy solution. Current traceability practices which retard agility are cast away and replaced with a disciplined, just-in-time approach. The novelty of our solution lies in a clear separation of trusted trace links from untrusted ones, the change in perspective from `living-with' inacurate traces toward rigorous and ongoing debridement of stale links from the trusted pool, and the notion of synthesizing available `project exhaust' as evidence to systematically construct or reconstruct purposed, highly-focused trace links.


Instant Dehazing of Images Using Polarization

We present an approach to easily remove the effects of haze from images. It is based on the fact that usually airlight scattered by atmospheric particles is partially polarized. Polarization filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, works under a wide range of atmospheric and viewing conditions. We analyze the image formation process, taking into account polarization effects of atmospheric scattering. We then invert the process to enable the removal of haze from images. The method can be used with as few as two images taken through a polarizer at different orientations. This method works instantly, without relying on changes of weather conditions. We present experimental results of complete dehazing in far from ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color. As a by product, the method also yields a range (depth) map of the scene, and information about properties of the atmospheric particles.


Types for Units-of-Measure: Theory and Practice

Units-of-measure are to science what types are to programming. In science and engineering, dimensional and unit consistency provides a first check on the correctness of an equation or formula, just as in programming the passing of a program by the type-checker eliminates one possible reason for failure. Units-of-measure errors can have catastrophic consequences, the most famous of which was the loss in September 1999 of NASA’s Mars Climate Orbiter probe, caused by a confusion between newtons (the SI unit of force) and lbf (poundforce, a unit of force sometimes used in the US). The report into the disaster made many recommendations [14]. Notably absent, though, was any suggestion that programming languages might assist in the prevention of such errors, either through static analysis tools, or through type-checking. Over the years, many people have suggested ways of extending programming languages with support for static checking of units-of-measure. It’s even possible to abuse the rich type systems of existing languages such as C++ and Haskell to achieve it, but at some cost in usability [18, 6]. More recently, Sun’s design for its Fortress programming language has included type system support for dimensions and units [2]. In this short course, we’ll look at the design supported by the F# programming language, the internals of its type system, and the theoretical basis for units safety. The tutorial splits into three parts:


Programming languages and dimensions

Scientists and engineers must ensure that the equations and formulae which they use are dimensionally consistent, but existing programming languages treat all numeric values as dimensionless. This thesis investigates the extension of programming languages to support the notion of physical dimension. A type system is presented similar to that of the programming language ML but extended with polymorphic dimension types. An algorithm which infers most general dimension types automatically is then described and proved correct. The semantics of the language is given by a translation into an explicitlytyped language in which dimensions are passed as arguments to functions. The operational semantics of this language is specified in the usual way by an evaluation relation defined by a set of rules. This is used to show that if a program is well-typed then no dimension errors can occur during its evaluation. More abstract properties of the language are investigated using a denotational semantics: these include a notion of invariance under changes in the units of measure used, analogous to parametricity in the polymorphic lambda calculus. Finally the dissertation is summarised and many possible directions for future research in dimension types and related type systems are described.


Development environments for autonomous mobile robots: A survey

Robotic Development Environments (RDEs) have come to play an increasingly important role in robotics research in general, and for the development of architectures for mobile robots in particular. Yet, no systematic evaluation of available RDEs has been performed; establishing a comprehensive list of evaluation criteria targeted at robotics applications is desirable that can subsequently be used to compare their strengths and weaknesses. Moreover, there are no practical evaluations of the usability and impact of a large selection of RDEs that provides researchers with the information necessary to select an RDE most suited to their needs, nor identifies trends in RDE research that suggest directions for future RDE development. This survey addresses the above by selecting and describing nine open source, freely available RDEs for mobile robots, evaluating and comparing them from various points of view. First, based on previous work concerning agent systems, a conceptual framework of four broad categories is established, encompassing the characteristics and capabilities that an RDE supports. Then, a practical evaluation of RDE usability in designing, implementing, and executing robot architectures is presented. Finally, the impact of specific RDEs on the field of robotics is addressed by providing a list of published applications and research projects that give concrete examples of areas in which systems have been used. The comprehensive evaluation and comparison of the nine RDEs concludes with suggestions of how to use the results of this survey and a brief discussion of future trends in RDE design.


VQM-based QoS/QoE mapping for streaming video

Numerous new broadband and multimedia applications have emerged in recent years, in which streaming video services are particularly of interest. Unlike data communication, streaming video requires a subjective measure of quality referred to as the Quality of Experience (QoE), but the subjective evaluation is very time consuming and complex. We are proposing an objective approach to provide mapping between QoE and Quality of Service (QoS), in which Video Quality Metric (VQM) is utilized to indicate QoE. We have analyzed the emulation results and derived a simple formula to estimate QoE from QoS parameters for streaming video under certain conditions. This method is non-intrusive and quick to use.


Nanoconnectomic upper bound on the variability of synaptic plasticity

Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes.


Online Attendance Management System Using RFID with Object Counter

Educational institutions’ administrators in our country and the whole world are concerned about regularity of student attendance. Student overall academic performance is affected by it. The conventional method of taking attendance by calling names or signing on paper is very time consuming, and hence inefficient. Radio Frequency Identification (RFID) based attendance system is one of the solutions to address this problem. A system that can automatically capture student’s attendance by flashing their student card at the RFID reader and save all the mentioned troubles was given by T.S.Lim et al. [1].The RFID system using web-based applications such as ASP.NET and IIS server to cater the recording and reporting of the students’ attendances was given by Abdul Aziz Mohammed et.al. [2]. Arulogun O. T. et al made an attempt to solve recurrent lecture attendance monitoring problem in developing countries using RFID technology [3]. Rajan Patel et al [4] presents the integration of ubiquitous computing systems into classroom for managing the students’ attendance using RFID technology. H.B. Hamid et al [5] gave a system that has been built using the web-based applications such as JSP, MySQL and Apache to cater the recording and reporting of the students’ attendances. NetBeans IDE 6.1 is used for developing the overall system. We have proposed the system in this paper using C#. Microsoft Visual Studio is used for the system designing. Also, the issue related to fake /false attendance through the RFID system has been addressed, we eliminate it by using a special object counter for the head count. Ankita Agrawal & Ashish Bansal 132


Model-based evaluation: from dependability to security

The development of techniques for quantitative, model-based evaluation of computer system dependability has a long and rich history. A wide array of model-based evaluation techniques is now available, ranging from combinatorial methods, which are useful for quick, rough-cut analyses, to state-based methods, such as Markov reward models, and detailed, discrete-event simulation. The use of quantitative techniques for security evaluation is much less common, and has typically taken the form of formal analysis of small parts of an overall design, or experimental red team-based approaches. Alone, neither of these approaches is fully satisfactory, and we argue that there is much to be gained through the development of a sound model-based methodology for quantifying the security one can expect from a particular design. In this work, we survey existing model-based techniques for evaluating system dependability, and summarize how they are now being extended to evaluate system security. We find that many techniques from dependability evaluation can be applied in the security domain, but that significant challenges remain, largely due to fundamental differences between the accidental nature of the faults commonly assumed in dependability evaluation, and the intentional, human nature of cyber attacks.


The use of socially assistive robots in the design of intelligent cognitive therapies for people with dementia

Currently the 2 percent growth rate for the world's older population exceeds the 1.2 rate for the world's population as a whole. By 2050, the number of individuals over the age 85 is projected to be three times more than there is today. Most of these individuals will need physical, emotional, and cognitive assistance. In this paper, we present a new adaptive robotic system based on the socially assistive robotics (SAR) technology that tries to provide a customized help protocol through motivation, encouragements, and companionship to users suffering from cognitive changes related to aging and/or Alzheimer's disease. Our results show that this approach can engage the patients and keep them interested in interacting with the robot, which, in turn, increases their positive behavior.


3D deep convolutional neural networks for amino acid environment similarity analysis

Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures. Our deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional hand-engineered features and successfully recapitulates known information about similar and different microenvironments. Models built from our predictions and substitution matrices achieve an 85% accuracy predicting outcomes of the T4 lysozyme mutation variants. Our substitution matrices contain rich information relevant to mutation analysis compared to well-established substitution matrices. Finally, we present a visualization method to inspect the individual contributions of each atom to the classification decisions. End-to-end trained deep learning networks consistently outperform methods using hand-engineered features, suggesting that the 3DCNN framework is well suited for analysis of protein microenvironments and may be useful for other protein structural analyses.


Wireless Technologies for IoT in Smart Cities

As cities continue to grow, numerous initiatives for Smart Cities are being conducted. The concept of Smart City encompasses several concepts being governance, economy, management, infrastructure, technology and people. This means that a Smart City can have different communication needs. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G or LTE (Long Term Evolution) have presented themselves as solutions to the communication needs of Smart City initiatives. However, as most of them employ unlicensed bands, interference and coexistence problems are increasing. In this paper, the wireless technologies available nowadays for IoT (Internet of Things) in Smart Cities are presented. Our contribution is a review of wireless technologies, their comparison and the problems that difficult coexistence among them. In order to do so, the characteristics and adequacy of wireless technologies to each domain are considered. The problems derived of over-crowded unlicensed spectrum and coexistence difficulties among each technology are discussed as well. Finally, power consumption concerns are addressed.


Chinese Image Text Recognition on grayscale pixels

This paper presents a novel scheme for Chinese text recognition in images and videos. It's different from traditional paradigms that binarize text images, fed the binarized text to an OCR engine and get the recognized results. The proposed scheme, named grayscale based Chinese Image Text Recognition (gCITR), implements the recognition directly on grayscale pixels via the following steps: image text over-segmentation, building recognition graph, Chinese character recognition and beam search determination. The advantages of gCITR lie in: (1) it does not heavily rely on the performance of binarization, which is not robust in practical and thus severely affects the performance of OCR, (2) grayscale image retains more information of the text thus facilitates the recognition. Experimental results on text from 13 TV news videos demonstrate the effectiveness of the proposed gCITR, from which significant performance gains are observed.


Applying Stochastic Integer Programming to Optimization of Resource Scheduling in Cloud Computing

Resource scheduling based on SLA(Service Level Agreement) in cloud computing is NP-hard problem. There is no efficient method to solve it. This paper proposes an optimal model for the problem and give an algorithm by applying stochastic integer programming technique. Applying Gröbner bases theory for solving the stochastic integer programming problem and the experimental results of the implementation are also presented.


ZooKeeper: Wait-free Coordination for Internet-scale Systems

In this paper, we describe ZooKeeper, a service for coordinating processes of distributed applications. Since ZooKeeper is part of critical infrastructure, ZooKeeper aims to provide a simple and high performance kernel for building more complex coordination primitives at the client. It incorporates elements from group messaging, shared registers, and distributed lock services in a replicated, centralized service. The interface exposed by ZooKeeper has the wait-free aspects of shared registers with an event-driven mechanism similar to cache invalidations of distributed file systems to provide a simple, yet powerful coordination service. The ZooKeeper interface enables a high-performance service implementation. In addition to the wait-free property, ZooKeeper provides a per client guarantee of FIFO execution of requests and linearizability for all requests that change the ZooKeeper state. These design decisions enable the implementation of a high performance processing pipeline with read requests being satisfied by local servers. We show for the target workloads, 2:1 to 100:1 read to write ratio, that ZooKeeper can handle tens to hundreds of thousands of transactions per second. This performance allows ZooKeeper to be used extensively by client applications.


Performance Analysis of Enhanced SPECK Algorithm

A secure lightweight block cipher is an effective security solution for applications running on resource-constrained devices. The SPECK family of lightweight block ciphers is designed for low-end devices that need data security. The randomness of the keystream produced by this algorithm is a good indicator of its security strength. Nevertheless, the computed proportion of sequences based on empirical test results falls beyond the range of acceptable interval, an evidence of non-randomness to each algorithm. In this study, the researchers enhanced SPECK (k-SPECK) through the addition of a key derivation function in its processes. Both SPECK and k - SPECK were evaluated with the two sophisticated statistical test suite. The result shows that k - SPECK outperformed SPECK in terms of randomness as its three variations successfully passed all the test of statistical packages NIST and DieHarder, making it more cryptographically secured. Similarly, software implementation was also evaluated showing that k - SPECK 128/192 and 128/256 performed faster in encryption cycle compared to SPECK.


FPGA Implementations of the Round Two SHA-3 Candidates

The second round of the NIST-run public competition is underway to find a new hash algorithm(s) for inclusion in the NIST Secure Hash Standard (SHA-3). This paper presents the full implementations of all of the second round candidates in hardware with all of their variants. In order to determine their computational efficiency, an important aspect in NIST's round two evaluation criteria, this paper gives an area/speed comparison of each design both with and without a hardware interface, thereby giving an overall impression of their performance in resource constrained and resource abundant environments. The implementation results are provided for a Virtex-5 FPGA device. The efficiency of the architectures for the hash functions are compared in terms of throughput per unit area.


PC-01: Introduction to computational thinking: Educational technology in primary and secondary education

This article describes the design and implementation of the course Introduction to Computational Thinking (PC-01) for primary and secondary education. The course introduces the “concepts” and core “processes” of Computational Thinking using a visual programming environment. The design of the course PC-01 includes a set of multimedia content, learning tools and technologies that help teachers and students in the teaching-learning process. These tools allow the teacher the successful delivery of the course content as well as the individual/collective monitoring of students' progress in the course. Students have access to their progress throughout the course and participate in creative and collaborative tasks. This course also serves to introduce transversely educational technologies to many students of primary and secondary education. The technological environment integrates the online teaching resources and the methodological tools of the course. The course uses Scratch 2.0 as the programming environment and Moodle as the learning platform. At present the course is being implemented in public schools in the Dominican Republic.


Soft Switching Circuit for Interleaved Boost Converters

A zero-voltage switching-zero-current switching interleaved boost converter is proposed in this paper. An active circuit branch in parallel with the main switches is added and it is composed of an auxiliary switch and a snubber capacitor. By using this interleaved converter topology, zero current turn-on and zero voltage turn-off of the main switches can be achieved and the reverse-recovery loss of boost diode can be reduced. In addition, the auxiliary switches are zero-voltage transmission during the whole switching transition. A prototype of boost converter rated at 1.2kW has been built to confirm the effectiveness of the converter


Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine

Electroencephalography (EEG) recordings are often obscured by physiological artifacts that can render huge amounts of data useless and thus constitute a key challenge in current brain-computer interface research. This paper presents a new algorithm that automatically and reliably removes artifacts from EEG based on blind source separation and support vector machine. Performance on a motor imagery task is compared for artifact-contaminated and preprocessed signals to verify the accuracy of the proposed approach. The results showed improved results over all datasets. Furthermore, the online applicability of the algorithm is investigated.


An End-to-End Multitask Learning Model for Fact Checking

With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. Here we decompose this problem into two parts: an entity linking task (retrieving relative Wikipedia pages) and recognizing textual entailment between the claim and selected pages. In this paper, we present an end-to-end multi-task learning with bi-direction attention (EMBA) model to classify the claim as “supports”, “refutes” or “not enough info” with respect to the pages retrieved and detect sentences as evidence at the same time. We conduct experiments on the FEVER (Fact Extraction and VERification) paper test dataset and shared task test dataset, a new public dataset for verification against textual sources. Experimental results show that our method achieves comparable performance compared with the baseline system.


OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems

In this paper, we present an approach for modeling 3D environments based on octrees using a probabilistic occupancy estimation. Our technique is able to represent full 3D models including free and unknown areas. It is available as an open-source library to facilitate the development of 3D mapping systems. We also provide a detailed review of existing approaches to 3D modeling. Our approach was thoroughly evaluated using different real-world and simulated datasets. The results demonstrate that our approach is able to model the data probabilistically while, at the same time, keeping the memory requirement at a minimum.


Proximal Tibiofibular Joint Instability and Treatment Approaches: A Systematic Review of the Literature.

PURPOSE
To evaluate the treatment options, outcomes, and complications associated with proximal tibiofibular joint (PTFJ) instability, which will aim to improve surgical treatment of PTFJ instability and aid surgeons in their decision making and treatment selection.

METHODS
A systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Inclusion criteria were as follows: PTFJ instability treatment techniques, PTFJ surgical outcomes, English language, and human studies. Exclusion criteria were cadaveric studies, animal studies, basic science articles, editorial articles, review articles, and surveys. Furthermore, we excluded studies that did not report patient follow-up time and studies without any patient-reported, clinical or radiographic outcomes at the final follow-up.

RESULTS
The systematic review identified 44 studies (96 patients) after inclusion and exclusion criteria application. For the treatment of PTFJ instability, there were 18 studies (35 patients) describing nonoperative management, 3 studies (4 patients) reported on open reduction, 11 studies (25 patients) reported on fixation, 4 studies (10 patients) that described proximal fibula resection, 3 studies (11 patients) reported on adjustable cortical button repair, 2 studies (3 patients) reported on ligament reconstructions, and 5 (8 patients) studies reported on biceps femoris tendon rerouting. The most (77% to 90%) PTFJ dislocations and instability were anterolateral/unspecified anterior dislocation or instability. Improved outcomes after all forms of PTFJ instability treatment were reported; however, high complication rates were associated with both PTFJ fixation (28%) and fibular head resection (20%).

CONCLUSIONS
Improved outcomes can be expected after surgical treatment of PTFJ instability. Proximal tibiofibular ligament reconstruction, specifically biceps rerouting and anatomic graft reconstruction, leads to improved outcomes with low complication rates. Nonoperative treatment is associated with persistent symptoms, whereas both fixation and fibular head resection are associated with high complication rates.

LEVEL OF EVIDENCE
Level IV, systematic review of level IV studies.


Knowledge Graph Embedding with Numeric Attributes of Entities

Knowledge Graph (KG) embedding projects entities and relations into low dimensional vector space, which has been successfully applied in KG completion task. The previous embedding approaches only model entities and their relations, ignoring a large number of entities’ numeric attributes in KGs. In this paper, we propose a new KG embedding model which jointly model entity relations and numeric attributes. Our approach combines an attribute embedding model with a translation-based structure embedding model, which learns the embeddings of entities, relations, and attributes simultaneously. Experiments of link prediction on YAGO and Freebase show that the performance is effectively improved by adding entities’ numeric attributes in the embedding model.


Improving Distributional Similarity with Lessons Learned from Word Embeddings

Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.


Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy.

A dual taxonomy is presented to reconcile 2 incongruous facts about antisocial behavior: (a) It shows impressive continuity over age, but (b) its prevalence changes dramatically over age, increasing almost 10-fold temporarily during adolescence. This article suggests that delinquency conceals 2 distinct categories of individuals, each with a unique natural history and etiology: A small group engages in antisocial behavior of 1 sort or another at every life stage, whereas a larger group is antisocial only during adolescence. According to the theory of life-course-persistent antisocial behavior, children's neuropsychological problems interact cumulatively with their criminogenic environments across development, culminating in a pathological personality. According to the theory of adolescence-limited antisocial behavior, a contemporary maturity gap encourages teens to mimic antisocial behavior in ways that are normative and adjustive.


A hot/cool-system analysis of delay of gratification: dynamics of willpower.

A 2-system framework is proposed for understanding the processes that enable--and undermine--self-control or "willpower" as exemplified in the delay of gratification paradigm. A cool, cognitive "know" system and a hot, emotional "go" system are postulated. The cool system is cognitive, emotionally neutral, contemplative, flexible, integrated, coherent, spatiotemporal, slow, episodic, and strategic. It is the seat of self-regulation and self-control. The hot system is the basis of emotionality, fears as well as passions--impulsive and reflexive--initially controlled by innate releasing stimuli (and, thus, literally under "stimulus control"): it is fundamental for emotional (classical) conditioning and undermines efforts at self-control. The balance between the hot and cool systems is determined by stress, developmental level, and the individual's self-regulatory dynamics. The interactions between these systems allow explanation of findings on willpower from 3 decades of research.


Risk and Rationality in Adolescent Decision Making: Implications for Theory, Practice, and Public Policy.

Crime, smoking, drug use, alcoholism, reckless driving, and many other unhealthy patterns of behavior that play out over a lifetime often debut during adolescence. Avoiding risks or buying time can set a different lifetime pattern. Changing unhealthy behaviors in adolescence would have a broad impact on society, reducing the burdens of disease, injury, human suffering, and associated economic costs. Any program designed to prevent or change such risky behaviors should be founded on a clear idea of what is normative (what behaviors, ideally, should the program foster?), descriptive (how are adolescents making decisions in the absence of the program?), and prescriptive (which practices can realistically move adolescent decisions closer to the normative ideal?). Normatively, decision processes should be evaluated for coherence (is the thinking process nonsensical, illogical, or self-contradictory?) and correspondence (are the outcomes of the decisions positive?). Behaviors that promote positive physical and mental health outcomes in modern society can be at odds with those selected for by evolution (e.g., early procreation). Healthy behaviors may also conflict with a decision maker's goals. Adolescents' goals are more likely to maximize immediate pleasure, and strict decision analysis implies that many kinds of unhealthy behavior, such as drinking and drug use, could be deemed rational. However, based on data showing developmental changes in goals, it is important for policy to promote positive long-term outcomes rather than adolescents' short-term goals. Developmental data also suggest that greater risk aversion is generally adaptive, and that decision processes that support this aversion are more advanced than those that support risk taking. A key question is whether adolescents are developmentally competent to make decisions about risks. In principle, barring temptations with high rewards and individual differences that reduce self-control (i.e., under ideal conditions), adolescents are capable of rational decision making to achieve their goals. In practice, much depends on the particular situation in which a decision is made. In the heat of passion, in the presence of peers, on the spur of the moment, in unfamiliar situations, when trading off risks and benefits favors bad long-term outcomes, and when behavioral inhibition is required for good outcomes, adolescents are likely to reason more poorly than adults do. Brain maturation in adolescence is incomplete. Impulsivity, sensation seeking, thrill seeking, depression, and other individual differences also contribute to risk taking that resists standard risk-reduction interventions, although some conditions such as depression can be effectively treated with other approaches. Major explanatory models of risky decision making can be roughly divided into (a) those, including health-belief models and the theory of planned behavior, that adhere to a "rational" behavioral decision-making framework that stresses deliberate, quantitative trading off of risks and benefits; and (b) those that emphasize nondeliberative reaction to the perceived gists or prototypes in the immediate decision environment. (A gist is a fuzzy mental representation of the general meaning of information or experience; a prototype is a mental representation of a standard or typical example of a category.) Although perceived risks and especially benefits predict behavioral intentions and risk-taking behavior, behavioral willingness is an even better predictor of susceptibility to risk taking-and has unique explanatory power-because adolescents are willing to do riskier things than they either intend or expect to do. Dual-process models, such as the prototype/willingness model and fuzzy-trace theory, identify two divergent paths to risk taking: a reasoned and a reactive route. Such models explain apparent contradictions in the literature, including different causes of risk taking for different individuals. Interventions to reduce risk taking must take into account the different causes of such behavior if they are to be effective. Longitudinal and experimental research are needed to disentangle opposing causal processes-particularly, those that produce positive versus negative relations between risk perceptions and behaviors. Counterintuitive findings that must be accommodated by any adequate theory of risk taking include the following: (a) Despite conventional wisdom, adolescents do not perceive themselves to be invulnerable, and perceived vulnerability declines with increasing age; (b) although the object of many interventions is to enhance the accuracy of risk perceptions, adolescents typically overestimate important risks, such as HIV and lung cancer; (c) despite increasing competence in reasoning, some biases in judgment and decision making grow with age, producing more "irrational" violations of coherence among adults than among adolescents and younger children. The latter occurs because of a known developmental increase in gist processing with age. One implication of these findings is that traditional interventions stressing accurate risk perceptions are apt to be ineffective or backfire because young people already feel vulnerable and overestimate their risk. In addition, research shows that experience is not a good teacher for children and younger adolescents, because they tend to learn little from negative outcomes (favoring the use of effective deterrents, such as monitoring and supervision), although learning from experience improves considerably with age. Experience in the absence of negative consequences may increase feelings of invulnerability and thus explain the decrease in risk perceptions from early to late adolescence, as exploration increases. Finally, novel interventions that discourage deliberate weighing of risks and benefits by adolescents may ultimately prove more effective and enduring. Mature adults apparently resist taking risks not out of any conscious deliberation or choice, but because they intuitively grasp the gists of risky situations, retrieve appropriate risk-avoidant values, and never proceed down the slippery slope of actually contemplating tradeoffs between risks and benefits.


Adolescent sexual risk behavior: a multi-system perspective.

Adolescents are at high risk for a number of negative health consequences associated with early and unsafe sexual activity, including infection with human immunodeficiency virus, other sexually transmitted diseases, and unintended pregnancy. As a result, researchers have attempted to identify those factors that influence adolescent sexual risk behavior so that meaningful prevention and intervention programs may be developed. We propose that research efforts so far have been hampered by the adoption of models and perspectives that are narrow and do not adequately capture the complexity associated with the adolescent sexual experience. In this article, we review the recent literature (i.e., 1990-1999) pertaining to the correlates of adolescent sexual risk-taking, and organize the findings into a multisystemic perspective. Factors from the self, family, and extrafamilial systems of influence are discussed. We also consider several methodological problems that limit the literature's current scope, and consider implications of the adoption of a multisystemic framework for future research endeavors. We conclude with a discussion of the implications of the available research for practitioners working to reduce sexual risk behavior among adolescents.


Separation of Fine Granular Mixtures in S-Plate-Type Electrostatic Separators

Plate-type electrostatic separators are commonly used for the selective sorting of conductive from nonconductive constituents of a granular mixture. This paper addresses the more delicate issue of granular materials that are characterized by finite nonzero resistivities. The study was conducted with three materials having similar granule size range and specific weight but slightly different electrical properties. Two sets of experiments were performed for evaluating the effect of electrode geometry on the behavior of each of the materials. These results were then employed for predicting the outcome of the electrostatic separation of binary mixtures of good and poor conductors. A third set of experiments validated the predictions and pointed out a couple of nonelectric factors that might deteriorate the performance of a plate-type electrostatic separator in an industrial environment.


Design of Permanent Magnet-Assisted Synchronous Reluctance Motors Made Easy

Electric motor design is a multi-variable problem which involves geometric dimensions of the stator and rotor. Presenting a unique solution for a family of optimization criteria has always been a challenge for motor designers. Several numerical tools such as finite element methods (FEM) have been developed to perform a precise analysis and predict the outcome of the design. However, limits in parametric analysis as well as mathematical and computational burden on numerical tools usually prohibit the designer in obtaining a unique solution for the design problem. These limits and demands in optimized solutions motivate the designer to use analytical models in order to perform a comprehensive parametric design. An accurate analytical model is crucial for this purpose. In this paper, an analytical model for permanent magnet assisted synchronous reluctance motor (PMa- SynRM) with four flux barriers and one cutout per pole is developed. Flux densities are found in the air gap, in the cutouts, and in the flux barriers; thus, the back-EMF developed by the permanent magnets is derived. Equations for the d-axis and the q-axis inductances are also obtained. Electromagnetic torque is finally derived using the co-energy method. The developed analytical model highlights the contribution of the reluctance variation and permanent magnets on the developed torque. Simulation results are obtained using both Matlab and Ansoft/Maxwell packages. These outcomes are supported by the experimental results obtained from a laboratory test bed.


Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System

A decision tree is a outstanding method for the data mining. In intrusion detection systems (IDSs), the data mining techniques are useful to detect the attack especially in anomaly detection. For the decision tree, we use the DARPA 98 Lincoln Laboratory Evaluation Data Set (DARPA Set) as the training data set and the testing data set. KDD 99 Intrusion Detection data set is also based on the DARPA Set. These three entities are widely used in IDSs. Hence, we describe the total process to generate the decision tree learned from the DARPA Sets. In this paper, we also evaluate the effective value of the decision tree as the data mining method for the IDSs, and the DARPA Set as the learning data set for the decision trees.


A highly area-efficient controller for capacitive touch screen panel systems

In this paper, a highly area-efficient controller for capacitive touch screen panels (TSPs) is proposed. The proposed controller uses a 10-bit successive approximation register analog-to-digital converter (SAR ADC) with an adder to compensate for the capacitance variation in the TSP and for the offset voltage variation in the charge amplifier of the sensing circuit. By using the proposed compensation method, the area of the controller can be reduced by 90.3% of the area of the conventional controllers. The measurement results showed that the signal-to-noise ratio (SNR) of the controller increases from 12.5 to 21.3 dB after compensation. Also, its spatial jitter decreases from ±1.5 to ±0.46 mm, which is 7% of the sensor pitch of 8 mm.


Could Data from Location-Based Social Networks Be Used to Support Urban Planning?

A great quantity of information is required to support urban planning. Usually there are many (not integrated) data sources, originating from different government bodies, in distinct formats and variable properties (e.g. reliability, completeness). The effort to handle these data, integrate and analyze them is high, taking to much time for the information to be available to help decision making. We argue that data from location-based social networks (LBSN) could be used to provide useful information in reasonable time, despite several limitations they have. To asses this, as a case study, we used data from different LBSN to calculate the Local Availability Index (IOL) for a Brazilian city. This index is part of a methodology to estimate quality of urban life inside cities and is used to support urban planning. The results suggest that data from LBSN are useful and could be used to provide insights for local governments.


IO Performance Interference among Consolidated n-Tier Applications: Sharing Is Better Than Isolation for Disks

The performance unpredictability associated with migrating applications into cloud computing infrastructures has impeded this migration. For example, CPU contention between co-located applications has been shown to exhibit counter-intuitive behavior. In this paper, we investigate IO performance interference through the experimental study of consolidated n-tier applications leveraging the same disk. Surprisingly, we found that specifying a specific disk allocation, e.g., limiting the number of Input/Output Operations Per Second (IOPs) per VM, results in significantly lower performance than fully sharing disk across VMs. Moreover, we observe severe performance interference among VMs can not be totally eliminated even with a sharing strategy (e.g., response times for constant workloads still increase over 1,100%). By using a micro-benchmark (Filebench) and an n-tier application benchmark systems (RUBBoS), we demonstrate the existence of disk contention in consolidated environments, and how performance loss occurs when co-located database systems in order to maintain database consistency flush their logs from memory to disk. Potential solutions to these isolation issues are (1) to increase the log buffer size to amortize the disk IO cost (2) to decrease the number of write threads to alleviate disk contention. We validate these methods experimentally and find a 64% and 57% reduction in response time (or more generally, a reduction in performance interference) for constant and increasing workloads respectively.


Performance of MIMO spatial multiplexing algorithms using indoor channel measurements and models

Several algorithms have recently been proposed for multiplexing multiple users in multiple input multiple output (MIMO) downlinks. The ability of a transmitter to accomplish this using spatial methods is generally dependent on whether the users’ channels are correlated. Up to this point, most of the multiplexing algorithms have been tested on uncorrelated Gaussian channels, a best-case scenario. In this paper, we examine the performance of multiplexing multiple users under more realistic channel conditions by using indoor channel measurements and a statistical model for test cases. We use a block zero-forcing algorithm to test performance at various user separation distances, optimizing for both maximum throughput under a power constraint and minimum power under a rate constraint. The results show that for the measured indoor environment (rich scattering, non-line-of-sight), a separation of five wavelengths is enough to achieve close to the maximum available performance for two users. Since many spatial multiplexing algorithms require channel state information (CSI) at the transmitter, we also examine the performance loss due to CSI error. The results show that a user can move up to one-half wavelength before the original channel measurement becomes unusable. Copyright # 2004 John Wiley & Sons, Ltd.


Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system

Collaborative filtering (CF) technique is capable of generating personalized recommendations. However, the recommender systems utilizing CF as their key algorithms are vulnerable to shilling attacks which insert malicious user profiles into the systems to push or nuke the reputations of targeted items. There are only a small number of labeled users in most of the practical recommender systems, while a large number of users are unlabeled because it is expensive to obtain their identities. In this paper, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed to take advantage of both types of data. It first trains a naïve Bayes classifier on a small set of labeled users, and then incorporates unlabeled users with EM-λ to improve the initial naïve Bayes classifier. Experiments on MovieLens datasets are implemented to compare the efficiency of Semi-SAD with supervised learning based detector and unsupervised learning based detector. The results indicate that Semi-SAD can better detect various kinds of shilling attacks than others, especially against obfuscated and hybrid shilling attacks.


A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning

In this paper, we show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the training set while (2) simultaneously fitting the trimmed model on the uncontaminated data that remains. To solve the resulting nonconvex optimization problem, we introduce a fast stochastic proximal-gradient algorithm that incorporates prior knowledge through nonsmooth regularization. For datasets of size n, our approach requires O(n/ε) gradient evaluations to reach ε-accuracy and, when a certain error bound holds, the complexity improves to O(κn log(1/ε)). These rates are n times better than those achieved by typical, full gradient methods.


It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation

Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.


Zero Shot Recognition with Unreliable Attributes

In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category’s attributes. For example, with classifiers for generic attributes like striped and four-legged, one can construct a classifier for the zebra category by enumerating which properties it possesses—even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute’s error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.


Switchboard: a matchmaking system for multiplayer mobile games

Supporting interactive, multiplayer games on mobile phones over cellular networks is a difficult problem. It is particularly relevant now with the explosion of mostly single-player or turn-based games on mobile phones. The challenges stem from the highly variable performance of cellular networks and the need for scalability (not burdening the cellular infrastructure, nor any server resources that a game developer deploys). We have built a service for matchmaking in mobile games -- assigning players to games such that game settings are satisfied as well as latency requirements for an enjoyable game. This requires solving two problems. First, the service needs to know the cellular network latency between game players. Second, the service needs to quickly group players into viable game sessions. In this paper, we present the design of our service, results from our experiments on predicting cellular latency, and results from efficiently grouping players into games.


Bistatic Synthetic Aperture Radar Imaging for Arbitrary Flight Trajectories

In this paper, we present an analytic, filtered backprojection (FBP) type inversion method for bistatic synthetic aperture radar (BISAR). We consider a BISAR system where a scene of interest is illuminated by electromagnetic waves that are transmitted, at known times, from positions along an arbitrary, but known, flight trajectory and the scattered waves are measured from positions along a different flight trajectory which is also arbitrary, but known. We assume a single-scattering model for the radar data, and we assume that the ground topography is known but not necessarily flat. We use microlocal analysis to develop the FBP-type reconstruction method. We analyze the computational complexity of the numerical implementation of the method and present numerical simulations to demonstrate its performance.


Modelling of IoT Traffic and Its Impact on LoRaWAN

The recent growth of IoT use-cases in a wide array of industrial, utility and environmental applications has necessitated the need for connectivity solutions with diverse requirements. Connectivity through BLE, Zigbee and 6LoPAN are examples of short-range IoT deployments. But to provide connectivity to a high density of devices over larger coverage areas, Low-Power Wide-Area Network (LPWAN) technologies in both licensed as well as unlicensed bands have been considered. In this paper, we consider modelling the traffic from IoT devices connected through LPWAN technologies. Due to diverse applications of IoT, it is not trivial to have a single traffic model to represent all of them, but the traffic can be broadly classified as either periodic, event-triggered, or a combination of both. We evaluate the performance of LoRaWAN, one such LPWAN technology, in the presence of a hybrid of both traffic types, where the event propagates spatially over time. In a practical deployment of sensor based IoT devices, the devices are usually densely deployed to ensure sufficient & reliable measurement. Thereby, when an event occurs, they exhibit spatial & temporal correlation in their traffic rate due to the natural phenomena of the metric they measure. We use the CMMPP model to represent such characteristic traffic from independent IoT devices triggered by an event. The characteristics of LoRa, the physical layer of LoRaWAN, is abstracted based on required signal strength and interference thresholds for different modulation parameters. Through system simulations, we demonstrate that there is a significant performance hit in LoRaWAN based networks, during the occurrence of events. In particular, using the packet delivery rate (PDR) as the metric, we found that while the system was able to handle regular updates from the devices with a PDR > 80%, event-driven traffic nearly impaired the network causing the PDR to drop below 10%.


Multi-Level Sequence GAN for Group Activity Recognition

We propose a novel semi supervised, Multi Level Sequential Generative Adversarial Network (MLS-GAN ) architecture for group activity recognition. In contrast to previous works which utilise manually annotated individual human action predictions, we allow the models to learn it’s own internal representations to discover pertinent subactivities that aid the final group activity recognition task. The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks. Action-based feature fusion is performed through novel gated fusion units that are able to consider long-term dependancies, exploring the relationships among all individual actions, to learn an intermediate representation or ‘action code’ for the current group activity. The network achieves it’s semi-supervised behaviour by allowing it to perform group action classification together with the adversarial real/fake validation. We perform extensive evaluations on different architectural variants to demonstrate the importance of the proposed architecture. Furthermore, we show that utilising both person-level and scene-level features facilitates the group activity prediction better than using only person-level features. Our proposed architecture outperforms current state-of-the-art results for sports and pedestrian based classification tasks on Volleyball and Collective Activity datasets, showing it’s flexible nature for effective learning of group activities. 1


Analyzing User Modeling on Twitter for Personalized News Recommendations

How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.


Project massive: self-regulation and problematic use of online gaming

A longitudinal design was employed to collect three waves of survey data over a 14 month period from 2790 online gamers. Respondents were asked questions about their gaming activity, motivations, personality, social and emotional environment, and the effect gaming has had on their lives. Prospective analysis was used to establish causal and temporal linkages among the repeatedly measured factors. While the data provide some indication that a player's reasons for playing do influence the development of problematic usage, these effects are overshadowed by the central importance of self-regulation in managing both the timing and amount of play. An individual's level of self-regulatory activity is shown to be very important in allowing them to avoid negative outcomes like problematic use. The role of depression is also discussed. With responsible use, online gaming appears to be a healthy recreational activity that provides millions of people with hours of social entertainment and adaptive diversion. However, failure to manage play behavior can lead to feelings of dependency.


Identifying Users With Alternate Behaviors of Lurking and Active Participation in Multilayer Social Networks

With the growing complexity of scenarios relating to online social networks (OSNs), there is an emergence of effective models and methods for understanding the characteristics and dynamics of multiple interconnected types of user relations. Profiles on different OSNs belonging to the same user can be linked using the multilayer structure, opening to unprecedented opportunities for user behavior analysis in a complex system. In this paper, we leverage the importance of studying the dichotomy between information-producers (contributors) and information-consumers (lurkers), and their interplay over a multilayer network, in order to effectively analyze such different roles a user may take on different OSNs. In this respect, we address the novel problem of identification and characterization of opposite behaviors that users may alternately exhibit over multiple layers of a complex network. We propose the first ranking method for alternate lurker-contributor behaviors on a multilayer OSN, dubbed mlALCR. Performance of mlALCR has been assessed quantitatively as well as qualitatively, and comparatively against methods designed for ranking either contributors or lurkers, on four real-world multilayer networks. Empirical evidence shows the significance and uniqueness of mlALCR in being able to mine alternate lurker-contributor behaviors over different layer networks.