title: Being Surprised by the Unsurprising: Earnings Seasonality and Stock Returns

abstract: We present evidence consistent with markets failing to properly price information in seasonal earnings patterns. Firms with historically larger earnings in one quarter of the year (“positive seasonality quarters”) have higher returns when those earnings are usually announced. Analysts have more positive forecast errors in positive seasonality quarters, consistent with the returns being driven by mistaken earnings estimates. We show that investors appear to overweight recent lower earnings following positive seasonality quarters, leading to pessimistic forecasts in the subsequent positive seasonality quarter. The returns are not explained by risk-based explanations, firm-specific information, increased volume, or idiosyncratic volatility.Received June 19, 2014; accepted April 25, 2016, by Editor David Hirshleifer.


title: An Overview of Diode Pumped Solid State (DPSS) Lasers

abstract: The primary purpose of text is to explain the mutual influences between the physical and dynamic processes in solids and their lasing properties. It begins with a brief overview of historical developments and the basics of solid state laser materials and design, then describes optical dynamics and energy transfer in solids and analyzes the properties of rare earth and transition metal ion doped crystals, glasses and ceramics. Diode laser pumps for solid state lasers are reviewed with special emphasis on current and future capabilities. The text also outlines the factors that influence the development of new laser materials, and reports on recent developments and technological advances in solid state lasers and photonics that use novel materials and techniques. Included are discussions of pumps for continuous wave or pulsed and side pumping configurations. The use of diode lasers instead of flashlamps as optical pump sources for solid state lasers offers significant advantages such as higher efficiency and longer lifetime. The potential applications of this technology to device singulation for electronic and power generation devices will be described.


title: Overview of BioNLP’09 Shared Task on Event Extraction

abstract: The paper presents the design and implementation of the BioNLP'09 Shared Task, and reports the final results with analysis. The shared task consists of three sub-tasks, each of which addresses bio-molecular event extraction at a different level of specificity. The data was developed based on the GENIA event corpus. The shared task was run over 12 weeks, drawing initial interest from 42 teams. Of these teams, 24 submitted final results. The evaluation results are encouraging, indicating that state-of-the-art performance is approaching a practically applicable level and revealing some remaining challenges.


title: Dynamic Hierarchical Classification for Patient Risk-of-Readmission

abstract: Congestive Heart Failure (CHF) is a serious chronic condition often leading to 50% mortality within 5 years. Improper treatment and post-discharge care of CHF patients leads to repeat frequent hospitalizations (i.e., readmissions). Accurately predicting patient's risk-of-readmission enables care-providers to plan resources, perform factor analysis, and improve patient quality of life. In this paper, we describe a supervised learning framework, Dynamic Hierarchical Classification (DHC) for patient's risk-of-readmission prediction. Learning the hierarchy of classifiers is often the most challenging component of such classification schemes. The novelty of our approach is to algorithmically generate various layers and combine them to predict overall 30-day risk-of-readmission. While the components of DHC are generic, in this work, we focus on congestive heart failure (CHF), a pressing chronic condition. Since healthcare data is diverse and rich and each source and feature-subset provides different insights into a complex problem, our DHC based prediction approach intelligently leverages each source and feature-subset to optimize different objectives (such as, Recall or AUC) for CHF risk-of-readmission. DHC's algorithmic layering capability is trained and tested over two real world datasets and is currently integrated into the clinical decision support tools at MultiCare Health System (MHS), a major provider of healthcare services in the northwestern US. It is integrated into a QlikView App (with EMR integration planned for Q2) and currently scores patients everyday, helping to mitigate readmissions and improve quality of care, leading to healthier outcomes and cost savings.


title: Unconstrained Sleep Apnea Monitoring Using Polyvinylidene Fluoride Film-Based Sensor

abstract: We established and tested an unconstrained sleep apnea monitoring method using a polyvinylidene (PVDF) film-based sensor for continuous and accurate monitoring of apneic events occurred during sleep. Twenty-six sleep apnea patients and six normal subjects participated in this study. Subjects' respiratory signals were measured using the PVDF-based sensor during polysomnography. The PVDF sensor comprised a 4 × 1 array, and a thin silicon pad was placed over the sensor to prevent damage. Total thickness of the merged system was approximately 1.1 mm which was thin enough to prevent the subject from being consciously aware of its presence. It was designed to be placed under subjects' backs and installed between a bed cover and mattress. The proposed method was based on the standard deviation of the PVDF signals, and it was applied to a test set for detecting apneic events. The method's performance was assessed by comparing the results with a sleep physician's manual scoring. The correlation coefficient for the apnea-hypopnea index (AHI) values between the methods was 0.94 (p < 0.001). The areas under the receiver operating curves at three AHI threshold levels (>5, >15, and >20) for sleep apnea diagnosis were 0.98, 0.99, and 0.98, respectively. For min-by-min apnea detection, the method classified sleep apnea with an average sensitivity of 72.9%, specificity of 90.6%, accuracy of 85.5%, and kappa statistic of 0.60. The developed system and method can be applied to sleep apnea detection in home or ambulatory monitoring.


title: Approach and avoidance sexual motives: Implications for personal and interpersonal well-being

abstract: This research provides the first empirical investigation of how approach and avoidance motives for engaging in sex in intimate relationships are associated with personal well-being and relationship quality. A 2-week daily experience study of college student dating couples tested specific predictions from the theoretical model and included both longitudinal and dyadic components. Whereas approach sex motives were positively associated with personal and interpersonal well-being, avoidance sex motives were negatively associated with well-being. Engaging in sex for avoidance motives was particularly detrimental to the maintenance of relationships over time. Perceptions of a partners motives for sex were also associated with well-being. Implications for the conceptualization of sexuality in relationships along these two dimensions are discussed. Sexual interactions in young adulthood can be positive forces that bring partners closer and make them feel good about themselves and their relationships. In the National Health and Social Life Survey (NHSLS), 78% of participants in monogamous dating relationships reported being either extremely or very pleased with their sexual relationship (Laumann, Gagnon, Michael, & Michaels, 1994). For instance, when asked to rate specific feelings they experienced after engaging in sex, a majority of the participants reported positive feelings (i.e., ‘‘felt loved,’’ ‘‘thrilled,’’ ‘‘wanted,’’ or ‘‘taken care of ’’). More generally, feelings of satisfaction with the sexual aspects of an intimate relationship contribute to overall relationship satisfaction and stability over time (e.g., Sprecher, 2002; see review by


title: Back-to-back converter control of grid-connected wind turbine to mitigate voltage drop caused by faults

abstract: Power electronic converters enable wind turbines, operating at variable speed, to generate electricity more efficiently. Among variable speed operating turbine generators, permanent magnetic synchronous generator (PMSG) has got more attentions due to low cost and maintenance requirements. In addition, the converter in a wind turbine with PMSG decouples the turbine from the power grid, which favors them for grid codes. In this paper, the performance of back-to-back (B2B) converter control of a wind turbine system with PMSG is investigated on a faulty grid. The switching strategy of the grid side converter is designed to improve voltage drop caused by the fault in the grid while the maximum available active power of wind turbine system is injected to the grid and the DC link voltage in the converter is regulated. The methodology of the converter control is elaborated in details and its performance on a sample faulty grid is assessed through simulation.


title: Hazards of Hospitalization: Residence Prior to Admission Predicts Outcomes

abstract: Purpose: Previous studies investigating adverse outcomes of hospitalized elders have focused on community-dwelling patients. Given the rapid growth of populations living in other settings, such as assisted living facilities, it is important to understand whether these patients are at higher risk of experiencing specific adverse outcomes during hospitalization, so that interventions can be developed to reduce risk. Methods: This is a prospective, observational study of 212 sequential patients admitted during a 1-month period in 2006 to a 38-bed Acute Care for Elders unit in Rochester, New York and followed until discharge. We categorized the patients by residence prior to admission (i.e., community, assisted living, and nursing home). Our outcome categories were: worsening function, delirium, depression, falls, pressure sores, and nursing home admission. Results: After adjusting for multiple characteristics, we found that patients admitted from assisted living facilities were at substantially higher risk than those admitted from the community for functional decline and falls. Patients from nursing homes had a trend toward increased risk for these outcomes, but the trend did not reach statistical significance. More than three fourths of assisted living facility residents were discharged to a nursing home after hospitalization, with a relative risk of 9.41 (p , .001) versus community-dwellers for this outcome. Implications: People who are admitted to the hospital from assisted living facilities are at high risk for falls and functional decline during hospitalization. Assisted living residents are at a particularly high risk of nursing home admission following hospitalization. Targeted preventive programs should be developed with a goal of reducing risk in this vulnerable population.


title: Geometry Optimize of Printed Circuit Board Stator Winding in Coreless Axial Field Permanent Magnet Motor

abstract: Axial Field Permanent Magnet Motor (AFPM) can be easily integrated into wheel-hub motor of Electrical Vehicle (EV) as impact structure and light weight. Improved hexagonal asymmetry coil is presented in Printed Circuit Board (PCB) stator of coreless AFPM. Geometry parameters of coil shape is optimized. According to the structure characteristics of coreless AFPM, dimension formulas of PCB coil are derived. Analytical calculation models of back-EMF and winding co-efficiency are built up. To maximize ratio of torque to copper loss, coil parameters such as radial length, inclined angle of active conductors etc. are optimized. Finally comparison and verification are performed by means of 3D finite element method and prototype test. The ratio of torque to copper loss is increased 30%. As a result, motor rated torque is also increased at certain motor size, heating load and dissipation condition.


title: A "Gen 2" RFID monitor based on the USRP

abstract: We have developed a low cost software radio based platform for monitoring EPC Gen 2 RFID traffic. The Gen 2 standard allows for a range of PHY layer configurations and does not specify exactly how to compose protocol messages to inventory tags. This has made it difficult to know how well the standard works, and how it is implemented in practice. Our platform provides much needed visibility into Gen 2 systems by capturing reader transmissions using the USRP2 and decoding them in real-time using software we have developed and released to the public. In essence, our platform delivers much of the functionality of expensive (


title: Comparison of angle-only filtering algorithms in 3D using EKF, UKF, PF, PFF, and ensemble KF

abstract: In our previous work, we compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) for the angle-only filtering (AOF) problem in 3D using Cartesian coordinates and modified spherical coordinates (MSC) for the relative state vector. We found that the UKF-MSC and EKF-MSC had the best performance in accuracy, the UKF-MSC being slightly better than the EKF-MSC. The PF didn't perform well compared with the EKF and UKF and had a higher computational cost. In this work, we compare the performance of the particle flow filter (PFF) with the other filters for the AOF problem. In addition, we also analyze the performance of two versions of the ensemble Kalman filter (EnKF) in this comparative study. We present numerical results from Monte Carlo simulations to analyze the state estimation accuracy and computational cost of these filters.


Category-Based Induction


title: Mapping Generative Models onto a Network of Digital Spiking Neurons

abstract: Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification, and are particularly interesting because of their potential for generative tasks. Inference and learning in these algorithms use a Markov Chain Monte Carlo procedure called Gibbs sampling, where a logistic function forms the kernel of this sampler. On the other side of the spectrum, neuromorphic systems have shown great promise for low-power and parallelized cognitive computing, but lack well-suited applications and automation procedures. In this work, we propose a systematic method for bridging the RBM algorithm and digital neuromorphic systems, with a generative pattern completion task as proof of concept. For this, we first propose a method of producing the Gibbs sampler using bio-inspired digital noisy integrate-and-fire neurons. Next, we describe the process of mapping generative RBMs trained offline onto the IBM TrueNorth neurosynaptic processor—a low-power digital neuromorphic VLSI substrate. Mapping these algorithms onto neuromorphic hardware presents unique challenges in network connectivity and weight and bias quantization, which, in turn, require architectural and design strategies for the physical realization. Generative performance is analyzed to validate the neuromorphic requirements and to best select the neuron parameters for the model. Lastly, we describe a design automation procedure which achieves optimal resource usage, accounting for the novel hardware adaptations. This work represents the first implementation of generative RBM inference on a neuromorphic VLSI substrate.


title: Frequency signature chipless RFID tag with enhanced data capacity

abstract: Frequency signature chipless RFID tag based on spurline resonator is presented in this letter. Resonant response of spurline is explained by analyzing the surface current distribution. Chipless tag ...


title: Excitation current control of a claw pole automotive alternator

abstract: This paper overviews the Lundell type of automotive alternator with claw poles. The machine construction will be summarized and the field winding will be the focus point. Mathematical equations of the field winding, field current and the excitation circuit losses will be determined. A simulation model that contains the detailed excitation circuit parameters will be established. Results will be compared with different PI controller coefficients.


Computational models of visual attention


title: Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks

abstract: Mobile applications are becoming increasingly computation-intensive, while the computing capability of portable mobile devices is limited. A powerful way to reduce the completion time of an application in a mobile device is to offload its tasks to nearby cloudlets, which consist of clusters of computers. Although there is a significant body of research in mobile cloudlet offloading technology, there has been very little attention paid to how cloudlets should be placed in a given network to optimize mobile application performance. In this paper we study cloudlet placement and mobile user allocation to the cloudlets in a wireless metropolitan area network (WMAN). We devise an algorithm for the problem, which enables the placement of the cloudlets at user dense regions of the WMAN, and assigns mobile users to the placed cloudlets while balancing their workload. We also conduct experiments through simulation. The simulation results indicate that the performance of the proposed algorithm is very promising.


title: A probabilistic model for item-based recommender systems

abstract: Recommender systems estimate the conditional probability P(χj|χi) of item χj being bought, given that a customer has already purchased item χi. While there are different ways of approximating this conditional probability, the expression is generally taken to refer to the frequency of co-occurrence of items in the same basket, or other user-specific item lists, rather than being seen as the co-occurrence of χj with χi as a proportion of all other items bought alongside χi. This paper proposes a probabilistic calculus for the calculation of conditionals based on item rather than basket counts. The proposed method has the consequence that items bough together as part of small baskets are more predictive of each other than if they co-occur in large baskets. Empirical results suggests that this may result in better take-up of personalised recommendations.


title: Experimental Study with Real-world Data for Android App Security Analysis using Machine Learning

abstract: Although Machine Learning (ML) based approaches have shown promise for Android malware detection, a set of critical challenges remain unaddressed. Some of those challenges arise in relation to proper evaluation of the detection approach while others are related to the design decisions of the same. In this paper, we systematically study the impact of these challenges as a set of research questions (i.e., hypotheses). We design an experimentation framework where we can reliably vary several parameters while evaluating ML-based Android malware detection approaches. The results from the experiments are then used to answer the research questions. Meanwhile, we also demonstrate the impact of some challenges on some existing ML-based approaches. The large (market-scale) dataset (benign and malicious apps) we use in the above experiments represents the real-world Android app security analysis scale. We envision this study to encourage the practice of employing a better evaluation strategy and better designs of future ML-based approaches for Android malware detection.


title: Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings

abstract: We present a natural language generator based on the sequence-to-sequence approach that can be trained to produce natural language strings as well as deep syntax dependency trees from input dialogue acts, and we use it to directly compare two-step generation with separate sentence planning and surface realization stages to a joint, one-step approach. We were able to train both setups successfully using very little training data. The joint setup offers better performance, surpassing state-of-the-art with regards to n-gram-based scores while providing more relevant outputs.


title: The Design and Implementation of AspectC++

abstract: Aspect-Oriented Programming (AOP) is a programming paradigm that supports the modular implementation of crosscutting concerns. Thereby, AOP improves the maintainability, reusability, and configurability of software in general. Although already popular in the Java domain, AOP is still not commonly used in conjunction with C/C++. For a broad adoption of AOP by the software industry, it is crucial to provide solid language and tool support. However, research and tool development for C++ is known to be an extremely hard and tedious task, as the language is overwhelmed with interacting features and hard to analyze. Getting AOP into the C++ domain is not just technical challenge. It is also the question of integrating AOP concepts with the philosophy of the C++ language, which is very different from Java. This paper describes the design and development of the AspectC++ language and weaver, which brings fully-fledged AOP support into the C++ domain.


title: Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection

abstract: Morphological reinflection is the task of generating a target form given a source form, a source tag and a target tag. We propose a new way of modeling this task with neural encoder-decoder models. Our approach reduces the amount of required training data for this architecture and achieves state-of-the-art results, making encoder-decoder models applicable to morphological reinflection even for low-resource languages. We further present a new automatic correction method for the outputs based on edit trees.


Traffic Sign Recognition Using Extreme Learning Classifier with Deep Convolutional Features


title: Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction

abstract: Relation extraction suffers from a performance loss when a model is applied to out-of-domain data. This has fostered the development of domain adaptation techniques for relation extraction. This paper evaluates word embeddings and clustering on adapting feature-based relation extraction systems. We systematically explore various ways to apply word embeddings and show the best adaptation improvement by combining word cluster and word embedding information. Finally, we demonstrate the effectiveness of regularization for the adaptability of relation extractors.


Analysis of Tweets for Prediction of Indian Stock Markets


title: Using Active Queue Management to Assist IoT Application Flows in Home Broadband Networks

abstract: Internet of Things (IoT) applications such as telehealth, smart appliances, and smart energy are becoming more common within the home. However, they must compete for bandwidth with traditional applications such as video streaming, video conferencing, and bulk file transfers. Such competition can be detrimental to the IoT applications when home gateways use traditional first-in-first-out (FIFO) queue management. Simply increasing bandwidth between the home gateway and the Internet Service Provider (ISP), even when possible, provides no guarantee of bandwidth for IoT applications since many traditional applications will consume as much bandwidth as is available. In this paper, we explore whether active queue management (AQM), now being implemented in home gateways, can provide protection for IoT flows. We investigate the effect of different AQM algorithms deployed at the home gateway in scenarios with multiple concurrent application flows. We find that deploying multiqueue FlowQueue Controlled Delay (FQ-CoDel) or the hybrid FlowQueue Proportional Integral Controller Enhanced (FQ-PIE) at the home gateway can provide excellent capacity sharing, flow isolation, and good protection in terms of throughput and queuing delays for IoT flows and other applications, which cannot be achieved with traditional FIFO or other single-queue AQMs such as Proportional Integral Controller Enhanced (PIE).


title: B15. Tri-band slotted bow-tie antenna design for RFID reader using hybrid CFO-NM algorithm

abstract: In this article, a dual bow-tie slot antenna for 915/2450/5800 MHz triple band RFID applications is presented. The size of the proposed antenna is determined by the middle resonant frequency. The lower and upper operating frequencies are obtained by inserting metal strip pairs near the ends of the slotted dual bow-tie without increasing the overall antenna area. The hybrid approach involving Central Force Optimization and Nelder-Mead (CFO-NM) algorithm is considered to optimize the antenna dimensions. The CFO-NM algorithm program was implemented using MATLAB-software which linked to the CST Microwave studio software to simulate the antenna. In addition the optimized antenna is simulated by the Finite Difference Time Domain (FDTD) method to validate the results.


Generating Topic-Oriented Summaries Using Neural Attention


title: Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks

abstract: We propose a deep learning model for identifying structure within experiment narratives in scientific literature. We take a sequence labeling approach to this problem, and label clauses within experiment narratives to identify the different parts of the experiment. Our dataset consists of paragraphs taken from open access PubMed papers labeled with rhetorical information as a result of our pilot annotation. Our model is a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells that labels clauses. The clause representations are computed by combining word representations using a novel attention mechanism that involves a separate RNN. We compare this model against LSTMs where the input layer has simple or no attention and a feature rich CRF model. Furthermore, we describe how our work could be useful for information extraction from scientific literature.


title: Visual consciousness and bodily self-consciousness

abstract: Purpose of review: In recent years, consciousness has become a central topic in cognitive neuroscience. This review focuses on the relation between bodily self-consciousness - the feeling of being a subject in a body - and visual consciousness - the subjective experience associated with the perception of visual signals. Recent findings: Findings from clinical and experimental work have shown that bodily self-consciousness depends on specific brain networks and is related to the integration of signals from multiple sensory modalities including vision. In addition, recent experiments have shown that visual consciousness is shaped by the body, including vestibular, tactile, proprioceptive, and motor signals. Summary: Several lines of evidence suggest reciprocal relationships between vision and bodily signals, indicating that a comprehensive understanding of visual and bodily self-consciousness requires studying them in unison.


title: An OpenCL™ Deep Learning Accelerator on Arria 10

abstract: Convolutional neural nets (CNNs) have become a practical means to perform vision tasks, particularly in the area of image classification. FPGAs are well known to be able to perform convolutions efficiently, however, most recent efforts to run CNNs on FPGAs have shown limited advantages over other devices such as GPUs. Previous approaches on FPGAs have often been memory bound due to the limited external memory bandwidth on the FPGA device. We show a novel architecture written in OpenCL(TM), which we refer to as a Deep Learning Accelerator (DLA), that maximizes data reuse and minimizes external memory bandwidth. Furthermore, we show how we can use the Winograd transform to significantly boost the performance of the FPGA. As a result, when running our DLA on Intel's Arria 10 device we can achieve a performance of 1020 img/s, or 23 img/s/W when running the AlexNet CNN benchmark. This comes to 1382 GFLOPs and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the state-of-the-art on FPGAs. Additionally, 23 img/s/W is competitive against the best publicly known implementation of AlexNet on nVidia's TitanX GPU.


title: Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos

abstract: Visual surveillance from low-altitude airborne platforms has been widely addressed in recent years. Moving vehicle detection is an important component of such a system, which is a very challenging task due to illumination variance and scene complexity. Therefore, a boosting Histogram Orientation Gradients (boosting HOG) feature is proposed in this paper. This feature is not sensitive to illumination change and shows better performance in characterizing object shape and appearance. Each of the boosting HOG feature is an output of an adaboost classifier, which is trained using all bins upon a cell in traditional HOG features. All boosting HOG features are combined to establish the final feature vector to train a linear SVM classifier for vehicle classification. Compared with classical approaches, the proposed method achieved better performance in higher detection rate, lower false positive rate and faster detection speed.


title: Structured Compressed Sensing: From Theory to Applications

abstract: Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.


title: Genetic mapping of the mouse proto-oncogene c-sis to chromosome 15.

abstract: The mouse homolog (c-sis) of the transforming gene of the simian sarcoma virus was mapped to chromosome 15 by the Southern blot analysis of DNA's from hamster-mouse somatic cell hybrids. Alterations in c-sis expression may thus play a role in the various murine neoplastic diseases characterized by rearrangements or duplications of chromosome 15.


title: Fuzzing with code fragments

abstract: Fuzz testing is an automated technique providing random data as input to a software system in the hope to expose a vulnerability. In order to be effective, the fuzzed input must be common enough to pass elementary consistency checks; a JavaScript interpreter, for instance, would only accept a semantically valid program. On the other hand, the fuzzed input must be uncommon enough to trigger exceptional behavior, such as a crash of the interpreter. The LangFuzz approach resolves this conflict by using a grammar to randomly generate valid programs; the code fragments, however, partially stem from programs known to have caused invalid behavior before. LangFuzz is an effective tool for security testing: Applied on the Mozilla JavaScript interpreter, it discovered a total of 105 new severe vulnerabilities within three months of operation (and thus became one of the top security bug bounty collectors within this period); applied on the PHP interpreter, it discovered 18 new defects causing crashes.


title: Introduction to side-channel attacks and fault attacks

abstract: Side-channel attacks and fault attacks use the physical interactions with cryptographic devices to achieve the shortcuts of recovering the secret key. For side-channel attacks, physical information leakage during the cryptographic calculation is measured and statistically analyzed to reveal the intermediate values. For fault attacks, computational faults are intentionally triggered to obtain faulty outputs or fault behaviors for the key recovery. Fault injections are also used to obtain special information leakage under the faulty environment. This paper gives a general overview of these physical attacks and discusses the necessity of the accurate complexity evaluation of their successful key recovery.


title: Automotive Technology and Human Factors Research: Past, Present, and Future

abstract: This paper reviews the history of automotive technology development and human factors research, largely by decade, since the inception of the automobile. The human factors aspects were classified into primary driving task aspects (controls, displays, and visibility), driver workspace (seating and packaging, vibration, comfort, and climate), driver’s condition (fatigue and impairment), crash injury, advanced driver-assistance systems, external communication access, and driving behavior. For each era, the paper describes the SAE and ISO standards developed, the major organizations and conferences established, the major news stories affecting vehicle safety, and the general social context. The paper ends with a discussion of what can be learned from this historical review and the major issues to be addressed. A major contribution of this paper is more than 180 references that represent the foundation of automotive human factors, which should be considered core knowledge and should be familiar to those in the profession.


title: Sensing Fabrics for Monitoring Physiological and Biomechanical Variables: E-textile solutions

abstract: In this paper is reported the experience gained in the last five years, in the implementation of wearable systems for personalized health care and their evolution in time. Sensing bio clothes for vital signs monitoring and wearable systems for gesture and posture recognition are specifically illustrated, resulting from the EU funded projects: Wealthy and My Heart.


title: Cloud Terminal: Secure Access to Sensitive Applications from Untrusted Systems

abstract: Current PC- and web-based applications provide insufficient security for the information they access, because vulnerabilities anywhere in a large client software stack can compromise confidentiality and integrity. We propose a new architecture for secure applications, Cloud Terminal, in which the only software running on the end host is a lightweight secure thin terminal, and most application logic is in a remote cloud rendering engine. The secure thin terminal has a very small TCB (23 KLOC) and no dependence on the untrusted OS, so it can be easily checked and remotely attested to. The terminal is also general-purpose: it simply supplies a secure display and input path to remote software. The cloud rendering engine runs an off-the-shelf application in a restricted VM hosted by the provider, but resource sharing between VMs lets one server support hundreds of users. We implement a secure thin terminal that runs on standard PC hardware and provides a responsive interface to applications like banking, email, and document editing. We also show that our cloud rendering engine can provide secure online banking for 5-10 cents per user per month.


title: Socialized Gaussian Process Model for Human Behavior Prediction in a Health Social Network

abstract: Modeling and predicting human behaviors, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. In this work, we propose a Socialized Gaussian Process (SGP) for socialized human behavior modeling. In the proposed SGP model, we naturally incorporates human's personal behavior factor and social correlation factor into a unified model, where basic Gaussian Process model is leveraged to capture individual's personal behavior pattern. Furthermore, we extend the Gaussian Process Model to socialized Gaussian Process (SGP) which aims to capture social correlation phenomena in the social network. The detailed experimental evaluation has shown the SGP model achieves the best prediction accuracy compared with other baseline methods.


title: A Review of SOAP Performance Optimization Techniques to Improve Communication in Web Services in Loosely Coupled

abstract: Web services (WS) implements Service-Oriented Architecture (SOA). WS extends World Wide Web (WWW) infrastructure. This provides a means of integrating software applications in loosely coupled distributed systems. WS communication is facilitated by Simple Object Access Protocol (SOAP). SOAP offers a simple and lightweight mechanism for exchanging structured and typed information among peers in a decentralized, distributed computing environment. However, SOAP’s transmitted data is represented in XML. XML documents are huge in size and verbose (highly redundant), and processing of XML information and its conversion to and fro memory data types are some of the major hindrance in performance for high performance applications. This survey paper gives an insight of previous researchers’ contributions on techniques used in optimizing SOAP in communication in WS in terms of bandwidth utilization and throughput. To optimize SOAP, several techniques covered include: client side caching, differential serialization, SOAP binding, compression, server side caching, and differential deserialization.


title: Graph-Based Compression of Dynamic 3D Point Cloud Sequences

abstract: This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames share some similarities, motion estimation is key to effective compression of these sequences. It, however, remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the point clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature-matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring a significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.


title: Designing an adaptive attention mechanism for relation classification

abstract: Entity pair provide essential information for identifying relation type. Aiming at this characteristic, Position Feature is widely used in current relation classification systems to highlight the words close to them. However, semantic knowledge involved in entity pair has not been fully utilized. To overcome this issue, we propose an Entity-pair-based Attention Mechanism, which is specially designed for relation classification. Recently, attention mechanism significantly promotes the development of deep learning in NLP. Inspired by this, for specific instance(entity pair, sentence), the corresponding entity pair information is incorporated as prior knowledge to adaptively compute attention weights for generating sentence representation. Experimental results on SemEval-2010 Task 8 dataset show that our method outperforms most of the state-of-the-art models, without external linguistic features.


title: On the Practicality of Motion Based Keystroke Inference Attack

abstract: Recent researches have shown that motion sensors may be used as a side channel to infer keystrokes on the touchscreen of smartphones. However, the practicality of this attack is unclear. For example, does this attack work on different devices, screen dimensions, keyboard layouts, or keyboard types? Does this attack depend on specific users or is it user independent? To answer these questions, we conducted a user study where 21 participants typed a total of 47,814 keystrokes on four different mobile devices in six settings. Our results show that this attack remains effective even though the accuracy is affected by user habits, device dimension, screen orientation, and keyboard layout. On a number-only keyboard, after the attacker tries 81 4-digit PINs, the probability that she has guessed the correct PIN is 65%, which improves the accuracy rate of random guessing by 81 times. Our study also indicates that inference based on the gyroscope is more accurate than that based on the accelerometer. We evaluated two classification techniques in our prototype and found that they are similarly effective.


title: Reconfigurable Monitoring System for Time-Frequency Analysis on Industrial Equipment Through STFT and DWT

abstract: Nowadays industry pays much attention to prevent failures that may interrupt production with severe consequences in cost, product quality, and safety. The most-analyzed parameters for monitoring dynamic characteristics and ensuring correct functioning of systems are electric current, voltage, and vibrations. System-on-chip (SoC) design is an approach to increase performance and overcome costs during equipment monitoring. This work presents the design and implementation of a low-cost SoC design that utilizes reconfigurable hardware and a customized embedded processor for time-frequency analysis on industrial equipment through short-time Fourier transform and discrete wavelet transform. Three study cases (electric current supply to an induction motor during startup transient, voltage supply to an induction motor through a variable speed drive, and vibration signals from industrial-robot links) show the suitability of the proposed monitoring system for time-frequency analysis of different signals in distinct industrial applications, and early diagnosis and prognosis of abnormalities in monitored systems.


title: Privacy in Social Media: Identification, Mitigation and Applications

abstract: The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. This data provides opportunities for researchers and service providers to study and better understand users' behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals' privacy. Users privacy in social media is an emerging task and has attracted increasing attention in recent years. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities, and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users' privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is a vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups 1) graph data anonymization and de-anonymization, 2) author identification, 3) profile attribute disclosure, 4) user location and privacy, and 5) recommender systems and privacy issues. We also discuss open problems and future research directions for user privacy issues in social media.


title: The influence of HR practices and job satisfaction on interpersonal deviance in the workplace

abstract: We examined the influence of human resource practices on interpersonal deviance at work through job satisfaction.A survey was carried out among 372 manufacturing employees of various occupational levels in manufacturing companies in the northern region of Malaysia. We collected data by distributing questionnaires to participants, with the assistance of human resource department. We asked them to indicate how often they know whether any of their workmates engaged in different types of interpersonal deviance. Regression analysis showed that human resource practices of job description, employment security, and internal career opportunities have significant relationships with interpersonal deviance. Hierarchical regression indicated that the dimensions of job satisfaction have a direct relationship with interpersonal deviance.The dimensions also mediated the relationship between human resource practices and interpersonal deviance.Implications to managers and practitioners are discussed.


title: Factored Language Models Tutorial

abstract: The Factored Language Model (FLM) is a e xible framework for incorporating various information sources, such as morphology and part-of-speech, into language modeling. FLMs have so far been successfully applied to tasks such as speech recognition and machine translation; it has the potential to be used in a wide variety of problems in estimating probability tables from sparse data. This tutorial serves as a comprehensive description of FLMs and related algorithms. We document the FLM functionalities as implemented in the SRI Language Modeling toolkit and provide an introductory walk-through using FLMs on an actual dataset. Our goal is to provide an easy-to-understand tutorial and reference for researchers interested in applying FLMs to their problems.


title: Thermoelectric properties of tetrahedrally bonded wide-gap stannite compounds Cu2ZnSn1-xInxSe4

abstract: It is usually accepted that good thermoelectric (TE) materials should be narrow-gap semiconductors. Here we show an example that the tetrahedrally bonded stannite compound Cu2ZnSnSe4 with a band gap of 1.44 eV can also exhibit a high figure of merit at intermediate temperature. The highly distorted structure strives for the relatively low thermal conductivity, and the tunability of the electrical properties were demonstrated through doping. The maximum ZT of Cu2ZnSn0.90In0.10Se4 reaches 0.95 at 850 K. This work may open a way for exploring high-performance TE materials with the family of widely existing tetrahedrally bonded semiconductors.


title: Why do people use gamification services?

abstract: Utilitarian, hedonic and social motivations to use gamification are investigated.Utilitarian and social motivations have a positive direct association with attitude.Utilitarian motivations' association with continued use is mediated by attitude.Hedonic motivations have a positive direct association with continued use. In recent years, technology has been increasingly harnessed for motivating and supporting people toward various individually and collectively beneficial behaviors. One of the most popular developments in this field has been titled gamification. Gamification refers to technologies that attempt to promote intrinsic motivations toward various activities, commonly, by employing design characteristic to games. However, a dearth of empirical evidence still exists regarding why people want to use gamification services. Based on survey data gathered from the users of a gamification service, we examine the relationship between utilitarian, hedonic and social motivations and continued use intention as well as attitude toward gamification. The results suggest that the relationship between utilitarian benefits and use is mediated by the attitude toward the use of gamification, while hedonic aspects have a direct positive relationship with use. Social factors are strongly associated with attitude, but show only a weak further association with the intentions to continue the use of a gamification service.


title: A SEM-neural network approach for predicting antecedents of m-commerce acceptance

abstract: The main goal is to analyze the individuals' use intention of m-commerce.The objective is to determine which variables are the most relevant.SEM was used to determine which variables had significant influence on m-commerce.Neural network model was used to rank the relative influence of predictors from SEM.This study provides a set of recommendations for companies. Higher penetration of powerful mobile devices - especially smartphones - and high-speed mobile internet access are leading to better offer and higher levels of usage of these devices in commercial activities, especially among young generations. The purpose of this paper is to determine the key factors that influence consumers' adoption of mobile commerce. The extended model incorporates basic TAM predictors, such as perceived usefulness and perceived ease of use, but also several external variables, such as trust, mobility, customization and customer involvement. Data was collected from 224 m-commerce consumers. First, structural equation modeling (SEM) was used to determine which variables had significant influence on m-commerce adoption. In a second phase, the neural network model was used to rank the relative influence of significant predictors obtained from SEM. The results showed that customization and customer involvement are the strongest antecedents of the intention to use m-commerce. The study results will be useful for m-commerce providers in formulating optimal marketing strategies to attract new consumers.


title: Shopping Path Analysis and Transaction Mining Based on RFID Technology

abstract: The RFID technology is gaining strong support from business for various applications such as supply chain management, access control, airline luggage management, etc. Due to the cost consideration, it is still not a replacement for the traditional item-level bar code. Therefore, tracking and analyzing the shopping paths and purchasing behaviors of customers in a superstore such as Wal-Mart is still a challenging task. In this paper, we propose a RFID deployment model for collecting shopping paths and purchased items of customers. Algorithms based on customer access matrix (CAM) and customer transaction mining (CTM) are then proposed for mining preferred shopping paths and their relationships to the purchased items. In the experiments, we build a dataset generator for generating shopping paths and purchased items of customers, and the results show that our model is applicable to real applications.


title: Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution

abstract: In this paper, we propose a fully convolutional network for iterative non-blind deconvolution. We decompose the non-blind deconvolution problem into image denoising and image deconvolution. We train a FCNN to remove noise in the gradient domain and use the learned gradients to guide the image deconvolution step. In contrast to the existing deep neural network based methods, we iteratively deconvolve the blurred images in a multi-stage framework. The proposed method is able to learn an adaptive image prior, which keeps both local (details) and global (structures) information. Both quantitative and qualitative evaluations on the benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of quality and speed.


title: A Comparison over Focused Web Crawling Strategies

abstract: In this paper we review and compare focused crawling strategies, studied and published during the past decade. Despite giant leaps in communication, storage and computing power in recent years, crawlers have always struggled to keep up with Web content generation and modification. Focused crawlers attempt to i) accelerate the crawling process, ii) maximize the harvest of high quality pages, iii) assign appropriate credit to different documents along a crawling path, such that short-term gains are not pursued at the expense of less obvious paths that ultimately yield larger sets of valuable pages. Beyond the review and comparison of the focused crawling strategies, we additionally propose additions to the corresponding architectures for further research.


title: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings

abstract: Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.


title: Perfect Absorber Metamaterial for Designing Low-RCS Patch Antenna

abstract: Perfect absorber metamaterial (PAM) to reduce antenna radar cross section (RCS) is proposed. The novel low-RCS antenna is based on the concept of the PAM consisting of a periodic array of metallic dendritic structure and metallic square film etched on each face of a single substrate. The PAM processes high electromagnetic wave absorptivity for a wide incident angle. In the present letter, the PAM patterns are printed on the surface of a patch antenna to reduce the antenna RCS. It is demonstrated that, as compared to an antenna without the PAM, the proposed antenna RCS can be reduced drastically due to the fact that the PAM absorbs electromagnetic wave. Meanwhile, the proposed antenna can still maintain good radiation characteristics.


title: Self-Adaptive Genetic Algorithms with Simulated Binary Crossover

abstract: Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs.


title: Hybrid Visual Servoing With Hierarchical Task Composition for Aerial Manipulation

abstract: In this letter, a hybrid visual servoing with a hierarchical task-composition control framework is described for aerial manipulation, i.e., for the control of an aerial vehicle endowed with a robot arm. The proposed approach suitably combines into a unique hybrid-control framework the main benefits of both image-based and position-based control schemes. Moreover, the underactuation of the aerial vehicle has been explicitly taken into account in a general formulation, together with a dynamic smooth activation mechanism. Both simulation case studies and experiments are presented to demonstrate the performance of the proposed technique.


title: Facial symmetry and the perception of beauty

abstract: Evolutionary, as well as cultural, pressures may contribute to our perceptions of facial attractiveness. Biologists predict that facial symmetry should be attractive, because it may signal mate quality. We tested the prediction that facial symmetry is attractive by manipulating the symmetry of individual faces and observing the effect on attractiveness, and by examining whether natural variations in symmetry (between faces) correlated with perceived attractiveness. Attractiveness increased when we increased symmetry, and decreased when we reduced symmetry, in individual faces (Experiment 1), and natural variations in symmetry correlated significantly with attractiveness (Experiments 1 and 1A). Perfectly symmetric versions, made by blending the normal and mirror images of each face, were preferred to less symmetric versions of the same faces (even when those versions were also blends) (Experiments 1 and 2). Similar results were found when subjects judged the faces on appeal as a potential life partner, suggesting that facial symmetry may affect human mate choice. We conclude that facial symmetry is attractive and discuss the possibility that this preference for symmetry may be biologically based.


title: Improving neural networks by preventing co-adaptation of feature detectors

abstract: When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random "dropout" gives big improvements on many benchmark tasks and sets new records for speech and object recognition.


title: Explainable Recommendation: A Survey and New Perspectives

abstract: Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems. It also facilitates system designers for better system debugging. In recent years, a large number of explainable recommendation approaches -- especially model-based methods -- have been proposed and applied in real-world systems. In this survey, we provide a comprehensive review for the explainable recommendation research. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research. 3) We summarize how explainable recommendation applies to different recommendation tasks. We also devote a chapter to discuss the explanation perspectives in broader IR and AI/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.


title: User-driven semantic mapping of tabular data

abstract: Governments and public administrations started recently to publish large amounts of structured data on the Web, mostly in the form of tabular data such as CSV files or Excel sheets. Various tools and projects have been launched aiming at facilitating the lifting of tabular data to reach semantically structured and linked data. However, none of these tools supported a truly incremental, pay-as-you-go data publication and mapping strategy, which enables effort sharing between data owners, community experts and consumers. In this article, we present an approach for enabling the user-driven semantic mapping of large amounts tabular data. We devise a simple mapping language for tabular data, which is easy to understand even for casual users, but expressive enough to cover the vast majority of potential tabular mappings use cases. We outline a formal approach for mapping tabular data to RDF. Default mappings are automatically created and can be revised by the community using a semantic wiki. The mappings are executed using a sophisticated streaming RDB2RDF conversion. We report about the deployment of our approach at the Pan-European data portal PublicData.eu, where we transformed and enriched almost 10,000 datasets accounting for 7.3 billion triples.


title: Guillain-Barré Syndrome outbreak associated with Zika virus infection in French Polynesia: a case-control study

abstract: Summary Background Between October, 2013, and April, 2014, French Polynesia experienced the largest Zika virus outbreak ever described at that time. During the same period, an increase in Guillain-Barre syndrome was reported, suggesting a possible association between Zika virus and Guillain-Barre syndrome. We aimed to assess the role of Zika virus and dengue virus infection in developing Guillain-Barre syndrome. Methods In this case-control study, cases were patients with Guillain-Barre syndrome diagnosed at the Centre Hospitalier de Polynesie Francaise (Papeete, Tahiti, French Polynesia) during the outbreak period. Controls were age-matched, sex-matched, and residence-matched patients who presented at the hospital with a non-febrile illness (control group 1; n=98) and age-matched patients with acute Zika virus disease and no neurological symptoms (control group 2; n=70). Virological investigations included RT-PCR for Zika virus, and both microsphere immunofluorescent and seroneutralisation assays for Zika virus and dengue virus. Anti-glycolipid reactivity was studied in patients with Guillain-Barre syndrome using both ELISA and combinatorial microarrays. Findings 42 patients were diagnosed with Guillain-Barre syndrome during the study period. 41 (98%) patients with Guillain-Barre syndrome had anti-Zika virus IgM or IgG, and all (100%) had neutralising antibodies against Zika virus compared with 54 (56%) of 98 in control group 1 (p Interpretation This is the first study providing evidence for Zika virus infection causing Guillain-Barre syndrome. Because Zika virus is spreading rapidly across the Americas, at risk countries need to prepare for adequate intensive care beds capacity to manage patients with Guillain-Barre syndrome. Funding Labex Integrative Biology of Emerging Infectious Diseases, EU 7th framework program PREDEMICS. and Wellcome Trust.


title: EPHECT II: Exposure assessment to household consumer products.

abstract: Within the framework of the EPHECT project (Emissions, exposure patterns and health effects of consumer products in the EU), irritative and respiratory health effects were assessed in relation to acute and long-term exposure to key and emerging indoor air pollutants emitted during household use of selected consumer products. In this context, inhalation exposure assessment was carried out for six selected 'target' compounds (acrolein, formaldehyde, benzene, naphthalene, d-limonene and α-pinene). This paper presents the methodology and the outcomes from the micro-environmental modelling of the 'target' pollutants following single or multiple use of selected consumer products and the subsequent exposure assessment. The results indicate that emissions from consumer products of benzene and α-pinene were not considered to contribute significantly to the EU indoor background levels, in contrast to some cases of formaldehyde and d-limonene emissions in Eastern Europe (mainly from cleaning products). The group of housekeepers in East Europe appears to experience the highest exposures to acrolein, formaldehyde and benzene, followed by the group of the retired people in North, who experiences the highest exposures to naphthalene and α-pinene. High exposure may be attributed to the scenarios developed within this project, which follow a 'most-representative worst-case scenario' strategy for exposure and health risk assessment. Despite the above limitations, this is the first comprehensive study that provides exposure estimates for 8 population groups across Europe exposed to 6 priority pollutants, as a result of the use of 15 consumer product classes in households, while accounting for regional differences in uses, use scenarios and ventilation conditions of each region.


title: Distributed video acquisition and annotation for sport-event summarization

abstract: This document presents the video data set that has been recently collected within the FP7 APIDIS project [1]. The acquisition setting was deployed around a basket-ball court, and consists in a set of 7 calibrated IP cameras, each one collecting 2 Mpixels frames at a rate higher than 20 frames/sec. After an approximate temporal synchronization of the video streams and the definition of an homography to link the ground points in every views, the video data have been augmented through the definition and collection of metadata. Those metadata are expected to support efficient browsing of the content, and automatic personalized summarization of the event captured by the distributed set of cameras. Metadata include (1) low-level feature distributions, (2) object trajectories, and (3) events of interest attributes. The data and metadata are publicly available upon email request to the APIDIS partners.


title: Detection of markers using deep learning for docking of autonomous underwater vehicle

abstract: Autonomous Underwater Vehicle (AUV) has limited energy capacity due to it being an embedded system. To overcome this limitation, the AUV can home into a docking station to recharge its battery. Several research has been conducted on the docking of AUV using vision. In some literatures, docking would fail if the target placed at the docking station is missing or disoriented from the camera view. This study proposes a deep learning system to detect the target markers to solve the disoriented view issues. The proposed system comprises of two phases which are training and testing. In training phase, there are region proposal, labeling data, developing convolutional neural network architecture, and network training. In testing phase, the trained network will be fed by various input data so as to measure the performance of the network. Result in this study shows that the system is able to locate and classify the target markers even though the view of the object of interest is disoriented. Future work may include the implementation of the developed system on real docking operation.


title: Big Graph Data Analytics on Single Machines – An Overview

abstract: Driven by a multitude of use cases, graph data analytics has become a hot topic in research and industry. Particularly on big graphs, performing complex analytical queries efficiently to derive new insights is a challenging task. Systems that aim at solving the technical part of this challenge are often referred to as graph processing systems. They allow expressing and executing analytic algorithms and queries, while hiding most of the technical details related to efficiently storing and processing graph data. Since 2010, work on graph processing systems for distributed systems as well as shared memory systems has virtually exploded. In this article, we give an overview of this work with the particular focus on graph processing systems for large multiprocessor machines. We describe the state of the art established in recent years and outline trends and challenges in research and development that point towards the future of graph processing systems.


title: Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments †

abstract: In search and rescue activities, unmanned aerial vehicles (UAV) should exploit sound information to compensate for poor visual information. This paper describes the design and implementation of a UAV-embedded microphone array system for sound source localization in outdoor environments. Four critical development problems included water-resistance of the microphone array, efficiency in assembling, reliability of wireless communication, and sufficiency of visualization tools for operators. To solve these problems, we developed a spherical microphone array system (SMAS) consisting of a microphone array, a stable wireless network communication system, and intuitive visualization tools. The performance of SMAS was evaluated with simulated data and a demonstration in the field. Results confirmed that the SMAS provides highly accurate localization, water resistance, prompt assembly, stable wireless communication, and intuitive information for observers and operators.


title: High-gain dielectric-loaded antipodal Fermi tapered slot antenna for MM-wave applications

abstract: Abstract — A high gain antipodal Fermi tapered slot antenna with sine corrugation is loaded with a dielectric for potential use in high data-rate wireless applications. The proposed antenna achieved a gain of 20 dB over the unlicensed millimeter-wave ISM-band, from 57 to 64 GHz. For the frequency range of 50-70 GHz, the total efficiency is around 93% and the simulated |S11| is less than -18 dB. To reduce costs and to simplify fabrication, the elliptical-shaped loaded dielectric and the proposed antenna are integrated in a single layer substrate. Keywords— Antipodal Fermi antenna; millimeter-wave; dielectric loading; high-gain antenna. I. I NTRODUCTION For imaging applications and high data-rate wireless systems operating at the millimeter wave (MMW) range, highly efficient and low-cost antennas are required. Some of the challenges such antennas must address are the required high gain, low sidelobe level (SLL), wideband, and low cross-polarization. The antipodal tapered slot antenna (ATSA) not only overcomes the above-mentioned challenges, it can also be incorporated with substrate integrated waveguide (SIW) feeding technology [1], and antenna arrays to enhance the gain [2]. Tapered slot antennas (TSAs) with different taper profiles have recently been proposed, such as linear TSAs (LTSA) [3], Vivaldi with exponential taper TSAs [4], and Fermi TSAs [5]. LTSA structures, based on SIW technology mainly designed to utilize broadband and achieve high gain, are presented in [6]. Reference [7] proposes adding rectangular corrugation slots to the conducting arms of antipodal Fermi antennas to improve the return loss, increase the gain, and reduce the SLL. Also, antipodal Fermi tapered slot antenna (AFTSA) with sine-corrugation was proposed in [8]. In this work, dielectric loading of the same substrate material is used to improve the gain and reduce the SLL of the proposed antenna. Compared with dielectrically-loaded ALTSA [9], the proposed antenna in this work shows lower SLL and higher gain. Moreover, it has better impedance matching and lower costs. Cutting a diamond-shape of dielectrically-loaded AFTSA not only increases the total efficiency, it also efficiently reduces the SLL and cross-polarization. II. A


title: GoCAD: GPU-Assisted Online Content-Adaptive Display Power Saving for Mobile Devices in Internet Streaming

abstract: During Internet streaming, a significant portion of the battery power is always consumed by the display panel on mobile devices. To reduce the display power consumption, backlight scaling, a scheme that intelligently dims the backlight has been proposed. To maintain perceived video appearance in backlight scaling, a computationally intensive luminance compensation process is required. However, this step, if performed by the CPU as existing schemes suggest, could easily offset the power savings gained from backlight scaling. Furthermore, computing the optimal backlight scaling values requires per-frame luminance information, which is typically too energy intensive for mobile devices to compute. Thus, existing schemes require such information to be available in advance. And such an offline approach makes these schemes impractical. To address these challenges, in this paper, we design and implement GoCAD, a GPU-assisted Online Content-Adaptive Display power saving scheme for mobile devices in Internet streaming sessions. In GoCAD, we employ the mobile device's GPU rather than the CPU to reduce power consumption during the luminance compensation phase. Furthermore, we compute the optimal backlight scaling values for small batches of video frames in an online fashion using a dynamic programming algorithm. Lastly, we make novel use of the widely available video storyboard, a pre-computed set of thumbnails associated with a video, to intelligently decide whether or not to apply our backlight scaling scheme for a given video. For example, when the GPU power consumption would offset the savings from dimming the backlight, no backlight scaling is conducted. To evaluate the performance of GoCAD, we implement a prototype within an Android application and use a Monsoon power monitor to measure the real power consumption. Experiments are conducted on more than 460 randomly selected YouTube videos. Results show that GoCAD can effectively produce power savings without affecting rendered video quality.


title: SciHadoop: Array-based query processing in Hadoop

abstract: Hadoop has become the de facto platform for large-scale data analysis in commercial applications, and increasingly so in scientific applications. However, Hadoop's byte stream data model causes inefficiencies when used to process scientific data that is commonly stored in highly-structured, array-based binary file formats resulting in limited scalability of Hadoop applications in science. We introduce Sci-Hadoop, a Hadoop plugin allowing scientists to specify logical queries over array-based data models. Sci-Hadoop executes queries as map/reduce programs defined over the logical data model. We describe the implementation of a Sci-Hadoop prototype for NetCDF data sets and quantify the performance of five separate optimizations that address the following goals for several representative aggregate queries: reduce total data transfers, reduce remote reads, and reduce unnecessary reads. Two optimizations allow holistic aggregate queries to be evaluated opportunistically during the map phase; two additional optimizations intelligently partition input data to increase read locality, and one optimization avoids block scans by examining the data dependencies of an executing query to prune input partitions. Experiments involving a holistic function show run-time improvements of up to 8x, with drastic reductions of IO, both locally and over the network.


title: The Regularization Effects of Anisotropic Noise in Stochastic Gradient Descent.

abstract: Understanding the generalization of deep learning has raised lots of concerns recently, where the learning algorithms play an important role in generalization performance, such as stochastic gradient descent (SGD). Along this line, we particularly study the anisotropic noise introduced by SGD, and investigate its importance for the generalization in deep neural networks. Through a thorough empirical analysis, it is shown that the anisotropic diffusion of SGD tends to follow the curvature information of the loss landscape, and thus is beneficial for escaping from sharp and poor minima effectively, towards more stable and flat minima. We verify our understanding through comparing this anisotropic diffusion with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics) and other types of position-dependent noise.


title: Coordinate Friendly Structures, Algorithms and Applications

abstract: This paper focuses on coordinate update methods, which are useful for solving problems involving large or high-dimensional datasets. They decompose a problem into simple subproblems, where each updates one, or a small block of, variables while fixing others. These methods can deal with linear and nonlinear mappings, smooth and nonsmooth functions, as well as convex and nonconvex problems. In addition, they are easy to parallelize. ::: The great performance of coordinate update methods depends on solving simple sub-problems. To derive simple subproblems for several new classes of applications, this paper systematically studies coordinate-friendly operators that perform low-cost coordinate updates. ::: Based on the discovered coordinate friendly operators, as well as operator splitting techniques, we obtain new coordinate update algorithms for a variety of problems in machine learning, image processing, as well as sub-areas of optimization. Several problems are treated with coordinate update for the first time in history. The obtained algorithms are scalable to large instances through parallel and even asynchronous computing. We present numerical examples to illustrate how effective these algorithms are.


title: Flash Cookies and Privacy

abstract: This is a pilot study of the use of 'Flash cookies' by popular websites. We find that more than 50% of the sites in our sample are using flash cookies to store information about the user. Some are using it to 'respawn' or re-instantiate HTTP cookies deleted by the user. Flash cookies often share the same values as HTTP cookies, and are even used on government websites to assign unique values to users. Privacy policies rarely disclose the presence of Flash cookies, and user controls for effectuating privacy preferences are lacking.


title: Design of Power-Rail ESD Clamp Circuit With Ultra-Low Standby Leakage Current in Nanoscale CMOS Technology

abstract: An ultra-low-leakage power-rail ESD clamp circuit, composed of the SCR device and new ESD detection circuit, has been proposed with consideration of gate current to reduce the standby leakage current. By controlling the gate current of the devices in the ESD detection circuit under a specified bias condition, the whole power-rail ESD clamp circuit can achieve an ultra-low standby leakage current. The new proposed circuit has been fabricated in a 1 V 65 nm CMOS process for experimental verification. The new proposed power-rail ESD clamp circuit can achieve 7 kV HBM and 325 V MM ESD levels while consuming only a standby leakage current of 96 nA at 1 V bias in room temperature and occupying an active area of only 49 mum 21 mum.


title: Contextual information fusion for intrusion detection: a survey and taxonomy

abstract: Research in cyber-security has demonstrated that dealing with cyber-attacks is by no means an easy task. One particular limitation of existing research originates from the uncertainty of information that is gathered to discover attacks. This uncertainty is partly due to the lack of attack prediction models that utilize contextual information to analyze activities that target computer networks. The focus of this paper is a comprehensive review of data analytics paradigms for intrusion detection along with an overview of techniques that apply contextual information for intrusion detection. A new research taxonomy is introduced consisting of several dimensions of data mining techniques, which create attack prediction models. The survey reveals the need to use multiple categories of contextual information in a layered manner with consistent, coherent, and feasible evidence toward the correct prediction of cyber-attacks.


title: RAM: Residual Attention Module for Single Image Super-Resolution.

abstract: Attention mechanisms are a design trend of deep neural networks that stands out in various computer vision tasks. Recently, some works have attempted to apply attention mechanisms to single image super-resolution (SR) tasks. However, they apply the mechanisms to SR in the same or similar ways used for high-level computer vision problems without much consideration of the different nature between SR and other problems. In this paper, we propose a new attention method, which is composed of new channel-wise and spatial attention mechanisms optimized for SR and a new fused attention to combine them. Based on this, we propose a new residual attention module (RAM) and a SR network using RAM (SRRAM). We provide in-depth experimental analysis of different attention mechanisms in SR. It is shown that the proposed method can construct both deep and lightweight SR networks showing improved performance in comparison to existing state-of-the-art methods.


title: Construction and Application of Radical Candor: Efficiency of Criticism at Work

abstract: Negative feedback has been repeatedly identified as beneficial for organizational learning, adaptability, and performance. Despite having these advantages, most organizations still do not use negative feedback to its full potential, as they fail to spread it correctly among their members. The application of negative feedback, therefore, faces several issues like misuse or process avoidance that are mostly driven by psychological factors. The purpose of this paper is to discuss potential benefits of a newly emerged organizational approach called radical candor for contemporary state of the art in feedback-related behavior and to propose how it might be successfully applied in organizational settings. We define radical candor as a proactive and compassionate engagement in an unpleasant and direct feedback process. Radical candor may help reduce the influence of psychological factors like extreme levels of attachment, harsh emotions or low self-esteem in order to make organizational members more engaged and satisfied with the negative feedback process. Organizations willing to apply radical candor might consider either official implementation from top to bottom (through managers and leadership) or unofficial intervention through the agents (employees of all levels) depending on company characteristics.


title: Topic Detection by Clustering Keywords

abstract: We consider topic detection without any prior knowledge of category structure or possible categories. Keywords are extracted and clustered based on different similarity measures using the induced k-bisecting clustering algorithm. Evaluation on Wikipedia articles shows that clusters of keywords correlate strongly with the Wikipedia categories of the articles. In addition, we find that a distance measure based on the Jensen-Shannon divergence of probability distributions outperforms the cosine similarity. In particular, a newly proposed term distribution taking co-occurrence of terms into account gives best results.


title: CONCEPT MAPPING AND TEXT WRITING AS LEARNING TOOLS IN PROBLEM-ORIENTED LEARNING

abstract: In two studies we investigated whether concept mapping or summary writing better support students while learning from authentic problems in the field of business. We interpret concept mapping and summary writing as elaboration tools aiming at helping students to understand new information, and to integrate it with prior knowledge. We hypothesize concept mapping to be superior to summary writing. Both studies had a pre-test and post-test design with two experimental groups (concept mapping and summary writing). In the first study, 26 students of a commercial high school (pre-university level) in their next to last year took part. In the second study 30 students aged 16-18 years from the pre-final year of pre-university education participated. Contrary to our expectations, in both studies the groups did not differ significantly in pre-test and post-test scores. However, the concept mapping condition seems to better support meaningful argumentation and elaboration of information.


title: Trying to improve communication and collaboration with information technology: An action research project which failed

abstract: Discusses the use of information technology to facilitate communication and collaboration. In this action research project a groupware product called Lotus NotesTM was implemented to facilitate communication and collaboration amongst the senior management team. Although there was a real need for change, and the project received strong support from senior management on the basis that it would enable radical changes in coordination within the workgroup, these radical changes did not occur. The authors analyse the reasons for failure, and suggest that the project failed because of institutional forces which inhibited dramatic changes in work habits.


Predictive Computing and Information Security


title: A hardware efficient chaotic ring oscillator based true random number generator

abstract: In this work, we present a hardware efficient chaotic ring oscillator based true random number generator in multiple oscillator sampling topology. Recently introduced ring oscillator based true random number generators use significant number of rings for accumulating available jitter to a useful level. They require large silicon area and consume considerable amount of power dissipation whereas, the proposed circuit can boost jitter by a factor of 178 with only a few components. The simplicity of the proposed circuit offers high integration potential with inherent low area and power consumption advantages over conventional designs. Random numbers generated by the proof of concept prototype passed all NIST statistical tests without any post processing.


title: To Update or Not to Update: Insights From a Two-Year Study of Android App Evolution

abstract: Although there are over 1,900,000 third-party Android apps in the Google Play Store, little is understood about how their security and privacy characteristics, such as dangerous permission usage and the vulnerabilities they contain, have evolved over time. Our research is two-fold: we take quarterly snapshots of the Google Play Store over a two-year period to understand how permission usage by apps has changed; and we analyse 30,000 apps to understand how their security and privacy characteristics have changed over the same two-year period. Extrapolating our findings, we estimate that over 35,000 apps in the Google Play Store ask for additional dangerous permissions every three months. Our statistically significant observations suggest that free apps and popular apps are more likely to ask for additional dangerous permissions when they are updated. Worryingly, we discover that Android apps are not getting safer as they are updated. In many cases, app updates serve to increase the number of distinct vulnerabilities contained within apps, especially for popular apps. We conclude with recommendations to stakeholders for improving the security of the Android ecosystem.


title: Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection

abstract: Generative Adversarial Networks (GANs) have been successfully used in a large number of domains. This paper proposes the use of GANs for generating network traffic in order to mimic other types of traffic. In particular, our method modifies the network behavior of a real malware in order to mimic the traffic of a legitimate application, and therefore avoid detection. By modifying the source code of a malware to receive parameters from a GAN, it was possible to adapt the behavior of its Command and Control (C2) channel to mimic the behavior of Facebook chat network traffic. In this way, it was possible to avoid the detection of new-generation Intrusion Prevention Systems that use machine learning and behavioral characteristics. A real-life scenario was successfully implemented using the Stratosphere behavioral IPS in a router, while the malware and the GAN were deployed in the local network of our laboratory, and the C2 server was deployed in the cloud. Results show that a GAN can successfully modify the traffic of a malware to make it undetectable. The modified malware also tested if it was being blocked and used this information as a feedback to the GAN. This work envisions the possibility of self-adapting malware and self-adapting IPS.


title: On Robot Revolution and Taxation

abstract: Advances in artificial intelligence are resulting in the rapid automation of the work force. The tools that are used to automate are called robots. Bill Gates proposed that in order to deal with the problem of the loss of jobs and reduction of the tax revenue we ought to tax the robots. The problem with taxing the robots is that it is not easy to know what a robot is. This article studies the definition of a robot and the implication of advances in robotics on taxation. It is evident from this article that it is a difficult task to establish what a robot is and what is not a robot. It concludes that taxing robots is the same as increasing corporate tax.


title: DeepTAM: Deep Tracking and Mapping

abstract: We present a system for keyframe-based dense camera tracking and depth map estimation that is entirely learned. For tracking, we estimate small pose increments between the current camera image and a synthetic viewpoint. This significantly simplifies the learning problem and alleviates the dataset bias for camera motions. Further, we show that generating a large number of pose hypotheses leads to more accurate predictions. For mapping, we accumulate information in a cost volume centered at the current depth estimate. The mapping network then combines the cost volume and the keyframe image to update the depth prediction, thereby effectively making use of depth measurements and image-based priors. Our approach yields state-of-the-art results with few images and is robust with respect to noisy camera poses. We demonstrate that the performance of our 6 DOF tracking competes with RGB-D tracking algorithms. We compare favorably against strong classic and deep learning powered dense depth algorithms.


title: Prompt Indian Coin Recognition with Rotation Invariance Using Image Subtraction Technique

abstract: This paper detects Indian coins of different denomination. The spiraling business transaction at vending machines and automated systems working on token have spurred better coin recognition techniques saddled with increased robustness.These techniques facilitate transaction making it easier in all forms of trade. Keeping all the essential factors in mind a system has been created which recognizes coin based on image subtraction technique. The process performs 3 checks(radius, coarse and fine)on the input image. The stated subsequent checks enable the technique to endorse Rotation Invariance, thus obviating the need of placing the coin at a certain angle. Also, the technique does away with the requirement of placing the front face of the coin up. Subtraction between the input object image and database image is performed. Further, plotting the resultant values gives minima which if less than a standard threshold establishes the recognition of the coin. Results of MATLAB based simulations have been reported.


title: Muscle Activation Patterns While Lifting Stable and Unstable Loads on Stable and Unstable Surfaces

abstract: In an attempt to mimic everyday activities that are performed in 3-dimensional environments, exercise programs have been designed to integrate training of the trunk muscles with training of the extremities. Many believe that the most effective way to recruit the core stabilizing muscles is to execute traditional exercise movements on unstable surfaces. However, physical activity is rarely performed with a stable load on an unstable surface; usually, the surface is stable, and the external resistance is not. The purpose of this study was to evaluate muscle activity of the prime movers and core stabilizers while lifting stable and unstable loads on stable and unstable surfaces during the seated overhead shoulder press exercise. Thirty resistance-trained subjects performed the shoulder press exercise for 3 sets of 3 repetitions under 2 load (barbell and dumbbell) and 2 surface (exercise bench and Swiss ball) conditions at a 10 repetition maximum relative intensity. Surface electromyography (EMG) measured muscle activity for 8 muscles (anterior deltoid, middle deltoid, trapezius, triceps brachii, rectus abdominis, external obliques, and upper and lower erector spinae). The average root mean square of the EMG signal was calculated for each condition. The results showed that as the instability of the exercise condition increased, the external load decreased. Triceps activation increased with external resistance, where the barbell/bench condition had the greatest EMG activation and the dumbbell/Swiss ball condition had the least. The upper erector spinae had greater muscle activation when performing the barbell presses on the Swiss ball vs. the bench. The findings provide little support for training with a lighter load using unstable loads or unstable surfaces.


title: Design, measurement and equivalent circuit synthesis of high power HF transformer for three-phase composite dual active bridge topology

abstract: High voltage high frequency (HF) transformer provides the isolation between high and low dc link voltages in dual active bridge (DAB) converters. Such DAB converters are finding wide applications as an intermediate DC-DC converter in transformerless intelligent power substation (TIPS), which is proposed as an alternative for conventional distribution-transformer connecting 13.8 kV and 480 V grids. The design of HF transformer used in DAB stage of such application is very challenging considering the required isolation, size and cost. In this paper, the specification generation, design, characterization, test and measurement results on a 10kHz HF transformer are presented, highlighting the above challenges.


title: sk_p: a neural program corrector for MOOCs

abstract: We present a novel technique for automatic program correction in MOOCs, capable of fixing both syntactic and semantic errors without manual, problem specific correction strategies. Given an incorrect student program, it generates candidate programs from a distribution of likely corrections, and checks each candidate for correctness against a test suite. The key observation is that in MOOCs many programs share similar code fragments, and the seq2seq neural network model, used in the natural-language processing task of machine translation, can be modified and trained to recover these fragments. Experiment shows our scheme can correct 29% of all incorrect submissions and out-performs state of the art approach which requires manual, problem specific correction strategies.


title: Load-balanced and locality-aware scheduling for data-intensive workloads at extreme scales

abstract: Data-driven programming models such as many-task computing MTC have been prevalent for running data-intensive scientific applications. MTC applies over-decomposition to enable distributed scheduling. To achieve extreme scalability, MTC proposes a fully distributed task scheduling architecture that employs as many schedulers as the compute nodes to make scheduling decisions. Achieving distributed load balancing and best exploiting data locality are two important goals for the best performance of distributed scheduling of data-intensive applications. Our previous research proposed a data-aware work-stealing technique to optimize both load balancing and data locality by using both dedicated and shared task ready queues in each scheduler. Tasks were organized in queues based on the input data size and location. Distributed key-value store was applied to manage task metadata. We implemented the technique in MATRIX, a distributed MTC task execution framework. In this work, we devise an analytical suboptimal upper bound of the proposed technique, compare MATRIX with other scheduling systems, and explore the scalability of the technique at extreme scales. Results show that the technique is not only scalable but can achieve performance within 15% of the suboptimal solution. Copyright © 2015 John Wiley & Sons, Ltd.


title: A Phase-Based Iris Recognition Algorithm

abstract: This paper presents an efficient algorithm for iris recognition using phase-based image matching. The use of phase components in two-dimensional discrete Fourier transforms of iris images makes possible to achieve highly robust iris recognition with a simple matching algorithm. Experimental evaluation using the CASIA iris image database (ver. 1.0 and ver. 2.0) clearly demonstrates an efficient performance of the proposed algorithm.


title: Vehicle Active Steering Control System Based on Human Mechanical Impedance Properties of the Arms

abstract: This paper presents the experimental data of human mechanical impedance properties (HMIPs) of the arms measured in steering operations according to the angle of a steering wheel (limbs posture) and the steering torque (muscle cocontraction). The HMIP data show that human stiffness/viscosity has the minimum/maximum value at the neutral angle of the steering wheel in relax (standard condition) and increases/decreases for the amplitude of the steering angle and the torque, and that the stability of the arms' motion in handling the steering wheel becomes high around the standard condition. Next, a novel methodology for designing an adaptive steering control system based on the HMIPs of the arms is proposed, and the effectiveness was then demonstrated via a set of double-lane-change tests, with several subjects using the originally developed stationary driving simulator and the 4-DOF driving simulator with a movable cockpit.