title: Hand gesture based user interface for computer using a camera and projector

abstract: In this paper, we propose a hand gesture based human computer interaction system comprising of a webcam and a pocket projector. The projector projects the display on the wall on any other plain surface. User can interact with the projected screen using his fingertips which are tracked in air by the camera using ‘Camshift’ tracker. A comparative study of different methods of hands and fingertips detection has been made. A robust method has been developed to detect and recognize single stroke gestures traced with fingertips, which are then translated into actions.


Traumatic exposures, posttraumatic stress disorder, and cognitive functioning in World Trade Center responders


title: Video summarization based on user log enhanced link analysis

abstract: Efficient video data management calls for intelligent video summarization tools that automatically generate concise video summaries for fast skimming and browsing. Traditional video summarization techniques are based on low-level feature analysis, which generally fails to capture the semantics of video content. Our vision is that users unintentionally embed their understanding of the video content in their interaction with computers. This valuable knowledge, which is difficult for computers to learn autonomously, can be utilized for video summarization process. In this paper, we present an intelligent video browsing and summarization system that utilizes previous viewers' browsing log to facilitate future viewers. Specifically, a novel ShotRank notion is proposed as a measure of the subjective interestingness and importance of each video shot. A ShotRank computation framework is constructed to seamlessly unify low-level video analysis and user browsing log mining. The resulting ShotRank is used to organize the presentation of video shots and generate video skims. Experimental results from user studies have strongly confirmed that ShotRank indeed represents the subjective notion of interestingness and importance of each video shot, and it significantly improves future viewers' browsing experience.


title: Fractal dimension based neurofeedback in serious games

abstract: EEG-based technology is widely used in serious game design since more wireless headsets that meet consumer criteria for wearability, price, portability, and ease-of-use are coming to the market. Originally, such technologies were mostly used in different medical applications, Brain Computer Interfaces (BCI) and neurofeedback games. The algorithms adopted in such applications are mainly based on power spectrum analysis, which may not be fully revealing the nonlinear complexity of the brain activities. In this paper, we first review neurofeedback games, EEG processing methods, and algorithms, and then propose a new nonlinear fractal dimension based approach to neurofeedback implementation targeting EEG-based serious games design. Only one channel is used in the proposed concentration quantification algorithm. The developed method was compared with other methods used for the concentration level recognition in neurofeedback games. The result analysis demonstrated an efficiency of the proposed approach. We designed and implemented new EEG-based 2D and 3D neurofeedback games that make the process of brain training more enjoyable.


title: Modeling tone and intonation in Mandarin and English as a process of target approximation

abstract: This paper reports the development of a quantitative target approximation (qTA) model for generating F0 contours of speech. The qTA model simulates the production of tone and intonation as a process of syllable-synchronized sequential target approximation [Xu, Y. (2005). “Speech melody as articulatorily implemented communicative functions,” Speech Commun. 46, 220–251]. It adopts a set of biomechanical and linguistic assumptions about the mechanisms of speech production. The communicative functions directly modeled are lexical tone in Mandarin and lexical stress in English and focus in both languages. The qTA model is evaluated by extracting function-specific model parameters from natural speech via supervised learning (automatic analysis by synthesis) and comparing the F0 contours generated with the extracted parameters to those of natural utterances through numerical evaluation and perceptual testing. The F0 contours generated by the qTA model with the learned parameters were very close to the natural co...


title: On the Importance of Text Analysis for Stock Price Prediction

abstract: We investigate the importance of text analysis for stock price prediction. In particular, we introduce a system that forecasts companies’ stock price changes (UP, DOWN, STAY) in response to financial events reported in 8-K documents. Our results indicate that using text boosts prediction accuracy over 10% (relative) over a strong baseline that incorporates many financially-rooted features. This impact is most important in the short term (i.e., the next day after the financial event) but persists for up to five days.


title: SemEval-2015 Task 3: Answer Selection in Community Question Answering

abstract: Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval2015 Task 3 on Answer Selection in cQA, which included two subtasks: (a) classifying answers as good, bad, or potentially relevant with respect to the question, and (b) answering a YES/NO question with yes, no, or unsure, based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. We used crowdsourcing on Amazon Mechanical Turk to label a large English training dataset, which we released to the research community. Thirteen teams participated in the challenge with a total of 61 submissions: 24 primary and 37 contrastive. The best systems achieved an official score (macro-averaged F1) of 57.19 and 63.7 for the English subtasks A and B, and 78.55 for the Arabic subtask A.


title: From Separate Formal Specifications to Certified Integrated Visual Modelling Techniques and Environments (Position Statement)

abstract: In this position statement we discuss the state of the art and role of formal specification and modelling techniques in different periods with special focus on the work of the TFS-group at TU-Berlin. In the past (1970 – 1990) single formal specification techniques have been developed with little impact on practical software development. In the present (1990 – 2010) integrated and visual modelling techniques have gained more and more importance. For the future (2010 – 2020) we try to sketch the idea of a Certified Integrated Visual Modelling Technique and Environment based on an integration of graph theory, graph transformation and Petri net theory, short Dynamic Graph and Net Theory.


title: Active Fault Tolerant Control of a quadrotor UAV based on gainscheduled PID control

abstract: In this paper, an Active Fault-Tolerant Control (AFTC) technique is developed and applied to an unmanned quadrotor helicopter UAV (Unmanned Aerial Vehicle, known also as Qball-X4) with 6 degrees of freedom based on a Gain-Scheduled Proportional-Integral Derivative (GS-PID) control technique. For implementing such an AFTC system, a Fault-Detection and Diagnosis (FDD) block is essential and implemented to detect and identify the actuator fault. The FDD block is implemented based on the OptiTrack visual feedback for providing information needed by GS-PID to switch from one set of pre-tuned controller gains for normal (pre-fault) condition to another set of controller gains tuned for faulty (post-fault) conditions in the presence of an actuator fault in the Qball-X4 UAV. Finally, experimental testing results are presented to demonstrate the effectiveness of the proposed active fault-tolerant control strategy based on the GS-PID control technique.


title: Distributed Privacy-Preserving Access Control in Sensor Networks

abstract: The owner and users of a sensor network may be different, which necessitates privacy-preserving access control. On the one hand, the network owner need enforce strict access control so that the sensed data are only accessible to users willing to pay. On the other hand, users wish to protect their respective data access patterns whose disclosure may be used against their interests. This paper presents DP2AC, a Distributed Privacy-Preserving Access Control scheme for sensor networks, which is the first work of its kind. Users in DP2AC purchase tokens from the network owner whereby to query data from sensor nodes which will reply only after validating the tokens. The use of blind signatures in token generation ensures that tokens are publicly verifiable yet unlinkable to user identities, so privacy-preserving access control is achieved. A central component in DP2AC is to prevent malicious users from reusing tokens, for which we propose a suite of distributed token reuse detection (DTRD) schemes without involving the base station. These schemes share the essential idea that a sensor node checks with some other nodes (called witnesses) whether a token has been used, but they differ in how the witnesses are chosen. We thoroughly compare their performance with regard to TRD capability, communication overhead, storage overhead, and attack resilience. The efficacy and efficiency of DP2AC are confirmed by detailed performance evaluations.


title: Muscle Strength and Speed Performance in Youth Soccer Players

abstract: This study aimed to examine the relationship between maximum leg extension strength and sprinting performance in youth elite male soccer players. Sixty-three youth players (12.5 ± 1.3 years) performed 5 m, flying 15 m and 20 m sprint tests and a zigzag agility test on a grass field using timing gates. Two days later, subjects performed a one-repetition maximum leg extension test (79.3 ± 26.9 kg). Weak to strong correlations were found between leg extension strength and the time to perform 5 m (r = -0.39, p = 0.001), flying 15 m (r = -0.72, p < 0.001) and 20 m (r = -0.67, p < 0.001) sprints; between body mass and 5 m (r = -0.43, p < 0.001), flying 15 m (r = -0.75, p < 0.001), 20 m (r = -0.65, p < 0.001) sprints and agility (r =-0.29, p < 0.001); and between height and 5 m (r = -0.33, p < 0.01) and flying 15 m (r = -0.74, p < 0.001) sprints. Our results show that leg muscle strength and anthropometric variables strongly correlate with sprinting ability. This suggests that anthropometric characteristics should be considered to compare among youth players, and that youth players should undergo strength training to improve running speed.


title: Virtual Reality and the Role of the Prefrontal Cortex in Adults and Children

abstract: In this review, the neural underpinnings of the experience of presence are outlined. Firstly, it is shown that presence is associated with activation of a distributed network, which includes the dorsal and ventral visual stream, the parietal cortex, the premotor cortex, mesial temporal areas, the brainstem and the thalamus. Secondly, the dorsolateral prefrontal cortex (DLPFC) is identified as a key node of the network as it modulates the activity of the network and the associated experience of presence. Thirdly, children lack the strong modulatory influence of the DLPFC on the network due to their unmatured frontal cortex. Fourthly, it is shown that presence-related measures are influenced by manipulating the activation in the DLPFC using transcranial direct current stimulation (tDCS) while participants are exposed to the virtual roller coaster ride. Finally, the findings are discussed in the context of current models explaining the experience of presence, the rubber hand illusion, and out-of-body experiences.


title: Coaxial 0.5 MV air-core pulse transformer

abstract: The paper describes the successful development of a novel design of high-voltage pulse transformer, having a coaxial primary winding and a toroidal secondary winding and a magnetic coupling coefficient between the windings in excess of 0.8. The air-core transformer operates in air, does not require any oil or pressurized gas, and it is capable of producing high-voltage impulses in excess of 0.5 MV with a corresponding load power of several GWs.


title: Effective named entity recognition for idiosyncratic web collections

abstract: Named Entity Recognition (NER) plays an important role in a variety of online information management tasks including text categorization, document clustering, and faceted search. While recent NER systems can achieve near-human performance on certain documents like news articles, they still remain highly domain-specific and thus cannot effectively identify entities such as original technical concepts in scientific documents. In this work, we propose novel approaches for NER on distinctive document collections (such as scientific articles) based on n-grams inspection and classification. We design and evaluate several entity recognition features---ranging from well-known part-of-speech tags to n-gram co-location statistics and decision trees---to classify candidates. In addition, we show how the use of external knowledge bases (either specific like DBLP or generic like DBPedia) can be leveraged to improve the effectiveness of NER for idiosyncratic collections. We evaluate our system on two test collections created from a set of Computer Science and Physics papers and compare it against state-of-the-art supervised methods. Experimental results show that a careful combination of the features we propose yield up to 85% NER accuracy over scientific collections and substantially outperforms state-of-the-art approaches such as those based on maximum entropy.


title: Pellet : A practical OWL-DL reasoner

abstract: In this paper, we present a brief overview of Pellet: a complete OWL-DL reasoner with acceptable to very good performance, extensive middleware, and a number of unique features. Pellet is the first sound and complete OWL-DL reasoner with extensive support for reasoning with individuals (including nominal support and conjunctive query), user-defined datatypes, and debugging support for ontologies. It implements several extensions to OWL-DL including a combination formalism for OWL-DL ontologies, a non-monotonic operator, and preliminary support for OWL/Rule hybrid reasoning. Pellet is written in Java and is open source.


title: Movie genre classification via scene categorization

abstract: This paper presents a method for movie genre categorization of movie trailers, based on scene categorization. We view our approach as a step forward from using only low-level visual feature cues, towards the eventual goal of high-level seman- tic understanding of feature films. Our approach decom- poses each trailer into a collection of keyframes through shot boundary analysis. From these keyframes, we use state-of- the-art scene detectors and descriptors to extract features, which are then used for shot categorization via unsuper- vised learning. This allows us to represent trailers using a bag-of-visual-words (bovw) model with shot classes as vo- cabularies. We approach the genre classification task by mapping bovw temporally structured trailer features to four high-level movie genres: action, comedy, drama or horror films. We have conducted experiments on 1239 annotated trailers. Our experimental results demonstrate that exploit- ing scene structures improves film genre classification com- pared to using only low-level visual features.


title: Optimizing the Hierarchical Prediction and Coding in HEVC for Surveillance and Conference Videos With Background Modeling

abstract: For the real-time and low-delay video surveillance and teleconferencing applications, the newly video coding standard HEVC can achieve much higher coding efficiency over H.264/AVC. However, we still argue that the hierarchical prediction structure in the HEVC low-delay encoder still does not fully utilize the special characteristics of surveillance and conference videos that are usually captured by stationary cameras. In this case, the background picture (G-picture), which is modeled from the original input frames, can be used to further improve the HEVC low-delay coding efficiency meanwhile reducing the complexity. Therefore, we propose an optimization method for the hierarchical prediction and coding in HEVC for these videos with background modeling. First, several experimental and theoretical analyses are conducted on how to utilize the G-picture to optimize the hierarchical prediction structure and hierarchical quantization. Following these results, we propose to encode the G-picture as the long-term reference frame to improve the background prediction, and then present a G-picture-based bit-allocation algorithm to increase the coding efficiency. Meanwhile, according to the proportions of background and foreground pixels in coding units (CUs), an adaptive speed-up algorithm is developed to classify each CU into different categories and then adopt different speed-up strategies to reduce the encoding complexity. To evaluate the performance, extensive experiments are performed on the HEVC test model. Results show our method can averagely save 39.09% bits and reduce the encoding complexity by 43.63% on surveillance videos, whereas those are 5.27% and 43.68% on conference videos.


title: How motivation, opportunity, and ability drive knowledge sharing: The constraining-factor model

abstract: Abstract We introduce and empirically test a theoretical metamodel that explains knowledge-sharing behavior among employees. Building on the well-established motivation–opportunity–ability (MOA) framework, we posit that knowledge sharing among employees is a function of their MOA to do so. Existing literature suggests that the interaction among motivation, opportunity, and ability drives knowledge-sharing behavior. In contrast, we specify a new model in which the “bottleneck” or constraining factor among the MOA variables determines the degree of knowledge sharing that occurs. This constraining-factor model (CFM) fits the data better than the traditional multiplicative model and reveals a new, qualitatively different portrait of knowledge sharing that resolves some of the puzzles in the previous literature. The CFM provides macro-level insights with respect to how operations managers can improve employee knowledge sharing by focusing on the bottleneck MOA variable. As a result, the CFM can help set strategic directions of related policies. The model emphasizes that, counter to conventional wisdom, the MOA variables should not be addressed independently, but rather in a dynamic and coordinated way.


title: Multilevel Analysis of Individual and Community Predictors of Smoking Prevalence and Frequency in China: 1991-2004

abstract: This study uses a series of nationally representative samples from China. We studied more than 4,000 households to explore factors associated with both the prevalence and the frequency of cigarette smoking over 13 years between 1991 and 2004. By introducing variables at both the individual and the community level, we found that some key variables are consistent predictors of smoking measures over time, thus they should be the targets of future intervention/prevention programs.


title: Anomaly Detection in Computer Security and an Application to File System Accesses ⋆

abstract: We present an overview of anomaly detection used in computer security, and provide a detailed example of a host-based Intrusion Detection System that monitors file systems to detect abnormal accesses. The File Wrapper Anomaly Detector (FWRAP) has two parts, a sensor that audits file systems, and an unsupervised machine learning system that computes normal models of those accesses. FWRAP employs the Probabilistic Anomaly Detection (PAD) algorithm previously reported in our work on Windows Registry Anomaly Detection. FWRAP represents a general approach to anomaly detection. The detector is first trained by operating the host computer for some amount of time and a model specific to the target machine is automatically computed by PAD. The model is then deployed to a real-time detector. In this paper we describe the feature set used to model file system accesses, and the performance results of a set of experiments using the sensor while attacking a Linux host with a variety of malware exploits. The PAD detector achieved impressive detection rates in some cases over 95% and about a 2% false positive rate when alarming on anomalous processes.


Rapid vocabulary addition to context-dependent decoder graphs.


title: A survey on Information Visualization in light of Vision and Cognitive sciences

abstract: Information visualization techniques are built on a context with too many factors, making it di cult to systematically deal with their underlying bases. In the intent of promoting a better comprehension, here, we survey concepts on vision, cognition, and Information Visualization organized in a theorization named Visual Expression Model. With a reduced level of complexity, our model organizes the bases of visualization techniques; nevertheless, it is complete enough to discuss guidelines related to design and analytical tasks. Organized in a coherent account, our work introduces the following contributions: (1) Theoretical compilation of vision, cognition, and Information Visualization; (2) Meticulous discussions supported by vast literature; and (3) Recommendations to have visualizations satisfy visualcognitive aspects. We expect our contributions will improve the practice of InfoVis by promoting comprehension and by proposing the use of simple recommendations.


A case report of ameloblastic fibroma: radiographic observation for 3 years and 8 months.


title: Steering approaches to Pareto-optimal multiobjective reinforcement learning

abstract: Abstract For reinforcement learning tasks with multiple objectives, it may be advantageous to learn stochastic or non-stationary policies. This paper investigates two novel algorithms for learning non-stationary policies which produce Pareto-optimal behaviour (w-steering and Q-steering), by extending prior work based on the concept of geometric steering. Empirical results demonstrate that both new algorithms offer substantial performance improvements over stationary deterministic policies, while Q-steering significantly outperforms w-steering when the agent has no information about recurrent states within the environment. It is further demonstrated that Q-steering can be used interactively by providing a human decision-maker with a visualisation of the Pareto front and allowing them to adjust the agent’s target point during learning. To demonstrate broader applicability, the use of Q-steering in combination with function approximation is also illustrated on a task involving control of local battery storage for a residential solar power system.


title: Body sensor network security: an identity-based cryptography approach

abstract: A body sensor network (BSN), is a network of sensors deployed on a person's body, usually for health care monitoring. Since the sensors collect personal medical data, security and privacy are important components in a body sensor network. At the same time, the collected data has to readily available in the event of an emergency. In this paper, we present IBE-Lite, a lightweight identity-based encryption suitable for sensors, and developed protocols based on IBE-Lite for a BSN.


title: A non-parametric mixture model for topic modeling over time

abstract: A single, stationary topic model such as latent Dirichlet allocation is inappropriate for modeling corpora that span long time periods, as the popularity of topics is likely to change over time. A number of models that incorporate time have been proposed, but in general they either exhibit limited forms of temporal variation, or require computationally expensive inference methods. In this paper we propose nonparametric Topics over Time (npTOT), a model for time-varying topics that allows an unbounded number of topics and flexible distribution over the temporal variations in those topics’ popularity. We develop a collapsed Gibbs sampler for the proposed model and compare against existing models on synthetic and real document sets.


title: The CLEAR 2006 Evaluation

abstract: This paper is a summary of the first CLEAR evaluation on CLassification of Events, Activities and Relationships - which took place in early 2006 and concluded with a two day evaluation workshop in April 2006. CLEAR is an international effort to evaluate systems for the multimodal perception of people, their activities and interactions. It provides a new international evaluation framework for such technologies. It aims to support the definition of common evaluation tasks and metrics, to coordinate and leverage the production of necessary multimodal corpora and to provide a possibility for comparing different algorithms and approaches on common benchmarks, which will result in faster progress in the research community. This paper describes the evaluation tasks, including metrics and databases used, that were conducted in CLEAR 2006, and provides an overview of the results. The evaluation tasks in CLEAR 2006 included person tracking, face detection and tracking, person identification, head pose estimation, vehicle tracking as well as acoustic scene analysis. Overall, more than 20 subtasks were conducted, which included acoustic, visual and audio-visual analysis for many of the main tasks, as well as different data domains and evaluation conditions.


title: Data Center Network Virtualization: A Survey

abstract: With the growth of data volumes and variety of Internet applications, data centers (DCs) have become an efficient and promising infrastructure for supporting data storage, and providing the platform for the deployment of diversified network services and applications (e.g., video streaming, cloud computing). These applications and services often impose multifarious resource demands (storage, compute power, bandwidth, latency) on the underlying infrastructure. Existing data center architectures lack the flexibility to effectively support these applications, which results in poor support of QoS, deployability, manageability, and defence against security attacks. Data center network virtualization is a promising solution to address these problems. Virtualized data centers are envisioned to provide better management flexibility, lower cost, scalability, better resources utilization, and energy efficiency. In this paper, we present a survey of the current state-of-the-art in data center networks virtualization, and provide a detailed comparison of the surveyed proposals. We discuss the key research challenges for future research and point out some potential directions for tackling the problems related to data center design.


title: THINKING TOWARDS STAKEHOLDER SATISFACTION IN HIGHER EDUCATION: AN APPLICATION OF PERFORMANCE PRISM

abstract: Quality of service and customer satisfaction is key concerned area in the today’s business. When considering education, quality of educational service and stakeholder satisfaction can be taken as more important. Most of the performance management systems are fails to address and consider the entire stakeholder needs after considering the importance and contributions of all stakeholders to the organization. The Performance Prism Model can be used as one of performance management and measurement tool which is closely looking at measurement from a stakeholder perspective. Performance prism model is taking consideration of two ways process as first identify the what are the want and needs of stakeholders and second to identify what are the organizations wants and need from its stakeholders. The purpose of the study is to review the performance prism model and to identify the possibility of application of this model into higher educational institutions. Finally presents the interrelation between stakeholder need, organizational capabilities, existing process, strategies and stakeholders’ role. The study concluded that use of performance prism model can be highly benefited in an academic institution and the process should start from identifying stakeholder needs, and then formulate strategies using existing capabilities and process and finally deliver better value with the support of all stakeholders in the system. Keyword: Performance Management, Academic Institution, performance prism, strategies, stakeholders


title: MACHINE LEARNING TECHNIQUES FOR INTRUSION DETECTION SYSTEM: A REVIEW

abstract: Intrusion detection is considered as one of the foremost research areas in network security, the challenge is to recognize unusual access that could lead to compromising the interconnected nodes. Anomaly-based intrusion detection system, that utilizes machine learning techniques such as single classifier and hybrid classifier have the capability to recognize unpredicted malevolent. In this paper, we examine different machine learning techniques that have been proposed for detecting intrusion by focusing on the hybrid classifier algorithms. The objective is to determine their strengths and weaknesses. From the comparison, we hope to identify the gap for developing an efficient intrusion detection system that is yet to be researched.


title: Efficient Computation of Collision Probabilities for Safe Motion Planning

abstract: We address the problem of safe motion planning. As mobile robots and autonomous vehicles become increasingly more prevalent in human-centered environments, the need to ensure safety in the sense of guaranteed collision free behaviour has taken renewed urgency. Achieving this when perceptual modules provide only noisy estimates of objects in the environment requires new approaches. Working within a probabilistic framework for describing the environment, we present methods for efficiently calculating a probabilistic risk of collision for a candidate path. This may be used to stratify a set of candidate trajectories by levels of a safety threshold. Given such a stratification, based on user-defined thresholds, motion synthesis techniques could optimise for secondary criteria with the assurance that a primary safety criterion is already being satisfied. A key contribution of this paper is the use of a `convolution trick' to factor the calculation of integrals providing bounds on collision risk, enabling an $O(1)$ computation even in cluttered and complex environments.


title: LOESS correction for length variation in gene set-based genomic sequence analysis

abstract: Motivation: Sequence analysis algorithms are often applied to sets of DNA, RNA or protein sequences to identify common or distinguishing features. Controlling for sequence length variation is critical to properly score sequence features and identify true biological signals rather than length-dependent artifacts. ::: ::: Results: Several cis-regulatory module discovery algorithms exhibit a substantial dependence between DNA sequence score and sequence length. Our newly developed LOESS method is flexible in capturing diverse score-length relationships and is more effective in correcting DNA sequence scores for length-dependent artifacts, compared with four other approaches. Application of this method to genes co-expressed during Drosophila melanogaster embryonic mesoderm development or neural development scored by the Lever motif analysis algorithm resulted in successful recovery of their biologically validated cis-regulatory codes. The LOESS length-correction method is broadly applicable, and may be useful not only for more accurate inference of cis-regulatory codes, but also for detection of other types of patterns in biological sequences. ::: ::: Availability: Source code and compiled code are available from http://thebrain.bwh.harvard.edu/LM_LOESS/ ::: ::: Contact: mlbulyk@receptor.med.harvard.edu ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


title: Visualization in the ECG QRS detection algorithms

abstract: Digital ECG data analysis is a trending concept in the field where applied computer science and medicine coincide. Therefore, in order to meet the requirements that arise, our R&D team has created an environment where developers can test different approaches in data processing. To assist the objective, the platform offers a number of features with the following main target goals: 1) to increase the effectiveness in conducting a proper medical diagnose, 2) to incorporate a unified format of storing the results of the diagnosis conducted and 3) to test various ECG QRS detection algorithms.


title: GaN-based high frequency totem-pole bridgeless PFC design with digital implementation

abstract: Totem-pole bridgeless PFC is a very promising topology for GaN devices because of very low reverse recovery. However, inherent challenges exist for this topology at zerocrossing point of AC voltage where inductor, current spike is observed which may cause higher harmonics and EMI issue. This paper looks deeply into this phenomena and found two main causes: the transition delay of low frequency leg and the minimum pulse width limit of high frequency leg. A DSP-based digital modulator is designed to address those two issues by applying lead time to the low frequency leg and digital dithering to increase equivalent resolution at the high frequency leg. Some other rules are also summarized to enable robust modulator design, which is the main challenge in this topology. A 500 kHz GaN totem-pole bridgeless PFC converter operating in continuous conduction mode verified the effectiveness of those methods to eliminate the current spike at zero-crossing. All those findings will help the implementation of wide band gap devices in very high frequency PFC circuit.


title: Treadmill training and overground gait: decision making for a toddler with spina bifida.

abstract: BACKGROUND AND PURPOSE ::: This case report describes a decision-making process that was used to progress a home-based intervention that coupled treadmill and walker stepping for a preambulatory toddler with spina bifida. ::: ::: ::: CASE DESCRIPTION ::: The toddler in this report had an L4-L5 level lesion, and began this home-based intervention at 18 months of age when she was pulling to stand. ::: ::: ::: INTERVENTION ::: The intervention included parameters for treadmill stepping that prepared this toddler for gait with orthotics and was progressed to overground walking with a walker using a decision-making algorithm based on data obtained from a parent log and coded video. ::: ::: ::: OUTCOMES ::: This toddler progressed from not stepping at the start of the study to ambulating 150 m with a walker at age 23 months, after 18 weeks of this intervention. ::: ::: ::: DISCUSSION AND CONCLUSION ::: The intervention and decision-making process used in this study were family centered and may be applicable to gait intervention with other populations.


title: Automatic recovery of the atmospheric light in hazy images

abstract: Most image dehazing algorithms require, for their operation, the atmospheric light vector, A, which describes the ambient light in the scene. Existing methods either rely on user input or follow error-prone assumptions such as the gray-world assumption. In this paper we present a new automatic method for recovering the atmospheric light vector in hazy scenes given a single input image. The method first recovers the vector's orientation, Â = A/∥A∥, by exploiting the abundance of small image patches in which the scene transmission and surface albedo are approximately constant. We derive a reduced formation model that describes the distribution of the pixels inside such patches as lines in RGB space and show how these lines are used for robustly extracting Â.


Image Halftoning by Error Diffusion: A Survey of Methods for Artifact Reduction


title: Analysing Cloud Services Reviews Using Opining Mining

abstract: There is increasing interest in sharing the experience of products and services on the web platform, and social media has opened a way for product and service providers to understand their consumers needs and expectations. This paper explores reviews by cloud consumers that reflect consumers experiences with cloud services. The reviews of around 6,000 cloud service users were analysed using sentiment analysis to identify the attitude of each review, and to determine whether the opinion expressed was positive, negative, or neutral. The analysis used two data mining tools, KNIME and RapidMiner, and the results were compared. We developed four prediction models in this study to predict the sentiment of users reviews. The proposed model is based on four supervised machine learning algorithms: K-Nearest Neighbour (k-NN), Nave Bayes, Random Tree, and Random Forest. The results show that the Random Forest predictions achieve 97.06% accuracy, which makes this model a better prediction model than the other three.


title: Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not

abstract: Human replicas may elicit unintended cold, eerie feelings in viewers, an effect known as the uncanny valley. Masahiro Mori, who proposed the effect in 1970, attributed it to inconsistencies in the replica’s realism with some of its features perceived as human and others as nonhuman. This study aims to determine whether reducing realism consistency in visual features increases the uncanny valley effect. In three rounds of experiments, 548 participants categorized and rated humans, animals, and objects that varied from computer animated to real. Two sets of features were manipulated to reduce realism consistency. (For humans, the sets were eyes–eyelashes–mouth and skin–nose–eyebrows.) Reducing realism consistency caused humans and animals, but not objects, to appear eerier and colder. However, the predictions of a competing theory, proposed by Ernst Jentsch in 1906, were not supported: The most ambiguous representations—those eliciting the greatest category uncertainty—were neither the eeriest nor the coldest. 2015 The Authors. Published by Elsevier B.V. ThisisanopenaccessarticleundertheCCBYlicense(http:// creativecommons.org/licenses/by/4.0/).


title: Engineering tolerance to industrially relevant stress factors in yeast cell factories

abstract: The main focus in development of yeast cell factories has generally been on establishing optimal activity of heterologous pathways and further metabolic engineering of the host strain to maximize product yield and titer. Adequate stress tolerance of the host strain has turned out to be another major challenge for obtaining economically viable performance in industrial production. Although general robustness is a universal requirement for industrial microorganisms, production of novel compounds using artificial metabolic pathways presents additional challenges. Many of the bio-based compounds desirable for production by cell factories are highly toxic to the host cells in the titers required for economic viability. Artificial metabolic pathways also turn out to be much more sensitive to stress factors than endogenous pathways, likely because regulation of the latter has been optimized in evolution in myriads of environmental conditions. We discuss different environmental and metabolic stress factors with high relevance for industrial utilization of yeast cell factories and the experimental approaches used to engineer higher stress tolerance. Improving stress tolerance in a predictable manner in yeast cell factories should facilitate their widespread utilization in the bio-based economy and extend the range of products successfully produced in large scale in a sustainable and economically profitable way.


title: A survey of transfer learning

abstract: Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.


title: Privacy Languages: Are we there yet to enable user controls?

abstract: Privacy protection is one of the most prominent concerns for web users. Despite numerous efforts, users remain powerless in controlling how their personal information should be used and by whom, and find limited options to actually opt-out of dominating service providers, who often process users information with limited transparency or respect for their privacy preferences. Privacy languages are designed to express the privacy-related preferences of users and the practices of organisations, in order to establish a privacy-preserved data handling protocol. However, in practice there has been limited adoption of these languages, by either users or data controllers. This survey paper attempts to understand the strengths and limitations of existing policy languages, focusing on their capacity of enabling users to express their privacy preferences. Our preliminary results show a lack of focus on normal web users, in both language design and their tooling design. This systematic survey lays the ground work for future privacy protection designs that aim to be centred around web users for empowering their control of data privacy.


title: Effects of Ketogenic Diets on Cardiovascular Risk Factors: Evidence from Animal and Human Studies

abstract: The treatment of obesity and cardiovascular diseases is one of the most difficult and important challenges nowadays. Weight loss is frequently offered as a therapy and is aimed at improving some of the components of the metabolic syndrome. Among various diets, ketogenic diets, which are very low in carbohydrates and usually high in fats and/or proteins, have gained in popularity. Results regarding the impact of such diets on cardiovascular risk factors are controversial, both in animals and humans, but some improvements notably in obesity and type 2 diabetes have been described. Unfortunately, these effects seem to be limited in time. Moreover, these diets are not totally safe and can be associated with some adverse events. Notably, in rodents, development of nonalcoholic fatty liver disease (NAFLD) and insulin resistance have been described. The aim of this review is to discuss the role of ketogenic diets on different cardiovascular risk factors in both animals and humans based on available evidence.


title: Illuminating Recommendation by Understanding the Explicit Item Relations

abstract: Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from “implicit” to “explicit” views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.


title: DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset

abstract: In the past decade, research in person re-identification (re-id) has exploded due to its broad use in security and surveillance applications. Issues such as inter-camera viewpoint, illumination and pose variations make it an extremely difficult problem. Consequently, many algorithms have been proposed to tackle these issues. To validate the efficacy of re-id algorithms, numerous benchmarking datasets have been constructed. While early datasets contained relatively few identities and images, several large-scale datasets have recently been proposed, motivated by data-driven machine learning. In this paper, we introduce a new large-scale real-world re-id dataset, DukeMTMC4ReID, using 8 disjoint surveillance camera views covering parts of the Duke University campus. The dataset was created from the recently proposed fully annotated multi-target multi-camera tracking dataset DukeMTMC[36]. A benchmark summarizing extensive experiments with many combinations of existing re-id algorithms on this dataset is also provided for an up-to-date performance analysis.


title: Suicidal Decapitation Using a Tractor Loader: A Case Report and Review of the Literature

abstract: In forensic practice, decapitated bodies are predominantly associated with decapitation by wheels of trains or with postmortem dismemberment following homicide. In the suicidal context, decapitation accounts for less than 1% of total suicide. Apart from decapitation by trains, other encountered methods involve suicidal hanging and vehicle-assisted ligature suicide. Reported here is a unique case of suicidal decapitation in a 45-year-old man using a tractor loader at the foot of a silo, on his farm. The head was recovered in the loader and there were several impact spots from the loader as well as blood on the silo wall. The autopsy revealed a complete decapitation wound with the severance plane located between the third and fourth cervical vertebra. A 1.5 cm wide abrasion on the anterior part of the neck and abrasions under the chin were noted. This very unique case of intentional suicidal decapitation is the first reported case of a planned system intended to create decapitation outside the unique case of homemade guillotine and the more common decapitation by train.


title: Design Criteria to Architect Continuous Experimentation for Self-Driving Vehicles

abstract: The software powering today's vehicles surpasses mechatronics as the dominating engineering challenge due to its fast evolving and innovative nature. In addition, the software and system architecture for upcoming vehicles with automated driving functionality is already processing ~750MB/s - corresponding to over 180 simultaneous 4K-video streams from popular video-on-demand services. Hence, self-driving cars will run so much software to resemble "small data centers on wheels" rather than just transportation vehicles. Continuous Integration, Deployment, and Experimentation have been successfully adopted for software-only products as enabling methodology for feedback-based software development. For example, a popular search engine conducts ~250 experiments each day to improve the software based on its users' behavior. This work investigates design criteria for the software architecture and the corresponding software development and deployment process for complex cyber-physical systems, with the goal of enabling Continuous Experimentation as a way to achieve continuous software evolution. Our research involved reviewing related literature on the topic to extract relevant design requirements. The study is concluded by describing the software development and deployment process and software architecture adopted by our self-driving vehicle laboratory, both based on the extracted criteria.


title: The Fake News Spreading Plague: Was it Preventable?

abstract: In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" won the Best Paper Prize of the WebSci'10 conference. Among its findings were the discovery and documentation of what was labeled a "Twitter bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can in- crease their impact on real-world societal issues.


title: Deep and Broad Learning on Content-Aware POI Recommendation

abstract: POI recommendation has attracted lots of research attentions recently. There are several key factors that need to be modeled towards effective POI recommendation - POI properties, user preference and sequential momentum of check- ins. The challenge lies in how to synergistically learn multi-source heterogeneous data. Previous work tries to model multi-source information in a flat manner, using either embedding based methods or sequential prediction models in a cross-related space, which cannot generate mutually reinforce results. In this paper, a deep and broad learning approach based on a Deep Context- aware POI Recommendation (DCPR) model was proposed to structurally learn POI and user characteristics. The proposed DCPR model includes three collaborative layers, a CNN layer for POI feature mining, an RNN layer for sequential dependency and user preference modeling, and an interactive layer based on matrix factorization to jointly optimize the overall model. Experiments over three data sets demonstrate that DCPR model achieves significant improvement over state-of-the-art POI recommendation algorithms and other deep recommendation models.


title: Understanding Cybercrime from Its Stakeholders' Perspectives: Part 2--Defenders and Victims

abstract: A comprehensive model and taxonomy of cybercrime, including all of its stakeholders, would contribute to better cybersecurity. Part 1 of this two-part series, which appeared in the January/February 2015 issue of IEEE Security a Privacy, explored cyberattackers and their motives in detail. Part 2 focuses on the other key stakeholders: defenders and victims of cybercrime.


title: A review of motion analysis methods for human Nonverbal Communication Computing ☆

abstract: Human Nonverbal Communication Computing aims to investigate how people exploit nonverbal aspects of their communication to coordinate their activities and social relationships. Nonverbal behavior plays important roles in message production and processing, relational communication, social interaction and networks, deception and impression management, and emotional expression. This is a fundamental yet challenging research topic. To effectively analyze Nonverbal Communication Computing, motion analysis methods have been widely investigated and employed. In this paper, we introduce the concept and applications of Nonverbal Communication Computing and also review some of the motion analysis methods employed in this area. They include face tracking, expression recognition, body reconstruction, and group activity analysis. In addition, we also discuss some open problems and the future directions of this area.


title: From everyday emotions to aesthetic emotions : Towards a unified theory of musical emotions

abstract: The sound of music may arouse profound emotions in listeners. But such experiences seem to involve a 'paradox', namely that music - an abstract form of art, which appears removed from our concerns ...


title: Phasing arbitrarily sampled multidimensional NMR data.

abstract: The recent re-introduction of the two-dimensional Fourier transformation (2D-FT) has allows for the transformation of arbitrarily sampled time domain signals. In this respect, radial sampling, where two incremented time dimensions (t(1) and t(2)) are sampled such that t(1)=taucosalpha and t(2)=tausinalpha, is especially appealing because of the relatively small leakage artifacts that occur upon Fourier transformation. Unfortunately radially sampled time domain data results in a fundamental artifact in the frequency domain manifested as a ridge of intensity extending through the peak positions perpendicular to +/- the radial sampling angle. Successful removal of the ridge artifacts using existing algorithms requires absorptive line shapes. Here we present two procedures for retrospective phase correction of arbitrarily sampled data.


Metaphor Detection in a Poetry Corpus


title: Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images

abstract: Abstract Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects. In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.


title: Don't let the X-bugs bite: Conquer elusive X-propagation issues early! Get them before they get you!

abstract: Designers spend many, many hours verifying that RTL provides the correct functionality and expect that gate level simulation produces the same results as RTL simulation. However, X-propagation is a major cause of differences between gate level and RTL simulation results, and issues cannot be detected by logical equivalence checkers. While most Xs are harmless at the RTL level, they can mask functional bugs. Resolving differences between gate level and RTL simulation results is painful and time consuming because Xs make correlation between the two difficult. X-propagation issues cause costly iterations, painful debug, and sometimes allow X-related functional bugs to slip through. This invited talk explains the common sources of Xs, shows how they can mask real functional issues and why they are difficult to avoid. It also presents a unique practical solution to assist designers in catching X-propagation bugs efficiently.


title: Carsharing and Personal Vehicle Services: Worldwide Market Developments and Emerging Trends

abstract: Carsharing (or short-term auto use) provides a flexible alternative that meets diverse transportation needs across the globe, while reducing the negative impacts of private vehicle ownership. More than 65 years ago, carsharing began appearing in Europe. It has expanded to approximately 1,100 cities worldwide, in 26 nations on five continents. This article provides a global perspective of carsharing growth and future developments from 2006 through 2015, employing data from three surveys conducted in 2006, 2008, and 2010. The authors explore the interview findings of 25 carsharing experts worldwide representing 25 of 26 nations, collected in 2010.


title: Mindfulness DOI 10.1007/s12671-013-0195-9 ORIGINAL PAPER Comparing Self-Compassion, Mindfulness, and Psychological Inflexibility as Predictors of Psychological Health

abstract: Self-compassion, mindfulness, and psychological inflexibility, constructs associated with mindfulness-based interventions, have demonstrated associations with multiple aspects of psychological health. However, a very limited body of research has analyzed the relative predictive strength among mindfulness-related constructs. Regression analyses were performed to determine the common and unique variance in psychological health predicted by these constructs and to compare their relative predictive strength in a nonclinical sample of 147 undergraduate students at a Mid-Atlantic university. Consistent with previous research, self-compassion demonstrated a stronger ability than single-factor mindfulness to predict variance in psychological health. However, results were mixed when a multifaceted measure of mindfulness was considered. Self-compassion predicted greater variance than multifaceted mindfulness when prediction was based on one total score, but not when individual subscales were analyzed. Psychological inflexibility predicted greater variance than did self-compassion for negative indicators of psychological health. Results suggest that self-compassion and psychological inflexibility may demonstrate greater associations with psychological health than single scores of mindfulness and that important predictive power is lost, particularly from the nonreactivity facet, when multifaceted mindfulness is consolidated into a single score.


title: Automatic registration of LiDAR and optical imagery using depth map stereo

abstract: Automatic fusion of aerial optical imagery and untextured LiDAR data has been of significant interest for generating photo-realistic 3D urban models in recent years. However, unsupervised, robust registration still remains a challenge. This paper presents a new registration method that does not require priori knowledge such as GPS/INS information. The proposed algorithm is based on feature correspondence between a LiDAR depth map and a depth map from an optical image. Each optical depth map is generated from edge-preserving dense correspondence between the image and another optical image, followed by ground plane estimation and alignment for depth consistency. Our two-pass RANSAC with Maximum Likelihood estimation incorporates 2D-2D and 2D-3D correspondences to yield robust camera pose estimation. Experiments with a LiDAR-optical imagery dataset show promising results, without using initial pose information.


title: Automated Tuberculosis Diagnosis Using Fluorescence Images from a Mobile Microscope

abstract: In low-resource areas, the most common method of tuberculosis (TB) diagnosis is visual identification of rod-shaped TB bacilli in microscopic images of sputum smears. We present an algorithm for automated TB detection using images from digital microscopes such as CellScope [2], a novel, portable device capable of brightfield and fluorescence microscopy. Automated processing on such platforms could save lives by bringing healthcare to rural areas with limited access to laboratory-based diagnostics. Our algorithm applies morphological operations and template matching with a Gaussian kernel to identify candidate TB-objects. We characterize these objects using Hu moments, geometric and photometric features, and histograms of oriented gradients and then perform support vector machine classification. We test our algorithm on a large set of CellScope images (594 images corresponding to 290 patients) from sputum smears collected at clinics in Uganda. Our object-level classification performance is highly accurate, with Average Precision of 89.2%±2.1%. For slide-level classification, our algorithm performs at the level of human readers, demonstrating the potential for making a significant impact on global healthcare.


title: TaiChi: A Fine-Grained Action Recognition Dataset

abstract: In this paper, we introduce TaiChi which is a fine-grained action dataset. It consists of unconstrained user-uploaded web videos containing camera motion and partial occlusions which pose new challenges to fine-grained action recognition compared to the existing datasets. In this dataset, 2,772 samples of 58 fine-grained action classes are manually annotated. Additionally, we provide the baseline action recognition results using the state-of-the-art Improved Dense Trajectory feature and Fisher Vector representation with an MAP (Mean Average Precision) of 51.39%.


Histopathological evaluation of in vitro Ichthyophthirius multifiliis culture in Red koi (Aka Matsuba) tissue


title: Learning Complex Swarm Behaviors by Exploiting Local Communication Protocols with Deep Reinforcement Learning

abstract: Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. Although there have been recent advances of deep RL algorithms applied to multi-agent systems, learning communication protocols while simultaneously learning the behavior of the agents is still beyond the reach of deep RL algorithms. However, while it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building, building a communication link, and pushing an intruder. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols.


title: 6D SLAM—3D Mapping Outdoor Environments

abstract: 6D SLAM (simultaneous localization and mapping) or 6D concurrent localization and mapping of mobile robots considers six dimensions for the robot pose, namely, the x, y, and z coordinates and the roll, yaw, and pitch angles. Robot motion and localization on natural surfaces, e.g., driving outdoor with a mobile robot, must regard these degrees of freedom. This paper presents a robotic mapping method based on locally consistent 3D laser range scans. Iterative Closest Point scan matching, combined with a heuristic for closed loop detection and a global relaxation method, results in a highly precise mapping system. A new strategy for fast data association, cached kd-tree search, leads to feasible computing times. With no ground-truth data available for outdoor environments, point relations in maps are compared to numerical relations in uncalibrated aerial images in order to assess the metric validity of the resulting 3D maps. © 2007 Wiley Periodicals, Inc.


title: Machine learning with adversaries: byzantine tolerant gradient descent

abstract: We study the resilience to Byzantine failures of distributed implementations of Stochastic Gradient Descent (SGD). So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones. Causes of failures include software bugs, network asynchrony, biases in local datasets, as well as attackers trying to compromise the entire system. Assuming a set of n workers, up to f being Byzantine, we ask how resilient can SGD be, without limiting the dimension, nor the size of the parameter space. We first show that no gradient aggregation rule based on a linear combination of the vectors proposed by the workers (i.e, current approaches) tolerates a single Byzantine failure. We then formulate a resilience property of the aggregation rule capturing the basic requirements to guarantee convergence despite f Byzantine workers. We propose Krum, an aggregation rule that satisfies our resilience property, which we argue is the first provably Byzantine-resilient algorithm for distributed SGD. We also report on experimental evaluations of Krum.


Hidradenitis Suppurativa


title: Performance Analysis of Enhanced SPECK Algorithm

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


title: Angle of Arrival Localization in {LTE} Using {MIMO} Pre-Coder Index Feedback

abstract: The paper presents a new angle of arrival (AoA) positioning technique for long term evolution (LTE) cellular systems. The method quantifies the directional correlation between TX antenna bore-sight and different multiple-input-multiple-output (MIMO) pre-coder indices. This correlation is used to develop an AoA positioning method that only builds on functionality and hardware available in standard LTE base stations. Measurements from a Swedish test network indicate that the method performs as expected.


title: Design of broadband orthomode transducer based on double ridged waveguide

abstract: A Ku broadband OMT for satellite communication applications is designed. This OMT consists of a stepped double ridged waveguide junction, a power combiner and an axial branch. It is similar to that of B⊘ifot junction, but has no septum and pins which increase the complexity of the structure. The OMT with a size of 42×44×100mm3 can cover from 10.44GHz to 14.93GHz (35.4% bandwidth). The return losses are better than 27dB and the isolation is greater than 70dB. The OMT has excellent performance and can be easily manufactured. In this design, the mode matching technique is used to optimize the components of the OMT.


title: ABMQ: An Agent-Based Modeler and Simulator for Self-Organization in MANETs using Qt

abstract: Agent-Based Modeling and Simulation (ABMS) is a simple and yet powerful method for simulation of interactions among individual agents. Using ABMS, different phenomena can be modeled and simulated without spending additional time on unnecessary complexities. Although ABMS is well-matured in many different fields such as economic, social, and natural phenomena, it has not received much attention in the context of mobile ad-hoc networks (MANETs). In this paper, we present ABMQ, a powerful Agent-Based platform suitable for modeling and simulation of self-organization in wireless networks, and particularly MANETs. By utilizing the unique potentials of Qt Application Framework, ABMQ provides the ability to easily model and simulate self-organizing algorithms, and then reuse the codes and models developed during simulation process for building real third-party applications for several desktop and mobile platforms, which substantially decreases the development time and cost, and prevents probable bugs that can happen as a result of rewriting codes.


title: An anomaly detection approach for multiple monitoring data series based on latent correlation probabilistic model

abstract: Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of monitoring data in the process. The main task of surveillance focuses on detecting anomalies in these routinely collected monitoring data, intended to help detect possible faults in the equipment. However, with the rapid increase in the volume of monitoring data, it is a nontrivial task to scan all the monitoring data to detect anomalies. In this paper, we propose an approach called latent correlation-based anomaly detection (LCAD) that efficiently and effectively detects potential anomalies from a large number of correlative isomerous monitoring data series. Instead of focusing on one or more isomorphic monitoring data series, LCAD identifies anomalies by modeling the latent correlation among multiple correlative isomerous monitoring data series, using a probabilistic distribution model called the latent correlation probabilistic model, which helps to detect anomalies according to their relations with the model. Experimental results on real-world data sets show that when dealing with a large number of correlative isomerous monitoring data series, LCAD yields better performances than existing anomaly detection approaches.


title: Fault detection for networked control systems via reduced-order filtering

abstract: For the networked control system (NCSs) with short network-induced time delay, the online fault detection method is proposed. The Markov jumping model for NCSs is established under the condition that the network-induced time delay can be governed by the Markov chain. The feasible solution of the reduced order fault detection filter is achieved based on the robust filtering method, and the non-convex optimization problem with the constraint of matrix rank is obtained. The local optimal solution of the optimization problem is considered based on the alternating projection method and the parameters of the fault detection filter are presented. Finally, the numerical examples show that the proposed approach can restrain the impact of the delays, and detect the faults quickly and effectively.


title: VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R

abstract: Visualization of orthogonal (disjoint) or overlapping datasets is a common task in bioinformatics. Few tools exist to automate the generation of extensively-customizable, high-resolution Venn and Euler diagrams in the R statistical environment. To fill this gap we introduce VennDiagram, an R package that enables the automated generation of highly-customizable, high-resolution Venn diagrams with up to four sets and Euler diagrams with up to three sets. The VennDiagram package offers the user the ability to customize essentially all aspects of the generated diagrams, including font sizes, label styles and locations, and the overall rotation of the diagram. We have implemented scaled Venn and Euler diagrams, which increase graphical accuracy and visual appeal. Diagrams are generated as high-definition TIFF files, simplifying the process of creating publication-quality figures and easing integration with established analysis pipelines. The VennDiagram package allows the creation of high quality Venn and Euler diagrams in the R statistical environment.


title: Storing, Tracking, and Querying Provenance in Linked Data

abstract: The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triplestores. We present methods extending a native RDF store to efficiently handle the storage, tracking, and querying of provenance in RDF data. We describe a reliable and understandable specification of the way results were derived from the data and how particular pieces of data were combined to answer a query. Subsequently, we present techniques to tailor queries with provenance data. We empirically evaluate the presented methods and show that the overhead of storing and tracking provenance is acceptable. Finally, we show that tailoring a query with provenance information can also significantly improve the performance of query execution.


title: The Effect of Personal Relationship and Consultative Task Behaviors on Buyer Perceptions of Salesperson Trust, Expertise, and Loyalty

abstract: Creating long-lasting business partnerships between buyers and sellers continues to be a major concern of marketing managers and scholars. The effect of salesperson personal relationship and consultative task behaviors on buyer perceptions, however, has not been thoroughly explored. In response, this study investigates the connection between sales behaviors and buyer assessment of salesperson attributes and loyalty. The results support the contention that consultative task and personal relationship behaviors play a vital role in influencing buyer perceptions of expertise, trust, and relationship loyalty.


title: Creating a Labeled Dataset for Medical Misinformation in Health Forums

abstract: The dissemination of medical misinformation online presents a challenge to human health. Machine learning techniques provide a unique opportunity for decreasing the cognitive load associated with deciding upon whether any given user comment is likely to contain misinformation, but a paucity of labeled data of medical misinformation makes supervised approaches a challenge. In order to ameliorate this condition, we present a new labeled dataset of misinformative and non-misinformative comments developed over posted questions and comments on a health discussion forum. This required extraction of candidate misinformative entries from the corpus using information retrieval techniques, development of a codex and labeling strategy for the dataset, and the creation of features for use in machine learning tasks. By identifying the nine most descriptive features with regard to classification as misinformative or non-misinformative through the use of Recursive Feature Elimination, we achieved a classification accuracy of 90.1%, where the dataset is comprised 85.8% of non-misinformative comments. In our opinion, this dataset and analysis will aid the machine learning community in the development of an online misinformation classification system over user-generated content such as medical forum posts.


title: Enhancing Routing Protocol for Low Power and Lossy Networks

abstract: With the rise the of the Internet of Things (IoT) the Routing Protocol for Low Power and Lossy Networks (RPL) gained a lot of interest in the research community mainly for its flexibility to cope with different network topologies and its ability to offer features like Auto-Configuration, Self-Healing, Loop avoidance and detection, etc. Based on certain routing metrics, RPL's Objective Function (OF) assigns ranks to the nodes in the network then selects and optimizes the routes. This paper overviews the most used objective functions then proposes a modification on the Minimum Rank with Hysteresis Objective Function (MRHOF) which takes into consideration two metrics instead of one, to get more reliable and optimized routes.


title: Understanding Diversity The Importance of Social Acceptance

abstract: Two studies investigated how people define and perceive diversity in the historically majority-group dominated contexts of business and academia. We hypothesized that individuals construe diversity as both the numeric representation of racial minorities and the social acceptance of racial minorities within a group. In Study 1, undergraduates’ (especially minorities’) perceptions of campus diversity were predicted by perceived social acceptance on a college campus, above and beyond perceived minority representation. Study 2 showed that increases in a company’s representation and social acceptance independently led to increases in perceived diversity of the company among Whites. Among non-Whites, representation and social acceptance only increased perceived diversity of the company when both qualities were high. Together these findings demonstrate the importance of both representation and social acceptance to the achievement of diversity in groups and that perceiver race influences the relative importance o...


title: Evaluation of the analyses of tert-butyldimethylsilyl derivatives of naphthenic acids by gas chromatography-electron impact mass spectrometry.

abstract: Naphthenic acids are a complex mixture of carboxylic acids with the general formula CnH(2n+Z)O2 and they are natural, toxic components of crude oils. GC-MS analyses of tert-butyldimethylsilyl esters of naphthenic acids are used to estimate component distribution within naphthenic acids mixtures. Our evaluations of the GC-MS method showed that ions from column bleed erroneously appear as C14 Z = -4 acids and that correcting for heavy isotopes of C and Si do not significantly affect ion distribution plots. Overall, the GC-MS method appears to overestimate the relative proportion of low-molecular-mass acids.


title: A Low-Cost Multi-failure Resilient Replication Scheme for High Data Availability in Cloud Storage

abstract: Replication is a common approach to enhance data availability in cloud storage systems. Previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures while increasing the data availability with the limited resource. The schemes for correlated machine failures must create a constant number of replicas for each data object, which neglects diverse data popularities and cannot utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability (hence minimize SLA violations) while minimizing cost caused by replication in order to maximize the revenue. In this paper, we build a nonlinear integer programming model to maximize data availability in both types of failures and minimize the cost caused by replication. Based on the model's solution for the replication degree of each data object, we propose a low-cost multi-failure resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance cost and storage cost. Extensive numerical results from trace parameters and experiments from real-world Amazon S3 show that MRR achieves high data availability, low data loss probability and low consistency maintenance cost and storage cost compared to previous replication schemes.


title: Interference-Free Pilots Insertion for MIMO-GFDM Channel Estimation

abstract: Generalized Frequency Division Multiplexing (GFDM) is a flexible non-orthogonal waveform. Due to its flexibility it can be served as a framework to emulate diverse multi-carrier waveforms including orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain equalization (SC- FDE). Nevertheless, inter-symbol- and inter-carrier- interference may arise in GFDM if the filter roll-off factor is larger than zero. In multiple-input multiple-output (MIMO) scenarios, also inter-antenna- interference further challenges the receiver design. In this paper, we focus on pilot-aided channel estimation for GFDM. In contrast to our prior works, we propose a technique to insert the pilot symbols in a manner such that they are orthogonal to the data symbols in the frequency domain. Based on this design, frequency-domain channel estimation algorithms initially developed for OFDM become straightforwardly applicable. We also examine the impact of such pilot design on the signal properties, including power spectral density (PSD) and peak-to- average-power ratio (PAPR). At the end of the paper, the performance of a MIMO-GFDM system is investigated and compared with the conventional MIMO-OFDM systems.


title: Intrusion prevention system of automotive network CAN bus

abstract: Automotive networks based on the Controller Area Network (CAN) bus (ISO 11898) family of protocols have been shown to be vulnerable to exploits by hackers who are outside the vehicle. These networks can be compromised in a manner which could jeopardize vehicle occupants. One well-publicized exploit led to a costly automotive recall that affected more than a million vehicles. Other exploits can allow criminals to steal cars without physically breaking into them. While no fatalities have yet occurred, hackers could trigger an event that led to accident involving serious injury or even death. The CAN bus connects electronic control units (ECUs), some of which are required for safety and emissions systems such as the anti-lock braking and fuel injection systems. In addition to controlling required functions, other ECUs provide consumer-oriented features such as infotainment and lighting. Whether factory installed or aftermarket add-ons, each ECU introduces attack vectors into the overall automotive network. This research focuses on securing these vehicle networks, specifically the CAN bus. This paper will analyze the current vulnerabilities and describe our design for a real-time intrusion prevention system (IPS) that neutralizes attacks by actively monitoring the CAN bus and eliminating malicious messages.


title: Active graphene–silicon hybrid diode for terahertz waves

abstract: Graphene has demonstrated the ability to modulate terahertz (THz) waves by optical or electrical excitation, but modulation depths have been low. Here, Li et al. demonstrate enhanced modulation and polarity-dependent THz attenuation using external voltage bias and photoexcitation on a graphene–silicon film.


title: Designing transcranial magnetic stimulation systems

abstract: We explain the process of designing optimized transcranial magnetic stimulation systems and outline a method for identifying optimal system parameters such as the number of turns, the capacitor size, the working voltage, and the size of the stimulation coil. The method combines field analysis, linear and nonlinear circuit analysis, and neural strength-duration response parameters. The method uses boundary-element analysis to predict the electric field as a function of depth, frequency, current, and excitation coil size. It then uses the field analysis to determine the inductance as a function of size and, in general, current when a saturable core is used. Circuit analysis allows these electric field computations to be indexed against system parameters, and optimized for total system energy and stimulation coil size. System optimizations depend on desired stimulation depth. A distinguishing feature of the method is that it inherently treats excitation frequency as an unknown to be determined from optimization.


title: Model-driven multi-omic data analysis elucidates metabolic immunomodulators of macrophage activation

abstract: Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Our study demonstrates metabolism's role in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors.


title: Learning from Corrupted Binary Labels via Class-Probability Estimation

abstract: Many supervised learning problems involve learning from samples whose labels are corrupted in some way. For example, each label may be flipped with some constant probability (learning with label noise), or one may have a pool of unlabelled samples in lieu of negative samples (learning from positive and unlabelled data). This paper uses class-probability estimation to study these and other corruption processes belonging to the mutually contaminated distributions framework (Scott et al., 2013), with three conclusions. First, one can optimise balanced error and AUC without knowledge of the corruption parameters. Second, given estimates of the corruption parameters, one can minimise a range of classification risks. Third, one can estimate corruption parameters via a class-probability estimator (e.g. kernel logistic regression) trained solely on corrupted data. Experiments on label noise tasks corroborate our analysis.


title: Tools for teaching introductory programming: what works?

abstract: 1. SUMMARY In the past decade educators have developed a myriad of tools to help novices learn to program. Different tools emerge as new features or combinations of features are employed. In this panel we consider the features of recent tools that have garnered significant interest in the computer science education community. These including narrative tools which support programming to tell a story (e.g., Alice [6], Jeroo [8]), visual programming tools which support the construction of programs through a drag-and-drop interface (e.g., JPie [3], Alice [6], Karel Universe), flow-model tools (e.g., Raptor [1], Iconic Programmer [2], VisualLogic) which construct programs through connecting program elements to represent order of computation, specialized output realizations (e.g., Lego Mindstorms [5], JES [7]) that provide execution feedback in nontextual ways, like multimedia or kinesthetic robotics, and tiered language tools (e.g., ProfessorJ [4], RoboLab) in which novices can use more sophisticated versions of a language as their expertise develops.


title: SMASH - A Cryptographic Hash Function

abstract: This paper presents a new hash function design, which is different from the popular designs of the MD4-family. Seen in the light of recent attacks on MD4, MD5, SHA-0, SHA-1, and on RIPEMD, there is a need to consider other hash function design strategies. The paper presents also a concrete hash function design named SMASH. One version has a hash code of 256 bits and appears to be at least as fast as SHA-256.


title: Incremental Joint POS Tagging and Dependency Parsing in Chinese

abstract: We address the problem of joint part-of-speech (POS) tagging and dependency parsing in Chinese. In Chinese, some POS tags are often hard to disambiguate without considering longrange syntactic information. Also, the traditional pipeline approach to POS tagging and dependency parsing may suffer from the problem of error propagation. In this paper, we propose the first incremental approach to the task of joint POS tagging and dependency parsing, which is built upon a shift-reduce parsing framework with dynamic programming. Although the incremental approach encounters difficulties with underspecified POS tags of look-ahead words, we overcome this issue by introducing so-called delayed features. Our joint approach achieved substantial improvements over the pipeline and baseline systems in both POS tagging and dependency parsing task, achieving the new state-of-the-art performance on this joint task.


title: Unsupervised incremental STDP learning using forced firing of dormant or idle neurons

abstract: Spiking neural networks with hardware implementations of Spike Timing Dependent Plasticity (STDP) present a promising solution to energy efficient real-time machine learning. Online real-time learning, however, requires that new training information be considered by an already trained network without reinforcing previous data. Learning new information without severely altering previously learned data proves difficult in such networks. We present a spiking neural network capable of unsupervised incremental learning that exploits STDP to gracefully adapt the network under changing input conditions using a technique called forced firing. Our simulations validate this design with accuracy results comparable to networks that require data reinforcement.


A data mining approach to analysis and prediction of movie


title: A diphenoxo bridged antiferromagnetically coupled dimer of copper(II) having bridging methanol

abstract: A dinuclear copper(II) complex, (mu2-MeOH)bis(mu(2)-phenoxide)dicopper complex with N-(3-aminopropyl)salicylaidimine, has been synthesised and characterised by X-ray structure determination. Variable temperature magnetic susceptibility measurement shows that it is strongly antiferromagnetically coupled. (C) 2003 Elsevier Science B.V. All rights reserved.


title: A second type of magnitude effect: Reinforcer magnitude differentiates delay discounting between substance users and controls

abstract: Basic research on delay discounting, examining preference for smaller-sooner or larger-later reinforcers, has demonstrated a variety of findings of considerable generality. One of these, the magnitude effect, is the observation that individuals tend to exhibit greater preference for the immediate with smaller magnitude reinforcers. Delay discounting has also proved to be a useful marker of addiction, as demonstrated by the highly replicated finding of greater discounting rates in substance users compared to controls. However, some research on delay discounting rates in substance users, particularly research examining discounting of small-magnitude reinforcers, has not found significant differences compared to controls. Here, we hypothesize that the magnitude effect could produce ceiling effects at small magnitudes, thus obscuring differences in delay discounting between groups. We examined differences in discounting between high-risk substance users and controls over a broad range of magnitudes of monetary amounts ($0.10, $1.00, $10.00, $100.00, and $1000.00) in 116 Amazon Mechanical Turk workers. We found no significant differences in discounting rates between users and controls at the smallest reinforcer magnitudes ($0.10 and $1.00) and further found that differences became more pronounced as magnitudes increased. These results provide an understanding of a second form of the magnitude effect: That is, differences in discounting between populations can become more evident as a function of reinforcer magnitude.


title: IT Security Governance: A Framework based on ISO 38500

abstract: ISO 38500 is an international standard for IT governance. The guidelines of ISO 38500 can also be applied at the IT security functional level in order to guide the governance of IT security. This paper proposes the use of a strategic information security management (ISM) framework to implement guidelines of ISO 38500. This approach provides several strategic advantages to the organization by 1) aligning IT security initiatives to business strategy; 2) providing a mechanism for establishing and tracking security metrics; and 3) enhancing the overall maturity of business, IT and IT security processes. The framework also leverages tools such as COBIT, the Balanced Scorecard and SSE-CMM in order to implement IT security governance and continuous improvement practices. Using extant literature, this paper identifies certain challenges and solutions with respect to the governance of IT security. For practitioners, it highlights relevant links between principles of ISO 38500 and IT governance, provides an over-arching contextual framework to drive IT security governance, and demonstrates mitigation solutions for IT security governance challenges. For academics, the paper makes theoretical contributions, by relating IT security governance to business strategy and proposing that firms develop dynamic governance capabilities (Pavlou and El Sawy, 2010) or organizational learning ladders (Ciborra and Andreu, 2010).


title: Symmetry-Breaking Convergence Analysis of Certain Two-layered Neural Networks with ReLU nonlinearity

abstract: In this paper, we use dynamical system to analyze the nonlinear weight dynamics of two-layered bias-free networks in the form of $g(x; w) = \sum_{j=1}^K \sigma(w_j \cdot x)$, where $\sigma(\cdot)$ is ReLU nonlinearity. We assume that the input $x$ follow Gaussian distribution. The network is trained using gradient descent to mimic the output of a teacher network of the same size with fixed parameters $w*$ using $l_2$ loss. We first show that when $K = 1$, the nonlinear dynamics can be written in close form, and converges to $w*$ with at least $(1-\epsilon)/2$ probability, if random weight initializations of proper standard derivation ($\sim 1/\sqrt{d}$) is used, verifying empirical practice. For networks with many ReLU nodes ($K \ge 2$), we apply our close form dynamics and prove that when the teacher parameters $\{w*_j\}_{j=1}^K$ forms orthonormal bases, (1) a symmetric weight initialization yields a convergence to a saddle point and (2) a certain symmetry-breaking weight initialization yields global convergence to $w*$ without local minima. To our knowledge, this is the first proof that shows global convergence in nonlinear neural network without unrealistic assumptions on the independence of ReLU activations. In addition, we also give a concise gradient update formulation for a multilayer ReLU network when it follows a teacher of the same size with $l_2$ loss. Simulations verify our theoretical analysis.


Continuous Auditing in ERP System Environments: The Current State and Future Directions


title: Transelliptical graphical models

abstract: We advocate the use of a new distribution family—the transelliptical—for robust inference of high dimensional graphical models. The transelliptical family is an extension of the nonparanormal family proposed by Liu et al. (2009). Just as the nonparanormal extends the normal by transforming the variables using univariate functions, the transelliptical extends the elliptical family in the same way. We propose a nonparametric rank-based regularization estimator which achieves the parametric rates of convergence for both graph recovery and parameter estimation. Such a result suggests that the extra robustness and flexibility obtained by the semiparametric transelliptical modeling incurs almost no efficiency loss. We also discuss the relationship between this work with the transelliptical component analysis proposed by Han and Liu (2012).


title: Driving and Braking Torque Distribution Methods for Front- and Rear-Wheel-Independent Drive-Type Electric Vehicles on Roads With Low Friction Coefficient

abstract: This paper focuses on the development of a front- and rear-wheel-independent drive-type electric vehicle (EV) (FRID EV) as a next-generation EV. The ideal characteristics of a FRID EV promote good performance and safety and are the result of structural features that independently control the driving and braking torques of the front and rear wheels. The first characteristic is the failsafe function. This function enables vehicles to continue running without any unexpected or sudden stops, even if one of the propulsion systems fails. The second characteristic is a function that performs efficient acceleration and deceleration on all road surfaces. This function works by distributing the driving or braking torques to the front and rear wheels, taking into consideration load movement. The third characteristic ensures that the vehicle runs safely on roads with a low friction coefficient (μ), such as icy roads. In this paper, we propose a driving torque distribution method when cornering and a braking torque distribution method; these methods are related to the third characteristic, and they are particularly effective when driving on roads with ultralow μ. We verify the effectiveness of the proposed torque control methods through simulations and experiments on the ultralow-μ road surface with a μ of 0.1.