title: Group-level emotion recognition using deep models on image scene, faces, and skeletons

abstract: This paper presents the work submitted to the Group-level Emotion Recognition sub-challenge, which is part of the 5th Emotion Recognition in the Wild (EmotiW 2017) Challenge. The task of this sub-challenge is to classify the emotion of a group of people in each image as positive, neutral or negative. To address this task, a hybrid network that incorporates global scene features, skeleton features of the group, and local facial features is developed. Specifically, deep convolutional neural networks (CNNs) are first trained on the faces of the group, the whole images and the skeletons of the group, and then fused to perform the group-level emotion prediction. Experimental results show that the proposed network achieves 80.05% and 80.61% on the validation and testing sets, respectively, outperforming the baseline of 52.97% and 53.62%.


title: Principles of Critical Discourse Analysis

abstract: This paper discusses some principles of critical discourse analy- sis, such as the explicit sociopolitical stance of discourse analysts, and a focus on dominance relations by elite groups and institutions as they are being enacted, legitimated or otherwise reproduced by text and talk. One of the crucial elements of this analysis of the relations between power and discourse is the patterns of access to (public) discourse for different social groups. Theoretically it is shown that in order to be able to relate power and discourse in an explicit way, we need the cognitive interface of models. knowledge, attitudes and ideologies and other social represen- tations of the social mind, which also relate the individual and the social, and the micro- and the macro-levels of social structure. Finally, the argu- ment is illustrated with an analysis of parliamentary debates about ethnic affairs.


title: Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification

abstract: This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images -- one of the main ingredients of zero-shot learning -- by formulating it as a metric learning problem. The optimized empirical criterion associates two types of sub-task constraints: metric discriminating capacity and accurate attribute prediction. This results in a novel expression of zero-shot learning not requiring the notion of class in the training phase: only pairs of image/attributes, augmented with a consistency indicator, are given as ground truth. At test time, the learned model can predict the consistency of a test image with a given set of attributes , allowing flexible ways to produce recognition inferences. Despite its simplicity, the proposed approach gives state-of-the-art results on four challenging datasets used for zero-shot recognition evaluation.


title: Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining

abstract: We introduce a skill discovery method for reinforcement learning in continuous domains that constructs chains of skills leading to an end-of-task reward. We demonstrate experimentally that it creates appropriate skills and achieves performance benefits in a challenging continuous domain.


title: Stock Trading by Modelling Price Trend with Dynamic Bayesian Networks

abstract: We study a stock trading method based on dynamic bayesian networks to model the dynamics of the trend of stock prices. We design a three level hierarchical hidden Markov model (HHMM). There are five states describing the trend in first level. Second and third levels are and concrete hidden Markov models to produce the observed patterns. To train the HHMM, we adapt a semi-supervised learning so that the trend states of first layer is manually labelled. The inferred probability distribution of first level are used as an indicator for the trading signal, which is more natural and reasonable than technical indicators. Experimental results on representative 20 companies of Korean stock market show that the proposed HHMM outperforms a technical indicator in trading performances.


title: A review on transfer learning for brain-computer interface classification

abstract: Due to the non-stationarity nature and poor signal-to-noise ratio (SNR) of brain signals, repeated time-consuming calibration is one of the biggest problems for today's brain-computer interfaces (BCIs). In order to reduce calibration time, many transfer learning methods have been proposed to extract discriminative or stationary information from other subjects or prior sessions for target classification task. In this paper, we review the existing transfer learning methods used for BCI classification problems and organize them into three cases based on different transfer strategies. Besides, we list the datasets used in these BCI studies.


title: An RFID attendance and monitoring system for university applications

abstract: The main objective of this paper is to enhance the university's monitoring system taking into account factors such as reliability, time saving, and easy control. The proposed system consists of a mobile RFID solution in a logical context. The system prototype and its small scale application was a complete success. However, the more practical phase will not be immediately ready because a large setup is required and a part of the existing system has to be completely disabled. Some software modifications in the RFID system can be easily done in order for the system to be ready for a new application. In this paper, advantages and disadvantages of the proposed RFID system will be presented.


title: An Analysis of Citation Recommender Systems: Beyond the Obvious

abstract: As science advances, the academic community has published millions of research papers. Researchers devote time and effort to search relevant manuscripts when writing a paper or simply to keep up with current research. In this paper, we consider the problem of citation recommendation by extending a set of known-to-be-relevant references. Our analysis shows the degrees of cited papers in the subgraph induced by the citations of a paper, called projection graph, follow a power law distribution. Existing popular methods are only good at finding the long tail papers, the ones that are highly connected to others. In other words, the majority of cited papers are loosely connected in the projection graph but they are not going to be found by existing methods. To address this problem, we propose to combine author, venue and keyword information to interpret the citation behavior behind those loosely connected papers. Results show that different methods are finding cited papers with widely different properties. We suggest multiple recommended lists by different algorithms could satisfy various users for a real citation recommendation system.


title: SQLizer: query synthesis from natural language

abstract: This paper presents a new technique for automatically synthesizing SQL queries from natural language (NL). At the core of our technique is a new NL-based program synthesis methodology that combines semantic parsing techniques from the NLP community with type-directed program synthesis and automated program repair. Starting with a program sketch obtained using standard parsing techniques, our approach involves an iterative refinement loop that alternates between probabilistic type inhabitation and automated sketch repair. We use the proposed idea to build an end-to-end system called SQLIZER that can synthesize SQL queries from natural language. Our method is fully automated, works for any database without requiring additional customization, and does not require users to know the underlying database schema. We evaluate our approach on over 450 natural language queries concerning three different databases, namely MAS, IMDB, and YELP. Our experiments show that the desired query is ranked within the top 5 candidates in close to 90% of the cases and that SQLIZER outperforms NALIR, a state-of-the-art tool that won a best paper award at VLDB'14.


title: Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study

abstract: Summary Background Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. Methods In a cross-sectional study we extracted data on 40 morbidities from a database of 1 751 841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. Findings 42·2% (95% CI 42·1–42·3) of all patients had one or more morbidities, and 23·2% (23·08–23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210 500 vs 194 996). Onset of multimorbidity occurred 10–15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9–11·2% in most deprived area vs 5·9%, 5·8%–6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59–6·90 for five or more disorders vs 1·95, 1·93–1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21–2·32 vs 1·08, 1·05–1·11). Interpretation Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Funding Scottish Government Chief Scientist Office.


title: High-bandwidth high-power gradient driver for magnetic resonance imaging with digital control

abstract: Gradient coils for magnetic resonance imaging (MRI) require large currents (> 500 A) for the gradient field strength, as well as high voltage (> 1600 V) for fast slew rates. Additionally, extremely high fidelity, reproducing the command signal, is critical for image quality. A new driver topology recently proposed can provide the high power and operate at high switching frequency allowing high bandwidth control. The paper presents additional improvements to the new driver architecture, and more importantly, describes the digital control design and implementation, crucial to achieve the required performance level. The power stage and control have been build and tested with the experimental results showing that the performance achieved with the new digital control capability, more than fulfills the system requirements


title: Multisource Transfer Double DQN Based on Actor Learning

abstract: Deep reinforcement learning (RL) comprehensively uses the psychological mechanisms of “trial and error” and “reward and punishment” in RL as well as powerful feature expression and nonlinear mapping in deep learning. Currently, it plays an essential role in the fields of artificial intelligence and machine learning. Since an RL agent needs to constantly interact with its surroundings, the deep Q network (DQN) is inevitably faced with the need to learn numerous network parameters, which results in low learning efficiency. In this paper, a multisource transfer double DQN (MTDDQN) based on actor learning is proposed. The transfer learning technique is integrated with deep RL to make the RL agent collect, summarize, and transfer action knowledge, including policy mimic and feature regression, to the training of related tasks. There exists action overestimation in DQN, i.e., the lower probability limit of action corresponding to the maximum Q value is nonzero. Therefore, the transfer network is trained by using double DQN to eliminate the error accumulation caused by action overestimation. In addition, to avoid negative transfer, i.e., to ensure strong correlations between source and target tasks, a multisource transfer learning mechanism is applied. The Atari2600 game is tested on the arcade learning environment platform to evaluate the feasibility and performance of MTDDQN by comparing it with some mainstream approaches, such as DQN and double DQN. Experiments prove that MTDDQN achieves not only human-like actor learning transfer capability, but also the desired learning efficiency and testing accuracy on target task.


title: From Facial Expression Recognition to Interpersonal Relation Prediction

abstract: Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.


title: Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla.

abstract: BD1-5, OP11, and OD1 bacteria have been widely detected in anaerobic environments, but their metabolisms remain unclear owing to lack of cultivated representatives and minimal genomic sampling. We uncovered metabolic characteristics for members of these phyla, and a new lineage, PER, via cultivation-independent recovery of 49 partial to near-complete genomes from an acetate-amended aquifer. All organisms were nonrespiring anaerobes predicted to ferment. Three augment fermentation with archaeal-like hybrid type II/III ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO) that couples adenosine monophosphate salvage with CO(2) fixation, a pathway not previously described in Bacteria. Members of OD1 reduce sulfur and may pump protons using archaeal-type hydrogenases. For six organisms, the UGA stop codon is translated as tryptophan. All bacteria studied here may play previously unrecognized roles in hydrogen production, sulfur cycling, and fermentation of refractory sedimentary carbon.


title: A robotic knee exoskeleton for walking assistance and connectivity topology exploration in EEG signal

abstract: Disability of lower limb affects voluntary movements and results in a low quality of life. Although people with disabled lower limb can restore their movements through assistive devices like wheelchair, such manner is not as natural as that healthy people perform locomotion. It would be better to regain movements by restoring the function of lower limb itself. Hence, an orthosis that can be worn on the disable lower limb might be a good choice. In addition, it is of interest to explore brain activities during walking with and without an orthosis. In this paper, we introduced a robotic knee exoskeleton that can provide assistive torque to facilitate walking. Three different conditions were employed here, namely free walking (FW, without exoskeleton), zero force (ZF, with the exoskeleton but no torque was provided), and assistive force (AF, torque was provided by the exoskeleton). During the walking, electrophysiological signals were simultaneously recorded. Partial directed coherence (PDC) was employed to measure connectivity strengths between channels and the obtained effective connectivity network was quantitatively assessed through a standard graph theoretical analysis framework. We found that the clustering coefficient was significantly increased when assistive torque was provided by the exoskeleton compared with FW and ZF. A decrease in path length was also found when assistive torque was provided. Our findings demonstrated that topological patterns of brain activities were distinctly different when people received assistive torque during walking. This study could be of meaningful significance on further orthosis development from the perspective of brain electrophysiological activity and insight into the understanding for brain plasticity and neural rehabilitation.


title: BROADBAND E-H SHAPED MICROSTRIP PATCH ANTENNA FOR WIRELESS SYSTEMS

abstract: A broadband inverted E-H shaped microstrip patch antenna is proposed and experimentally investigated. The antenna employs novel E-H shaped patch with L-probe feed technique. Prototype of the proposed antenna has been fabricated and measured for electromagnetic analysis including the impedance bandwidth, radiation pattern, and antenna gain. The designed antenna has a dimension of 80mm by 50mm, leading to broad bandwidths covering 1.76GHz to 2.38GHz. Stable radiation patterns across the operating bandwidth are observed. In addition, a parametric study is conducted to facilitate the design and optimization process.


title: Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity using Convolutional Neural Networks

abstract: This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints; next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN). We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches.


title: High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

abstract: We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.


Deception detection in Russian texts.


title: From Mental Disorder to Iatrogenic Hypogonadism: Dilemmas in Conceptualizing Gender Identity Variants as Psychiatric Conditions

abstract: The categorization of gender identity variants (GIVs) as “mental disorders” in the Diagnostic and Statistical Manual of Mental Disorders (DSM) of the American Psychiatric Association is highly controversial among professionals as well as among persons with GIV. After providing a brief history of GIV categorizations in the DSM, this paper presents some of the major issues of the ongoing debate: GIV as psychopathology versus natural variation; definition of “impairment” and “distress” for GID; associated psychopathology and its relation to stigma; the stigma impact of the mental-disorder label itself; the unusual character of “sex reassignment surgery” as a psychiatric treatment; and the consequences for health and mental-health services if the disorder label is removed. Finally, several categorization options are examined: Retaining the GID category, but possibly modifying its grouping with other syndromes; narrowing the definition to dysphoria and taking “disorder” out of the label; categorizing GID as a neurological or medical rather than a psychiatric disorder; removing GID from both the DSM and the International Classification of Diseases (ICD); and creating a special category for GIV in the DSM. I conclude that—as also evident in other DSM categories—the decision on the categorization of GIVs cannot be achieved on a purely scientific basis, and that a consensus for a pragmatic compromise needs to be arrived at that accommodates both scientific considerations and the service needs of persons with GIVs.


title: The Heckman Correction for Sample Selection and Its Critique

abstract: This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman's (1976, 1979) two-step estimator for estimating selection models. Such models occur frequently in empirical work, especially in microeconometrics when estimating wage equations or consumer expenditures. It is shown that exploratory work to check for collinearity problems is strongly recommended before deciding on which estimator to apply. In the absence of collinearity problems, the full-information maximum likelihood estimator is preferable to the limited-information two-step method of Heckman, although the latter also gives reasonable results. If, however, collinearity problems prevail, subsample OLS (or the Two-Part Model) is the most robust amongst the simple-to-calculate estimators. Copyright 2000 by Blackwell Publishers Ltd


title: Employing automatic approach and avoidance tendencies for the assessment of implicit personality self-concept: The Implicit Association Procedure (IAP

abstract: An ew chronometric procedure, the Implicit Association Procedure (IAP), was adapted to assess the implicit personality self-concept of shyness. A sample of 300 participants completed a shyness-inducing role play and, before or after the role play, a shyness IAP, a shyness Implicit Association Test (IAT), and direct self-ratings. The experimental group was instructed to fake nonshyness. The control group did not receive this instruction. IAT and IAP were unaffected by position effects, and were less susceptible to faking than direct self-ratings with regard to mean levels and correlates. Under faking, correlations between direct and indirect measures decreased, and direct but not indirect measures showed higher correlations with social desirability and lower correlations with observed shyness. Despite many similarities, the true correlation between IAT and IAP was estimated only .61, indicating high method-specific variance in both procedures. The findings suggest that indirect measures are more robust against faking than traditional self-ratings but do not yet meet psychometric criteria for practical assessment purposes.


Authentication via keystroke dynamics


title: Software metrics: roadmap

abstract: Software metrics as a subject area is over 30 years old, but it has barely penetrated into mainstream software engineering. A key reason for this is that most software metrics activities have not addressed their most important requirement: to provide information to support quantitative managerial decision-making during the software lifecycle. Good support for decision-making implies support for risk assessment and reduction. Yet traditional metrics approaches, often driven by regression-based models for cost estimation and defects prediction, provide little support for managers wishing to use measurement to analyse and minimise risk. The future for software metrics lies in using relatively simple existing metrics to build management decision-support tools that combine different aspects of software development and testing and enable managers to make many kinds of predictions, assessments and trade-offs during the software life-cycle. Our recommended approach is to handle the key factors largely missing from the usual metrics approaches, namely: causality, uncertainty, and combining different (often subjective) evidence. Thus the way forward for software metrics research lies in causal modelling (we propose using Bayesian nets), empirical software engineering, and multi-criteria decision aids.


Frontal fi brosing alopecia and lichen planopilaris in HLA-identical mother and daughter


title: A Novel Flower Pollination Based Global Maximum Power Point Method for Solar Maximum Power Point Tracking

abstract: To maximize solar photovoltaic (PV) output under dynamic weather conditions, maximum power point tracking (MPPT) controllers are incorporated in solar PV systems. However, the occurrence of multiple peaks due to partial shading adds complexity to the tracking process. Even though conventional and soft computing techniques are widely used to solve MPPT problem, conventional methods exhibit limited performance due to fixed step size, whereas soft computing techniques are restricted by insufficient randomness after reaching the vicinity of maximum power. Hence, in this paper, a new flower pollination algorithm (FPA) with the ability to reach global peak is proposed. Optimization process in FPA method performs global and local search in single stage and it is a key tool for its success in MPPT application. The ruggedness of the algorithm is tested with zero, weak, and strong shade pattern. Further, comprehensive performance estimation via simulation and hardware are carried out for FPA method and are quantified with conventional perturb and observe and particle swarm optimization (PSO) methods. Results obtained with FPA method show superiority in energy saving and proved to be economical.


title: Approximation Algorithms for Correlated Knapsacks and Non-martingale Bandits

abstract: In the stochastic knapsack problem, we are given a knapsack of size B, and a set of items whose sizes and rewards are drawn from a known probability distribution. To know the actual size and reward we have to schedule the item -- when it completes, we get to know these values. The goal is to schedule the items (possibly making adaptive decisions based on the sizes seen so far) to maximize the expected total reward of items which successfully pack into the knapsack. We know constant-factor approximations when (i) the rewards and sizes are independent, and (ii) we cannot prematurely cancel items after we schedule them. What if either or both assumptions are relaxed? Related stochastic packing problems are the multi-armed bandit (and budgeted learning) problems, here one is given several arms which evolve in a specified stochastic fashion with each pull, and the goal is to (adaptively) decide which arms to pull, in order to maximize the expected reward obtained after B pulls in total. Much recent work on this problem focuses on the case when the evolution of each arm follows a martingale, i.e., when the expected reward from one pull of an arm is the same as the reward at the current state. What if the rewards do not form a martingale? In this paper, we give O(1)-approximation algorithms for the stochastic knapsack problem with correlations and/or cancellations. Extending the ideas developed here, we give O(1)-approximations for MAB problems without the martingale assumption. Indeed, we can show that previously proposed linear programming relaxations for these problems have large integrality gaps. So we propose new time-indexed LP relaxations, using a decomposition and "gap-filling" approach, we convert these fractional solutions to distributions over strategies, and then use the LP values and the time ordering information from these strategies to devise randomized adaptive scheduling algorithms.


title: Comparison of dilute acid and ionic liquid pretreatment of switchgrass: Biomass recalcitrance, delignification and enzymatic saccharification.

abstract: The efficiency of two biomass pretreatment technologies, dilute acid hydrolysis and dissolution in an ionic liquid, are compared in terms of delignification, saccharification efficiency and saccharide yields with switchgrass serving as a model bioenergy crop. When subject to ionic liquid pretreatment (dissolution and precipitation of cellulose by anti-solvent) switchgrass exhibited reduced cellulose crystallinity, increased surface area, and decreased lignin content compared to dilute acid pretreatment. Pretreated material was characterized by powder X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy and chemistry methods. Ionic liquid pretreatment enabled a significant enhancement in the rate of enzyme hydrolysis of the cellulose component of switchgrass, with a rate increase of 16.7-fold, and a glucan yield of 96.0% obtained in 24h. These results indicate that ionic liquid pretreatment may offer unique advantages when compared to the dilute acid pretreatment process for switchgrass. However, the cost of the ionic liquid process must also be taken into consideration.


title: IoT Elements, Layered Architectures and Security Issues: A Comprehensive Survey

abstract: The use of the Internet is growing in this day and age, so another area has developed to use the Internet, called Internet of Things (IoT). It facilitates the machines and objects to communicate, compute and coordinate with each other. It is an enabler for the intelligence affixed to several essential features of the modern world, such as homes, hospitals, buildings, transports and cities. The security and privacy are some of the critical issues related to the wide application of IoT. Therefore, these issues prevent the wide adoption of the IoT. In this paper, we are presenting an overview about different layered architectures of IoT and attacks regarding security from the perspective of layers. In addition, a review of mechanisms that provide solutions to these issues is presented with their limitations. Furthermore, we have suggested a new secure layered architecture of IoT to overcome these issues.


title: Emotion Recognition in Animated Compared to Human Stimuli in Adolescents with Autism Spectrum Disorder

abstract: There is equivocal evidence as to whether there is a deficit in recognising emotional expressions in Autism spectrum disorder (ASD). This study compared emotion recognition in ASD in three types of emotion expression media (still image, dynamic image, auditory) across human stimuli (e.g. photo of a human face) and animated stimuli (e.g. cartoon face). Participants were 37 adolescents (age 11–16) with a diagnosis of ASD (33 male, 4 female). 42 males and 39 females served as typically developing, age-matched controls. Overall there was significant advantage for control groups over the ASD group for emotion recognition in human stimuli but not animated stimuli, across modalities. For static animated images specifically, those with ASD significantly outperformed controls. The findings are consistent with the ASD group using atypical explicit strategies.


title: Seeing Stars When There Aren’t Many Stars: Graph-Based Semi-Supervised Learning For Sentiment Categorization

abstract: We present a graph-based semi-supervised learning algorithm to address the sentiment analysis task of rating inference. Given a set of documents (e.g., movie reviews) and accompanying ratings (e.g., "4 stars"), the task calls for inferring numerical ratings for unlabeled documents based on the perceived sentiment expressed by their text. In particular, we are interested in the situation where labeled data is scarce. We place this task in the semi-supervised setting and demonstrate that considering unlabeled reviews in the learning process can improve rating-inference performance. We do so by creating a graph on both labeled and unlabeled data to encode certain assumptions for this task. We then solve an optimization problem to obtain a smooth rating function over the whole graph. When only limited labeled data is available, this method achieves significantly better predictive accuracy over other methods that ignore the unlabeled examples during training.


title: Set Constraint Model and Automated Encoding into SAT: Application to the Social Golfer Problem

abstract: On the one hand, constraint satisfaction problems allow one to expressively model problems. On the other hand, propositional satisfiability problem (SAT) solvers can handle huge SAT instances. We thus present a technique to expressively model set constraint problems and to encode them automatically into SAT instances. We apply our technique to the social golfer problem and we also use it to break symmetries of the problem. Our technique is simpler, more expressive, and less error-prone than direct modeling. The SAT instances that we automatically generate contain less clauses than improved direct instances such as in Triska and Musliu (Ann Oper Res 194(1):427–438, 2012), and with unit propagation they also contain less variables. Moreover, they are well-suited for SAT solvers and they are solved faster as shown when solving difficult instances of the social golfer problem.


title: Materiality and Organizing: Social Interaction in a Technological World

abstract: Ask a person on the street whether new technologies bring about important social change and you are likely to hear a resounding "yes." But the answer is less definitive amongst academics who study technology and social practice. Scholarly writing has been heavily influenced by the ideology of technological determinism - the belief that some types or technologically driven social changes are inevitable and cannot be stopped. Rather than argue for or against notions of determinism, the authors in this book ask how the materiality (the arrangement of physical, digital, or rhetorical materials into particular forms that endure across differences in place and time) of technologies, ranging from computer-simulation tools and social media, to ranking devices and rumours, is actually implicated in the process of formal and informal organizing. The book builds a new theoretical framework to consider the important socio-technical changes confronting people's everyday experiences in and outside of work. Leading scholars in the field contribute original chapters examining the complex interactions between technology and the social, between artefact and humans. The discussion spans multiple disciplines, including management, information systems, informatics, communication, sociology, and the history of technology, and opens up a new area of research regarding the relationship between materiality and organizing.


title: Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

abstract: Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation. We study the more challenging problem of learning DCNNs for semantic image segmentation from either (1) weakly annotated training data such as bounding boxes or image-level labels or (2) a combination of few strongly labeled and many weakly labeled images, sourced from one or multiple datasets. We develop Expectation-Maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings. Extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentation benchmark, while requiring significantly less annotation effort. We share source code implementing the proposed system at https://bitbucket.org/deeplab/deeplab-public.


title: Summarization evaluation using relative utility

abstract: We present a series of experiments to demonstrate the validity of Relative Utility (RU) as a measure for evaluating extractive summarizers. RU is applicable in both single-document and multi-document summarization, is extendable to arbitrary compression rates with no extra annotation effort, and takes into account both random system performance and interjudge agreement. Our results using the JHU summary corpus indicate that RU is a reasonable and often superior alternative to several common evaluation metrics.


title: Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions

abstract: Designing soft robots poses considerable challenges: automated design approaches may be particularly appealing in this field, as they promise to optimize complex multi-material machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution in order to let soft robots (both morphologies and controllers) spontaneously evolve within physically-realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this paper a powerful evolutionary system is put in place in order to perform a broad investigation on the free-form evolution of walking and swimming soft robots in different environments. Three sets of experiments are reported, tackling different aspects of the evolution of soft locomotion. The first two sets explore the effects of different material properties on the evolution of terrestrial and aquatic soft locomotion: particularly, we show how different materials lead to the evolution of different morphologies, behaviors, and energy-performance tradeoffs. It is found that within our simplified physics world stiffer robots evolve more sophisticated and effective gaits and morphologies on land, while softer ones tend to perform better in water. The third set of experiments starts investigating the effect and potential benefits of major environmental transitions (land - water) during evolution. Results provide interesting morphological exaptation phenomena, and point out a potential asymmetry between land-water and water-land transitions: while the first type of transition appears to be detrimental, the second one seems to have some beneficial effects.


title: CURA: A Framework for Quality-Retaining Power Saving on Mobile OLED Displays

abstract: Organic Light-Emitting Diode (OLED) technology is regarded as a promising alternative to mobile displays. In this article, we introduce the design, algorithm, and implementation of a novel framework called CURA for quality-retaining power saving on mobile OLED displays. First, we link human visual attention to OLED power saving and model the OLED image scaling optimization problem. The objective is to minimize the power required to display an image without adversely impacting the user’s visual experience. Then, we present the algorithm used to solve the modeled problem, and prove its optimality even without an accurate power model. Finally, based on the framework, we implement two practical applications on a commercial OLED mobile tablet. The results of experiments conducted on the tablet with real images demonstrate that CURA can reduce significant OLED power consumption while retaining the visual quality of images.


title: Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

abstract: The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over time in such dynamic knowledge graphs is not yet well understood. To this end, we present Know-Evolve, a novel deep evolutionary knowledge network that learns non-linearly evolving entity representations over time. The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings. We demonstrate significantly improved performance over various relational learning approaches on two large scale real-world datasets. Further, our method effectively predicts occurrence or recurrence time of a fact which is novel compared to prior reasoning approaches in multi-relational setting.


title: A Novel Compact and Polarization-Dependent Mushroom-Type EBG Using CSRR for Dual/Triple-Band Applications

abstract: In this letter, a novel compact electromagnetic band-gap (EBG) structure constructed by etching a complementary split ring resonator (CSRR) on the patch of a conventional mushroom-type EBG (CMT-EBG) is proposed. The first bandgap is defined in all directions in the surface structure. Compared to the CMT-EBG structure, the CSRR-based EBG presents a 28% size reduction in the start frequency of the first bandgap. However, asymmetrical frames of the CSRR-based EBG result in different properties at X and Y-directions. Another two tunable bandgaps in Y-direction are observed. Thus, the proposed EBG can be used for multi-band applications, such as dual/triple-band antennas. The EBGs have been constructed and measured.


title: Products of two atoms in Krull monoids and arithmetical characterizations of class groups☆

abstract: Let H be a Krull monoid with finite class group G such that every class contains a prime divisor and let D ( G ) be the Davenport constant of G . Then a product of two atoms of H can be written as a product of at most D ( G ) atoms. We study this extremal case and consider the set U { 2 , D ( G ) } ( H ) defined as the set of all l ? N with the following property: there are two atoms u , v ? H such that u v can be written as a product of l atoms as well as a product of D ( G ) atoms. If G is cyclic, then U { 2 , D ( G ) } ( H ) = { 2 , D ( G ) } . If G has rank two, then we show that (apart from some exceptional cases) U { 2 , D ( G ) } ( H ) = 2 , D ( G ) ? { 3 } . This result is based on the recent characterization of all minimal zero-sum sequences of maximal length over groups of rank two. As a consequence, we are able to show that the arithmetical factorization properties encoded in the sets of lengths of a rank 2 prime power order group uniquely characterizes the group.


title: The Fiction of South Asians in North America and the Caribbean: A Critical Study of English-Language Works Since 1950

abstract: This study establishes connections between the themes and methodologies of writers within the South Asian diaspora in the New World, and serves both serious analysts as well as beginning readers of South Asian fiction. It is an impartial study that analyzes the stylistic excellence of South Asian fiction and the clearly emergent motifs of the writers, recognizing the value of the interplay of cultural differences and the need for resolution of those differences. The book begins with a discussion of the works of Indo-Caribbean novelists Samuel Selvon and V.S. Naipaul, author of A House for Mr. Biswas and The Enigma of Arrival, thereby establishing parallels between the immigration patterns of the South Asian diaspora who first emigrated to the Caribbean long before significant numbers of South Asians came to the United States. Next, the fiction of Ruth Prawer Jhabvala (Heat and Dust), the non-fictional narratives of Ved Mehta (Face to Face), and the satire and social criticism of Bharati Mukherjee (Wife) and Chitra Banerjee Divakaruni (Sister of My Heart) are discussed. New literary voices such as those of Bapsi Sidhwa (An American Brat), Pulitzer Prize winner Jhumpa Lahiri, whose characters, plots and themes deal with universal human experiences, Akhil Sharma, Manil Suri and Samrat Upadhyay are studied for the new directions and new methods they offer. A sub-genre of young adult fiction is discovered in the novels of Dhan Gopal Mukerji, such as in his Gay-Neck: The Story of a Pigeon, and more recently in the works of Mitali Perkins and Indi Rana. Recent expatriate novelists from South Asia such as Anita Desai, Amitav Chosh, Vikram Chandra and the American editions of Vikram Seth's novels are appraised together with contemporary Indo-Canadian novelists and Indo-Caribbean novelists resident in Canada.


title: A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query

abstract: Sensor Network has been widely used in all fields, and a large number of heterogeneous data is produced. In order to make these data shared and integrated semantically, semantic web technology is introduced in the sensor network. In this paper, we make a comprehensive survey about Semantic Sensor Web (SSW) from the perspective of ontology, mapping and query respectively. Firstly, we present state of the art of SSW from three aspects: ontologies before SSN ontology, SSN ontology and the extensions of SSN ontology, in which the extensions of SSN ontology are introduced emphatically. Secondly, we discuss the mapping from the stream data to ontology and focus on the comparison of the mapping languages in order to find out the best mapping method of SSW. Thirdly, we describe the research status of stream data query languages and analyze the characteristics, the application fields, the related researches and tools of the query languages in detail. Finally, the future research directions of SSW are proposed.


title: Address Resolution Protocol (ARP): Spoofing Attack and Proposed Defense

abstract: Networks have become an integral part of today’s world. The ease of deployment, low-cost and high data rates have contributed significantly to their popularity. There are many protocols that are tailored to ease the process of establishing these networks. Nevertheless, security-wise precautions were not taken in some of them. In this paper, we expose some of the vulnerability that exists in a commonly and widely used network protocol, the Address Resolution Protocol (ARP) protocol. Effectively, we will implement a user friendly and an easy-to-use tool that exploits the weaknesses of this protocol to deceive a victim’s machine and a router through creating a sort of Man-in-the-Middle (MITM) attack. In MITM, all of the data going out or to the victim machine will pass first through the attacker’s machine. This enables the attacker to inspect victim’s data packets, extract valuable data (like passwords) that belong to the victim and manipulate these data packets. We suggest and implement a defense mechanism and tool that counters this attack, warns the user, and exposes some information about the attacker to isolate him. GNU/Linux is chosen as an operating system to implement both the attack and the defense tools. The results show the success of the defense mechanism in detecting the ARP related attacks in a very simple and efficient way.


title: Brody disease: insights into biochemical features of SERCA1 and identification of a novel mutation.

abstract: Brody disease is an inherited disorder of skeletal muscle function characterized by increasing impairment of relaxation during exercise. The autosomal recessive form can be caused by mutations in the ATP2A1 gene, which encodes for the sarcoplasmic/endoplasmic reticulum Ca-ATPase 1 (SERCA1) protein. We studied 2 siblings affected by Brody disease. The patients complained of exercise-induced delay of muscle relaxation and stiffness since childhood and had gene analysis of ATP2A1. Morphologic and biochemical studies were performed on a muscle biopsy from 1 patient. The biopsy showed fiber size variation and increased numbers of fibers with internal nuclei. Ultrastructural examination revealed dilatation of lateral cisternae and proliferation of tubular elements of the sarcoplasmic reticulum. By immunohistochemistry, SERCA1 was expressed in a normal pattern, but sarcoplasmic reticulum Ca-ATPase activity was significantly reduced. Immunoblotting after high-resolution 2-dimensional gel electrophoresis showed a significant difference in the amount of SERCA1 protein between the patient and controls. Both patients were found to have 2 previously unreported in-frame deletions in ATP2A1. Because SERCA1 protein has specific biochemical characteristics in our patient, these results underline the importance of a pathologic and biochemical analyses for the diagnosis. In addition, we describe 2 novel mutations in the ATP2A1 gene.


title: Rotor Yoke Thickness of Coreless High-Speed Axial-Flux Permanent Magnet Generator

abstract: Recently, high-speed axial flux generator (HSAFG) has come to attention because of its some advantages such as high power density and efficiency. This paper presents a modeling and designing procedure of HSAFG with some details. The back iron of the permanent magnet is the main design subject investigated in this paper. Optimized thickness of the back-iron used in the rotor of two ends is determined for a HSAFG by carefully considerations of the terminal voltage, output power, and efficiency. These all vary by thickness of back-iron variation significantly. It is illustrated that a very small thickness of the rotor yoke reduces the terminal voltage and efficiency and a very large value of it increases the rotor inertia, mechanical problems and cost of the machine with no significant improvement of the machine performance characteristics. It is concluded that back-iron thickness of the rotor plays a singular and important role in the HSAFG and requires more carefully attention when designing the machine.


title: Geometric Particle Swarm Optimisation

abstract: Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms. This connection enables us to generalize PSO to virtually any solution representation in a natural and straightforward way. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces.


title: An Efficient Frame-Content Based Intra Frame Rate Control for High Efficiency Video Coding

abstract: Rate control plays an important role in the rapid development of high-fidelity video services. As the High Efficiency Video Coding (HEVC) standard has been finalized, many rate control algorithms are being developed to promote its commercial use. The HEVC encoder adopts a new R-lambda based rate control model to reduce the bit estimation error. However, the R-lambda model fails to consider the frame-content complexity that ultimately degrades the performance of the bit rate control. In this letter, a gradient based R-lambda (GRL) model is proposed for the intra frame rate control, where the gradient can effectively measure the frame-content complexity and enhance the performance of the traditional R-lambda method. In addition, a new coding tree unit (CTU) level bit allocation method is developed. The simulation results show that the proposed GRL method can reduce the bit estimation error and improve the video quality in HEVC all intra frame coding.


title: Lip Reading in the Wild

abstract: Our aim is to recognise the words being spoken by a talking face, given only the video but not the audio. Existing works in this area have focussed on trying to recognise a small number of utterances in controlled environments (e.g. digits and alphabets), partially due to the shortage of suitable datasets.


title: An analysis of the health status of the United Arab Emirates: the ‘Big 4’ public health issues

abstract: Background: The United Arab Emirates (UAE) is a rapidly developing country composed of a multinational population with varying educational backgrounds, religious beliefs, and cultural practices, which pose a challenge for population-based public health strategies. A number of public health issues significantly contribute to morbidity and mortality in the UAE. This article summarises the findings of a panel of medical and public health specialists from UAE University and various government health agencies commissioned to report on the health status of the UAE population. Methods: A systematic literature search was conducted to retrieve peer-reviewed articles on health in the UAE, and unpublished data were provided by government health authorities and local hospitals. Results: The panel reviewed and evaluated all available evidence to list and rank (1=highest priority) the top four main public health issues: 1) Cardiovascular disease accounted for more than 25% of deaths in 2010; 2) Injury caused 17% of mortality for all age groups in 2010; 3) Cancer accounted for 10% of all deaths in 2010, and the incidence of all cancers is projected to double by 2020; and 4) Respiratory disorders were the second most common non-fatal condition in 2010. Conclusion: The major public health challenges posed by certain personal (e.g. ethnicity, family history), lifestyle, occupational, and environmental factors associated with the development of chronic disease are not isolated to the UAE; rather, they form part of a global health problem, which requires international collaboration and action. Future research should focus on population-based public health interventions that target the factors associated with the development of various chronic diseases. Keywords: globalisation of public health; non-communicable disease; United Arab Emirates (Published: 5 February 2013) Citation: Glob Health Action 2013, 6 : 20100 - http://dx.doi.org/10.3402/gha.v6i0.20100 To access the supplementary material to this article please see Supplementary files in the column to the right (under ReadingTools).


title: Chambolle's Projection Algorithm for Total Variation Denoising

abstract: Denoising is the problem of removing the inherent noise from an image. The standard noise model is additive white Gaussian noise, where the observed image f is related to the underlying true image u by the degradation model f = u + �, andis supposed to be at each pixel inde- pendently and identically distributed as a zero-mean Gaussian random variable. Since this is an ill-posed problem, Rudin, Osher and Fatemi introduced the total variation as a regularizing term. It has proved to be quite efficient for regularizing images without smoothing the bound- aries of the objects. This paper focuses on the simple description of the theory and on the implementation of Cham- bolle's projection algorithm for minimizing the total variation of a grayscale image. Further- more, we adapt the algorithm to the vectorial total variation for color images. The implementa- tion is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.


title: Catastrophic failure and fault-tolerant design of IGBT power electronic converters - an overview

abstract: Reliability is one of the key issues for the application of Insulated Gate Bipolar Transistors (IGBTs) in power electronic converters. Many efforts have been devoted to the reduction of IGBT wear out failure induced by accumulated degradation and catastrophic failure triggered by single-event overstress. The wear out failure under field operation could be mitigated by scheduled maintenances based on lifetime prediction and condition monitoring. However, the catastrophic failure is difficult to be predicted and thus may lead to serious consequence of power electronic converters. To obtain a better understanding of catastrophic failure of IGBTs, the state-of-the-art research on their failure behaviors and failure mechanisms is presented in this paper. Moreover, various fault-tolerant design methods, to prevent converter level malfunctions in the event of IGBT failure, are also reviewed.


title: Risk Assessment Model for Assessing NBFCs' (Asset Financing) Customers

abstract: Non-banking financial companies (NBFCs) form an integral part of the Indian financial system. The history of the NBFC Industry in India is a story of under-regulation followed by over-regulation. Policy makers have swung from one extreme position to another in their attempt to set controls and then restrain them so that they do not curb the growth of the industry. This report covers the industry.


title: Enhancing information retrieval and resource discovery from data using the Semantic Web

abstract: Data are everywhere. Often the sheer quantities of data that is stored or archived in repositories or digital libraries make it difficult to navigate data; information in the data is obscured, particularly where we have Big Data. Traditionally, we record metadata on our data items to assist data classification and some information retrieval. The Semantic Web enables us to further unlock and enrich our data, by exploring how different data are related or connected. Using ontologies and Linked Data we can declare, navigate and discover semantic relationships. Relationships exist both locally, within the data, and globally, such that we can enhance our data with information retrieved from a wider context. To illustrate how the application of Semantic Web technologies aids data discovery and information retrieval, I discuss two case studies: (1) Sharing Ancient Wisdoms (SAWS), a Dynamic Library of information on selected ancient wisdom literature; and (2) the DEFRA DTC archive, a repository of data about freshwater quality in the UK.


title: IXPs: mapped?

abstract: Internet exchange points (IXPs) are an important ingredient of the Internet AS-level ecosystem - a logical fabric of the Internet made up of about 30,000 ASes and their mutual business relationships whose primary purpose is to control and manage the flow of traffic. Despite the IXPs' critical role in this fabric, little is known about them in terms of their peering matrices (i.e., who peers with whom at which IXP) and corresponding traffic matrices (i.e., how much traffic do the different ASes that peer at an IXP exchange with one another). In this paper, we report on an Internet-wide traceroute study that was specifically designed to shed light on the unknown IXP-specific peering matrices and involves targeted traceroutes from publicly available and geographically dispersed vantage points. Based on our method, we were able to discover and validate the existence of about 44K IXP-specific peering links - nearly 18K more links than were previously known. In the process, we also classified all known IXPs depending on the type of information required to detect them. Moreover, in view of the currently used inferred AS-level maps of the Internet that are known to miss a significant portion of the actual AS relationships of the peer-to-peer type, our study provides a new method for augmenting these maps with IXP-related peering links in a systematic and informed manner.


title: Spatial patterns of plastic debris along Estuarine shorelines.

abstract: The human population generates vast quantities of waste material. Macro (>1 mm) and microscopic (<1 mm) fragments of plastic debris represent a substantial contamination problem. Here, we test hypotheses about the influence of wind and depositional regime on spatial patterns of micro- and macro-plastic debris within the Tamar Estuary, UK. Debris was identified to the type of polymer using Fourier-transform infrared spectroscopy (FT-IR) and categorized according to density. In terms of abundance, microplastic accounted for 65% of debris recorded and mainly comprised polyvinylchloride, polyester, and polyamide. Generally, there were greater quantities of plastic at downwind sites. For macroplastic, there were clear patterns of distribution for less dense items, while for microplastic debris, clear patterns were for denser material. Small particles of sediment and plastic are both likely to settle slowly from the water-column and are likely to be transported by the flow of water and be deposited in areas where the movements of water are slower. There was, however, no relationship between the abundance of microplastic and the proportion of clay in sediments from the strandline. These results illustrate how FT-IR spectroscopy can be used to identify the different types of plastic and in this case was used to indicate spatial patterns, demonstrating habitats that are downwind acting as potential sinks for the accumulation of debris.


title: Gamifying informal learning activities using interactive displays: an empirical investigation of students’ learning and engagement

abstract: Interactive displays (IDs) are increasingly employed in informal learning environments, where they are seen as a medium for enhancing students’ creativity, and engagement. Due to the larger space they provide and thus the larger interaction area, they allow for group-work, working in parallel, co-creating artifacts or co-experiencing the interaction in a playful manner. In particular, gaming activities in IDs enhance students’ mental exercise and fantasy and promote students engagement through rewards and collaboration. However, despite the increased prevalence of interactive displays and gamification, we know very little about how designers and instructors can gamify their learning activities by taking advantage of the IDs. In this paper, a framework for developing gamified activities for interactive displays is presented. For the empirical evaluation, pre-post attitudinal surveys and cognitive tests along with photos and observations were recorded and used. The contribution of this article is twofold: 1) an adaptable framework for developing gamified activities on interactive displays (GAID), and 2) the results of a field study where students have been engaged with an interactive display application during an extracurricular activity. By incorporating GAID to a traditional informal learning activity, it is found that students’ knowledge acquisition, satisfaction, enjoyment and intention to participate on similar events in the future are significantly improved.


title: BlOMECHANlCAL ANALYSIS OF VERTICAL JUMP WITH DIFFERENT FOREFOOT MORPHOLOGY

abstract: This study examined biomechanical differences between habitually barefoot male and habitually shod male during vertical jump. Foot morphology was measured with Easy-Foot-Scan. Foot kinetics and ankle kinematics were obtained from EMED pressure platform and Vicon motion analysis system as completing vertical jumps under barefoot condition. The results showed that habitually barefoot subjects had a significantly larger minimal distance between hallux and other toes. habitually unshod subjects showed larger loading under hallux and medial forefoot, while habitually shod subjects presented larger loading under medial and central forefoot. in addition, habitually barefoot male had smaller ankle plantarflexion, eversion and external rotation during vertical jump. Differences of kinematics and kinetics during vertical jump might attribute to the morphological differences in the toes region, which possibly explain the foot injury risks between habitually barefoot and habitually shod individuals.


Functional Near Infrared Spectroscope for Cognition Brain Tasks by Wavelets Analysis and Neural Networks


'Lots done, more to do': the current state of agile systems development research


title: Classifying objects based on their visual similarity to target categories

abstract: Visual similarity relationships underlie a host of human behaviors, and determining these relationships is crucial both to the understanding of these behaviors and the construction of automated systems designed for human use. We conducted a large-scale web-based experiment in which subjects rank ordered random objects according to their visual similarity to object classes. We then constructed a computational model, using Adaboost with color, texture, and shape features, to quantify the visual similarity between these objects and the target classes, and to perform the same similarity ranking task. The model and subjects showed good agreement in the objects judged to be most and least similar to the target categories. Our data also suggest that color, texture, and shape features are all useful for classification, and that the specific weighting of these features depends on the target object class. Moreover, we show that these target-like properties constitute a learnable class, as it was possible to train a classifier on target-like objects (i.e., without positive samples) to recognize actual targets.


title: Suitable Single-Phase to Three-Phase AC–DC–AC Power Conversion System

abstract: This paper presents a single-phase to three-phase power conversion system with parallel rectifier and series inverter to cope with single-phase to three-phase asymmetry. Such converter guarantees both reduction in the input current processed by rectifier circuit and reduction of the output voltage processed by inverter circuit. It is worth to mention that, in spite of proposing a topology with features no longer observed in the technical literature, this paper presents a comprehensive model of the proposed converter, modulation strategy and a general comparison with the conventional configuration. Simulated and experimental results are also presented.


title: Support vector regression with modified firefly algorithm for stock price forecasting

abstract: The support vector regression (SVR) has been employed to deal with stock price forecasting problems. However, the selection of appropriate kernel parameters is crucial to obtaining satisfactory forecasting performance. This paper proposes a novel approach for forecasting stock prices by combining the SVR with the firefly algorithm (FA). The proposed forecasting model has two stages. In the first stage, to enhance the global convergence speed, a modified version of the FA, which is termed the MFA, is developed in which the dynamic adjustment strategy and the opposition-based chaotic strategy are introduced. In the second stage, a hybrid SVR model is proposed and combined with the MFA for stock price forecasting, in which the MFA is used to optimize the SVR parameters. Finally, comparative experiments are conducted to show the applicability and superiority of the proposed methods. Experimental results show the following: (1) Compared with other algorithms, the proposed MFA algorithm possesses superior performance, and (2) The proposed MFA-SVR prediction procedure can be considered as a feasible and effective tool for forecasting stock prices.


title: The Cooperative Brain

abstract: Cooperation is essential for the functioning of human societies. To better understand how cooperation both succeeds and fails, recent research in cognitive neuroscience has begun to explore novel paradigms to examine how cooperative mechanisms may be encoded in the brain. By combining functional neuroimaging techniques with simple but realistic tasks adapted from experimental economics, this approach allows for the discrimination and modeling of processes that are important in cooperative behavior. Here, we review evidence demonstrating that many of the processes underlying cooperation overlap with rather fundamental brain mechanisms, such as, for example, those involved in reward, punishment and learning. In addition, we review how social expectations induced by an interactive context and the experience of social emotions may influence cooperation and its associated underlying neural circuitry, and we describe factors that appear important for generating cooperation, such as the provision of incentives. These findings illustrate how cognitive neuroscience can contribute to the development of more accurate, brain-based, models of cooperative decision making.


title: Space/time trade-offs in hash coding with allowable errors

abstract: In this paper trade-offs among certain computational factors in hash coding are analyzed. The paradigm problem considered is that of testing a series of messages one-by-one for membership in a given set of messages. Two new hash-coding methods are examined and compared with a particular conventional hash-coding method. The computational factors considered are the size of the hash area (space), the time required to identify a message as a nonmember of the given set (reject time), and an allowable error frequency. The new methods are intended to reduce the amount of space required to contain the hash-coded information from that associated with conventional methods. The reduction in space is accomplished by exploiting the possibility that a small fraction of errors of commission may be tolerable in some applications, in particular, applications in which a large amount of data is involved and a core resident hash area is consequently not feasible using conventional methods. In such applications, it is envisaged that overall performance could be improved by using a smaller core resident hash area in conjunction with the new methods and, when necessary, by using some secondary and perhaps time-consuming test to “catch” the small fraction of errors associated with the new methods. An example is discussed which illustrates possible areas of application for the new methods. Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.


title: Miniaturized water quality monitoring pH and conductivity sensors

abstract: Abstract The development of cost-effective, durable and sensitive on-line water quality monitoring sensors that can be installed across water distribution networks has attracted attention to increase the frequency of monitoring and hence reduce the risk of accidental or deliberate contaminations or improve routine control of water quality. This paper presents microsensors fabricated and implemented in a serpentine channel interface for the measurement of pH and conductivity, two important water quality parameters. The performance of these sensors was tested in both still (static) and flowing (dynamic) water. The serpentine channel interface, numerically optimized, provides a constant flow and a constant outlet pressure condition for dynamic experiments. Tests conducted for evaluating the effect of the exposure time on the sensor performance show no change in the sensor response even after one month. Finally, the pH and conductivity sensors were compared against the common commercial sensors used for evaluating water quality. The results show that the pH and conductivity sensors are as precise as the commercial sensors in both static and dynamic conditions. However, the cost of the sensors presented here is significantly lower than that of the commercial sensors.


title: The implications of embodiment for behavior and cognition: animal and robotic case studies

abstract: In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.


title: Logo Recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers

abstract: The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology.


title: Target words selection for a Persian brain-computer-interface-based speller using language model

abstract: Utilizing a language model in a brain-computer-interface-based (BCI-based) speller has been proven helpful in improving the performance of the system. Since it is important to evaluate the effect of the language model on the system, it is necessary to choose the words in a way that they can represent different levels of difficulty based on the language model. In this paper, we will give a brief introduction to the Persian BCI speller that we are going to develop. Then, we will explain how we select words with three levels of difficulty based on both a character-level and a word-level language model. We consider three levels of difficulty, named Easy, Medium and Hard, for which we have selected 3, 6 and 3 words, respectively. We then make a sentence for each of the words, so that the words are presented within a context. By doing so, the language model can predict the possibility of occurrence for each word. The novelty of this paper is in the fact that so far, there have not been implemented any Persian BCI spellers utilizing a language model as an aid in character recognition using the brain signals.


title: A 32 mW 1.25 GS/s 6b 2b/Step SAR ADC in 0.13$\ \mu$m CMOS

abstract: A 1.25 GS/s 6b ADC is implemented in a 0.13 mum digital CMOS process by time-interleaving two SAR ADCs with 2.5 GHz internal clock frequency that converts 6 bits in 3 cycles. 5.5b ENOB at 1.25 GS/s and 5.8b ENOB at 1 GS/s are achieved without any off-line calibration, error correction or post processing. The entire ADC consumes 32 mW at 1.25 GS/s including T/H and reference buffers, and occupies 0.09 mm2 .


title: Text clustering using enhanced PLSA with word correlation

abstract: As a popular topic model, Probabilistic Latent Semantic Analysis (PLSA) has been widely applied in text clustering due to its reliability and practicability. While independence assumption contributes to its practicability, it loses the rich local information between words, which in some cases will result in incoherent topics. In this paper, we propose an enhanced PLSA model embedded with word correlation (EWPLSA) for text clustering. The new model can utilize a wide range of pairwise semantic constraints between words to get more meaningful topics. We study how to consider context information of all the words, trained with the global semantic distribution in a global manner. The relationship between similar words will be amplified in a global perspective in the next iteration. However, it will also be restricted by other words because all word contexts are considered. In this way, instead of generating independently, the topic assignment of each word is affected by other correlative words. In addition, we initialize several important parameters before the first iteration of the EM algorithm according to the local correlation information between words, leading to the reduction of iteration number. Experiments on two datasets have demonstrated the effectiveness of our method.


title: Robust optimization for decision-making under endogenous uncertainty

abstract: Abstract This paper contemplates the use of robust optimization as a framework for addressing problems that involve endogenous uncertainty, i.e., uncertainty that is affected by the decision maker’s strategy. To that end, we extend generic polyhedral uncertainty sets typically considered in robust optimization into sets that depend on the actual decisions. We present the derivation of robust counterpart models in this setting, and we discuss relevant algorithmic considerations for solving these models to guaranteed optimality. Besides capturing the functional changes in parameter correlations that may be induced by given decisions, we show how the use of our decision-dependent uncertainty sets allows us to also eradicate conservatism effects from parameters that become irrelevant in view of the optimal decisions. We quantify these benefits via a number of case studies, demonstrating our proposed framework’s versatility to be utilized in the context of various applications.


title: A simulation model for outpatient appointment scheduling with patient unpunctuality

abstract: This paper is to investigate the appointment system of an outpatient department in a large hospital in China in order to reduce its long patient waiting time. Through a sensitivity analysis using actual data, the study shows that patient unpunctuality and doctor consultation time have significant impacts on the patient waiting time. To facilitate the existing practice without making strong assumptions, a simulation model is developed to identify proper appointment rules for appointment scheduling with severe patient unpunctuality. In specific, four outpatient environments, characterised by a preset appointment time interval with variable doctor consultation times, were simulated and 100 scenarios were run for each environment to identify a proper appointment rule that has the least patient average waiting time (PAWT). Results indicate that PAWT is consistently shorter for environments when patients are punctual and, moreover, is barely affected by the length of appointment time interval. However, for environments with patient unpunctuality, a proper appointment rule could potentially reduce PAWT up to 34% (i.e., reducing the appointment time interval from 1.0 hour to 0.5 hour). The developed simulation model may serve as an efficient tool to enhance outpatient appointment scheduling with patient unpunctuality. Possible extensions to the presented model are also discussed.


title: GPS enabled employee registration and attendance tracking system

abstract: GPS enables a management to maintain Staff attendance and employee registration through mobile application, this application facilitates the staffs to login through mobile phone and track other staff members' whereabouts through mobile phone. In the present scenario manual registration through biometric systems is commonly in practice. The staff will be kept on informed about their attendance constantly by the admin when they login and log out so that the staff can keep a track on their attendance by using this application. The admin can track the location of any staff member using latitude, longitude and IMSI number.


title: Understanding predictability and exploration in human mobility

abstract: Predictive models for human mobility have important applications in many fields such as traffic control, ubiquitous computing and contextual advertisement. The predictive performance of models in literature varies quite broadly, from as high as 93% to as low as under 40%. In this work we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users for periods between 3 months and one year. We show that it is easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover we demonstrate how the temporal and spatial resolution of the data can have strong influence on the accuracy of prediction. Finally we uncover that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.


title: Early childhood sexual abuse increases suicidal intent

abstract: Childhood sexual abuse has been consistently associated with suicidal behavior. We studied suicide attempt features in depressed individuals sexually abused as children. On average, sexual abuse started before age 9. It frequently coexisted with physical abuse. Suicide attempters more often had personality disorders and had endured abuse for longer, but did not differ in terms of other clinical characteristics from non-attempters. Earlier onset of sexual abuse and its duration were associated with more suicide attempts. However, when personality disorders were included in the regression model, only these disorders predicted number of attempts. The severity of sexual abuse and the coexistence of physical abuse were correlated with age at first suicide attempt. However, only severity of sexual abuse was marginally associated with age at first suicide attempt in the regression model. Finally, the earlier the age of onset of sexual abuse, the higher the intent, even after controlling for age, sex and personality disorders. This suggests that the characteristics of childhood sexual abuse, especially age of onset, should be considered when studying the risk for suicidal behavior in abused populations.


title: Array design techniques of microstrip comb-line antennas in millimeter-wave band

abstract: Traveling-wave array design techniques are applied to microstrip comb-line antennas in the millimeter-wave band. The simple design procedure is demonstrated. To neglect the effect of reflection waves in the design, a radiating element with a reflection-canceling slit and a stub-integrated radiating element are proposed. Matching performance is also improved.


title: A Highly Stable and Reliable 13.56-MHz RFID Tag IC for Contactless Payment

abstract: Radio frequency identification (RFID) has been widely used for contactless payment. However, modifying sensitive data such as balance stored in an RFID tag over an air interface requires high stability and reliability, particularly when the tag is being moved away from the RFID reader (such phenomenon is called card tear). Previous countermeasures of card tear operation are mainly based on contact smart card; however, when the tag is contactless, the variation of magnetic field should be also taken into account. To solve card tear problem, this paper presents a fully integrated 13.56-MHz RFID tag IC with reliable power supply and antitearing mechanism. Proposed are the following solutions: 1) to provide a 4-kV multifunction electrostatic discharge protection circuit with rectifier and limiter; 2) to develop a low dropout circuit and a low voltage detection circuit; 3) to design a well-run antitearing mechanism, which can protect the tag from being incorrectly written; and 4) to design a digital circuit with several functions, including 3DES, micro control unit, and electrically erasable programmable read-only memory. The proposed RFID tag IC was fabricated using a 0.18-μm process with an area of 3 mm2. The measured results indicate that the tag operates in a stable and reliable mode without any problem caused by card tear.


title: Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT

abstract: Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspondence problems that rely on descriptor matching. In this paper we compare features from various layers of convolutional neural nets to standard SIFT descriptors. We consider a network that was trained on ImageNet and another one that was trained without supervision. Surprisingly, convolutional neural networks clearly outperform SIFT on descriptor matching. This paper has been merged with arXiv:1406.6909


Exploiting Language Models to Classify Events from Twitter


title: A review of trisomy X (47,XXX)

abstract: Trisomy X is a sex chromosome anomaly with a variable phenotype caused by the presence of an extra X chromosome in females (47,XXX instead of 46,XX). It is the most common female chromosomal abnormality, occurring in approximately 1 in 1,000 female births. As some individuals are only mildly affected or asymptomatic, it is estimated that only 10% of individuals with trisomy X are actually diagnosed. The most common physical features include tall stature, epicanthal folds, hypotonia and clinodactyly. Seizures, renal and genitourinary abnormalities, and premature ovarian failure (POF) can also be associated findings. Children with trisomy X have higher rates of motor and speech delays, with an increased risk of cognitive deficits and learning disabilities in the school-age years. Psychological features including attention deficits, mood disorders (anxiety and depression), and other psychological disorders are also more common than in the general population. Trisomy X most commonly occurs as a result of nondisjunction during meiosis, although postzygotic nondisjunction occurs in approximately 20% of cases. The risk of trisomy X increases with advanced maternal age. The phenotype in trisomy X is hypothesized to result from overexpression of genes that escape X-inactivation, but genotype-phenotype relationships remain to be defined. Diagnosis during the prenatal period by amniocentesis or chorionic villi sampling is common. Indications for postnatal diagnoses most commonly include developmental delays or hypotonia, learning disabilities, emotional or behavioral difficulties, or POF. Differential diagnosis prior to definitive karyotype results includes fragile X, tetrasomy X, pentasomy X, and Turner syndrome mosaicism. Genetic counseling is recommended. Patients diagnosed in the prenatal period should be followed closely for developmental delays so that early intervention therapies can be implemented as needed. School-age children and adolescents benefit from a psychological evaluation with an emphasis on identifying and developing an intervention plan for problems in cognitive/academic skills, language, and/or social-emotional development. Adolescents and adult women presenting with late menarche, menstrual irregularities, or fertility problems should be evaluated for POF. Patients should be referred to support organizations to receive individual and family support. The prognosis is variable, depending on the severity of the manifestations and on the quality and timing of treatment.


title: Independent and Personal SMS Spam Filtering

abstract: The amount of Short Message Service (SMS) spam is increasing. SMS spam should be put into the spam folder, not the inbox. Some solutions have been proposed that for the most part use Text Classification techniques. However, they need another computer system to create the filtering system using a large amount of SMS data in advance and install the filtering system into the mobile phone to filter incoming SMS. This kind of solution reduces independence because the user has to store received SMS into computer to train or update the filtering system or data and user can get the filtering system. Storing SMS, which may consist of private data especially SMS ham, leads to privacy problem. Obviously, it also increases hardware cost and increases communication cost between mobile phone and computer system. Thus, we propose an independent filtering system that does not need computer system support. The training, filtering and updating processes were done on mobile phone. Our proposed approach filters SMS spam on an independent mobile phone while obtaining reasonable accuracy, minimum storage consumption and acceptable processing time.


title: LeadLine: Interactive visual analysis of text data through event identification and exploration

abstract: Text data such as online news and microblogs bear valuable insights regarding important events and responses to such events. Events are inherently temporal, evolving over time. Existing visual text analysis systems have provided temporal views of changes based on topical themes extracted from text data. But few have associated topical themes with events that cause the changes. In this paper, we propose an interactive visual analytics system, LeadLine, to automatically identify meaningful events in news and social media data and support exploration of the events. To characterize events, LeadLine integrates topic modeling, event detection, and named entity recognition techniques to automatically extract information regarding the investigative 4 Ws: who, what, when, and where for each event. To further support analysis of the text corpora through events, LeadLine allows users to interactively examine meaningful events using the 4 Ws to develop an understanding of how and why. Through representing large-scale text corpora in the form of meaningful events, LeadLine provides a concise summary of the corpora. LeadLine also supports the construction of simple narratives through the exploration of events. To demonstrate the efficacy of LeadLine in identifying events and supporting exploration, two case studies were conducted using news and social media data.


title: Balance control of two-wheeled self-balancing robot based on Linear Quadratic Regulator and Neural Network

abstract: Two-wheeled self-balancing robot is a kind of unstable, nonlinear, strong coupling system. On the basis of analyzing the method of Linear Quadratic Regulator(LQR) and PID-BP-RBF, this paper proposed a new balance control method based on LQR and Neural Network(NN)(LQR-NN).In this method, the balance controller is designed as a LQR controller contained a neural network inside. The LQR's optimal parameters are used to initialize the neural network, which would make the network have the optimum initial values and converge fast. The new method can overcome the inaccuracy modeling because of system linearization based on LQR, and also has the self-turning mechanism without great computation load which the NN method brings. Experiments show that the balance controller based on LQR-NN has better balancing control to the robot and also improved the system's robustness significantly.


title: Threshold voltage mismatch and intra-die leakage current in digital CMOS circuits

abstract: Due to device and voltage scaling scenarios for present and future deep-submicron CMOS technologies, it is inevitable that the off-state current (I/sub off/) of MOSFET transistors increases as the technology minimum dimensions scale down. Experimental evidence shows that the leakage current distribution of modern deep-submicron designs not only has a higher mean value but it also presents a larger variability as well. In this paper, we investigate the impact of threshold voltage mismatch as one plausible source for this increased variability. In digital circuit design, it is commonly assumed that the threshold voltage difference (mismatch) of static CMOS cells is negligible. However, threshold voltage mismatch (/spl Delta/V/sub to/) has a two-sided effect on the off-state current. Namely, the total cell's current can increase or decrease depending upon the direction of the V/sub t/ mismatch shift. This effect can be so severe that I/sub off/ can increase by more than one order of magnitude with respect to its nominal value due only to V/sub to/ mismatch. We further show through experimental results that the V/sub to/ mismatch of paired transistors working in the subthreshold regime can be worse by a factor of two as compared to transistors working in the saturation or linear regions. A factor of two larger spread is obviously quite devastating in terms of area, speed, and power consumption, should it be desired to attain the same I/sub off/ level as for a V/sub to/ mismatch characterized out of the subthreshold regime.


title: EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild

abstract: Research in face perception and emotion theory requires very large annotated databases of images of facial expressions of emotion. Annotations should include Action Units (AUs) and their intensities as well as emotion category. This goal cannot be readily achieved manually. Herein, we present a novel computer vision algorithm to annotate a large database of one million images of facial expressions of emotion in the wild (i.e., face images downloaded from the Internet). First, we show that this newly proposed algorithm can recognize AUs and their intensities reliably across databases. To our knowledge, this is the first published algorithm to achieve highly-accurate results in the recognition of AUs and their intensities across multiple databases. Our algorithm also runs in real-time (>30 images/second), allowing it to work with large numbers of images and video sequences. Second, we use WordNet to download 1,000,000 images of facial expressions with associated emotion keywords from the Internet. These images are then automatically annotated with AUs, AU intensities and emotion categories by our algorithm. The result is a highly useful database that can be readily queried using semantic descriptions for applications in computer vision, affective computing, social and cognitive psychology and neuroscience, e.g., "show me all the images with happy faces" or "all images with AU 1 at intensity c."


title: Motivation for Achievement in Mathematics: Findings, Generalizations, and Criticisms of the Research

abstract: In this review we examine recent research in the area of motivation in mathematics education and discuss findings from research perspectives in this domain. We note consistencies across research perspectives that suggest a set of generalizable conclusions about the contextual factors, cognitive processes, and benefits of interventions that affect students’ and teachers’ motivational attitudes. Criticisms are leveled concerning the lack of theoretical guidance driving the conduct and interpretation of the majority of studies in the field. Few researchers have attempted to extend current theories of motivation in ways that are consistent with the current research on learning and classroom discourse. In particular, researchers interested in studying motivation in the content domain of school mathematics need to examine the relationship that exists between mathematics as a socially constructed field and students’ desire to achieve.


title: Knowledge of the Firm and the Evolutionary Theory of the Multinational Corporation

abstract: Firms are social communities that specialize in the creation and internal transfer of knowledge. The multinational corporation arises not out of the failure of markets for the buying and selling of knowledge, but out of its superior efficiency as an organizational vehicle by which to transfer this knowledge across borders. We test the claim that firms specialize in the internal transfer of tacit knowledge by empirically examining the decision to transfer the capability to manufacture new products to wholly owned subsidiaries or to other parties. The empirical results show that the less codifiable and the harder to teach is the technology, the more likely the transfer will be to wholly owned operations. This result implies that the choice of transfer mode is determined by the efficiency of the multinational corporation in transferring knowledge relative to other firms, not relative to an abstract market transaction. The notion of the firm as specializing in the transfer and recombination of knowledge is the foundation to an evolutionary theory of the multinational corporation


title: Rowhammer.js: A Remote Software-Induced Fault Attack in JavaScript

abstract: A fundamental assumption in software security is that a memory location can only be modified by processes that may write to this memory location. However, a recent study has shown that parasitic effects in DRAM can change the content of a memory cell without accessing it, but by accessing other memory locations in a high frequency. This so-called Rowhammer bug occurs in most of today's memory modules and has fatal consequences for the security of all affected systems, e.g., privilege escalation attacks. ::: ::: All studies and attacks related to Rowhammer so far rely on the availability of a cache flush instruction in order to cause accesses to DRAM modules at a sufficiently high frequency. We overcome this limitation by defeating complex cache replacement policies. We show that caches can be forced into fast cache eviction to trigger the Rowhammer bug with only regular memory accesses. This allows to trigger the Rowhammer bug in highly restricted and even scripting environments. ::: ::: We demonstrate a fully automated attack that requires nothing but a website with JavaScript to trigger faults on remote hardware. Thereby we can gain unrestricted access to systems of website visitors. We show that the attack works on off-the-shelf systems. Existing countermeasures fail to protect against this new Rowhammer attack.