title: Directed symbolic execution

abstract: In this paper, we study the problem of automatically finding program executions that reach a particular target line. This problem arises in many debugging scenarios; for example, a developer may want to confirm that a bug reported by a static analysis tool on a particular line is a true positive. We propose two new directed symbolic execution strategies that aim to solve this problem: shortest-distance symbolic execution (SDSE) uses a distance metric in an interprocedural control flow graph to guide symbolic execution toward a particular target; and call-chain-backward symbolic execution (CCBSE) iteratively runs forward symbolic execution, starting in the function containing the target line, and then jumping backward up the call chain until it finds a feasible path from the start of the program. We also propose a hybrid strategy, Mix-CCBSE, which alternates CCBSE with another (forward) search strategy. We compare these three with several existing strategies from the literature on a suite of six GNU Coreutils programs. We find that SDSE performs extremely well in many cases but may fail badly. CCBSE also performs quite well, but imposes additional overhead that sometimes makes it slower than SDSE. Considering all our benchmarks together, Mix-CCBSE performed best on average, combining to good effect the features of its constituent components.


title: Leveraging Term Banks for Answering Complex Questions: A Case for Sparse Vectors

abstract: While open-domain question answering (QA) systems have proven effective for answering simple questions, they struggle with more complex questions. Our goal is to answer more complex questions reliably, without incurring a significant cost in knowledge resource construction to support the QA. One readily available knowledge resource is a term bank, enumerating the key concepts in a domain. We have developed an unsupervised learning approach that leverages a term bank to guide a QA system, by representing the terminological knowledge with thousands of specialized vector spaces. In experiments with complex science questions, we show that this approach significantly outperforms several state-of-the-art QA systems, demonstrating that significant leverage can be gained from continuous vector representations of domain terminology.


title: Measuring and Reducing College Students' Procrastination

abstract: We examined college students' procrastination when studying for weekly in-class quizzes. Two schedules of online practice quiz delivery were compared using a multiple baseline design. When online study material was made available noncontingently, students usually procrastinated. When access to additional study material was contingent on completing previous study material, studying was more evenly distributed. Overall, the mean gain in percentage correct scores on weekly in-class quizzes relative to pretests was greater during contingent access than during noncontingent access conditions.


title: Film-Type Sensor Materials PVDF and EMFi in Measurement of Cardiorespiratory Signals— A Review

abstract: In this paper, some recent results obtained with film-type sensor materials polyvinylidenefluoride (PVDF) and electromechanical film (EMFi) are presented. The materials generate a voltage when they are mechanically deformed, however, because of their capacitive nature only the change of an external force can be measured. Hence, the sensors made of these materials are useful especially in the measurements of physiological pulsatile signals (e.g., heart rate and respiration). The entire measurement process with the PVDF and EMFi sensors is considered here; from the design and construction of the sensors and measurement electronics to the analysis of the measured data. The paper also presents some examples of the physiological signals (heart rate, respiration, and heart sounds) measured with the PVDF and EMFi sensors. Flexible and thin sensor materials are useful especially in physiological applications where the sensor is integrated into clothing or into daily life objects (e.g., a chair or a bed). The sensor attachments to human can be minimized and the measurement systems can be designed to be unobtrusive and comfortable for the user.


title: Service Mediation in multihop Bluetooth Low Energy networks based on NDN approach

abstract: The 4.1 version of Bluetooth Low Energy (BLE) specification introduced the support of scatternet topology. As a result, the range limit of the technology is no more present, which enables the creation of extensive, and complex network applications. However the modifications were done only in the lower layers, leaving the Attribute Protocol (ATT), and the Generic Attribute Profile (GATT) layers untouched, which is a significant drawback in the development of multihop network applications. In this paper the concept of Service Mediation is introduced, that is based on Named Data Networking (NDN) approach, and not only holds the solution of this problem within, but also shows great potential to create intelligent, automatic and teachable networks and applications. The experimental development and the preliminary validation tests showed satisfactory results also, which enables the approach to be used in real-world environments.


title: A novel low-power full-adder cell with new technique in designing logical gates based on static CMOS inverter

abstract: A new low-power full-adder based on CMOS inverter is presented. This full-adder is comprised of inverters. Universal gates such as NOR, NAND and MAJORITY-NOT gates are implemented with a set of inverters and non-conventional implementation of them. In the proposed design approach the time consuming XOR gates are eliminated. As full-adders are frequently employed in a tree-structured configuration for high-performance arithmetic circuits, a cascaded simulation structure is employed to evaluate the full-adders in a realistic application environment. The circuits being studied were optimized for energy efficiency using [email protected] and 90nm CMOS process technologies. The proposed full-adder shows full swing logic, balanced outputs and strong output drivability. It is also observed that the presented design can be utilized in many cases especially whenever the lowest possible power consumption is targeted. Circuits layout implementations and checking their functionality have been done using Cadence IC package and Synopsys HSpice, respectively.


title: Convergence of MANET and WSN in IoT Urban Scenarios

abstract: Ubiquitous smart environments, equipped with low-cost and easy-deployable wireless sensor networks (WSNs) and widespread mobile ad hoc networks (MANETs), are opening brand new opportunities in wide-scale urban monitoring. Indeed, MANET and WSN convergence paves the way for the development of brand new Internet of Things (IoT) communication platforms with a high potential for a wide range of applications in different domains. Urban data collection, i.e., the harvesting of monitoring data sensed by a large number of collaborating sensors, is a challenging task because of many open technical issues, from typical WSN limitations (bandwidth, energy, delivery time, etc.) to the lack of widespread WSN data collection standards, needed for practical deployment in existing and upcoming IoT scenarios. In particular, effective collection is crucial for classes of smart city services that require a timely delivery of urgent data such as environmental monitoring, homeland security, and city surveillance. After surveying the existing WSN interoperability efforts for urban sensing, this paper proposes an original solution to integrate and opportunistically exploit MANET overlays, impromptu, and collaboratively formed over WSNs, to boost urban data harvesting in IoT. Overlays are used to dynamically differentiate and fasten the delivery of urgent sensed data over low-latency MANET paths by integrating with latest emergent standards/specifications for WSN data collection. The reported experimental results show the feasibility and effectiveness (e.g., limited coordination overhead) of the proposed solution.


title: Larval and Post-Larval Stages of Pacific Oyster (Crassostrea gigas) Are Resistant to Elevated CO2

abstract: The average pH of surface oceans has decreased by 0.1 unit since industrialization and is expected to decrease by another 0.3–0.7 units before the year 2300 due to the absorption of anthropogenic CO2. This human-caused pH change is posing serious threats and challenges to the Pacific oyster (Crassostrea gigas), especially to their larval stages. Our knowledge of the effect of reduced pH on C. gigas larvae presently relies presumptively on four short-term (<4 days) survival and growth studies. Using multiple physiological measurements and life stages, the effects of long-term (40 days) exposure to pH 8.1, 7.7 and 7.4 on larval shell growth, metamorphosis, respiration and filtration rates at the time of metamorphosis, along with the juvenile shell growth and structure of the C. gigas, were examined in this study. The mean survival and growth rates were not affected by pH. The metabolic, feeding and metamorphosis rates of pediveliger larvae were similar, between pH 8.1 and 7.7. The pediveligers at pH 7.4 showed reduced weight-specific metabolic and filtration rates, yet were able to sustain a more rapid post-settlement growth rate. However, no evidence suggested that low pH treatments resulted in alterations to the shell ultrastructures (SEM images) or elemental compositions (i.e., Mg/Ca and Sr/Ca ratios). Thus, larval and post-larval forms of the C. gigas in the Yellow Sea are probably resistant to elevated CO2 and decreased near-future pH scenarios. The pre-adapted ability to resist a wide range of decreased pH may provide C. gigas with the necessary tolerance to withstand rapid pH changes over the coming century.


title: Controlling Robot Morphology from Incomplete Measurements

abstract: Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions. Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body-mounted robotic arm gathers the missing data.


title: A high performance 76.5 GHz FMCW RADAR for advanced driving assistance system

abstract: Frequency modulated Continuous Wave (FMCW) is continuous wave RADAR widely used for Intelligent Adaptive Cruise Control (ACC) and Collision Warning System (CWS). The paper presents a design and simulation of millimeter wave FMCW RADAR. Paper introduces a mathematical modeling of a FMCW RADAR including its target model. Design of a 76.5 GHz FMCW RADAR & its target model by using Advanced Design System (ADS) software and target range & velocity at various conditions are employed in the simulated FMCW RADAR system to verify the operation of the system.


title: Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction

abstract: We consider a crowdsourcing model in which $n$ workers are asked to rate the quality of $n$ items previously generated by other workers. An unknown set of $\alpha n$ workers generate reliable ratings, while the remaining workers may behave arbitrarily and possibly adversarially. The manager of the experiment can also manually evaluate the quality of a small number of items, and wishes to curate together almost all of the high-quality items with at most an $\epsilon$ fraction of low-quality items. Perhaps surprisingly, we show that this is possible with an amount of work required of the manager, and each worker, that does not scale with $n$: the dataset can be curated with $\tilde{O}\Big(\frac{1}{\beta\alpha^3\epsilon^4}\Big)$ ratings per worker, and $\tilde{O}\Big(\frac{1}{\beta\epsilon^2}\Big)$ ratings by the manager, where $\beta$ is the fraction of high-quality items. Our results extend to the more general setting of peer prediction, including peer grading in online classrooms.


title: Towards Assessing Changes in Degree of Depression through Facebook

abstract: Depression is typically diagnosed as being present or absent. However, depression severity is believed to be continuously distributed rather than dichotomous. Severity may vary for a given patient daily and seasonally as a function of many variables ranging from life events to environmental factors. Repeated population-scale assessment of depression through questionnaires is expensive. In this paper we use survey responses and status updates from 28,749 Facebook users to develop a regression model that predicts users’ degree of depression based on their Facebook status updates. Our user-level predictive accuracy is modest, significantly outperforming a baseline of average user sentiment. We use our model to estimate user changes in depression across seasons, and find, consistent with literature, users’ degree of depression most often increases from summer to winter. We then show the potential to study factors driving individuals’ level of depression by looking at its most highly correlated language features.


title: A force-feedback exoskeleton for upper-limb rehabilitation in virtual reality

abstract: This paper presents the design and the clinical validation of an upper-limb force-feedback exoskeleton, the L-EXOS, for robotic-assisted rehabilitation in virtual reality VR. The L-EXOS is a five degrees of freedom exoskeleton with a wearable structure and anthropomorphic workspace that can cover the full range of motion of human arm. A specific VR application focused on the reaching task was developed and evaluated on a group of eight post-stroke patients, to assess the efficacy of the system for the rehabilitation of upper limb. The evaluation showed a significant reduction of the performance error in the reaching task paired t-test, p < 0.02.


title: Mid-level Elements for Object Detection

abstract: Building on the success of recent discriminative mid-level elements, we propose a surprisingly simple approach for object detection which performs comparable to the current state-of-the-art approaches on PASCAL VOC comp-3 detection challenge (no external data). Through extensive experiments and ablation analysis, we show how our approach effectively improves upon the HOG-based pipelines by adding an intermediate mid-level representation for the task of object detection. This representation is easily interpretable and allows us to visualize what our object detector "sees". We also discuss the insights our approach shares with CNN-based methods, such as sharing representation between categories helps.


title: A Peer-to-Peer Anonymizing Network Layer

abstract: A new and distinct cultivar of Crossandra infundibuliformis to be known as 'Diane', the new cultivar being principally distinguished from the commonly cultivated variety by its larger sized and more broadly shaped leaves, its more robust and compact habit of growth and its flowers whose color is a darker, richer shade of salmon orange and whose petals are more rounded and larger in size than those of the commonly cultivated variety.


Employee learning orientation, transformational leadership, and employee creativity: The mediating role of employee creative self-efficacy. The Academy of Management


title: Exploring Peopleware in Continuous Delivery

abstract: Traditionally, releasing new software has been a fragile and painful procedure. This view has been challenged by a new approach to software deployment, where the goal is to always be able to deploy the system. The transition from manual releases to instant deployments requires a high degree of automation. Furthermore, the transition requires a new mindset, where both developers and operators act together to deliver value to end users. As this process involves humans and cooperation, developers and their attitude towards the new way of working is important. To this end, in this paper we study the developer perspective of applying continuous delivery in the light of interviewing practitioners working for projects where continuous delivery practices are applied. More precisely, we place the focus on how continuous delivery practices affect software developers and what are the prerequisites for achieving continuous delivery. Based on our research, higher quality and other benefits of continuous delivery can be achieved by giving developers more responsibility. Still, the added responsibilities do not necessarily increase stress, but can actually decrease it, and at the same time improve motivation and job satisfaction.


FormResNet : Formatted Residual Learning for Image Restoration 1


title: Improved Visual Secret Sharing Scheme for QR Code Applications

abstract: Quick response (QR) codes have been widely used in applications, such as data storage and high-speed machine reading. Anyone can gain access to the information stored in QR codes; therefore, they are unsuitable for encoding secret information without the addition of cryptography or other protection. In this paper, we propose a visual secret sharing scheme to encode a secret QR code into several shares. In contrast with other techniques, the shares in our scheme are valid QR codes that can be decoded with some specific meaning by a standard QR code reader, thereby avoiding raising suspicion in potential attackers. Moreover, the secret message is recovered by XOR-ing the qualified shares, an operation that can easily be performed using smartphones or other QR scanning devices. Experimental results show that the proposed scheme is both feasible and reasonably secure. Our scheme’s high sharing efficiency is also highlighted in this paper.


title: Chroma Toolbox: Matlab Implementations for Extracting Variants of Chroma-Based Audio Features

abstract: Chroma-based audio features, which closely correlate to the aspect of harmony, are a well-established tool in processing and analyzing music data. There are many ways of computing and enhancing chroma features, which results in a large number of chroma variants with different properties. In this paper, we present a chroma toolbox [13], which contains MATLAB implementations for extracting various types of recently proposed pitch-based and chroma-based audio features. Providing the MATLAB implementations on a welldocumented website under a GNU-GPL license, our aim is to foster research in music information retrieval. As another goal, we want to raise awareness that there is no single chroma variant that works best in all applications. To this end, we discuss two example applications showing that the final music analysis result may crucially depend on the initial feature design step.


title: Compact Multiresonator-Loaded Planar Antenna for Multiband Operation

abstract: A compact multiresonator-loaded planar antenna for multiband operation is presented. Multiresonator-loaded structures including a pair of symmetrical edge resonators and a T-shaped stub resonator are employed. By loading these resonators, a U-shaped monopole antenna can yield three independent resonances while achieving a compact size of $18\times 19\ {\rm mm}^{2}$ . The experimental results demonstrate that the proposed antenna is capable of operating over the 2.40–2.51 GHz, 3.35–3.94 GHz and 5.02–6.63 GHz frequency ranges, which is suitable for WLAN 2.4/5.2/5.8 GHz and WiMAX 3.5 GHz applications.


title: Personas: practice and theory

abstract: i Personasi is an interaction design technique with considerable potential for software product development. In three years of use, our colleagues and we have extended Alan Cooperis technique to make Personas a powerful complement to other usability methods. After describing and illustrating our approach, we outline the psychological theory that explains why Personas are more engaging than design based primarily on scenarios. As Cooper and others have observed, Personas can engage team members very effectively. They also provide a conduit for conveying a broad range of qualitative and quantitative data, and focus attention on aspects of design and use that other methods do not.


title: Trait-based perspectives of leadership.

abstract: The trait-based perspective of leadership has a long but checkered history. Trait approaches dominated the initial decades of scientific leadership research. Later, they were disdained for their inability to offer clear distinctions between leaders and nonleaders and for their failure to account for situational variance in leadership behavior. Recently, driven by greater conceptual, methodological, and statistical sophistication, such approaches have again risen to prominence. However, their contributions are likely to remain limited unless leadership researchers who adopt this perspective address several fundamental issues. The author argues that combinations of traits and attributes, integrated in conceptually meaningful ways, are more likely to predict leadership than additive or independent contributions of several single traits. Furthermore, a defining core of these dominant leader trait patterns reflects a stable tendency to lead in different ways across disparate organizational domains. Finally, the author summarizes a multistage model that specifies some leader traits as having more distal influences on leadership processes and performance, whereas others have more proximal effects that are integrated with, and influenced by, situational parameters.


title: COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOVEL NOISE ENHANCED DATA ANALYSIS METHOD

abstract: The phenomenon of mode-mixing caused by intermittence signals is an annoying problem in Empirical Mode Decomposition (EMD) method. The noise assisted method of Ensemble EMD (EEMD) has not only effectively resolved this problem but also generated a new one, which tolerates the residue noise in the signal reconstruction. Of course, the relative magnitude of the residue noise could be reduced with large enough ensemble, it would be too time consuming to implement. An improved algorithm of noise enhanced data analysis method is suggested in this paper. In this approach, the residue of added white noises can be extracted from the mixtures of data and white noises via pairs of complementary ensemble IMFs with positive and negative added white noises. Though this new approach yields IMF with the similar RMS noise as EEMD, it effectively eliminated residue noise in the IMFs. Numerical experiments were conducted to demonstrate the new approach and also illustrate the problems of mode splitting and translation.


title: Theory learning as stochastic search in the language of thought

abstract: Abstract We present an algorithmic model for the development of children's intuitive theories within a hierarchical Bayesian framework, where theories are described as sets of logical laws generated by a probabilistic context-free grammar. We contrast our approach with connectionist and other emergentist approaches to modeling cognitive development. While their subsymbolic representations provide a smooth error surface that supports efficient gradient-based learning, our symbolic representations are better suited to capturing children's intuitive theories but give rise to a harder learning problem, which can only be solved by exploratory search. Our algorithm attempts to discover the theory that best explains a set of observed data by performing stochastic search at two levels of abstraction: an outer loop in the space of theories and an inner loop in the space of explanations or models generated by each theory given a particular dataset. We show that this stochastic search is capable of learning appropriate theories in several everyday domains and discuss its dynamics in the context of empirical studies of children's learning.


title: Formal notations are diagrams: Evidence from a production task

abstract: Although a general sense of the magnitude, quantity, or numerosity of objects is common in both untrained people and animals, the abilities to deal exactly with large quantities and to reason precisely in complex but well-specified situations—to behave formally, that is—are skills unique to people trained in symbolic notations. These symbolic notations typically employ complex, hierarchically embedded structures, which all extant analyses assume are constructed by concatenative, rule-based processes. The primary goal of this article is to establish, using behavioral measures on naturalistic tasks, that some of the same cognitive resources involved in representing spatial relations and proximities are also involved in representing symbolic notations—in short, that formal notations are a kind of diagram. We examined self-generated productions in the domains of handwritten arithmetic expressions and typewritten statements in a formal logic. In both tasks, we found substantial evidence for spatial representational schemes even in these highly symbolic domains.


title: Dynamic monocular machine vision

abstract: A new approach to real-time machine vision in dynamic scenes is presented based on special hardware and methods for feature extraction and information processing. Using integral spatio-temporal models, it by-passes the nonunique inversion of the perspective projection by applying recursive least squares filtering. By prediction error feedback methods similar to those used in modern control theory, all spatial state variables including the velocity components are estimated. Only the last image of the sequence needs to be evaluated, thereby alleviating the real-time image sequence processing task.


title: Accelerated proximal gradient methods for nonconvex programming

abstract: Nonconvex and nonsmooth problems have recently received considerable attention in signal/image processing, statistics and machine learning. However, solving the nonconvex and nonsmooth optimization problems remains a big challenge. Accelerated proximal gradient (APG) is an excellent method for convex programming. However, it is still unknown whether the usual APG can ensure the convergence to a critical point in nonconvex programming. In this paper, we extend APG for general nonconvex and nonsmooth programs by introducing a monitor that satisfies the sufficient descent property. Accordingly, we propose a monotone APG and a nonmonotone APG. The latter waives the requirement on monotonic reduction of the objective function and needs less computation in each iteration. To the best of our knowledge, we are the first to provide APG-type algorithms for general nonconvex and nonsmooth problems ensuring that every accumulation point is a critical point, and the convergence rates remain O(1/k2) when the problems are convex, in which k is the number of iterations. Numerical results testify to the advantage of our algorithms in speed.


title: Prediction of users' facial attractiveness on an online dating website

abstract: Online dating websites are popular platforms for adults to search for their life partners. Because on online dating websites, a user's profile image is an important factor determining other's impressions, we focus on profile images and analyze users' visual attractiveness in this study. Facial attractiveness is strongly related to our perception of aesthetics and therefore we believe our investigation can somewhat contribute to artwork analysis. We use pre-trained convolutional neural networks (CNN) to extract visual features and propose a new method to rank users' attractiveness from their online dating interactions. For both genders, we predict users' facial attractiveness by supervised machine learning. Our experimental results show that deep representations of profile images are powerful to capture facial attributes' differences and perform well in predicting users' attractiveness. The correlation coefficient of 0.462 for male users and the correlation coefficient of 0.387 for females users is obtained for regression. The accuracy of 75% for females and the accuracy of 78.8% for males is obtained for 2-level classification.


title: Challenges for protecting the privacy of health information: required certification can leave common vulnerabilities undetected

abstract: The use of electronic health record (EHR) systems by medical professionals enables the electronic exchange of patient data, yielding cost and quality of care benefits. The United States American Recovery and Reinvestment Act (ARRA) of 2009 provides up to $34 billion for meaningful use of certified EHR systems. But, will these certified EHR systems provide the infrastructure for secure patient data exchange? As a window into the ability of current and emerging certification criteria to expose security vulnerabilities, we performed exploratory security analysis on a proprietary and an open source EHR. We were able to exploit a range of common code-level and design-level vulnerabilities. These common vulnerabilities would have remained undetected by the 2011 security certification test scripts from the Certification Commission for Health Information Technology, the most widely used certification process for EHR systems. The consequences of these exploits included, but were not limited to: exposing all users' login information, the ability of any user to view or edit health records for any patient, and creating a denial of service for all users. Based upon our results, we suggest that an enhanced set of security test scripts be used as entry criteria to the EHR certification process. Before certification bodies spend the time to certify that an EHR application is functionally complete, they should have confidence that the software system meets a basic level of security competence.


title: A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer

abstract: Reducing the number of features whilst maintaining an acceptable classification accuracy is a fundamental step in the process of constructing cancer predictive models. In this work, we introduce a novel hybrid (MI-LDA) feature selection approach for the diagnosis of ovarian cancer. This hybrid approach is embedded within a global optimization framework and offers a promising improvement on feature selection and classification accuracy processes. Global Mutual Information (MI) based feature selection optimizes the search process of finding best feature subsets in order to select the highly correlated predictors for ovarian cancer diagnosis. The maximal discriminative cancer predictors are then passed to a Linear Discriminant Analysis (LDA) classifier, and a Genetic Algorithm (GA) is applied to optimise the search process with respect to the estimated error rate of the LDA classifier (MI-LDA). Experiments were performed using an ovarian cancer dataset obtained from the FDA-NCI Clinical Proteomics Program Databank. The performance of the hybrid feature selection approach was evaluated using the Support Vector Machine (SVM) classifier and the LDA classifier. A comparison of the results revealed that the proposed (MI-LDA)-LDA model outperformed the (MI-LDA)-SVM model on selecting the maximal discriminative feature subset and achieved the highest predictive accuracy. The proposed system can therefore be used as an efficient tool for finding predictors and patterns in serum (blood)-derived proteomic data for the detection of ovarian cancer.


title: Software Defined Unified Device Management for Smart Environments

abstract: Applications such as street lights, parking meters, traffic light and congestion sensors, safety cameras, air quality sensors, weather sensors, remote personal healthcare monitors, and acoustic detection define the new world of “Smart Environments” that we live in. The technologies driving these applications are customized to the environment in which they run. The backhauling infrastructure to support each application is as diverse as the application itself. An abstraction which hides the complexities of such disparate systems presenting a unified view is desired. Integrated management platform aids in comprehensive management of the infrastructure. “Smartness” also means it is ready to use by just plug and play. A plug-in based implementation supports multiple vendor devices while a unified northbound controller ensures minimal vendor lock for the applications running on the top.


title: Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner

abstract: There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.


title: Spatial but not verbal cognitive deficits at age 3 years in persistently antisocial individuals.

abstract: Previous studies have repeatedly shown verbal intelligence deficits in adolescent antisocial individuals, but it is not known whether these deficits are in place prior to kindergarten or, alternatively, whether they are acquired throughout childhood. This study assesses whether cognitive deficits occur as early as age 3 years and whether they are specific to persistently antisocial individuals. Verbal and spatial abilities were assessed at ages 3 and 11 years in 330 male and female children, while antisocial behavior was assessed at ages 8 and 17 years. Persistently antisocial individuals (N = 47) had spatial deficits in the absence of verbal deficits at age 3 years compared to comparisons (N = 133), and also spatial and verbal deficits at age 11 years. Age 3 spatial deficits were independent of social adversity, early hyperactivity, poor test motivation, poor test comprehension, and social discomfort during testing, and they were found in females as well as males. Findings suggest that early spatial deficits contribute to persistent antisocial behavior whereas verbal deficits are developmentally acquired. An early-starter model is proposed whereby early spatial impairments interfere with early bonding and attachment, reflect disrupted right hemisphere affect regulation and expression, and predispose to later persistent antisocial behavior.


title: Clusters and Grids for Distributed and Parallel Knowledge Discovery

abstract: Parallel and Distributed Knowledge Discovery (PDKD) is emerging as a possible killer application for clusters and grids of computers. The need to process large volumes of data and the availability of parallel data mining algorithms, makes it possible to exploit the increasing computational power of clusters at low costs. On the other side, grid computing is an emerging "standard" to develop and deploy distributed, high performance applications over geographic networks, in different domains, and in particular for data intensive applications. This paper proposes an approach to integrate cluster of computers within a grid infrastructure to use them, enriched by specific data mining services, as the deployment platform for high performance distributed data mining and knowledge discovery.


title: Low-density granulocytes: a distinct class of neutrophils in systemic autoimmunity

abstract: Recent studies have renewed the interest on the potential role that neutrophils play in the development of systemic lupus erythematosus (SLE) and other autoimmune conditions. A distinct subset of proinflammatory, low-density granulocytes (LDGs) isolated from the peripheral blood mononuclear cell fractions of patients with SLE has been described. While the origin and role of LDGs needs to be fully characterized, there is evidence that these cells may contribute to lupus pathogenesis and to the development of end-organ damage through heightened proinflammatory responses, altered phagocytic capacity, enhanced ability to synthesize type I interferons, and to kill endothelial cells. Furthermore, these cells readily form neutrophil extracellular traps, a phenomenon that may promote autoantigen externalization and organ damage. This review examines the biology and potential origin of LDGs, describes the ultrastructural characteristics of these cells, and discusses their putative pathogenic role in systemic autoimmune diseases.


Beyond Mechanical Turk : An Analysis of Paid Crowd Work Platforms


title: CDR analysis using Big Data technology

abstract: Call Detail Records (CDR) is a valuable source of information; it opens new opportunities for telecom industry and maximize its revenues as well as it helps the community to raise its standard of living in many different ways. However, we need to analyze CDRs in order to extract its big value. But CDRs has a huge volume, variety of data and high data rate, while current telecom systems are designed without these issues in mind. CDRs can be seen as Big Data source, and hence, it is applicable to use Big Data technologies (storage, processing and analysis) in CDR analytics. There are considerable research efforts to address the CDR analysis challenges. This paper presents the use of Big Data technology in CDR analysis by giving some CDR analytics based application examples, highlighting their architecture, the utilized Big Data tools and techniques, and the CDR use case scenarios.


title: Studies in Scientometrics I Transience and Continuance in Scientific Authorship

abstract: Estuda o fenomeno de transiencia/permanencia na frente de pesquisa pela analise do fluxo anual de autores de uma populacao amostrada derivada do banco de dados resultante de sete anos de publicacao de "Science Citation Index" e "Who is publishing in Science". Para o periodo total analisado (1964-1970) foram encontrados 281 autores transientes e 19 autores permanentes, numa populacao total de 506 autores. Derivou-se, a seguir, um modelo para analise quantitativa do fluxo padrao de autores, verificando-se que ha uma taxa de natalidade ("annual recruitment") e uma taxa de mortalidade ("annual terminatio") de cuja superposicao resulta uma taxa de mortalidade infantil (transiencia). O modelo final permite definir a existencia de um nucleo de autores altamente permanentes, correspondente a 20% da populacao. Os autores transientes constituem 22% da populacao anual e 2/3 dos autores novos ("newcomers"). Foram ainda identificadas outras categorias de autores na comunidade cientifica. Ha uma clara correlacao entre estas propriedades demograficas e as taxas (maior e menor) de produtividade dos autores, cuja distribuicao e consistente com as leis de Lotka e Price. Isto permite derivar a expectativa de permanencia na frente de pesquisa, que sera proporcional ao tempo ativo de publicacao. Temos de um lado da escala, a maioria dos autores com uma expectativa de vida minima e taxa de produtividade baixa (75% dos autores produzindo 25% dos trabalhos); no lado oposto estao os autores do nucleo permanente (20%), com menor taxa de mortalidade e maior taxa de produtividade (mais que a metade dos trabalhos). Esta situacao e resultante de um feedback positivo ou efeito Matthew — e parece ser tao intrinseca que, tudo leva a crer, nossa sociedade deve atingir de modo a adaptar sua estrutura institucional a feicao da produtividade e demografia cientificas. Abstract Investigation of the transience / continuance phenomenon occurring at a research front. The annual output of authors in a random sample derived from seven years data from Science Citation Index and Who is publishing in Science was analysed. In the whole period (1964-1970) there are 281 transient authors and 19 continuant authors, for a total population of 506 authors. By deriving a quantitative model for the author flow pattern analysis, it was shown that there is a birth rate (annual recruitment) and a death rate (annual termination) which overlap to give an infant mortality (transience). By refining the model, it was possible to define a core of continuant authors, which amounts to 20% of those publishing. The transient authors constitute 22% of the annual population and 2/3 of the newcomers to publication. The other identified components of a scientific community are the recruits, terminators, non-core publishing continuants and non-publishing continuants. These demographic properties are clearly associated with the lowest and highest rates of authors' productivity, the distributions of which folkw Lotka's and Price's laws with great regularity. Thus it was possible to derive a lifetime expectancy at the research front which will be proportional to the time of active publication. On one end of the scale there is a majority of authors with a minimum life expectancy and a low average productivity (75% of the authors produce 25% of the papers); at the opposite extreme there are authors in the permanent nucleus (20%) with less average mortality and greater average productivity (more than half of the papers) All this is a result of the positive feedback or Mathew Principle in scientific publication. This situation seems so intrinsic that it must be regarded at the way in which society has adjusted its institutional structure to fit the cloth of scientific productivity and demography.


title: Economics of WiFi offloading: Trading delay for cellular capacity

abstract: Cellular networks are facing severe traffic overloads due to the proliferation of smart handheld devices and traffic-hungry applications. A cost-effective and practical solution is to offload cellular data through WiFi. Recent theoretical and experimental studies show that a scheme, referred to as delayed WiFi offloading, can significantly save the cellular capacity by delaying users' data and exploiting mobility and thus increasing chance of meeting WiFi APs (Access Points). Despite a huge potential of WiFi offloading in alleviating mobile data explosion, its success largely depends on the economic incentives provided to users and operators to deploy and use delayed offloading. In this paper, we study how much economic benefits can be generated due to delayed WiFi offloading, by modeling a market based on a two-stage sequential game between a monopoly provider and users. We also provide extensive numerical results computed using a set of parameters from the real traces and Cisco's projection of traffic statistics in year 2015. In both analytical and numerical results, we model a variety of practical scenarios and control knobs in terms of traffic demand and willingness to pay of users, spatio-temporal dependence of pricing and traffic, and diverse pricing and delay tolerance. We demonstrate that delayed WiFi offloading has considerable economic benefits, where the increase ranges from 21% to 152% in the provider's revenue, and from 73% to 319% in the users' surplus, compared to on-the-spot WiFi offloading.


title: Real-time people detection and tracking using 3D depth estimation

abstract: Detecting and tracking people in video have a wide variety of applications in computer vision. However the development of robust methodologies for these purposes are challenging due to complexity of the scenes. In this paper, we propose a real-time algorithm for people detection and tracking by tridimensional depth estimation in indoor environment. The approach is based on people detection and stereo processing techniques for 3D depth estimation of the analyzed scene. The prototype implementation showed satisfactory results for people detection and tracking within pre-recorded video and also in real-time captured video by a stereo camera. The performance on standard hardware and using open source software library achieved a frame rate of up to 7 fps.


title: Exploring the intellectual structure of research on ‘born globals’ and INVs: A literature review using bibliometric methods

abstract: The present research paper shows the results of an analysis of the existing literature on one of the topics that has sparked the most interest amongst scholars and researchers in the fields of international management and entrepreneurship: born global firms and international new ventures (INVs). Concretely, with the aim of identifying and visualising the intellectual structure of research on this phenomenon, a total of 124 research papers whose titles contain the above terms are analysed. The methodology is mainly based on the bibliometric techniques of document citation and co-citation analyses and the analysis of social networks (SNA).


title: Feature Selection based on Mutual Information for Machine learning prediction of Petroleum reservoir properties

abstract: The application of machine learning models such as ::: support vector machine (SVM) and artificial neural networks ::: (ANN) in predicting reservoir properties has been effective in the ::: recent years when compared with the traditional empirical ::: methods. Despite that the machine learning models suffer a lot in ::: the faces of uncertain data which is common characteristics of ::: well log dataset. The reason for uncertainty in well log dataset ::: includes a missing scale, data interpretation and measurement ::: error problems. Feature Selection aimed at selecting feature ::: subset that is relevant to the predicting property. In this paper a ::: feature selection based on mutual information criterion is ::: proposed, the strong point of this method relies on the choice of ::: threshold based on statistically sound criterion for the typical ::: greedy feedforward method of feature selection. Experimental ::: results indicate that the proposed method is capable of improving ::: the performance of the machine learning models in terms of ::: prediction accuracy and reduction in training time.


title: Rehabilitation, etiology, and self-regulation: The comprehensive good lives model of treatment for sexual offenders.

abstract: In this paper, we draw upon two sets of theoretical resources to develop a comprehensive theory of sexual offender rehabilitation named the Good Lives Model-Comprehensive (GLM-C). The original Good Lives Model (GLM-O) forms the overarching values and principles guiding clinical practice in the GLM-C. In addition, the latest sexual offender theory (i.e., the Integrated Theory of Sexual Offending; ITSO) provides a clear etiological grounding for these principles. The result is a more substantial and improved rehabilitation model that is able to conceptually link latest etiological theory with clinical practice. Analysis of the GLM-C reveals that it also has the theoretical resources to secure currently used self-regulatory treatment practice within a meaningful structure.


title: Early Teacher–Child Relationships and the Trajectory of Children's School Outcomes through Eighth Grade

abstract: This study followed a sample of 179 children from kindergarten through eighth grade to examine the extent to which kindergarten teachers' perceptions of their relationships with students predict a range of school outcomes. Kindergarten teachers rated children's behavior and the quality of the teacher-child relationship. Followup data from first through eighth grade were organized by epoch and included academic grades, standardized test scores, work-habit ratings, and discipline records. Relational Negativity in kindergarten, marked by conflict and dependency, was related to academic and behavioral outcomes through eighth grade, particularly for children with high levels of behavior problems in kindergarten and for boys generally. These associations remained significant after controlling for gender, ethnicity, cognitive ability, and behavior ratings. The results have implications for theories of the determinants of school success, the role of adult-child relationships in development, and a range of early intervention and prevention efforts.


Cognitive radar network design and applications


title: Remote control with accelerometer-based hand gesture recognition for interaction in digital TV

abstract: At the present, the digital TV allows the access to a greater amount of content and to execute interactive applications. The remote control used to control the digital TV systems is, in most cases, still solved by the traditional infrared remote control, which has become a limiting factor on the user interaction with the TV. This paper introduces the design and development of an interaction device for use in the context of digital TV in Argentina. The proposed device can be considered an evolution of the classic remote control, in which the functionality of hand gesture recognition is implemented as a natural and friendly interface for controlling digital TV systems of the home. A gestural dictionary of 20 types of gestures was adopted. The recognized gestures are translated into control commands for digital TV systems. As the hand gesture recognition is a pattern classification problem, two techniques based on artificial neural networks were explored, in order to compare results and to select the tool that best fits the problem in question. The pattern classifier design was described in detail, in order to properly select the hardware platform, fulfilling requirements of low-cost and fast execution of pattern classification algorithms. An interaction device of low-cost and excellent recognition precision was developed, for enhancing and enriching the user experience.


title: A hybrid neuro-evolutive algorithm for neural network optimization

abstract: This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes, moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System that is used to design the neural network's architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established state-of-the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process.


title: DR$^{2}$-Net: Deep Residual Reconstruction Network for Image Compressive Sensing

abstract: Abstract Most traditional algorithms for compressive sensing image reconstruction suffer from the intensive computation. Recently, deep learning-based reconstruction algorithms have been reported, which dramatically reduce the time complexity than iterative reconstruction algorithms. In this paper, we propose a novel Deep Residual Reconstruction Network (DR2-Net) to reconstruct the image from its Compressively Sensed (CS) measurement. The DR2-Net is proposed based on two observations: (1) linear mapping could reconstruct a high-quality preliminary image, and (2) residual learning could further improve the reconstruction quality. Accordingly, DR2-Net consists of two components, i.e., linear mapping network and residual network, respectively. Specifically, the fully-connected layer in neural network implements the linear mapping network. We then expand the linear mapping network to DR2-Net by adding several residual learning blocks to enhance the preliminary image. Extensive experiments demonstrate that the DR2-Net outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.04, 0.1, and 0.25, respectively. The code of DR2-Net has been released on: https://github.com/coldrainyht/caffe_dr2 .


title: A lumped-element power divider/combiner suitable for high power applications

abstract: This paper proposes a new lumped element power divider/combiner, having minimum number of elements and being suitable for high power applications. The proposed topology is analyzed using conventional even and odd mode analysis, and values of the lumped elements are determined. Stress analysis also is done and maximum voltage/current/power stress on each element is calculated. At last, a prototype is designed and implemented in VHF band. Results from this prototype show good agreement with results obtained from simulations.


title: Digitised Historical Text: Does it have to be mediOCRe?

abstract: This paper reports on experiments to improve the Optical Character Recognition (ocr) quality of historical text as a preliminary step in text mining. We analyse the quality of ocred text compared to a gold standard and show how it can be improved by performing two automatic correction steps. We also demonstrate the impact this can have on named entity recognition in a preliminary extrinsic evaluation. This work was performed as part of the Trading Consequences project which is focussed on text mining of historical documents for the study of nineteenth century trade in the British Empire.


title: Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy

abstract: To maximize task scheduling performance and minimize nonreasonable task allocation in clouds, this paper proposes a method based on a two-stage strategy. At the first stage, a job classifier motivated by a Bayes classifier’s design principle is utilized to classify tasks based on historical scheduling data. A certain number of virtual machines (VMs) of different types are accordingly created. This can save time of creating VMs during task scheduling. At the second stage, tasks are matched with concrete VMs dynamically. Dynamic task scheduling algorithms are accordingly proposed. Experimental results show that they can effectively improve the cloud’s scheduling performance and achieve the load balancing of cloud resources in comparison with existing methods. Note to Practitioners — Task scheduling is one of the challenging problems in cloud computing, especially when deadline and cost are considered. As an important actuator, virtual machines (VMs) play a vital role for cloud task scheduling. To meet task deadlines, one needs to save the time of creating VMs, task waiting time, and executing time. To minimize the task execution cost, one needs to schedule tasks onto their most suitable VMs for execution. We propose a cloud task scheduling framework based on a two-stage strategy to do so. It precreates VMs according to historical scheduling data, therefore saving time for tasks to wait for creating VMs. It matches tasks with their most suitable VMs dynamically, therefore saving their execution cost. Under the premise of meeting task deadlines, it minimizes the waiting time of VMs to schedule tasks, thus minimizing the cost to be paid by users who utilize VMs. The readily deployable algorithms are designed and illustrated to improve cloud task scheduling and execution results in comparison with those using traditional methods.


title: ATTITUDES AND SOCIAL COGNITION Stereotype Threat and the Intellectual Test Performance of African Americans

abstract: Stereotype threat is being at risk of confirming, as self-characte ristic, a negative stereotype about one's group. Studies 1 and 2 varied the stereotype vulnerability of Black participants taking a difficult verbal test by varying whether or not their performance was ostensibly diagnostic of ability, and thus, whether or not they were at risk of fulfilling the racial stereotype about their intellectual ability. Reflecting the pressure of this vulnerability, Blacks underperformed in relation to Whites in the ability-diagnostic condition but not in the nondiagnostic condition (with Scholastic Aptitude Tests controlled). Study 3 validated that ability-diagnosticity cognitively activated the racial stereotype in these participants and motivated them not to conform to it, or to be judged by it. Study 4 showed that mere salience of the stereotype could impair Blacks' performance even when the test was not ability diagnostic. The role of stereotype vulnerability in the standardized test performance of ability-stigmatized groups is discussed. Not long ago, in explaining his career-long preoccupation with the American Jewish experience, the novelist Philip Roth said that it was not Jewish culture or religion per se that fascinated him, it was what he called the Jewish "predicament." This is an apt term for the perspective taken in the present research. It focuses on a social-psychological predicament that can arise from widely-known negative stereotypes about one's group. It is this: the existence of such a stereotype means that anything one does or any of one's features that conform to it make the stereotype more plausible as a self-characterization in the eyes of others, and perhaps even in one's own eyes. We call this predicament stereotype threat and argue that it is experienced, essentially, as a self-evaluative threat. In form, it is a predicament that can beset the members of any group about whom negative stereotypes exist. Consider the stereotypes elicited by the terms yuppie, feminist, liberal, or White male. Their prevalence in society raises the possibility for potential targets that the stereotype is true of them and, also, that other people will see them that way. When the allegations of the stereotype are importantly


title: Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

abstract: Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.


title: Game theoretic study on blockchain based secure edge networks

abstract: Blockchain has been applied to study data privacy and network security recently. In this paper, we propose a punishment scheme based on the action record on the blockchain to suppress the attack motivation of the edge servers and the mobile devices in the edge network. The interactions between a mobile device and an edge server are formulated as a blockchain security game, in which the mobile device sends a request to the server to obtain real-time service or launches attacks against the server for illegal security gains, and the server chooses to perform the request from the device or attack it. The Nash equilibria (NEs) of the game are derived and the conditions that each NE exists are provided to disclose how the punishment scheme impacts the adversary behaviors of the mobile device and the edge server.


title: Design and analysis of a novel 6-DOF redundant actuated parallel robot with compliant hinges for high precision positioning

abstract: This paper presents the design and modeling of a new 6-DOF 8-PSS/SPS compliant dual redundant parallel robot with wide-range flexure hinges. This robot can achieve either high accurate positioning or rough positioning as well as a 6-DOF active vibration isolation and excitation to the payload placed on the moving platform. Adopting a kind of wide-range flexure hinge, we establish the kinematics model of the macro parallel mechanism system via the stiffness model and Newton–Raphson method, then we build up the dynamics model using Kane’s method for the micro-motion system. The investigations of this paper will provide suggestions to improve the structure and control algorithm optimization for a novel compliant dual redundant parallel mechanism in order to achieve the feature of larger workspace, higher motion precision and better dynamic characteristics. The results will be helpful in modifying the structure of the prototype platform to enhance its high kinematics and dynamics properties.


title: APRN Survey on Roles, Functions, and Competencies

abstract: Nursing education is undergoing a major transition with a shift in advanced practice registered nurse (APRN) educational preparation from a master's degree to the clinical doctorate that is targeted to be fully implemented by 2015. This paradigm shift in nursing education is expanding the role and s


title: NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

abstract: Speckle noise is an inherent problem in coherent imaging systems like synthetic aperture radar. It creates strong intensity fluctuations and hampers the analysis of images and the estimation of local radiometric, polarimetric or interferometric properties. SAR processing chains thus often include a multi-looking (i.e., averaging) filter for speckle reduction, at the expense of a strong resolution loss. Preservation of point-like and fine structures and textures requires to adapt locally the estimation. Non-local means successfully adapt smoothing by deriving data-driven weights from the similarity between small image patches. The generalization of non-local approaches offers a flexible framework for resolution-preserving speckle reduction. We describe a general method, NL-SAR, that builds extended non-local neighborhoods for denoising amplitude, polarimetric and/or interferometric SAR images. These neighborhoods are defined on the basis of pixel similarity as evaluated by multi-channel comparison of patches. Several non-local estimations are performed and the best one is locally selected to form a single restored image with good preservation of radar structures and discontinuities. The proposed method is fully automatic and handles single and multi-look images, with or without interferometric or polarimetric channels. Efficient speckle reduction with very good resolution preservation is demonstrated both on numerical experiments using simulated data, airborne and spaceborne radar images. The source code of a parallel implementation of NL-SAR is released with the paper.


title: Bayesian Rule Sets for Interpretable Classification

abstract: A Rule Set model consists of a small number of short rules for interpretable classification, where an instance is classified as positive if it satisfies at least one of the rules. The rule set provides reasons for predictions, and also descriptions of a particular class. We present a Bayesian framework for learning Rule Set models, with prior parameters that the user can set to encourage the model to have a desired size and shape in order to conform with a domain-specific definition of interpretability. We use an efficient inference approach for searching for the MAP solution and provide theoretical bounds to reduce computation. We apply Rule Set models to ten UCI data sets and compare the performance with other interpretable and non-interpretable models.


title: Computation for ChIP-seq and RNA-seq studies

abstract: Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.


title: Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients

abstract: Epileptic seizure prediction has steadily evolved from its conception in the 1970s, to proof-of-principle experiments in the late 1980s and 1990s, to its current place as an area of vigorous, clinical and laboratory investigation. As a step toward practical implementation of this technology in humans, we present an individualized method for selecting electroencephalogram (EEG) features and electrode locations for seizure prediction focused on precursors that occur within ten minutes of electrographic seizure onset. This method applies an intelligent genetic search process to EEG signals simultaneously collected from multiple intracranial electrode contacts and multiple quantitative features derived from these signals. The algorithm is trained on a series of baseline and preseizure records and then validated on other, previously unseen data using split sample validation techniques. The performance of this method is demonstrated on multiday recordings obtained from four patients implanted with intracranial electrodes during evaluation for epilepsy surgery. An average probability of prediction (or block sensitivity) of 62.5% was achieved in this group, with an average block false positive (FP) rate of 0.2775 FP predictions/h, corresponding to 90.47% specificity. These findings are presented as an example of a method for training, testing and validating a seizure prediction system on data from individual patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical deployment.


title: Multi-agent Diverse Generative Adversarial Networks

abstract: We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and its conditional variants to address the well known problem of mode collapse. First, MAD-GAN is a multi-agent GAN architecture incorporating multiple generators and one discriminator. Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample. Intuitively, to succeed in this task, the discriminator must learn to push different generators towards different identifiable modes. We perform extensive experiments on synthetic and real datasets and compare MAD-GAN with different variants of GAN. We show high quality diverse sample generations for challenging tasks such as image-to-image translation and face generation. In addition, we also show that MAD-GAN is able to disentangle different modalities when trained using highly challenging diverse-class dataset (e.g. dataset with images of forests, icebergs, and bedrooms). In the end, we show its efficacy on the unsupervised feature representation task. In Appendix, we introduce a similarity based competing objective (MAD-GAN-Sim) which encourages different generators to generate diverse samples based on a user defined similarity metric. We show its performance on the image-to-image translation, and also show its effectiveness on the unsupervised feature representation task.


title: A fuzzy neural network for extracting facial patterns

abstract: A multistage fuzzy neural network is presented for extracting facial patterns from images. The system can handle some image variations due to changes in pose, illumination, etc. It contains three modules: preprocessing, face-components extraction, and final decision-making. The performance of the system is evaluated by conducting experiments on seven large test sets.


title: miRTex: A Text Mining System for miRNA-Gene Relation Extraction

abstract: MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http://proteininformationresource.org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes.


title: Discovering Opinion Changes in Online Reviews via Learning Fine-Grained Sentiments

abstract: Recent years have shown rapid advancement in understanding consumers' behaviors and opinions through collecting and analyzing data from online social media platforms. While abundant research has been undertaken to detect users' opinions, few tools are available for understanding events where user opinions change drastically. In this paper, we propose a novel framework for discovering consumer opinion changing events. To detect subtle opinion changes over time, we first develop a novel fine-grained sentiment classification method by leveraging word embedding and convolutional neural networks. The method learns sentiment-enhanced word embedding, both for words and phrases, to capture their corresponding syntactic, semantic, and sentimental characteristics. We then propose an opinion shift detection algorithm that is based on the Kullback-Leibler divergence of temporal opinion category distributions, and conducted experiments on online reviews from Yelp. The results show that the proposed approach can effectively classify fine-grained sentiments of reviews and can discover key moments that correspond to consumer opinion shifts in response to events that relate to a product or service.


title: The vision-based hand gesture recognition using blob analysis

abstract: As technology becomes the part of human life for decades, the relationship between human and computer called human-computer interaction (HCI) is important to study for improving the system to serve the human need. HCI can be applied in various areas including medical system which is valuable for the elder who is not able to walk or express the feelings by words. The intuitive approach is the development of algorithm by using hand gestures. This paper proposes a dynamic hand gesture recognition algorithm for elder people. The algorithm implements in a vision-based hand gesture recognition using optical flow and blob analysis to track six dynamic hand gestures and classify their meanings. The experiment provided good results for all six hand gestures in detection, tracking, and classification procedures.


title: miR-206 controls LXRα expression and promotes LXR-mediated cholesterol efflux in macrophages

abstract: Liver X receptors (LXRα and LXRβ) are key transcription factors in cholesterol metabolism that regulate cholesterol biosynthesis/efflux and bile acid metabolism/excretion in the liver and numerous organs. In macrophages, LXR signaling modulates cholesterol handling and the inflammatory response, pathways involved in atherosclerosis. Since regulatory pathways of LXR transcription control are well understood, in the present study we aimed at identifying post-transcriptional regulators of LXR activity. MicroRNAs (miRs) are such post-transcriptional regulators of genes that in the canonical pathway mediate mRNA inactivation. In silico analysis identified miR-206 as a putative regulator of LXRα but not LXRβ. Indeed, as recently shown, we found that miR-206 represses LXRα activity and expression of LXRα and its target genes in hepatic cells. Interestingly, miR-206 regulates LXRα differently in macrophages. Stably overexpressing miR-206 in THP-1 human macrophages revealed an up-regulation and miR-206 knockdown led to a down-regulation of LXRα and its target genes. In support of these results, bone marrow-derived macrophages (BMDMs) from miR-206 KO mice also exhibited lower expression of LXRα target genes. The physiological relevance of these findings was proven by gain- and loss-of-function of miR-206; overexpression of miR-206 enhanced cholesterol efflux in human macrophages and knocking out miR-206 decreased cholesterol efflux from MPMs. Moreover, we show that miR-206 expression in macrophages is repressed by LXRα activation, while oxidized LDL and inflammatory stimuli profoundly induced miR-206 expression. We therefore propose a feed-back loop between miR-206 and LXRα that might be part of an LXR auto-regulatory mechanism to fine tune LXR activity.


title: Digital Wildfires: Propagation, Verification, Regulation, and Responsible Innovation

abstract: Social media platforms provide an increasingly popular means for individuals to share content online. Whilst this produces undoubted societal benefits, the ability for content to be spontaneously posted and reposted creates an ideal environment for rumour and false/malicious information to spread rapidly. When this occurs it can cause significant harm and can be characterised as a “digital wildfire.” In this article, we demonstrate that the propagation and regulation of digital wildfires form important topics for research and conduct an overview of existing work in this area. We outline the relevance of a range of work from the computational and social sciences, including a series of insights into the propagation of rumour and false/malicious information. We argue that significant research gaps remain—for instance, there is an absence of systematic studies on the effects of digital wildfires and there is a need to combine empirical research with a consideration of how the responsible governance of social media can be determined. We propose an agenda for research that establishes a methodology to explore in full the propagation and regulation of unverified content on social media. This agenda promotes high-quality interdisciplinary research that will also inform policy debates.


title: On Graph Query Optimization in Large Networks

abstract: The dramatic proliferation of sophisticated networks has resulted in a growing need for supporting effective querying and mining methods over such large-scale graph-structured data. At the core of many advanced network operations lies a common and critical graph query primitive: how to search graph structures efficiently within a large network? Unfortunately, the graph query is hard due to the NP-complete nature of subgraph isomorphism. It becomes even challenging when the network examined is large and diverse. In this paper, we present a high performance graph indexing mechanism, SPath, to address the graph query problem on large networks. SPath leverages decomposed shortest paths around vertex neighborhood as basic indexing units, which prove to be both effective in graph search space pruning and highly scalable in index construction and deployment. Via SPath, a graph query is processed and optimized beyond the traditional vertex-at-a-time fashion to a more efficient path-at-a-time way: the query is first decomposed to a set of shortest paths, among which a subset of candidates with good selectivity is picked by a query plan optimizer; Candidate paths are further joined together to help recover the query graph to finalize the graph query processing. We evaluate SPath with the state-of-the-art GraphQL on both real and synthetic data sets. Our experimental studies demonstrate the effectiveness and scalability of SPath, which proves to be a more practical and efficient indexing method in addressing graph queries on large networks.


title: From Hitting to Tattling to Gossip: An Evolutionary Rationale for the Development of Indirect Aggression:

abstract: Adult humans are characterized by low rates of intra-group physical aggression. Since children tend to be more physically aggressive, an evolutionary developmental account shows promise for explaining how physical aggression is suppressed in adults. I argue that this is achieved partly through extended dominance hierarchies, based on indirect reciprocity and linguistic transmission of reputational information, mediated by indirectly aggressive competition. Reviewing the literature on indirect and related forms of aggression provides three pieces of evidence for the claim that evolutionarily old impulses towards physical aggression are socialized into indirect aggression in humans: (i) physical aggression falls in early childhood over the same age range at which indirect aggression increases; (ii) the same individuals engage in both direct and indirect aggression; and (iii) socially dominant individuals practice indirect aggression more frequently. Consideration of the developmental course of indirect aggr...


title: Blur-Invariant Deep Learning for Blind-Deblurring

abstract: In this paper, we investigate deep neural networks for blind motion deblurring. Instead of regressing for the motion blur kernel and performing non-blind deblurring outside of the network (as most methods do), we propose a compact and elegant end-to-end deblurring network. Inspired by the data-driven sparse-coding approaches that are capable of capturing linear dependencies in data, we generalize this notion by embedding non-linearities into the learning process. We propose a new architecture for blind motion deblurring that consists of an autoencoder that learns the data prior, and an adversarial network that attempts to generate and discriminate between clean and blurred features. Once the network is trained, the generator learns a blur-invariant data representation which when fed through the decoder results in the final deblurred output.


title: Completeness theorems for non-cryptographic fault-tolerant distributed computation

abstract: Every function of n inputs can be efficiently computed by a complete network of n processors in such a way that: If no faults occur, no set of size t n /2 of players gets any additional information (other than the function value), Even if Byzantine faults are allowed, no set of size t n /3 can either disrupt the computation or get additional information. Furthermore, the above bounds on t are tight!


title: Adverse physiological and psychological effects of screen time on children and adolescents: Literature review and case study

abstract: Abstract A growing body of literature is associating excessive and addictive use of digital media with physical, psychological, social and neurological adverse consequences. Research is focusing more on mobile devices use, and studies suggest that duration, content, after-dark-use, media type and the number of devices are key components determining screen time effects. Physical health effects: excessive screen time is associated with poor sleep and risk factors for cardiovascular diseases such as high blood pressure, obesity, low HDL cholesterol, poor stress regulation (high sympathetic arousal and cortisol dysregulation), and Insulin Resistance. Other physical health consequences include impaired vision and reduced bone density. Psychological effects: internalizing and externalizing behavior is related to poor sleep. Depressive symptoms and suicidal are associated to screen time induced poor sleep, digital device night use, and mobile phone dependency. ADHD-related behavior was linked to sleep problems, overall screen time, and violent and fast-paced content which activates dopamine and the reward pathways. Early and prolonged exposure to violent content is also linked to risk for antisocial behavior and decreased prosocial behavior. Psychoneurological effects: addictive screen time use decreases social coping and involves craving behavior which resembles substance dependence behavior. Brain structural changes related to cognitive control and emotional regulation are associated with digital media addictive behavior. A case study of a treatment of an ADHD diagnosed 9-year-old boy suggests screen time induced ADHD-related behavior could be inaccurately diagnosed as ADHD. Screen time reduction is effective in decreasing ADHD-related behavior. Conclusions Components crucial for psychophysiological resilience are none-wandering mind (typical of ADHD-related behavior), good social coping and attachment, and good physical health. Excessive digital media use by children and adolescents appears as a major factor which may hamper the formation of sound psychophysiological resilience.


title: A survey on instance selection for active learning

abstract: Active learning aims to train an accurate prediction model with minimum cost by labeling most informative instances. In this paper, we survey existing works on active learning from an instance-selection perspective and classify them into two categories with a progressive relationship: (1) active learning merely based on uncertainty of independent and identically distributed (IID) instances, and (2) active learning by further taking into account instance correlations. Using the above categorization, we summarize major approaches in the field, along with their technical strengths/weaknesses, followed by a simple runtime performance comparison, and discussion about emerging active learning applications and instance-selection challenges therein. This survey intends to provide a high-level summa- rization for active learning and motivates interested readers to consider instance-selection approaches for designing effective active learning solutions.


Survey Graph clustering


title: A Frequency-Domain Analysis of Latch Comparator Offset due to Load Capacitor Mismatch

abstract: This brief presents a frequency-domain analysis of the latch comparator offset due to load capacitor mismatch. Although the analysis is applied to the static latch comparator, the developed method can be extended to the dynamic latch comparator.


title: Surface Charge of Variable Porosity Al2O3(s) and SiO2(s) Adsorbents

abstract: The surface charge properties of two SiO2 and three Al2O3 mineral adsorbents with varying degrees of framework porosity were investigated using discontinuous titration and ion adsorption methodologies. Points of zero net charge (p.z.n.c) for porous and non-porous SiO2 were 7. Variation of surface charge density with aqueous proton concentration was nearly identical within a mineral type (i.e., SiO2 or Al2O3) irrespective of the degree of framework porosity, indicating that the densities of dissociable surface sites are equivalent, when normalized to surface area. The results suggest that the use of titration methods alone may be insufficient for thorough surface charge characterization, particularly at low and high pH. Proton titrations should be coupled with concurrent ion adsorption measurements to confirm surface charge development. Discontinuous proton titration and ion adsorption data, which were in agreement in the slightly acidic through slightly basic pH range, both indicated that p.z.n.c. was equal to the point of zero net proton charge (p.z.n.p.c.) for the variable charge minerals investigated.


title: Neighborhood preserving embedding

abstract: Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling a probability distribution that has support on or near a submanifold of Euclidean space. In this paper, we propose a novel subspace learning algorithm called neighborhood preserving embedding (NPE). Different from principal component analysis (PCA) which aims at preserving the global Euclidean structure, NPE aims at preserving the local neighborhood structure on the data manifold. Therefore, NPE is less sensitive to outliers than PCA. Also, comparing to the recently proposed manifold learning algorithms such as Isomap and locally linear embedding, NPE is defined everywhere, rather than only on the training data points. Furthermore, NPE may be conducted in the original space or in the reproducing kernel Hilbert space into which data points are mapped. This gives rise to kernel NPE. Several experiments on face database demonstrate the effectiveness of our algorithm


title: Emotional Branding and the Strategic Value of the Doppelgänger Brand Image

abstract: Emotional branding is widely heralded as a key to marketing success. However, little attention has been given to the risks posed by this strategy. This article argues that emotional-branding strategies are conducive to the emergence of a doppelganger brand image, which is defined as a family of disparaging images and meanings about a brand that circulate throughout popular culture. This article’s thesis is that a doppelganger brand image can undermine the perceived authenticity of an emotional-branding story and, thus, the identity value that the brand provides to consumers. The authors discuss how the tenets of emotional branding paradoxically encourage the formation and propagation of doppelganger brand imagery. This article develops the counterintuitive proposition that rather than merely being a threat to be managed, a doppelganger brand image can actually benefit a brand by providing early warning signs that an emotional-branding story is beginning to lose its cultural resonance. This article demonstrates these ideas through an analysis of the doppelganger brand image that is beginning to haunt a paragon of emotional branding—Starbucks. The authors conclude with a discussion of how marketing managers can proactively use the insights gained by analyzing a doppelganger brand image.


title: Improved step size adaptation for the MO-CMA-ES

abstract: The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is an evolutionary algorithm for continuous vector-valued optimization. It combines indicator-based selection based on the contributing hypervolume with the efficient strategy parameter adaptation of the elitist covariance matrix adaptation evolution strategy (CMA-ES). Step sizes (i.e., mutation strengths) are adapted on individual-level using an improved implementation of the 1/5-th success rule. In the original MO-CMA-ES, a mutation is regarded as successful if the offspring ranks better than its parent in the elitist, rank-based selection procedure. In contrast, we propose to regard a mutation as successful if the offspring is selected into the next parental population. This criterion is easier to implement and reduces the computational complexity of the MO-CMA-ES, in particular of its steady-state variant. The new step size adaptation improves the performance of the MO-CMA-ES as shown empirically using a large set of benchmark functions. The new update scheme in general leads to larger step sizes and thereby counteracts premature convergence. The experiments comprise the first evaluation of the MO-CMA-ES for problems with more than two objectives.


title: Webly Supervised Semantic Segmentation

abstract: We propose a weakly supervised semantic segmentation algorithm that uses image tags for supervision. We apply the tags in queries to collect three sets of web images, which encode the clean foregrounds, the common backgrounds, and realistic scenes of the classes. We introduce a novel three-stage training pipeline to progressively learn semantic segmentation models. We first train and refine a class-specific shallow neural network to obtain segmentation masks for each class. The shallow neural networks of all classes are then assembled into one deep convolutional neural network for end-to-end training and testing. Experiments show that our method notably outperforms previous state-of-the-art weakly supervised semantic segmentation approaches on the PASCAL VOC 2012 segmentation benchmark. We further apply the class-specific shallow neural networks to object segmentation and obtain excellent results.


title: Water allocation, inefficiency and inequity in a government irrigation system

abstract: Abstract Efficient and equitable allocation of irrigation water in a large multi-user system must consider spatial, temporal and seasonal differences in farm water use. In developing countries, centralized government management of canal systems typically allocates water according to simple, fixed rules. Water allocation over a six-year period is analyzed for a Philippine Government system, and compared with estimated variability in farmer water utilization. Inflexibility of the present allocation is found to be inefficient, with inequitable distribution of irrigation benefits among farmers.


title: The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

abstract: Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.


title: Distributed resource allocation in virtualized wireless cellular networks based on ADMM

abstract: In next generation wireless mobile networks, network virtualization will become an important key technology. In this paper, we firstly propose a resource allocation scheme for enabling efficient resource allocation in wireless network virtualization. Then, we formulate the resource allocation strategy as an optimization problem, considering not only the revenue earned by serving end users of virtual networks, but also the cost of leasing infrastructure from infrastructure providers. In addition, we develop an efficient alternating direction method of multipliers (ADMM)-based distributed virtual resource allocation algorithm in virtualized wireless networks. Simulation results are presented to show the effectiveness of the proposed scheme.


title: Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping

abstract: Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called “Path-Based Isomap”. Similar to Isomap, we exploit geodesic paths to find the low-dimensional embedding. However, instead of preserving pairwise geodesic distances, the low-dimensional embedding is computed via a path-mapping algorithm. Due to the much fewer number of paths compared to number of data points, a significant improvement in time and memory complexity with a comparable performance is achieved. The method demonstrates state-of-the-art performance on well-known synthetic and real-world datasets, as well as in the presence of noise.


title: From few to many: generative models for recognition under variable pose and illumination

abstract: Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearance-based methods which, unlike previous appearance-based approaches, require only a small set of training images to generate a rich representation that models this variability. Specifically, from as few as three images of an object in fixed pose seen under slightly varying but unknown lighting, a surface and an albedo map are reconstructed. These are then used to generate synthetic images with large variations in pose and illumination and thus build a representation useful for object recognition. Our methods have been tested within the domain of face recognition on a subset of the Yale Face Database B containing 4050 images of 10 faces seen under variable pose and illumination. This database was specifically gathered for testing these generative methods. Their performance is shown to exceed that of popular existing methods.


title: Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction

abstract: Photometric stereo (PS) is an established technique for high-detail reconstruction of 3D geometry and appearance. To correct for surface integration errors, PS is often combined with multiview stereo (MVS). With dynamic objects, PS reconstruction also faces the problem of computing optical flow (OF) for image alignment under rapid changes in illumination. Current PS methods typically compute optical flow and MVS as independent stages, each one with its own limitations and errors introduced by early regularization. In contrast, scene flow methods estimate geometry and motion, but lack the fine detail from PS. This paper proposes photogeometric scene flow (PGSF) for high-quality dynamic 3D reconstruction. PGSF performs PS, OF, and MVS simultaneously. It is based on two key observations: (i) while image alignment improves PS, PS allows for surfaces to be relit to improve alignment, (ii) PS provides surface gradients that render the smoothness term in MVS unnecessary, leading to truly data-driven, continuous depth estimates. This synergy is demonstrated in the quality of the resulting RGB appearance, 3D geometry, and 3D motion.


title: Phase synchrony and coherence analyses of EEG as tools to discriminate between children with and without attention deficit disorder

abstract: Phase synchrony and model-based magnitude squared (MS) coherence analyses of the EEG are investigated for their usefulness in the discrimination between non-ADHD and ADHD children. We observed that phase synchrony is higher ‐ on average ‐ for non-ADHD participants than for the ADHD group. Based on the relatively small sample available to us, we find that a Euclidean distance-based classifier may ‐ with selection of proper electrode pairs, EEG rhythm, and task ‐ discriminate between the EEG of non-ADHD and ADHD children with an average percentage of correct classification of 63.2%. MS coherence was observed to be considerably higher at low frequencies (i.e. below 1 Hz) for ADHD subjects than for the majority of the nonADHD individuals. We also observedthat the u rhythm MS coherence (approximately 4‐6 Hz) is generally higher for non-ADHD subjects than for ADHD individuals. The combination of these effects was observed on four out of the seven non-ADHD subjects and on all six of the ADHD subjects.


Extraction of Hyponymy, Meronymy and Antonymy Relation Pairs : A Brief Survey