title: OPPORTUNITIES, PRECIPITATORS AND CRIMINAL DECISIONS: A REPLY TO WORTLEY'S CRITIQUE OF SITUATIONAL CRIME PREVENTION

abstract: Clarke's classification of situational crime prevention tech- niques is designed to provide a conceptual analysis of situational strategies, and to offer practical guidance on their use in reducing criminal opportunities. It has developed in parallel with a long program of empirical research, conducted by many researchers, on the situ- ational determinants and the prevention of a wide variety of crimes. For this reason the classification has been subject to constant revision and updating, of which Clarke's (1997) version, which lists 16 such techniques, is the latest. Recently, Wortley (2001) has suggested the need to augment the existing classification, which deals with the analysis of situational opportunities, with a complementary analysis of situational precipitators. These are factors within the crime setting it- self that may prompt, provoke, pressure, or permit an individual to of- fend. The present chapter examines the assumptions underlying the development of situational crime prevention, and offers some views about the theoretical and practical significance of Wortley's suggested additions and revisions. It concludes by proposing a revised classifica-


title: Conversational Recommendation System with Unsupervised Learning

abstract: We will demonstrate a conversational products recommendation agent. This system shows how we combine research in personalized recommendation systems with research in dialogue systems to build a virtual sales agent. Based on new deep learning technologies we developed, the virtual agent is capable of learning how to interact with users, how to answer user questions, what is the next question to ask, and what to recommend when chatting with a human user. Normally a descent conversational agent for a particular domain requires tens of thousands of hand labeled conversational data or hand written rules. This is a major barrier when launching a conversation agent for a new domain. We will explore and demonstrate the effectiveness of the learning solution even when there is no hand written rules or hand labeled training data.


title: 2008b). The automaticity of emotion recognition

abstract: Evolutionary accounts of emotion typically assume that humans evolved to quickly and efficiently recognize emotion expressions because these expressions convey fitness-enhancing messages. The present research tested this assumption in 2 studies. Specifically, the authors examined (a) how quickly perceivers could recognize expressions of anger, contempt, disgust, embarrassment, fear, happiness, pride, sadness, shame, and surprise; (b) whether accuracy is improved when perceivers deliberate about each expression’s meaning (vs. respond as quickly as possible); and (c) whether accurate recognition can occur under cognitive load. Across both studies, perceivers quickly and efficiently (i.e., under cognitive load) recognized most emotion expressions, including the self-conscious emotions of pride, embarrassment, and shame. Deliberation improved accuracy in some cases, but these improvements were relatively small. Discussion focuses on the implications of these findings for the cognitive processes underlying emotion recognition.


title: Location recognition using prioritized feature matching

abstract: We present a fast, simple location recognition and image localization method that leverages feature correspondence and geometry estimated from large Internet photo collections. Such recovered structure contains a significant amount of useful information about images and image features that is not available when considering images in isolation. For instance, we can predict which views will be the most common, which feature points in a scene are most reliable, and which features in the scene tend to co-occur in the same image. Based on this information, we devise an adaptive, prioritized algorithm for matching a representative set of SIFT features covering a large scene to a query image for efficient localization. Our approach is based on considering features in the scene database, and matching them to query image features, as opposed to more conventional methods that match image features to visual words or database features. We find this approach results in improved performance, due to the richer knowledge of characteristics of the database features compared to query image features. We present experiments on two large city-scale photo collections, showing that our algorithm compares favorably to image retrieval-style approaches to location recognition.


title: A twin study of generalized anxiety disorder symptoms, panic disorder symptoms and post-traumatic stress disorder in men

abstract: Abstract Generalized anxiety disorder (GAD), panic disorder (PD) and post-traumatic stress disorder (PTSD) often co-occur. We investigated whether and to what degree genetic and environmental contributions overlap among symptoms of GAD, symptoms of PD and PTSD. Subjects were 3327 monozygotic and dizygotic male–male twin pair members of the Vietnam Era Twin Registry who participated in a 1992 telephone administration of the Diagnostic Interview Schedule Version 3 Revised (DIS3R). Genetic model fitting was performed to estimate the magnitude of genetic and environmental contributions to the lifetime co-occurrence of GAD symptoms, PD symptoms and PTSD. The liability for GAD symptoms was due to a 37.9% additive genetic contribution common to PD symptoms and PTSD. Liability for PD symptoms was due to a 20.7% additive genetic contribution common to GAD symptoms and PTSD, and a 20.1% additive genetic influence specific to PD symptoms. Additive genetic influences common to symptoms of GAD and PD accounted for 21.3% of the genetic variance in PTSD. Additive genetic influences specific to PTSD accounted for 13.6% of the genetic variance in PTSD. Remaining variance for all three disorders was due to unique environmental factors both common and specific to each phenotype. These results suggest that these disorders each have etiologically distinct components and also have significant genetic and unique environmental contributions in common.


title: A robust sparse signal decomposition framework for baseline wander removal from ECG signal

abstract: In this paper, we present a new method based on sparse signal decomposition for effectively removing baseline wander (BW) noise from the ECG signal. Our proposed method not only eliminates BW noise from ECG signal but also preserves the morphological shape of local waves of the signal. Our proposed method is tested and validated on real ECG signals taken from MIT-BIH arrhythmia database. Comparative results depict the superior performance of the proposed method over the state of art methods.


title: Ultrastructure of the normal adult human female prostate gland (Skene’s gland)

abstract: The predominant cells of female prostatic glands lining their lumen were found to be tall cylindrical secretory cells with short stubby microvilli, protuberances of the apical cytoplasm, and with bleb formation. Abundant secretory vacuoles and granules, rough endoplasmic reticulum, developed Golgi complexes and numerous mitochondria are characteristic of their active secretory configuration with apocrine (apical blebs) and merocrine (secretory vacuoles and granules) type of secretion. Basal (reserve) cells were seen to be located between the secretory (luminal) cells and the basement membrane. Their ground cytoplasm is dense with rough endoplasmic reticulum and mitochondria. Their nuclei, unlike those of secretory cells, possess more peripheral condensed chromatin, denser dispersed chromatin and sporadic nucleoli. Besides the two basic types of mature prostatic cells intermediary cells were also seen, located between the basal and secretory cells or in their close vicinity. Their cytoplasm exhibits numerous profiles of rough endoplasmic reticulum and free ribosomes. Secretory vacuoles and granules were mostly practically absent (type 1 intermediary cells) so that they resembled basal (reserve) cells. In some of them, however, as in secretory cells, such secretory elements do gradually appear (type 2 intermediary cells). The finding of intermediary cells in the lining of prostatic glands supports the role of basal (reserve) cells in the renewal of cells in glands of the female prostate. The first ultrastructural analysis of the normal female prostate performed by transmission electron microscopy showed that, as in the postpubertal male, the prostatic glands in the adult female display mature secretory and basal cells. The results of the presented study further corroborate the contemporary concept of the female prostate as a functional genitourinary organ.


title: News Stream Summarization using Burst Information Networks

abstract: This paper studies summarizing key information from news streams. We propose simple yet effective models to solve the problem based on a novel and promising representation of text streams ‐ Burst Information Networks (BINets). A BINet can be aware of redundant information, allows global analysis of a text stream, and can be efficiently built and dynamically updated, which perfectly fits the demands of text stream summarization. Extensive experiments show that the BINet-based approaches are not only efficient and can be used in a real-time online summarization setting, but also can generate high-quality summaries, outperforming the state-of-the-art approach.


title: Bayesian lasso regression

abstract: The lasso estimate for linear regression corresponds to a posterior mode when independent, double-exponential prior distributions are placed on the regression coefficients. This paper introduces new aspects of the broader Bayesian treatment of lasso regression. A direct characterization of the regression coefficients' posterior distribution is provided, and computation and inference under this characterization is shown to be straightforward. Emphasis is placed on point estimation using the posterior mean, which facilitates prediction of future observations via the posterior predictive distribution. It is shown that the standard lasso prediction method does not necessarily agree with model-based, Bayesian predictions. A new Gibbs sampler for Bayesian lasso regression is introduced. Copyright 2009, Oxford University Press.


title: Inferring social roles and statuses in social networks

abstract: Users in online social networks play a variety of social roles and statuses. For example, users in Twitter can be represented as advertiser, content contributor, information receiver, etc; users in Linkedin can be in different professional roles, such as engineer, salesperson and recruiter. Previous research work mainly focuses on using categorical and textual information to predict the attributes of users. However, it cannot be applied to a large number of users in real social networks, since much of such information is missing, outdated and non-standard. In this paper, we investigate the social roles and statuses that people act in online social networks in the perspective of network structures, since the uniqueness of social networks is connecting people. We quantitatively analyze a number of key social principles and theories that correlate with social roles and statuses. We systematically study how the network characteristics reflect the social situations of users in an online society. We discover patterns of homophily, the tendency of users to connect with users with similar social roles and statuses. In addition, we observe that different factors in social theories influence the social role/status of an individual user to various extent, since these social principles represent different aspects of the network. We then introduce an optimization framework based on Factor Conditioning Symmetry, and we propose a probabilistic model to integrate the optimization framework on local structural information as well as network influence to infer the unknown social roles and statuses of online users. We will present experiment results to show the effectiveness of the inference.


title: From sectoral systems of innovation to socio-technical systems Insights about dynamics and change from sociology and institutional theory

abstract: In the last decade ‘sectoral systems of innovation’ have emerged as a new approach in innovation studies. This article makes four contributions to the approach by addressing some open issues. The first contribution is to explicitly incorporate the user side in the analysis. Hence, the unit of analysis is widened from sectoral systems of innovation to socio-technical systems. The second contribution is to suggest an analytical distinction between systems, actors involved in them, and the institutions which guide actor’s perceptions and activities. Thirdly, the article opens up the black box of institutions, making them an integral part of the analysis. Institutions should not just be used to explain inertia and stability. They can also be used to conceptualise the dynamic interplay between actors and structures. The fourth contribution is to address issues of change from one system to another. The article provides a coherent conceptual multi-level perspective, using insights from sociology, institutional theory and innovation studies. The perspective is particularly useful to analyse long-term dynamics, shifts from one socio-technical system to another and the co-evolution of technology and society.


title: A Study of Deterministic Jitter in Crystal Oscillators

abstract: Crystal oscillators are widely used in electronic systems to provide reference timing signals. Even though they are designed to be highly stable, their performance can be deteriorated by several types of random noise sources and deterministic interferences. This paper investigates the phenomenon of timing jitter in crystal oscillators induced by the injection of deterministic interferences. It is shown that timing jitter is closely related to the phase and amplitude modulations of the oscillator response. A closed-form variational macromodel is proposed to quantify timing jitter as well as to qualitatively explain the interference mechanism. Analytical results and efficient numerical simulations are developed to explore how timing jitter depends on the frequency of the interfering signal. The methodology is tested by a crystal Pierce oscillator.


title: SOMKE: Kernel Density Estimation Over Data Streams by Sequences of Self-Organizing Maps

abstract: In this paper, we propose a novel method SOMKE, for kernel density estimation (KDE) over data streams based on sequences of self-organizing map (SOM). In many stream data mining applications, the traditional KDE methods are infeasible because of the high computational cost, processing time, and memory requirement. To reduce the time and space complexity, we propose a SOM structure in this paper to obtain well-defined data clusters to estimate the underlying probability distributions of incoming data streams. The main idea of this paper is to build a series of SOMs over the data streams via two operations, that is, creating and merging the SOM sequences. The creation phase produces the SOM sequence entries for windows of the data, which obtains clustering information of the incoming data streams. The size of the SOM sequences can be further reduced by combining the consecutive entries in the sequence based on the measure of Kullback-Leibler divergence. Finally, the probability density functions over arbitrary time periods along the data streams can be estimated using such SOM sequences. We compare SOMKE with two other KDE methods for data streams, the M-kernel approach and the cluster kernel approach, in terms of accuracy and processing time for various stationary data streams. Furthermore, we also investigate the use of SOMKE over nonstationary (evolving) data streams, including a synthetic nonstationary data stream, a real-world financial data stream and a group of network traffic data streams. The simulation results illustrate the effectiveness and efficiency of the proposed approach.


title: Directions in hybrid intelligence: complementing AI systems with human intelligence

abstract: Hybrid intelligence systems combine machine and human intelligence to overcome the shortcomings of existing AI systems. This paper reviews recent research efforts towards developing hybrid systems focusing on reasoning methods for optimizing access to human intelligence and on gaining comprehensive understanding of humans as helpers of AI systems. It concludes by discussing short and long term research directions.


title: Robust Non-Intrusive Load Monitoring (NILM) with unknown loads

abstract: A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households.


title: Coordination of Multiagents Interacting Under Independent Position and Velocity Topologies

abstract: We consider the coordination control for multiagent systems in a very general framework where the position and velocity interactions among agents are modeled by independent graphs. Different algorithms are proposed and analyzed for different settings, including the case without leaders and the case with a virtual leader under fixed position and velocity interaction topologies, as well as the case with a group velocity reference signal under switching velocity interaction. It is finally shown that the proposed algorithms are feasible in achieving the desired coordination behavior provided the interaction topologies satisfy the weakest possible connectivity conditions. Such conditions relate only to the structure of the interactions among agents while irrelevant to their magnitudes and thus are easy to verify. Rigorous convergence analysis is preformed based on a combined use of tools from algebraic graph theory, matrix analysis as well as the Lyapunov stability theory.


title: Using physiological measures in conjunction with other UX approaches for better understanding of the player's gameplay experiences

abstract: The goal of video games is to challenge and entertain the players. Successful video games deliver experience that impact players on a level of arousal. Therefore undertaking a user experience (UX) study is crucial to ensure that a game achieves both critical and financial success. However, traditional usability methods (observation, subjective reporting, questionnaire, and interview) have a number of limitations on game user research. In this study we capture player's physiological measures during a gameplay session, to indicate micro-events that have caused changes in their body signals. At the post-gameplay interviews we ask participants to comment and describe their feelings on the selected events. The aim of this study is not to over-interpret physiological measures, but on using blips in measures to help identify key points in a game, which we then use to investigate further with the participant. This approach provides a method that can identify not only the negative user experience and usability issues but also the events which have a positive impact on player's experience.


title: Submentalizing or Mentalizing in a Level 1 Perspective-Taking Task: A Cloak and Goggles Test

abstract: It has been proposed that humans possess an automatic system to represent mental states ('implicit mentalizing'). The existence of an implicit mentalizing system has generated considerable debate however, centered on the ability of various experimental paradigms to demonstrate unambiguously such mentalizing. Evidence for implicit mentalizing has previously been provided by the 'dot perspective task,' where participants are slower to verify the number of dots they can see when an avatar can see a different number of dots. However, recent evidence challenged a mentalizing interpretation of this effect by showing it was unaltered when the avatar was replaced with an inanimate arrow stimulus. Here we present an extension of the dot perspective task using an invisibility cloaking device to render the dots invisible on certain trials. This paradigm is capable of providing unambiguous evidence of automatic mentalizing, but no such evidence was found. Two further well-powered experiments used opaque and transparent goggles to manipulate visibility but found no evidence of automatic mentalizing, nor of individual differences in empathy or perspective-taking predicting performance, contradicting previous studies using the same design. The results cast doubt on the existence of an implicit mentalizing system, suggesting that previous effects were due to domain-general processes. (PsycINFO Database Record


title: Job Satisfaction in Britain: Individual and Job Related Factors

abstract: Recently there has been a resurgence of interest in the analysis of job satisfaction variables. Job satisfaction is correlated with labour market behaviour such as productivity, quits and absenteeism. In this paper four different measures of job satisfaction are related to a variety of personal and job characteristics. The data used are from the 28 240 British employees in the Workplace Employee Relations Survey, 1997. This data set is larger and more recent than in any previous studies. Four measures of job satisfaction that have not previously been used are considered: satisfaction with influence over job; satisfaction with amount of pay; satisfaction with sense of achievement; and satisfaction with respect from supervisors. The paper contributes to the literature by analysing job satisfaction with respect to industrial composition and occupations. One of the striking findings is that those in the education and health sectors are less satisfied with their pay but more satisfied with their sense of achievement. Further, it is found that employees who received job training were more satisfied than those who had no training opportunities. Unlike previous studies, it is found that married individuals have lower job satisfaction levels than the unmarried. Other results confirm those in the literature, such as women being more satisfied than men, and a U-shaped relationship between satisfaction and age.


title: Online bagging and boosting

abstract: Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been used mainly in batch mode, i.e., they require the entire training set to be available at once and, in some cases, require random access to the data. In this paper, we present online versions of bagging and boosting that require only one pass through the training data. We build on previously presented work by describing some theoretical results. We also compare the online and batch algorithms experimentally in terms of accuracy and running time.


title: A survey of document image classification: problem statement, classifier architecture and performance evaluation

abstract: Document image classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. There is great diversity in document image classifiers: they differ in the problems they solve, in the use of training data to construct class models, and in the choice of document features and classification algorithms. We survey this diverse literature using three components: the problem statement, the classifier architecture, and performance evaluation. This brings to light important issues in designing a document classifier, including the definition of document classes, the choice of document features and feature representation, and the choice of classification algorithm and learning mechanism. We emphasize techniques that classify single-page typeset document images without using OCR results. Developing a general, adaptable, high-performance classifier is challenging due to the great variety of documents, the diverse criteria used to define document classes, and the ambiguity that arises due to ill-defined or fuzzy document classes.


title: An empirical methodology for developing stockmarket trading systems using artificial neural networks

abstract: A great deal of work has been published over the past decade on the application of neural networks to stockmarket trading. Individual researchers have developed their own techniques for designing and testing these neural networks, and this presents a difficulty when trying to learn lessons and compare results. This paper aims to present a methodology for designing robust mechanical trading systems using soft computing technologies, such as artificial neural networks. This paper describes the key steps involved in creating a neural network for use in stockmarket trading, and places particular emphasis on designing these steps to suit the real-world constraints the neural network will eventually operate in. Such a common methodology brings with it a transparency and clarity that should ensure that previously published results are both reliable and reusable.


title: The effect of restaurant attributes on customers' expectations and experiences in formal full service restaurants in Port Elizabeth, South Africa

abstract: The purpose of this study was to assess the effect of restaurant attributes on customers’ expectations and experiences in formal full service restaurants. The attributes included in this research were food, service and ambience as independent variables and expectations and experiences as dependent variables. The aims were to: (a) assess restaurant attributes that are important for customers’ expectations and experiences, (b) to determine which restaurant attributes had a significant relationship with customers’ expectations and experiences. The questionnaire was based on Markovic, Raspor & Markovic’s (2010) research. In order to meet the surveys’ goals, correlation coefficient and regression analysis were conducted. The results of correlation coefficient reveal that all three restaurant attributes had a significant correlation (p<0.05) with expectations. The strongest correlation with expectations was service (r=0.76). Customers’ experiences showed that all the attributes had a weak to moderate (r≤0.5) positive correlation with customers’ experiences. The strongest correlation with experiences was food (r=0.54). The first regression model showed that all three dining attributes were significantly related (p<0.05) to customers’ expectations. The level of service (t=10.73) was rated as the most important attribute for expectations. The second regression model showed that all three dining attributes were significantly related (p<0.05) to experiences. The second model indicated that respondents rated food (t=7.51) as the most important attribute for experience. The results reveal that although good food is an essential component for customers’ experiences, however, the level of service plays a pivotal role for customers’ expectations in formal full service restaurants.


title: A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts

abstract: Owing to demand characteristics of spare part, demand forecasting for spare parts is especially difficult. Based on the properties of spare part demand, we develop a hybrid forecasting approach, which can synthetically evaluate autocorrelation of demand time series and the relationship of explanatory variables with demand of spare part. In the described approach, support vector machines (SVMs) are adapted to forecast occurrences of nonzero demand of spare part, and a hybrid mechanism for integrating the SVM forecast results and the relationship of occurrence of nonzero demand with explanatory variables is proposed. Using real data sets of 30 kinds of spare parts from a petrochemical enterprise in China, we show that our method produces more accurate forecasts of distribution of lead-time demands of spare parts than do current methods across almost all the lead times.


title: A Mobile Vision System for Urban Detection with Informative Local Descriptors

abstract: We present a computer vision system for the detection and identification of urban objects from mobile phone imagery, e.g., for the application of tourist information services. Recognition is based on MAP decision making over weak object hypotheses from local descriptor responses in the mobile imagery. We present an improvement over the standard SIFT key detector [7] by selecting only informative (i-SIFT) keys for descriptor matching. Selection is applied first to reduce the complexity of the object model and second to accelerate detection by selective filtering. We present results on the MPG-20 mobile phone imagery with severe illumination, scale and viewpoint changes in the images, performing with ≈ 98% accuracy in identification, efficient (100%) background rejection, efficient (0%) false alarm rate, and reliable quality of service under extreme illumination conditions, significantly improving standard SIFT based recognition in every sense, providing - important for mobile vision - runtimes which are ≈ 8 (≈24) times faster for the MPG-20 (ZuBuD) database.


title: A multipath channel model for wideband aeronautical telemetry

abstract: The paper presents a multipath channel model for wideband aeronautical telemetry links at DoD test ranges. Channel sounding data were collected at Edwards AFB, California, at both L-band and lower S-band. Frequency domain analysis techniques were used to evaluate candidate channel models. The channel model is composed of three propagation paths: a line-of-sight path and two specular reflections. The first specular reflection is characterized by a relative amplitude of 70% to 96% of the line-of-sight amplitude and and a delay of 10-80 ns. This path is the result of "ground bounces" off the dry lake bed at Edwards and is a typical terrain feature at DoD test ranges located in the western USA. The amplitude and delay of this path are defined completely by the flight path geometry. The second path is a much lower amplitude path with a longer delay. The gain of this path is well modeled as a zero-mean complex Gaussian random variable. The relative amplitude is on the order of 2% to 8% of the line-of-sight amplitude. The mean excess delay is 155 ns with an RMS delay spread of 74 ns.


title: Improving palliative care with deep learning

abstract: Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.


title: PERGAMON A real-coded genetic algorithm for training recurrent neural networks

abstract: Abstract The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference.


title: Overview of the High Efficiency Video Coding (HEVC) Standard

abstract: High Efficiency Video Coding (HEVC) is currently being prepared as the newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. The main goal of the HEVC standardization effort is to enable significantly improved compression performance relative to existing standards-in the range of 50% bit-rate reduction for equal perceptual video quality. This paper provides an overview of the technical features and characteristics of the HEVC standard.


title: Frameworks for entity matching: A comparison

abstract: Entity matching is a crucial and difficult task for data integration. Entity matching frameworks provide several methods and their combination to effectively solve different match tasks. In this paper, we comparatively analyze 11 proposed frameworks for entity matching. Our study considers both frameworks which do or do not utilize training data to semi-automatically find an entity matching strategy to solve a given match task. Moreover, we consider support for blocking and the combination of different match algorithms. We further study how the different frameworks have been evaluated. The study aims at exploring the current state of the art in research prototypes of entity matching frameworks and their evaluations. The proposed criteria should be helpful to identify promising framework approaches and enable categorizing and comparatively assessing additional entity matching frameworks and their evaluations.


NEXMark – A Benchmark for Queries over Data Streams DRAFT


title: Interpretable Credit Application Predictions With Counterfactual Explanations

abstract: We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations. Counterfactual explanations expose the minimal changes required on the input data to obtain a different result e.g., approved vs rejected application. Despite their effectiveness, counterfactuals are mainly designed for changing an undesired outcome of a prediction i.e. loan rejected. Counterfactuals, however, can be difficult to interpret, especially when a high number of features are involved in the explanation. Our contribution is two-fold: i) we propose positive counterfactuals, i.e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to generate more interpretable counterfactuals. Experiments on the HELOC loan applications dataset show that our contribution outperforms the baseline counterfactual generation strategy, by leading to smaller and hence more interpretable counterfactuals.


title: Invertible Residual Networks

abstract: We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation. Typically, enforcing invertibility requires partitioning dimensions or restricting network architectures. In contrast, our approach only requires adding a simple normalization step during training, already available in standard frameworks. Invertible ResNets define a generative model which can be trained by maximum likelihood on unlabeled data. To compute likelihoods, we introduce a tractable approximation to the Jacobian log-determinant of a residual block. Our empirical evaluation shows that invertible ResNets perform competitively with both state-of-the-art image classifiers and flow-based generative models, something that has not been previously achieved with a single architecture.


title: Fuzzy relevance vector machine for learning from unbalanced data and noise

abstract: Handing unbalanced data and noise are two important issues in the field of machine learning. This paper proposed a complete framework of fuzzy relevance vector machine by weighting the punishment terms of error in Bayesian inference process of relevance vector machine (RVM). Above problems can be learned within this framework with different kinds of fuzzy membership functions. Experiments on both synthetic data and real world data demonstrate that fuzzy relevance vector machine (FRVM) is effective in dealing with unbalanced data and reducing the effects of noises or outliers.


title: Model-Driven Software Development: Technology, Engineering, Management

abstract: Part I: Introduction. 1. Introduction. 2. MDSD - Basic Ideas and Terminology. 3. Case Study: A Typical Web Application. 4. Concept Formation. 5. Classification. Part II: Domain Architectures. 6. Metamodeling. 7. MDSD-Capable Target Architectures. 8. Building Domain Architectures. 9. Code Generation Techniques. 10. Model Transformation Techniques. 11. MDSD Tools: Roles, Architecture, Selection Criteria, and Pointers. 12. The MDA Standard. Part III: Processes and Engineering. 13. MDSD Process Building Blocks and Best Practices. 14. Testing. 15. Versioning. 16. Case Study: Embedded Component Infrastructures. 17. Case Study: An Enterprise System. Part IV: Management. 18. Decision Support. 1.9 Organizational Aspects. 20. Adoption Strategies for MDSD. References. Index.


title: Bandwidth Investigation on Half-Height Pin in Ridge Gap Waveguide

abstract: Gap waveguide is a promising transmission structure, especially for millimeter-wave (mmW) and terahertz applications. It does not require conductive connection between the upper and the lower plates, which makes this technology gain advantages over conventional rectangular waveguides and substrate integrated waveguides in an mmW and terahertz regime. Different fabrication methods for gap waveguides should be employed for different frequency bands applied, such as molding, milling, die forming, electrical discharge machining, microelectromechanical systems, and 3-D printing. Therefore, different pin forms used in gap waveguides are required to match the applied fabrication methods. In this paper, a new pin form, the half-height-pin form, in gap waveguides is proposed for reducing the fabrication cost, and its stopband characteristics are investigated and compared with the previous full-height pins in gap waveguides at the $V$ -band. A device of a double-ridged gap waveguide with two 90° bends for verifying the stopband characteristic analysis of the new pin form has been designed and manufactured. The measured data confirm our analysis and simulations.


title: Satellite handover techniques for LEO networks

abstract: Low earth orbit satellite constellations could play an important role in future mobile communication networks due to their characteristics, such as global coverage and low propagation delays. However, because of the non-stationarity of the satellites, a call may be subjected to handovers, which can be cell or satellite handovers. Quite many techniques have been proposed in the literature dealing with the cell handover issue. In this paper, a satellite handover procedure is proposed, that investigates and exploits the partial satellite diversity (namely, the existing common coverage area between contiguous satellites) in order to provide an efficient handover strategy, based always on a tradeoff of blocking and forced termination probabilities for a fair treatment of new and handover calls. Three different criteria were examined for the selection of a satellite. Each one of them could be applied either to new or handover calls, therefore we investigated nine different service schemes. A simulation tool was implemented in order to compare the different service schemes and simulation results are presented at the end of the paper.


title: Neural networks for sentiment analysis on Twitter

abstract: The online medium has become a significant way that people express their opinions online. Sentiment analysis can be used to find out the polarity of an opinion, such as positive, negative, or neutral. Sentiment analysis has applications such as companies getting their customer's opinions on their products, political sentiment analysis, or opinions on movie reviews. Recent research has involved looking at text from online blogs, tweets, online movie reviews, etc. to try and classify the text as being positive, negative, or neutral. For this research, a feedforward neural network will be experimented with for sentiment analysis of tweets. The training set of tweets are collected using the Twitter API using positive and negative keywords. The testing set of tweets are collected using the same positive and negative keywords.


title: Boosting Combinatorial Problem Modeling with Machine Learning

abstract: In the past few years, the area of Machine Learning (ML) has witnessed tremendous advancements, becoming a pervasive technology in a wide range of applications. One area that can significantly benefit from the use of ML is Combinatorial Optimization. The three pillars of constraint satisfaction and optimization problem solving, i.e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness. In this survey we focus on the modeling component, whose effectiveness is crucial for solving the problem. The modeling activity has been traditionally shaped by optimization and domain experts, interacting to provide realistic results. Machine Learning techniques can tremendously ease the process, and exploit the available data to either create models or refine expert-designed ones. In this survey we cover approaches that have been recently proposed to enhance the modeling process by learning either single constraints, objective functions, or the whole model. We highlight common themes to multiple approaches and draw connections with related fields of research.


Commonsense Knowledge Aware Conversation Generation with Graph Attention


title: On the Interplay between Incentive Compatibility and Envy Freeness

abstract: We study mechanisms for an allocation of goods among agents, where agents have no incentive to lie about their true values (incentive compatible) and for which no agent will seek to exchange outcomes with another (envy-free). Mechanisms satisfying each requirement separately have been studied extensively, but there are few results on mechanisms achieving both. We are interested in those allocations for which there exist payments such that the resulting mechanism is simultaneously incentive compatible and envy-free. Cyclic monotonicity is a characterization of incentive compatible allocations, local efficiency is a characterization for envy-free allocations. We combine the above to give a characterization for allocations which are both incentive compatible and envy free. We show that even for allocations that allow payments leading to incentive compatible mechanisms, and other payments leading to envy free mechanisms, there may not exist any payments for which the mechanism is simultaneously incentive compatible and envy-free. The characterization that we give lets us compute the set of Pareto-optimal mechanisms that trade off envy freeness for incentive compatibility.


title: caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research.

abstract: The authors report on the development of the Cancer Tissue Information Extraction System (caTIES)dan application that supports collaborative tissue banking and text mining by leveraging existing natural language processing methods and algorithms, grid communication and security frameworks, and query visualization methods. The system fills an important need for textderived clinical data in translational research such as tissue-banking and clinical trials. The design of caTIES addresses three critical issues for informatics support of translational research: (1) federation of research data sources derived from clinical systems; (2) expressive graphical interfaces for concept-based text mining; and (3) regulatory and security model for supporting multicenter collaborative research. Implementation of the system at several Cancer Centers across the country is creating a potential network of caTIES repositories that could provide millions of de-identified clinical reports to users. The system provides an end-to-end application of medical natural language processing to support multiinstitutional translational research programs.


title: Statistical Analysis of Big Data on Pharmacogenomics

abstract: This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed.


The Great Time Series Classification Bake Off : A Review and Experimental Evaluation of Recently Proposed Algorithms


title: Tactile Sensing Using Force Sensing Resistors and a Super-Resolution Algorithm

abstract: This paper presents a tactile sensor consisting of an array of force sensing resistors (FSRs). The tactile sensing array can be seen as a coordinated system of touch sensors. The low spatial resolution measured with the FSRs compared to other force or pressure sensors required the use of a super-resolution algorithm. Super-resolution algorithms are often used in digital image processing to enhance the resolution of images. Multiple images taken from slightly different orientations are superimposed in such a way that a single higher-resolution image is obtained. Different touch sensors are briefly discussed and the use of FSRs is motivated. Image-registration techniques are discussed and the super-resolution algorithm developed for the application is presented. Some tests performed using the tactile sensor in a neck palpation device and the results of these tests are also presented.


title: Fast and Accurate Closest Point Search on Triangulated Surfaces and its Application to Head Motion Estimation

abstract: The Iterative Closest Point (ICP) algorithm is widely used to register two roughly aligned surfaces. Its most expensive step is the search for the closest point. Many efficient variants have been proposed to speed up this step, however they do not guarantee that the closest point on a triangulated surface is found. Instead they might result in an approximation of the closest point. In this paper we present a method for the closest point search that is fast and exact. The method was implemented and used to evaluate the accuracy of head motion estimation using dense 3D data obtained from a stereo camera system. The results show that the accuracy of the estimated head motion is better than 1 mm for translational movements and better than 1 degree for rotations. Key-Words: Iterative Closest Point, 3D Registration, Stereo, Head Motion Estimation


title: Early versus late fusion in semantic video analysis

abstract: Semantic analysis of multimodal video aims to index segments of interest at a conceptual level. In reaching this goal, it requires an analysis of several information streams. At some point in the analysis these streams need to be fused. In this paper, we consider two classes of fusion schemes, namely early fusion and late fusion. The former fuses modalities in feature space, the latter fuses modalities in semantic space. We show by experiment on 184 hours of broadcast video data and for 20 semantic concepts, that late fusion tends to give slightly better performance for most concepts. However, for those concepts where early fusion performs better the difference is more significant.


Comparing Website Fingerprinting Attacks and Defenses


title: Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification

abstract: In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants. We will look at how the various characteristics of visual stimulation affect the measured electro-physiological response of the brain and describe the optimal parameters found that elicit a steady-state visually evoked potential (SSVEP) in certain parts of the cerebral cortex where it can be reliably perceived by the electrode of the EEG device. After that, we continue with a description of the advanced machine learning pipeline model that can perform confident classification of the collected EEG data in order to (a) reliably distinguish signal from noise (about 85% validation score) and (b) reliably distinguish between EEG records collected from different human participants (about 80% validation score). Finally, we demonstrate that the proposed method works reliably even with an inexpensive (less than $100) consumer-grade EEG sensing device and with participants who do not have previous experience with EEG technology (EEG illiterate). All this in combination opens up broad prospects for the development of new types of consumer devices, [e.g.] based on virtual reality helmets or augmented reality glasses where EEG sensor can be easily integrated. The proposed method can be used to improve an online user experience by providing [e.g.] password-less user identification for VR / AR applications. It can also find a more advanced application in intensive care units where collected EEG data can be used to classify the level of conscious awareness of patients during anesthesia or to automatically detect hardware failures by classifying the input signal as noise.


title: Interactive record/replay for web application debugging

abstract: During debugging, a developer must repeatedly and manually reproduce faulty behavior in order to inspect different facets of the program's execution. Existing tools for reproducing such behaviors prevent the use of debugging aids such as breakpoints and logging, and are not designed for interactive, random-access exploration of recorded behavior. This paper presents Timelapse, a tool for quickly recording, reproducing, and debugging interactive behaviors in web applications. Developers can use Timelapse to browse, visualize, and seek within recorded program executions while simultaneously using familiar debugging tools such as breakpoints and logging. Testers and end-users can use Timelapse to demonstrate failures in situ and share recorded behaviors with developers, improving bug report quality by obviating the need for detailed reproduction steps. Timelapse is built on Dolos, a novel record/replay infrastructure that ensures deterministic execution by capturing and reusing program inputs both from the user and from external sources such as the network. Dolos introduces negligible overhead and does not interfere with breakpoints and logging. In a small user evaluation, participants used Timelapse to accelerate existing reproduction activities, but were not significantly faster or more successful in completing the larger tasks at hand. Together, the Dolos infrastructure and Timelapse developer tool support systematic bug reporting and debugging practices.


title: Detecting Algorithmically Generated Domain-Flux Attacks With DNS Traffic Analysis

abstract: Recent botnets such as Conficker, Kraken, and Torpig have used DNS-based "domain fluxing" for command-and-control, where each Bot queries for existence of a series of domain names and the owner has to register only one such domain name. In this paper, we develop a methodology to detect such "domain fluxes" in DNS traffic by looking for patterns inherent to domain names that are generated algorithmically, in contrast to those generated by humans. In particular, we look at distribution of alphanumeric characters as well as bigrams in all domains that are mapped to the same set of IP addresses. We present and compare the performance of several distance metrics, including K-L distance, Edit distance, and Jaccard measure. We train by using a good dataset of domains obtained via a crawl of domains mapped to all IPv4 address space and modeling bad datasets based on behaviors seen so far and expected. We also apply our methodology to packet traces collected at a Tier-1 ISP and show we can automatically detect domain fluxing as used by Conficker botnet with minimal false positives, in addition to discovering a new botnet within the ISP trace. We also analyze a campus DNS trace to detect another unknown botnet exhibiting advanced domain-name generation technique.


title: High accuracy indoor localization: A WiFi-based approach

abstract: Indoor positioning systems (IPS) based on Wi-Fi signals are gaining popularity recently. IPS based on Received Signal Strength Indicator (RSSI) could only achieve a precision of several meters due to the strong temporal and spatial variation of indoor environment. On the other hand, IPS based on Channel State Information (CSI) drive the precision into the sub-meter regime with several access points (AP). However, the performance degrades with fewer APs mainly due to the limit of bandwidth. In this paper, we propose a Wi-Fi-based time-reversal indoor positioning system (WiFi-TRIPS) using the location-specific fingerprints generated by CSIs with a total bandwidth of 1 GHz. WiFi-TRIPS consists of an offline phase and an online phase. In the offline phase, CSIs are collected in different 10 MHz bands from each location-of-interest and the timing and frequency synchronization errors are compensated. We perform a bandwidth concatenation to combine CSIs in different bands into a single fingerprint of 1 GHz. In the online phase, we evaluate the time-reversal resonating strength using the fingerprint from an unknown location and those in the database for location estimation. Extensive experiment results demonstrate a perfect 5cm precision in an 20cm × 70cm area in a non-line-of-sight office environment with one link measurement.


title: Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes

abstract: Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actions and receives observations and rewards from the environment. Many POMDP solution methods are based on computing a belief state, which is a probability distribution over possible states in which the agent could be. The action choice of the agent is then based on the belief state. The belief state is computed based on a model of the environment, and the history of actions and observations seen by the agent. However, reward information is not taken into account in updating the belief state. In this paper, we argue that rewards can carry useful information that can help disambiguate the hidden state. We present a method for updating the belief state which takes rewards into account. We present experiments with exact and approximate planning methods on several standard POMDP domains, using this belief update method, and show that it can provide advantages, both in terms of speed and in terms of the quality of the solution obtained.


title: The multi‐tasking paradox: perceptions, problems and strategies

abstract: Purpose – The purpose of this article is to describe multi‐tasking behaviour in the workplace; to link its cause to the increasing prevalence of low‐cost information and communications technologies and to the changing organizational structures that have evolved to meet the demands and opportunities of these technologies.Design/methodology/approach – This article is a presentation of the current literature on multi‐tasking behaviour among knowledge workers with a selective bibliography addressing empirical research into the behavioural, managerial and technological aspects of this phenomenon. It then expands to comprehensive coverage of the literature on past and current thinking about task structuring, strategies for coping in a multi‐tasking environment and the changing nature of work and organizations, which fuels the need to multi‐task in response to these changes.Findings – Among knowledge workers, multi‐tasking behaviour appears to be an inevitable consequence of the presence of increasingly easy acc...


title: Spatially Augmented Reality

abstract: To create an effective illusion of virtual objects coexisting with the real world, see-through HMD-based Augmented Reality techniques supplement the user's view with images of virtual objects. We introduce here a new paradigm, Spatially Augmented Reality (SAR), where virtual objects are rendered directly within or on the user's physical space. A key benefit of SAR is that the user does not need to wear a head-mounted display. Instead, with the use of spatial displays, wide field of view and possibly high-resolution images of virtual objects can be integrated directly into the environment. For example, the virtual objects can be realized by using digital light projectors to "paint" 2D/3D imagery onto real surfaces, or by using built-in flat panel displays. In this paper we present the rendering method used in our implementation and discuss the fundamentally different visible artifacts that arise as a result of errors in tracker measurements. Finally, we speculate about how SAR techniques might be combined with see-through AR to provide an even more compelling AR experience.


title: Optimizing CNNs on Multicores for Scalability, Performance and Goodput

abstract: Convolutional Neural Networks (CNN) are a class of Ar- tificial Neural Networks (ANN) that are highly efficient at the pattern recognition tasks that underlie difficult AI prob- lems in a variety of domains, such as speech recognition, object recognition, and natural language processing. CNNs are, however, computationally intensive to train. This paper presents the first characterization of the per- formance optimization opportunities for training CNNs on CPUs. Our characterization includes insights based on the structure of the network itself (i.e., intrinsic arithmetic inten- sity of the convolution and its scalability under parallelism) as well as dynamic properties of its execution (i.e., sparsity of the computation). Given this characterization, we present an automatic framework called spg-CNN for optimizing CNN training on CPUs. It comprises of a computation scheduler for efficient parallel execution, and two code generators: one that opti- mizes for sparsity, and the other that optimizes for spatial reuse in convolutions. We evaluate spg-CNN using convolutions from a variety of real world benchmarks, and show that spg-CNN can train CNNs faster than state-of-the-art approaches by an order of magnitude.


title: MAUI: a multimodal affective user interface

abstract: Human intelligence is being increasingly redefined to include the all-encompassing effect of emotions upon what used to be considered 'pure reason'. With the recent progress of research in computer vision, speech/prosody recognition, and bio-feedback, real-time recognition of affect will enhance human-computer interaction considerably, as well as assist further progress in the development of new emotion theories.In this article, we describe how affect, moods and emotions closely interact with cognition and how affect and emotion are the quintessential multimodal processes in humans. We then propose an adaptive system architecture designed to sense the user's emotional and affective states via three multimodal subsystems (V, K, A): namely (1) the Visual (from facial images and videos), (2) Kinesthetic (from autonomic nervous system (ANS) signals), and (3) Auditory (from speech). The results of the system sensing are then integrated into the multimodal perceived multimodal anthropomorphic interface agent then adapts its interface by responding most appropriately to the current emotional states of its user, and provides intelligent multi-modal feedback to the user.


title: Scribbles to vectors: preparation of scribble drawings for CAD interpretation

abstract: This paper describes the work carried out on off-line paper based scribbles such that they can be incorporated into a sketch-based interface without forcing designers to change their natural drawing habits. In this work, the scribbled drawings are converted into a vectorial format which can be recognized by a CAD system. This is achieved by using pattern analysis techniques, namely the Gabor filter to simplify the scribbled drawing. Vector line are then extracted from the resulting drawing by means of Kalman filtering.


title: Natural Language Interfaces to Database (NLIDB): Question handling and unit conversion

abstract: Natural Language Interfaces to Database is a type of database interface that allows the user to access the data using natural language. Recent development used as the base of mis paper was made by Wibisono in 2013. Wibisono developed a domain-independent NLIDB using Stanford Dependency Parser as a tool in processing user input. This paper presents a solution to two of Wibisono's NLIDB problems, which are inability to process question-type queries and unit conversion. User input will first be identified, whether it is a question-type query or a directive-type query. Methods from Wibisono's NLIDB are then modified in order to be able to process question-type query. Unit conversion is done using a library called JScience in two phases of the process, before the SQL is produced (if necessary), and before the data is shown to the user. The proposed system was tested and was able to process some questions that follow a pattern and also to convert data in measurement units and some currency units.


Investigation of Senone-based Long-Short Term Memory RNNs for Spoken Language Recognition.


title: Domain Ontology Induction Using Word Embeddings

abstract: Ontology, the shared formal conceptualization of domain information, has been shown to have multiple applications in modeling, processing and understanding natural language text. In this work, we use distributed word vectors out of various recent language models from Deep Learning for semi-automated domain ontology creation for closed domains. We cover all major aspects of Domain Ontology Induction or Learning like concept identification, attribute identification, taxonomical and non-taxonomical relationship identification using the distributed word vectors. Preliminary results show that simple clustering based methods using distributed word vectors from these language models outperforms methods using models like LSI in ontology learning for closed domains.


title: Can characters reveal your native language? A language-independent approach to native language identification

abstract: A common approach in text mining tasks such as text categorization, authorship identification or plagiarism detection is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. In this work, an approach that uses character n-grams as features is proposed for the task of native language identification. Instead of doing standard feature selection, the proposed approach combines several string kernels using multiple kernel learning. Kernel Ridge Regression and Kernel Discriminant Analysis are independently used in the learning stage. The empirical results obtained in all the experiments conducted in this work indicate that the proposed approach achieves state of the art performance in native language identification, reaching an accuracy that is 1.7% above the top scoring system of the 2013 NLI Shared Task. Furthermore, the proposed approach has an important advantage in that it is language independent and linguistic theory neutral. In the cross-corpus experiment, the proposed approach shows that it can also be topic independent, improving the state of the art system by 32.3%.


title: Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs

abstract: Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantitative results to answer following research questions for the dataset: 1) What loss functions to use for training DCNN from scratch on ChestX-ray14 dataset that demonstrates high class imbalance and label co occurrence? 2) How to use cascading to model label dependency and to improve accuracy of the deep learning model?


title: An End-to-End Compression Framework Based on Convolutional Neural Networks

abstract: Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high level vision applications such as recognition and understanding. However, it is rarely used to solve low-level vision problems such as image compression studied in this paper. Here, we move forward a step and propose a novel compression framework based on CNNs. To achieve high-quality image compression at low bit rates, two CNNs are seamlessly integrated into an end-to-end compression framework. The first CNN, named compact convolutional neural network (ComCNN), learns an optimal compact representation from an input image, which preserves the structural information and is then encoded using an image codec (e.g., JPEG, JPEG2000 or BPG). The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end. To make two CNNs effectively collaborate, we develop a unified end-to-end learning algorithm to simultaneously learn ComCNN and RecCNN, which facilitates the accurate reconstruction of the decoded image using RecCNN. Such a design also makes the proposed compression framework compatible with existing image coding standards. Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with state-of-the-art deblocking or denoising post-processing methods.


title: Cloud Computing: A Review

abstract: Cloud computing is the next generation in computation. Cloud computing is a recent trend in IT that moves computing and data away from desktop and portable PCs into large data centers. It implies a service oriented architecture through offering software’s and platforms as services, reduced information technology overhead for the enduser, great flexibility, reduced total cost of ownership, on demand services,security,Efficiency and Bandwidth etc.. Keyword– Cloud Computing, Virtualization, On Demand Service, Hypervisor, Virtual Machine.


title: Mobile robot localization by GPS and sequential appearance-based place recognition

abstract: In this paper, we propose a mobile robot localization by GPS observations and appearance-based place recognition. These are critical issues for achieving a high accuracy and stable localization due to some challenging problems that GPS observations face still such as multipath and signal lost, especially under situations where there are narrow streets and so on. The appearance-based place recognition method that is combined with positional information has capability to overcome the issue. We apply both of observations derived from GPS and appearance-based place recognition to a mobile robot localization for the sake of achieving robust localization. Moreover sequential appearance-based place recognition makes it possible to recognize their own position even when we navigate a robot on a night. Our system uses not only multiple observations but also dead reckoning with gyrodometry model. To verify the validity of the proposed method, our experiment is conducted through an outdoor course.


title: Connected Car: Quantified Self becomes Quantified Car

abstract: The automotive industry could be facing a situation of profound change and opportunity in the coming decades. There are a number of influencing factors such as increasing urban and aging populations, self-driving cars, 3D parts printing, energy innovation, and new models of transportation service delivery (Zipcar, Uber). The connected car means that vehicles are now part of the connected world, continuously Internet-connected, generating and transmitting data, which on the one hand can be helpfully integrated into applications, like real-time traffic alerts broadcast to smartwatches, but also raises security and privacy concerns. This paper explores the automotive connected world, and describes five killer QS (Quantified Self)-auto sensor applications that link quantified-self sensors (sensors that measure the personal biometrics of individuals like heart rate) and automotive sensors (sensors that measure driver and passenger biometrics or quantitative automotive performance metrics like speed and braking activity). The applications are fatigue detection, real-time assistance for parking and accidents, anger management and stress reduction, keyless authentication and digital identity verification, and DIY diagnostics. These kinds of applications help to demonstrate the benefit of connected world data streams in the automotive industry and beyond where, more fundamentally for human progress, the automation of both physical and now cognitive tasks is underway.


title: A Study of Entanglement in a Categorical Framework of Natural Language

abstract: In both quantum mechanics and corpus linguistics based on vector spaces, the notion of entanglement provides a means for the various subsystems to communicate with each other. In this paper we examine a number of implementations of the categorical framework of Coecke et al. [4] for natural language, from an entanglement perspective. Specifically, our goal is to better understand in what way the level of entanglement of the relational tensors (or t he lack of it) affects the compositional structures in practical situations. Our findings reveal tha t a number of proposals for verb construction lead to almost separable tensors, a fact that considerably s implifies the interactions between the words. We examine the ramifications of this fact, and we show t hat the use of Frobenius algebras mitigates the potential problems to a great extent. Finally, we briefly examine a machine learning method that creates verb tensors exhibiting a sufficient lev el of entanglement.


title: Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner

abstract: Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries -- captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after adaptation. Utilizing critics during inference further gives another 4.5% boost.


A Design Framework for Cell Production Systems. From the Viewpoint of Autonomy and Decentralization.


AN EFFICIENT APPROACH TO SOLVING THE MINIMUM SUDOKU PROBLEM


title: PMCVec: Distributed Phrase Representation for Biomedical Text Processing

abstract: Abstract Distributed semantic representation of biomedical text can be beneficial for text classification, named entity recognition, query expansion, human comprehension, and information retrieval. Despite the success of high-quality vector space models such as Word2Vec and GloVe, they only provide unigram word representations and the semantics for multi-word phrases can only be approximated by composition. This is problematic in biomedical text processing where technical phrases for diseases, symptoms, and drugs should be represented as single entities to capture the correct meaning. In this paper, we introduce PMCVec , an unsupervised technique that generates important phrases from PubMed abstracts and learns embeddings for single words and multi-word phrases simultaneously. Evaluations performed on benchmark datasets produce significant performance gains both qualitatively and quantitatively.


title: Special Issue on The Semantic Web for the Legal Domain - Guest Editors’ Editorial: The Next Step

abstract: Ontology-driven systems with reasoning capabilities in the legal field are now better understood. Legal concepts are not discrete, but make up a dynamic continuum between common sense terms, specific technical use, and professional knowledge, in an evolving institutional reality. Thus, the tension between a plural understanding of regulations and a more general understanding of law is bringing into view a new landscape in which general legal frameworks — grounded in well-known legal theories stemming from 20th-century c. legal positivism or sociological jurisprudence — is made compatible with specific forms of rights management on the Web. In this sense, Semantic Web tools are not only being designed for information retrieval, classification, clustering, and knowledge management. They can also be understood as regulatory tools, i.e. as components of the contemporary legal architecture, to be used by multiple stakeholders — front-line practitioners, policy-makers, legal drafters, companies, market agents, and citizens. That is the issue broadly addressed in this Special Issue on the Semantic Web for the Legal Domain, over-viewing the work carried out over the last fifteen years, and seeking to foster new research in this field, beyond the state of the art.


title: Context Threading: A Flexible and Efficient Dispatch Technique for Virtual Machine Interpreters

abstract: Direct-threaded interpreters use indirect branches to dispatch bytecodes, but deeply-pipelined architectures rely on branch prediction for performance. Due to the poor correlation between the virtual program's control flow and the hardware program counter, which we call the context problem, direct threading's indirect branches are poorly predicted by the hardware, limiting performance. Our dispatch technique, context threading, improves branch prediction and performance by aligning hardware and virtual machine state. Linear virtual instructions are dispatched with native calls and returns, aligning the hardware and virtual PC. Thus, sequential control flow is predicted by the hardware return stack. We convert virtual branching instructions to native branches, mobilizing the hardware's branch prediction resources. We evaluate the impact of context threading on both branch prediction and performance using interpreters for Java and OCaml on the Pentium and PowerPC architectures. On the Pentium IV, our technique reduces mean mispredicted branches by 95%. On the PowerPC, it reduces mean branch stall cycles by 75% for OCaml and 82% for Java. Due to reduced branch hazards, context threading reduces mean execution time by 25% for Java and by 19% and 37% for OCaml on the P4 and PPC970, respectively. We also combine context threading with a conservative inlining technique and find its performance comparable to that of selective inlining.


title: Integration of eHealth Tools in the Process of Workplace Health Promotion: Proposal for Design and Implementation

abstract: BACKGROUND ::: Electronic health (eHealth) and mobile health (mHealth) tools can support and improve the whole process of workplace health promotion (WHP) projects. However, several challenges and opportunities have to be considered while integrating these tools in WHP projects. Currently, a large number of eHealth tools are developed for changing health behavior, but these tools can support the whole WHP process, including group administration, information flow, assessment, intervention development process, or evaluation. ::: ::: ::: OBJECTIVE ::: To support a successful implementation of eHealth tools in the whole WHP processes, we introduce a concept of WHP (life cycle model of WHP) with 7 steps and present critical and success factors for the implementation of eHealth tools in each step. ::: ::: ::: METHODS ::: We developed a life cycle model of WHP based on the World Health Organization (WHO) model of healthy workplace continual improvement process. We suggest adaptations to the WHO model to demonstrate the large number of possibilities to implement eHealth tools in WHP as well as possible critical points in the implementation process. ::: ::: ::: RESULTS ::: eHealth tools can enhance the efficiency of WHP in each of the 7 steps of the presented life cycle model of WHP. Specifically, eHealth tools can support by offering easier administration, providing an information and communication platform, supporting assessments, presenting and discussing assessment results in a dashboard, and offering interventions to change individual health behavior. Important success factors include the possibility to give automatic feedback about health parameters, create incentive systems, or bring together a large number of health experts in one place. Critical factors such as data security, anonymity, or lack of management involvement have to be addressed carefully to prevent nonparticipation and dropouts. ::: ::: ::: CONCLUSIONS ::: Using eHealth tools can support WHP, but clear regulations for the usage and implementation of these tools at the workplace are needed to secure quality and reach sustainable results.


A Parallel and Scalable Processor for JSON Data


BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset


title: DnaSP, DNA polymorphism analyses by the coalescent and other methods

abstract: Summary: DnaSP is a software package for the analysis of DNA polymorphism data. Present version introduces several new modules and features which, among other options allow: (1) handling big data sets (∼5 Mb per sequence); (2) conducting a large number of coalescent-based tests by Monte Carlo computer simulations; (3) extensive analyses of the genetic differentiation and gene flow among populations; (4) analysing the evolutionary pattern of preferred and unpreferred codons; (5) generating graphical outputs for an easy visualization of results. Availability: The software package, including complete documentation and examples, is freely available to academic users from: http://www.ub.es/dnasp


title: Watergate: Visual Exploration of Water Trajectories in Protein Dynamics

abstract: The function of proteins is tightly related to their interactions with other molecules. The study of such interactions often requires to track the molecules that enter or exit specific regions of the proteins. This is investigated with molecular dynamics simulations, producing the trajectories of thousands of water molecules during hundreds of thousands of time steps. To ease the exploration of such rich spatio-temporal data, we propose a novel workflow for the analysis and visualization of large sets of water-molecule trajectories. Our solution consists of a set of visualization techniques, which help biochemists to classify, cluster, and filter the trajectories and to explore the properties and behavior of selected subsets in detail. Initially, we use an interactive histogram and a time-line visualization to give an overview of all water trajectories and select the interesting ones for further investigation. Further, we depict clusters of trajectories in a novel 2D representation illustrating the flows of water molecules. These views are interactively linked with a 3D representation where we show individual paths, including their simplification, as well as extracted statistical information displayed by isosurfaces. The proposed solution has been designed in tight collaboration with experts to support specific tasks in their scientific workflows. They also conducted several case studies to evaluate the usability and effectiveness of our new solution with respect to their research scenarios. These confirmed that our proposed solution helps in analyzing water trajectories and in extracting the essential information out of the large amount of input data.


Leak Detection via Dynamic Taint Analysis


title: Anaesthesia in arthrogryposis multiplex congenita: case report.

abstract: The anaesthetic management of an 11-year-old male with arthrogryposis multiplex congenita (AMC) with myopathic features is described. The child presented with an obstructing renal calculus and a pyelolithotomy via an anterior transverse subcostal extraperitoneal incision was performed. In view of the associated hypoplastic musculature, severe scoliosis and expected increased sensitivity to various sedatives and inhalational anaesthetic agents, low-dose ketamine (10-15 micrograms X kg-1 X min-1) was administered by continuous infusion, with a satisfactory result.


title: Filtering Spam E-Mail from Mixed Arabic and English Messages: A Comparison of Machine Learning Techniques

abstract: Spam is one of the main problems in emails communications. As the volume of non-english language spam increases, little work is done in this area. For example, in Arab world users receive spam written mostly in arabic, english or mixed Arabic and english. To filter this kind of messages, this research applied several machine learning techniques. Many researchers have used machine learning techniques to filter spam email messages. This study compared six supervised machine learning classifiers which are maximum entropy, decision trees, artificial neural nets, naive bayes, support system machines and k-nearest neighbor. The experiments suggested that words in Arabic messages should be stemmed before applying classifier. In addition, in most cases, experiments showed that classifiers using feature selection techniques can achieve comparable or better performance than filters do not used them.


title: Kodkod: A relational model finder

abstract: The key design challenges in the construction of a SAT-based relational model finder are described, and novel techniques are proposed to address them. An efficient model finder must have a mechanism for specifying partial solutions, an effective symmetry detection and breaking scheme, and an economical translation from relational to boolean logic. These desiderata are addressed with three new techniques: a symmetry detection algorithm that works in the presence of partial solutions, a sparse-matrix representation of relations, and a compact representation of boolean formulas inspired by boolean expression diagrams and reduced boolean circuits. The presented techniques have been implemented and evaluated, with promising results.


title: RUM Extractor: A Facebook Extractor for Data Analysis

abstract: Social Network Analysis (SNA) is a field of study that focuses on analyzing user profiles and participations on social network channels in order to model relationships between people and to predict certain behaviors or knowledge. To achieve their goals, researchers, interested in SNA, have to extract content and structure from the numerous social networks available today. Existing tools, which help in this task, often require substantial pre-processing or good programming skills which may not be available for all SNA researchers. This paper describes RUM, a data extraction tool which allows researchers to easily extract several types of content and structure that are available on Facebook pages. Consequently, the extracted data can be saved and analyzed. RUM Extractor is easy to set up and use, and it gives flexible options to users to specify the type and amount of content and structure they want to retrieve. The paper also demonstrates how RUM can be exploited by collecting and further analyzing data collected from two popular Arabic news pages.


title: A reliable gateway for in-vehicle networks based on LIN, CAN, and FlexRay

abstract: This article describes a reliable gateway for in-vehicle networks. Such networks include local interconnect networks, controller area networks, and FlexRay. There is some latency when transferring a message from one node (source) to another node (destination). A high probability of error exists due to different protocol specifications such as baud-rate, and message frame format. Therefore, deploying a reliable gateway is a challenge to the automotive industry. We propose a reliable gateway based on the OSEK/VDX components for in-vehicle networks. We also examine the gateway system developed, and then we evaluate the performance of our proposed system.


title: Multilevel Inverter Topology for Renewable Energy Grid Integration

abstract: In this paper, a novel three-phase parallel grid-connected multilevel inverter topology with a novel switching strategy is proposed. This inverter is intended to feed a microgrid from renewable energy sources (RES) to overcome the problem of the polluted sinusoidal output in classical inverters and to reduce component count, particularly for generating a multilevel waveform with a large number of levels. The proposed power converter consists of $n$ two-level $(n+1)$ phase inverters connected in parallel, where $n$ is the number of RES. The more the number of RES, the more the number of voltage levels, the more faithful is the output sinusoidal waveform. In the proposed topology, both voltage pulse width and height are modulated and precalculated by using a pulse width and height modulation so as to reduce the number of switching states (i.e., switching losses) and the total harmonic distortion. The topology is investigated through simulations and validated experimentally with a laboratory prototype. Compliance with the $\text{IEEE 519-1992}$ and $\text{IEC 61000-3-12}$ standards is presented and an exhaustive comparison of the proposed topology is made against the classical cascaded H-bridge topology.


title: Intelligent Fixture Design through a Hybrid System of Artificial Neural Network and Genetic Algorithm

abstract: In designing fixtures for machining operations, clamping scheme is a complex and highly nonlinear problem that entails the frictional contact between the workpiece and the clamps. Such parameters as contact area, state of contact, clamping force, wear and damage in the contact area and deformation of the component are of special interest. A viable fixture plan must include the optimum values of clamping forces. Along research efforts carried out in this area, this comprehensive problem in fixture design needs further investigation. In this study, a hybrid learning system that uses nonlinear finite element analysis (FEA) with a supportive combination of artificial neural network (ANN) and genetic algorithm (GA) is discussed. A frictional model of workpart--fixture system under cutting and clamping forces is solved through FEA. Training and querying an ANN takes advantage of the results of FEA. The ANN is required to recognize a pattern between the clamping forces and state of contact in the workpiece--fixture system and the workpiece maximum elastic deformation. Using the identified pattern, a GA-based program determines the optimum values for clamping forces that do not cause excessive deformation/stress in the component. The advantage of this work against similar studies is manifestation of exact state of contact between clamp elements and workpart. The results contribute to automation of fixture design task and computer aided process planning (CAPP).


title: Multi-Agent Path Finding with Delay Probabilities

abstract: Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to large MAPF instances by searching for MAPF plans on 2 levels: The high-level search resolves collisions between agents, and the low-level search plans paths for single agents under the constraints imposed by the high-level search. We make the following contributions to solve the MAPF problem with imperfect plan execution with small average makespans: First, we formalize the MAPF Problem with Delay Probabilities (MAPF-DP), define valid MAPF-DP plans and propose the use of robust plan-execution policies for valid MAPF-DP plans to control how each agent proceeds along its path. Second, we discuss 2 classes of decentralized robust plan-execution policies (called Fully Synchronized Policies and Minimal Communication Policies) that prevent collisions during plan execution for valid MAPF-DP plans. Third, we present a 2-level MAPF-DP solver (called Approximate Minimization in Expectation) that generates valid MAPF-DP plans.


title: DiFS: distributed flow scheduling for adaptive routing in hierarchical data center networks

abstract: Data center networks leverage multiple parallel paths connecting end host pairs to offer high bisection bandwidth for cluster computing applications. However, state of the art routing protocols such as Equal Cost Multipath (ECMP) is load-oblivious due to static flow-to-link assignments. They may cause bandwidth loss due to flow collisions. Recently proposed centralized scheduling algorithm or host based adaptive routing that require network-wide condition information may suffer from scalability problems. In this paper, we present Distributed Flow Scheduling (DiFS) based Adaptive Routing for hierarchical data center networks, which is a localized and switch-only solution. DiFS allows switches to cooperate to avoid over-utilized links and find available paths without centralized control. DiFS is scalable and can react quickly to dynamic traffic, because it is independently executed on switches and requires no synchronization. DiFS provides global bounds of flow balance based on local optimization. Extensive experiments show that the aggregate throughput of DiFS using various traffic patterns is much better than that of ECMP, and is similar to or higher than those of two representative protocols that use network-wide optimization.


title: Semi-supervised learning with very few labeled training examples

abstract: In semi-supervised learning, a number of labeled examples are usually required for training an initial weakly useful predictor which is in turn used for exploiting the unlabeled examples. However, in many real-world applications there may exist very few labeled training examples, which makes the weakly useful predictor difficult to generate, and therefore these semisupervised learning methods cannot be applied. This paper proposes a method working under a two-view setting. By taking advantages of the correlations between the views using canonical component analysis, the proposed method can perform semi-supervised learning with only one labeled training example. Experiments and an application to content-based image retrieval validate the effectiveness of the proposed method.


title: A practical solution to the string stability problem in autonomous vehicle following

abstract: This work describes an improved autonomous vehicle following control scheme based on inter-vehicle communication. A previously developed autonomous following algorithm uses an on-board laser scanning radar sensor (LIDAR) to measure the ego-vehicle's relative position with respect to its preceding vehicle, and the steering command is calculated for the ego-vehicle to follow the preceding vehicle. In this control scheme, the performance of the following vehicle largely depends on the behavior of the preceding vehicle. String stability becomes a serious issue when this control algorithm is applied to a platoon of vehicles. The deterioration of road tracking performance is another concern. The road tracking error of one vehicle is passed on to following vehicles, and the errors may accumulate as they propagate in the upstream direction. It is shown in the paper that inter-vehicle communication is a practical solution to this problem. Experimental and simulation results are presented.


title: The hormone resistin links obesity to diabetes

abstract: Diabetes mellitus is a chronic disease that leads to complications including heart disease, stroke, kidney failure, blindness and nerve damage. Type 2 diabetes, characterized by target-tissue resistance to insulin, is epidemic in industrialized societies and is strongly associated with obesity; however, the mechanism by which increased adiposity causes insulin resistance is unclear. Here we show that adipocytes secrete a unique signalling molecule, which we have named resistin (for resistance to insulin). Circulating resistin levels are decreased by the anti-diabetic drug rosiglitazone, and increased in diet-induced and genetic forms of obesity. Administration of anti-resistin antibody improves blood sugar and insulin action in mice with diet-induced obesity. Moreover, treatment of normal mice with recombinant resistin impairs glucose tolerance and insulin action. Insulin-stimulated glucose uptake by adipocytes is enhanced by neutralization of resistin and is reduced by resistin treatment. Resistin is thus a hormone that potentially links obesity to diabetes.


title: Learning an Intrinsic Image Decomposer Using Synthesized RGB-D Dataset

abstract: Intrinsic image decomposition refers to recover the albedo and shading from images, which is an ill-posed problem in signal processing. As realistic labeled data are severely lacking, it is difficult to apply learning methods in this issue. In this letter, we propose using a synthesized dataset to facilitate the solving of this problem. A physically based renderer is used to generate color images and their underlying ground-truth albedo and shading from three-dimensional models. Additionally, we render a Kinect-like noisy depth map for each instance. We utilize this synthetic dataset to train a deep neural network for intrinsic image decomposition and further fine-tune it for real-world images. Our model supports both RGB and RGB-D as input, and it employs both high-level and low-level features to avoid blurry outputs. Experimental results verify the effectiveness of our model on realistic images.


Questionnaire Evaluation with Factor Analysis and Cronbach’s Alpha An


title: Battery management system simulation using SystemC

abstract: One of the major problems with electric vehicles is the battery. The battery must be adequately monitored in order to optimize its performances and to maximize its life, to know when it's time to recharge it, or when the charging has been completed, or it's time to buy a new one. The battery's monitoring is the goal of the battery management system (BMS), which must be carefully designed. Moreover, the BMS must report its data to the outer world, and this means that it cannot work alone. If we want to use some kind of simulation to help the design of an effective BMS, we need a simulation model that can be easily attached to other hardware simulation models, such as CAN bus' or Bluetooth models. In this work we present and validate a BMS SystemC simulation model. Both the design and the validation of this BMS are carried on using real-world scenarios and data.