title: Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach

abstract: There is much interest in rule extraction from neural networks and a plethora of different methods have been proposed for this purpose. We discuss the merits of pedagogical and decompositional approaches to rule extraction from trained neural networks, and show that some currently used methods for binary data comply with a theoretical formalism for extraction of Boolean rules from continuously valued logic. This formalism is extended into a generic methodology for rule extraction from smooth decision surfaces fitted to discrete or quantized continuous variables independently of the analytical structure of the underlying model, and in a manner that is efficient even for high input dimensions. This methodology is then tested with Monks' data, for which exact rules are obtained and to Wisconsin's breast cancer data, where a small number of high-order rules are identified whose discriminatory performance can be directly visualized.


title: A 47,XXY Female with Gender Identity Disorder

abstract: According toDSM-IV-TR,gender identitydisorder (GID)(also known as transsexualism) has two features: a strong and persistent cross-gender identification and persistent discomfort about one’s assigned gender or a sense of inappropriateness in the role of that gender. It is reportedly a rare disorder, with prevalence rates between 1:12,000 and 1:40,000 in western countries (Landen, Walinder, & Lundstrom, 1996). Though the etiology is largely unknown, genetic factors have been reported to play an important role, based on reports of twin concordance and families with several affected members (Green, 2000). Althoughchromosomalabnormalitieshavenotbeen reported to occur at an increased rate in these patients, rare reports of chromosomal abnormalities in GID have been reported (Table 1). Klinefelter’s syndrome (KS) is the most common sex chromosome disorder and the second most common condition caused by the presence of extra chromosomes. The condition exists in roughly one out of every 1,000 males and is characterized by chromosomal abnormality in the form of 47,XXY karyotype with a male phenotype. Males with KS may have a mosaic 47,XXY/46,XY constitutional karyotype and varying degrees of spermatogenic failure. Mosaicism 47,XXY/46,XX with clinical features suggestive of Klinefelter syndrome is very rare. Thus far, only about 10 cases have been described in the literature (Velissariou et al., 2006). Few reports of sex reversal in KS have been reported. Mutations in theSRY orSOX genehavebeen implicated insex reversal (Cameron & Sinclair, 1997). As per our knowledge, so far there are only three reports of complete sex reversal in this syndrome searchable on PubMed (Rottger et al., 2000; SaavedraCastillo, Cortes-Gutierrez, Davila-Rodriguez, Reyes-Martinez, OThangaraj,Gupta,Chakravarty,& Singh, 1998). We present a rare case, probably the first of its kind, in which a XXY female presented to us with transsexualism. X, a 24-year-old, single, graduate, employed female patient was referred to our department for psychiatric evaluation in view of her request for sex reassignment surgery. She introduced herself to us as a male. On psychiatric interview, she reported that, since her childhood, she had experienced sexual interest in girls, preferred dressing like a boy, played mainly with boys, would take the role of boys in games, and had been predominantly interested in masculine activities, such as playing football. She would find it difficult to use female restrooms and would often be teased by people around her. She could not understand what and who she was and, at times, would even consider herself a hermaphrodite. It was revealed that she had feelingsofdiscomfortwithherown gender, and thedesire to be male since her childhood. Birth history revealed that she was a full term, normal vaginal delivery. Parents had no doubt regarding the anatomical sex of her and reared her like a female. There were no family conflicts or any difference in her rearing as compared to her other siblings. There was neither significant family history nor any history suggestive of mental subnormality or psychosis. Menstrual history revealed that her first and only menstruation occurred at the age of 15–16 years. This was very scanty. She was started on hormonal therapy, but did not resume menstruation. She has homosexual contact with a female and was not guilty of the same. The Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults (Deogracias et al., 2007) was applied. It is a scale designed to assess gender identity (gender dysphoria) A. Khandelwal R. C. Jiloha Department of Psychiatry, Maulana Azad Medical College and Associated GB Pant Hospital, New Delhi, India


title: Grasp taxonomy based on force distribution

abstract: Human grasp has been studied extensively and many taxonomies have been developed to classify different grasp types. In this work, we collect a comprehensive list of grasp types and investigate the force distribution patterns among them. We conduct a human study and collect force data in various grasp types. Data collection is performed by utilizing a data glove that has seventeen force sensors. We analyze the measured forces and identify different patterns in force distribution. Employing voting technique over various clustering methods, we propose a robust clustering for the force patterns, namely the grasp taxonomy in force domain. The proposed grasp taxonomy can be exploited in designing robotic prosthetic hands, designing grasp controllers and action recognition in human-robot interaction.


title: Using Model Checking to Analyze the System Behavior of the LHC Production Grid

abstract: DIRAC (Distributed Infrastructure with Remote Agent Control) is the grid solution designed to support production activities as well as user data analysis for the Large Hadron Collider "beauty" experiment. It consists of cooperating distributed services and a plethora of light-weight agents delivering the workload to the grid resources. Services accept requests from agents and running jobs, while agents actively fulfill specific goals. Services maintain database back-ends to store dynamic state information of entities such as jobs, queues, or requests for data transfer. Agents continuously check for changes in the service states, and react to these accordingly. The logic of each agent is rather simple, the main source of complexity lies in their cooperation. These agents run concurrently, and communicate using the services' databases as a shared memory for synchronizing the state transitions. Despite the effort invested in making DIRAC reliable, entities occasionally get into inconsistent states. Tracing and fixing such behaviors is difficult, given the inherent parallelism among the distributed components and the size of the implementation. In this paper we present an analysis of DIRAC with mCRL2, process algebra with data. We have reverse engineered two critical and related DIRAC subsystems, and subsequently modeled their behavior with the mCRL2 toolset. This enabled us to easily locate race conditions and live locks which were confirmed to occur in the real system. We further formalized and verified several behavioral properties of the two modeled subsystems.


title: Trajectory generator for autonomous vehicles in urban environments

abstract: Nowadays, some developments in the vehicle industry permit a safe and comfortable driving. However, several manufactures and research groups are still working in the improvement of the control strategies and path smoothing algorithms. In this paper, a new trajectory generation approach for autonomous vehicles in urban scenarios, considering parametric equations, is proposed. An algorithm that considers Bezier curves and circumference parametric equations for a real vehicle, specifically in roundabout and urban intersections is presented. This approach is generated in real time and can be adapted to dynamic changes in the route. A smooth trajectory generator computationally efficient and easily implementable is proposed. Moreover, this new trajectory generator reduces the control actions, generated with to a fuzzy controller. Some trials have been performed in an urban circuit with promising performance.


Object Detection with YOLO on Artwork Dataset


Solving PDE problems with uncertainty using neural-networks


title: Adversarial discriminative sim-to-real transfer of visuo-motor policies

abstract: Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real world. In the tra...


title: LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations

abstract: Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent successes in combining deep neural networks with reinforcement learning have sparked significant new interest in this domain. However, practical solutions remain elusive due to large training data requirements, algorithmic instability, and lack of standard tools. In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks. While prior work has frequently explored applications in simulations, LIFT centers on utilizing human expertise to learn from demonstrations, thus lowering online training times. We further introduce TensorForce, a TensorFlow library for applied deep reinforcement learning exposing a unified declarative interface to common RL algorithms, thus providing a backend to LIFT. We demonstrate the utility of LIFT in two case studies in database compound indexing and resource management in stream processing. Results show LIFT controllers initialized from demonstrations can outperform human baselines and heuristics across latency metrics and space usage by up to 70%.


title: An anatomy of a YouTube meme

abstract: Launched in 2005 as a video-sharing website, YouTube has become an emblem of participatory culture. A central feature of this website is the dazzling number of derivative videos, uploaded daily by many thousands. Using the ‘meme’ concept as an analytic tool, this article aims at uncovering the attributes common to ‘memetic videos’ – popular clips that generate extensive user engagement by way of creative derivatives. Drawing on YouTube popularity-measurements and on user-generated playlists, a corpus of 30 prominent memetic videos was assembled. A combined qualitative and quantitative analysis of these videos yielded six common features: focus on ordinary people, flawed masculinity, humor, simplicity, repetitiveness and whimsical content. Each of these attributes marks the video as incomplete or flawed, thereby invoking further creative dialogue. In its concluding section, the article addresses the skyrocketing popularity of mimicking in contemporary digital culture, linking it to economic, social and cul...


title: An efficient parameter extraction methodology for the EKV MOST model

abstract: This paper presents a new parameter extraction methodology, based on an accurate and continuous MOS model dedicated to low-voltage and low-current analog circuit design and simulation (EKV MOST Model). The extraction procedure provides the key parameters from the pinch-off versus gate voltage characteristic, measured at constant current from a device biased in moderate inversion. Unique parameter sets, suitable for statistical analysis, describe the device behavior in all operating regions and over all device geometries. This efficient and simple method is shown to be accurate for both submicron bulk CMOS and fully depleted SOI technologies.


title: Coordinated multipoint transmission and reception in LTE-advanced: deployment scenarios and operational challenges

abstract: 3GPP has completed a study on coordinated multipoint transmission and reception techniques to facilitate cooperative communications across multiple transmission and reception points (e.g., cells) for the LTE-Advanced system. In CoMP operation, multiple points coordinate with each other in such a way that the transmission signals from/to other points do not incur serious interference or even can be exploited as a meaningful signal. The goal of the study is to evaluate the potential performance benefits of CoMP techniques and the implementation aspects including the complexity of the standards support for CoMP. This article discusses some of the deployment scenarios in which CoMP techniques will likely be most beneficial and provides an overview of CoMP schemes that might be supported in LTE-Advanced given the modern silicon/DSP technologies and backhaul designs available today. In addition, practical implementation and operational challenges are discussed. We also assess the performance benefits of CoMP in these deployment scenarios with traffic varying from low to high load.


title: Radar Pulse Compression for Point Target and Distributed Target Using Neural Network

abstract: An important study of the responses to the point target and the distributed target of the radar echoes processed by a neural network pulse compression (NNPC) algorithm is presented in this paper. For whatever the purpose of a radar system, both of the point target and distributed target echoes are received simultaneously. It is always necessary and helpful to discriminate them clearly while detecting the desired target, which will reduce the influence for each other in pulse compression processing. However, in most of the pulse compression algorithms, it is only considered the radar purpose to process one type of the targets but neglect the other. This will make either the presence of a point target's range sidelobes masking and corrupting the observation of the weak distributed target nearby or a distributed target with extended range interfering with the detection of the neighboring point target. By completely considering the interactions of a point target with a distributed target, we acquire all the possible data occurred in the procedure. Using these valid data, we can train the backpropagation (BP) network to construct it as a well performance of NNPC. To compare with the traditional algorithms such as direct autocorrelation filter (ACF), least squares (LS) inverse filter, and linear programming (LP) filter based on 13-element Barker code (B13 code), the proposed NNPC provides the requirements of high signal-to-sidelobe ratio, low integrated sidelobe level (ISL), and high target discrimination ratio. Simulation results show that this NNPC algorithm has significant advantages in targets discrimination ability, range resolution ability, and noise rejection performance while processing the interaction of point target with distributed target, which are superior to the traditional algorithms.


title: Neural Graph Learning: Training Neural Networks Using Graphs

abstract: Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural networks, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training framework with a graph-regularised objective, namely Neural Graph Machines, that can combine the power of neural networks and label propagation. This work generalises previous literature on graph-augmented training of neural networks, enabling it to be applied to multiple neural architectures (Feed-forward NNs, CNNs and LSTM RNNs) and a wide range of graphs. The new objective allows the neural networks to harness both labeled and unlabeled data by: (a)~allowing the network to train using labeled data as in the supervised setting, (b)~biasing the network to learn similar hidden representations for neighboring nodes on a graph, in the same vein as label propagation. Such architectures with the proposed objective can be trained efficiently using stochastic gradient descent and scaled to large graphs, with a runtime that is linear in the number of edges. The proposed joint training approach convincingly outperforms many existing methods on a wide range of tasks (multi-label classification on social graphs, news categorization, document classification and semantic intent classification), with multiple forms of graph inputs (including graphs with and without node-level features) and using different types of neural networks.


title: ESLAMI, WILLIAMS: FACTORED SHAPES AND APPEARANCES 1 Factored Shapes and Appearances for Parts-based Object Understanding

abstract: We present a novel generative framework for learning parts-based representations of object classes. Our model, Factored Shapes and Appearances (FSA), employs a highly factored representation to reason about appearance and shape variability across datasets of images. We propose Markov Chain Monte Carlo sampling schemes for efficient inference and learning, and evaluate the model on a number of datasets. Here we consider datasets that exhibit large amounts of variability, both in the shapes of objects in the scene, and in their appearances. We show that the FSA model extracts meaningful parts from training data, and that its parameters and representation can be used to perform a range of tasks, including object parsing, segmentation and fine-grained categorisation.


title: Pharmaceutical Excipients: A review

abstract: Excipients play an important role in formulating a dosage form. These are the ingredients which along with Active Pharmaceutical Ingredients make up the dosage forms. Excipients act as protective agents, bulking agents and can also be used to improve bioavailability of drugs in some instances, the following review discusses the various types and sources of excipients along with their uses, and these can be used for different activities. Specific excipients are best suited for a particular dosage form; the selection criterion for excipients and various interactions that an excipient can undergo during its course of stay in formulation has been discussed in this review. Some excipient interactions can be detrimental and need to be avoided. This has been detailed out in the interaction section. Excipients as like other active pharmaceutical ingredients need to be stabilized and standardized; the following review gives brief information about standardization and stabilization process alongwith the safety evaluation parameters of the excipients.


Board of Director Composition, and Ownership and Their Relationship to Patents: An Empirical Study


title: Formatting Time-Aligned ASR Transcripts for Readability

abstract: We address the problem of formatting the output of an automatic speech recognition (ASR) system for readability, while preserving word-level timing information of the transcript. Our system enriches the ASR transcript with punctuation, capitalization and properly written dates, times and other numeric entities, and our approach can be applied to other formatting tasks. The method we describe combines hand-crafted grammars with a class-based language model trained on written text and relies on Weighted Finite State Transducers (WFSTs) for the preservation of start and end time of each word.


title: A Broad Overview and Review of CRISPR-Cas Technology and Stem Cells

abstract: The pinnacle of four decades of research, induced pluripotent stem cells (iPSCs), and genome editing with the advent of clustered, regularly interspaced, short palindromic repeats (CRISPR) now promise to take drug development and regenerative medicine to new levels and to enable the interrogation of disease mechanisms with a hitherto unimaginable level of model fidelity. Autumn 2014 witnessed the first patient receiving iPSCs differentiated into retinal pigmented epithelium to treat macular degeneration. Technologies such as 3D bioprinting may now exploit these advances to manufacture organs in a dish. As enticing as these prospects are, these technologies demand a deeper understanding, which will lead to improvements in their safety and efficacy. For example, precise and more efficient reprogramming for iPSC production is a requisite for wider clinical adoption. Improving awareness of the roles of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) and genomic epigenetic status will contribute to the achievement of these aims. Similarly, increased efficiency, avoidance of off-target effects, and expansion of available target sequences are critical to the uptake of genome editing technology. In this review, we survey the historical development of genetic manipulation and stem cells. We explore the potential of genetic manipulation of iPSCs for in vitro disease modeling, generation of new animal models, and clinical applicability. We highlight the aspects that define CRISPR-Cas as a breakthrough technology, look at gene correction, and consider some important ethical and societal implications of this approach.


title: Inducible Heme Oxygenase 1 (HMOX1) Promotes Osteoblastogenesis, and Inhibits Osteoclastogenesis and Myeloma-Induced Bone Disease

abstract: Abstract 627 Cytotherapy with mesenchymal stem cells (MSCs) has been shown to promote bone formation, inhibit bone disease and reduce multiple myeloma (MM) growth in myelomatous bone (Yaccoby et al., 2006; Li et al., 2011). At the cellular level, the infused MSCs act as a bystander cells that activate endogenous osteoblasts and suppress osteoclast activity. To shed light on molecular mechanisms associated with the cytotherapeutic effects of MSCs we exploited the SCID-hu model engrafted with the Hg MM line. Hg cells are only maintained by passaging in SCID-hu/SCID-rab mice and produce severe bone disease. Human global gene expression profile (GEP) was performed in nonmyelomatous implanted bones (n=5), and in myelomatous implanted bones injected with human MSCs (1×106 cells/bone) and analyzed immediately (control group, n=8) or 24 hours later (cytotherapy, n=6). Based on stringent criteria, (e.g. ≥2 folds, p Disclosures: Barlogie:Celgene, Genzyme, Novartis, Millennium: Consultancy, Honoraria, Patents & Royalties. Shaughnessy:Myeloma Health, Celgene, Genzyme, Novartis: Consultancy, Employment, Equity Ownership, Honoraria, Patents & Royalties.


title: Solution of the epidemic model by Adomian decomposition method

abstract: In this article the problem of the spread of a non-fatal disease in a population which is assumed to have constant size over the period of the epidemic is considered. Mathematical modeling of the problem leads to a system of non-linear ordinary differential equations. Adomian decomposition method is employed to solve this system of non-linear ordinary differential equations, the problem of convergence has been addressed.


title: A GA-based feature selection and parameters optimization for support vector machines

abstract: Support Vector Machines, one of the new techniques for pattern classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM classification accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. We tried several real-world datasets using the proposed GA-based approach and the Grid algorithm, a traditional method of performing parameters searching. Compared with the Grid algorithm, our proposed GA-based approach significantly improves the classification accuracy and has fewer input features for support vector machines. q 2005 Elsevier Ltd. All rights reserved.


title: Interactive sketching of urban procedural models

abstract: 3D modeling remains a notoriously difficult task for novices despite significant research effort to provide intuitive and automated systems. We tackle this problem by combining the strengths of two popular domains: sketch-based modeling and procedural modeling. On the one hand, sketch-based modeling exploits our ability to draw but requires detailed, unambiguous drawings to achieve complex models. On the other hand, procedural modeling automates the creation of precise and detailed geometry but requires the tedious definition and parameterization of procedural models. Our system uses a collection of simple procedural grammars, called snippets, as building blocks to turn sketches into realistic 3D models. We use a machine learning approach to solve the inverse problem of finding the procedural model that best explains a user sketch. We use non-photorealistic rendering to generate artificial data for training convolutional neural networks capable of quickly recognizing the procedural rule intended by a sketch and estimating its parameters. We integrate our algorithm in a coarse-to-fine urban modeling system that allows users to create rich buildings by successively sketching the building mass, roof, facades, windows, and ornaments. A user study shows that by using our approach non-expert users can generate complex buildings in just a few minutes.


title: The 5G-Enabled Tactile Internet: Applications, requirements, and architecture

abstract: Powered by next generation mobile networking capabilities, the Tactile Internet will be able to transport touch and actuation in real-time. Enabled by suitable robotics and haptics equipment at the edges, and an unprecedented communications network, the Tactile Internet will provide a true paradigm in creating skill-set delivery networks. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. Specifically, the paper presents some of the most important Tactile Internet applications, outlines the key technical requirements, and covers end-to-end architectural aspects of the Tactile Internet.


The Million Musical Tweet Dataset - What We Can Learn From Microblogs


title: Measuring aesthetic emotions: A review of the literature and a new assessment tool

abstract: Aesthetic perception and judgement are not merely cognitive processes, but also involve feelings. Therefore, the empirical study of these experiences requires conceptualization and measurement of aesthetic emotions. Despite the long-standing interest in such emotions, we still lack an assessment tool to capture the broad range of emotions that occur in response to the perceived aesthetic appeal of stimuli. Elicitors of aesthetic emotions are not limited to the arts in the strict sense, but extend to design, built environments, and nature. In this article, we describe the development of a questionnaire that is applicable across many of these domains: the Aesthetic Emotions Scale (Aesthemos). Drawing on theoretical accounts of aesthetic emotions and an extensive review of extant measures of aesthetic emotions within specific domains such as music, literature, film, painting, advertisements, design, and architecture, we propose a framework for studying aesthetic emotions. The Aesthemos, which is based on this framework, contains 21 subscales with two items each, that are designed to assess the emotional signature of responses to stimuli’s perceived aesthetic appeal in a highly differentiated manner. These scales cover prototypical aesthetic emotions (e.g., the feeling of beauty, being moved, fascination, and awe), epistemic emotions (e.g., interest and insight), and emotions indicative of amusement (humor and joy). In addition, the Aesthemos subscales capture both the activating (energy and vitality) and the calming (relaxation) effects of aesthetic experiences, as well as negative emotions that may contribute to aesthetic displeasure (e.g., the feeling of ugliness, boredom, and confusion).


Two Principles for Robot


title: Using fuzzy inference system for architectural space analysis

abstract: Though architectural space is the main source and the only indispensable component of any architectural construction, in many cases its boundaries are uncertain, leading intuitive spatial design. Creating a mathematical model of architectural space with concrete results will offer many possibilities for design process in analysing spatial organization, independently from in architect's experience and intuitions. This paper presents a fuzzy inference system based spatial analysis model for spatial analysis for architectural design which brings many advantages to design process. The aim of this article is to investigate the potential of a fuzzy system with a Mamdami inference engine, considering different numbers of membership functions. Two venues have been selected and the fuzzy inference system based spatial analysis model is applied. For better judgement, outcomes of the model have been compared to depthmap analysis model. The results of the model indicate that fuzzy inference system based spatial analysis model performs very well, even with the limited and imprecise data. Such prototype can evolve into a tool for identifying spatial formations for improvements during the architectural design process.


title: Walking-wheeling dual mode strategy for humanoid robot, DRC-HUBO+

abstract: For robots, a flexible approach is very important. Even when the robot is a humanoid, it does not have to perfectly mimic human motion. If necessary, the humanoid robot can adapt to other technologies even when these technologies make it different from humans. In a real environment or a disaster environment, humanoid robots are a good solution to assist humans. However, the stability problem of biped locomotion remains. In this paper, we introduce a new strategy of attaching a wheel to our humanoid robot in order to achieve mobility efficiency with a bipedal walking and wheel driven mode. Additionally, this strategy can enhance the stability. Through the proposed method, the humanoid robot can traverse uneven terrain and climb stairs with a walking motion, and can move safely with wheels on the ground. In addition, a method will be presented that can enhance the performance of wheel movement by using redundant joints and sensors on the humanoid robot. Various experiments and the DARPA Robotics Challenge results validate the efficiency and the feasibility of the proposed strategy.


title: Interface Theories for Component-based Design

abstract: We classify component-based models of computation into component models and interface models. A component model specifies for each component howthe component behaves in an arbitrary environment; an interface model specifies for each component what the component expects from the environment. Component models support compositional abstraction, and therefore component-based verification. Interface models support compositional refinement, and therefore componentbased design. Many aspects of interface models, such as compatibility and refinement checking between interfaces, are properly viewed in a gametheoretic setting, where the input and output values of an interface are chosen by different players.


title: Bored in the USA: Experience Sampling and Boredom in Everyday Life

abstract: We report new evidence on the emotional, demographic, and situational correlates of boredom from a rich experience sample capturing 1.1 million emotional and time-use reports from 3,867 U.S. adults. Subjects report boredom in 2.8% of the 30-min sampling periods, and 63% of participants report experiencing boredom at least once across the 10-day sampling period. We find that boredom is more likely to co-occur with negative, rather than positive, emotions, and is particularly predictive of loneliness, anger, sadness, and worry. Boredom is more prevalent among men, youths, the unmarried, and those of lower income. We find that differences in how such demographic groups spend their time account for up to one third of the observed differences in overall boredom. The importance of situations in predicting boredom is additionally underscored by the high prevalence of boredom in specific situations involving monotonous or difficult tasks (e.g., working, studying) or contexts where one’s autonomy might be constrained (e.g., time with coworkers, afternoons, at school). Overall, our findings are consistent with cognitive accounts that cast boredom as emerging from situations in which engagement is difficult, and are less consistent with accounts that exclusively associate boredom with low arousal or with situations lacking in meaning.


Progressive Web Apps: the Definite Approach to Cross-Platform Development?


title: Data-aware process mining: discovering decisions in processes using alignments

abstract: Process discovery, i.e., learning process models from event logs, has attracted the attention of researchers and practitioners. Today, there exists a wide variety of process mining techniques that are able to discover the control-flow of a process based on event data. These techniques are able to identify decision points, but do not analyze data flow to find rules explaining why individual cases take a particular path. Fortunately, recent advances in conformance checking can be used to align an event log with data and a process model with decision points. These alignments can be used to generate a well-defined classification problem per decision point. This way data flow and guards can be discovered and added to the process model.


title: Markerless tracking using planar structures in the scene

abstract: We describe a markerless camera tracking system for augmented reality that operates in environments which contain one or more planes. This is a common special case, which we show significantly simplifies tracking. The result is a practical, reliable, vision-based tracker. Furthermore, the tracked plane imposes a natural reference frame, so that the alignment of the real and virtual coordinate systems is rather simpler than would be the case with a general structure-and-motion system. Multiple planes can be tracked, and additional data such as 2D point tracks are easily incorporated.


title: Facial expression recognition using constructive feedforward neural networks

abstract: A new technique for facial expression recognition is proposed, which uses the two-dimensional (2D) discrete cosine transform (DCT) over the entire face image as a feature detector and a constructive one-hidden-layer feedforward neural network as a facial expression classifier. An input-side pruning technique, proposed previously by the authors, is also incorporated into the constructive learning process to reduce the network size without sacrificing the performance of the resulting network. The proposed technique is applied to a database consisting of images of 60 men, each having five facial expression images (neutral, smile, anger, sadness, and surprise). Images of 40 men are used for network training, and the remaining images of 20 men are used for generalization and testing. Confusion matrices calculated in both network training and generalization for four facial expressions (smile, anger, sadness, and surprise) are used to evaluate the performance of the trained network. It is demonstrated that the best recognition rates are 100% and 93.75% (without rejection), for the training and generalizing images, respectively. Furthermore, the input-side weights of the constructed network are reduced by approximately 30% using our pruning method. In comparison with the fixed structure back propagation-based recognition methods in the literature, the proposed technique constructs one-hidden-layer feedforward neural network with fewer number of hidden units and weights, while simultaneously provide improved generalization and recognition performance capabilities.


title: A survey of collectives

abstract: Due to the increasing sophistication and miniaturization of computational components, complex, distributed systems of interacting agents are becoming ubiquitous. Such systems, where each agent aims to optimize its own performance, but there is a well-defined set of system-level performance criteria, are called collectives. The fundamental problem in analyzing and designing such systems is in determining how the combined actions of a large number of agents lead to “coordinated” behavior on the global scale. Examples of artificial systems that exhibit such behavior include packet routing across a data network, control of an array of communication satellites, coordination of multiple rovers, and dynamic job scheduling across a distributed computer grid. Examples of natural systems include ecosystems, economies, and the organelles within a living cell.


title: Extracorporeal photopheresis (ECP) in the treatment of chronic graft-versus-host disease (GVHD)

abstract: The aim of our study was to assess the efficacy of extracorporeal photopheresis (ECP) in chronic graft-versus-host disease (GVHD). Eleven patients with chronic cutaneous GVHD were studied. Four had mucosal involvement and five had pulmonary involvement. All had failed to improve on first- and second-line therapy. Three patients received ECP alone; the remainder continued to receive steroids and/or immunosuppressive therapy. Patients received ECP twice monthly for 4 months and then once monthly for 3 months. They were evaluated by serial skin scores, mucosal and skin photography, pulmonary function tests, biochemical and haematological parameters. Nine patients showed objective evidence of cutaneous improvement with a mean reduction in skin score of 48% overall. In the 10th patient, skin scores and oral involvement improved on twice monthly ECP but deteriorated when reduced to once monthly. The final patient died from renal failure secondary to cyclosporin toxicity. Two out of five patients with lung involvement showed a mild improvement in pulmonary function tests. Liver function tests were abnormal in five patients; they improved in one and deteriorated in three. All patients receiving concomitant immunosuppressive/steroid therapy were able to reduce drug dosages by trial completion. Our results indicate that ECP can benefit patients with cutaneous and mucosal chronic GVHD who have failed on first- and second-line therapies. The effect on the systemic manifestations of GVHD is less consistent.


title: LoRa indoor coverage and performance in an industrial environment: Case study

abstract: LoRa is a long range, low power, low bit rate, single hop wireless communication technology. It is intended to be used for Internet of Things (IoT) networks, where devices are battery powered and limited bandwidth is needed. In combination with its scalability and the low end device price, LoRa is a candidate technology for low bandwidth industrial applications with a high number of communication devices spread across large areas. The use case for this paper is taken from the flower industry, where a large number of trolleys need to communicate with a server during their movement across the auction floor area. Once trolleys are outside of the auction floor they can use the public LoRaWAN network to communicate with the server, without switching communication technology. The LoRaWAN network consists of multiple end nodes and a single gateway per cell, acting as a transparent bridge between the end nodes and the network server. The measurements show that with a single LoRa gateway we can cover an indoor area of around 34000m2 only with spreading factor 7, while for spreading factor 12 the total covered area will be even higher. Also, the area outside the factory is covered when switching to spreading factor 12. We also show that the number of nodes (trolleys) that can be served by a gateway in such a case can be as high as 6000.


title: Mean square exponential stability of impulsive stochastic reaction-diffusion Cohen-Grossberg neural networks with delays

abstract: In this paper, we establish a method to study the mean square exponential stability of the zero solution of impulsive stochastic reaction-diffusion Cohen-Grossberg neural networks with delays. By using the properties of M-cone and inequality technique, we obtain some sufficient conditions ensuring mean square exponential stability of the zero solution of impulsive stochastic reaction-diffusion Cohen-Grossberg neural networks with delays. The sufficient conditions are easily checked in practice by simple algebra methods and have a wider adaptive range. Two examples are also discussed to illustrate the efficiency of the obtained results.


title: The critical role of retrieval practice in long-term retention

abstract: Learning is usually thought to occur during episodes of studying, whereas retrieval of information on testing simply serves to assess what was learned. We review research that contradicts this traditional view by demonstrating that retrieval practice is actually a powerful mnemonic enhancer, often producing large gains in long-term retention relative to repeated studying. Retrieval practice is often effective even without feedback (i.e. giving the correct answer), but feedback enhances the benefits of testing. In addition, retrieval practice promotes the acquisition of knowledge that can be flexibly retrieved and transferred to different contexts. The power of retrieval practice in consolidating memories has important implications for both the study of memory and its application to educational practice.


title: Bug Prioritization to Facilitate Bug Report Triage

abstract: The large number of new bug reports received in bug repositories of software systems makes their management a challenging task. Handling these reports manually is time consuming, and often results in delaying the resolution of important bugs. To address this issue, a recommender may be developed which automatically prioritizes the new bug reports. In this paper, we propose and evaluate a classification based approach to build such a recommender. We use the Naive Bayes and Support Vector Machine (SVM) classifiers, and present a comparison to evaluate which classifier performs better in terms of accuracy. Since a bug report contains both categorical and text features, another evaluation we perform is to determine the combination of features that better determines the priority of a bug. To evaluate the bug priority recommender, we use precision and recall measures and also propose two new measures, Nearest False Negatives (NFN) and Nearest False Positives (NFP), which provide insight into the results produced by precision and recall. Our findings are that the results of SVM are better than the Naive Bayes algorithm for text features, whereas for categorical features, Naive Bayes performance is better than SVM. The highest accuracy is achieved with SVM when categorical and text features are combined for training.


title: Does Rap or Rock Music Provoke Violent Behavior

abstract: This study examined whether or not watching a violent music video would provoke individuals to answer questions with violent responses. Eleven participants watched a violent music video, 11 participants watched a nonviolent music video, and 11 participants were in the control group and did not watch any videos. It was found that watching the violent music video containing violent lyrics, aggressive behavior, and degrading behaviors toward women did make an individual feel and react more violently with regards to responses to questions about fictitious scenarios. The conclusion was that watching violent music videos does negatively affect behavior.


title: Robust Tracking Using Region Proposal Networks

abstract: Recent advances in visual tracking showed that deep Convolutional Neural Networks (CNN) trained for image classification can be strong feature extractors for discriminative trackers. However, due to the drastic difference between image classification and tracking, extra treatments such as model ensemble and feature engineering must be carried out to bridge the two domains. Such procedures are either time consuming or hard to generalize well across datasets. In this paper we discovered that the internal structure of Region Proposal Network (RPN)'s top layer feature can be utilized for robust visual tracking. We showed that such property has to be unleashed by a novel loss function which simultaneously considers classification accuracy and bounding box quality. Without ensemble and any extra treatment on feature maps, our proposed method achieved state-of-the-art results on several large scale benchmarks including OTB50, OTB100 and VOT2016. We will make our code publicly available.


Generic Tools, Specific Languages


title: Hierarchical Question-Image Co-Attention for Visual Question Answering

abstract: A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling "where to look" or visual attention, it is equally important to model "what words to listen to" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN). Our model improves the state-of-the-art on the VQA dataset from 60.3% to 60.5%, and from 61.6% to 63.3% on the COCO-QA dataset. By using ResNet, the performance is further improved to 62.1% for VQA and 65.4% for COCO-QA.


title: The Midline Forehead Flap in Nasal Reconstruction

abstract: Abstract An ancient Indian medical document, the Sushruta Samhita, describes a technique of using a flap from the forehead for nasal reconstruction. The forehead flap remains the workhorse for major nasal resurfacing today. Contemporary nasal reconstruction with forehead flaps uses the well-established concept of facial and nasal subunits, restoring the three-dimensional morphology by replacing missing tissue with like tissue. This article covers the history of forehead flap surgery, current concepts in flap design, surgical steps, potential complications, defect analysis, lining, framework, and cover as a means of restoring the three-dimensional nasal morphology.


title: Face recognition with enhanced local directional patterns

abstract: This paper presents a novel approach based on enhanced local directional patterns (ELDP) to face recognition, which adopts local edge gradient information to represent face images. Specially, each pixel of every facial image sub-block gains eight edge response values by convolving the local 3x3 neighborhood with eight Kirsch masks, respectively. ELDP just utilizes the directions of the most prominent edge response value and the second most prominent one. Then, these two directions are encoded into a double-digit octal number to produce the ELDP codes. The ELDP dominant patterns (ELDP^d) are generated by statistical analysis according to the occurrence rates of the ELDP codes in a mass of facial images. Finally, the face descriptor is represented by using the global concatenated histogram based on ELDP or ELDP^d extracted from the face image which is divided into several sub-regions. The performances of several single face descriptors not integrated schemes are evaluated in face recognition under different challenges via several experiments. The experimental results demonstrate that the proposed method is more robust to non-monotonic illumination changes and slight noise without any filter.


title: Prader-Willi syndrome and Angelman syndrome

abstract: Prader–Willi syndrome (PWS) and Angelman syndrome (AS) are two distinct neurogenetic disorders in which imprinted genes on the proximal long arm of chromosome 15 are affected. Although the SNORD116 gene cluster has become a prime candidate for PWS, it cannot be excluded that other paternally expressed genes in the chromosomal region 15q11q13 contribute to the full phenotype. AS is caused by a deficiency of the UBE3A gene, which in the brain is expressed from the maternal allele only. The most frequent genetic lesions in both disorders are a de novo deletion of the chromosomal region 15q11q13, uniparental disomy 15, an imprinting defect or, in the case of AS, a mutation of the UBE3A gene. Microdeletions in a small number of patients with PWS and AS have led to the identification of the chromosome 15 imprinting center (IC). The IC consists of two critical elements, which act in cis to regulate imprinting in the whole chromosome 15q11q13 imprinted domain. © 2010 Wiley-Liss, Inc.


title: Using the augmented reality sandbox for advanced learning in geoscience education

abstract: Augmented reality is influential in achieving experiential learning in several subjects. In a geoscience classroom where spatial thinking skills and visualization of real-world phenomena is key to enhance understanding, experiential learning through the use of augmented reality sandbox and augmented reality constructs can be very effective. Augmented reality is being successfully used for holistic training in critical applications of medicine, aerospace, robotics and now it is increasingly getting popular in formal education. Geoscience, being is an observational science, poses a challenge for an educator as delivering an understanding of earth processes through geological time scales difficult to achieve in the confines of a classroom. Field based studies have always been complementing geoscience education but they are becoming a logistic headache for many educational institutions. Augmented reality, through the draping of virtual reality onto a physical model, presents a realistic simulation of earth processes which facilitates the design and implementation of experiential learning modes in a geoscience classroom. The current article discusses the Augmented Reality construct and its integration in tutorials aimed at achieving experiential learning for geoscience students. We also discussed extending the use of similar constructs for transitioning student understanding in real-world problems and applications.


title: Classification of K-Pop Dance Movements Based on Skeleton Information Obtained by a Kinect Sensor

abstract: This paper suggests a method of classifying Korean pop (K-pop) dances based on human skeletal motion data obtained from a Kinect sensor in a motion-capture studio environment. In order to accomplish this, we construct a K-pop dance database with a total of 800 dance-movement data points including 200 dance types produced by four professional dancers, from skeletal joint data obtained by a Kinect sensor. Our classification of movements consists of three main steps. First, we obtain six core angles representing important motion features from 25 markers in each frame. These angles are concatenated with feature vectors for all of the frames of each point dance. Then, a dimensionality reduction is performed with a combination of principal component analysis and Fisher’s linear discriminant analysis, which is called fisherdance. Finally, we design an efficient Rectified Linear Unit (ReLU)-based Extreme Learning Machine Classifier (ELMC) with an input layer composed of these feature vectors transformed by fisherdance. In contrast to conventional neural networks, the presented classifier achieves a rapid processing time without implementing weight learning. The results of experiments conducted on the constructed K-pop dance database reveal that the proposed method demonstrates a better classification performance than those of conventional methods such as KNN (K-Nearest Neighbor), SVM (Support Vector Machine), and ELM alone.


title: Does computer-assisted learning improve learning outcomes? Evidence from a randomized experiment in migrant schools in Beijing

abstract: The education of the disadvantaged population has been a long-standing challenge to education systems in both developed and developing countries. Although computer-assisted learning (CAL) has been considered one alternative to improve learning outcomes in a cost-effective way, the empirical evidence of its impacts on improving learning outcomes is mixed. This paper uses a randomized field experiment to explore the effects of CAL on student academic and non-academic outcomes for students in migrant schools in Beijing. Our results show that a remedial CAL program held out of regular school hours improved the student standardized math scores by 0.15 standard deviations and most of the program effect took place within 2 months after the start of the program. Students with less-educated parents benefited more from the program. Moreover, CAL also significantly increased the students’ interest in learning.


title: Executive functions in extremely low birth weight and late-preterm preschoolers: Effects on working memory and response inhibition

abstract: Executive function (EF) refers to fundamental capacities that underlie more complex cognition and have ecological relevance across the individual's lifespan. However, emerging executive functions have rarely been studied in young preterm children (age 3) whose critical final stages of fetal development are interrupted by their early birth. We administered four novel touch-screen computerized measures of working memory and inhibition to 369 participants born between 2004 and 2006 (52 Extremely Low Birth Weight [ELBW]; 196 late preterm; 121 term-born). ELBW performed worse than term-born on simple and complex working memory and inhibition tasks and had the highest percentage of incomplete performance on a continuous performance test. The latter finding indicates developmental immaturity and the ELBW group's most at-risk preterm status. Additionally, late-preterm participants performed worse compared with term-born on measures of complex working memory but did not differ from those term-born on response inhi...


title: Coping with divided attention: the advantage of familiarity

abstract: The ability of an animal to perform a task successfully is limited by the amount of attention being simultaneously focused on other activities. One way in which individuals might reduce the cost of divided attention is by preferentially focusing on the most beneficial tasks. In territorial animals where aggression is lower among familiar individuals, the decision to associate preferentially with familiar conspecifics may therefore confer advantages by allowing attention to be switched from aggression to predator vigilance and feeding. Wild juvenile brown trout were used to test the prediction that familiar fishes respond more quickly than unfamiliar fishes to a simulated predator attack. Our results confirm this prediction by demonstrating that familiar trout respond 14% faster than unfamiliar individuals to a predator attack. The results also show that familiar fishes consume a greater number of food items, foraging at more than twice the rate of unfamiliar conspecifics. To the best of our knowledge, these results provide the first evidence that familiarity-biased association confers advantages through the immediate fitness benefits afforded by faster predator-evasion responses and the long-term benefits provided by increased feeding opportunities.


title: AMAZING: A sentiment mining and retrieval system

abstract: With the rapid growth of e-commerce, there are a great number of customer reviews on the e-commerce websites. Generally, potential customers usually wade through a lot of on-line reviews in order to make an informed decision. However, retrieving sentiment information relevant to customer's interest still remains challenging. Developing a sentiment mining and retrieval system is a good way to overcome the problem of overloaded information in customer reviews. In this paper, we propose a sentiment mining and retrieval system which mines useful knowledge from consumer product reviews by utilizing data mining and information retrieval technology. A novel ranking mechanism taking temporal opinion quality (TOQ) and relevance into account is developed to meet customers' information need. Besides the trend movement of customer reviews and the comparison between positive and negative evaluation are presented visually in the system. Experimental results on a real-world data set show the system is feasible and effective.


title: Measuring Polarization in Twitter Enabled in Online Political Conversation: The Case of 2016 US Presidential Election

abstract: Political Polarization is the divergence of attitudes toward ideological extremes and it is more likely to happen in groups of like-minded individuals. We measured polarization on Twitter during the 2016 United States Presidential elections, analyzing Twitter backchanneling conversations occurring during the three Presidential debates. The polarization metric we use is based on an existing metaphor of the electric dipole. In each debate the polarization dynamic followed a U-shaped pattern, with polarization being high at the beginning of the debate, decreasing over time, and finally bouncing back to a value that was higher than the initial one. The temporary decline of polarization is due to the increase in interaction with participants holding opposite opinions, but apparently this interaction is more conducive to confrontation than to revision of existing beliefs. We argue that the characteristics of Twitter are generally conducive to polarized and manipulable online debate.


title: Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing

abstract: This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing. The theoretical and numerical foundations are tackled before the applications are discussed. Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more. The presentation is elegant and engaging. Sparse and Redundant Representations is intended for graduate students in applied mathematics and electrical engineering, as well as applied mathematicians, engineers, and researchers who are active in the fields of signal and image processing.


title: Forecasting dynamic public transport Origin-Destination matrices with long-Short term Memory recurrent neural networks

abstract: A considerable number of studies have been undertaken on using smart card data to analyse urban mobility. Most of these studies aim to identify recurrent passenger habits, reveal mobility patterns, reconstruct and predict passenger flows, etc. Forecasting mobility demand is a central problem for public transport authorities and operators alike. It is the first step to efficient allocation and optimisation of available resources. This paper explores an innovative approach to forecasting dynamic Origin-Destination (OD) matrices in a subway network using long Short-term Memory (LSTM) recurrent neural networks. A comparison with traditional approaches, such as calendar methodology or Vector Autoregression is conducted on a real smart card dataset issued from the public transport network of Rennes Metropole, France. The obtained results show that reliable short-term prediction (over a 15 minutes time horizon) of OD pairs can be achieved with the proposed approach. We also experiment with the effect of taking into account additional data about OD matrices of nearby transport systems (buses in this case) on the prediction accuracy.


title: Model-driven Composition of Context-aware Web Services Using ContextUML and Aspects

abstract: Service oriented architectures (SOAs) are constantly gaining ground for the provision of business to business as well as user-centric services, mainly in the form of Web services technology. SOAs enable service providers to design and deploy new,composite service offerings out of existing component services. In order to match end-user expectations with respect to personalization and ease of use, these services should be designed in a manner that allows them to exhibit a certain level of context-awareness which is a basic element towards a richer end-user experience. However, in the majority of such services, context-handling is still tightly coupled with the core functionality of the service, resulting in a design which is difficult to implement and maintain. The paper proposes the decoupling of core service logic from context-related functionality by adopting a model-driven approach based on a modified version of the ContextUML metamodel. Core service logic and context handling are treated as separate concerns at the modeling level as well as in the resulting source code where aspect oriented programming (AOP) encapsulates context-dependent behavior in discrete code modules. The design of a restaurant finder service is used to portray the modified ContextUML metamodel and the service modeling process which is covered in full. Respective code snippets belonging to the executable version of the service (part of work in progress) are also provided, illustrating the transition from model to code and the resulting separation of concerns.


title: The design and evaluation of multitouch marking menus

abstract: Despite the considerable quantity of research directed towards multitouch technologies, a set of standardized UI components have not been developed. Menu systems provide a particular challenge, as traditional GUI menus require a level of pointing precision inappropriate for direct finger input. Marking menus are a promising alternative, but have yet to be investigated or adapted for use within multitouch systems. In this paper, we first investigate the human capabilities for performing directional chording gestures, to assess the feasibility of multitouch marking menus. Based on the positive results collected from this study, and in particular, high angular accuracy, we discuss our new multitouch marking menu design, which can increase the number of items in a menu, and eliminate a level of depth. A second experiment showed that multitouch marking menus perform significantly faster than traditional hierarchal marking menus, reducing acquisition times in both novice and expert usage modalities.


title: Vision-based Markov localization for long-term autonomy

abstract: Lifelong autonomous operation has gained much attention in the field of mobile robotics in recent years. In the context of robot navigation based on vision, lifelong applications include scenarios with substantial perceptual changes due to changes in season, illumination and weather. In this paper, we present an approach to localize a mobile robot, equipped with a low frequency camera, with respect to an image sequence recorded in a different season. Our approach employs a discrete Bayes filter with a sensor model based on whole image descriptors. We compute a similarity matrix over all image descriptors and leverage the sequential nature of typical image streams with a flexible transition scheme in the Bayes filter framework. Since we compute a probability distribution over the entire state space, our approach can handle complex trajectories that may include same season loop-closures as well as fragmented sub-sequences. Furthermore, we show that decorrelating the similarity matrix results in an improved localization performance. Through an extensive experimental evaluation on challenging datasets we demonstrate that our approach outperforms state-of-the-art techniques. Novel vision-based localization approach under challenging perceptual conditions.The approach successfully handles complex vehicle trajectories.Exploits decorrelation of the similarity matrix to improve localization accuracy.


title: Electrical collector topologies for offshore wind power plants: A survey

abstract: This paper gives an overview of various collector topologies for offshore wind farms. A comparative analysis of different DC and AC collector system topologies for offshore wind power plant are presented. The advantages and disadvantages of collector system topologies in terms of cost, reliability and efficiency are discussed. The paper discusses various updated collector topologies like radial, single sided ring, double sided ring, star and hybrid AC-DC etc. in AC collector systems and series, parallel, hybrid and matrix interconnected topology etc in DC collector systems. At the end alternative topologies based on bypassing method for radial, double sided topology are explained.


title: Antidepressant activity of zinc and magnesium in view of the current hypotheses of antidepressant action

abstract: The clinical efficacy of current antidepressant therapies is unsatisfactory; antidepressants induce a variety of unwanted effects, and, moreover, their therapeutic mechanism is not clearly understood. Thus, a search for better and safer agents is continuously in progress. Recently, studies have demonstrated that zinc and magnesium possess antidepressant properties. Zinc and magnesium exhibit antidepressant-like activity in a variety of tests and models in laboratory animals. They are active in forced swim and tail suspension tests in mice and rats, and, furthermore, they enhance the activity of conventional antidepressants (e.g., imipramine and citalopram). Zinc demonstrates activity in the olfactory bulbectomy, chronic mild and chronic unpredictable stress models in rats, while magnesium is active in stress-induced depression-like behavior in mice. Clinical studies demonstrate that the efficacy of pharmacotherapy is enhanced by supplementation with zinc and magnesium. The antidepressant mechanisms of zinc and magnesium are discussed in the context of glutamate, brain-derived neurotrophic factor (BDNF) and glycogen synthase kinase-3 (GSK-3) hypotheses. All the available data indicate the importance of zinc and magnesium homeostasis in the psychopathology and therapy of affective disorders.


Multi-label classification of music by emotion


title: Push-Pull DC-DC converter with planar transformer for PV applications

abstract: There is a trend toward increasing the performance of power electronics converters used in photovoltaic (PV) systems. This paper presents the design and implementation of a Push-Pull DC-DC converter prototype. This power converter typically integrates systems for photovoltaic applications due to high voltage conversion ratio. Is also presented the design of the high frequency planar transformer, which presents several advantages in relation to the conventional transformer.


title: An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems

abstract: Provision of training data sets is one of the core requirements for event-based supervised NILM (Non-Intrusive Load Monitoring) algorithms. Due to diversity in appliances' technologies, in-situ training by users is often required. This process might require continuous user-interaction to ensure that a high quality training data set is provided. Pre-populating a training data set could potentially reduce the need for user-system interaction. In this study, a heuristic unsupervised clustering algorithm is presented and evaluated to enable autonomous partitioning of appliances signature space (i.e. feature space) for applications in electricity consumption disaggregation. The algorithm is based on hierarchical clustering and uses the characteristics of a cluster binary tree to determine the distance threshold for pruning the tree without a priori information. The algorithm determines the partition of a feature space recursively to account for multi-scale nature of the binary cluster tree. Evaluation of the algorithm was carried out using metrics for accuracy and cluster quality (proposed in this study) on a fully labeled data set that was collected and processed in a real residential setting. The algorithm performance in accurate partitioning of the feature space and the effect of different feature extraction techniques were presented and discussed.


Design, Simulation and Verification of Generalized Photovoltaic cells Model Using First Principles Modeling


title: Three-Dimensional Atom-Probe Tomographic Analyses of Lead-Telluride Based Thermoelectric Materials

abstract: Precipitates in bulk p-type thermoelectric materials, PbTe-SrTe and PbTe-PbS, are studied using three-dimensional (3-D) atom-probe tomography (APT). APT is capable of characterizing chemically materials in 3-D with subnano-scale spatial resolution on an atom-by-atom basis, which enables us to characterize secondary phases in the PbTe matrix as well as the dopant distributions at different imperfections. We demonstrate that APT provides accurate information about the compositions and morphologies of nanoprecipitates. In the PbTe-SrTe system, different morphology of precipitates is observed and the SrTe composition is confirmed. Also, segregation of Na dopants at mesoscale imperfections, dislocations and grain boundaries, and at matrix/precipitate interfaces is observed. In the PbTe-PbS system, PbS precipitates are observed. The PbS precipitates exhibit faceting, and have a morphology that depends on the bulk Na concentration. A predominance of {100} faceted precipitates is observed for 2 mol.% Na. Using 3-D APT, we demonstrate that Na segregation at matrix/precipitate interfaces is most likely responsible for the change in their morphologies, which occurs by reducing the interfacial free energy of {100} facets.


title: Adaptive over current protection for distribution feeders with distributed generators

abstract: The connection of distributed generators (DG) to distribution networks influences the performance of the networks. This paper focuses on the impact of DG on the feeder protection, specifically the impact on the overcurrent (OC) relay performance. The paper presents simulation results to show the extent of deterioration a DG can cause on the OC relay performance. The paper then presents an approach to solve this problem and restore the overcurrent relay performance.


title: Differentially Private Histogram Publication for Dynamic Datasets: an Adaptive Sampling Approach

abstract: Differential privacy has recently become a de facto standard for private statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. However, most of them focus on "one-time" release of a static dataset and do not adequately address the increasing need of releasing series of dynamic datasets in real time. A straightforward application of existing histogram methods on each snapshot of such dynamic datasets will incur high accumulated error due to the composibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the problem of releasing series of dynamic datasets in real time with differential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the current snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic threshold adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods.


title: From Executive Order to Judicial Approval: Tracing the History of Surveillance of U.S. Persons Abroad in Light of Recent Terrorism Investigations

abstract: INTRODUCTION"[W]e are seeing an increasing number of individuals - including U.S. citizens - who have become captivated by extremist ideology and have taken steps to carry out terrorist objectives, either at home or abroad. It's a disturbing trend that we have been intensely investigating in recent years and will continue to investigate and root out."1After lengthy negotiations, numerous competing legislative proposals, and the expiration of its predecessor legislation, Congress passed the Foreign Intelligence Surveillance Act (FISA) Amendments Act of 2008 (FAA) in July 2008, amending the FISA in several significant ways.2 Although Congress eventually passed the FAA with bipartisan support,3 the legislative debate focused on several issues, including the incidental collection of communications of U.S. persons and the minimization of U.S. person information; the potential reverse targeting of U.S. persons; and civil immunity for telecommunications companies allegedly involved with the Terrorist Surveillance Program (TSP). The FAA built on the framework of the expired Protect America Act of 20074 (PAA or Protect America Act) with respect to the targeting of non-U.S. persons overseas and included several provisions aimed at protecting the civil liberties of U.S. persons, such as certain prohibitions on the reverse targeting of U.S. persons and a more robust role for the Foreign Intelligence Surveillance Court (FISC) in certain matters.5 The FAA also created a procedure for electronic communication service providers to claim immunity regarding their alleged involvement in the TSP,6 and reaffirmed FISA (and certain provisions of Title III) as the exclusive means for the government to conduct electronic surveillance within the United States.7In one particular respect, the FAA represented a significant departure from the way the executive branch had collected foreign intelligence information for decades.8 For the first time, the FAA required the government to apply to the FISC for the authority to conduct surveillance of U.S. persons located outside the United States.9 While this provision received significant attention from supporters and opponents when originally proposed, it was the subject of little disagreement upon final passage of the FAA, in contrast to other issues such as retroactive immunity for telecommunications companies.10 One senator, noting the provision's significance, declared that the FAA did "more than Congress has ever done before to protect Americans' privacy regardless of where they are, anywhere in the world."11 This change preceded several significant counterterrorism investigations involving Americans, including the attempted bombing of Times Square and the 2008 Mumbai attacks. The Americans involved in those matters engaged in significant activities in furtherance of their plots outside the United States, such as receiving training from or conducting surveillance on behalf of their terrorist associates.To date, scholarship addressing the FAA has generally focused on aspects of the legislation other than the surveillance of U.S. persons overseas.12 This article examines the history of surveillance of U.S. persons overseas and the new provisions affecting such surveillance in light of several recent national security investigations indicating that an increasing number of U.S. persons are involved in terrorist activities. As noted in the FAA legislative debate, several million U.S. persons live, travel, or study overseas at any given time.13 To set the stage, Part I examines recent counterterrorism investigations involving the activities of United States persons abroad. Recent reports suggest that the number of U.S. persons involved in terrorist training, planning, or attacks abroad is on the rise.14 Government officials have similarly warned that "[h]omegrown terrorists represent a new and changing facet of the terrorist threat."15 Part II examines the history of surveillance of U. …


title: Transfer Learning for Visual Categorization: A Survey

abstract: Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.


title: Large scale visual recommendations from street fashion images

abstract: We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose two classes of data driven models in the Deterministic Fashion Recommenders (DFR) and Stochastic Fashion Recommenders (SFR) for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. The industrial applicability of proposed models is in the context of mobile fashion shopping. Finally, we also outline a large-scale annotated data set of fashion images Fashion-136K) that can be exploited for future research in data driven visual fashion.


title: Credit Risk Prediction Based on Machine Learning Methods

abstract: Credit scoring which helps to evaluate the capability of repayment of customers is one of the most important issues for loan institutions. Due to the rapid development of machine learning techniques in computer science area and other related subjects, various classification methods have been proposed to facilitate the implementation of machine learning methods for characterizing the repayment behavior of customers. In this research, we use the XGBoost algorithm to identify the bad customers who do noe pay money back from the good customers. A comprehensive study is conducted to compare the performance of XGBoost algorithm with logistic regression. The results show that the XGBoost algorithm works much better.


title: Human Pose Estimation Using Global and Local Normalization

abstract: In this paper, we address the problem of estimating the positions of human joints, i.e., articulated pose estimation. Recent state-of-the-art solutions model two key issues, joint detection and spatial configuration refinement, together using convolutional neural networks. Our work mainly focuses on spatial configuration refinement by reducing variations of human poses statistically, which is motivated by the observation that the scattered distribution of the relative locations of joints (e.g., the left wrist is distributed nearly uniformly in a circular area around the left shoulder) makes the learning of convolutional spatial models hard. We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation. In addition, our empirical results show that incorporating multi-scale supervision and multi-scale fusion into the joint detection network is beneficial. Experiment results demonstrate that our method consistently outperforms state-of-the-art methods on the benchmarks.


title: Towards Secure SPARQL Queries in Semantic Web Applications Using PHP

abstract: The Semantic Web (SW) is a significant advancement in the field of Internet technologies and an uncharted territory as far as security is concerned. In this paper we investigate and assess the impact of known attacks of SPARQL/SPARUL injections on Semantic Web applications developed in PHP. We highlight future challenges of developing robust Semantic Web applications using PHP. Our results demonstrate and quantify impacts on Confidentiality, Integrity and Availability (CIA) breaches of data in Semantic Web applications. Our recommendations are targeted to PHP developers, to encourage them to integrate security as early in their design and coding practice as possible.


title: Markerless Human Motion Capture for Gait Analysis

abstract: The aim of our study is to detect balance disorders and a tendency towards the falls in the elderly, knowing gait parameters. In this paper we present a new tool for gait analysis based on markerless human motion capture, from camera feeds. The system introduced here, recovers the 3D positions of several key points of the human body while walking. Foreground segmentation, an articulated body model and particle filtering are basic elements of our approach. No dynamic model is used thus this system can be described as generic and simple to implement. A modified particle filtering algorithm, which we call Interval Particle Filtering, is used to reorganise and search through the model's configurations search space in a deterministic optimal way. This algorithm was able to perform human movement tracking with success. Results from the treatment of a single cam feeds are shown and compared to results obtained using a marker based human motion capture system.


title: Balanced Antipodal Vivaldi Antenna With Dielectric Director for Near-Field Microwave Imaging

abstract: A balanced antipodal Vivaldi antenna is designed to be used as a sensor for a microwave breast cancer detection system. The antenna has the ability to send short electromagnetic pulses into the near-field, with low distortion, low loss and in a directional manner. The antenna directivity is further improved by the inclusion of a novel feature in the antenna aperture called a “director” which consists of a profiled piece of higher dielectric constant material. Several simulated results are successfully confirmed with measurements. Reflections of a tumor placed in a breast model are simulated for two cases, namely a balanced antipodal Vivaldi antenna with and without a director. Greater tumor responses are recorded with the director present, demonstrating the potential of this feature for microwave breast imaging.


title: An improved LDA algorithm for text classification

abstract: Latent Dirichlet Allocation is a classic topic model which can extract latent topic from large data corpus. This model assumes that if a document is relevant to a topic, then all tokens in the document are relevant to that topic. In this paper, we present an algorithm called gLDA for topic text classification by adding topic-category distribution parameter to LDA, which can make the document generated from the most relevant category. Gibbs sampling is employed to conduct approximate inference, and experiment results in two datasets show the effectiveness of this method.


title: Two-Switch Voltage Equalizer Using an LLC Resonant Inverter and Voltage Multiplier for Partially Shaded Series-Connected Photovoltaic Modules

abstract: Various kinds of differential power processing converters and voltage equalizers have been proposed for series-connected photovoltaic (PV) modules to prevent negative influences of partial shading, such as significant reduction in power generation and the occurrence of multiple maximum power points (MPPs), including local and global MPPs, that hinders and confuses MPP tracking algorithms to operate properly. However, since conventional topologies are based on multiple individual dc–dc converters, the required switch count increases proportionally to the number of modules connected in series, increasing the complexity. A two-switch voltage equalizer using an LLC resonant inverter and voltage multiplier is proposed in this paper. The circuitry can be dramatically simplified compared with conventional topologies due to the two-switch configuration. Detailed operation analyses for the LLC resonant inverter and voltage multiplier are separately performed. Experimental equalization tests emulating partially shaded conditions were performed for four PV modules connected in series. With the proposed voltage equalizer, local MPPs successfully disappeared, and extractable maximum power increased compared with those without equalization, demonstrating the effectiveness and performance of the proposed voltage equalizer.


title: 21.1 % efficient perc silicon solar cells on large scale by using inline sputtering for metallization

abstract: This work presents the first results for the production of highly efficient solar cells with industrial processes using the PERC structure. Two batches of FZ and Cz wafers were conducted to prove the applicability of a transfer from the clean room to an industrial standard. The front and rear side metallization was done by a new pilot system using the sputtering technology [1] The investigation of solar cell results, series resistance, contact resistance and a optical analysis are discussed later on.


title: Deep Dependency Substructure-Based Learning for Multidocument Summarization

abstract: Most extractive style topic-focused multidocument summarization systems generate a summary by ranking textual units in multiple documents and extracting a proper subset of sentences biased to the given topic. Usually, the textual units are simply represented as sentences or n-grams, which do not carry deep syntactic and semantic information. This article presents a novel extractive topic-focused multidocument summarization framework. The framework proposes a new kind of more meaningful and informative units named frequent Deep Dependency Sub-Structure (DDSS) and a topic-sensitive Multi-Task Learning (MTL) model for frequent DDSS ranking. Given a document set, first, we parse all the sentences into deep dependency structures with a Head-driven Phrase Structure Grammar (HPSG) parser and mine the frequent DDSSs after semantic normalization. Then we employ a topic-sensitive MTL model to learn the importance of these frequent DDSSs. Finally, we exploit an Integer Linear Programming (ILP) formulation and use the frequent DDSSs as the essentials for summary extraction. Experimental results on two DUC datasets demonstrate that our proposed approach can achieve state-of-the-art performance. Both the DDSS information and the topic-sensitive MTL model are validated to be very helpful for topic-focused multidocument summarization.


title: Desisting and persisting gender dysphoria after childhood: A qualitative follow-up study

abstract: The aim of this qualitative study was to obtain a better understanding of the developmental trajectories of persistence and desistence of childhood gender dysphoria and the psychosexual outcome of ...


title: Validation of heart rate extraction through an iPhone accelerometer

abstract: Ubiquitous medical technology may provide advanced utility for evaluating the status of the patient beyond the clinical environment. The iPhone provides the capacity to measure the heart rate, as the iPhone consists of a 3-axis accelerometer that is sufficiently sensitive to perceive tiny body movements caused by heart pumping. In this preliminary study, an iPhone was tested and evaluated as the reliable heart rate extractor to use for medical purpose by comparing with reference electrocardiogram. By comparing the extracted heart rate from acquired acceleration data with the extracted one from ECG reference signal, iPhone functioning as the reliable heart rate extractor has demonstrated sufficient accuracy and consistency.


title: Cannabis ‐ from cultivar to chemovar

abstract: The medicinal use of Cannabis is increasing as countries worldwide are setting up official programs to provide patients with access to safe sources of medicinal-grade Cannabis. An important question that remains to be answered is which of the many varieties of Cannabis should be made available for medicinal use. Drug varieties of Cannabis are commonly distinguished through the use of popular names, with a major distinction being made between Indica and Sativa types. Although more than 700 different cultivars have already been described, it is unclear whether such classification reflects any relevant differences in chemical composition. Some attempts have been made to classify Cannabis varieties based on chemical composition, but they have mainly been useful for forensic applications, distinguishing drug varieties, with high THC content, from the non-drug hemp varieties. The biologically active terpenoids have not been included in these approaches. For a clearer understanding of the medicinal properties of the Cannabis plant, a better classification system, based on a range of potentially active constituents, is needed. The cannabinoids and terpenoids, present in high concentrations in Cannabis flowers, are the main candidates. In this study, we compared cultivars obtained from multiple sources. Based on the analysis of 28 major compounds present in these samples, followed by principal component analysis (PCA) of the quantitative data, we were able to identify the Cannabis constituents that defined the samples into distinct chemovar groups. The study indicates the usefulness of a PCA approach for chemotaxonomic classification of Cannabis varieties. Copyright © 2012 John Wiley & Sons, Ltd.


title: Untangling Text Data Mining

abstract: The possibilities for data mining from large text collections are virtually untapped. Text expresses a vast, rich range of information, but encodes this information in a form that is difficult to decipher automatically. Perhaps for this reason, there has been little work in text data mining to date, and most people who have talked about it have either conflated it with information access or have not made use of text directly to discover heretofore unknown information. ::: ::: In this paper I will first define data mining, information access, and corpus-based computational linguistics, and then discuss the relationship of these to text data mining. The intent behind these contrasts is to draw attention to exciting new kinds of problems for computational linguists. I describe examples of what I consider to be real text data mining efforts and briefly outline recent ideas about how to pursue exploratory data analysis over text.


title: A Review of Eye Gaze in Virtual Agents, Social Robotics and HCI: Behaviour Generation, User Interaction and Perception

abstract: A person's emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: 'The face is the portrait of the mind; the eyes, its informers'. This presents a significant challenge for Computer Graphics researchers who generate artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human-human interactions. This review article provides an overview of the efforts made on tackling this demanding task. As with many topics in computer graphics, a cross-disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user. We begin with a discussion of the movement of the eyeballs, eyelids and the head from a physiological perspective and how these movements can be modelled, rendered and animated in computer graphics applications. Furthermore, we present recent research from psychology and sociology that seeks to understand higher level behaviours, such as attention and eye gaze, during the expression of emotion or during conversation. We discuss how these findings are synthesized in computer graphics and can be utilized in the domains of Human-Robot Interaction and Human-Computer Interaction for allowing humans to interact with virtual agents and other artificial entities. We conclude with a summary of guidelines for animating the eye and head from the perspective of a character animator.


title: Low cost microwave oscillator using substrate integrated waveguide cavity

abstract: A topology is proposed for designing a low-cost microwave oscillator. This new feedback oscillator makes use of a substrate integrated waveguide (SIW) cavity that acts as a frequency selector as well as a feedback-coupling device. The oscillator is stabilized by using an injection-locking scheme. A 12.02-GHz oscillator prototype was designed. Experimental results for phase noise, locking range, and quality factor of the new circuit are presented. An external Q of 178 was measured.


title: Metacestodes of Glossocercus auritus (Cyclophyllidea, Gryporhynchidae) in Poecilia reticulata (Pisces, Poeciliidae) from Brazil

abstract: Studies on fish parasites in Pampulha dam, Belo Horizonte, Minas Gerais, Brazil, found specimens of Poecilia reticulata Peters, 1859 harboring natural infection by larval stages of tapeworms. A total of 250 specimens of P. reticulata were collected and analyzed between February and August 2010, of which 23 were found infected (prevalence 9.2%) with one metacestode each (mean intensity 1, mean abundance 0.09). The analyses of the parasites, particularly the morphology of rostellar hooks, made it possible to identify Glossocercus auritus (Rudolphi, 1818). This is the first report of G. auritus metacestode in South America and P. reticulata is a newly known host for this parasite.


title: Preceptors’ perceptions of the elements of a successful and an unsuccessful orientation period for newly graduated nurses

abstract: AIM ::: To identify existing orientation patterns and to find elements that may enhance or impede successful orientation of newly graduated nurses. ::: ::: ::: BACKGROUND ::: Preceptors have first-hand information concerning orientation and their opinions should not be forgotten when organisations develop their orientation processes. ::: ::: ::: METHOD ::: An open-ended questionnaire was undertaken to collect data from preceptors (n = 172) about the current orientation patterns. Data were analysed by using deductive content analysis and by quantifying the phrases. ::: ::: ::: RESULTS ::: Communal commitment to the orientation process, strong professional orientation know-how and supportive leadership were the enhancing elements of successful orientation. Complex interpersonal relationship problems during orientation, inadequate orientation resources and the organisation's structural and functional problems were the impeding elements of successful orientation. ::: ::: ::: CONCLUSIONS ::: With the elements of successful orientation we ensure a supportive transition to practice for newly graduate nurses and possibilities to focus on good orientation practices for preceptors. ::: ::: ::: IMPLICATION FOR NURSING MANAGEMENT ::: Nurse leaders play an important part in ensuring newly graduated nurses have a safe nursing career starting point and, for preceptors, opportunities to provide orientation that is as good as possible. Supportive leadership, sufficient resources and good interpersonal relationships should be the leading principles during newly graduated nurses' orientation period.


title: A Compact, Low-Profile Metasurface-Enabled Antenna for Wearable Medical Body-Area Network Devices

abstract: We propose a compact conformal wearable antenna that operates in the 2.36-2.4 GHz medical body-area network band. The antenna is enabled by placing a highly truncated metasurface, consisting of only a two by two array of I-shaped elements, underneath a planar monopole. In contrast to previously reported artificial magnetic conducting ground plane backed antenna designs, here the metasurface acts not only as a ground plane for isolation, but also as the main radiator. An antenna prototype was fabricated and tested, showing a strong agreement between simulation and measurement. Comparing to previously proposed wearable antennas, the demonstrated antenna has a compact form factor of 0.5 λ ::: 0 ::: ×0.3 λ ::: 0 ::: ×0.028 λ ::: 0 ::: , all while achieving a 5.5% impedance bandwidth, a gain of 6.2 dBi, and a front-to-back ratio higher than 23 dB. Further numerical and experimental investigations reveal that the performance of the antenna is extraordinarily robust to both structural deformation and human body loading, far superior to both planar monopoles and microstrip patch antennas. Additionally, the introduced metal backed metasurface enables a 95.3% reduction in the specific absorption rate, making such an antenna a prime candidate for incorporation into various wearable devices.


title: Towards structured log analysis

abstract: Value of software log file analysis has been constantly increasing with the value of information to organizations. Log management tools still have a lot to deliver in order to empower their customers with the true strength of log information. In addition to the traditional uses such as testing software functional conformance, troubleshooting and performance benchmarking, log analysis has proven its capabilities in fields like intrusion detection and compliance evaluation. This is verified by the emphasis on log analysis in regulations like PCI DSS, FISMA, HIPAA and frameworks such as ISO 27001 and COBIT. In this paper we present an in depth analysis into current log analysis domains and common problems. A practical guide to the use of few popular log analysis tools is also included. Lack of proper support for structured analysis is identified as one major flaw in existing tools. After that, we describe a framework we developed for structured log analysis with the view of providing a solution to open problems in the domain. The core strength of the framework is its ability to handle many log file formats that are not well served by existing tools and providing sophisticated infrastructure for automating recurring log analysis procedures. We prove the usefulness of the framework with a simple experiment.