title: Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference

abstract: Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity.


title: High performance and fast object detection in road environments

abstract: In this paper, we present a high performance and fast object detection method based on a fully convolutional network (FCN) for advanced driver assistance systems (ADAS). Object detection methods based on deep learning have high performance but they require high computational complexity. Even if a method works on the high-performance graphics processing unit (GPU) hardware platform, it is hard to guarantee real-time processing. General object detectors based on deep learning try to localize too many classes of objects in various dynamic environments. The proposed detection method based on FCN improves detection performance and maintains real-time processing in road environments through various schemes related to the limitation of object class type, data augmentation, network architecture, and multi-ratio default boxes. Our experimental results show that the proposed method outperforms a previous method both in terms of performance and speed.


title: FCL: A general purpose library for collision and proximity queries

abstract: We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot.


title: Clustering Student Programming Assignments to Multiply Instructor Leverage

abstract: A challenge in introductory and intermediate programming courses is understanding how students approached solving a particular programming problem, in order to provide feedback on how they might improve. In both Massive Open Online Courses (MOOCs) and large residential courses, such feedback is difficult to provide for each student individually. To multiply the instructor's leverage, we would like to group student submissions according to the general problem-solving strategy they used, as the first stage of a ``feedback pipeline''. We describe ongoing explorations of a variety of clustering algorithms and similarity metrics using a corpus of over 800 student submissions to a simple programming assignment from a programming MOOC. We find that for a majority of submissions, it is possible to automatically create clusters such that an instructor ``eyeballing'' some representative submissions from each cluster can readily describe qualitatively what the common elements are in student submissions in that cluster. This information can be the basis for feedback to the students or for comparing one group of students' approach with another's.


title: Classification of individual articles from all of science by research level

abstract: A system of four research levels, designed to classify scientific journals from most applied to most basic, was introduced by Francis Narin and colleagues in the 1970s. Research levels have been used since that time to characterize research at institutional and departmental levels. Currently, less than half of all articles published are in journals that been classified by research level. There is thus a need for the notion of research level to be extended in a way that all articles can be so classified. This article reports on a new model – trained from title and abstract words and cited references – that classifies individual articles by research level. The model covers all of science, and has been used to classify over 25 million articles from Scopus by research level. The final model and set of classified articles are further characterized.


title: ACCURACY ANALYSIS OF KINECT DEPTH DATA

abstract: Abstract. This paper presents an investigation of the geometric quality of depth data obtained by the Kinect sensor. Based on the mathematical model of depth measurement by the sensor a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimetres up to about 4 cm at the maximum range of the sensor. The accuracy of the data is also found to be influenced by the low resolution of the depth measurements.


title: Visual information foraging in a focus + context visualization

abstract: Eye tracking studies of the Hyperbolic Tree browser [10] suggest that visual search in focus+context displays is highly affected by information scent (i.e., local cues, such as text summaries, used to assess and navigate toward distal information sources). When users detected a strong information scent, they were able to reach their goal faster with the Hyperbolic Tree browser than with a conventional browser. When users detected a weak scent or no scent, users exhibited less efficient search of areas with a high density of visual items. In order to interpret these results we present an integration of the CODE Theory of Visual Attention (CTVA) with information foraging theory. Development of the CTVA-foraging theory could lead to deeper analysis of interaction with visual displays of content, such as the World Wide Web or information visualizations.


title: Salesperson Selection, Training, and Development: Trends, Implications, and Research Opportunities

abstract: Building on the forces of change in selling identified in the introductory paper of this Special Issue of JPSSM, we identify and explicate trends, managerial implications, and research opportunities in two important domains of the selling field: training and development (TD) and selection. Part One focuses on TD of salespeople, and argues why a fresh perspective on salesperson TD is essential. Then, taking a knowledge, skills, and ability (KSA) approach, a framework for future TD content is proposed around three levels of KSAs: task-related KSAs, growth-related KSAs, and meta-KSAs. Part Two, on selection, focuses on several important predictors of salesperson success: cognitive ability tests, personality inventories, and biodata. Then, assessment methods are reviewed. Finally, we summarize with a discussion of emerging trends and research opportunities. The focus of the discussion is on both enhancing salesperson performance and affording scholars new research opportunities.


title: Reduction of Mutual Coupling Between Closely-Packed Antenna Elements

abstract: A simple ground plane structure that can reduce mutual coupling between closely-packed antenna elements is proposed and studied. The structure consists of a slitted pattern, without via's, etched onto a single ground plane and it is therefore low cost and straightforward to fabricate. It is found that isolations of more than -20 dB can be achieved between two parallel individual planar inverted-F antennas (PIFAs) sharing a common ground plane, with inter-antenna spacing (center to center) of 0.116 lambdao and ground plane size 0.331lambdao 2. At 2.31 GHz it is demonstrated that this translates into an edge to edge separation between antennas of just 10 mm. Similarly the structure can be applied to reduce mutual coupling between three or four radiating elements. In addition the mutual coupling between half wavelength patches and monopoles can also be reduced with the aid of the proposed ground plane structure. Results of parametric studies are also given in this paper. Both simulation and measurement results are used to confirm the suppression of mutual coupling between closely-packed antenna elements with our slitted ground plane.


General LTL Specification Mining


title: A design of 120-degree vertical polarized sector antenna using very short balanced antipodal Vivaldi antenna

abstract: The Vivaldi antenna is a very popular antenna element for those applications requiring a high-gain and a wideband traveling wave antenna element. The antenna usually has very long length L (/spl ges/3/spl lambda//sub 0/). Following basic traveling-wave antenna principles, the longer L, the higher the gain and narrower the beamwidth. Very few references appear in the literature where this antenna element is used in its very short form, i.e. (L/spl les//spl lambda//sub 0//2). A short form Vivaldi antenna is used for the sector antenna application. With the help of reflectors, the design can give a 120/spl deg/ horizontal beamwidth.


title: Federated authentication of things: demo abstract

abstract: In this demo, we will showcase FLAT, a federated identity model tailored to IoT. FLAT's authentication is lightweight because it uses only symmetric cryptosystems on the IoT client side and implicit certificates between the Identity and Service Providers. We show how FLAT can be used to increase security and privacy in automatic toll gate payment applications.


title: Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm

abstract: Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adversarial perturbations in a neighbourhood around an input. In this paper we focus on the $L_0$ norm and aim to compute, for a trained DNN and an input, the maximal radius of a safe norm ball around the input within which there are no adversarial examples. Then we define global robustness as an expectation of the maximal safe radius over a test data set. We first show that the problem is NP-hard, and then propose an approximate approach to iteratively compute lower and upper bounds on the network's robustness. The approach is \emph{anytime}, i.e., it returns intermediate bounds and robustness estimates that are gradually, but strictly, improved as the computation proceeds; \emph{tensor-based}, i.e., the computation is conducted over a set of inputs simultaneously, instead of one by one, to enable efficient GPU computation; and has \emph{provable guarantees}, i.e., both the bounds and the robustness estimates can converge to their optimal values. Finally, we demonstrate the utility of the proposed approach in practice to compute tight bounds by applying and adapting the anytime algorithm to a set of challenging problems, including global robustness evaluation, competitive $L_0$ attacks, test case generation for DNNs, and local robustness evaluation on large-scale ImageNet DNNs. We release the code of all case studies via GitHub.


title: Lessons from technology development for energy and sustainability

abstract: There are lessons from recent history of technology introductions which should not be forgotten when considering alternative energy technologies for carbon dioxide emission reductions. The growth of the ecological footprint of a human population about to increase from 7B now to 9B in 2050 raises serious concerns about how to live both more efficiently and with less permanent impacts on the finite world. One present focus is the future of our climate, where the level of concern has prompted actions across the world in mitigation of the emissions of CO 2 . An examination of successful and failed introductions of technology over the last 200 years generates several lessons that should be kept in mind as we proceed to 80% decarbonize the world economy by 2050. I will argue that all the actions taken together until now to reduce our emissions of carbon dioxide will not achieve a serious reduction, and in some cases, they will actually make matters worse. In practice, the scale and the different specific engineering challenges of the decarbonization project are without precedent in human history. This means that any new technology introductions need to be able to meet the huge implied capabilities. An altogether more sophisticated public debate is urgently needed on appropriate actions that (i) considers the full range of threats to humanity, and (ii) weighs more carefully both the upsides and downsides of taking any action, and of not taking that action.


title: TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing

abstract: We investigate a new approach to the design of distributed, shared-nothing RDF engines. Our engine, coined "TriAD", combines join-ahead pruning via a novel form of RDF graph summarization with a locality-based, horizontal partitioning of RDF triples into a grid-like, distributed index structure. The multi-threaded and distributed execution of joins in TriAD is facilitated by an asynchronous Message Passing protocol which allows us to run multiple join operators along a query plan in a fully parallel, asynchronous fashion. We believe that our architecture provides a so far unique approach to join-ahead pruning in a distributed environment, as the more classical form of sideways information passing would not permit for executing distributed joins in an asynchronous way. Our experiments over the LUBM, BTC and WSDTS benchmarks demonstrate that TriAD consistently outperforms centralized RDF engines by up to two orders of magnitude, while gaining a factor of more than three compared to the currently fastest, distributed engines. To our knowledge, we are thus able to report the so far fastest query response times for the above benchmarks using a mid-range server and regular Ethernet setup.


title: Sparse Representation-Based Open Set Recognition

abstract: We propose a generalized Sparse Representation-based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms.


title: CloCom: Mining existing source code for automatic comment generation

abstract: Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences. In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.


title: An iterative model-based approach to cochannel speech separation

abstract: Cochannel speech separation aims to separate two speech signals from a single mixture. In a supervised scenario, the identities of two speakers are given, and current methods use pre-trained speaker models for separation. One issue in model-based methods is the mismatch between training and test signal levels. We propose an iterative algorithm to adapt speaker models to match the signal levels in testing. Our algorithm first obtains initial estimates of source signals using unadapted speaker models and then detects the input signal-to-noise ratio (SNR) of the mixture. The input SNR is then used to adapt the speaker models for more accurate estimation. The two steps iterate until convergence. Compared to search-based SNR detection methods, our method is not limited to given SNR levels. Evaluations demonstrate that the iterative procedure converges quickly in a considerable range of SNRs and improves separation results significantly. Comparisons show that the proposed system performs significantly better than related model-based systems.


title: Towards Model-Based Failure-Management for Automotive Software

abstract: Failure management is a particular challenge problem in the automotive domain. Today's cars host a network of 30 to 80 electronic control units (ECUs), distributed over up to five interconnected in-car networks supporting hundreds to thousands of software- defined functions. This high degree of distribution of hard- and software components is a key contributor to the difficulty of failure management in vehicle. This paper addresses comprehensive failure management, starting from domain models for logical and deployment models of automotive software. These models capture interaction patterns as a critical part of both logical and deployment architectures, introducing failure detection and mitigation as "wrapper" services to "unmanaged services", i.e. services without failure management. We show how these models can be embedded into an interaction-centric development process, which captures failure management information across development phases. Finally, we exploit the failure management models to verify that a particular architecture meets its requirements under the stated failure hypothesis.


title: Research on converting cultivated land into forests with GPS management system in application

abstract: GPS is a satellite-based positioning and time transfering GPS positioning function has a wide range of applications in the modern measurement and a more modern building. GPS technology is also widely used in modernization building, and now GPS has also been shown to be an important civilian communities of interest, and increasingly wide range of applicated. GPS management system solve the relevant problems of grain plots to forestry engineering effectively. Through the combination of information communication tool and the science and technology of management, a big problem China in environmental protection has been solved. This paper introduces the application of GPS management system in the grain plots to forestry engineering in detail, analyses the deficiency of the existing manual measurement method and expounds the advantages of the GPS management system.


Design of edge termination on non-uniform 100-V super-junction trench power MOSFET


title: RNA modifications and structures cooperate to guide RNA–protein interactions

abstract: An emerging body of evidence indicates that post-transcriptional gene regulation relies not only on the sequence of mRNAs but also on their folding into intricate secondary structures and on the chemical modifications of the RNA bases. These features, which are highly dynamic and interdependent, exert direct control over the transcriptome and thereby influence many aspects of cell function. Here, we consider how the coupling of RNA modifications and structures shapes RNA-protein interactions at different steps of the gene expression process.


title: Proximity-Coupled Multiband Microstrip Antenna for Wireless Applications

abstract: This letter presents a new design of a multiband microstrip antenna with a proximity-coupled feed for operating in the LTE2300 (2300–2400 MHz), Bluetooth (2400–2485 MHz), WiMAX (3.3–3.7 GHz), and WLAN (5.15–5.35 GHz, 5.725–5.825 GHz) bands. In addition, it also covers 6-dB impedance bandwidth across the UMTS (1920–2170 MHz) band. The proposed antenna consists of a corner-truncated rectangular patch with a rectangular slot, meandered microstrip feed, and defected ground plane. The antenna is fabricated using 0.8-mm-thick FR4 substrate with a dielectric constant of 4.4 and has a small size of only $27\times 24~\hbox{mm}^{2}$ . The antenna shows a stable gain over the operating bands and good radiation characteristics. The simulated and measured results are shown to have good agreements.


title: Empirical properties of asset returns: stylized facts and statistical issues

abstract: We present a set of stylized empirical facts emerging from the statistical analysis of price variations in various types of financial markets. We first discuss some general issues common to all statistical studies of financial time series. Various statistical properties of asset returns are then described: distributional properties, tail properties and extreme fluctuations, pathwise regularity, linear and nonlinear dependence of returns in time and across stocks. Our description emphasizes properties common to a wide variety of markets and instruments. We then show how these statistical properties invalidate many of the common statistical approaches used to study financial data sets and examine some of the statistical problems encountered in each case.


title: Monitoring Health Changes in Congestive Heart Failure Patients Using Wearables and Clinical Data

abstract: In this work we present systems to monitor the health of a wide variety of high risk patients living with Congestive Heart Failure. For critical hospitalized patients, we introduce a deep learning framework for hospital records that uses a Word2Vec vector space representation to learn from a combination of structured data and unstructured text. The deep learning framework is able to assess patient risk, and accurately predict medical outcomes into the future. For less critical patients living at home, we also present algorithms for remote monitoring that can track a patient’s changing health indicators using a wearable heart-rate sensors. The pool of individuals living with congestive heart failure is very diverse, which can make multifaceted approaches to health monitoring, such as those presented in this work, attractive for observing large pools of high-risk patients. Collectively the methods presented in this paper allow us to continuously monitor a person living with CHF through hospitalization, discharge, and into their home.


title: A deep representation for depth images from synthetic data

abstract: Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach.


title: Analysis of security features in DLMS/COSEM: Vulnerabilities and countermeasures

abstract: The goal of this article is to identify weaknesses of the security layer in DLMS/COSEM according to Green Book Version 8. In the latest versions 7.3 and 8 of the Green Book, different security layers were added to protect sensitive and personal data. Security requirements of countries with a high level of data protection necessitated this step. Therefore, Green Book Version 8 intends to ensure confidentiality, availability and integrity of data using state-of-the-art methods in cryptography. This paper identifies and describes vulnerabilities and suggests effective countermeasures.


title: The AdaBoost algorithm for vehicle detection based on CNN features

abstract: Vehicle detection is the key technology of Intelligent Transportation System(ITS), Self-Driving Cars and Active Driving Assist. Existing methods have some problems with the robustness and real-time performance. This paper proposes the AdaBoost algorithm for vehicle detection based on CNN features. We set up a shallower CNN than traditional one to extract features to reduce the complexity of the network. On this basis, AdaBoost classifier with the ability for rapid classification is used to complete the vehicle detection. The combination of shallow CNN and AdaBoost algorithm improves the robustness and real-time performance of vehicle detection. The result of the experiments proves that the algorithm not only improves the real-time performance but also the recognition rate.


title: Workflow Patterns

abstract: Differences in features supported by the various contemporary commercial workflow management systems point to different insights of suitability and different levels of expressive power. The challenge, which we undertake in this paper, is to systematically address workflow requirements, from basic to complex. Many of the more complex requirements identified, recur quite frequently in the analysis phases of workflow projects, however their implementation is uncertain in current products. Requirements for workflow languages are indicated through workflow patterns. In this context, patterns address business requirements in an imperative workflow style expression, but are removed from specific workflow languages. The paper describes a number of workflow patterns addressing what we believe identify comprehensive workflow functionality. These patterns provide the basis for an in-depth comparison of a number of commercially availablework flow management systems. As such, this paper can be seen as the academic response to evaluations made by prestigious consulting companies. Typically, these evaluations hardly consider the workflow modeling language and routing capabilities, and focus more on the purely technical and commercial aspects.


title: Miniaturized Dual-Loop NFC Antenna With a Very Small Slot Clearance for Metal-Cover Smartphone Applications

abstract: A near-field communication (NFC) dual-loop antenna with a miniaturized size of $15\,\, \text {mm} \times 25 \,\,\text {mm}$ is initially proposed for full metal-cover smartphone applications. To achieve a small clearance zone on the metal cover, a small rectangular slot of size $8 \,\,\text {mm} \times 16\,\, \text {mm}$ is loaded into the metal cover. To allow good inductive coupling, the NFC antenna is only 0.37 mm beneath the center of the rectangular slot. Because of that, the direction of the eddy current induced on the metal cover will be the same as the dual-loop NFC antenna. It is noteworthy that the prototype NFC antenna integrated with a single rectangular slot loaded full metal cover was certified by the Europay, MasterCard, and Visa test.


title: A highly efficient method for extracting FSMs from flattened gate-level netlist

abstract: This paper proposes a novel method for extracting Finite State Machines (FSMs) from flattened gate-level netlist. The proposed method which employs a potential state register elimination technique and a two-level FSM separation strategy is highly applicable to control-intensive circuits. The potential state register elimination technique is based on control signal identification whereas the two-level FSM separation strategy is based on enable tree identification and the strongly connected components algorithm. To demonstrate the efficacy and to illustrate the unique features of the proposed FSM extraction method, the Synopsys DesignWare DW8051 microcontroller is used as the benchmark circuit for comparison and simulations. Results show that the proposed method reduces the complexity of the extracted FSMs in terms of number of state registers in an FSM by more than 90% as compared to the reported technique.


title: Disentangling the Latent Space of (Variational) Autoencoders for NLP

abstract: We train multi-task (variational) autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders are attached, the better the models cluster sentences according to their syntactic similarity, as the representation space becomes less entangled. We compare standard unconstrained autoencoders to variational autoencoders and find significant differences. We achieve better disentanglement with the standard autoencoder, which goes against recent work on variational autoencoders in the visual domain.


title: DXR: A Toolkit for Building Immersive Data Visualizations

abstract: This paper presents DXR, a toolkit for building immersive data visualizations based on the Unity development platform. Over the past years, immersive data visualizations in augmented and virtual reality (AR, VR) have been emerging as a promising medium for data sense-making beyond the desktop. However, creating immersive visualizations remains challenging, and often require complex low-level programming and tedious manual encoding of data attributes to geometric and visual properties. These can hinder the iterative idea-to-prototype process, especially for developers without experience in 3D graphics, AR, and VR programming. With DXR, developers can efficiently specify visualization designs using a concise declarative visualization grammar inspired by Vega-Lite. DXR further provides a GUI for easy and quick edits and previews of visualization designs in-situ, i.e., while immersed in the virtual world. DXR also provides reusable templates and customizable graphical marks, enabling unique and engaging visualizations. We demonstrate the flexibility of DXR through several examples spanning a wide range of applications.


title: An Optimal Transport Approach to Robust Reconstruction and Simplification of 2D Shapes Fernando de Goes 1

abstract: We propose a robust 2D shape reconstruction and simplification algorithm which takes as input a defect-laden point set with noise and outliers. We introduce an optimal-transport driven approach where the input point set, considered as a sum of Dirac measures, is approximated by a simplicial complex considered as a sum of uniform measures on 0- and 1-simplices. A fine-to-coarse scheme is devised to construct the resulting simplicial complex through greedy decimation of a Delaunay triangulation of the input point set. Our method performs well on a variety of examples ranging from line drawings to grayscale images, with or without noise, features, and boundaries.


title: Single night video-game use leads to sleep loss and attention deficits in older adolescents

abstract: Abstract The present study investigated adolescent video-game use prior to bedtime and subsequent sleep, working memory and sustained attention performance. Participants were 21 healthy, good-sleeping adolescents (16 male) aged between 15 and 20 years ( M = 17.6 years, SD = 1.8). Time spent video-gaming and subsequent sleep was measured across one night in the sleep laboratory. There were significant correlations between time spent video-gaming and sleep and between video-gaming and sustained attention, but not working memory. Sleep duration, in turn, had a significant negative association with sustained attention performance. Mediation analyses revealed that the relationship between video-gaming and sustained attention was fully mediated by sleep duration. These results indicate that video-gaming affected the ability to sustain attention only in as much as it affected sleep. In order to minimise negative consequences of video-game playing, video-games should be used in moderation, avoiding use close to the sleep period, to obviate detriments to sleep and performance.


title: Game Theory of Mind

abstract: This paper introduces a model of ‘theory of mind’, namely, how we represent the intentions and goals of others to optimise our mutual interactions. We draw on ideas from optimum control and game theory to provide a ‘game theory of mind’. First, we consider the representations of goals in terms of value functions that are prescribed by utility or rewards. Critically, the joint value functions and ensuing behaviour are optimised recursively, under the assumption that I represent your value function, your representation of mine, your representation of my representation of yours, and so on ad infinitum. However, if we assume that the degree of recursion is bounded, then players need to estimate the opponent's degree of recursion (i.e., sophistication) to respond optimally. This induces a problem of inferring the opponent's sophistication, given behavioural exchanges. We show it is possible to deduce whether players make inferences about each other and quantify their sophistication on the basis of choices in sequential games. This rests on comparing generative models of choices with, and without, inference. Model comparison is demonstrated using simulated and real data from a ‘stag-hunt’. Finally, we note that exactly the same sophisticated behaviour can be achieved by optimising the utility function itself (through prosocial utility), producing unsophisticated but apparently altruistic agents. This may be relevant ethologically in hierarchal game theory and coevolution.


title: Regression and time series model selection in small samples

abstract: SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small, or when the number of fitted parameters is a moderate to large fraction of the sample size. The corrected method, called AICC, is asymptotically efficient if the true model is infinite dimensional. Furthermore, when the true model is of finite dimension, AICC is found to provide better model order choices than any other asymptotically efficient method. Applications to nonstationary autoregressive and mixed autoregressive moving average time series models are also discussed.


title: A multi-cue information based approach to contour detection by utilizing superpixel segmentation

abstract: Contour detection forms one of the primitive, yet inherent operations of computer vision systems. Owing to the significance of this fundamental task, a number of approaches have been proposed till date. This paper characterizes the functionality of a multi-scale feature-based edge detection strategy that exploits joint information from different feature-channels, modelled over a measure of spacial dispersion associated with structured discontinuities in an image. The issue of eliminating false edges is achieved by incorporating an iterative clustering procedure that divides the image into disjoint groups of perceptually semantic regions by constructing naturally adaptive region borders, thereby recovering precise object boundaries. From the experiments conducted on the BSDS300 dataset, it appears that the proposed detector achieves noteworthy performance by attaining promising detection results when compared to the state-of-the-art edge detection approaches.


title: Root Coverage Using the Coronally Positioned Flap With or Without a Subepithelial Connective Tissue Graft

abstract: Background: Various surgical techniques have been proposed for treating gingival recession. This randomized clinical trial compared the coronally positioned flap (CPF) alone or in conjunction with a subepithelial connective tissue graft (SCTG) in the treatment of gingival recession. Methods: Eleven non-smoking subjects with bilateral and comparable Miller Class I recession defects were selected. The defects, at least 3.0 mm deep, were randomly assigned to the test (CPF + SCTG) or control group (CPF alone). Recession depth (RD), probing depth (PD), clinical attachment level (CAL), width of keratinized tissue (KT), and gingival/mucosal thickness (GT) were assessed at baseline and 6 months postoperatively. Results: Recession depth was significantly reduced 6 months postoperatively (P <0.05) for both groups. Mean root coverage was 75% and 69% in the test and control groups, respectively. There were no significant differences between the two groups in RD, PD, or CAL, either at baseline or at 6 months postoperatively. However, at 6 months postoperatively, the test group showed a statistically significant increase in KT and GT compared to the control group (P <0.05). Conclusions: The results indicate that both surgical approaches are effective in addressing root coverage. However, when an increase in gingival dimensions (keratinized tissue width, gingival/ mucosal thickness) is a desired outcome, then the combined technique (CPF + SCTG) should be used. J Periodontol 2004;75: 413-419.


title: Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh

abstract: Agricultural crop production depends on various factors such as biology, climate, economy and geography. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies. Applying such methodologies and techniques on historical yield of crops, it is possible to obtain information or knowledge which can be helpful to farmers and government organizations for making better decisions and policies which lead to increased production. In this paper, our focus is on application of data mining techniques to extract knowledge from the agricultural data to estimate crop yield for major cereal crops in major districts of Bangladesh.


title: Protein Ligation in Living Cells Using Sortase

abstract: Sortagging is a versatile method for site-specific modification of proteins as applied to a variety of in vitro reactions. Here, we explore possibilities of adapting the sortase method for use in living cells. For intracellular sortagging, we employ the Ca²⁺-independent sortase A transpeptidase (SrtA) from Streptococcus pyogenes. Substrate proteins were equipped with the C-terminal sortase-recognition motif (LPXTG); we used proteins with an N-terminal (oligo)glycine as nucleophiles. We show that sortase-dependent protein ligation can be achieved in Saccharomyces cerevisiae and in mammalian HEK293T cells, both in the cytosol and in the lumen of the endoplasmic reticulum (ER). ER luminal sortagging enables secretion of the reaction products, among which circular polypeptides. Protein ligation of substrate and nucleophile occurs within 30 min of translation. The versatility of the method is shown by protein ligation of multiple substrates with green fluorescent protein-based nucleophiles in different intracellular compartments.


title: A 98.3% Efficient GaN Isolated Bidirectional DC–DC Converter for DC Microgrid Energy Storage System Applications

abstract: This paper presents a novel 400 to 12 V isolated bidirectional dc–dc converter based on a phase-shift-controlled-modified dual-active-bridge power stage. The proposed converter consists of a half-bridge and center tap with active clamp circuit, which has promising performance for low-voltage high-current applications. 650 V gallium-nitride high electron mobility transistors are used on the high voltage side to avoid issues encountered using Si superjunction MOSFETs in phase-shift-controlled-bidirectional power conversions. The operation principle and power transfer characteristic are obtained based on a time-domain analysis of the inductor current. Design methodology and criteria and converter's efficiency analysis are discussed. Both the analysis and experiments verify that the proposed converter is capable of achieving low power loss and high power density in soft-switching and hard-switching modes. Experimental results are presented for a 1-kW, 400 V-to-12 V dc–dc prototype converter operating at 100 kHz switching frequency. A power density of 30 W/in3 and a peak efficiency of 98.3% in a wide input/output voltage range are achieved.


title: Pre-experimental Familiarization Increases Hippocampal Activity for Both Targets and Lures in Recognition Memory: An fMRI Study

abstract: In the present study, items pre-exposed in a familiarization series were included in a list discrimination task to manipulate memory strength. At test, participants were required to discriminate strong targets and strong lures from weak targets and new lures. This resulted in a concordant pattern of increased "old" responses to strong targets and lures. Model estimates attributed this pattern to either equivalent increases in memory strength across the two types of items (unequal variance signal detection model) or equivalent increases in both familiarity and recollection (dual process signal detection [DPSD] model). Hippocampal activity associated with strong targets and lures showed equivalent increases compared with missed items. This remained the case when analyses were restricted to high-confidence responses considered by the DPSD model to reflect predominantly recollection. A similar pattern of activity was observed in parahippocampal cortex for high-confidence responses. The present results are incompatible with "noncriterial" or "false" recollection being reflected solely in inflated DPSD familiarity estimates and support a positive correlation between hippocampal activity and memory strength irrespective of the accuracy of list discrimination, consistent with the unequal variance signal detection model account.


title: Environmental Determinants of Chronic Disease and Medical Approaches: Recognition, Avoidance, Supportive Therapy, and Detoxification

abstract: The World Health Organization warns that chronic, noncommunicable diseases are rapidly becoming epidemic worldwide. Escalating rates of neurocognitive, metabolic, autoimmune and cardiovascular diseases cannot be ascribed only to genetics, lifestyle, and nutrition; early life and ongoing exposures, and bioaccumulated toxicants may also cause chronic disease. Contributors to ill health are summarized from multiple perspectives—biological effects of classes of toxicants, mechanisms of toxicity, and a synthesis of toxic contributors to major diseases. Healthcare practitioners have wide-ranging roles in addressing environmental factors in policy and public health and clinical practice. Public health initiatives include risk recognition and chemical assessment then exposure reduction, remediation, monitoring, and avoidance. The complex web of disease and environmental contributors is amenable to some straightforward clinical approaches addressing multiple toxicants. Widely applicable strategies include nutrition and supplements to counter toxic effects and to support metabolism; as well as exercise and sweating, and possibly medication to enhance excretion. Addressing environmental health and contributors to chronic disease has broad implications for society, with large potential benefits from improved health and productivity.


title: Bandwidth Enhancement of a Monopolar Patch Antenna With V-Shaped Slot for Car-to-Car and WLAN Communications

abstract: A monopolar patch antenna with a V-shaped slot for car-to-car (C2C) and wireless local area network (WLAN) communications is presented in this paper. To widen the impedance bandwidth of the antenna, techniques for adding a shorting pin and a V-shaped slot are applied to an equilateral triangular patch. By properly placing the shorting pins on an equilateral triangular patch, two operating modes, i.e., TM10 and TM20, are obtained. The presence of the V-shaped slot can generate an additional TM11 mode. These three resonances found in the operating frequency bandwidth resulted in a wideband characteristic. The proposed antenna can operate from 4.82 to 6.67 GHz for the reflection coefficient $\le -$ 10 dB with the gain of around 5.0 dBi. In addition, an omnidirectional radiation pattern is yielded by a coaxial center-fed probe excitation. The antenna has a thickness of 0.09 $\boldsymbol{\lambda}_\mathbf{g}$ (at the center frequency of 5.5 GHz), which is easily hidden on the roof of a vehicle for C2C communication. This proposed design can also be used as indoor base-station antennas for WLAN communication.


title: A linear constant current LED driver without off-chip inductor and capacitor

abstract: A linear constant current light-emitting diode (LED) driver without off-chip inductor and capacitor is presented in this paper. The proposed driver consists of one resistor, one diode bridge and one controller integrated circuits (IC). The controller IC employs four LED strings and sequences their conduction to shape the output current in proportion to the input voltage. The controller IC was designed and implemented by using HHNEC 1μm 700V process. The experimental results show that high PF of 97.93% and efficiency of 90.41% under the input voltage of 220V|rms utility voltage were obtained.


title: A Supervised Learning Framework for Arbitrary Lagrangian-Eulerian Simulations

abstract: The Arbitrary Lagrangian-Eulerian (ALE) method is used in a variety of engineering and scientific applications for enabling multi-physics simulations. Unfortunately, the ALE method can suffer from simulation failures that require users to adjust parameters iteratively in order to complete a simulation. In this paper, we present a supervised learning framework for predicting conditions leading to simulation failures. To our knowledge, this is the first time machine learning has been applied to ALE simulations. We propose a novel learning representation for mapping the ALE domain onto a supervised learning formulation. We analyze the predictability of these failures and evaluate our framework using well-known test problems.


title: Path Planning for an Underwater Glider in Ocean Currents

abstract: Underwater gliders are a class of AUVs designed for high endurance over long distances, but their reduced velocity makes them more susceptible to ocean currents during deployment. Thus, feasible paths need to be generated through the ocean current field. This paper proposes a method for determining energy-optimal paths that account for the influence of ocean currents. The proposed technique is based on Rapidly-Exploring Random Trees (RRTs). Using real ocean current and bathymetry data, results produce comparable paths to grid based methods, and offer an improvement in terms of avoiding high-energy shallow regions. Future work will focus on heuristically biasing the RRT growth to further improve the generated paths, and implementation of the algorithms on a glider platform.


Predicting cardiovascular risk: so what do we do now?


Critical Thinking: A Literature Review Research Report


title: Obesity, Fat Distribution, and Weight Gain as Risk Factors for Clinical Diabetes in Men

abstract: OBJECTIVE ::: To investigate the relation between obesity, fat distribution, and weight gain through adulthood and the risk of non-insulin-dependent diabetes mellitus (NIDDM). ::: ::: ::: RESEARCH DESIGN AND METHODS ::: We analyzed data from a cohort of 51,529 U.S. male health professionals, 40-75 years of age in 1986, who completed biennial questionnaires sent out in 1986, 1988, 1990, and 1992. During 5 years of follow-up (1987-1992), 272 cases of NIDDM were diagnosed among men without a history of diabetes, heart disease, and cancer in 1986 and who provided complete health information. Relative risks (RRs) associated with different anthropometric measures were calculated controlling for age, and multivariate RRs were calculated controlling for smoking, family history of diabetes, and age. ::: ::: ::: RESULTS ::: We found a strong positive association between overall obesity as measured by body mass index (BMI) and risk of diabetes. Men with a BMI of > or = 35 kg/m2 had a multivariate RR of 42.1 (95% confidence interval [CI] 22.0-80.6) compared with men with a BMI < 23.0 kg/m2. BMI at age 21 and absolute weight gain throughout adulthood were also significant independent risk factors for diabetes. Fat distribution, measured by waist-to-hip ratio (WHR), was a good predictor of diabetes only among the top 5%, while waist circumference was positively associated with the risk of diabetes among the top 20% of the cohort. ::: ::: ::: CONCLUSIONS ::: These data suggest that waist circumference may be a better indicator than WHR of the relationship between abdominal adiposity and risk of diabetes. Although early obesity, absolute weight gain throughout adulthood, and waist circumference were good predictors of diabetes, attained BMI was the dominant risk factor for NIDDM; even men of average relative weight had significantly elevated RRs.


title: Deep analysis for development of RDF, RDFS and OWL ontologies with protege

abstract: In SW (SW), Ontology is an important layer architecture that is develop with the help of web ontology language (OWL) and makes the web semantic. OWL is designs and creates metadata that required for developing SW applications. It is an essential part of SW. In this paper we design an education and non-education domain ontology framework which is developed by using protege an ontology editor tool developed by Stanford University. We also show the output for both ontologies by using pellet reasoner based on the proposed ontology frameworks. We analyze from our result that OWL improves the inference compare to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). OWL also provides more relevant information compare to RDF and RDFS and OWL precision and recall is better compare to RDF and RDFS. In our paper we used Pellet reasoner that retrieve relevant information from the education and non education ontology and protege 4.3 alpha tool for the construction of education and non education domain ontology.


title: SPICE Model of Memristor with Nonlinear Dopant Drift

abstract: A mathematical model of the prototype of memristor, manufactured in 2008 in Hewlett-Packard Labs, is described in the paper. It is shown that the hitherto published approaches to the modeling of boundary conditions need not conform with the requirements for the behavior of a practical circuit element. The described SPICE model of the memristor is thus constructed as an open model, enabling additional modifications of non-linear boundary conditions. Its functionality is illustrated on computer simulations.


title: Improving Knowledge Work Processes

abstract: Are traditional reengineering approaches appropriate for improving knowledge work processes? In a study of thirty organizations, the authors found that improvement methods for knowledge work ranged from the classical top-down approach to a more laissez-faire philosophy that allowed professionals to design and execute their own work. Companies should probably choose an intermediate approach that reflects the type of knowledge work, the organizational culture, and the project's business requirements. They must recognize that the nature of knowledge work is different from administrative and operational work and that the people who perform it resist structured approaches.


title: Unsupervised Learning of Image Manifolds by Semidefinite Programming

abstract: Can we detect low dimensional structure in high dimensional data sets of images? In this paper, we propose an algorithm for unsupervised learning of image manifolds by semidefinite programming. Given a data set of images, our algorithm computes a low dimensional representation of each image with the property that distances between nearby images are preserved. More generally, it can be used to analyze high dimensional data that lies on or near a low dimensional manifold. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on actual images of faces, handwritten digits, and solid objects.


title: Interleukins and Inflammatory Markers in In-Stent Restenosis after Femoral Percutaneous Transluminal Angioplasty

abstract: Background Inflammatory activity may influence results of percutaneous transluminal angioplasty (PTA). The purpose of this study was to evaluate the relationship between (1) proinflammatory markers (interleukin [IL]-6, IL-8, tumor necrosis factor α (TNF-α), and highly sensitive C-reactive protein [CRP]); (2) type 1 T helper cell marker (IL-12); and (3) Type 2 T helper cell marker (transforming growth factor-β [TGF-β]) and in-stent restenosis, 6 months after femoral PTA with stent implantation. Methods We performed a single-center prospective study with 26 patients with peripheral artery disease requiring PTA and stenting. As control, we studied 26 patients who were submitted to diagnostic angiography. Serum samples were collected before stent implantation, 24 hr and 6 months after the procedure. To detect restenosis, a new angiography was obtained at 6 months. Results Restenosis was observed in 10 (38.5%) patients who underwent PTA and stenting. There was a trend to increased levels of IL-6, TNF-α, TGF-β, and IL-12 24 hr after PTA and stenting compared with pretreatment. IL-8 levels showed a statistically significant reduction 24 hours after versus pretreatment (P Conclusions No inflammatory marker was independently identified as risk factor for in-stent restenosis, 6 months after femoral PTA with stent implantation. The question that remains is whether acute phase reactants will be clinically useful to predict the individual risk for in-stent restenosis.


title: SnortView: visualization system of snort logs

abstract: False detection is a major issue in deploying and maintaining Network-based Intrusion Detection Systems (NIDS). Traditionally, it is recommended to customize its signature database (DB) to reduce false detections. However, it requires quite deep knowledge and skills to appropriately customize the signature DB. Inappropriate customization causes the increase of false negatives as well as false positives. In this paper, we propose a visualization system of a NIDS log, named SnortView, which supports administrators in analyzing NIDS alerts much faster and much more easily. Instead of customizing the signature DB, we propose to utilize visualization to recognize not only each alert but also false detections. The system is based on a 2-D time diagram and alerts are shown as icons with different styles and colors. In addition, the system introduces some visualization techniques such as overlayed statistical information, source-destination matrix, and so on. The system was used to detect real attacks while recognizing some false detections.


title: Annotation and Detection of Emotion in a Task-oriented Human-Human Dialog Corpus

abstract: Automatic emotion detection is potentially important for customer care in the context of call center services. A major difficulty is that the expression of emotion is quite complex in the context of agent-client dialogs. Due to unstated rules of politeness it is common to have shaded emotions, in which the interlocutors attempt to limit their internal attitude (frustration, anger, disappointment). Furthermore, the telephonic quality speech complicates the tasks of detecting perceptual cues and of extracting relevant measures from the signal. Detecting real emotions can be helpful to follow the evolution of the human-computer dialogs, enabling dynamic modification of the dialog strategies and influencing the final outcome. This paper reports on three studies: the first concerns the use of multi-level annotations including emotion tags for diagnosis of the dialog state; the second investigates automatic emotion detection using linguistic information; and the third reports on two perceptual tests for identifying emotions as well as the prosodic and textual cues which signal them. Two experimental conditions were considered – with and without the capability of listening the audio signal. Finally, we propose a new set of emotions/attitudes tags suggested by the previous experiments and discuss perspectives.


title: The influence of perceived value on purchase intention in social commerce context

abstract: The purpose of this paper is to examine the effects of perceived benefits, i.e. utilitarian value, hedonic value and social value, as well as perceived risk, on purchase intention in social commerce context.,To cast light on the factors motivating users’ intention to purchase in the context of social commerce, data of 277 users of social commerce in China were collected via an online survey.,Results show that satisfaction significantly and positively affects users’ purchase intention in social commerce context. In addition, utilitarian, hedonic and social values have significant and positive impacts on satisfaction and purchase intention; and utilitarian value is found to be the most salient factor influencing purchase intention, while hedonic value has the greatest effect on satisfaction. Moreover, perceived risk significantly and negatively affects satisfaction.,Extant research on social commerce has mainly focused on investigating how the general perceived value affects user behavior, but has less considered different dimensions of perceived value. Moreover, prior studies have explored the roles of utilitarian and hedonic values on user behavior; however, there is a lack of research on the effect of social value. The current study attempts to fill these research gaps.


title: Mining distance-based outliers in near linear time with randomization and a simple pruning rule

abstract: Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.


title: A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements

abstract: Ready or not, the digitalization of information has come, and privacy is standing out there, possibly at stake. Although digital privacy is an identified priority in our society, few systematic, effective methodologies exist that deal with privacy threats thoroughly. This paper presents a comprehensive framework to model privacy threats in software-based systems. First, this work provides a systematic methodology to model privacy-specific threats. Analogous to STRIDE, an information flow–oriented model of the system is leveraged to guide the analysis and to provide broad coverage. The methodology instructs the analyst on what issues should be investigated, and where in the model those issues could emerge. This is achieved by (i) defining a list of privacy threat types and (ii) providing the mappings between threat types and the elements in the system model. Second, this work provides an extensive catalog of privacy-specific threat tree patterns that can be used to detail the threat analysis outlined above. Finally, this work provides the means to map the existing privacy-enhancing technologies (PETs) to the identified privacy threats. Therefore, the selection of sound privacy countermeasures is simplified.


title: Directed Network Community Detection: A Popularity and Productivity Link Model

abstract: In this paper, we consider the problem of community detection in directed networks by using probabilistic models. Most existing probabilistic models for community detection are either symmetric in which incoming links and outgoing links are treated equally or conditional in which only one type (i.e., either incoming or outgoing) of links is modeled. We present a probabilistic model for directed network community detection that aims to model both incoming links and outgoing links simultaneously and differentially. In particular, we introduce latent variables node productivity and node popularity to explicitly capture outgoing links and incoming links, respectively. We demonstrate the generality of the proposed framework by showing that both symmetric models and conditional models for community detection can be derived from the proposed framework as special cases, leading to better understanding of the existing models. We derive efficient EM algorithms for computing the maximum likelihood solutions to the proposed models. Extensive empirical studies verify the effectiveness of the new models as well as the insights obtained from the unified framework. keywords: community detection, popularity, productivity, stochastic block model, directed network


Are educational computer microgames engaging and effective for knowledge acquisition at high-schools ? A quasi-experimental study


title: Relationship Between Microbiota of the Colonic Mucosa vs Feces and Symptoms, Colonic Transit, and Methane Production in Female Patients With Chronic Constipation.

abstract: BACKGROUND & AIMS ::: In fecal samples from patients with chronic constipation, the microbiota differs from that of healthy subjects. However, the profiles of fecal microbiota only partially replicate those of the mucosal microbiota. It is not clear whether these differences are caused by variations in diet or colonic transit, or are associated with methane production (measured by breath tests). We compared the colonic mucosal and fecal microbiota in patients with chronic constipation and in healthy subjects to investigate the relationships between microbiota and other parameters. ::: ::: ::: METHODS ::: Sigmoid colonic mucosal and fecal microbiota samples were collected from 25 healthy women (controls) and 25 women with chronic constipation and evaluated by 16S ribosomal RNA gene sequencing (average, 49,186 reads/sample). We assessed associations between microbiota (overall composition and operational taxonomic units) and demographic variables, diet, constipation status, colonic transit, and methane production (measured in breath samples after oral lactulose intake). ::: ::: ::: RESULTS ::: Fourteen patients with chronic constipation had slow colonic transit. The profile of the colonic mucosal microbiota differed between constipated patients and controls (P < .05). The overall composition of the colonic mucosal microbiota was associated with constipation, independent of colonic transit (P < .05), and discriminated between patients with constipation and controls with 94% accuracy. Genera from Bacteroidetes were more abundant in the colonic mucosal microbiota of patients with constipation. The profile of the fecal microbiota was associated with colonic transit before adjusting for constipation, age, body mass index, and diet; genera from Firmicutes (Faecalibacterium, Lactococcus, and Roseburia) correlated with faster colonic transit. Methane production was associated with the composition of the fecal microbiota, but not with constipation or colonic transit. ::: ::: ::: CONCLUSIONS ::: After adjusting for diet and colonic transit, the profile of the microbiota in the colonic mucosa could discriminate patients with constipation from healthy individuals. The profile of the fecal microbiota was associated with colonic transit and methane production (measured in breath), but not constipation.


title: Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation

abstract: We combine two of the most popular approaches to automated Grammatical Error Correction (GEC): GEC based on Statistical Machine Translation (SMT) and GEC based on Neural Machine Translation (NMT). The hybrid system achieves new state-of-the-art results on the CoNLL-2014 and JFLEG benchmarks. This GEC system preserves the accuracy of SMT output and, at the same time, generates more fluent sentences as it typical for NMT. Our analysis shows that the created systems are closer to reaching human-level performance than any other GEC system reported so far.


title: Framework for Abstractive Summarization using Text-to-Text Generation

abstract: We propose a new, ambitious framework for abstractive summarization, which aims at selecting the content of a summary not from sentences, but from an abstract representation of the source documents. This abstract representation relies on the concept of Information Items (InIt), which we define as the smallest element of coherent information in a text or a sentence. Our framework differs from previous abstractive summarization models in requiring a semantic analysis of the text. We present a first attempt made at developing a system from this framework, along with evaluation results for it from TAC 2010. We also present related work, both from within and outside of the automatic summarization domain.


title: PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

abstract: We present PredRNN++, an improved recurrent network for video predictive learning. In pursuit of a greater spatiotemporal modeling capability, our approach increases the transition depth between adjacent states by leveraging a novel recurrent unit, which is named Causal LSTM for re-organizing the spatial and temporal memories in a cascaded mechanism. However, there is still a dilemma in video predictive learning: increasingly deep-in-time models have been designed for capturing complex variations, while introducing more difficulties in the gradient back-propagation. To alleviate this undesirable effect, we propose a Gradient Highway architecture, which provides alternative shorter routes for gradient flows from outputs back to long-range inputs. This architecture works seamlessly with causal LSTMs, enabling PredRNN++ to capture short-term and long-term dependencies adaptively. We assess our model on both synthetic and real video datasets, showing its ability to ease the vanishing gradient problem and yield state-of-the-art prediction results even in a difficult objects occlusion scenario.


title: An investigation into using parallel data for far-field speech recognition

abstract: Far-field speech recognition is an important yet challenging task due to low signal to noise ratio. In this paper, three novel deep neural network architectures are explored to improve the far-field speech recognition accuracy by exploiting the parallel far-field and close-talk recordings. All three novel architectures use multi-task learning for the model optimization but focus on three different ideas: dereverberation and recognition joint-learning, close-talk and far-field model knowledge sharing, and environment-code aware training. Experiments on the AMI single distant microphone (SDM) task show that each of the proposed method can boost accuracy individually, and additional improvement can be obtained with appropriate integration of these models. Overall we reduced the error rate by 10% relatively on the SDM set by exploiting the IHM data.


title: FPGA: what's in it for a database?

abstract: While there seems to be a general agreement that next years' systems will include many processing cores, it is often overlooked that these systems will also include an increasing number of different cores (we already see dedicated units for graphics or network processing). Orchestrating the diversity of processing functionality is going to be a major challenge in the upcoming years, be it to optimize for performance or for minimal energy consumption. We expect field-programmable gate arrays (FPGAs or "programmable hardware") to soon play the role of yet another processing unit, found in commodity computers. It is clear that the new resource is going to be too precious to be ignored by database systems, but it is unclear how FPGAs could be integrated into a DBMS. With a focus on database use, this tutorial introduces into the emerging technology, demonstrates its potential, but also pinpoints some challenges that need to be addressed before FPGA-accelerated database systems can go mainstream. Attendees will gain an intuition of an FPGA development cycle, receive guidelines for a "good" FPGA design, but also learn the limitations that hardware-implemented database processing faces. Our more high-level ambition is to spur a broader interest in database processing on novel hardware technology.


Parkinsons Disease Classification using Neural Network and Feature Selection


Story Comprehension for Predicting What Happens Next


title: Beyond Assortativity: Proclivity Index for Attributed Networks (ProNe)

abstract: If Alice is majoring in Computer Science, can we guess the major of her friend Bob? Even harder, can we determine Bob’s age or sexual orientation? Attributed graphs are ubiquitous, occurring in a wide variety of domains; yet there is limited literature on the study of the interplay between the attributes associated to nodes and edges connecting them. Our work bridges this gap by addressing the following questions: Given the network structure, (i) which attributes and (ii) which pairs of attributes show correlation? Prior work has focused on the first part, under the name of assortativity (closely related to homophily). In this paper, we propose ProNe, the first measure to handle pairs of attributes (e.g., major and age). The proposed ProNe is (a) thorough, handling both homophily and heterophily (b) general, quantifying correlation of a single attribute or a pair of attributes (c) consistent, yielding a zero score in the absence of any structural correlation. Furthermore, ProNe can be computed fast in time linear in the network size and is highly useful, with applications in data imputation, marketing, personalization and privacy protection.


title: ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks

abstract: We propose ENCASE to combine expert features and DNNs (Deep Neural Networks) together for ECG classification. We first explore and implement expert features from statistical area, signal processing area and medical area. Then, we build DNNs to automatically extract deep features. Besides, we propose a new algorithm to find the most representative wave (called centerwave) among long ECG record, and extract features from centerwave. Finally, we combine these features together and put them into ensemble classifiers. Experiment on 4-class ECG data classification reports 0.84 F\ score, which is much better than any of the single model.


title: Depth map estimation using census transform for light field cameras

abstract: Depth estimation for the lense-array type cameras is a challenging problem because of sensor noise and radiometric distortion which is a global brightness change between sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against the sensor noise and the radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial map obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. We refine an initial map by the optimization which uses the assumption that the variations of the depth values in the depth map and of the pixel values in the texture-less objects are similar. Experiments show that our method outperforms the conventional methods.


title: A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip

abstract: In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use of deep neural networks to jointly predict taxi trip time and distance. We propose a model, called ST-NN (Spatio-Temporal Neural Network), which first predicts the travel distance between an origin and a destination GPS coordinate, then combines this prediction with the time of day to predict the travel time. The beauty of ST-NN is that it uses only the raw trips data without requiring further feature engineering and provides a joint estimate of travel time and distance. We compare the performance of ST-NN to that of state-of-the-art travel time estimation methods, and we observe that the proposed approach generalizes better than state-of-the-art methods. We show that ST-NN approach significantly reduces the mean absolute error for both predicted travel time and distance, about 17% for travel time prediction. We also observe that the proposed approach is more robust to outliers present in the dataset by testing the performance of ST-NN on the datasets with and without outliers.


title: Recommending patents based on latent topics

abstract: The availability of large volumes of granted patents and applications, all publicly available on the Web, enables the use of sophisticated text mining and information retrieval methods to facilitate access and analysis of patents. In this paper we investigate techniques to automatically recommend patents given a query patent. This task is critical for a variety of patent-related analysis problems such as finding relevant citations, research of relevant prior art, and infringement analysis. We investigate the use of latent Dirichlet allocation and Dirichlet multinomial regression to represent patent documents and to compute similarity scores. We compare our methods with state-of-the-art document representations and retrieval techniques and demonstrate the effectiveness of our approach on a collection of US patent publications.


title: Red, green, and blue LEDs for white light illumination

abstract: The rapid improvement of the white light efficacy achievable with light-emitting diodes (LEDs) opens up new opportunities in the general illumination market. An LED light source made of red, green, and blue LEDs (RGB-LEDs) can provide the unique feature of color variability, allowing the user to select the desired color point of the lamp. The white light color accuracy required in the general illumination market is a challenge for LEDs. The variation in lumen output and wavelength for nominally identical LEDs and the change in these parameters with temperature and time result in an unacceptably high variability in the color point of white light from RGB-LEDs. In this paper, we show that these problems can be overcome with suitable feedback control schemes that can be implemented in a practical LED lamp. We present results of experiment and theoretical modeling that shows the performance that can be achieved with a number of different control schemes.


title: Antenna azimuth position control with PID, fuzzy logic and sliding mode controllers

abstract: In our age, the most important technology that provides communication without selecting location and time is satellite communications undoubtedly. The antenna position control systems are used in providing that communication with high tracing accuracy. The goal of this study is to ensure the minimum angle deviation after the antenna is rotated. For this purpose, the antenna position control system is designed in MATLAB/Simulink and PID, fuzzy logic and sliding mode controllers are performed to the system. The simulation results are compared and the most suitable control method is determined.


title: Mobile Learning for Education: Benefits and Challenges

abstract: Education and training is the process by which the wisdom, knowledge and skills of one generation are passed on to the next. Today there are two forms of education and training: conventional education and distance education. Mobile learning, or "M-Learning", offers modern ways to support learning process through mobile devices, such as handheld and tablet computers, MP3 players, smart phones and mobile phones.This document introduces the subject of mobile learning for education purposes. It examines what impact mobile devices have had on teaching and learning practices and goes on to look at the opportunities presented by the use of digital media on mobile devices. The main purpose of this paper is to describe the current state of mobile learning, benefits, challenges, and it’s barriers to support teaching and learning. Data for this paper were collected through bibliographic and internet research from January to March 2013. Four key areas will be addressed in this paper: 1. An analysis of Mobile Learning. 2. Differentiating E-Learning from Mobile Learning 3. Value and Benefits of Mobile Learning 4. Challenges and Barriers of Mobile Learning:Study showed that M-Learning as a Distance learning brought great benefits to society include : Training when it is needed, Training at any time; Training at any place; Learnercentred content; Avoidance of re-entry to work problems; Training for taxpayers, and those fully occupied during university lectures and sessions at training centres; and The industrialisation of teaching and learning. And also, notebooks, mobile Tablets, iPod touch, and iPads are very popular devices for mobile learning because of their cost and availability of apps.


title: A Flooding Warning System based on RFID Tag Array for Energy Facility

abstract: Passive radio-frequency identification (RFID) tags are widely used due to its economic cost and satisfactory performance. So far, passive RFID tags are mostly applied to identify certain objects, such as underground pipe identification under buried conditions. However, there is lack of study on the application of buried tags for further application. In this paper, the performances of buried RFID tags are studied to develop a flooding warning system based on RFID tag array for energy facility such as power stations. In this study, the corresponding signal strength received by the RFID reader is evaluated when the RFID tags are buried by seven materials respectively. The results show that flood warning detector can be constructed using passive RFID tag array and reader.


title: Pattern Codification Strategies in Structured Light Systems

abstract: Coded structured light is considered one of the most reliable techniques for recovering the surface of objects. This technique is based on projecting a light pattern and viewing the illuminated scene from one or more points of view. Since the pattern is coded, correspondences between image points and points of the projected pattern can be easily found. The decoded points can be triangulated and 3D information is obtained. We present an overview of the existing techniques, as well as a new and definitive classification of patterns for structured light sensors. We have implemented a set of representative techniques in this field and present some comparative results. The advantages and constraints of the different patterns are also discussed.


title: Semantics-based obfuscation-resilient binary code similarity comparison with applications to software plagiarism detection

abstract: Existing code similarity comparison methods, whether source or binary code based, are mostly not resilient to obfuscations. In the case of software plagiarism, emerging obfuscation techniques have made automated detection increasingly difficult. In this paper, we propose a binary-oriented, obfuscation-resilient method based on a new concept, longest common subsequence of semantically equivalent basic blocks, which combines rigorous program semantics with longest common subsequence based fuzzy matching. We model the semantics of a basic block by a set of symbolic formulas representing the input-output relations of the block. This way, the semantics equivalence (and similarity) of two blocks can be checked by a theorem prover. We then model the semantics similarity of two paths using the longest common subsequence with basic blocks as elements. This novel combination has resulted in strong resiliency to code obfuscation. We have developed a prototype and our experimental results show that our method is effective and practical when applied to real-world software.


title: Modeling air-to-ground path loss for low altitude platforms in urban environments

abstract: The reliable prediction of coverage footprint resulting from an airborne wireless radio base station, is at utmost importance, when it comes to the new emerging applications of air-to-ground wireless services. These applications include the rapid recovery of damaged terrestrial wireless infrastructure due to a natural disaster, as well as the fulfillment of sudden wireless traffic overload in certain spots due to massive movement of crowds. In this paper, we propose a statistical propagation model for predicting the air-to-ground path loss between a low altitude platform and a terrestrial terminal. The prediction is based on the urban environment properties, and is dependent on the elevation angle between the terminal and the platform. The model shows that air-to-ground path loss is following two main propagation groups, characterized by two different path loss profiles. In this paper we illustrate the methodology of which the model was deduced, as well as we present the different path loss profiles including the occurrence probability of each.


title: Extraction of Informative Blocks from Web Pages

abstract: Typically Web pages always contain a large amount of banner ads, navigation bars, and copyright notices etc. Such irrelevant information is not part of the main contents of the pages, they will seriously harm Web mining and searching. In this paper, we develop and evaluate a method that utilizes both the visual features and the semantic information to extract informative blocks. We first partition a web page into semantic blocks using vision-based page segmentation. The visual and the semantic information got by LSI (Latent Semantic Indexing) are extracted to form the feature-vector of the block. Second we manually annotate informative or uninformative labels to the blocks. The labeled blocks are used as training dataset to train a classification model. Then the informative blocks can be extracted through the model. Our experiments show that the proposed EIBA (Extract Informative Block Arithmetic) is able to dramatically improve the results in near-duplicate detection and classification tasks.


The Influence of Nicotinamide on Health and Disease in the Central Nervous System


title: Performance Analysis of a Hybrid Downlink-Uplink Cooperative NOMA Scheme

abstract: This paper proposes a novel hybrid downlinkuplink cooperative NOMA (HDU-CNOMA) scheme to achieve a better tradeoff between spectral efficiency and signal reception reliability than the conventional cooperative NOMA schemes. In particular, the proposed scheme enables the strong user to perform a cooperative transmission and an interference-free uplink transmission simultaneously during the cooperative phase, at the expense of a slightly decrease in signal reception reliability at the weak user. We analyze the outage probability, diversity order, and outage throughput of the proposed scheme. Simulation results not only confirm the accuracy of the developed analytical results, but also unveil the spectral efficiency gains achieved by the proposed scheme over a baseline cooperative NOMA scheme and a non-cooperative NOMA scheme.


title: An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures

abstract: We present two new methods for approximating the Kullback-Liebler (KL) divergence between two mixtures of Gaussians. The first method is based on matching between the Gaussian elements of the two Gaussian mixture densities. The second method is based on the unscented transform. The proposed methods are utilized for image retrieval tasks. Continuous probabilistic image modeling based on mixtures of Gaussians together with KL measure for image similarity, can be used for image retrieval tasks with remarkable performance. The efficiency and the performance of the KL approximation methods proposed are demonstrated on both simulated data and real image data sets. The experimental results indicate that our proposed approximations outperform previously suggested methods.


title: Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision

abstract: Semantic segmentation is a popular visual recognition task whose goal is to estimate pixel-level object class labels in images. This problem has been recently handled by deep convolutional neural networks (DCNNs), and the state-of-theart techniques achieve impressive records on public benchmark data sets. However, learning DCNNs demand a large number of annotated training data while segmentation annotations in existing data sets are significantly limited in terms of both quantity and diversity due to the heavy annotation cost. Weakly supervised approaches tackle this issue by leveraging weak annotations such as image-level labels and bounding boxes, which are either readily available in existing large-scale data sets for image classification and object detection or easily obtained thanks to their low annotation costs. The main challenge in weakly supervised semantic segmentation then is the incomplete annotations that miss accurate object boundary information required to learn segmentation. This article provides a comprehensive overview of weakly supervised approaches for semantic segmentation. Specifically, we describe how the approaches overcome the limitations and discuss research directions worthy of investigation to improve performance.


title: Quality Control in Crowdsourcing Systems: Issues and Directions

abstract: As a new distributed computing model, crowdsourcing lets people leverage the crowd's intelligence and wisdom toward solving problems. This article proposes a framework for characterizing various dimensions of quality control in crowdsourcing systems, a critical issue. The authors briefly review existing quality-control approaches, identify open issues, and look to future research directions. In the Web extra, the authors discuss both design-time and runtime approaches in more detail.


title: Pubsub: An Efficient Publish/Subscribe System

abstract: PUBSUB is a versatile, efficient, and scalable publish/subscribe system. This paper describes the architecture of PUBSUB together with some of its current capabilities. A version of PUBSUB optimized for event processing was benchmarked against the publish/subscribe systems BE-Tree and Siena, which also are optimized for event processing. PUBSUB processes events faster than Siena and BE-tree. On our tests, the speedup of the fastest version of PUBSUB relative to Siena was 98% on an average. The speedup range relative to BE-Tree was from 1.23 to 1.48 and averaged 1.36 on the uniform tests and PUBSUB was comparable to BE-tree on the Zipf tests. The faster times in PUBSUB were a result of very efficient data structures used in PUBSUB to store the subscriptions, and the fast matching algorithms developed to match events to subscriptions.


title: SemEval-2016 Task 2: Interpretable Semantic Textual Similarity

abstract: Comunicacio presentada al 10th International Workshop on Semantic Evaluation (SemEval-2016), celebrat els dies 16 i 17 de juny de 2016 a San Diego, California.


Biological Alphabets and DNA-based Cryptography


title: Automatically classifying question types for consumer health questions.

abstract: We present a method for automatically classifying consumer health questions. Our thirteen question types are designed to aid in the automatic retrieval of medical answers from consumer health resources. To our knowledge, this is the first machine learning-based method specifically for classifying consumer health questions. We demonstrate how previous approaches to medical question classification are insufficient to achieve high accuracy on this task. Additionally, we describe, manually annotate, and automatically classify three important question elements that improve question classification over previous techniques. Our results and analysis illustrate the difficulty of the task and the future directions that are necessary to achieve high-performing consumer health question classification.


title: Junction-based table detection in camera-captured document images

abstract: In this paper, we present a method that locates tables and their cells in camera-captured document images. In order to deal with this problem in the presence of geometric and photometric distortions, we develop new junction detection and labeling methods, where junction detection means to find candidates for the corners of cells, and junction labeling is to infer their connectivity. We consider junctions as the intersections of curves, and so we first develop a multiple curve detection algorithm. After the junction detection, we encode the connectivity information (including false detection) between the junctions into 12 labels, and design a cost function reflecting pairwise relationships as well as local observations. The cost function is minimized via the belief propagation algorithm, and we can locate tables and their cells from the inferred labels. Also, in order to handle multiple tables in a single page, we propose a table area detection method. Our method is based on the well-known recursive X-Y cut, however, we modify the method so that we can also deal with curved seams caused by the geometric distortions. For the evaluation of our method, we build a data set that includes a variety of camera-captured table images and make the set publicly available. Experimental results on the set show that our method successfully locates tables and their cells in camera-captured images.


title: On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach

abstract: Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving anomaly detection performance, in this paper we introduce recently-developed deep learning (DL) in machine learning into the combustors anomaly detection application. Specifically, we use deep learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures. And we then use the learned features as the input to a neural network classifier for performing combustor anomaly detection. Since such deep learned features potentially better capture complex relations among all sensor measurements and the underlying combustor behavior than handcrafted features do, we expect the learned features can lead to a more accurate and robust anomaly detection. Using the data collected from a real-world gas turbine combustion system, we demonstrated that the proposed deep learning based anomaly detection significantly indeed improved combustor anomaly detection performance.


Complete Neural Population in the Retina


title: Electromagnetic compatibility (EMC) for integration and use of near field communication (NFC) in aircraft

abstract: For portable electronic devices (PEDs), e.g. smartphones or tablets, near field communication (NFC) enables easy and convenient man-machine interaction by simply tapping a PED to a tangible NFC user interface. Usage of NFC technology in the air transport system is supposed to facilitate travel processes and self-services for passengers and to support digital interaction with other participating stakeholders. One of the potential obstacles to benefit from NFC technology in the aircraft cabin is the lack of an explicit qualification guideline for electromagnetic compatibility (EMC) testing. In this paper, we propose a methodology for EMC testing and for characterizing NFC devices and their emissions according to aircraft industry standards (RTCA DO-160, DO-294, DO-307 and EUROCAE ED-130). A potential back-door coupling scenario of radiated NFC emissions and possible effects to nearby aircraft wiring are discussed. A potential front-door-coupling effect on NAV/COM equipment is not investigated in this paper.


title: DroidFuzzer: Fuzzing the Android Apps with Intent-Filter Tag

abstract: The Android system is getting more and more popular on the mobile devices. Thus, lots of apps have sprung up to facilitate people's daily life. However, many of the apps are released without sufficient testing work, so the users encounter a sudden app crash now and then. This will undoubtedly impact the user's experience and even lead to economic loss. Because current testing tools on Android apps mainly focus on the motion event on the screen, like click event, bugs concerned with data handling module in an app is neglected. In this paper, we propose an automated testing method to fuzz testing the Android apps. The test targets are the Activities which accept outside MIME data. These Activities are picked out by analyzing the Intent-filter tag in the AndroidManifest.xml file. An automated fuzzing tool, DroidFuzzer, is implemented based on the method. Finally, experiments are conducted to prove the effectiveness of it.


title: Memory formation under stress: Quantity and quality

abstract: Stress shapes memory. Depending on the timing of the stress exposure facilitating and impairing effects of stress are reported on how much is learned and remembered. Beyond such stress-induced changes in the quantity of memory, recent research suggests that stress also affects the contribution of multiple memory systems to performance. Under stress, rigid ‘habit’ memory gets favored over more flexible ‘cognitive’ memory. Thus, stress has an impact on the way we learn and remember, that is the quality of memory. This shift between different behavioral strategies on “environmental demands” may facilitate adaptive responses. Here, we review stress effects on both quantity and quality of memory and address possible implications of these effects for the understanding of stress-related psychiatric disorders.