title: Long range iris recognition: A survey

abstract: The term iris refers to the highly textured annular portion of the human eye that is externally visible. An iris recognition system exploits the richness of these textural patterns to distinguish individuals. Iris recognition systems are being used in a number of human recognition applications such as access control, national ID schemes, border control, etc. To capture the rich textural information of the iris pattern regardless of the eye color, traditional iris recognition systems utilize near-infrared (NIR) sensors to acquire images of the iris. This, however, restricts the iris image acquisition distance to close quarters (less than 1m). Over the last several years, there have been numerous attempts to design and implement iris recognition systems that operate at longer standoff distances ranging from 1m to 60m. Such long range iris acquisition and recognition systems can provide high user convenience and improved throughput. This paper reviews the state-of-the-art design and implementation of iris-recognition-at-a-distance (IAAD) systems. In this regard, the design of such a system from both the image acquisition (hardware) and image processing (algorithms) perspectives are presented. The major contributions of this paper include: (1) discussing the significance and applications of IAAD systems in the context of human recognition, (2) providing a review of existing IAAD systems, (3) presenting a complete solution to the design problem of an IAAD system, from both hardware and algorithmic perspectives, (4) discussing the use of additional ocular information, along with iris, for improving IAAD accuracy, and (5) discussing the current research challenges and providing recommendations for future research in IAAD.


title: OpenGL multipipe SDK: a toolkit for scalable parallel rendering

abstract: We describe OpenGL multipipe SDK (MPK), a toolkit for scalable parallel rendering based on OpenGL. MPK provides a uniform application programming interface (API) to manage scalable graphics applications across many different graphics subsystems. MPK-based applications run seamlessly from single-processor, single-pipe desktop systems to large multi-processor, multipipe scalable graphics systems. The application is oblivious of the system configuration, which can be specified through a configuration file at run time. To scale application performance, MPK uses a decomposition system that supports different modes for task partitioning and implements optimized CPU-based composition algorithms. MPK also provides a customizable image composition interface, which can be used to apply post-processing algorithms on raw pixel data obtained from executing sub-tasks on multiple graphics pipes in parallel. This can be used to implement parallel versions of any CPU-based algorithm, not necessarily used for rendering. In this paper, we motivate the need for a scalable graphics API and discuss the architecture of MPK. We present MPK's graphics configuration interface, introduce the notion of compound-based decomposition schemes and describe our implementation. We present some results from our work on a couple of target system architectures and conclude with future directions of research in this area.


title: Stock prices and Macroeconomic Performance in Pakistan: An Analysis

abstract: This paper analyzes long-term equilibrium relationships between the Karachi stock exchange index and a group of macroeconomic variables. The macroeconomic variables are represented by the gross domestic product, the consumer price index, M2 and the exchange rate. We employ a multiple regression model to explore such relationships during 1991 to 2012. Our results indicated a "causal" relationship between the stock market and the economy analysis of our results indicates that KSE 100 index has a strong positive impact on GDP and M2 in Pakistan. Whereas it has a negative and significant impact on CPI and exchange rate in Pakistan. Granger causality test shows that KSE 100 index Granger causes GDP, CPI, M2, EXRT, AGRI, FDI and BOT and the direction of causality runs from KSE 100 index to these variables.


title: Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

abstract: The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks ANNs are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six-step procedure. The back-propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.


title: Gaming in the clouds: QoE and the users' perspective

abstract: Abstract Cloud Gaming is a new kind of service, which combines the successful concepts of Cloud Computing and Online Gaming. It provides the entire game experience to the users remotely from a data center. The player is no longer dependent on a specific type or quality of gaming hardware, but is able to use common devices. The end device only needs a broadband internet connection and the ability to display High Definition (HD) video. While this may reduce hardware costs for users and increase the revenue for developers by leaving out the retail chain, it also raises new challenges for service quality in terms of bandwidth and latency for the underlying network. In this paper we present the results of a subjective user study we conducted into the user-perceived quality of experience (QoE) in Cloud Gaming. We design a measurement environment, that emulates this new type of service, define tests for users to assess the QoE, derive Key Influence Factors (KIF) and influences of content and perception from our results.


title: The effect of mindfulness-based cognitive therapy for prevention of relapse in recurrent major depressive disorder: A systematic review and meta-analysis

abstract: Abstract Background Mindfulness-based cognitive therapy (MBCT) is a group-based clinical intervention program designed to reduce relapse or recurrence of major depressive disorder (MDD) by means of systematic training in mindfulness meditation combined with cognitive-behavioral methods. Objective By means of a meta-analysis to evaluate the effect of MBCT for prevention of relapse or recurrence among patients with recurrent MDD in remission. Method Electronic databases were searched and researchers were contacted for further relevant studies. Studies were coded for quality. Meta-analyses were performed by means of the Cochrane Collaboration Review Manager 5.1. Results Six randomized controlled trials with a total of 593 participants were included in the meta-analysis. MBCT significantly reduced the risk of relapse/recurrence with a risk ratio of 0.66 for MBCT compared to treatment as usual or placebo controls, corresponding to a relative risk reduction of 34%. In a pre-planned subgroup analysis the relative risk reduction was 43% for participants with three or more previous episodes, while no risk reduction was found for participants with only two episodes. In two studies, MBCT was at least as effective as maintenance antidepressant medication. Conclusion Results of this meta-analysis indicate that MBCT is an effective intervention for relapse prevention in patients with recurrent MDD in remission, at least in case of three or more previous MDD episodes.


title: A novel broadband EBG using multi-period mushroom-like structure

abstract: A novel broadband electromagnetic band-gap (EBG) structure is presented using multi-period mushroom-like structure with different patch size cascaded. Direct transmission method is used to determine the band-gap of the EBG structure. The effects of the unit number and patch size on the mushroomlike EBG structure are investigated. Two kinds of unit with different patch size are cascaded to enlarge the band-gap of EBG structure, which achieves almost 87.1%. The simulation results show that the band-gap almost covers the stop-band produced by the two uniform configurations with different patch size respectively.


Improving Children’s Speech Recognition Through Out-of-Domain Data Augmentation


title: Iterative Refinement in Floating Point

abstract: Iterative refinement reduces the roundoff errors in the computed solution to a system of linear equations. Only one step requires higher precision arithmetic. If sufficiently high precision is used, the final result is shown to be very accurate.


title: Cyclic deadlock prediction and avoidance for zone-controlled AGV system

abstract: Abstract Automated guided vehicles (AGVs) are now becoming popular in automated materials handling systems, flexible manufacturing systems and even containers handling applications at seaports. Its performance affects the efficiency of the entire system. Deadlock formation is a serious problem as it stalls the AGVS. The objective of this paper is to develop an efficient AGV deadlock prediction and avoidance algorithm for container port operations. Deadlock in a broad sense is a situation in which at least a part of the system stalls. There are a lot of situations in which the system may stall and most of these situations can be avoided through the control and navigation system. This paper discusses the development of an efficient strategy for predicting and avoiding the deadlocks in a large scale AGVS A variety of deadlock-detecting algorithms are available, but these methods work mainly for manufacturing system where the network layout is simple and uses only a small number of AGVs. The AGVS under study has complex layout and involves close to 80 AGVs. The proposed solution is implemented using the Automod simulation software, and the performance of the technique is presented. Simulation shows that all the potential deadlock situations can be detected and avoided via this methodology. Also, the proposed strategy is computationally efficient and is able to provide the movement decision to the vehicles within the 1.5-s sampling time.


NovaSearch at ImageCLEFmed 2016 Subfigure Classification Task


title: Trajectorybased modeling of human actions with motion reference point

abstract: Human action recognition in videos is a challenging problem with wide applications. State-of-the-art approaches often adopt the popular bag-of-features representation based on isolated local patches or temporal patch trajectories, where motion patterns like object relationships are mostly discarded. This paper proposes a simple representation specifically aimed at the modeling of such motion relationships. We adopt global and local reference points to characterize motion information, so that the final representation can be robust to camera movement. Our approach operates on top of visual codewords derived from local patch trajectories, and therefore does not require accurate foreground-background separation, which is typically a necessary step to model object relationships. Through an extensive experimental evaluation, we show that the proposed representation offers very competitive performance on challenging benchmark datasets, and combining it with the bag-of-features representation leads to substantial improvement. On Hollywood2, Olympic Sports, and HMDB51 datasets, we obtain 59.5%, 80.6% and 40.7% respectively, which are the best reported results to date.


title: Adaptive Denoising Autoencoders: A Fine-Tuning Scheme to Learn from Test Mixtures

abstract: This work aims at a test-time fine-tune scheme to further improve the performance of an already-trained Denoising AutoEncoder DAE in the context of semi-supervised audio source separation. Although the state-of-the-art deep learning-based DAEs show sensible denoising performance when the nature of artifacts is known in advance, the scalability of an already-trained network to an unseen signal with an unknown characteristic of deformation is not well studied. To handle this problem, we propose an adaptive fine-tuning scheme where we define a test-time target variables so that a DAE can learn from the newly available sources and the mixing environments in the test mixtures. In the proposed network topology, we stack an AutoEncoder AE trained from clean source spectra of interest on top of a DAE trained from a variety of available mixture spectra. Hence, the bottom DAE outputs are used as the input to the top AE, which is to check the purity of the once denoised DAE output. Then, the top AE error is used to fine-tune the bottom DAE during the test phase. Experimental results on audio source separation tasks demonstrate that the proposed fine-tuning technique can further improve the sound quality of a DAE during the test procedure.


title: Studying Software Engineers: Data Collection Techniques for Software Field Studies

abstract: Software engineering is an intensively people-oriented activity, yet too little is known about how designers, maintainers, requirements analysts and all other types of software engineers perform their work. In order to improve software engineering tools and practice, it is therefore essential to conduct field studies, i.e. to study real practitioners as they solve real problems. To do so effectively, however, requires an understanding of the techniques most suited to each type of field study task. In this paper, we provide a taxonomy of techniques, focusing on those for data collection. The taxonomy is organized according to the degree of human intervention each requires. For each technique, we provide examples from the literature, an analysis of some of its advantages and disadvantages, and a discussion of how to use it effectively. We also briefly talk about field study design in general, and data analysis.


title: Circulating Tumor Cell Clusters Are Oligoclonal Precursors of Breast Cancer Metastasis

abstract: Circulating tumor cell clusters (CTC clusters) are present in the blood of patients with cancer but their contribution to metastasis is not well defined. Using mouse models with tagged mammary tumors, we demonstrate that CTC clusters arise from oligoclonal tumor cell groupings and not from intravascular aggregation events. Although rare in the circulation compared with single CTCs, CTC clusters have 23- to 50-fold increased metastatic potential. In patients with breast cancer, single-cell resolution RNA sequencing of CTC clusters and single CTCs, matched within individual blood samples, identifies the cell junction component plakoglobin as highly differentially expressed. In mouse models, knockdown of plakoglobin abrogates CTC cluster formation and suppresses lung metastases. In breast cancer patients, both abundance of CTC clusters and high tumor plakoglobin levels denote adverse outcomes. Thus, CTC clusters are derived from multicellular groupings of primary tumor cells held together through plakoglobin-dependent intercellular adhesion, and though rare, they greatly contribute to the metastatic spread of cancer.


title: Nonintrusive load monitoring (NILM) using an Artificial Neural Network in embedded system with low sampling rate

abstract: A Nonintrusive load monitoring (NILM) system is an energy demand monitoring and load identification system that only uses one instrument installed at main power distribution board. In this paper authors have used low sampling rate of monitored data to detect any change of power signal that obtained a 1 Hz sampling rate of active power from energy meter. Using Artificial Neural Network (ANN) for training steady-state real power and reactive power signatures. This paper point to four appliances including air conditioner television refrigerator and rice cooker. The results showed that in simulation test can disaggregation of appliances in correct detection rate 98% and in the installation test can disaggregation of appliances in correct detection rate 95%.


title: Multi-task Sequence to Sequence Learning

abstract: Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the oneto-many setting - where the encoder is shared between several tasks such as machine translation and syntactic parsing, (b) the many-to-one setting - useful when only the decoder can be shared, as in the case of translation and image caption generation, and (c) the many-to-many setting - where multiple encoders and decoders are shared, which is the case with unsupervised objectives and translation. Our results show that training on a small amount of parsing and image caption data can improve the translation quality between English and German by up to 1.5 BLEU points over strong single-task baselines on the WMT benchmarks. Furthermore, we have established a new state-of-the-art result in constituent parsing with 93.0 F1. Lastly, we reveal interesting properties of the two unsupervised learning objectives, autoencoder and skip-thought, in the MTL context: autoencoder helps less in terms of perplexities but more on BLEU scores compared to skip-thought.


title: A Gold Standard Dependency Corpus for English

abstract: We present a gold standard annotation of syntactic dependencies in the English Web Treebank corpus using the Stanford Dependencies standard. This resource addresses the lack of a gold standard dependency treebank for English, as well as the limited availability of gold standard syntactic annotations for informal genres of English text. We also present experiments on the use of this resource, both for training dependency parsers and for evaluating dependency parsers like the one included as part of the Stanford Parser. We show that training a dependency parser on a mix of newswire and web data improves performance on that type of data without greatly hurting performance on newswire text, and therefore gold standard annotations for non-canonical text can be valuable for parsing in general. Furthermore, the systematic annotation effort has informed both the SD formalism and its implementation in the Stanford Parser’s dependency converter. In response to the challenges encountered by annotators in the EWT corpus, we revised and extended the Stanford Dependencies standard, and improved the Stanford Parser’s dependency converter.


title: Assessment of “juvenile psychopathy” and its association with violence: a critical review*

abstract: Interest in the construct of psychopathy as it applies to children and adolescents has become an area of considerable research interest in the past 5-10 years, in part due to the clinical utility of psychopathy as a predictor of violence among adult offenders. Despite interest in "juvenile psychopathy" in general and its relationship to violence in particular, relatively few studies specifically have examined whether operationalizations of this construct among children and adolescents predict various forms of aggression. This article critically reviews this literature, as well as controversies regarding the assessment of adult psychopathic "traits" among juveniles. Existing evidence indicates a moderate association between measures of psychopathy and various forms of aggression, suggesting that this construct may be relevant for purposes of short-term risk appraisal and management among juveniles. However, due to the enormous developmental changes that occur during adolescence and the absence of longitudinal research on the stability of this construct (and its association with violence), we conclude that reliance on psychopathy measures to make decisions regarding long-term placements for juveniles is contraindicated at this time. Language: en


title: Hierarchies of octrees for efficient 3D mapping

abstract: In this paper, we present a novel multi-resolution approach to efficiently mapping 3D environments. Our representation models the environment as a hierarchy of probabilistic 3D maps, in which each submap is updated and transformed individually. In addition to the formal description of the approach, we present an implementation for tabletop manipulation tasks and an information-driven exploration algorithm for autonomously building a hierarchical map from sensor data. We evaluate our approach using real-world as well as simulated data. The results demonstrate that our method is able to efficiently represent 3D environments at high levels of detail. Compared to a monolithic approach, our maps can be generated significantly faster while requiring significantly less memory.


title: Practical and Optimal LSH for Angular Distance

abstract: We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent. Unlike earlier algorithms with this property (e.g., Spherical LSH [Andoni, Indyk, Nguyen, Razenshteyn 2014], [Andoni, Razenshteyn 2015]), our algorithm is also practical, improving upon the well-studied hyperplane LSH [Charikar, 2002] in practice. We also introduce a multiprobe version of this algorithm, and conduct experimental evaluation on real and synthetic data sets. ::: We complement the above positive results with a fine-grained lower bound for the quality of any LSH family for angular distance. Our lower bound implies that the above LSH family exhibits a trade-off between evaluation time and quality that is close to optimal for a natural class of LSH functions.


title: A phase I safety and pharmacokinetic study of ATX-101: injectable, synthetic deoxycholic acid for submental contouring.

abstract: ATX-101 (deoxycholic acid [DCA] injection) is a proprietary formulation of pure synthetic DCA. When injected into subcutaneous fat, ATX-101 results in focal adipocytolysis, the targeted destruction of fat cells. ATX-101 is undergoing investigation as an injectable drug for contouring the submental area by reducing submental fat (SMF). The purpose of this study was to evaluate the safety and pharmacokinetics (PK) of the maximal therapeutic dose of ATX-101 (100 mg total dose). Following PK evaluation of endogenous DCA, subjects (N=24) received subcutaneous injections of ATX-101 (2 mg/cm2, with or without 0.9% benzyl alcohol) into SMF; PK evaluation was repeated periodically over 24 hours. Endogenous DCA plasma concentrations measured prior to injection were highly variable within and between subjects. Similarly, following ATX-101 injection, DCA plasma concentrations were highly variable, peaked rapidly, and returned to the range observed for endogenous values by 24 hours postdose. All subjects experienced at least 1 adverse event (AE). No death, serious AE, or AE-related discontinuations occurred. The majority of AEs were transient, associated with the area treated, and of mild or moderate severity. No clinically significant changes were reported for laboratory test results, vital signs, or Holter electrocardiograms postdosing. These data support the favorable safety and efficacy observations of ATX-101 as an injectable drug to reduce SMF.


title: M\"obius disjointness for homogeneous dynamics

abstract: We prove Sarnak's M\"{o}bius disjointness conjecture for all unipotent translations on homogeneous spaces of real connected Lie groups. Namely, we show that if $G$ is any such group, $\Gamma\subset G$ a lattice, and $u\in G$ an Ad-unipotent element, then for every $x\in\Gamma\backslash G$ and every continuous, bounded function $f$ on $\Gamma\backslash G$, the sequence $f(xu^{n})$ cannot correlate with the M\"{o}bius function on average.


title: Turbo-Like Beamforming Based on Tabu Search Algorithm for Millimeter-Wave Massive MIMO Systems

abstract: For millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems, codebook-based analog beamforming (including transmit precoding and receive combining) is usually used to compensate the severe attenuation of mmWave signals. However, conventional beamforming schemes involve complicated search among predefined codebooks to find out the optimal pair of analog precoder and analog combiner. To solve this problem, by exploring the idea of turbo equalizer together with the tabu search (TS) algorithm, we propose a Turbo-like beamforming scheme based on TS, which is called Turbo-TS beamforming in this paper, to achieve near-optimal performance with low complexity. Specifically, the proposed Turbo-TS beamforming scheme is composed of the following two key components: 1) Based on the iterative information exchange between the base station (BS) and the user, we design a Turbo-like joint search scheme to find out the near-optimal pair of analog precoder and analog combiner; and 2) inspired by the idea of the TS algorithm developed in artificial intelligence, we propose a TS-based precoding/combining scheme to intelligently search the best precoder/combiner in each iteration of Turbo-like joint search with low complexity. Analysis shows that the proposed Turbo-TS beamforming can considerably reduce the searching complexity, and simulation results verify that it can achieve near-optimal performance.


title: Survey on LBP based texture descriptors for image classification

abstract: The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors).Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets.As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases.


title: Molecular and cellular reorganization of neural circuits in the human lineage

abstract: To better understand the molecular and cellular differences in brain organization between human and nonhuman primates, we performed transcriptome sequencing of 16 regions of adult human, chimpanzee, and macaque brains. Integration with human single-cell transcriptomic data revealed global, regional, and cell-type-specific species expression differences in genes representing distinct functional categories. We validated and further characterized the human specificity of genes enriched in distinct cell types through histological and functional analyses, including rare subpallial-derived interneurons expressing dopamine biosynthesis genes enriched in the human striatum and absent in the nonhuman African ape neocortex. Our integrated analysis of the generated data revealed diverse molecular and cellular features of the phylogenetic reorganization of the human brain across multiple levels, with relevance for brain function and disease.


title: GENERALIZED INVERSES IN REPRODUCING KERNEL SPACES" AN APPROACH TO REGULARIZATION OF LINEAR OPERATOR EQUATIONS*

abstract: In this paper a study of generalized inverses of linear operators in reproducing kernel Hilbert spaces (RKHS) is initiated. Explicit expressions for generalized inverses and minimal-norm solutions of linear operator equations in RKHS are obtained in several forms. The relation between the regularization operator of the equation $Af = g$ and the generalized inverse of the operator A in RKHS is demonstrated. In particular, it is shown that they are the same if the range of the operator is closed in an appropriate RKHS. Finally, properties of the regularized pseudosolutions in this setting are studied.


title: Automated classification of security requirements

abstract: Requirement engineers are not able to elicit and analyze the security requirements clearly, that are essential for the development of secure and reliable software. Proper identification of security requirements present in the Software Requirement Specification (SRS) document has been a problem being faced by the developers. As a result, they are not able to deliver the software free from threats and vulnerabilities. Thus, in this paper, we intend to mine the descriptions of security requirements present in the SRS document and thereafter develop the classification models. The security-based descriptions are analyzed using text mining techniques and are then classified into four types of security requirements viz. authentication-authorization, access control, cryptography-encryption and data integrity using J48 decision tree method. Corresponding to each type of security requirement, a prediction model has been developed. The effectiveness of the prediction models is evaluated against requirement specifications collected from 15 projects which have been developed by MS students at DePaul University. The result analysis indicated that all the four models have performed very well in predicting their respective type of security requirements.


title: A 28GHz self-contained power amplifier for 5G applications in 28nm FD-SOI CMOS

abstract: This paper presents a 28 GHz CMOS balanced Power Amplifier (PA) with integrated quadrature hybrid couplers to achieve robust load insensitivity for 5G phased array applications. The proposed balanced PA achieves a saturated output power (Psat) of 18.7dBm and a maximum Power Added Efficiency (PAE max ) of 12.4% with 17.5dB gain. It consumes 154mW and occupies 0.66mm2 of die area. Each power cell is based on class-AB Segmented Biased Push-Pull (SBPP) topology to improve both the AM-AM and AM-PM conversion. The circuit is implemented in 28nm UTBB FD-SOI CMOS technology taking, advantage of the back gate for threshold voltage adjustment.


title: Business process mining from e-commerce web logs

abstract: The dynamic nature of the Web and its increasing importance as an economic platform create the need of new methods and tools for business efficiency. Current Web analytic tools do not provide the necessary abstracted view of the underlying customer processes and critical paths of site visitor behavior. Such information can offer insights for businesses to react effectively and efficiently. We propose applying Business Process Management (BPM) methodologies to e-commerce Website logs, and present the challenges, results and potential benefits of such an approach. ::: ::: We use the Business Process Insight (BPI) platform, a collaborative process intelligence toolset that implements the discovery of loosely-coupled processes, and includes novel process mining techniques suitable for the Web. Experiments are performed on custom click-stream logs from a large online travel and booking agency. We first compare Web clicks and BPM events, and then present a methodology to classify and transform URLs into events. We evaluate traditional and custom process mining algorithms to extract business models from real-life Web data. The resulting models present an abstracted view of the relation between pages, exit points, and critical paths taken by customers. Such models show important improvements and aid high-level decision making and optimization of e-commerce sites compared to current state-of-art Web analytics.


title: Information demand and stock market volatility

abstract: We study information demand and supply at the firm and market level using data for 30 of the largest stocks traded on NYSE and NASDAQ. Demand is approximated in a novel manner from weekly internet search volume time series drawn from the recently released Google Trends database. Our paper makes contributions in four main directions. First, although information demand and supply tend to be positively correlated, their dynamic interactions do not allow conclusive inferences about the information discovery process. Second, demand for information at the market level is significantly positively related to historical and implied measures of volatility and to trading volume, even after controlling for market return and information supply. Third, information demand increases significantly during periods of higher returns. Fourth, analysis of the expected variance risk premium confirms for the first time empirically the hypothesis that investors demand more information as their level of risk aversion increases.


LBNL-62937 Microgrids : An Overview of Ongoing Research , Development , and Demonstration Projects


title: Probabilistic Dimensionality Reduction via Structure Learning

abstract: We propose an alternative probabilistic dimensionality reduction framework that can naturally integrate the generative model and the locality information of data. Based on this framework, we present a new model, which is able to learn a set of embedding points in a low-dimensional space by retaining the inherent structure from high-dimensional data. The objective function of this new model can be equivalently interpreted as two coupled learning problems, i.e., structure learning and the learning of projection matrix. Inspired by this interesting interpretation, we propose another model, which finds a set of embedding points that can directly form an explicit graph structure. We proved that the model by learning explicit graphs generalizes the reversed graph embedding method, but leads to a natural interpretation from Bayesian perspective. This can greatly facilitate data visualization and scientific discovery in downstream analysis. Extensive experiments are performed that demonstrate that the proposed framework is able to retain the inherent structure of datasets and achieve competitive quantitative results in terms of various performance evaluation criteria.


title: Stacked Hourglass Networks for Human Pose Estimation

abstract: This work introduces a novel convolutional network architecture for the task of human pose estimation. Features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body. We show how repeated bottom-up, top-down processing used in conjunction with intermediate supervision is critical to improving the performance of the network. We refer to the architecture as a “stacked hourglass” network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. State-of-the-art results are achieved on the FLIC and MPII benchmarks outcompeting all recent methods.


title: Personalizing Information Using Users' Online Social Networks: A Case Study of CiteULike

abstract: This paper aims to assess the feasibility of a new and less-focused type of online sociability (the watching network) as a useful information source for personalized recommendations. In this paper, we recommend scientific articles of interests by using the shared interests between target users and their watching connections. Our recommendations are based on one typical social bookmarking system, CiteULike. The watching network-based recommendations, which use a much smaller size of user data, produces suggestions that are as good as the conventional Collaborative Filtering technique. The results demonstrate that the watching network is a useful information source and a feasible foundation for information personalization. Furthermore, the watching network is substitutable for anonymous peers of the Collaborative Filtering recommendations. This study shows the expandability of social network-based recommendations to the new type of online social networks.


title: Does Perpetrating Violence Damage Mental Health? Differences Between Forcibly Recruited and Voluntary Combatants in DR Congo

abstract: As a consequence of the ongoing conflict in the Democratic Republic of the Congo (DRC), combatants are constantly involved in various forms of violence. Findings concerning the impact of perpetrating violence on mental health are contradictory, ranging from increasing to buffering the risk for mental ill health. The present study investigated the impact of perpetrating violence on mental health. In total, 204 forcibly recruited and voluntary male combatants (mean age = 24.61 years) from different armed groups in the eastern DRC took part in the study. In a semistructured interview, respondents were questioned about appetitive aggression and posttraumatic stress disorder (PTSD) as well as self-experienced violence and self-perpetrated violent offending. A multivariate analysis of variance (η(2) = .23) revealed that voluntary combatants perpetrated more violent acts (η(2) = .06) and showed higher appetitive aggression η(2) = .03). A moderated multiple regression analysis (R(2) = .20) showed that perpetrating violence was positively related to PTSD in forcibly recruited combatants, but not in voluntary combatants. Thus, perpetrating violence may not necessarily qualify as a traumatic stressor. Further studies might consider assessing the combatant's perception of committing violent acts. Language: en


title: Real time occlusion handling using Kalman Filter and mean-shift

abstract: Tracking objects using Mean Shift algorithm fails when there is a full/partial occlusion or when the background color and the desired object are close. In this paper we proposed a method using Kalman Filter and Mean Shift for handling these situations. Using similarity measure of Mean Shift algorithm we are able to detect an occlusion. Kalman Filter comes into the play for occlusion handling in a Buffer-Mode Process. We implemented this algorithm both on PC and DSP 64x+ Texas Instrument and the results are both tabulated. The results reveal the ability of our method to locate the object soon after occlusion disappearance.


title: Real-time single camera SLAM using fiducial markers

abstract: In this paper, a real-time single camera simultaneous localization and mapping (SLAM), that uses artificial landmarks is proposed. Proposed method uses the extended Kalman filter (EKF) to estimate robot pose and landmarks position. The core of the approach is the online creation of a map of fiducial markers in the environment within a probabilistic framework. Our key contributions include a development of measurement model of fiducial markers, solutions for global registration of fiducial markers, and the development of calibration-free indoor localization method. We present a detailed method to estimate the 3D location of fiducial markers from an image and how the robot is positioned. Simulation and experimental results for a self-developed mobile robot are both presented.


title: Line Segment Based Scan Matching for Concurrent Mapping and Localization of a Mobile Robot

abstract: This paper describes a new approach to align the laser scans in order to build a consistent map of the environment of a mobile robot. There are no physical landmarks and the environment is completely unknown. Our method finds the nearest matching lines in the scans and resolves the data correspondence problem. The global consistency and final map is achieved by the formulation of geometrical constraints and solving them using conjugate gradient method. The method can handle the multiple loop closing problem. This is easy to implement and fully integrated with a complete navigational architecture. The proposed algorithm is implemented using C++ programming libraries and tested in a real-time robotic simulation environment.


title: The Pyramid Method: Incorporating human content selection variation in summarization evaluation

abstract: Human variation in content selection in summarization has given rise to some fundamental research questions: How can one incorporate the observed variation in suitable evaluation measuresq How can such measures reflect the fact that summaries conveying different content can be equally good and informativeq In this article, we address these very questions by proposing a method for analysis of multiple human abstracts into semantic content units. Such analysis allows us not only to quantify human variation in content selection, but also to assign empirical importance weight to different content units. It serves as the basis for an evaluation method, the Pyramid Method, that incorporates the observed variation and is predictive of different equally informative summaries. We discuss the reliability of content unit annotation, the properties of Pyramid scores, and their correlation with other evaluation methods.


title: Hybrid impedance control of 7-DOF redundant manipulator with dual compliant surface

abstract: A control strategy based on the hybrid impedance control of 7-DOF manipulator is investigated to achieve force control and obstacle avoidance. And the redundancy resolution method is improved, the kinematic related function (arm angle) of manipulator is considered as the additional user-specified task, and a damping coefficients matrix is introduced, hence a singularity-robust inverse kinematics at acceleration level is proposed. The obstacle avoidance control of redundant manipulator is implemented in obstacle avoidance compliant surface. Furthermore, a virtual torque reacted on the additional compliant surface is introduced by artificial potential functions of obstacle, The control strategy based on the non-contact impedance control is adopted to deal with virtual contact. And a numerical simulation system based on Matlab/Simulink is built to verify the control strategy, the simulation results demonstrate the validity and feasibility of the proposed control strategy.


title: Unsupervised Labeling for Supervised Anomaly Detection in Enterprise and Cloud Networks

abstract: Identifying anomalous events in the network is one of the vital functions in enterprises, ISPs, and datacenters to protect the internal resources. With its importance, there has been a substantial body of work for network anomaly detection using supervised and unsupervised machine learning techniques with their own strengths and weaknesses. In this work, we take advantage of the both worlds of unsupervised and supervised learning methods. The basic process model we present in this paper includes (i) clustering the training data set to create referential labels, (ii) building a supervised learning model with the automatically produced labels, and (iii) testing individual data points in question using the established learning model. By doing so, it is possible to construct a supervised learning model without the provision of the associated labels, which are often not available in practice. To attain this process, we set up a new property defining anomalies in the context of clustering, based on our observations from anomalous events in network, by which the referential labels can be obtained. Through our extensive experiments with a public data set (NSL-KDD), we will show that the presented method perform very well, yielding fairly comparable performance to the traditional method running with the original labels provided in the data set, with respect to the accuracy for anomaly detection.


title: A novel approach of palm-line extraction

abstract: Palm-lines, including the principal lines and wrinkles, can describe a palmprint clearly. This paper presents a novel approach of palm-line extraction for the online palmprints. This approach is composed of two stages: coarse-level extraction stage and fine-level extraction stage. In the first stage, morphological operations are used to extract palm-lines in different directions. In the second stage, for each extracted line, a recursive process is devised to further extract and trace the palm-line using the local information of the extracted part. Experimental results show that the proposed approach is suitable for palm-line extraction.


title: Experimental Investigation of a Self-Biased Microstrip Circulator

abstract: An experimental investigation of an above-ferromagnetic resonant self-biased hexagonal ferrite circulator is detailed in this paper. The concept of the ldquoregion of opportunityrdquo is invoked to establish a relationship between the ferrite's geometry and its MH large-signal data for self-biased operation. This concept is used to design a working three-port nonreciprocal device that will serve as a basis for experimentally ascertaining best case insertion losses (ILs), return losses (RLs), and/or isolations (ISs) for circulator applications. Furthermore, we show that this nonreciprocal device when externally tuned as a circulator exhibits RLs and ISs of better than 15 dB across a narrow band; the ILs are on the order of 1.5 dB within this same band. This IL is correlated to the best case IL. Results confirm the conclusion that a ferrite with Hc = 4.0 kOe, 4piMs = 4.375 kG , and DeltaH = 1.862 kOe is suitable for self-biased circulator applications.


Application Of Wavelet Transform For Fault Diagnosisof Rolling Element Bearings


title: Monocular camera-based face liveness detection by combining eyeblink and scene context

abstract: This paper presents a face liveness detection system against spoofing with photographs, videos, and 3D models of a valid user in a face recognition system. Anti-spoofing clues inside and outside a face are both exploited in our system. The inside-face clues of spontaneous eyeblinks are employed for anti-spoofing of photographs and 3D models. The outside-face clues of scene context are used for anti-spoofing of video replays. The system does not need user collaborations, i.e. it runs in a non-intrusive manner. In our system, the eyeblink detection is formulated as an inference problem of an undirected conditional graphical framework which models contextual dependencies in blink image sequences. The scene context clue is found by comparing the difference of regions of interest between the reference scene image and the input one, which is based on the similarity computed by local binary pattern descriptors on a series of fiducial points extracted in scale space. Extensive experiments are carried out to show the effectiveness of our system.


title: Internet of Things (IoT): Application systems and security vulnerabilities

abstract: IoT (Internet of Things) is a system where objects are embedded with sensor technology to interact with each other over wireless communication medium to generate, exchange and transfer data without human interaction. It provides interaction between physical world and cyber world which is known as cyber physical systems. IoT can be implemented as smart cities, smart homes, traffic management systems etc. Research has proven that, any system with the characteristics of remote management, dynamic topology, resource constraints and wireless communication medium are always susceptible to security issues. This research aims to investigate and identify the specific types of IoT applications suitable for UAE, analyse the potential security threats associated them and their impacts on these applications. A thorough exploration and review of the available literature has been done to identify the IoT application systems and related cyber vulnerabilities.


title: Dopaminergic Contributions to Vocal Learning

abstract: Although the brain relies on auditory information to calibrate vocal behavior, the neural substrates of vocal learning remain unclear. Here we demonstrate that lesions of the dopaminergic inputs to a basal ganglia nucleus in a songbird species (Bengalese finches, Lonchura striata var. domestica ) greatly reduced the magnitude of vocal learning driven by disruptive auditory feedback in a negative reinforcement task. These lesions produced no measureable effects on the quality of vocal performance or the amount of song produced. Our results suggest that dopaminergic inputs to the basal ganglia selectively mediate reinforcement-driven vocal plasticity. In contrast, dopaminergic lesions produced no measurable effects on the birds9 ability to restore song acoustics to baseline following the cessation of reinforcement training, suggesting that different forms of vocal plasticity may use different neural mechanisms. SIGNIFICANCE STATEMENT During skill learning, the brain relies on sensory feedback to improve motor performance. However, the neural basis of sensorimotor learning is poorly understood. Here, we investigate the role of the neurotransmitter dopamine in regulating vocal learning in the Bengalese finch, a songbird with an extremely precise singing behavior that can nevertheless be reshaped dramatically by auditory feedback. Our findings show that reduction of dopamine inputs to a region of the songbird basal ganglia greatly impairs vocal learning but has no detectable effect on vocal performance. These results suggest a specific role for dopamine in regulating vocal plasticity.


title: Single- and two-stage inverter-based grid-connected photovoltaic power plants with ride-through capability under grid faults

abstract: Grid-connected distributed generation sources interfaced with voltage source inverters (VSIs) need to be disconnected from the grid under: 1) excessive dc-link voltage; 2) excessive ac currents; and 3) loss of grid-voltage synchronization. In this paper, the control of single- and two-stage grid-connected VSIs in photovoltaic(PV) power plants is developed to address the issue of inverter disconnecting under various grid faults. Inverter control incorporates reactive power support in the case of voltage sags based on the grid codes' (GCs) requirements to ride-through the faults and support the grid voltages. A case study of a 1-MW system simulated in MATLAB/Simulink software is used to illustrate the proposed control. Problems that may occur during grid faults along with associated remedies are discussed. The results presented illustrate the capability of the system to ride-through different types of grid faults.


Othering in the nursing context: A concept analysis


title: Homotopy Path Planning for Terrestrial Robots Using Spherical Algorithm

abstract: Autonomous and semiautonomous mobile robots play an important role in making tasks in environments considered hostile or dangerous for a human being. In order to execute many of the required tasks, robots need, in its architecture, a module of navigation with an appropriate path planning algorithm. This paper presents the development and implementation of a methodology for path planning of a mobile robot using a spherical algorithm and homotopy continuation methods (HCMs). The first section is a brief introduction about HCMs. Subsequently, the homotopy path planning method and spherical path tracking algorithm are explained, as well as the upgraded version and its main features. Then, the main contributions of this paper are presented and the effectiveness of the proposed method is proved. Besides, numerical examples, implementation results in multiple platforms including a 32-b microcontroller and robot operating system, are displayed. Finally, some favorable results from a comparative between the proposed methodology against sampling-based planners using the open motion planning library are presented. Note to Practitioners —This paper presents a numerical routine for homotopy path planning that can be implemented easily using different programming languages or programs like C, C++, and Maple CAS software, among many others. The aim of this paper is to introduce a numerical algorithm suitable to be implemented in different platforms even with low hardware resources. The main advantage of this method is that it is not probabilistic and it is easy to implement by any engineer with basic knowledge of numerical methods. The numerical simulations show that it is possible to resolve in milliseconds environment maps composed by several hundreds of obstacles.


title: Design of a circular polarized nearly square microstrip patch antenna with offset feed

abstract: This paper presents the design of a circular polarized nearly square patch antenna using an offset microstrip feed. By using an offset feed instead of the normal corner feed, it is shown that the area of the perturbation segment must increase in order to maintain circular polarization. Consequently, the effect of manufacturing errors on the axial ratio is reduced. Using a fixed offset feed position, the dimensions of the antenna are uniquely obtained. The antenna is matched using a simple matching network consisting of a short length of microstrip line. A matched antenna was designed to operate at 2.45 GHz, and an axial ratio of 20 dB was obtained experimentally. The predicted, simulated and practical results are shown to be in good agreement.


title: Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform

abstract: Design of a transferable time series anomaly detection method.Novel deep neural network structure facilitates learning short and long-term pattern interdependencies.Detection of anomalies in the Seismic Electrical Signal for predicting earthquake activity.Detection of road anomalies using smartphone data, facilitating crowdsourcing applications. The quest for more efficient real-time detection of anomalies in time series data is critically important in numerous applications and systems ranging from intelligent transportation, structural health monitoring, heart disease, and earthquake prediction. Although the range of application is wide, anomaly detection algorithms are usually domain specific and build on experts knowledge. Here a new signal processing algorithm inspired by the deep learning paradigm is presented that combines wavelets, neural networks, and Hilbert transform. The algorithm performs robustly and is transferable. The proposed neural network structure facilitates learning short and long-term pattern interdependencies; a task usually hard to accomplish using standard neural network training algorithms. The paper provides guidelines for selecting the neural network's buffer size, training algorithm, and anomaly detection features. The algorithm learns the system's normal behavior and does not require the existence of anomalous data for assessing its statistical significance. This is an essential attribute in applications that require customization. Anomalies are detected by analysing hierarchically the instantaneous frequency and amplitude of the residual signal using probabilistic Receiver Operating Characteristics. The method is shown to be able to automatically detect anomalies in the Seismic Electrical Signal that could be used to predict earthquake activity. Furthermore, the method can be used in combination with crowdsourcing of smartphone data to locate road defects such as potholes and bumps for intervention and repair.


title: A print book preference: Caregivers report higher child enjoyment and more adult–child interactions when reading print than electronic books

abstract: Abstract Prior research has indicated that parents of young children prefer their children read print over electronic books. In this study we addressed whether this preference is associated with differences in child enjoyment and engagement or joint caregiver–child interactions during reading. Caregivers of children ages 1–4 years reported their children not only read traditional books more than electronic books, but enjoyed them more and paid more attention to them. Caregivers also reported participating in more adult–child interactions when reading print than electronic books. This research is important because it indicates that caregivers and children may not tend to engage with electronic formats in optimal ways. The result may be a cycle of lower-quality interaction and lower-quality learning with electronic books.


title: Robust multiple kernel K-means using ℓ 2;1 -norm

abstract: The k-means algorithm is one of the most often used method for data clustering. However, the standard k-means can only be applied in the original feature space. The kernel k-means, which extends k-means into the kernel space, can be used to capture the non-linear structure and identify arbitrarily shaped clusters. Since both the standard k-means and kernel k-means apply the squared error to measure the distances between data points and cluster centers, a few outliers will cause large errors and dominate the objection function. Besides, the performance of kernel method is largely determined by the choice of kernel. Unfortunately, the most suitable kernel for a particular task is often unknown in advance. In this paper, we first present a robust k-means using l2,1-norm in the feature space and then extend it to the kernel space. To recap the powerfulness of kernel methods, we further propose a novel robust multiple kernel k-means (RMKKM) algorithm that simultaneously finds the best clustering label, the cluster membership and the optimal combination of multiple kernels. An alternating iterative schema is developed to find the optimal value. Extensive experiments well demonstrate the effectiveness of the proposed algorithms.


title: Parallel k nearest neighbor graph construction using tree-based data structures

abstract: Construction of a nearest neighbor graph is often a neces- ::: sary step in many machine learning applications. However, ::: constructing such a graph is computationally expensive, es- ::: pecially when the data is high dimensional. Python's open ::: source machine learning library Scikit-learn uses k-d trees ::: and ball trees to implement nearest neighbor graph construc- ::: tion. However, this implementation is ine cient for large ::: datasets. In this work, we focus on exploiting these under- ::: lying tree-based data structures to optimize parallel execu- ::: tion of the nearest neighbor algorithm. We present parallel ::: implementations of nearest neighbor graph construction us- ::: ing such tree structures, with parallelism provided by the ::: OpenMP and the Galois framework. We empirically show ::: that our parallel and exact approach is e cient as well as ::: scalable, compared to the Scikit-learn implementation. We ::: present the rst implementation of k-d trees and ball trees ::: using Galois. Our results show that k-d trees are faster when ::: the number of dimensions is small (2d N); ball trees on ::: the other hand scale well with the number of dimensions. ::: Our implementation of ball trees in Galois has almost linear ::: speedup on a number of datasets irrespective of the size and ::: dimensionality of the data.


title: Processing Faces and Facial Expressions

abstract: This paper reviews processing of facial identity and expressions. The issue of independence of these two systems for these tasks has been addressed from different approaches over the past 25 years. More recently, neuroimaging techniques have provided researchers with new tools to investigate how facial information is processed in the brain. First, findings from “traditional” approaches to identity and expression processing are summarized. The review then covers findings from neuroimaging studies on face perception, recognition, and encoding. Processing of the basic facial expressions is detailed in light of behavioral and neuroimaging data. Whereas data from experimental and neuropsychological studies support the existence of two systems, the neuroimaging literature yields a less clear picture because it shows considerable overlap in activation patterns in response to the different face-processing tasks. Further, activation patterns in response to facial expressions support the notion of involved neural substrates for processing different facial expressions.


Privacy concerns and protection measures in online behavioural advertising


title: Amphibot I: an amphibious snake-like robot

abstract: This article presents a project that aims at constructing a biologically inspired amphibious snake-like robot. The robot is designed to be capable of anguilliform swimming like sea-snakes and lampreys in water and lateral undulatory locomotion like a snake on ground. Both the structure and the controller of the robot are inspired by elongate vertebrates. In particular, the locomotion of the robot is controlled by a central pattern generator (a system of coupled oscillators) that produces travelling waves of oscillations as limit cycle behavior. We present the design considerations behind the robot and its controller. Experiments are carried out to identify the types of travelling waves that optimize speed during lateral undulatory locomotion on ground. In particular, the optimal frequency, amplitude and wavelength are thus identified when the robot is crawling on a particular surface. © 2004 Elsevier B.V. All rights reserved.


title: The Continuous Configuration Model: A Null for Community Detection on Weighted Networks.

abstract: Community detection is the process of grouping strongly connected nodes in a network. Many community detection methods for un-weighted networks have a theoretical basis in a null model, which provides an interpretation of resulting communities in terms of statistical significance. In this paper, we introduce a null for sparse weighted networks called the continuous configuration model. We prove a Central Limit Theorem for sums of edge weights under the model, and propose a community extraction method called CCME which combines this result with an iterative multiple testing framework. To benchmark the method, we provide a simulation framework that incorporates the continuous configuration model as a way to plant null or "background" nodes in weighted networks with communities. We show CCME to be competitive with existing methods in accurately identifying both disjoint and overlapping communities, while being particularly effective in ignoring background nodes when they exist. We present two real-world data sets with potential background nodes and analyze them with CCME, yielding results that correspond to known features of the data.


title: CMU-ML-07-116 Dynamic Non-Parametric Mixture Models and The Recurrent Chinese Restaurant Process a

abstract: Clustering is an important data mining task for exploration and visualization of different data types like news stories, scientific publications, weblogs, etc. Due to the evolving nature of these data, evolutionary clustering, also known as dynamic clustering, has recently emerged to cope with the challenges of mining temporally smooth clusters over time. A good evolutionary clustering algorithm should be able to fit the data well at each time epoch, and at the same time results in a smooth cluster evolution that provides the data analyst with a coherent and easily interpretable model. In this paper we introduce the temporal Dirichlet process mixture model (TDPM) as a framework for evolutionary clustering. TDPM is a generalization of the DPM framework for clustering that automatically grows the number of clusters with the data. In our framework, the data is divided into epochs; all data points inside the same epoch are assumed to be fully exchangeable, whereas the temporal order is maintained across epochs. Moreover, The number of clusters in each epoch is unbounded: the clusters can retain, die out or emerge over time, and the actual parameterization of each cluster can also evolve over time in a Markovian fashion. We give a detailed and intuitive construction of this framework using the recurrent Chinese restaurant process (RCRP) metaphor, as well as a Gibbs sampling algorithm to carry out posterior inference in order to determine the optimal cluster evolution. We demonstrate our model over simulated data by using it to build an infinite dynamic mixture of Gaussian factors, and over real dataset by using it to build a simple non-parametric dynamic clustering-topic model and apply it to analyze the NIPS12 document collection.


title: Major trends in knowledge management research: a bibliometric study

abstract: This study provides an overview of the knowledge management literature from 1980 through 2014. We employ bibliometric and text mining analyses on a sample of 500 most cited articles to examine the impact of factors such as number of authors, references, pages, and keywords on the number of citations that they received. We also investigate major trends in knowledge management literature including the contribution of different countries, variations across publication years, and identifying active research areas and major journal outlets. Our study serves as a resource for future studies by shedding light on how trends in knowledge management research have evolved over time and demonstrating the characteristics of the most cited articles in this literature. Specifically, our results reveal that the most cited articles are from United States and United Kingdom. The most prolific year in terms of the number of published articles is 2009 and in terms of the number of citations is 2012. We also found a positive relationship between the number of publications' keywords, references, and pages and the number of citations that they have received. Finally, the Journal of Knowledge Management has the largest share in publishing the most cited articles in this field.


title: Unsupervised learning algorithms for intrusion detection

abstract: This work summarizes our research on the topic of the application of unsupervised learning algorithms to the problem of intrusion detection, and in particular our main research results in network intrusion detection. We proposed a novel, two tier architecture for network intrusion detection, capable of clustering packet payloads and correlating anomalies in the packet stream. We show the experiments we conducted on such architecture, we give performance results, and we compare our achievements with other comparable existing systems.


title: Understanding the dark side of domain parking

abstract: Domain parking is a booming business with millions of dollars in revenues. However, it is also among the least regulated: parked domains have been routinely found to connect to illicit online activities even though the roles they play there have never been clarified. In this paper, we report the first systematic study on this "dark side" of domain parking based upon a novel infiltration analysis on domains hosted by major parking services. The idea here is to control the traffic sources (crawlers) of the domain parking ecosystem, some of its start nodes (parked domains) and its end nodes (advertisers and traffic buyers) and then "connect the dots", delivering our own traffic to our end nodes across our own start nodes with other monetization entities (parking services, ad networks, etc) in-between. This provided us a unique observation of the whole monetization process and over one thousand seed redirection chains where some ends were under our control. From those chains, we were able to confirm the presence of click fraud, traffic spam and traffic stealing. To further understand the scope and magnitude of this threat, we extracted a set of salient features from those seed chains and utilized them to detect illicit activities on 24 million monetization chains we collected from leading parking services over 5.5 months. This study reveals the pervasiveness of those illicit monetization activities, parties responsible for them and the revenues they generate which approaches 40% of the total revenue for some parking services. Our findings point to an urgent need for a better regulation of domain parking.


title: Chess Evolution Visualization

abstract: We present a chess visualization to convey the changes in a game over successive generations. It contains a score chart, an evolution graph and a chess board, such that users can understand a game from global to local viewpoints. Unlike current graphical chess tools, which focus only on highlighting pieces that are under attack and require sequential investigation, our visualization shows potential outcomes after a piece is moved and indicates how much tactical advantage the player can have over the opponent. Users can first glance at the score chart to roughly obtain the growth and decline of advantages from both sides, and then examine the position relations and the piece placements, to know how the pieces are controlled and how the strategy works. To achieve this visualization, we compute the decision tree using artificial intelligence to analyze a game, in which each node represents a chess position and each edge connects two positions that are one-move different. We then merge nodes representing the same chess position, and shorten branches where nodes on them contain only two neighbors, in order to achieve readability. During the graph rendering, the nodes containing events such as draws, effective checks and checkmates, are highlighted because they show how a game is ended. As a result, our visualization helps players understand a chess game so that they can efficiently learn strategies and tactics. The presented results, evaluations, and the conducted user studies demonstrate the feasibility of our visualization design.


title: Automotive ECU Software Reprogramming Method Based on Ethernet Backbone Network to Save Time

abstract: In this paper, a method for parallel reprogramming automotive electronic control units that reduces the total reprogramming time is proposed. The method is implemented in an in-vehicle domain control unit which performs the role of the gateway between the backbone network and its own sub-network. We built a test environment that consists of an in-vehicle network system based on Ethernet backbone to measure, analyze and evaluate the reprogramming performance. Experiment results show that the proposed parallel reprogramming method can reduce the total reprogramming time at least 39% compare to the sequential reprogramming method with the increasing number of reprogramming target ECUs connecting different sub-networks.


title: Accurate phase measurement of passive non-reciprocal quasi-optical components

abstract: A method for accurate measurement of the phase of passive non-reciprocal quasi-optical components is presented. The measurements are performed using a two-port free-space measurement system that is composed of two horn-fed axially aligned lenses calibrated using a through-reflect-match (TRM) technique. The proposed method for accurate phase measurement consists in measuring the component twice, i.e. once facing the first port and then facing the second port. A simple relationship is derived between the phase at the desired location, recorded phase values and the phase due to the thickness of the sample. The method was verified by measuring two different structures at Ka band, i.e. a polarisation-sensitive frequency-selective surface (FSS) and a multilayer isotropic dielectric sample. Another method requiring two identical components is used to measure the phase of the reflection coefficient based on a Fabry-Perot interferometer (FPI) architecture. Neither time-domain gating nor data smoothing were applied to the results. The S-parameter magnitude and phase results are in good agreement with simulations.


title: Facebook and the others. Potentials and obstacles of Social Media for teaching in higher education

abstract: Social Media tools are seen by many authors as powerful drivers of change for teaching and learning practices, in terms of openness, interactivity and sociability. However, extensive surveys about actual use that are carried out with large samples at a national level are rare. This study reports the results of a survey addressed to the Italian academic staff, with the aim of identifying the uses of Social Media in the field of university teaching practices. The response rate was 10.5%, corresponding to 6139. The respondents were asked to identify frequency of use, motivations, teaching practices and obstacles related to the use of a number of tools: generic social network sites (Twitter, Facebook), professional and academic networking services (LinkedIn, ResearchGate and Academia.edu), tools to write and comment (blogs, wikis) and to archive and retrieve content material for lectures and group work (podcasts, YouTube and Vimeo, SlideShare). Analyses of data tested which socio-demographic variables mostly affected frequency of use, and the relationships between motivations, ways of use, barriers to use and the scientific discipline. The results show that Social Media use is still rather limited and restricted and that academics are not much inclined to integrate these devices into their practices for several reasons, such as cultural resistance, pedagogical issues or institutional constraints. However, there are differences among academics in the ways they use Social Media or perceive them, mostly depending on the scientific discipline of teaching. Overall, the results emphasise ambivalent attitudes towards the benefits and challenges of Social Media in the context of higher education with obstacles prevailing over advantages. We examine the frequency of use of Social Media for teaching purposes.The variable most associated with frequency of use is scientific discipline.We present motivations and ways to use Social Media tools in teaching.A principal component analysis reveals three main factors as obstacles to use.Results show a complex scenario where potentials and barriers are intertwined.


title: VRID-1: A basic vehicle re-identification dataset for similar vehicles

abstract: Vehicle re-identification is a process of recognising a vehicle at different locations. It has attracted increasing amounts of attention due to the rapidly-increasing number of vehicles. Identification of two vehicles of the same model is even more difficult than the identification of identical twin humans. Further-more, there is no vehicle re-identification dataset that considers the interference caused by the presence of other vehicles of the same model. Therefore, to provide a fair comparison and facilitate future research into vehicle re-identification, this paper constructs a new dataset called the vehicle re-identification dataset-1 (1 VRID-1). VRID-1 contains 10,000 images captured in daytime of 1,000 individual vehicles of the ten most common vehicle models. For each vehicle model, there are 100 individual vehicles, and for each of these, there are ten images captured at different locations. The images in VRID-1 were captured by 326 surveillance cameras, and thus there are various vehicles poses and levels of illumination. Yet, it provides images of good enough quality for the evaluation of vehicle re-identification in a practical surveillance environment. In addition, according to the characteristics of vehicle morphology, this paper proposes a deep learning-based method to extract multi-dimensional robust features for vehicle re-identification using convolutional neural networks. Experimental results on the VRID-1 dataset demonstrate that it can deal with interference from vehicles of the same model, and is effective and practical for vehicle re-identification.


title: A qualitative exploration of responses to self-compassion in a non-clinical sample

abstract: Research suggests that being self-compassionate can have myriad benefits, including life satisfaction, health-promoting behaviours and improved mental health. Given the possible advantages of being self-compassionate, it seems critical to explore how to promote this in the general population. This qualitative study aimed to understand responses to the idea of being compassionate to oneself within the general population. Semi-structured interviews were conducted in the North East of England between October 2014 and February 2015, they were analysed using thematic analysis. As part of an intervention study, non-clinical volunteers watched a psychoeducation video about the concept of self-compassion and then participated in one of four meditation exercises. Following this, participants were interviewed about their responses to the idea of being self-compassionate. Three themes were identified: Benefits of Self-Compassion; Being Self-Compassionate and Barriers to Self-Compassion. Participants believed that being self-compassionate would be beneficial, for both themselves and the world, but they believed that self-compassion would make them vulnerable and that others would judge them. Thus, participants were afraid to be the first ones to be self-compassionate and stated that, in order for self-compassion to be acceptable, we need to change the perspective of western culture. These findings underscore the importance of understanding society's role in a person's ability to be self-compassionate. In order to reap the benefits of self-compassion, we need to create a culture that accepts and encourages this. As practitioners, we are in a position to lead in self-compassion and to encourage other leaders to promote this as a preventative mental health strategy.


title: Semantic bottleneck for computer vision tasks

abstract: This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. More specifically, our proposition is to introduce what we call a semantic bottleneck in the processing pipeline, which is a crossing point in which the representation of the image is entirely expressed with natural language , while retaining the efficiency of numerical representations. We show that our approach is able to generate semantic representations that give state-of-the-art results on semantic content-based image retrieval and also perform very well on image classification tasks. Intelligibility is evaluated through user centered experiments for failure detection.


Interactive Instance-based Evaluation of Knowledge Base Question Answering


title: Fault tolerant permanent magnet motor drives for electric vehicles

abstract: This paper presents a novel five-phase fault tolerant interior permanent magnet (IPM) motor drive with higher performance and reliability for electric vehicles applications. A new machine design along with efficient control strategy is developed for fault tolerant operation of electric drive without severely compromising the drive performance. Fault tolerance is achieved by employing a five phase fractional-slot concentrated windings configuration IPM motor drive, with each phase electrically, magnetically, thermally and physically independent of all the others. The proposed electric drive system presents higher torque density, negligible cogging torque, and about ±0.5% torque ripple. Power converter requirements are discussed and control strategies to minimize the impact of machine or converter fault are developed. Besides, all the requirement of a fault tolerant operation, including high phase inductance and negligible mutual coupling between phases are met. Analytical and finite element analysis and comparison case studies are presented.


title: Agmatine rescues autistic behaviors in the valproic acid‐induced animal model of autism

abstract: Abstract Autism spectrum disorder (ASD) is an immensely challenging developmental disorder characterized primarily by two core behavioral symptoms of social communication deficits and restricted/repetitive behaviors. Investigating the etiological process and identifying an appropriate therapeutic target remain as formidable challenges to overcome ASD due to numerous risk factors and complex symptoms associated with the disorder. Among the various mechanisms that contribute to ASD, the maintenance of excitation and inhibition balance emerged as a key factor to regulate proper functioning of neuronal circuitry. Interestingly, our previous study involving the valproic acid animal model of autism (VPA animal model) has demonstrated excitatory-inhibitory imbalance (E/I imbalance) due to enhanced differentiation of glutamatergic neurons and reduced GABAergic neurons. Here, we investigated the potential of agmatine, an endogenous NMDA receptor antagonist, as a novel therapeutic candidate in ameliorating ASD symptoms by modulating E/I imbalance using the VPA animal model. We observed that a single treatment of agmatine rescued the impaired social behaviors as well as hyperactive and repetitive behaviors in the VPA animal model. We also observed that agmatine treatment rescued the overly activated ERK1/2 signaling in the prefrontal cortex and hippocampus of VPA animal models, possibly, by modulating over-excitability due to enhanced excitatory neural circuit. Taken together, our results have provided experimental evidence suggesting a possible therapeutic role of agmatine in ameliorating ASD-like symptoms in the VPA animal model of ASD.


title: Channel Modeling of Miniaturized Battery-Powered Capacitive Human Body Communication Systems

abstract: Objective: The purpose of this contribution is to estimate the path loss of capacitive human body communication (HBC) systems under practical conditions. Methods: Most prior work utilizes large grounded instruments to perform path loss measurements, resulting in overly optimistic path loss estimates for wearable HBC devices. In this paper, small battery-powered transmitter and receiver devices are implemented to measure path loss under realistic assumptions. A hybrid electrostatic finite element method simulation model is presented that validates measurements and enables rapid and accurate characterization of future capacitive HBC systems. Results: Measurements from form-factor-accurate prototypes reveal path loss results between 31.7 and 42.2 dB from 20 to 150 MHz. Simulation results matched measurements within 2.5 dB. Comeasurements using large grounded benchtop vector network analyzer (VNA) and large battery-powered spectrum analyzer (SA) underestimate path loss by up to 33.6 and 8.2 dB, respectively. Measurements utilizing a VNA with baluns, or large battery-powered SAs with baluns still underestimate path loss by up to 24.3 and 6.7 dB, respectively. Conclusion: Measurements of path loss in capacitive HBC systems strongly depend on instrumentation configurations. It is thus imperative to simulate or measure path loss in capacitive HBC systems utilizing realistic geometries and grounding configurations. Significance: HBC has a great potential for many emerging wearable devices and applications; accurate path loss estimation will improve system-level design leading to viable products.


title: The Ecology of Homelessness

abstract: This article proposes an ecological model of homelessness drawn from a broad review of the research and practice literature. The spectrum of biopsychosocial risks associated with pathways into homelessness is reviewed, followed by examination of social and individual consequences resulting from periods of homelessness. The temporal dimensions of homelessness, including “first-time,” “episodic,” and “chronic,” are defined and discussed along with the continuum of living arrangements experienced by homeless individuals and families. These constituent domains of the ecology of homelessness are presented in a conceptual map depicting the relationships and interactions among the parts of the model. The intention is to provide a coherent and cogent map to encourage comprehensive integrated efforts as individuals, agencies, and communities strive to prevent and resolve homelessness.


title: DeadBolt: Securing IoT Deployments

abstract: In this paper, we introduce DeadBolt, a new security framework for managing IoT network access. DeadBolt hides all of the devices in an IoT deployment behind an access point that implements deny-by-default policies for both incoming and outgoing traffic. The DeadBolt AP also forces high-end IoT devices to use remote attestation to gain network access; attestation allows the devices to prove that they run up-to-date, trusted software. For lightweight IoT devices which lack the ability to attest, the DeadBolt AP uses virtual drivers (essentially, security-focused virtual network functions) to protect lightweight device traffic. For example, a virtual driver might provide network intrusion detection, or encrypt device traffic that is natively cleartext. Using these techniques, and several others, DeadBolt can prevent realistic attacks while imposing only modest performance costs.


title: Do Psychosocial and Study Skill Factors Predict College Outcomes? A Meta-Analysis.

abstract: This study examines the relationship between psychosocial and study skill factors (PSFs) and college outcomes by meta-analyzing 109 studies. On the basis of educational persistence and motivational theory models, the PSFs were categorized into 9 broad constructs: achievement motivation, academic goals, institutional commitment, perceived social support, social involvement, academic self-efficacy, general self-concept, academic-related skills, and contextual influences. Two college outcomes were targeted: performance (cumulative grade point average; GPA) and persistence (retention). Meta-analyses indicate moderate relationships between retention and academic goals, academic self-efficacy, and academic-related skills (ps = .340, .359, and .366, respectively). The best predictors for GPA were academic self-efficacy and achievement motivation (ps = .496 and .303, respectively). Supplementary regression analyses confirmed the incremental contributions of the PSF over and above those of socioeconomic status, standardized achievement, and high school GPA in predicting college outcomes.


title: Do television and electronic games predict children's psychosocial adjustment? Longitudinal research using the UK Millennium Cohort Study

abstract: Background Screen entertainment for young children has been associated with several aspects of psychosocial adjustment. Most research is from North America and focuses on television. Few longitudinal studies have compared the effects of TV and electronic games, or have investigated gender differences. Purpose To explore how time watching TV and playing electronic games at age 5 years each predicts change in psychosocial adjustment in a representative sample of 7 year-olds from the UK. Methods Typical daily hours viewing television and playing electronic games at age 5 years were reported by mothers of 11 014 children from the UK Millennium Cohort Study. Conduct problems, emotional symptoms, peer relationship problems, hyperactivity/inattention and prosocial behaviour were reported by mothers using the Strengths and Difficulties Questionnaire. Change in adjustment from age 5 years to 7 years was regressed on screen exposures; adjusting for family characteristics and functioning, and child characteristics. Results Watching TV for 3 h or more at 5 years predicted a 0.13 point increase (95% CI 0.03 to 0.24) in conduct problems by 7 years, compared with watching for under an hour, but playing electronic games was not associated with conduct problems. No associations were found between either type of screen time and emotional symptoms, hyperactivity/inattention, peer relationship problems or prosocial behaviour. There was no evidence of gender differences in the effect of screen time. Conclusions TV but not electronic games predicted a small increase in conduct problems. Screen time did not predict other aspects of psychosocial adjustment. Further work is required to establish causal mechanisms.


title: Mimicking Very Efficient Network for Object Detection

abstract: Current CNN based object detectors need initialization from pre-trained ImageNet classification models, which are usually time-consuming. In this paper, we present a fully convolutional feature mimic framework to train very efficient CNN based detectors, which do not need ImageNet pre-training and achieve competitive performance as the large and slow models. We add supervision from high-level features of the large networks in training to help the small network better learn object representation. More specifically, we conduct a mimic method for the features sampled from the entire feature map and use a transform layer to map features from the small network onto the same dimension of the large network. In training the small network, we optimize the similarity between features sampled from the same region on the feature maps of both networks. Extensive experiments are conducted on pedestrian and common object detection tasks using VGG, Inception and ResNet. On both Caltech and Pascal VOC, we show that the modified 2.5× accelerated Inception network achieves competitive performance as the full Inception Network. Our faster model runs at 80 FPS for a 1000×1500 large input with only a minor degradation of performance on Caltech.


title: The performance of touch screen soft buttons

abstract: The introduction of a new generation of attractive touch screen-based devices raises many basic usability questions whose answers may influence future design and market direction. With a set of current mobile devices, we conducted three experiments focusing on one of the most basic interaction actions on touch screens: the operation of soft buttons. Issues investigated in this set of experiments include: a comparison of soft button and hard button performance; the impact of audio and vibrato-tactile feedback; the impact of different types of touch sensors on use, behavior, and performance; a quantitative comparison of finger and stylus operation; and an assessment of the impact of soft button sizes below the traditional 22 mm recommendation as well as below finger width.


title: Monitoring Blockchains with Self-Organizing Maps

abstract: Blockchains such as those used by the Bitcoin and Ethereum cryptocurrencies provide a global, observable record of all transactions and associated data. Analyzing blockchain data is useful for tasks such as detecting fraudulent activities, studying the use and growth of the system, and understanding its levels of anonymity and traceability. Such analysis is challenging due to the high volume and rapidly changing characteristics of popular blockchains. In particular, online (soft real-time) analysis of blockchains requires methods that adapt organically to changes in the data. This paper describes such a method based on self-organizing maps and reports on experiments using the Bitcoin blockchain data.


title: NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models

abstract: With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.


title: miRBase: integrating microRNA annotation and deep-sequencing data

abstract: miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences. Deep-sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. We have mapped reads from short RNA deep-sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations. miRBase is available online at: http://www.mirbase.org/.


title: Distributed Sparse Linear Regression

abstract: The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems. This paper develops algorithms to estimate the regression coefficients via Lasso when the training data are distributed across different agents, and their communication to a central processing unit is prohibited for e.g., communication cost or privacy reasons. A motivating application is explored in the context of wireless communications, whereby sensing cognitive radios collaborate to estimate the radio-frequency power spectrum density. Attaining different tradeoffs between complexity and convergence speed, three novel algorithms are obtained after reformulating the Lasso into a separable form, which is iteratively minimized using the alternating-direction method of multipliers so as to gain the desired degree of parallelization. Interestingly, the per agent estimate updates are given by simple soft-thresholding operations, and inter-agent communication overhead remains at affordable level. Without exchanging elements from the different training sets, the local estimates consent to the global Lasso solution, i.e., the fit that would be obtained if the entire data set were centrally available. Numerical experiments with both simulated and real data demonstrate the merits of the proposed distributed schemes, corroborating their convergence and global optimality. The ideas in this paper can be easily extended for the purpose of fitting related models in a distributed fashion, including the adaptive Lasso, elastic net, fused Lasso and nonnegative garrote.


title: Camera array-based HDR image synthesis method

abstract: For traditional high dynamic range(HDR) image synthesis method could not adapt to the dynamic illumination,the paper proposed a camera array-based different exposure images alignment and HDR image synthesis method.Firstly it captured different exposure images with the camera array whose calibration parameters had been calculated out,according to light field synthetic aperture photography theory,aligned different exposure images,and then did a second alignment to aligned images with the median threshold bitmap(MTB).According to different cameras' response curves fitted,it generated a HDR ima-ge with different exposure images second aligned.The experiments show that the proposed method can effectively synthesize HDR images in the dynamic illumination conditions,and achieves good results.


title: An improved k-medoids clustering algorithm

abstract: In this paper, we present an improved k-medoids clustering algorithm based on CF-Tree. The algorithm based on the clustering features of BIRCH algorithm, the concept of k-medoids algorithm has been improved. We preserve all the training sample data in an CF-Tree, then use k-medoids method to cluster the CF in leaf nodes of CF-Tree. Eventually, we can get k clusters from the root of the CF-Tree. This algorithm improves obviously the drawbacks of the k-medoids algorithm, such as the time complexity, scalability on large dataset, and can't find the clusters of sizes different very much and the convex shapes. Experiments show that this algorithm enhances the quality and scalability of clustering.


title: A parallel approximate SS-ELM algorithm based on MapReduce for large-scale datasets

abstract: Abstract Extreme Learning Machine (ELM) algorithm not only has gained much attention of many scholars and researchers, but also has been widely applied in recent years especially when dealing with big data because of its better generalization performance and learning speed. The proposal of SS-ELM (semi-supervised Extreme Learning Machine) extends ELM algorithm to the area of semi-supervised learning which is an important issue of machine learning on big data. However, the original SS-ELM algorithm needs to store the data in the memory before processing it, so that it could not handle large and web-scale data sets which are of frequent appearance in the era of big data. To solve this problem, this paper firstly proposes an efficient parallel SS-ELM (PSS-ELM) algorithm on MapReduce model, adopting a series of optimizations to improve its performance. Then, a parallel approximate SS-ELM Algorithm based on MapReduce (PASS-ELM) is proposed. PASS-ELM is based on the approximate adjacent similarity matrix (AASM) algorithm, which leverages the Locality-Sensitive Hashing (LSH) scheme to calculate the approximate adjacent similarity matrix, thus greatly reducing the complexity and occupied memory. The proposed AASM algorithm is general, because the calculation of the adjacent similarity matrix is the key operation in many other machine learning algorithms. The experimental results have demonstrated that the proposed PASS-ELM algorithm can efficiently process very large-scale data sets with a good performance, without significantly impacting the accuracy of the results.