title: Discovery of Ranking Fraud for Mobile Apps

abstract: Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of globalanomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps' ranking, rating and review behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.


title: Complex brain networks: graph theoretical analysis of structural and functional systems

abstract: In recent years, the principles of network science have increasingly been applied to the study of the brain's structural and functional organization. Bullmore and Sporns review this growing field of research and discuss its contributions to our understanding of brain function.


title: A personal route prediction system based on trajectory data mining

abstract: This paper presents a system where the personal route of a user is predicted using a probabilistic model built from the historical trajectory data. Route patterns are extracted from personal trajectory data using a novel mining algorithm, Continuous Route Pattern Mining (CRPM), which can tolerate different kinds of disturbance in trajectory data. Furthermore, a client-server architecture is employed which has the dual purpose of guaranteeing the privacy of personal data and greatly reducing the computational load on mobile devices. An evaluation using a corpus of trajectory data from 17 people demonstrates that CRPM can extract longer route patterns than current methods. Moreover, the average correct rate of one step prediction of our system is greater than 71%, and the average Levenshtein distance of continuous route prediction of our system is about 30% shorter than that of the Markov model based method.


title: M.: Interactive modeling of city layouts using layers of procedural content

abstract: In this paper, we present new solutions for the interactive modeling of city layouts that combine the power of procedural modeling with the flexibility of manual modeling. Procedural modeling enables us to quickly generate large city layouts, while manual modeling allows us to hand-craft every aspect of a city. We introduce transformation and merging operators for both topology preserving and topology changing transformations based on graph cuts. In combination with a layering system, this allows intuitive manipulation of urban layouts using operations such as drag and drop, translation, rotation etc. In contrast to previous work, these operations always generate valid, i.e., intersection-free layouts. Furthermore, we introduce anchored assignments to make sure that modifications are persistent even if the whole urban layout is regenerated.


title: Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms

abstract: This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.


title: 1.6 V Nanogenerator for Mechanical Energy Harvesting Using PZT Nanofibers

abstract: Energy harvesting technologies that are engineered to miniature sizes, while still increasing the power delivered to wireless electronics,(1, 2) portable devices, stretchable electronics,(3) and implantable biosensors,(4, 5) are strongly desired. Piezoelectric nanowire- and nanofiber-based generators have potential uses for powering such devices through a conversion of mechanical energy into electrical energy.(6) However, the piezoelectric voltage constant of the semiconductor piezoelectric nanowires in the recently reported piezoelectric nanogenerators(7-12) is lower than that of lead zirconate titanate (PZT) nanomaterials. Here we report a piezoelectric nanogenerator based on PZT nanofibers. The PZT nanofibers, with a diameter and length of approximately 60 nm and 500 μm, were aligned on interdigitated electrodes of platinum fine wires and packaged using a soft polymer on a silicon substrate. The measured output voltage and power under periodic stress application to the soft polymer was 1.63 V and 0.03 ...


title: CUNY-UIUC-SRI TAC-KBP2011 Entity Linking System Description

abstract: In this paper we describe a joint effort by the City University of New York (CUNY), University of Illinois at Urbana-Champaign (UIUC) and SRI International at participating in the mono-lingual entity linking (MLEL) and cross-lingual entity linking (CLEL) tasks for the NIST Text Analysis Conference (TAC) Knowledge Base Population (KBP2011) track. The MLEL system is based on a simple combination of two published systems by CUNY (Chen and Ji, 2011) and UIUC (Ratinov et al., 2011). Therefore, we mainly focus on describing our new CLEL system. In addition to a baseline system based on name translation, machine translation and MLEL, we propose two novel approaches. One is based on a cross-lingual name similarity matrix, iteratively updated based on monolingual co-occurrence, and the other uses topic modeling to enhance performance. Our best systems placed 4th in mono-lingual track and 2nd in cross-lingual track.


title: On the TDD subframe structure for beyond 4G radio access network

abstract: The purpose of a Beyond 4G (B4G) radio access technology, is to cope with the expected exponential increase of mobile data traffic in local area (LA). The requirements related to physical layer control signaling latencies and to hybrid ARQ (HARQ) round trip time (RTT) are in the order of ~1ms. In this paper, we propose a flexible orthogonal frequency division multiplexing (OFDM) based time division duplex (TDD) physical subframe structure optimized for B4G LA environment. We show that the proposed optimizations allow very frequent link direction switching, thus reaching the tight B4G HARQ RTT requirement and significant control signaling latency reductions compared to existing LTE-Advanced and WiMAX technologies.


title: Flawed Self-Assessment Implications for Health, Education, and the Workplace

abstract: Research from numerous corners of psychological inquiry suggests that self-assessments of skill and character are often flawed in substantive and systematic ways. We review empirical findings on the imperfect nature of self-assessment and discuss implications for three real-world domains: health, education, and the workplace.In general, people's self-views hold only a tenuous to modest relationship with their actual behavior and performance. The correlation between self-ratings of skill and actual performance in many domains is moderate to meager—indeed, at times, other people's predictions of a person's outcomes prove more accurate than that person's self-predictions. In addition, people overrate themselves. On average, people say that they are “above average” in skill (a conclusion that defies statistical possibility), overestimate the likelihood that they will engage in desirable behaviors and achieve favorable outcomes, furnish overly optimistic estimates of when they will complete future projects, an...


title: A 25 Gb/s Hybrid-Integrated Silicon Photonic Source-Synchronous Receiver With Microring Wavelength Stabilization

abstract: Single-mode wavelength-division multiplexing (WDM) optical links are an attractive technology to meet the growing interconnect bandwidth demand in data center applications. This paper presents a multi-channel hybridintegrated photonic receiver based on microring drop filters and waveguide photodetectors implemented in a 130 nm SOI process and high-speed optical front-ends designed in 65 nm CMOS. The source-synchronous receiver utilizes an LC injection-locked oscillator (ILO) in the clock path for improved jitter filtering, while maintaining correlated jitter tracking with the data channels. Receiver sensitivity is improved with a large input-stage feedback resistor transimpedance amplifier (TIA) cascaded with an adaptively-tuned continuous-time linear equalizer (CTLE). In order to stabilize the microring drop filter resonance wavelength, a peak-detector-based thermal tuning loop is implemented with a 0.7 nm range at 43 μW/GHz efficiency. When tested with a waveguide photodetector with 0.45 A/W responsivity, the receiver achieves -8.0 dBm OMA sensitivity at a BER = 10 ::: -12 ::: with a jitter tolerance corner frequency near 20 MHz and a per-channel power consumption of 17 mW including amortized clocking power.


title: Automatic Defect Detection and Classification Technique from Image: A Special Case Using Ceramic Tiles

abstract: Quality control is an important issue in the ceramic tile industry. On the other hand maintaining the rate of production with respect to time is also a major issue in ceramic tile manufacturing. Again, price of ceramic tiles also depends on purity of texture, accuracy of color, shape etc. Considering this criteria, an automated defect detection and classification technique has been proposed in this report that can have ensured the better quality of tiles in manufacturing process as well as production rate. Our proposed method plays an important role in ceramic tiles industries to detect the defects and to control the quality of ceramic tiles. This automated classification method helps us to acquire knowledge about the pattern of defect within a very short period of time and also to decide about the recovery process so that the defected tiles may not be mixed with the fresh tiles.


title: Pushing the contextual envelope: developing and diffusing IS theory for health information systems research

abstract: Abstract The healthcare sector is a crucial and socially challenging component of modern economies. Information systems (IS) research could contribute to the effective development, application and use of information technologies to manage and coordinate health services. Healthcare also provides opportunities to develop or refine IS theory because of its unique institutional context. To profile IS research in health-related settings, we examine the publication of health information systems research (HISR) in 17 IS journals since 1985. Our analysis revealed a small but growing body of HISR literature. These publications are concentrated in “HISR-friendly journals” and employ a variety of strategies for balancing general IS theories and knowledge with attention to the institutional characteristics of healthcare. We consider the strengths and limitations of these strategies in advancing HISR within the IS field and for contributing to multidisciplinary HISR knowledge.


title: Event-related cortical desynchronization detected by power measurements of scalp EEG ☆

abstract: Abstract Changes in the background EEG activity occurring at the same time as visual and auditory evoked potentials, as well as during the interstimulus interval in a CNV paradigm were analysed in human subjects, using serial power measurements of overlapping EEG segments. The analysis was focused on the power of the rhythmic activity within the alpha band (RAAB power). A decrease in RAAB power occurring during these event-related phenomena was indicative of desynchronization. Phasic, i.e. short lasting, localised desynchronization was present during sensory stimulation, and also preceding the imperative signal and motor response (motor preactivation) in the CNV paradigm.


title: Systemic lupus erythematosus: diagnostic application of magnetization transfer ratio histograms in patients with neuropsychiatric symptoms--initial results.

abstract: PURPOSE ::: To explore the diagnostic potential of magnetization transfer ratio (MTR) histogram analysis in patients with neuropsychiatric systemic lupus erythematosus (SLE) by using multivariate discriminant analysis (MDA). ::: ::: ::: MATERIALS AND METHODS ::: Volumetric magnetization transfer imaging was performed in nine patients with active non-thromboembolic, neuropsychiatric SLE, 10 patients with SLE who had had neuropsychiatric SLE previously, 10 patients with SLE but no history of neuropsychiatric SLE, 10 patients with inactive multiple sclerosis, and 10 healthy control subjects. For each subject, an MTR histogram of the whole brain was generated, and an MDA score was produced for each histogram. Each patient was assigned to a clinical subgroup on the basis of these MDA scores. For assignment, binary comparisons between subgroups were made. The accuracy of this classification method was assessed and compared with that of conventional MTR histogram analysis. ::: ::: ::: RESULTS ::: With MDA, the success rate of binary classification was 60%-100%, depending on which two groups were compared. When the different clinical subgroups were separated, MDA parameters were always better than conventional MTR histogram parameters, with P values ranging from.05 to less than 1 x 10(-6) of those attained with the best conventional parameter. ::: ::: ::: CONCLUSION ::: With MDA, MTR histograms of brain tissue may provide diagnostic information for individual patients in the clinical context of SLE.


title: Multi-Modal Curriculum Learning for Semi-Supervised Image Classification

abstract: Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.


title: Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey

abstract: The distributed nature and dynamic topology of Wireless Sensor Networks (WSNs) introduces very special requirements in routing protocols that should be met. The most important feature of a routing protocol, in order to be efficient for WSNs, is the energy consumption and the extension of the network's lifetime. During the recent years, many energy efficient routing protocols have been proposed for WSNs. In this paper, energy efficient routing protocols are classified into four main schemes: Network Structure, Communication Model, Topology Based and Reliable Routing. The routing protocols belonging to the first category can be further classified as flat or hierarchical. The routing protocols belonging to the second category can be further classified as Query-based or Coherent and non-coherent-based or Negotiation-based. The routing protocols belonging to the third category can be further classified as Location-based or Mobile Agent-based. The routing protocols belonging to the fourth category can be further classified as QoS-based or Multipath-based. Then, an analytical survey on energy efficient routing protocols for WSNs is provided. In this paper, the classification initially proposed by Al-Karaki, is expanded, in order to enhance all the proposed papers since 2004 and to better describe which issues/operations in each protocol illustrate/enhance the energy-efficiency issues.


title: A 700MHz to 2.5GHz Cascode GaAs Power Amplifier for Multi-Band Pico-Cell Achieving 20dB Gain, 40dBm to 45dBm OIP3 and 66% Peak PAE

abstract: Recent development and technology advancement in wireless communication systems to accommodate higher data rate poses a great challenge for wideband operation. This is due to the employment of modulation scheme, such as OFDM, which is subject to high peak to average ratio. Hence communication systems, such as LTE have to operate in multiple operating bands at the moment in order to transmit the OFDM signals linearly with high gain. The power amplifier serves as the bottle neck for high gain linear operation over wide bandwidth. This paper addresses this issue and further presents a novel integrated feedback and analog pre-distortion linearization technique as a solution to achieve wideband flat gain and linear output power while preserving the power amplifier’s power added efficiency (PAE). A maximally flat gain is achieved through the dual parallel feedback technique. The analog pre-distorter on the other hand introduces an optimum third order nonlinear signal components cancellation mechanism over wide frequency range. A prototype of 700 MHz to 2.5 GHz power amplifier is implemented in $0.25~\mu \text{m}$ PHEMT. It achieves an input and output return loss of less than −10 dB followed by flat power gain of 20 dB across the operating band, while sustaining an unconditional stability performance up to 20 GHz. With 1-dB compression point output power (P1dB) of 24.0 dBm, the PA delivers an OIP3 of more than 40 dBm with peak PAE of 66%. The fully integrated circuit consumes an area of 0.8 mm2. The proposed circuit serves to be a good solution to be integrated as a part of the wideband transmitter system for picocell application.


title: A survey on trajectory clustering analysis

abstract: This paper comprehensively surveys the development of trajectory clustering. Considering the critical role of trajectory data mining in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory clustering has attracted growing attention. Existing trajectory clustering methods can be grouped into three categories: unsupervised, supervised and semi-supervised algorithms. In spite of achieving a certain level of development, trajectory clustering is limited in its success by complex conditions such as application scenarios and data dimensions. This paper provides a holistic understanding and deep insight into trajectory clustering, and presents a comprehensive analysis of representative methods and promising future directions.


title: Participatory Design that Matters—Facing the Big Issues

abstract: At a time where computer technology is putting human lives and work under pressure, we discuss how to provide alternatives. We look back at Participatory Design (PD) which was originally about possibilities and alternatives as much as it was about specific solutions. The paper aims to revitalize and revise PD to help people influence big issues. The agenda for this is set through proposing a set of key elements for realizing new, important possibilities. We discuss the possible changes of partnership with users, call for a new role of researchers as activists, debate how to work with demanding visions for lasting impact, and democratic control.We focus on high technological ambitions, on deployment of working prototypes, on alliances, and on scaling up, all seen as important for a PD that matters. We conclude the paper with an invitation to participate in the continued discussion, codesign and realization of a PD that matters.


title: An agent-based collaborative information processing system for mixed reality applications — Part A: Agent-aware computing

abstract: In this paper, we present an agent-based collaborative computing framework for mixed reality applications. The comprehensive HCI issues in VR, AR, and MR, have been discussed with a task analysis. An agent-aware computing framework is introduced using agent-based system design. By using human-aware computing, system-aware computing and humansystem aware computing methods, the agent-aware computing framework addresses the complex issues in mixed reality fusion, QoE-QoS management, confidentiality, and security in mixed reality.


title: Conservatism in Accounting Part I: Explanations and Implications

abstract: This paper is the first in a two‐part series on conservatism in accounting. Part I examines alternative explanations for conservatism in accounting and their implications for accounting regulators....


title: The JARID2–PRC2 duality

abstract: Polycomb group proteins (PcG) are required for proper developmental regulation and cell fate commitment in metazoans. Recently, four studies reported the identification of JARID2, a JmjC domain-containing protein, as a component of the Polycomb-repressive complex 2 (PRC2), which is involved in implementing histone H3 Lys 27 methylation and transcriptional repression during development. Here, we discuss the implications of these studies for an improved understanding of PcG function in development.


title: Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data

abstract: Neural networks have been an active research area for decades. However, privacy bothers many when the training dataset for the neural networks is distributed between two parties, which is quite common nowadays. Existing cryptographic approaches such as secure scalar product protocol provide a secure way for neural network learning when the training dataset is vertically partitioned. In this paper, we present a privacy preserving algorithm for the neural network learning when the dataset is arbitrarily partitioned between the two parties. We show that our algorithm is very secure and leaks no knowledge (except the final weights learned by both parties) about other party’s data. We demonstrate the efficiency of our algorithm by experiments on real world data.


title: Data-efficient hand motor imagery decoding in EEG-BCI by using Morlet wavelets & Common Spatial Pattern algorithms

abstract: EEG-based Brain Computer Interfaces (BCIs) are quite noisy brain signals recorded from the scalp (electroencephalography, EEG) to translate the user's intent into action. This is usually achieved by looking at the pattern of brain activity across many trials while the subject is imagining the performance of an instructed action - the process known as motor imagery. Nevertheless, existing motor imagery classification algorithms do not always achieve good performances because of the noisy and non-stationary nature of the EEG signal and inter-subject variability. Thus, current EEG BCI takes a considerable upfront toll on patients, who have to submit to lengthy training sessions before even being able to use the BCI. In this study, we developed a data-efficient classifier for left/right hand motor imagery by combining in our pattern recognition both the oscillation frequency range and the scalp location. We achieve this by using a combination of Morlet wavelet and Common Spatial Pattern theory to deal with nonstationarity and noise. The system achieves an average accuracy of 88% across subjects and was trained by about a dozen training (10–15) examples per class reducing the size of the training pool by up to a 100-fold, making it very data-efficient way for EEG BCI.


title: Interactive Markerless Articulated Hand Motion Tracking Using RGB and Depth Data

abstract: Tracking the articulated 3D motion of the hand has important applications, for example, in human-computer interaction and teleoperation. We present a novel method that can capture a broad range of articulated hand motions at interactive rates. Our hybrid approach combines, in a voting scheme, a discriminative, part-based pose retrieval method with a generative pose estimation method based on local optimization. Color information from a multi-view RGB camera setup along with a person-specific hand model are used by the generative method to find the pose that best explains the observed images. In parallel, our discriminative pose estimation method uses fingertips detected on depth data to estimate a complete or partial pose of the hand by adopting a part-based pose retrieval strategy. This part-based strategy helps reduce the search space drastically in comparison to a global pose retrieval strategy. Quantitative results show that our method achieves state-of-the-art accuracy on challenging sequences and a near-real time performance of 10 fps on a desktop computer.


title: Development of a hybrid web application firewall to prevent web based attacks

abstract: Firewall and intrusion detection systems are used by the purposes of preventing information loss and weakness on internet and providing security for web applications. However attacks to web applications do not only come from network layer. Web applications use Hyper Text Transfer Protocol (HTTP) and attacks come from this protocol to web pages. Tools used for providing security on network layer become inefficient for HTTP attacks. These attacks to web applications can be prevented by detection of HTTP. In this study, a hybrid web application firewall is developed by using proposed signature based detection and anomaly detection methods, to prevent attacks by detection of HTTP requests.


title: Visualizing Whole-Brain DTI Tractography with GPU-based Tuboids and LoD Management

abstract: Diffusion tensor imaging (DTI) of the human brain, coupled with tractography techniques, enable the extraction of large- collections of three-dimensional tract pathways per subject. These pathways and pathway bundles represent the connectivity between different brain regions and are critical for the understanding of brain related diseases. A flexible and efficient GPU-based rendering technique for DTI tractography data is presented that addresses common performance bottlenecks and image-quality issues, allowing interactive render rates to be achieved on commodity hardware. An occlusion query-based pathway LoD management system for streamlines/streamtubes/tuboids is introduced that optimizes input geometry, vertex processing, and fragment processing loads, and helps reduce overdraw. The tuboid, a fully-shaded streamtube impostor constructed entirely on the GPU from streamline vertices, is also introduced. Unlike full streamtubes and other impostor constructs, tuboids require little to no preprocessing or extra space over the original streamline data. The supported fragment processing levels of detail range from texture-based draft shading to full raycast normal computation, Phong shading, environment mapping, and curvature-correct text labeling. The presented text labeling technique for tuboids provides adaptive, aesthetically pleasing labels that appear attached to the surface of the tubes. Furthermore, an occlusion query aggregating and scheduling scheme for tuboids is described that reduces the query overhead. Results for a tractography dataset are presented, and demonstrate that LoD-managed tuboids offer benefits over traditional streamtubes both in performance and appearance.


title: Iterative policy learning in end-to-end trainable task-oriented neural dialog models

abstract: In this paper, we present a deep reinforcement learning (RL) framework for iterative dialog policy optimization in end-to-end task-oriented dialog systems. Popular approaches in learning dialog policy with RL include letting a dialog agent to learn against a user simulator. Building a reliable user simulator, however, is not trivial, often as difficult as building a good dialog agent. We address this challenge by jointly optimizing the dialog agent and the user simulator with deep RL by simulating dialogs between the two agents. We first bootstrap a basic dialog agent and a basic user simulator by learning directly from dialog corpora with supervised training. We then improve them further by letting the two agents to conduct task-oriented dialogs and iteratively optimizing their policies with deep RL. Both the dialog agent and the user simulator are designed with neural network models that can be trained end-to-end. Our experiment results show that the proposed method leads to promising improvements on task success rate and total task reward comparing to supervised training and single-agent RL training baseline models.


title: Estimating CT image from MRI data using 3D fully convolutional networks

abstract: Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during CT imaging, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiation. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a 3D deep learning based method to address this challenging problem. Specifically, a 3D fully convolutional neural network (FCN) is adopted to learn an end-to-end nonlinear mapping from MR image to CT image. Compared to the conventional convolutional neural network (CNN), FCN generates structured output and can better preserve the neighborhood information in the predicted CT image. We have validated our method in a real pelvic CT/MRI dataset. Experimental results show that our method is accurate and robust for predicting CT image from MRI image, and also outperforms three state-of-the-art methods under comparison. In addition, the parameters, such as network depth and activation function, are extensively studied to give an insight for deep learning based regression tasks in our application.


title: On the Application of Danskin's Theorem to Derivative-Free Minimax Optimization

abstract: Motivated by Danskin's theorem, gradient-based methods have been applied with empirical success to solve minimax problems that involve non-convex outer minimization and non-concave inner maximization. On the other hand, recent work has demonstrated that Evolution Strategies (ES) algorithms are stochastic gradient approximators that seek robust solutions. In this paper, we address black-box (gradient-free) minimax problems that have long been tackled in a coevolutionary setup. To this end and guaranteed by Danskin's theorem, we employ ES as a stochastic estimator for the descent direction. The proposed approach is validated on a collection of black-box minimax problems. Based on our experiments, our method's performance is comparable with its coevolutionary counterparts and favorable for high-dimensional problems. Its efficacy is demonstrated on a real-world application.


title: Myelination and the trophic support of long axons

abstract: In addition to their role in providing myelin for rapid impulse propagation, the glia that ensheath long axons are required for the maintenance of normal axon transport and long-term survival. This presumably ancestral function seems to be independent of myelin membrane wrapping. Here, I propose that ensheathing glia provide trophic support to axons that are metabolically isolated, and that myelin itself might cause such isolation. This glial support of axonal integrity may be relevant for a number of neurological and psychiatric diseases.


title: Acne scarring: A review and current treatment modalities

abstract: Acne is a prevalent condition in society and often results in secondary damage in the form of scarring. Of course, prevention is the optimal method to avoid having to correct the physically or emotionally troublesome scars. However, even with the best efforts, scars will certainly arise. This article attempts to give a broad overview of multiple management options, whether medically, surgically, or procedurally based. The hope is that a general knowledge of the current available alternatives will be of value to the physician when confronted with the difficult task of developing a treatment plan for acne-scarred individuals, even in challenging cases.


title: Head-Driven Statistical Models for Natural Language Parsing

abstract: This article describes three statistical models for natural language parsing. The models extend methods from probabilistic context-free grammars to lexicalized grammars, leading to approaches in which a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree. Independence assumptions then lead to parameters that encode the X-bar schema, subcategorization, ordering of complements, placement of adjuncts, bigram lexical dependencies, wh-movement, and preferences for close attachment. All of these preferences are expressed by probabilities conditioned on lexical heads. The models are evaluated on the Penn Wall Street Journal Treebank, showing that their accuracy is competitive with other models in the literature. To gain a better understanding of the models, we also give results on different constituent types, as well as a breakdown of precision/recall results in recovering various types of dependencies. We analyze various characteristics of the models through experiments on parsing accuracy, by collecting frequencies of various structures in the treebank, and through linguistically motivated examples. Finally, we compare the models to others that have been applied to parsing the treebank, aiming to give some explanation of the difference in performance of the various models.


title: PLL-based Pitch Detection and Tracking for Audio Signals

abstract: Pitch detection and tracking can be used for several audio effects, such as time and pitch scaling and the generation of harmonic signals related to the detected fundamental frequency. A variety of audio effects has efficiently been implemented by time-domain processing. Pitch tracking of monophonic audio signals can also be performed by time domain techniques. This paper introduces a novel phase lock loop based frequency demodulator for pitch detection and tracking. We will derive the guidelines for parameters of such a kind of pitch tracker and show performance evaluations for a certain type of input signals. The resulting implementation is suited for producing real-time pitch tracking on simple hardware systems with low computational complexity.


title: Implementing the Davis–Putnam Method

abstract: The method proposed by Davis, Putnam, Logemann, and Loveland for propositional reasoning, often referred to as the Davis–Putnam method, is one of the major practical methods for the satisfiability (SAT) problem of propositional logic. We show how to implement the Davis–Putnam method efficiently using the trie data structure for propositional clauses. A new technique of indexing only the first and last literals of clauses yields a unit propagation procedure whose complexity is sublinear to the number of occurrences of the variable in the input. We also show that the Davis–Putnam method can work better when unit subsumption is not used. We illustrate the performance of our programs on some quasigroup problems. The efficiency of our programs has enabled us to solve some open quasigroup problems.


title: Creating Autonomous Vehicle Systems

abstract: Abstract This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition,...


title: A multiband inverted-F antenna for metal rim mobile phone application

abstract: This Multiband inverted f antenna is proposed for mobiles and Smartphone application for WWAN/LTE operations which is surrounded by metal frame with two open slit nearby the border (outside limit) of antenna. The strategy used in this antenna is that metal is separated into three parts which governs for the better radiation of proposed multiband antenna. The three part of metal rim provides advantage for the antenna is that it is capable to cover bands GSM/TCS/PCS/UMTS/LTE frequency band on which antenna can operate respectively. Inspection of the suggested antenna is done for the operation and to get remarkable radiation for WWAN/LTE and respective operating brands. The proposed antenna is fully designed and the results of reflection coefficient, VSWR, far field region for 2D polar plot have been simulated. Graph between frequency and s11 and 2D polar plot showing the exact behavior of suggested antenna. The application of proposed antenna suited for WWAN/LTE.


title: Ranking of fuzzy numbers based on centroid point and spread

abstract: Centroid and spread are commonly used approaches in ranking fuzzy numbers. Some experts rank fuzzy numbers using centroid or spread alone while others tend to integrate them together. Although a lot of methods for ranking fuzzy numbers that are related to both approaches have been presented, there are still limitations whereby the ranking obtained is inconsistent with human intuition. This paper proposes a novel method for ranking fuzzy numbers that integrates the centroid point and the spread approaches and overcomes the limitations and weaknesses of most existing methods. Proves and justifications with regard to the proposed ranking method are also presented.


title: Beam-Scanning Reflectarray Antennas

abstract: A detailed overview of various design methodologies and enabling technologies for beam-scanning reflectarray antennas is presented in this article. Numerous advantages of reflectarrays over reflectors and phased arrays are delineated, and representative beam-scanning reflectarray antenna designs are reviewed. For limited field-of-view beam-scanning systems, utilizing the reflector nature of the reflectarray antenna and the feed-tuning technique can provide a simple solution with good performance. On the other hand, for applications where wideangle scan coverage is required, utilizing the array nature of the reflectarray and the aperture phase-tuning approach are the more suitable choices. There are various enabling technologies available for both design methodologies, making them a suitable choice for the new generation of high-speed, high-gain beam-scanning antennas.


title: Dawn of the Selfie Era: The Whos, Wheres, and Hows of Selfies on Instagram

abstract: Online interactions are increasingly involving images, especially those containing human faces, which are naturally attention grabbing and more effective at conveying feelings than text. To understand this new convention of digital culture, we study the collective behavior of sharing \textit{selfies} on Instagram and present how people appear in selfies and which patterns emerge from such interactions. Analysis of millions of photos shows that the amount of selfies has increased by 900 times from 2012 to 2014. Selfies are an effective medium to grab attention; they generate on average 1.1--3.2 times more likes and comments than other types of content on Instagram. Compared to other content, interactions involving selfies exhibit variations in homophily scores (in terms of age and gender) that suggest they are becoming more widespread. Their style also varies by cultural boundaries in that the average age and majority gender seen in selfies differ from one country to another. We provide explanations of such country-wise variations based on cultural and socioeconomic contexts.


title: Arabic sign language recognition using the leap motion controller

abstract: Sign language is important for facilitating communication between hearing impaired and the rest of society. Two approaches have traditionally been used in the literature: image-based and sensor-based systems. Sensor-based systems require the user to wear electronic gloves while performing the signs. The glove includes a number of sensors detecting different hand and finger articulations. Image-based systems use camera(s) to acquire a sequence of images of the hand. Each of the two approaches has its own disadvantages. The sensor-based method is not natural as the user must wear a cumbersome instrument while the imagebased system requires specific background and environmental conditions to achieve high accuracy. In this paper, we propose a new approach for Arabic Sign Language Recognition (ArSLR) which involves the use of the recently introduced Leap Motion Controller (LMC). This device detects and tracks the hand and fingers to provide position and motion information. We propose to use the LMC as a backbone of the ArSLR system. In addition to data acquisition, the system includes a preprocessing stage, a feature extraction stage, and a classification stage. We compare the performance of Multilayer Perceptron (MLP) neural networks with the Nave Bayes classifier. Using the proposed system on the Arabic sign alphabets gives 98% classification accuracy with the Nave Bayes classifier and more than 99% using the MLP.


title: Cavity-backed planar slot array antenna with a single waveguide-fed sub-array

abstract: A cavity-backed slot array antenna using a single waveguide fed sub-array is presented. A subarray consists of 2/spl times/4 slot elements backed by a single cavity. By proper choice of dimensions it is shown that the proposed antenna has characteristics of high radiation efficiency and broad frequency bandwidth. Antenna characteristics for the array antenna with 256 elements are measured in Ku-band.


title: High resolution low power 0.6µm CMOS 40MHz dynamic latch comparator

abstract: In order to diminish circuit complexity and power dissipation, the simple configuration of the dynamic latch comparator is revisited. This paper proposes and analyzes a comparator that consists of a preamplifier using negative resistance as a load and a double regenerative dynamic latch. A discussion of the dynamic latch is presented and the effect of transistor sizes in the time constant and the offset voltage is explained briefly. The comparator is designed in a 0.6µm CMOS technology. Monte Carlo simulation using Virtuoso® Spectre shows that the comparator has a resolution of 1.8mV and dissipates 750µW of power when working at 40MHz.


title: Real-time 3D reconstruction at scale using voxel hashing

abstract: Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale. We contribute an online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure. Our system uses a simple spatial hashing scheme that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure. Surface data is only stored densely where measurements are observed. Additionally, data can be streamed efficiently in or out of the hash table, allowing for further scalability during sensor motion. We show interactive reconstructions of a variety of scenes, reconstructing both fine-grained details and large scale environments. We illustrate how all parts of our pipeline from depth map pre-processing, camera pose estimation, depth map fusion, and surface rendering are performed at real-time rates on commodity graphics hardware. We conclude with a comparison to current state-of-the-art online systems, illustrating improved performance and reconstruction quality.


title: Multiple-Group Confirmatory Factor Analysis in R--A Tutorial in Measurement Invariance with Continuous and Ordinal Indicators.

abstract: Multiple-group confirmatory factor analysis (MG-CFA) is among the most productive extensions of structural equation modeling. Many researchers conducting cross-cultural or longitudinal studies are interested in testing for measurement and structural invariance. The aim of the present paper is to provide a tutorial in MG-CFA using the freely available R-packages lavaan, semTools, and semPlot. The combination of these packages enable a highly efficient analysis of the measurement models both for normally distributed as well as ordinal data. Data from two freely available datasets – the first with continuous the second with ordered indicators will be used to provide a walk-through the individual steps.


title: Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

abstract: We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and actual interactions are used to fill a replay buffer and the sampling ratio between demonstrations and transitions is automatically tuned via a prioritized replay mechanism. Typically, carefully engineered shaping rewards are required to enable the agents to efficiently explore on high dimensional control problems such as robotics. They are also required for model-based acceleration methods relying on local solvers such as iLQG (e.g. Guided Policy Search and Normalized Advantage Function). The demonstrations replace the need for carefully engineered rewards, and reduce the exploration problem encountered by classical RL approaches in these domains. Demonstrations are collected by a robot kinesthetically force-controlled by a human demonstrator. Results on four simulated insertion tasks show that DDPG from demonstrations out-performs DDPG, and does not require engineered rewards. Finally, we demonstrate the method on a real robotics task consisting of inserting a clip (flexible object) into a rigid object.


title: A Fast-Response Pseudo-PWM Buck Converter With PLL-Based Hysteresis Control

abstract: Hysteresis voltage-mode control is a simple and fast control scheme for switched-mode power converters. However, it is well-known that the switching frequency of a switched-mode power converter with hysteresis control depends on many factors such as loading current and delay of the controller which vary from time to time. It results in a wide noise spectrum and leads to difficulty in shielding electro-magnetic interference. In this work, a phase-lock loop (PLL) is utilized to control the hysteresis level of the comparator used in the controller, while not interfering with the intrinsic behavior of the hysteresis controller. Some design techniques are used to solve the integration problem and to improve the settling speed of the PLL. Moreover, different control modes are implemented. A buck converter with proposed control scheme is fabricated using a commercial 0.35-μ m CMOS technology. The chip area is 1900 μm × 2200 μm. The switching frequency is locked to 1 MHz, and the measured frequency deviation is within ±1%. The measured load transient response between 160 and 360 mA is 5 μ s only.


title: A Formal Monitoring-Based Framework for Software Development and Analysis

abstract: A formal framework for software development and analysis is presented, which aims at reducing the gap between formal specification and implementation by integrating the two and allowing them together to form a system. It is called monitoring-oriented programming (MOP), since runtime monitoring is supported and encouraged as a fundamental principle. Monitors are automatically synthesized from formal specifications and integrated at appropriate places in the program, according to user-configurable attributes. Violations and/or validations of specifications can trigger user-defined code at any points in the program, in particular recovery code, outputting/sending messages, or raising exceptions. The major novelty of MOP is its generality w.r.t. logical formalisms: it allows users to insert their favorite or domain-specific specification formalisms via logic plug-in modules. A WWW repository has been created, allowing MOP users to download and upload logic plug-ins. An experimental prototype tool, called Java-MOP, is also discussed, which currently supports most but not all of the desired MOP features.


title: Implementation and Performance Analysis of Digital Controllers for Brushless DC Motor Drives

abstract: This paper presents design and digital implementation of a fuzzy controller for achieving improved performance of Brushless dc (BLDC) servomotor drive. The performance of fuzzy and PID controller-based BLDC servomotor drives is investigated under different operating conditions such as change in reference speed, parameter variations, load disturbance, etc. BLDC servomotors are used in aerospace, instrumentation systems, space vehicles, electric vehicles, robotics, and industrial control applications. In such applications, conventional controllers like P, PI, and PID are being used with the BLDC servomotor drive control systems to achieve satisfactory transient and steady-state responses. However, the major problem associated with the conventional PID controller is that the tuned gain parameters obtained for such BLDC servomotor drive control systems do not yield better transient and steady-state responses under different operating conditions such as parameter variations, load disturbances, etc. In this paper, design and implementation of fuzzy controller is presented and its performance is compared with PID controller to show its capability to track the error and usefulness of fuzzy controller in control applications.


title: Lyapunov-Function and Proportional-Resonant-Based Control Strategy for Single-Phase Grid-Connected VSI With LCL Filter

abstract: This paper presents a new control strategy based on Lyapunov-function and proportional-resonant (PR) controller for single-phase grid-connected LCL-filtered voltage-source inverters (VSIs). While Lyapunov-function-based control guarantees the global stability of the system, the PR controller is employed to process the grid current error and determine the inverter current reference. However, it is shown that the conventional Lyapunov-function-based control (CLFBC) together with the PR control cannot damp the inherent resonance of the LCL filter. Therefore, this control approach is modified by adding a capacitor voltage loop so as to achieve the desired resonance damping. In addition, a transfer function from the reference grid current to actual grid current is formulated in terms of the LCL-filter parameters and their possible variations in the proposed control strategy. An important consequence of using the PR controller is that the need for performing first and second derivative operations in the generation of inverter current reference is eliminated. Also, a zero steady-state error in the grid current is guaranteed in the case of variations in the LCL-filter parameters. The computer simulations and experimental results obtained from a 3.3-kW system show that the proposed control strategy exhibits a good performance in achieving the required control objectives such as fast dynamic response, zero steady-state error, global stability, and sinusoidal grid current with low total harmonic distortion (THD).


title: Extrinsic calibration of camera and 3D laser sensor system

abstract: Robots are typically equipped with multiple complementary sensors such as cameras and laser range finders. Camera generally provides dense 2D information while range sensors give sparse and accurate depth information in the form of a set of 3D points. In order to represent the different data sources in a common coordinate system, extrinsic calibration is needed. This paper presents a pipeline for extrinsic calibration a zed setero camera with Velodyne LiDAR puck using a novel self-made 3D marker whose edges can be robustly detected in the image and 3d point cloud. Our approach first estimate the large sensor displacement using just a single frame. then we optimize the coarse results by finding the best align of edges in order to obtain a more accurate calibration. Finally, the ratio of the 3D points correctly projected onto proper image segments is used to evaluate the accuracy of calibration.


title: Vertical reduction mammaplasty combined with a superomedial pedicle in gigantomastia.

abstract: Vertical reduction mammaplasty using a superomedial pedicle is a well-accepted technique giving good results in mild to moderate breast hypertrophy. We describe modifications of the vertical reduction technique to achieve safe reductions even for very large breasts and minimize unsightly scarring, skin necrosis and poor shape. Over the past 4 years, 162 patients have undergone bilateral breast reduction using the vertical mammaplasty technique with a superomedial dermoglandular pedicle. We present a retrospective study of 23 cases of gigantomastia (reductions over 1100g) who underwent bilateral reduction mammaplasty, using our technique. The mean age was 49 years, BMIs ranged from 28 to 52 kg/m. The mean suprasternal notch-to-nipple distance was 40.5 cm on the right and 41.4 cm on the left. The average resection weight per breast was 1303 g on the right, and 1245 g on the left side. The suprasternal notch-to-nipple distance was reduced by between 13.2 and 36.0 cm (mean, 16.1 cm). Mean follow-up was 14 months. We observed a superficial infection in 2 patients, a deep hematoma in one patient, partial necrosis of the nipple-areola complex in 1, and 2 patients needed correction surgery due to dog-ear formation. By using the described modifications, the nipple and areola were safely transposed on a superomedial dermoglandular pedicle producing good breast shapes, while scarring and complications in vertical reduction mammaplasty for oversized breasts were effectively minimized.


title: Modeling, parameter identification and model-based control of a lightweight robotic manipulator

abstract: Nowadays many robotic tasks require close and compliant tracking of desired positions, paths, or forces. To achieve these goals, model-based control schemes provide a possible solution, allowing to directly consider the nonlinear dynamics. One of the key challenges, however, is the derivation of suitable models, which allow sufficiently fast evaluation, as well as the parameterization of these models based on measurements. In this work we outline and review a structured approach for model-based controller design for robots. In a first step we derive suitable models for multi-link robots. In a second step we review how such models can be parameterized and how optimal identification experiments can be designed. Based on the model we then derive a simple model based controller to experimentally validate the results considering a lightweight robot. The single steps of the derivation and controller design are supported by a newly developed freely available model toolbox for the considered lightweight robot.


title: Opportunities in mobile crowd sensing

abstract: Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area.


title: Understanding Computational Thinking before Programming: Developing Guidelines for the Design of Games to Learn Introductory Programming through Game-Play

abstract: This paper outlines an innovative game-based approach to learning introductory programming that is grounded in the development of computational thinking at an abstract conceptual level, but also provides a direct contextual relationship between game-play and learning traditional introductory programming. The paper proposes a possible model for, and guidelines in support of, this games-based approach contextualized by an analysis of existing research into the issues of learning programming and game based learning approaches. Furthermore, the proposed game-based learning model focuses not only on procedural and applied knowledge and associated skills acquisition in computational thinking, but also provides contextualised theoretical knowledge on Computer Science concepts. By way of illustration, the authors introduce a game prototype currently being developed to combine a puzzle solving game-play that uses Computer Science concepts as the game elements.


title: Pushing CDN-ISP collaboration to the limit

abstract: Today a spectrum of solutions are available for istributing content over the Internet, ranging from commercial CDNs to ISP-operated CDNs to content-provider-operated CDNs to peer-to-peer CDNs. Some deploy servers in just a few large data centers while others deploy in thousands of locations or even on millions of desktops. Recently, major CDNs have formed strategic alliances with large ISPs to provide content delivery network solutions. Such alliances show the natural evolution of content delivery today driven by the need to address scalability issues and to take advantage of new technology and business opportunities. In this paper we revisit the design and operating space of CDN-ISP collaboration in light of recent ISP and CDN alliances. We identify two key enablers for supporting collaboration and improving content delivery performance: informed end-user to server assignment and in-network server allocation. We report on the design and evaluation of a prototype system, NetPaaS, that materializes them. Relying on traces from the largest commercial CDN and a large tier-1 ISP, we show that NetPaaS is able to increase CDN capacity on-demand, enable coordination, reduce download time, and achieve multiple traffic engineering goals leading to a win-win situation for both ISP and CDN.


title: Self-adaptive parameters in genetic algorithms

abstract: Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a genetic algorithm’s parameters can be encoded within the chromosome of each individual. The parameters’ values are entirely dependent on the evolution mechanism and on the problem context. Our preliminary results show that a GA is able to learn and evaluate the quality of self-set parameters according to their degree of contribution to the resolution of the problem. These results are indicative of a promising approach to the development of GAs with self-adaptive parameter settings that do not require the user to pre-adjust parameters at the outset.


title: Data Markets in the Cloud: An Opportunity for the Database Community

abstract: Cloud-computing is transforming many aspects of data management. Most recently, the cloud is seeing the emergence of digital markets for data and associated services. We observe that our community has a lot to oer in building successful cloud-based data markets. We outline some of the key challenges that such markets face and discuss the associated research problems that our community can help solve.


title: Low birth weight of contemporary African Americans: an intergenerational effect of slavery?

abstract: The average birth weight in the contemporary African-American population is about 250 g lower than the average birth weight of European Americans. Differences in genetic and socioeconomic factors present between these two groups can explain only part of birth weight variation. I propose a hypothesis that the low birth weight of contemporary African Americans not only results from the difference in present exposure to lifestyle factors known to affect fetal development but also from conditions experienced during the period of slavery. Slaves had poor nutritional status during all stages of life because of the inadequate dietary intake accompanied by high energetic costs of physical work and infectious diseases. The concept of "fetal programming" suggests that physiology and metabolism including growth and fat accumulation of the developing fetus, and, thus its birth weight, depend on intergenerational signal of environmental quality passed through generations of matrilinear ancestors. I suggest that several generations that have passed since the abolition of slavery in the United States (1865) has not been enough to obliterate the impact of slavery on the current biological and health condition of the African-American population.


title: An evaluation of 2D SLAM techniques available in Robot Operating System

abstract: In this work, a study of several laser-based 2D Simultaneous Localization and Mapping (SLAM) techniques available in Robot Operating System (ROS) is conducted. All the approaches have been evaluated and compared in 2D simulations and real world experiments. In order to draw conclusions on the performance of the tested techniques, the experimental results were collected under the same conditions and a generalized performance metric based on the k-nearest neighbors concept was applied. Moreover, the CPU load of each technique is examined. This work provides insight on the weaknesses and strengths of each solution. Such analysis is fundamental to decide which solution to adopt according to the properties of the intended final application.


title: A time-based collective factorization for topic discovery and monitoring in news

abstract: Discovering and tracking topic shifts in news constitutes a new challenge for applications nowadays. Topics evolve,emerge and fade, making it more difficult for the journalist -or the press consumer- to decrypt the news. For instance, the current Syrian chemical crisis has been the starting point of the UN Russian initiative and also the revival of the US France alliance. A topical mapping representing how the topics evolve in time would be helpful to contextualize information. As far as we know, few topic tracking systems can provide such temporal topic connections. In this paper, we introduce a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. The framework learns jointly the topics evolution and their time dependencies. It offers the user the ability to control, through one unique hyper-parameter, the tradeoff between the past accumulated knowledge and the current observed data. We show, on semi-synthetic datasets and on Yahoo News articles, that our method is competitive with state-of-the-art techniques while providing a simple way to monitor topics evolution (including emerging and disappearing topics).


title: The therapist user interface of a virtual reality exposure therapy system in the treatment of fear of flying

abstract: The use of virtual reality (VR) technology to support the treatment of patients with phobia, such as the fear of flying, is getting considerable research attention. Research mainly focuses on the patient experience and the effect of the treatment. In this paper, however, the focus is on the interaction therapists have with the system. Two studies are presented in which the therapist user interface is redesigned and evaluated. The first study was conducted in 2001 with the introduction of the system into the clinic. The original user interface design was compared with a redesign that was based on interviews with therapists. The results of a user study with five therapists and 11 students showed significant usability improvement. In 2008 a follow-up study was conducted on how therapists were now using the redesigned system. Using a direct observation approach six therapists were observed during a total of 14 sessions with patients. The analysis showed that: 93% of the exposures had similar patterns, therapists triggered 20 inappropriate sound recordings (e.g. the pilot giving height information while taking off), and more complex airplane simulation functions (e.g. roll control to make turns with the airplane) were only used by a therapist who was also a pilot. This resulted in a second redesign of the user interface, which allowed therapists to select flight scenarios (e.g. a flight with extra long taxiing, a flight with multiple taking off and landing sessions) instead of controlling the simulation manually. This new design was again evaluated with seven therapists. Again, results showed significant usability improvements. These findings led to five design guidelines with the main tenet in favour of a treatment-focused user interface (i.e. specific flying scenario) instead of a simulation-focused user interface (i.e. specific airplane controls).


title: A Capstone Course on Agile Software Development Using Scrum

abstract: In this paper, an undergraduate capstone course in software engineering is described that not only exposes students to agile software development, but also makes it possible to observe the behavior of developers using Scrum for the first time. The course requires students to work as Scrum Teams, responsible for the implementation of a set of user stories defined by a project domain expert playing the role of the Product Owner. During the course, data on project management activities are collected in order to analyze the amount of work completed, compliance with the release and iteration plans, productivity, ability in effort estimation, and the like. The paper discusses the achievement of teaching goals and provides empirical evaluation of students' progress in estimation and planning skills. A summary of lessons learned and recommendations is given, reflecting the issues to be considered when teaching courses in agile software development. Surveys of students have shown that they were overwhelmingly positive about the course, indicating that the course fully met or even exceeded their expectations.


title: Designing vehicle tracking system - an open source approach

abstract: Vehicle tracking system has been extensively used for fleet management, asset tracking, surveillance, stolen vehicle recovery and many more. Advances in modern technologies like ubiquitous computing, Internet of Things (IoT) coupled with the availability of the economical hardware building blocks is creating a new wave of vehicular tracking systems. In this work we present VERTIGUO (VEhiculaR TrackInG Using Opensource approach), a GPS,GSM and GPRS technology based vehicular tracking system, that is accurate, robust, flexible, economical and feature rich. Unlike the traditional COTS (Commercial Of The Shelf) vehicular tracking system, that are closed and confined to smartphone and PC’s, our vehicular tracking system infrastructure is open sourced and is available for the research fraternity to test, experiment and add more features. The hardware (HW) is developed by leveraging our expertize on open source HW platform. The software (SW) infrastructure can track the vehicles through a web interface on smartphones and PC or through an SMS on normal GSM based feature phones. This application will work for all mobile phones provided they are on a GSM network. In this paper we describe our system architecture, prototype and results obtained in our field trials.


ISPRS Commission V Symposium 'Image Engineering and Vision Metrology' DIGITAL CAMERA CALIBRATION METHODS: CONSIDERATIONS AND COMPARISONS


Cloud tracking with optical flow for short-term solar forecasting


title: Intuitionistic Tableau Extracted

abstract: This paper presents a formalization of a sequent presentation of intuitionisitic propositional logic and proof of decidability. The proof is implemented in the Nuprl system and the resulting proof object yields a "correct-by-construction" program for deciding intuitionisitc propositional sequents. The extracted program turns out to be an implementation of the tableau algorithm. If the argument to the resulting decision procedure is a valid sequent, a formal proof of that fact is returned, otherwise a counter-example in the form of a Kripke Countermodel is returned. The formalization roughly follows Aitken, Constable and Underwood's presentation in [1] but a number of adjustments and corrections have been made to ensure the extracted program is clean (no non-computational junk) and efficient.


title: An Anomaly Detection Approach for Scale-Out Storage Systems

abstract: Scale-out storage systems (SoSS) have become increasingly important for meeting availability requirements of web services in cloud platforms. To enhance data availability, SoSS rely on a variety of built-in fault-tolerant mechanisms, including replication, redundant network topologies, advanced request scheduling, and other failover techniques. However, performance issues in cloud services still remain one of the main causes of discontentment among their tenants. In this paper, we propose an anomaly detection approach for SoSS that predicts cloud anomalies caused by memory and network faults. To evaluate our prediction model, we built a testbed simulating a virtual data center using VMware. Experimental results confirm that the injected faults are likely to undermine the data availability in SoSS. They suggest that although unsupervised learning has been the most common method for anomaly detection, a supervisedbased implementation of the same model reduces the false positive rate by roughly 10%. Our analysis also points out that probing SoSS-specific monitoring data at the VM-level contributes to improve the anomaly prediction efficiency


title: An Image Correction Method of Fisheye Lens Based on Bilinear Interpolation

abstract: Fisheye lens has a big viewing field, with the range between 180° to 270° which works at fixed angle, without spinning. It is widely applied in recent years. However, there is obvious distortion of the image, it is rarely used in the identification of objects. Spherical projection method was used to rectify the image. The method was introduced from the aspects of imaging, projection model, distortion model, bilinear interpolation adjusting etc. The result image has a high resolution, homogeneous illumination distribution suggesting that the method is practical.


title: Unsupervised learning with mixed numeric and nominal data

abstract: Presents a similarity-based agglomerative clustering (SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure proposed by D.W. Goodall (1966) for biological taxonomy, that gives greater weight to uncommon feature value matches in similarity computations and makes no assumptions about the underlying distributions of the feature values, is adopted to define the similarity measure between pairs of objects. An agglomerative algorithm is employed to construct a dendrogram, and a simple distinctness heuristic is used to extract a partition of the data. The performance of the SBAC algorithm has been studied on real and artificially-generated data sets. The results demonstrate the effectiveness of this algorithm in unsupervised discovery tasks. Comparisons with other clustering schemes illustrate the superior performance of this approach.


title: The Five-Factor Model of Personality Traits and Organizational Citizenship Behaviors: A Meta-Analysis

abstract: Using meta-analytic tests based on 87 statistically independent samples, we investigated the relationships between the five-factor model (FFM) of personality traits and organizational citizenship behaviors in both the aggregate and specific forms, including individual-directed, organization-directed, and changeoriented citizenship. We found that Emotional Stability, Extraversion, and Openness/Intellect have incremental validity for citizenship over and above Conscientiousness and Agreeableness, 2 wellestablished FFM predictors of citizenship. In addition, FFM personality traits predict citizenship over and above job satisfaction. Finally, we compared the effect sizes obtained in the current meta-analysis with the comparable effect sizes predicting task performance from previous meta-analyses. As a result, we found that Conscientiousness, Emotional Stability, and Extraversion have similar magnitudes of relationships with citizenship and task performance, whereas Openness and Agreeableness have stronger relationships with citizenship than with task performance. This lends some support to the idea that personality traits are (slightly) more important determinants of citizenship than of task performance. We conclude with proposed directions for future research on the relationships between FFM personality traits and specific forms of citizenship, based on the current findings.


Systems Perspective of Amazon Mechanical Turk for Organizational Research: Review and Recommendations


title: Analytics over large-scale multidimensional data: the big data revolution!

abstract: In this paper, we provide an overview of state-of-the-art research issues and achievements in the field of analytics over big data, and we extend the discussion to analytics over big multidimensional data as well, by highlighting open problems and actual research trends. Our analytical contribution is finally completed by several novel research directions arising in this field, which plays a leading role in next-generation Data Warehousing and OLAP research.


title: Generalizability vs. Robustness: Adversarial Examples for Medical Imaging

abstract: In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data. To this end, we utilize adversarial examples, images that fool machine learning models, while looking imperceptibly different from original data, as a measure to evaluate the robustness of a variety of medical imaging models. Through extensive experiments on skin lesion classification and whole brain segmentation with state-of-the-art networks such as Inception and UNet, we show that models that achieve comparable performance regarding generalizability may have significant variations in their perception of the underlying data manifold, leading to an extensive performance gap in their robustness.


title: Concatenate text embeddings for text classification

abstract: Text embedding has gained a lot of interests in text classification area. This paper investigates the popular neural document embedding method Paragraph Vector as a source of evidence in document ranking. We focus on the effects of combining knowledge-based with knowledge-free document embeddings for text classification task. We concatenate these two representations so that the classification can be done more accurately. The results of our experiments show that this approach achieves better performances on a popular dataset.


title: Traumatic brain injury and vestibulo-ocular function: current challenges and future prospects

abstract: Normal function of the vestibulo-ocular reflex (VOR) coordinates eye movement with head movement, in order to provide clear vision during motion and maintain balance. VOR is generated within the semicircular canals of the inner ear to elicit compensatory eye movements, which maintain stability of images on the fovea during brief, rapid head motion, otherwise known as gaze stability. Normal VOR function is necessary in carrying out activities of daily living (eg, walking and riding in a car) and is of particular importance in higher demand activities (eg, sports-related activities). Disruption or damage in the VOR can result in symptoms such as movement-related dizziness, blurry vision, difficulty maintaining balance with head movements, and even nausea. Dizziness is one of the most common symptoms following traumatic brain injury (TBI) and is considered a risk factor for a prolonged recovery. Assessment of the vestibular system is of particular importance following TBI, in conjunction with oculomotor control, due to the intrinsic neural circuitry that exists between the ocular and vestibular systems. The purpose of this article is to review the physiology of the VOR and the visual-vestibular symptoms associated with TBI and to discuss assessment and treatment guidelines for TBI. Current challenges and future prospects will also be addressed.


title: Matching user accounts based on user generated content across social networks

abstract: Matching user accounts can help us build better users' profiles and benefit many applications. It has attracted much attention from both industry and academia. Most of existing works are mainly based on rich user profile attributes. However, in many cases, user profile attributes are unavailable, incomplete or unreliable, either due to the privacy settings or just because users decline to share their information. This makes the existing schemes quite fragile. Users often share their activities on different social networks. This provides an opportunity to overcome the above problem. We aim to address the problem of user identification based on User Generated Content (UGC). We first formulate the problem of user identification based on UGCs and then propose a UGC-based user identification model. A supervised machine learning based solution is presented. It has three steps: firstly, we propose several algorithms to measure the spatial similarity, temporal similarity and content similarity of two UGCs; secondly, we extract the spatial, temporal and content features to exploit these similarities; afterwards, we employ the machine learning method to match user accounts, and conduct the experiments on three ground truth datasets. The results show that the proposed method has given excellent performance with F1 values reaching 89.79%, 86.78% and 86.24% on three ground truth datasets, respectively. This work presents the possibility of matching user accounts with high accessible online data.


title: Epipolar-plane image analysis: An approach to determining structure from motion

abstract: We present a technique for building a three-dimensional description of a static scene from a dense sequence of images. These images are taken in such rapid succession that they form a solid block of data in which the temporal continuity from image to image is approximately equal to the spatial continuity in an individual image. The technique utilizes knowledge of the camera motion to form and analyze slices of this solid. These slices directly encode not only the three-dimensional positions of objects, but also such spatiotemporal events as the occlusion of one object by another. For straight-line camera motions, these slices have a simple linear structure that makes them easier to analyze. The analysis computes the three-dimensional positions of object features, marks occlusion boundaries on the objects, and builds a three-dimensional map of “free space.” In our article, we first describe the application of this technique to a simple camera motion, and then show how projective duality is used to extend the analysis to a wider class of camera motions and object types that include curved and moving objects.


title: The impact of colour on Website appeal and users’ cognitive processes

abstract: One of the challenges today in human–computer interaction is to design systems that are not only usable but also appealing to users. In order to contribute to meet this challenge, our general objective in the present study was to enhance current understanding of the perceptual features that favour users’ interactions with Websites. This is a particularly important issue, as users’ first impressions when they land on a site determine whether or not they stay on it. We conducted two experimental studies, focusing on one specific perceptual feature: Website colour. The first study investigated designers’ and users’ preferred colours for a Web homepage. Although researchers generally flag up differences between designers and users, we found that the latter also had several favourite colours in common. On the basis of these initial results, three colours were selected for a second study exploring colour in relation to an entire Website. The main originality of this second study lays in the fact that we used both subjective and objective measurements to gauge the impact of colour, analyzing not only users’ judgments but also their Website navigation and the items of information they memorized. Results of this second study showed that colours were a determining factor in the way that users interacted with the Website. Their influence was also observed afterwards, when users were asked to exploit the information they had gleaned from the Website. As such, these findings will have a practical value for Website designers.


title: Moments in Time Dataset: One Million Videos for Event Understanding

abstract: We present the Moments in Time Dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds. Modeling the spatial-audio-temporal dynamics even for actions occurring in 3 second videos poses many challenges: meaningful events do not include only people, but also objects, animals, and natural phenomena; visual and auditory events can be symmetrical in time (“opening” is “closing” in reverse), and either transient or sustained. We describe the annotation process of our dataset (each video is tagged with one action or activity label among 339 different classes), analyze its scale and diversity in comparison to other large-scale video datasets for action recognition, and report results of several baseline models addressing separately, and jointly, three modalities: spatial, temporal and auditory. The Moments in Time dataset, designed to have a large coverage and diversity of events in both visual and auditory modalities, can serve as a new challenge to develop models that scale to the level of complexity and abstract reasoning that a human processes on a daily basis.


title: IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning

abstract: In order to reach human performance on complexvisual tasks, artificial systems need to incorporate a sig-nificant amount of understanding of the world in termsof macroscopic objects, movements, forces, etc. Inspiredby work on intuitive physics in infants, we propose anevaluation benchmark which diagnoses how much a givensystem understands about physics by testing whether itcan tell apart well matched videos of possible versusimpossible events constructed with a game engine. Thetest requires systems to compute a physical plausibilityscore over an entire video. It is free of bias and cantest a range of basic physical reasoning concepts. Wethen describe two Deep Neural Networks systems aimedat learning intuitive physics in an unsupervised way,using only physically possible videos. The systems aretrained with a future semantic mask prediction objectiveand tested on the possible versus impossible discrimi-nation task. The analysis of their results compared tohuman data gives novel insights in the potentials andlimitations of next frame prediction architectures.


title: Bandwidth Enhancement for Low Frequency Meander Line Antenna

abstract: A simple and efiective method of bandwidth enhancement for the printed meander line antenna (MLA) is proposed. This approach is characterized by symmetrically printing two meandering sections on both sides of a dielectric substrate and connecting them via shorting pins at the bottom of meandering sections, which are connected to a capacitive stripe for impedance matching. The illustrative equivalent circuit and the corresponding principle of bandwidth enhancement of this double-layered MLA are presented. The measured results of these double-layered and single-layered MLAs manifest the validity of our design approach.


title: Nested Chinese Restaurant Franchise Processes: Applications to User Tracking and Document Modeling

abstract: Much natural data is hierarchical in nature. Moreover, this hierarchy is often shared between different instances. We introduce the nested Chinese Restaurant Franchise Process to obtain both hierarchical tree-structured representations for objects, akin to (but more general than) the nested Chinese Restaurant Process while sharing their structure akin to the Hierarchical Dirichlet Process. ::: ::: Moreover, by decoupling the structure generating part of the process from the components responsible for the observations, we are able to apply the same statistical approach to a variety of user generated data. In particular, we model the joint distribution of microblogs and locations for Twitter for users. This leads to a 40% reduction in location uncertainty relative to the best previously published results. Moreover, we model documents from the NIPS papers dataset, obtaining excellent perplexity relative to (hierarchical) Pachinko allocation and LDA.


title: Lazy evaluation methods for detecting complex events

abstract: The goal of Complex Event Processing (CEP) systems is to efficiently detect complex patterns over a stream of primitive events. A pattern of particular significance is a sequence, where we are interested in identifying that a number of primitive events have arrived on the stream in a predefined order. Many popular CEP systems employ Non-deterministic Finite Automata (NFA) arranged in a chain topology to detect such sequences. Existing NFA-based mechanisms incrementally extend previously observed prefixes of a sequence until a match is found. Consequently, each newly arriving event needs to be processed to determine whether a new prefix is to be initiated or an existing one extended. This approach may be very inefficient when events at the beginning of the sequence are very frequent. We address the problem by introducing a lazy evaluation mechanism that is able to process events in descending order of selectivity. We employ this mechanism in a chain topology NFA, which waits until the most selective event in the sequence arrives and then adds events to partial matches according to a predetermined order of selectivity. In addition, we propose a tree topology NFA that does not require the selectivity order to be defined in advance. Finally, we experimentally evaluate our mechanism on real-world stock trading data, demonstrating a performance gain of two orders of magnitude, with significantly reduced memory resource requirements.


title: Automated modeling and robotic grasping of unknown three-dimensional objects

abstract: This paper describes the development of a novel vision-based modeling and grasping system for three-dimensional (3D) objects whose shape and location are unknown a priori. Our approach integrates online computer vision-based 3D object modeling with online 3D grasp planning and execution. A single wrist-mounted video camera is moved around the stationary object to obtain images from multiple viewpoints. Object silhouettes are extracted from these images and used to form a 3D solid model of the object. To refine the model, the object's top surface is modeled by scanning with a wrist-mounted line laser while recording images. The laser line in each image is used to form a 3D surface model that is combined with the silhouette result. The grasp planning algorithm is designed for the parallel-jaw grippers that are commonly used in industry. The algorithm analyses the solid model, generates a robust force closure grasp, and outputs the required gripper position and orientation for grasping the object. The robot then automatically picks up the object. Experiments are performed with two real-world 3D objects, a metal bracket and a hex nut. The shape, position and orientation of the objects are not known by the system a priori. The time required to compute an object model and plan a grasp was less than 4 s for each object. The experimental results demonstrate that the automated grasping system can obtain suitable models and generate successful grasps, even when the objects are not lying parallel to the supporting table.


title: EvolTrack: improving design evolution awareness in software development

abstract: Software differs from most manufactured products because it is intangible. This characteristic makes it difficult to detect, control, and understand how it evolves. This paper presents an approach based on software visualization that can detect and externalize design evolution made in a software project during its initial development or at any further phase. By using this approach, a developer can be aware of the current state of the software as a whole and can additionally verify if the current design, also called emerging design, is evolving according to the team expectations and leader guidance, preventing problems caused by misunderstandings of the expected software solution. The approach was evaluated with free/open source software (FOSS) projects. The results indicate that the approach behaves as expected when applied to real software development projects, with minor performance bottlenecks.


title: Security Analysis of the Telegram IM

abstract: Telegram is a popular instant messaging service, a self-described fast and secure solution. It introduces its own home-made cryptographic protocol MTProto instead of using already known solutions, which was criticised by a significant part of the cryptographic community [2, 8]. In this article we will briefly introduce the protocol to provide context to the reader and then present two major findings we discovered as part of our security analysis performed in late 2016. First, the undocumented obfuscation method Telegram uses, and second, a replay attack vulnerability we discovered. The analysis was mainly focused on the MTProto protocol and the Telegram's official client for Android.


title: Easily Extendable Compact Planar UWB MIMO Antenna Array

abstract: This letter presents a highly isolated compact four-element planar ultrawideband (UWB) multiple-input-multiple-output (MIMO) antenna array configuration. The main advantages of the proposed array configuration are that it requires no isolation/decoupling circuit and the configuration is easily extendable to larger size array. The array consists of novel miniaturized slotted annular ring monopole antenna and each element in the array is placed orthogonal to its adjacent elements. The fabricated structure provides good impedance bandwidth matching and high isolation between elements over the range from 3 to 15 GHz. The absence of decoupling circuit results in overall compact size of the proposed design. The prototypes are fabricated and tested. The simulated and measured results are in good agreement. Moreover, the envelope correlation coefficient and channel capacity loss of the array are calculated, which shows good MIMO performance. The proposed monopole antenna structure supports multielement UWB MIMO antenna array design with easy extension of elements and without any decoupling circuit. An example of eight-element array is also investigated.


title: Api hyperlinking via structural overlap

abstract: This paper presents a tool Altair that automatically generates API function cross-references, which emphasizes reliable structural measures and does not depend on specific client code. Altair ranks related API functions for a given query according to pair-wise overlap, i.e., how they share state, and clusters tightly related ones into meaningful modules. Experiments against several popular C software packages show that Altair recommends related API functions for a given query with remarkably more precise and complete results than previous tools, that it can extract modules from moderate-sized software (e.g., Apache with 1000+ functions) at high precision and recall rates (e.g., both exceeding 70% for two modules in Apache), and that the computation can finish within a few seconds.


title: Classification and clustering via dictionary learning with structured incoherence and shared features

abstract: A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, as in k-means type of approaches that model the data as distributions around discrete points, we optimize for a set of dictionaries, one for each cluster, for which the signals are best reconstructed in a sparse coding manner. Thereby, we are modeling the data as a union of learned low dimensional subspaces, and data points associated to subspaces spanned by just a few atoms of the same learned dictionary are clustered together. An incoherence promoting term encourages dictionaries associated to different classes to be as independent as possible, while still allowing for different classes to share features. This term directly acts on the dictionaries, thereby being applicable both in the supervised and unsupervised settings. Using learned dictionaries for classification and clustering makes this method robust and well suited to handle large datasets. The proposed framework uses a novel measurement for the quality of the sparse representation, inspired by the robustness of the l 1 regularization term in sparse coding. In the case of unsupervised classification and/or clustering, a new initialization based on combining sparse coding with spectral clustering is proposed. This initialization clusters the dictionary atoms, and therefore is based on solving a low dimensional eigen-decomposition problem, being applicable to large datasets. We first illustrate the proposed framework with examples on standard image and speech datasets in the supervised classification setting, obtaining results comparable to the state-of-the-art with this simple approach. We then present experiments for fully unsupervised clustering on extended standard datasets and texture images, obtaining excellent performance.


title: Hierarchical Boundary-Aware Neural Encoder for Video Captioning

abstract: The use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded continuously by a recurrent layer, we propose a novel LSTM cell which can identify discontinuity points between frames or segments and modify the temporal connections of the encoding layer accordingly. We evaluate our approach on three large-scale datasets: the Montreal Video Annotation dataset, the MPII Movie Description dataset and the Microsoft Video Description Corpus. Experiments show that our approach can discover appropriate hierarchical representations of input videos and improve the state of the art results on movie description datasets.


title: Cloud Computing Security--Trends and Research Directions

abstract: Cloud Computing is increasingly becoming popular as many enterprise applications and data are moving into cloud platforms. However, a major barrier for cloud adoption is real and perceived lack of security. In this paper, we take a holistic view of cloud computing security - spanning across the possible issues and vulnerabilities connected with virtualization infrastructure, software platform, identity management and access control, data integrity, confidentiality and privacy, physical and process security aspects, and legal compliance in cloud. We present our findings from the points of view of a cloud service provider, cloud consumer, and third-party authorities such as Govt. We also discuss important research directions in cloud security in areas such as Trusted Computing, Information Centric Security and Privacy Preserving Models. Finally, we sketch a set of steps that can be used, at a high level, to assess security preparedness for a business application to be migrated to cloud.


The advantages and disadvantages of MMORPG video games for learning English as a second language