title: The diagnostic and therapeutic difficulties of the recurrent lower respiratory tract infections in children with neurological disorders

abstract: Abstract Background and aims Severe clinical course of the respiratory tract infections is very frequent in children with neurological disorders. The aim of this study was to establish the diagnostic and therapeutic procedures giving the best results in this group of children. Methods The symptoms, clinical picture, diagnostics, treatment an outcome of lower respiratory system infections in children with neurological disorders were analyzed. The patients, according to the type of neurological dysfunction, were divided into 5 groups: progressive encephalophaties, cerebral palsy, developmental delay, chromosomal anomalies and other dysmorphic syndromes, neuromuscular diseases. The patients were analyzed according to: lower respiratory tract infections risk factors, diagnostic signs of acute lower respiratory tract infections, outcome and treatment. Results In the study group the most numerous were children with cerebral palsy and with progressive encephalopaties. Those patients required target, broad-spectrum antibiotic therapy, intensive chest physiotherapy, energetic and metabolic deficits supplementation, using prokinetics and careful administration of mucolitics. Conclusions In the treatment of the respiratory tract diseases in children with neurological disorders cooperation of pneumologist, neurologist, gastrologist and physioterapeutist is necessary.


title: Understanding the attitudes and perceptions of vegetarian and plant-based diets to shape future health promotion programs

abstract: Abstract Vegetarian diets have become a prominent area of interest. However, little research has focused on the attitudes and perceptions on vegetarian diets. The purpose of this scoping review was to methodologically search the literature and summarize public perceptions and attitudes towards a vegetarian diet. Using the information found from this review, the Health Belief Model can be applied to shape future health promotion initiatives to move individuals towards a vegetarian or plant-based diet. The main findings indicate that vegetarian diets are generally perceived in a positive light. There are many barriers to consuming this diet such as health concerns, an unwillingness to make dietary changes, and an enjoyment of eating meat.


title: The evolving brand logic : a service-dominant logic perspective

abstract: The meanings of brand and branding have been evolving over the past several decades. This evolution is converging on a new conceptual logic, which views brand in terms of collaborative, value co-creation activities of firms and all of their stakeholders and brand value in terms of the stakeholders’ collectively perceived value-in-use. The authors argue that this new logic parallels and reflects the related, evolving service-dominant (S-D) logic in marketing. They provide an historical account of the branding literature, organize it into eras, and connect it to the evolution in marketing as captured by S-D logic. The analysis provides further support for the S-D logic of marketing and suggests a related research agenda for furthering the understanding of brand and branding. It also suggests that marketing managers might benefit from investing resources in building strong brand relationships with all of their stakeholders and a service-dominant firm philosophy built around brand value co-creation.


Detection of Unhealthy Region of plant leaves using Neural Network


Principles of energetics and stability in human locomotion


title: A 4.39–5.26 GHz LC-Tank CMOS Voltage-Controlled Oscillator With Small VCO-Gain Variation

abstract: A wide band CMOS LC-tank voltage controlled oscillator (VCO) with small VCO gain (KVCO) variation was developed. For small KVCO variation, serial capacitor bank was added to the LC-tank with parallel capacitor array. Implemented in a 0.18 mum CMOS RF technology, the proposed VCO can be tuned from 4.39 GHz to 5.26 GHz with the VCO gain variation less than 9.56%. While consuming 3.5 mA from a 1.8 V supply, the VCO has -113.65 dBc/Hz phase noise at 1 MHz offset from the carrier.


title: An FPGA based low cost receiver for ultrasonic anemometer

abstract: This paper introduces a low cost receiver for wind velocity measurement using FPGA with multiple ultrasonic transceivers. Wind speed is derived from the time taken by ultrasonic sound to travel between an ultrasonic transmitter and receiver. The receiver section of ultrasonic anemometer acquires the data for time of flight calculation from transmitter section and wind velocity is calculated. Ultrasonic anemometer is more advantageous over mechanical or other type of anemometers. The existing ultrasonic system for three dimensional wind velocity measurement is highly expensive. The proposed system can achieve an accuracy of 0.05m/s at a lower cost. Simulations are done in Xilinx ISE. For the hardware implementation, Xilinx Spartan 3E FPGA board is used.


title: Comparing a computer agent with a humanoid robot

abstract: HRI researchers interested in social robots have made large investments in humanoid robots. There is still sparse evidence that peoples' responses to robots differ from their responses to computer agents, suggesting that agent studies might serve to test HRI hypotheses. To help us understand the difference between people's social interactions with an agent and a robot, we experimentally compared people's responses in a health interview with (a) a computer agent projected either on a computer monitor or life-size on a screen, (b) a remote robot projected life-size on a screen, or (c) a collocated robot in the same room. We found a few behavioral and large attitude differences across these conditions. Participants forgot more and disclosed least with the collocated robot, next with the projected remote robot, and then with the agent. They spent more time with the collocated robot and their attitudes were most positive toward that robot. We discuss tradeoffs for HRI research of using collocated robots, remote robots, and computer agents as proxies of robots.


title: Automatic Vectorization for MATLAB

abstract: Dynamic array-based languages such as MATLAB provide a wide range of built-in operations which can be efficiently applied to all elements of an array. Historically, MATLAB and Octave programmers have been advised to manually transform loops to equivalent “vectorized” computations in order to maximize performance. In this paper we present the techniques and tools to perform automatic vectorization, including handling for loops with calls to user-defined functions. We evaluate the technique on 9 benchmarks using two interpreters and two JIT-based platforms and show that automatic vectorization is extremely effective for the interpreters on most benchmarks, and moderately effective on some benchmarks in the JIT context.


title: Active disturbance rejection control for automated steering in vehicles and controller tuning

abstract: This paper applies active disturbance rejection control (ADRC) to automated steering in vehicles. The controller is designed on a linear vehicle model and its performance is evaluated on a nonlinear vehicle model. A tuning method is proposed to obtain the acceptable values of the controller parameters by making the control system satisfy the requirements on stability and system performance. The stability analysis is realized through determining the eigenvalues of the control system. The ADRC controller is compared with two well-tuned PID controllers. Comparison and simulation results indicate that the ADRC controller performs better and is more robust against the parameter variations and the external disturbance.


Rethinking Binary Neural Network for Accurate Image Classification and Semantic Segmentation


title: The Two-Portal Approach for Internal Fixation of Scapular Fractures

abstract: Objectives ::: Wide approach to the posterior aspect of the scapula for internal fixation of unstable fractures of neck of scapula and glenoid.


title: A toolkit of designs for mixing Discrete Event Simulation and System Dynamics

abstract: In recent years there has been significant interest in multimethodology and the mixing of OR/MS methods, including Discrete Event Simulation (DES) with System Dynamics (SD). Several examples of mixing DES and SD are described in the literature but there is no overarching framework which characterises the spectrum of options available to modellers. This paper draws on a sample of published case studies, in conjunction with the theoretical literature on mixing methods, to propose a toolkit of designs for mixing DES and SD which can be implemented as a set of questions which a modeller should ask in order to guide the choice of design and inform the associated project methodology. The impetus for this work was the perceived need to transfer insight from reported practice in order to formalise how the two methods can be and have been mixed.


title: Second-Order Digital Inequality: The Case of E-Commerce

abstract: “Second-order digital inequality" describes that certain individuals profit less from digital opportunities not only due to limited access but also due to limited abilities to use information and communication technologies (ICT). This study extends research on second-order digital inequality to the realm of e-commerce. We introduce a novel conceptualization of effective, potentially beneficial, e-commerce use that encompasses two dimensions: (1) the diversity of e-commerce platforms used by an individual; (2) the degree to which an individual uses supporting e-commerce features. Building on technology acceptance theory and social psychology, we argue that socio-economically disadvantaged individuals are less likely to use e-commerce effectively than socio-economically advantaged individuals. We empirically test our hypotheses on clickstream data that tracks the online behavior of 2819 US e-commerce users for six month. Our findings reveal that, despite equal access, the socio-economically advantaged use e-commerce more effectively regarding both dimensions. Implications for research and practice are discussed.


title: Efficient Likelihood Learning of a Generic CNN-CRF Model for Semantic Segmentation

abstract: Deep Models, such as Convolutional Neural Networks (CNNs), are omnipresent in computer vision, as well as, structured models, such as Conditional Random Fields (CRFs). Combining them brings many advantages, foremost the ability to in-cooperate prior knowledge into CNNs, e.g. by explicitly modelling the dependencies between output variables. In this work we present a CRF model were unary factors are dependent on a CNN. Our main contribution is an efficient and scalable, maximum likelihood-based, learning procedure to infer all model parameters jointly. Previous work either concentrated on piecewise-training, or maximum likelihood learning of restricted model families, such as Gaussian CRFs or CRFs with a few variables only. In contrast, we are the first to perform maximum likelihood learning for large-sized factor graphs with non-parametric potentials. We have applied our model to the task of semantic labeling of body parts in depth images. We show that it is superior to selected competing models and learning strategies. Furthermore, we empirically observe that our model can capture shape and context information of relating body parts.


Ever Wondered What Playing Video Games Does to Your Brain?


title: Region-based fully convolutional siamese networks for robust real-time visual tracking

abstract: Partial occlusions and deformations in visual object tracking are still very challenging. Existing Convolutional Neural Networks (CNNs) trackers either fail to handle these issues or can just run in low speed. In this paper, we present a real-time tracker which is robust to occlusions and deformations based on a Region-based, Fully Convolutional Siamese Network (R-FCSN). In the proposed R-FCSN, the information of regions is extracted separately by the proposition of positionsensitive score maps. Combining these score maps via adaptive weights leads to accurate location of the target on a new frame. The experiments illustrate that our method outperforms state-of-the-art approaches, and can handle the cases of object deformation and occlusion at about 51 FPS.


title: Efficient Routing in Intermittently Connected Mobile Networks: The Multiple-Copy Case

abstract: Intermittently connected mobile networks are wireless networks where most of the time there does not exist a complete path from the source to the destination. There are many real networks that follow this model, for example, wildlife tracking sensor networks, military networks, vehicular ad hoc networks, etc. In this context, conventional routing schemes fail, because they try to establish complete end-to-end paths, before any data is sent. ::: To deal with such networks researchers have suggested to use flooding-based routing schemes. While flooding-based schemes have a high probability of delivery, they waste a lot of energy and suffer from severe contention which can significantly degrade their performance. Furthermore, proposed efforts to reduce the overhead of flooding-based schemes have often been plagued by large delays. With this in mind, we introduce a new family routing schemes that "spray" a few message copies into the network, and then route each copy independently towards the destination. We show that, if carefully designed, spray routing not only performs significantly fewer transmissions per message, but also has lower average delivery delays than existing schemes; furthermore, it is highly scalable and retains good performance under a large range of scenarios. ::: Finally, we use our theoretical framework proposed in our 2004 paper to analyze the performance of spray routing. We also use this theory to show how to choose the number of copies to be sprayed and how to optimally distribute these copies to relays.


title: A Pareto Optimal D* Search Algorithm for Multiobjective Path Planning

abstract: Path planning is one of the most vital elements of mobile robotics, providing the agent with a collision-free route through the workspace. The global path plan can be calculated with a variety of informed search algorithms, most notably the A* search method, guaranteed to deliver a complete and optimal solution that minimizes the path cost. D* is widely used for its dynamic replanning capabilities. Path planning optimization typically looks to minimize the distance traversed from start to goal, but many mobile robot applications call for additional path planning objectives, presenting a multiobjective optimization (MOO) problem. Common search algorithms, e.g. A* and D*, are not well suited for MOO problems, yielding suboptimal results. The search algorithm presented in this paper is designed for optimal MOO path planning. The algorithm incorporates Pareto optimality into D*, and is thus named D*-PO. Non-dominated solution paths are guaranteed by calculating the Pareto front at each search step. Simulations were run to model a planetary exploration rover in a Mars environment, with five path costs. The results show the new, Pareto optimal D*-PO outperforms the traditional A* and D* algorithms for MOO path planning.


title: Quality of life in patients with severe malocclusion before treatment.

abstract: The aim of this study was to determine the occurrence of oral health impacts among patients with severe malocclusions and dentofacial deformities before treatment. A further aim was to evaluate the effect of gender or the type of malocclusion on the oral impacts. The study comprised 151 adult patients who were referred for orthodontic or surgical-orthodontic treatment to the Oral and Maxillofacial Department, Oulu University Hospital, Finland during the years 2001-2004. The study group consisted of 92 females and 59 males with a mean age of 35.5 years [standard deviation (SD) 11.5 years, range 16-64 years]. A self-completed Oral Health Impact Profile (OHIP)-14 questionnaire was used to measure oral impacts during a 1 month reference period. The prevalence, extent, and severity scores were calculated from the OHIP-14. Malocclusions were registered at clinical examination. The prevalence and mean extent and severity scores were compared among malocclusion groups and between genders. Statistical significance was evaluated with Mann-Whitney, Kruskall-Wallis, Chi-squared, and Fisher's exact tests. The prevalence of oral impacts perceived fairly or very often was 70.2 per cent. The mean severity and extent scores were 17.2 (SD 10.5, range 0-45) and 2.5 (SD 2.6, range 0-10), respectively. Physical pain as well as psychological discomfort and disability were the most commonly perceived oral impacts. Being self-conscious, feeling tense, having difficulties in relaxing, and being somewhat irritable with other people were more common in females than in males. No differences were observed in oral impacts among the malocclusion groups. Compared with a 'normal' population, patients with severe malocclusions report high levels of oral impacts. Females reported oral impacts more often than males.


title: CryptoLock (and Drop It): Stopping Ransomware Attacks on User Data

abstract: Ransomware is a growing threat that encrypts auser's files and holds the decryption key until a ransom ispaid by the victim. This type of malware is responsible fortens of millions of dollars in extortion annually. Worse still, developing new variants is trivial, facilitating the evasion of manyantivirus and intrusion detection systems. In this work, we presentCryptoDrop, an early-warning detection system that alerts a userduring suspicious file activity. Using a set of behavior indicators, CryptoDrop can halt a process that appears to be tampering witha large amount of the user's data. Furthermore, by combininga set of indicators common to ransomware, the system can beparameterized for rapid detection with low false positives. Ourexperimental analysis of CryptoDrop stops ransomware fromexecuting with a median loss of only 10 files (out of nearly5,100 available files). Our results show that careful analysis ofransomware behavior can produce an effective detection systemthat significantly mitigates the amount of victim data loss.


title: The Effects of Using Mobile Devices on Student Achievement in Language Learning: A Meta-Analysis

abstract: The use of mobile technologies has recently received great attention in language learning. Most research evaluates the effects of employing mobile devices in language learning and explores the design of mobile-learning interventions that can maximize the benefits of new technologies. However, it is still unclear whether the use of mobile devices in language learning is more effective than other instructional approaches. It is also not clear whether the effects of mobile-device use vary in different settings. Our meta-analysis will explore these questions about mobile technology use in language learning. Based on the specific inclusion and exclusion criteria, 22 d-type effect sizes from 20 studies were calculated for the meta-analysis. We adopted the random-effects model, and the estimated average effect was 0.51 (se = 0.10). This is a moderate positive overall effect of using mobile devices on language acquisition and language-learning achievement. Moderator analyses under the mixed-effects model examined six features; effects varied significantly only by test type and source of the study. The overall effect and the effects of these moderators of mobile-device use on achievement in language learning are discussed.


title: GECAF: a framework for developing context-aware pervasive systems

abstract: Context-aware systems differ in the way they interact with users, the way they interpret the context of their entities and the actions they take. Each system (or system type) is developed in its own way with no common architecture currently available. This fact makes the development of every context aware system a challenge. To address this issue, a generic and extensible framework is needed so that it can be used for developing various systems. This paper proposes a generic framework that uses the Pipe-and-Filter software architectural style, which is a well-known style, allowing developers to create new systems by arranging existing components into various arrangements to meet the system's requirements. The paper also analyses the state of the art architectures to study their functionality and architectural organisation. The analysis was then used as a basis for comparing and evaluating the proposed framework.


Supplementary Material : Video Summarization with Long Short-term Memory


Decreasing Off -Task Behaviors in an Elementary Classroom


title: Event Related Potential investigation of Initial Accent processing in French.

abstract: This study investigates stress processing through the Event-Related brain Potential (ERP) technique. It aims at evaluating whether French listeners can perceive and discriminate the Initial Accent (IA) and whether IA is encoded in the phonological representation. Participants listened to trisyllabic words in two stress-pattern conditions, with (+IA) or without (-IA) initial accenting, in an oddball paradigm. The EEG was recorded in both a passive and an active listening task, and in two di erent oddball versions: one where standard stimuli were +IA words and deviants -IA words, and the reverse for the other version (-IA standard, +IA deviant). Behavioral results show faster processing and less errors for +IA stimuli. ERP results show larger MisMatch Negativity component for -IA words, pointing out 1) that French listeners are sensitive to f0 manipulation, and 2) that +IA is the preferred stress template in French. Altogether, our results indicate that French listeners not only discriminate stress patterns but that IA is encoded in long-term memory, hence phonologically relevant.


title: A versatile pipeline for the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture

abstract: Understanding how individual cells interact to form tissues in animals and plants is a key problem in cell and developmental biology. To be able to answer this question researchers need to use microscopy to observe the cells in a tissue, extract structural information from the images, and then generate three-dimensional digital models of the tissue. However, the software solutions that are currently available are limited, and reconstructing three-dimensional tissue from microscopy images remains problematic. To meet this challenge, Morales-Navarrete et al. extended the free software platform called MotionTracking, which had been used previously for two-dimensional work. The software now combines a series of new and established algorithms for analysing fluorescence microscopy images that make it possible to identify the different structures that make up a tissue and then create and analyse a three-dimensional model. Morales-Navarrete et al. used the software to analyse liver tissue from mice. The resulting model revealed that liver cells called hepatocytes are arranged in particular zones within the tissue according to their size and DNA content. The software was also applied successfully to analyse lung and kidney tissue, which demonstrates that the approach can be used to create three-dimensional models of a variety of tissues. Morales-Navarrete et al.’s approach can rapidly generate accurate models of larger tissues than were previously possible. Therefore, it provides researchers with a powerful tool to analyse the different features of tissues. This tool will be useful for many areas of research: from understanding of how cells form tissues, to diagnosing diseases based on the changes to features in particular tissues.


title: Distributed Detection of Sensor Worms Using Sequential Analysis and Remote Software Attestations

abstract: Recent work has demonstrated that self-propagating worms are a real threat to sensor networks. Since worms can enable an adversary to quickly compromise an entire sensor network, they must be detected and stopped as quickly as possible. To meet this need, we propose a worm propagation detection scheme for sensor networks. The proposed scheme applies a sequential analysis to detect worm propagation by leveraging the intuition that a worm’s communication pattern is different from benign traffic. In particular, a worm in a sensor network requires a long sequence of packets propagating hop-by-hop to each new infected node in turn. We thus have detectors that observe communication patterns in the network, a worm spreading hop-by-hop will quickly create chains of connections that would not be seen in normal traffic. Once detector nodes identify the worm propagation pattern, they initiate remote software attestations to detect infected nodes. Through analysis and simulation, we demonstrate that the proposed scheme effectively and efficiently detects worm propagation. In particular, it blocks worm propagation while restricting the fraction of infected nodes to at most 13.5% with an overhead of at most 0.63 remote attestations per node per time slot.


title: A Slot Loaded Circularly Polarized Patch Antenna for UHF RFID Reader

abstract: The design of a simple UHF (ultrahigh frequency) RFID (radio frequency identification) reader antenna that operates within the 900 MHz band (902-928 MHz) is studied. To generate circular polarization (CP) radiation, a novel method of loading a semicircular slot into the main circular radiating patch is proposed. To allow optimum impedance matching with enhanced CP bandwidth, the L-shaped probe-feed technique is employed in this design. From the experimental results, besides achieving a gain level of more than 7 dBic, the proposed antenna can also yield an impedance bandwidth (10-dB return loss) from 880 to 1100 MHz, while good CP performances between 901 to 930 MHz are exhibited. To further validate the experimental results, parametric studies are also carried out via simulation.


title: A SIW-Based vivaldi array antenna for 5G wireless communication systems

abstract: A Vivaldi array antenna based on a substrate integrated waveguide (SIW) is investigated for operating bandwidth at 42 GHz band which is candidate for the 5th generation (5G) mobile communication systems. The antenna consists of a 1-to-8 way SIW power divider and 8 Vivaldi element antennas which are implemented on one substrate. Details of the simulation and measurement results are presented and discussed. Proposed antenna achieves an impendence bandwidth of 14.76% (38.9 GHz ∼ 45.1 GHz) with reflection coefficient less than −10 dB and is practical for millimeter-wave (mmWave) application of 5G.


title: Gathering Additional Feedback on Search Results by Multi-Armed Bandits with Respect to Production Ranking

abstract: Given a repeatedly issued query and a document with a not-yet-confirmed potential to satisfy the users' needs, a search system should place this document on a high position in order to gather user feedback and obtain a more confident estimate of the document utility. On the other hand, the main objective of the search system is to maximize expected user satisfaction over a rather long period, what requires showing more relevant documents on average. The state-of-the-art approaches to solving this exploration-exploitation dilemma rely on strongly simplified settings making these approaches infeasible in practice. We improve the most flexible and pragmatic of them to handle some actual practical issues. The first one is utilizing prior information about queries and documents, the second is combining bandit-based learning approaches with a default production ranking algorithm. We show experimentally that our framework enables to significantly improve the ranking of a leading commercial search engine.


title: Performance debugging for distributed systems of black boxes

abstract: Many interesting large-scale systems are distributed systems of multiple communicating components. Such systems can be very hard to debug, especially when they exhibit poor performance. The problem becomes much harder when systems are composed of "black-box" components: software from many different (perhaps competing) vendors, usually without source code available. Typical solutions-provider employees are not always skilled or experienced enough to debug these systems efficiently. Our goal is to design tools that enable modestly-skilled programmers (and experts, too) to isolate performance bottlenecks in distributed systems composed of black-box nodes.We approach this problem by obtaining message-level traces of system activity, as passively as possible and without any knowledge of node internals or message semantics. We have developed two very different algorithms for inferring the dominant causal paths through a distributed system from these traces. One uses timing information from RPC messages to infer inter-call causality; the other uses signal-processing techniques. Our algorithms can ascribe delay to specific nodes on specific causal paths. Unlike previous approaches to similar problems, our approach requires no modifications to applications, middleware, or messages.


title: WebPUM: A Web-based recommendation system to predict user future movements

abstract: Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an online prediction using Web usage mining system and propose a novel approach for classifying user navigation patterns to predict users' future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy.


title: Classification with scattering operators

abstract: A scattering vector is a local descriptor including multiscale and multi-direction co-occurrence information. It is computed with a cascade of wavelet decompositions and complex modulus. This scattering representation is locally translation invariant and linearizes deformations. A supervised classification algorithm is computed with a PCA model selection on scattering vectors. State of the art results are obtained for handwritten digit recognition and texture classification.


title: An SDN-Based CDN/ISP Collaboration Architecture for Managing High-Volume Flows

abstract: The collaboration of Internet service providers (ISPs) and content distribution network (CDN) providers was shown to be beneficial for both parties in a number of recent works. Influencing CDN edge server (surrogate) selection allows the ISP to manage the rising amount of traffic emanating from CDNs to reduce the operational expenditures (OPEX) of his infrastructure, e.g., by preventing peered traffic. At the same time, including the ISP's hidden network knowledge in the surrogate selection process influences the quality of service a CDN provider can deliver positively. As a large amount of CDN traffic is video-on-demand traffic, this paper investigates the topic of CDN/ISP collaboration from a perspective of high-volume long-living flows. These types of flows are hardly manageable with state-of-the-art Dynamic Name Service (DNS)-based redirection, as a reassignment of flows during the session is difficult to achieve. Consequently, varying load of surrogates caused by flash crowds and congestion events in the ISP's network are hard to compensate. This paper presents a novel approach promoting ISP and CDN collaboration based on a minimal deployment of software-defined networking switches in the ISP's network. The approach complements standard DNS-based redirection by allowing for a migration of high-volume flows between surrogates in the backend even if the communication has state information, such as Hyper Text Transfer Protocol sessions. In addition to a proof-of-concept, the evaluation identifies factors influencing performance and shows large performance increases when compared to standard DNS-based redirection.


title: Teaching on a Budget: Agents Advising Agents in Reinforcement Learning

abstract: This paper introduces a teacher-student framework for reinforcement learning. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two experimental domains: Mountain Car and Pac-Man. Our results show that the same amount of advice, given at different moments, can have different effects on student learning, and that teachers can significantly affect student learning even when students use different learning methods and state representations.


title: A holistic cyber security implementation framework

abstract: Purpose – The purpose of this paper is to propose a holistic cyber security implementation framework (HCS-IF) that lays out the ground for a conceptual, coherent, systematic, overarching and consolidated approach to implement cyber security strategies (CSSs). Design/methodology/approach – The HCS-IF is conceptually proposed to address the actual needs that are extracted from literature review. The HCS-IF uses and integrates a set of high-level conceptual security controls, solutions, processes, entities, tools, techniques or mechanisms that are already known in the domains of information security management, software engineering and project management to address the identified needs. Findings – The HCS-IF components and controls collectively interact and cooperate to implement CSSs. The proposed framework is compared with other related frameworks, and the results show that the HCS-IF outperforms other frameworks on most of the suggested comparison criteria. Originality/value – From a practical standpoint,...


title: IGBT gate-drive with PCB Rogowski coil for improved short circuit detection and current turn-off capability

abstract: In this paper, a gate drive using gate boosting and double-stage turn off including voltage clamping as well as with detection of overcurrent and a too high di/dt during turn on is discussed in detail. Besides the gate drive, also the design of a PCB-Rogowski coil, which is used for measuring currents and for di/dt detection, is explained and different designs are compared. The presented coil has a bandwidth of more than 28MHz and a propagation delay of 11 ns.


title: DIGITAL PHEROMONES FOR AUTONOMOUS COORDINATION OF SWARMING UAV'S

abstract: Modern UAV’s reduce the threat to human operators, but do not decrease the manpower requirements. Each aircraft requires a flight crew of one to three, so deploying large numbers of UAV’s requires committing and coordinating many human warfighters. Insects perform impressive feats of coordination without direct inter-agent coordination, by sensing and depositing pheromones (chemical scent markers) in the environment [14]. We have developed a novel technology for coordinating the movements of multiple UAV’s based on a computational analog of pheromone dynamics. The control logic is simple enough that it can be executed autonomously by a UAV, enabling a single human to monitor an entire swarm of UAV’s. This paper describes the technology, its application to UAV coordination, and the results we have obtained.


title: Single-session gamified virtual reality exposure therapy for spider phobia vs. traditional exposure therapy: study protocol for a randomized controlled non-inferiority trial

abstract: BACKGROUND ::: Traditional one-session exposure therapy (OST) in which a patient is gradually exposed to feared stimuli for up to 3 h in a one-session format has been found effective for the treatment of specific phobias. However, many individuals with specific phobia are reluctant to seek help, and access to care is lacking due to logistic challenges of accessing, collecting, storing, and/or maintaining stimuli. Virtual reality (VR) exposure therapy may improve upon existing techniques by facilitating access, decreasing cost, and increasing acceptability and effectiveness. The aim of this study is to compare traditional OST with in vivo spiders and a human therapist with a newly developed single-session gamified VR exposure therapy application with modern VR hardware, virtual spiders, and a virtual therapist. ::: ::: ::: METHODS/DESIGN ::: Participants with specific phobia to spiders (N = 100) will be recruited from the general public, screened, and randomized to either VR exposure therapy (n = 50) or traditional OST (n = 50). A behavioral approach test using in vivo spiders will serve as the primary outcome measure. Secondary outcome measures will include spider phobia questionnaires and self-reported anxiety, depression, and quality of life. Outcomes will be assessed using a non-inferiority design at baseline and at 1, 12, and 52 weeks after treatment. ::: ::: ::: DISCUSSION ::: VR exposure therapy has previously been evaluated as a treatment for specific phobias, but there has been a lack of high-quality randomized controlled trials. A new generation of modern, consumer-ready VR devices is being released that are advancing existing technology and have the potential to improve clinical availability and treatment effectiveness. The VR medium is also particularly suitable for taking advantage of recent phobia treatment research emphasizing engagement and new learning, as opposed to physiological habituation. This study compares a market-ready, gamified VR spider phobia exposure application, delivered using consumer VR hardware, with the current gold standard treatment. Implications are discussed. ::: ::: ::: TRIAL REGISTRATION ::: ClinicalTrials.gov identifier NCT02533310. Registered on 25 August 2015.


title: ANN-based sediment yield models for Vamsadhara

abstract: Most universally accepted feed-forward error back-propagation artificial neural network models, supported by batch- and pattern-learning, daily, weekly, ten-daily and monthly sediment yield were developed for the Vamsadhara River basin of India. The fast gradient descent optimisation technique improved with variable learning rate (α) and momentum term (β) was used for optimisation. In the process of optimisation and updating of weights, criteria adopted to terminate the process of learning was selected as a per-decided high number of iteration and the other is the generalisation of model through crossvalidation. In all cases of model formulation, the data were normalised with the maximum value of the variable of the series individually. The pattern-learned models were found superior to batch-learned models. High numbers of iterations adopted for model development were found to reduce the value of the objective function, but with model’s over-learning and that is reflected? Unclear what is meant by an increase and decrease of the performance in calibration and cross-validation, respectively. The generalised pattern- learned models for different time scales were compared with linear transfer function models and it was found that the pattern-learned models developed with generalisation through cross-validation were superior in general, except weekly for the study area.


title: Indian sign language recognition

abstract: Understanding human motions can be posed as a pattern recognition problem. Applications of pattern recognition in information processing problems are diverse ranging from Speech, Handwritten character recognition to medical research and astronomy. Humans express time-varying motion patterns (gestures), such as a wave, in order to convey a message to a recipient. If a computer can detect and distinguish these human motion patterns, the desired message can be reconstructed, and the computer can respond appropriately. This paper represents a framework for a human computer interface capable of recognizing gestures from the Indian sign language. The complexity of Indian sign language recognition system increases due to the involvement of both the hands and also the overlapping of the hands. Alphabets and numbers have been recognized successfully. This system can be extended for words and sentences Recognition is done with PCA (Principal Component analysis). This paper also proposes recognition with neural networks. Further it is proposed that number of finger tips and the distance of fingertips from the centroid of the hand can be used along with PCA for robustness and efficient results.


title: A comparison of facial feature representation methods for automatic facial expression recognition

abstract: A machine translation system that can convert South African Sign Language video to English audio or text and vice versa in real-time would be immensely beneficial to the Deaf and hard of hearing. Sign language gestures are characterised and expressed by five distinct parameters: hand location; hand orientation; hand shape; hand movement and facial expressions. The aim of this research is to recognise facial expressions and to compare the following feature descriptors: local binary patterns; compound local binary patterns and histogram of oriented gradients in two testing environments, a subset of the BU3D-FE dataset and the CK+ dataset. The overall accuracy, accuracy across facial expression classes, robustness to test subjects, and the ability to generalise of each feature descriptor within the context of automatic facial expression recognition are analysed as part of the comparison procedure. Overall, HOG proved to be a more robust feature descriptor to the LBP and CLBP. Furthermore, the CLBP can generally be considered to be superior to the LBP, but the LBP has greater potential in terms of its ability to generalise.


title: A Survey on Successors of LEACH Protocol

abstract: Even after 16 years of existence, low energy adaptive clustering hierarchy (LEACH) protocol is still gaining the attention of the research community working in the area of wireless sensor network (WSN). This itself shows the importance of this protocol. Researchers have come up with various and diverse modifications of the LEACH protocol. Successors of LEACH protocol are now available from single hop to multi-hop scenarios. Extensive work has already been done related to LEACH and it is a good idea for a new research in the field of WSN to go through LEACH and its variants over the years. This paper surveys the variants of LEACH routing protocols proposed so far and discusses the enhancement and working of them. This survey classifies all the protocols in two sections, namely, single hop communication and multi-hop communication based on data transmission from the cluster head to the base station. A comparitive analysis using nine different parameters, such as energy efficiency, overhead, scalability complexity, and so on, has been provided in a chronological fashion. The article also discusses the strong and the weak points of each and every variants of LEACH. Finally the paper concludes with suggestions on future research domains in the area of WSN.


Amlexanox Suppresses Osteoclastogenesis and Prevents Ovariectomy-Induced Bone Loss


title: Graphs in Scientific Visualization: A Survey

abstract: Graphs represent general node-link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history, yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph-based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely partition-wise, relationship-wise, structure-wise and provenance-wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph-based visualization pipeline. We also point out research trends and remaining challenges in graph-based representations and techniques for scientific visualization.


Disaster Operations and Decision Making


title: An Engagement-Based Customer Lifetime Value System for E-commerce

abstract: A comprehensive understanding of individual customer value is crucial to any successful customer relationship management strategy. It is also the key to building products for long-term value returns. Modeling customer lifetime value (CLTV) can be fraught with technical difficulties, however, due to both the noisy nature of user-level behavior and the potentially large customer base. Here we describe a new CLTV system that solves these problems. This was built at Groupon, a large global e-commerce company, where confronting the unique challenges of local commerce means quickly iterating on new products and the optimal inventory to appeal to a wide and diverse audience. Given current purchaser frequency we need a faster way to determine the health of individual customers, and given finite resources we need to know where to focus our energy. Our CLTV system predicts future value on an individual user basis with a random forest model which includes features that account for nearly all aspects of each customer's relationship with our platform. This feature set includes those quantifying engagement via email and our mobile app, which give us the ability to predict changes in value far more quickly than models based solely on purchase behavior. We further model different customer types, such as one-time buyers and power users, separately so as to allow for different feature weights and to enhance the interpretability of our results. Additionally, we developed an economical scoring framework wherein we re-score a user when any trigger events occur and apply a decay function otherwise, to enable frequent scoring of a large customer base with a complex model. This system is deployed, predicting the value of hundreds of millions of users on a daily cadence, and is actively being used across our products and business initiatives.


title: Brain age predicts mortality

abstract: Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N=2001), then tested in the Lothian Birth Cohort 1936 (N=669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.


title: Security, Privacy, and Fairness in Fog-Based Vehicular Crowdsensing

abstract: Fog-based vehicular crowdsensing is an emerging paradigm where vehicles use onboard sensors to collect and share data with the aim of measuring phenomena of common interest. Unlike traditional mobile crowdsensing, fog nodes are introduced specifically to meet the requirements for location- specific applications and location-aware data management in vehicular ad hoc networks. In this article, we examine the architecture, applications, and especially security, privacy, and fairness of fog-based vehicular crowdsensing. Specifically, we first introduce the overall infrastructure and some promising applications, including parking navigation, road surface monitoring, and traffic collision reconstruction. We then study the security, privacy, and fairness requirements in fog-based vehicular crowdsensing, and describe the possible solutions to achieve security assurance, privacy preservation, and incentive fairness. By defining interesting future directions, this article is expected to draw more attention into this emerging area.


title: Design, implementation and test results of a robust control method for a powered ankle foot orthosis (AFO)

abstract: There are various methods to control a powered AFO. As different as they are in their approach each of them has certain advantages as well as difficulties. What is still needed is a robust control concept that meets the requirements for ankle gait assistance. A new, stiffness-control model has been developed that divides the stance phase of gait into five zones using either velocity or stiffness control for each zone. The design and implementation of this new control algorithm as well as some first test results are presented.


title: Fisheye image rectification for efficient large-scale stereo

abstract: Stereo vision using fisheye lenses has received greater attention in recent years due to its vast number of applications. As an essential prerequisite for stereo processing, this work proposes a fisheye image rectification method for efficient large-scale stereo. The scene distance was formulated within the spherical model. Reprojecting the original images using transverse Mercator projection enabled us to proof a theorem for which distance computing could be simplified to only a division operation of a constant and the disparity. Experimental results shows that stereo processing using this method could achieve much faster computational speed than existing approaches while maintaining satisfying matching quality.


title: 25% Ahead of Schedule and just at "Step 2" of the SAMI

abstract: The Sidky Agile Measurement Index (SAMI) is a 5step value-based roadmap to agility. The SAMI is designed to help guide organizations seeking to become more agile. Each of SAMI’s 5 steps (Collaborative, Evolutionary, Integrated, Adaptive and Encompassing) aims to instill a new value in teams and organizations. This report shows how an organization that is just at Step 2 of the SAMI has realized a number of tangible business benefits. Some of these notable benefits are (a) 25% increase in the speed to market, (b) 45% decrease in the number of bugs reported after delivery of the system, (c) unprecedented customer satisfaction, and (d) 19% decrease in employee turnover because of the substantial increase in the team moral.


title: Searching by Talking: Analysis of Voice Queries on Mobile Web Search

abstract: The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries, rather than type them. While this kind of voice search is still in its infancy, it is gradually becoming more widespread. In this paper, we examine the logs of a commercial search engine's mobile interface, and compare the spoken queries to the typed-in queries. We place special emphasis on the semantic and syntactic characteristics of the two types of queries. %Our analysis suggests that voice queries focus more on audio-visual content and question answering, and less on social networking and adult domains. We also conduct an empirical evaluation showing that the language of voice queries is closer to natural language than typed queries. Our analysis reveals further differences between voice and text search, which have implications for the design of future voice-enabled search tools.


title: Chipless RFID tag with integrated resistive and capacitive sensors

abstract: A wireless sensor node based on a chipless RFID tag with integrated sensor element is presented. The chipless tag is multiresonator-based and encodes data as spectral signature. The sensor element is attached to a dedicated resonator of the chipless tag. Two types of sensor elements are used - resistive and capacitive. The resistive sensor output changes the Q factor of the spiral resonator effectively changing its insertion loss at the resonant frequency. The capacitive sensor output changes the spiral resonator's resonant frequency. The wireless chipless sensor node can be used in various automated monitoring applications.


title: Robust Color Guided Depth Map Restoration

abstract: One of the most challenging issues in color guided depth map restoration is the inconsistency between color edges in guidance color images and depth discontinuities on depth maps. This makes the restored depth map suffer from texture copy artifacts and blurring depth discontinuities. To handle this problem, most state-of-the-art methods design complex guidance weight based on guidance color images and heuristically make use of the bicubic interpolation of the input depth map. In this paper, we show that using bicubic interpolated depth map can blur depth discontinuities when the upsampling factor is large and the input depth map contains large holes and heavy noise. In contrast, we propose a robust optimization framework for color guided depth map restoration. By adopting a robust penalty function to model the smoothness term of our model, we show that the proposed method is robust against the inconsistency between color edges and depth discontinuities even when we use simple guidance weight. To the best of our knowledge, we are the first to solve this problem with a principled mathematical formulation rather than previous heuristic weighting schemes. The proposed robust method performs well in suppressing texture copy artifacts. Moreover, it can better preserve sharp depth discontinuities than previous heuristic weighting schemes. Through comprehensive experiments on both simulated data and real data, we show promising performance of the proposed method.


Boosting Algorithms as Gradient Descent in Function Space


title: MRCP-RM: A Technique for Resource Allocation and Scheduling of MapReduce Jobs with Deadlines

abstract: Resource allocation and scheduling on clouds are required to harness the power of the underlying resource pool such that the service provider can meet the quality of service requirements of users, which are often captured in service level agreements (SLAs). This paper focuses on resource allocation and scheduling on clouds and clusters that process MapReduce jobs with SLAs. The resource allocation and scheduling problem is modelled as an optimization problem using constraint programming, and a novel MapReduce Constraint Programming based Resource Management algorithm (MRCP-RM) is devised that can effectively process an open stream of MapReduce jobs where each job is characterized by an SLA comprising an earliest start time, a required execution time, and an end-to-end deadline. A detailed performance evaluation of MRCP-RM is conducted for an open system subjected to a stream of job arrivals using both simulation and experimentation on a real system. The experiments on a real system are performed on a Hadoop cluster (deployed on Amazon EC2) that runs our new Hadoop Constraint Programming based Resource Management algorithm (HCP-RM) that incorporates a technique for handling data locality. The results of the performance evaluation demonstrate the effectiveness of MRCP-RM/HCP-RM in generating a schedule that leads to a low proportion of jobs missing their deadlines ( P ) and also provide insights into system behaviour and performance. In the simulation experiments, it is observed that MRCP-RM achieves on average an 82 percent lower P compared to a technique from the existing literature when processing a synthetic workload from Facebook. Furthermore, in the experiments performed on a Hadoop cluster deployed on Amazon EC2, it is observed that HCP-RM achieved on average a 63 percent lower P compared to an EDF-Scheduler for a wide variety of workload and system parameters experimented with.


title: Head pose estimation through multi-class face segmentation

abstract: The aim of this work is to explore the usefulness of face semantic segmentation for head pose estimation. We implement a multi-class face segmentation algorithm and we train a model for each considered pose. Given a new test image, the probabilities associated to face parts by the different models are used as the only information for estimating the head orientation. A simple algorithm is proposed to exploit such probabilites in order to predict the pose. The proposed scheme achieves competitive results when compared to most recent methods, according to mean absolute error and accuracy metrics. Moreover, we release and make publicly available a face segmentation dataset1 consisting of 294 images belonging to 13 different poses, manually labeled into six semantic regions, which we used to train the segmentation models.


title: Large-Scale BSP Graph Processing in Distributed Non-Volatile Memory

abstract: Processing large graphs is becoming increasingly important for many domains. Large-scale graph processing requires a large-scale cluster system, which is very expensive. Thus, for high-performance large-scale graph processing in small clusters, we have developed bulk synchronous parallel graph processing in distributed non-volatile memory that has lower bit cost, lower power consumption, and larger capacity than DRAM. When non-volatile memory is used, accessing non-volatile memory is a performance bottleneck because accesses to non-volatile memory are fine-grained random accesses and non-volatile memory has much larger latency than DRAM. Thus, we propose non-volatile memory group access method and the implementation for using non-volatile memory efficiently. Proposed method and implementation improve the access performance to non-volatile memory by changing fine-grained random accesses to random accesses the same size as a non-volatile memory page and hiding non-volatile memory latency with pipelining. An evaluation indicated that the proposed graph processing can hide the latency of non-volatile memory and has the proportional performance to non-volatile memory bandwidth. When non-volatile memory read/write mixture bandwidth is 4.2 GB/sec, the performance of proposed graph processing and the performance storing all data in main memory have the same order of magnitude (46%). In addition, the proposed graph processing had scalable performance for any number of nodes. The proposed method and implementation can process 125 times bigger graph than a DRAM-only system.


title: GA-Net: A Genetic Algorithm for Community Detection in Social Networks

abstract: The problem of community structure detection in complex networks has been intensively investigated in recent years. In this paper we propose a genetic based approach to discover communities in social networks. The algorithm optimizes a simple but efficacious fitness function able to identify densely connected groups of nodes with sparse connections between groups. The method is efficient because the variation operators are modified to take into consideration only the actual correlations among the nodes, thus sensibly reducing the research space of possible solutions. Experiments on synthetic and real life networks show the capability of the method to successfully detect the network structure.


title: A secure and efficient Ciphertext-Policy Attribute-Based Signcryption for Personal Health Records sharing in cloud computing

abstract: Abstract In recent years, Personal Health Record (PHR) system has attracted intensive attention due to its universal accessibility and low cost. The practical deployment of PHR system in cloud computing environments raises privacy and information security issues that should be addressed positively. Recently [Future Generation Computer Systems 52 (2015)], a Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) scheme (Liu et al. 2015) is proposed with the aim to securing PHR data stored at cloud servers, and it is claimed to provide confidentiality against chosen ciphertext attacks in selective-predicate model. Unfortunately, we show in this paper that the claim is incorrect. Besides, the CP-ABSC scheme Liu et al. (2015) cannot realize the property of public ciphertext verifiability which is an essential requirement of signcryption schemes to reduce unnecessary burden on the decryptor for decrypting invalid ciphertexts. In this paper, we propose a provable secure CP-ABSC scheme for cloud-based PHR sharing system that has ability to provide fine-grained access control, confidentiality, authenticity, signcryptor privacy and public verifiability, simultaneously. Our framework exploits expressive monotone boolean functions as signing and encryption predicates, and realizes security in the standard model. On the positive note, our construction exhibits short ciphertext size and requires less number of pairing computations compared to the existing schemes in the area.


title: Improved Black Hole Algorithm for Efficient Low Observable UCAV Path Planning in Constrained Aerospace

abstract: An essential task of UAV autonomy is automatic path planning. There are many evolutionary planners for Unmanned Aerial Vehicles (UAVs) that have been developed UAV community. In this paper a comparative study about performance of effective trajectory planners is done. Also an efficient version of black hole methodology has been introduced for single UCAV trajectory planning, and an enhancement is designed to communicate among stars and black hole based on relativity theory principles. By considering UCAV Dynamic properties and environment constraints, Developed path planner based on black hole algorithm can compute feasible and quasi-optimal trajectories for UCAV flight. Our comparison of algorithms shows that IBH generates desired optimal trajectories. Then path planning task of UCAV is performed. Simulations show advantage of IBH methodology.


title: Automatic Hot Spot Detection and Segmentation in Whole Body FDG-PET Images

abstract: We present a system for automatic hot spots detection and segmentation in whole body FDG-PET images. The main contribution of our system is threefold. First, it has a novel body-section labeling module based on spatial hidden-Markov models (HMM); this allows different processing policies to be applied in different body sections. Second, the competition diffusion (CD) segmentation algorithm, which takes into account body-section information, converts the binary thresholding results to probabilistic interpretation and detects hot-spot region candidates. Third, a recursive intensity mode-seeking algorithm finds hot spot centers efficiently, and given these centers, a clinically meaningful protocol is proposed to accurately quantify hot spot volumes. Experimental results show that our system works robustly despite the large variations in clinical PET images.


title: Recovering and using use-case-diagram-to-source-code traceability links

abstract: Use case diagrams (UCDs) are widely used to describe requirements and desired functionality of software products. However, UCDs are loosely linked to source code, and maintaining traces between the source code and elements of UCDs is a manual, tedious, and laborious process. These traces help programmers to understand code that they maintain and evolve. Our contribution is twofold. First, we offer a novel approach for automating part of the process of recovering traceability links (TLs) between types and variables in Java programs and elements of UCDs. We evaluate our prototype implementation on open-source and commercial software, and the results suggest that our approach can recover many TLs with a high degree of automation and precision. Second, we developed an Eclipse plugin that enables programmers to trace program types and variables to elements of UCDs and vice versa using recovered TLs. We conducted a case study that shows that programmers could maintain and evolve software more efficiently with our plugin. These results demonstrate that modest programmer effort to create TLs together with automated program mining and analysis is a promising approach than can increase program understanding while reducing programmer burden.


title: Stable feature selection via dense feature groups

abstract: Many feature selection algorithms have been proposed in the past focusing on improving classification accuracy. In this work, we point out the importance of stable feature selection for knowledge discovery from high-dimensional data, and identify two causes of instability of feature selection algorithms: selection of a minimum subset without redundant features and small sample size. We propose a general framework for stable feature selection which emphasizes both good generalization and stability of feature selection results. The framework identifies dense feature groups based on kernel density estimation and treats features in each dense group as a coherent entity for feature selection. An efficient algorithm DRAGS (Dense Relevant Attribute Group Selector) is developed under this framework. We also introduce a general measure for assessing the stability of feature selection algorithms. Our empirical study based on microarray data verifies that dense feature groups remain stable under random sample hold out, and the DRAGS algorithm is effective in identifying a set of feature groups which exhibit both high classification accuracy and stability.


title: Cortical-Inspired Open-Bigram Representation for Handwritten Word Recognition

abstract: Recent research in the cognitive process of reading hypothesized that we do not read words by sequentially recognizing letters, but rather by identifing open-bigrams, i.e. couple of letters that are not necessarily next to each other. In this paper, we evaluate an handwritten word recognition method based on original open-bigrams representation. We trained Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) to predict open-bigrams rather than characters, and we show that such models are able to learn the long-range, complicated and intertwined dependencies in the input signal, necessary to the prediction. For decoding, we decomposed each word of a large vocabulary into the set of constituent bigrams, and apply a simple cosine similarity measure between this representation and the bagged RNN prediction to retrieve the vocabulary word. We compare this method to standard word recognition techniques based on sequential character recognition. Experiments are carried out on two public databases of handwritten words (Rimes and IAM). The bigram decoder results with our bigram decoder are comparable to more conventional decoding methods based on sequences of letters.


PRIMARY IMMUNE DEFICIENCY : When to suspect and what to do


title: Distraction arthroplasty.

abstract: Few joint-preserving surgical options exist for the patient who has ankle arthritis refractory to conservative measures. Therefore, continuous effort is afforded to the development of additional treatment options for such patients. Distraction arthroplasty has been proposed as one of these options for the patient in whom fusion or joint replacement is not appropriate. Although the mechanism of action remains unknown, the reports of several researchers support the potential beneficial effects that can be obtained from joint distraction arthroplasty in the severely osteoarthritic ankle. Furthermore, the studies published to date suggest that these effects may not only persist for years but also improve as time progresses during the first several years after treatment. Although additional laboratory studies are needed to understand the biochemical and biomechanical effects of distraction, additional prospective clinical studies are also needed to further understand its efficacy and appropriate patient population. The data thus far suggests that joint distraction arthroplasty may be a viable alternative treatment to arthrodesis and replacement for the young patient who has a congruent, painful, mobile, arthritic ankle joint.


title: A survey on graphic processing unit computing for large-scale data mining

abstract: General purpose computation using Graphic Processing Units (GPUs) is a well-established research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfully improved the scalability of data mining algorithms to address significantly larger dataset sizes in many application areas. The popularization of distributed computing frameworks for big data mining opens up new opportunities for transformative solutions combining GPUs and distributed frameworks. This survey analyzes current trends in the use of GPU computing for large-scale data mining, discusses GPU architecture advantages for handling volume and velocity of data, identifies limitation factors hampering the scalability of the problems, and discusses open issues and future directions. ::: ::: For further resources related to this article, please visit the WIREs website.


title: A rare cause of forearm pain: anterior branch of the medial antebrachial cutaneous nerve injury: a case report

abstract: Introduction ::: Medial antebrachial cutaneous nerve (MACN) neuropathy is reported to be caused by iatrogenic reasons. Although the cases describing the posterior branch of MACN neuropathy are abundant, only one case caused by lipoma has been found to describe the anterior branch of MACN neuropathy in the literature. As for the reason for the forearm pain, we report the only case describing isolated anterior branch of MACN neuropathy which has developed due to repeated minor trauma.


title: Transfer Learning for Neural Semantic Parsing

abstract: The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with a focus on transfer learning. We explore three multi-task architectures for sequence-to-sequence modeling and compare their performance with an independently trained model. Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to a target task with smaller labeled data. We see absolute accuracy gains ranging from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and semantic auxiliary tasks.


title: Inferring Dynamic User Interests in Streams of Short Texts for User Clustering

abstract: User clustering has been studied from different angles. In order to identify shared interests, behavior-based methods consider similar browsing or search patterns of users, whereas content-based methods use information from the contents of the documents visited by the users. So far, content-based user clustering has mostly focused on static sets of relatively long documents. Given the dynamic nature of social media, there is a need to dynamically cluster users in the context of streams of short texts. User clustering in this setting is more challenging than in the case of long documents, as it is difficult to capture the users’ dynamic topic distributions in sparse data settings. To address this problem, we propose a dynamic user clustering topic model (UCT). UCT adaptively tracks changes of each user’s time-varying topic distributions based both on the short texts the user posts during a given time period and on previously estimated distributions. To infer changes, we propose a Gibbs sampling algorithm where a set of word pairs from each user is constructed for sampling. UCT can be used in two ways: (1) as a short-term dependency model that infers a user’s current topic distribution based on the user’s topic distributions during the previous time period only, and (2) as a long-term dependency model that infers a user’s current topic distributions based on the user’s topic distributions during multiple time periods in the past. The clustering results are explainable and human-understandable, in contrast to many other clustering algorithms. For evaluation purposes, we work with a dataset consisting of users and tweets from each user. Experimental results demonstrate the effectiveness of our proposed short-term and long-term dependency user clustering models compared to state-of-the-art baselines.


ody appreciation in adult women : Relationships with age nd body satisfaction


title: Learning to Translate: A Query-Specific Combination Approach for Cross-Lingual Information Retrieval

abstract: When documents and queries are presented in different languages, the common approach is to translate the query into the document language. While there are a variety of query translation approaches, recent research suggests that combining multiple methods into a single ”structured query” is the most effective. In this paper, we introduce a novel approach for producing a unique combination recipe for each query, as it has also been shown that the optimal combination weights differ substantially across queries and other task specifics. Our query-specific combination method generates statistically significant improvements over other combination strategies presented in the literature, such as uniform and task-specific weighting. An in-depth empirical analysis presents insights about the effect of data size, domain differences, labeling and tuning on the end performance of our approach.


title: Hashcash - A Denial of Service Counter-Measure

abstract: Hashcash was originally proposed as a mechanism to throttle systematic abuse of un-metered internet resources such as email, and anonymous remailers in May 1997. Five years on, this paper captures in one place the various applications, improvements suggested and related subsequent publications, and describes initial experience from experiments using hashcash. The hashcash CPU cost-function computes a token which can be used as a proof-of-work. Interactive and noninteractive variants of cost-functions can be constructed which can be used in situations where the server can issue a challenge (connection oriented interactive protocol), and where it can not (where the communication is store–and– forward, or packet oriented) respectively.


title: Therapeutic robocat for nursing home residents with dementia: Preliminary inquiry

abstract: Traditional pet therapy enhances individual wellbeing. However, there are situations where a substitute artificial companion (i.e., robotic pet) may serve as a better alternative because of insufficient available resources to care for a real pet, allergic responses to pets, or other difficulties. This pilot study, which compared the benefits of a robotic cat and a plush toy cat as interventions for elderly persons with dementia, was conducted at a special care unit of a large, not-for-profit nursing home. Various aspects of a person's engagement and affect were assessed through direct observations. Though not identical, similar trends were seen for the two cats. Interacting with the cats was linked with decreased agitation and increased pleasure and interest. The study is intended to pave the way for future research on robotherapy with nursing home residents.


title: Bridging the Gap: From Research to Practical Advice

abstract: Software engineers must solve practical problems under deadline pressure. They rely on the best-codified knowledge available, turning to weaker results and their expert judgment when sound science is unavailable. Meanwhile, software engineering researchers seek fully validated results, resulting in a lag to practical guidance. To bridge this gap, research results should be systematically distilled into actionable guidance in a way that respects differences in strength and scope among the results. Starting with the practitioners’ need for actionable guidance, this article reviews the evolution of software engineering research expectations, identifies types of results and their strengths, and draws on evidence-based medicine for a concrete example of deriving pragmatic guidance from mixed-strength research results. It advances a levels-of-evidence framework to allow researchers to clearly identify the strengths of their claims and the supporting evidence for their results and to work with practitioners to synthesize actionable recommendations from diverse types of evidence. This article is part of a special issue on software engineering’s 50th anniversary.


title: A Practical Security Architecture for In-Vehicle CAN-FD

abstract: The controller area network with flexible data rate (CAN-FD) is attracting attention as the next generation of in-vehicle network technology. However, security issues have not been completely taken into account when designing CAN-FD, although every bit of information transmitted could be critical to driver safety. If we fail to solve the security vulnerabilities of CAN-FD, we cannot expect Vehicle-Information and Communications Technology (Vehicle-ICT) convergence to continue to develop. Fortunately, secure in-vehicle CAN-FD communication environments can be constructed using the larger data payload of CAN-FD. In this paper, we propose a security architecture for in-vehicle CAN-FD as a countermeasure (designed in accordance with CAN-FD specifications). We considered the characteristics of the International Organization for Standardization (ISO) 26262 Automotive Safety Integrity Level and the in-vehicle subnetwork to design a practical security architecture. We also evaluated the feasibility of the proposed security architecture using three kinds of microcontroller unit and the CANoe software. Our evaluation findings may be used as an indicator of the performance level of electronic control units for manufacturing next-generation vehicles.


title: A Thermodynamic Coupling Mechanism for the Disaggregation of a Model Peptide Substrate by Chaperone SecB

abstract: Abstract Molecular chaperones prevent protein aggregationin vivo and in vitro. In a few cases, multichaperone systems are capable of dissociating aggregated state(s) of substrate proteins, although little is known of the mechanism of the process. SecB is a cytosolic chaperone, which forms part of the precursor protein translocation machinery in Escherichia coli. We have investigated the interaction of the B-chain of insulin with chaperone SecB by light scattering, pyrene excimer fluorescence, and electron spin resonance spectroscopy. We show that SecB prevents aggregation of the B-chain of insulin. We show that SecB is capable of dissociating soluble B-chain aggregates as monitored by pyrene fluorescence spectroscopy. The kinetics of dissociation of the B-chain aggregate by SecB has been investigated to understand the mechanism of dissociation. The data suggests that SecB does not act as a catalyst in dissociation of the aggregate to individual B-chains, rather it binds the small population of free B-chains with high affinity, thereby shifting the equilibrium from the ensemble of the aggregate toward the individual B-chains. Thus SecB can rescue aggregated, partially folded/misfolded states of target proteins by a thermodynamic coupling mechanism when the free energy of binding to SecB is greater than the stability of the aggregate. Pyrene excimer fluorescence and ESR methods have been used to gain insights on the bound state conformation of the B-chain to chaperone SecB. The data suggests that the B-chain is bound to SecB in a flexible extended state in a hydrophobic cleft on SecB and that the binding site accommodates approximately 10 residues of substrate.


title: Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks

abstract: In this paper, we propose the utterance-level permutation invariant training uPIT technique. uPIT is a practically applicable, end-to-end, deep-learning-based solution for speaker independent multitalker speech separation. Specifically, uPIT extends the recently proposed permutation invariant training PIT technique with an utterance-level cost function, hence eliminating the need for solving an additional permutation problem during inference, which is otherwise required by frame-level PIT. We achieve this using recurrent neural networks RNNs that, during training, minimize the utterance-level separation error, hence forcing separated frames belonging to the same speaker to be aligned to the same output stream. In practice, this allows RNNs, trained with uPIT, to separate multitalker mixed speech without any prior knowledge of signal duration, number of speakers, speaker identity, or gender. We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on nonnegative matrix factorization and computational auditory scene analysis, and compares favorably with deep clustering, and the deep attractor network. Furthermore, we found that models trained with uPIT generalize well to unseen speakers and languages. Finally, we found that a single model, trained with uPIT, can handle both two-speaker, and three-speaker speech mixtures.


title: Miniaturized electroencephalographic scalp electrode for optimal wearing comfort

abstract: Abstract Objective Current mainstream EEG electrode setups permit efficient recordings, but are often bulky and uncomfortable for subjects. Here we introduce a novel type of EEG electrode, which is designed for an optimal wearing comfort. The electrode is referred to as C-electrode where “C” stands for comfort. Methods The C-electrode does not require any holder/cap for fixation on the head nor does it use traditional pads/lining of disposable electrodes – thus, it does not disturb subjects. Fixation of the C-electrode on the scalp is based entirely on the adhesive interaction between the very light C-electrode/wire construction ( Results The quality of EEG recordings for all three types of experimental conditions was similar for standard and C-electrodes, i.e., for near-DC recordings (Bereitschaftspotential), standard rest EEG spectra (1–45 Hz) and very fast oscillations ∼600 Hz (somatosensory evoked potentials). The tests showed also that once being placed on a subject’s head, C-electrodes can be used for 9 h without any loss in EEG recording quality. Furthermore, we showed that C-electrodes can be effectively utilized for Brain-Computer Interfacing. C-electrodes proved to posses a high stability of mechanical fixation (stayed attached with 2.5 g accelerations). Subjects also reported not having any tactile sensations associated with wearing of C-electrodes. Conclusion C-electrodes provide optimal wearing comfort without any loss in the quality of EEG recordings. Significance We anticipate that C-electrodes can be used in a wide range of clinical, research and emerging neuro-technological environments.


title: Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases

abstract: Kutato is a system that takes as input a database of cases and produces a belief network that captures many of the dependence relations represented by those data. This system incorporates a module for determining the entropy of a belief network and a module for constructing belief networks based on entropy calculations. Kutato constructs an initial belief network in which all variables in the database are assumed to be marginally independent. The entropy of this belief network is calculated, and that arc is added that minimizes the entropy of the resulting belief network. Conditional probabilities for an arc are obtained directly from the database. This process continues until an entropy-based threshold is reached. We have tested the system by generating databases from networks using the probabilistic logic-sampling method, and then using those databases as input to Kutato. The system consistently reproduces the original belief networks with high fidelity.


title: Coupled Dictionary Training for Image Super-Resolution

abstract: In this paper, we propose a novel coupled dictionary training method for single-image super-resolution (SR) based on patchwise sparse recovery, where the learned couple dictionaries relate the low- and high-resolution (HR) image patch spaces via sparse representation. The learning process enforces that the sparse representation of a low-resolution (LR) image patch in terms of the LR dictionary can well reconstruct its underlying HR image patch with the dictionary in the high-resolution image patch space. We model the learning problem as a bilevel optimization problem, where the optimization includes an l1-norm minimization problem in its constraints. Implicit differentiation is employed to calculate the desired gradient for stochastic gradient descent. We demonstrate that our coupled dictionary learning method can outperform the existing joint dictionary training method both quantitatively and qualitatively. Furthermore, for real applications, we speed up the algorithm approximately 10 times by learning a neural network model for fast sparse inference and selectively processing only those visually salient regions. Extensive experimental comparisons with state-of-the-art SR algorithms validate the effectiveness of our proposed approach.


title: Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

abstract: The combination of modern Reinforcement Learning and Deep Learning approaches holds the promise of making significant progress on challenging applications requiring both rich perception and policy-selection. The Arcade Learning Environment (ALE) provides a set of Atari games that represent a useful benchmark set of such applications. A recent breakthrough in combining model-free reinforcement learning with deep learning, called DQN, achieves the best real-time agents thus far. Planning-based approaches achieve far higher scores than the best model-free approaches, but they exploit information that is not available to human players, and they are orders of magnitude slower than needed for real-time play. Our main goal in this work is to build a better real-time Atari game playing agent than DQN. The central idea is to use the slow planning-based agents to provide training data for a deep-learning architecture capable of real-time play. We proposed new agents based on this idea and show that they outperform DQN.


title: Fingertip-based interactive projector-camera system

abstract: Most current challenge of hand touch detection in interactive projector system is how to recover the 3D information of fingertips with a single camera. In this paper, a projector-camera system is proposed that enables users to interact with computer by touching on arbitrary surfaces with bare-hand. The realization of touch detection is through a novel vision-based algorithm performing in three steps: (1) foreground extraction based on predicted image generated from geometric and photometric calibration matrix; (2) fingertip detection by the curvature of hand contour points; (3) touch detection by active vision. Extensive experiments on foreground extraction, fingertip detection, and touch judgment are presented to show the robust performance of this system. Proposed an interactive projector system that can recover 3D information of fingertips with a single camera.Perform foreground extraction by subtracting a predicted image generated from geometric and photometric calibration matrix.Proposed a fingertip detection algorithm based on the curvature of hand contour points.


title: Authentication anomaly detection: a case study on a virtual private network

abstract: The authentication logs on a network can provide a trove of information for discovering potential anomalies in login attempts. Using such logs collected by a production Virtual Private Network device over a period of 15 months, we generate a diurnal model of network accesses. These models are used to detect anomalous authentications, which merit further investigation by a security analyst. We intend that this work will dramatically reduce the amount time spent by analysts identifying anomalous events and allow them to focus on in-depth analysis of these anomalies. Our work makes two contributions: a novel approach of mining authentication data, and the use of geographic distance as a metric to evaluate Virtual Private Network connections. We demonstrate the success of our model using real-world case analysis.


title: Deep Multimodal Reinforcement Network with Contextually Guided Recurrent Attention for Image Question Answering

abstract: Image question answering (IQA) has emerged as a promising interdisciplinary topic in computer vision and natural language processing fields. In this paper, we propose a contextually guided recurrent attention model for solving the IQA issues. It is a deep reinforcement learning based multimodal recurrent neural network. Based on compositional contextual information, it recurrently decides where to look using reinforcement learning strategy. Different from traditional “static” soft attention, it is deemed as a kind of “dynamic” attention whose objective is designed based on reinforcement rewards purposefully towards IQA. The finally learned compositional information incorporates both global context and local informative details, which is demonstrated to benefit for generating answers. The proposed method is compared with several state-of-the-art methods on two public IQA datasets, including COCO-QA and VQA from dataset MS COCO. The experimental results demonstrate that our proposed model outperforms those methods and achieves better performance.


title: StarsRace: A mobile collaborative seriuos game for obesity

abstract: Many researchers in the literature participated in solving the obesity epidemic that has spread worryingly nowadays. One of these solutions is the mobile serious games. In this paper, we designed, developed and deployed a mobile serious game entitled StarsRace. It is a collaborative game, which two players involve in one match. This competition depends on rapid arrival to pre-defined geographical points (representing by stars images) on the map. Players collect stars form these locations and gain points. The therapist with respect to the players' profiles distributed those stars. To make the game more fun, we have added an aid toolbox that can increase the competition. Lastly, we evaluate our game using an evaluating model proposed by Nokia Research Center. We find that our game cover nearly all the model criteria.


title: Words in puddles of sound : modelling psycholinguistic effects in speech segmentation *

abstract: There are numerous models of how speech segmentation may proceed in infants acquiring their first language. We present a framework for considering the relative merits and limitations of these various approaches. We then present a model of speech segmentation that aims to reveal important sources of information for speech segmentation, and to capture psycholinguistic constraints on children's language perception. The model constructs a lexicon based on information about utterance boundaries and deduces phonotactic constraints from the discovered lexicon. Compared to other models of speech segmentation, our model performs well in terms of accuracy, computational tractability and the number of components of the model. Finally, our model also reflects the psycholinguistic effects of language learning, in terms of the early advantage for segmentation provided by the child's name, and by revealing the overlap in usefulness of information for segmentation and for grammatical categorization of the language.


title: Deep Lidar CNN to Understand the Dynamics of Moving Vehicles

abstract: Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich information of RGB images. Deep Learning solutions applied to the data of other sensors typically mounted on autonomous cars (e.g. lidars or radars) are not explored much. In this paper we propose a novel solution to understand the dynamics of moving vehicles of the scene from only lidar information. The main challenge of this problem stems from the fact that we need to disambiguate the proprio-motion of the “observer” vehicle from that of the external “observed” vehicles. For this purpose, we devise a CNN architecture which at testing time is fed with pairs of consecutive lidar scans. However, in order to properly learn the parameters of this network, during training we introduce a series of so-called pretext tasks which also leverage on image data. These tasks include semantic information about vehicleness and a novel lidar-flow feature which combines standard image-based optical flow with lidar scans. We obtain very promising results and show that including distilled image information only during training, allows improving the inference results of the network at test time, even when image data is no longer used.


title: Pictorial Illustrations Still Improve Students' Learning from Text

abstract: Research conducted primarily during the 1970s and 1980s supported the assertion that carefully constructed text illustrations generally enhance learners' performance on a variety of text-dependent cognitive outcomes. Research conducted throughout the 1990s still strongly supports that assertion. The more recent research has extended pictures-in-text conclusions to alternative media and technological formats and has begun to explore more systematically the “whys,” “whens,” and “for whoms” of picture facilitation, in addition to the “whethers” and “how muchs.” Consideration is given here to both more and less conventional types of textbook illustration, with several “tenets for teachers” provided in relation to each type.


title: What You Want (and Do Not Want) Affects What You See (and Do Not See): Avoidance Social Goals and Social Events

abstract: Two studies examined the influence of approach and avoidance social goals on memory for and evaluation of ambiguous social information. Study 1 found that individual differences in avoidance social goals were associated with greater memory of negative information, negatively biased interpretation of ambiguous social cues, and a more pessimistic evaluation of social actors. Study 2 experimentally manipulated social goals and found that individuals high in avoidance social motivation remembered more negative information and expressed more dislike for a stranger in the avoidance condition than in the approach condition. Results suggest that avoidance social goals are associated with emphasizing potential threats when making sense of the social environment.


title: Deep Exploration via Bootstrapped DQN

abstract: Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy exploration, bootstrapped DQN carries out temporally-extended (or deep) exploration; this can lead to exponentially faster learning. We demonstrate these benefits in complex stochastic MDPs and in the large-scale Arcade Learning Environment. Bootstrapped DQN substantially improves learning times and performance across most Atari games.


title: Emotional modulation of interval timing and time perception

abstract: Like other senses, our perception of time is not veridical, but rather, is modulated by changes in environmental context. Anecdotal experiences suggest that emotions can be powerful modulators of time perception; nevertheless, the functional and neural mechanisms underlying emotion-induced temporal distortions remain unclear. Widely accepted pacemaker-accumulator models of time perception suggest that changes in arousal and attention have unique influences on temporal judgments and contribute to emotional distortions of time perception. However, such models conflict with current views of arousal and attention suggesting that current models of time perception do not adequately explain the variability in emotion-induced temporal distortions. Instead, findings provide support for a new perspective of emotion-induced temporal distortions that emphasizes both the unique and interactive influences of arousal and attention on time perception over time. Using this framework, we discuss plausible functional and neural mechanisms of emotion-induced temporal distortions and how these temporal distortions may have important implications for our understanding of how emotions modulate our perceptual experiences in service of adaptive responding to biologically relevant stimuli.