title: Unsupervised Interesting Places Discovery in Location-Based Social Sensing

abstract: This paper presents an unsupervised approach toaccurately discover interesting places in a city from location-basedsocial sensing applications, a new sensing applicationparadigm that collects observations of physical world fromLocation-based Social Networks (LBSN). While there are alarge amount of prior works on personalized Point of Interests(POI) recommendation systems, they used supervised learningapproaches that did not work for users who have little orno historic (training) data. In this paper, we focused onan interesting place discovery problem where the goal isto accurately discover the interesting places in a city thataverage people may have strong interests to visit (e.g., parks, museums, historic sites, etc.) using unsupervised approaches. Inparticular, we develop a new Physical-Social-aware InterestingPlace Discovery (PSIPD) scheme which jointly exploits thelocation's physical dependency and the visitor's social dependencyto solve the interesting place discovery problem using anunsupervised approach. We compare our solution with state-of-the-art baselines using two real world data traces from LBSN. The results showed that our approach achieved significantperformance improvements compared to all baselines in termsof both estimation accuracy and ranking performance.


title: Cooperative Device-to-Device Communications: Social Networking Perspectives

abstract: With an increasing demand to improve spectrum efficiency and energy efficiency in cellular networks, device-to-device (D2D) communications have drawn a lot of attention from both academia and industry. Cooperative D2D networking, which allows for cooperation among mobile users (MUs) via D2D links while communicating with other MUs or base stations (BSs), is one of the most promising techniques in D2D communications. In cooperative D2D communications, two major issues need to be investigated: network performance evaluation and peer selection. In order to enhance evaluation accuracy while maintaining data privacy, it is essential to employ social interactions among MUs in cooperative D2D communications. In this article, we investigate social aware cooperative D2D networks. We first introduce social relationship formation, and then social learning based cooperative network evaluation and social relationship based peer selection are investigated, respectively. Numerical results show the effectiveness of the proposed methods.


title: Structure of niobium-tungsten alloy films produced by metal sputtering

abstract: The solid-solution alloy coatings are obtained via ion-plasma sputtering and the codeposition of ultradispersed Nb and W particles in the entire concentration range of a double system. Samples with 15.8–90.6 at % W are prepared. The sputtered metal particles have sizes ranging between 0.1 and 0.9 nm. The solid-solution alloy structures are based on niobium and tungsten if the tungsten concentration of the coating is, respectively, less and more than 40 at %. At certain ratios between the sizes of ultradispersed W particles and a certain sputterer power, a photoinduced increase in the mobility of particles is observed. As a result, a pure tungsten phase segregates from the tungsten solid solution in niobium and its particles coagulate in the nanocluster superlattice. It is established that superstructure formation depends mainly on the size factor. On account of growth in the thickness of the niobium and tungsten sublayers, metallic phases and solid solutions appear in the coatings and the nanocluster superlattices of one metal are formed in the matrix of another.


title: Lactic acid bacteria: promising supplements for enhancing the biological activities of kombucha

abstract: Kombucha is sweetened black tea that is fermented by a symbiosis of bacteria and yeast embedded within a cellulose membrane. It is considered a health drink in many countries because it is a rich source of vitamins and may have other health benefits. It has previously been reported that adding lactic acid bacteria (Lactobacillus) strains to kombucha can enhance its biological functions, but in that study only lactic acid bacteria isolated from kefir grains were tested. There are many other natural sources of lactic acid bacteria. In this study, we examined the effects of lactic acid bacteria from various fermented Vietnamese food sources (pickled cabbage, kefir and kombucha) on kombucha's three main biological functions: glucuronic acid production, antibacterial activity and antioxidant ability. Glucuronic acid production was determined by high-performance liquid chromatography-mass spectrometry, antibacterial activity was assessed by the agar-well diffusion method and antioxidant ability was evaluated by determining the 2,2-diphenyl-1-picrylhydrazyl radical scavenging capacity. Four strains of food-borne pathogenic bacteria were used in our antibacterial experiments: Listeria monocytogenes ATCC 19111, Escherichia coli ATCC 8739, Salmonella typhimurium ATCC 14028 and Bacillus cereus ATCC 11778. Our findings showed that lactic acid bacteria strains isolated from kefir are superior to those from other sources for improving glucuronic acid production and enhancing the antibacterial and antioxidant activities of kombucha. This study illustrates the potential of Lactobacillus casei and Lactobacillus plantarum isolated from kefir as biosupplements for enhancing the bioactivities of kombucha.


title: Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

abstract: This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.


title: A dual-switch push-pull converter with LLC resonant tank at secondary side for high reliable and high power aerospace applications

abstract: A new push-pull converter with features of soft switching and anti-shoot-through capability is proposed for aerospace applications. By embedding LLC resonant tank at the secondary side, the zero-voltage switching for MOSFETs and zero-current switching for Diodes can be achieved. Based on the fundamental harmonic approximation analysis, the operation principle of the proposed converter is analyzed, which is similar to the traditional LLC converters. A 500W prototype was built to verify the merits of the proposed converter. Experimental results show that all the power devices can be operated under soft-switching conditions, while achieving high conversion efficiency and high reliability.


title: Fast Finite Shearlet Transform : a tutorial

abstract: 3 Computation of the shearlet transform 13 3.1 Finite discrete shearlets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 A discrete shearlet frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.3 Inversion of the shearlet transform . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4 Smooth shearlets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.5 Implementation details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.5.1 Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.5.2 Computation of spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.6 Short documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.7 Download & Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.8 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.9 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32


title: REVIEW ARTICLE Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems— A Review, Part I

abstract: This paper is the second of a two-part review of methods for automated fault detection and diagnostics (FDD) and prognostics whose intent is to increase awareness of the HVAC&R research and development community to the body of FDD and prognostics developments in other fields as well as advancements in the field of HVAC&R. The first part of the review focused on generic FDD and prognostics, provided a framework for categorizing methods, described them, and identified their primary strengths and weaknesses (Katipamula and Brambley 2005). In this paper we address research and applications specific to the fields of HVAC&R, provide a brief discussion on the current state of diagnostics in buildings, and discuss the future of automated diagnostics in buildings.


title: Psychological Vulnerability and Problem Gambling: An Application of Durand Jacobs’ General Theory of Addictions to Electronic Gaming Machine Playing in Australia

abstract: The aim of this study was to conduct an empirical investigation of the validity of Jacobs’ (in J Gambl Behav 2:15–31, 1986) general theory of addictions in relation to gambling problems associated with electronic gaming machines (EGM). Regular EGM gamblers (n = 190) completed a series of standardised measures relating to psychological and physiological vulnerability, substance use, dissociative experiences, early childhood trauma and abuse and problem gambling (the Problem Gambling Severity Index). Statistical analysis using structural equation modelling revealed clear relationships between childhood trauma and life stressors and psychological vulnerability, dissociative-like experiences and problem gambling. These findings confirm and extend a previous model validated by Gupta and Derevensky (in J Gambl Stud 14: 17–49, 1998) using an adolescent population. The significance of these findings are discussed for existing pathway models of problem gambling, for Jacobs’ theory, and for clinicians engaged in assessment and intervention.


title: Difficulty in Video Games: Understanding the Effects of Dynamic Difficulty Adjustment in Video Games on Player Experience

abstract: This thesis investigates the impact of difficulty in the context of Dynamic Difficulty Adjustment (DDA) in video games on the player experience (PX). A series of quantitative and qualitative studies were employed to compare the effects of DDA systems on the PX and to gain an understanding of the lived PX of players engaging with such systems. This research contributes to game design knowledge and the current limited understanding of the impact of DDA systems on the PX. The findings are of relevance to practitioners and researchers in the domain of entertainment games as well as other related domains such as games for education and health care.


title: A framework for a video analysis tool for suspicious event detection

abstract: This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video data that exists in today's world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation of the event classification and detection technique is presented and a future experiment to refine this technique is proposed. These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation scheme proposed in this paper.


title: Perceptual evaluation of tone mapping operators with real-world scenes

abstract: A number of successful tone mapping operators for contrast compression ::: have been proposed due to the need to visualize high dynamic range ::: (HDR) images on low dynamic range devices. They were inspired by ::: fields as diverse as image processing, photographic practice, and ::: modeling of the human visual systems (HVS). The variety of approaches ::: calls for a systematic perceptual evaluation of their performance. We conduct a psychophysical experiment based on a direct comparison between the appearance of real-world scenes and HDR images of these scenes displayed on a low dynamic range monitor. In our experiment, HDR images are tone mapped by seven existing tone mapping operators. The primary interest of this psychophysical experiment is to assess the differences in how tone mapped images are perceived by human observers and to find out which attributes of image appearance account for these differences when tone mapped images are compared directly with their corresponding real-world scenes rather than ::: with each other. The human subjects rate image naturalness, overall contrast, overall brightness, and detail reproduction in dark ::: and bright image regions with respect to the corresponding real-world ::: scene. The results indicate substantial differences in perception of images produced by individual tone mapping operators. We observe a clear distinction between global and local operators in favor of the latter, and we classify the tone mapping operators according to naturalness and appearance attributes.


title: AdaBoost learning for fabric defect detection based on HOG and SVM

abstract: In this paper, a novel fabric detect detection scheme based on HOG and SVM is proposed. Firstly, each block-based feature of the image is encoded using the histograms of orientated gradients (HOG), which are insensitive to various lightings and noises. Then, a powerful feature selection algorithm, AdaBoost, is performed to automatically select a small set of discriminative HOG features in order to achieve robust detection results. In the end, support vector machine (SVM) is used to classify the fabric defects. Experimental results demonstrate the efficiency of our proposed algorithm.


title: Examining cognitive function across the lifespan using a mobile application

abstract: Many studies conducted in a laboratory or university setting are limited by funding, personnel, space, and time constraints. In the present study, we introduce a method of data collection using a mobile application that circumvents these typical experiment administration issues. Using the application, we examined cross-sectional age differences in cognitive function. We obtained data from more than 15,000 participants and replicated specific patterns of age-related differences in cognition. Using a subset of these participants, we also examined the processing speed account of age-related cognitive differences, and the association of exercise and leisure activity with cognitive function across the lifespan. We discuss the relative advantages and disadvantages of data collection with a mobile application, and provide recommendations for the use of this method in research.


title: An analysis of shared leadership, diversity, and team creativity in an e-learning environment

abstract: This study suggests a team creativity model including shared leadership, diversity, and knowledge sharing.To sophistically measure shared leadership, we employed a social network approach by calculating density.Our results showed that shared leadership and knowledge sharing positively contributed to team creativity.Knowledge sharing played a partially mediating role between shared leadership and team creativity.Knowledge sharing had a fully mediating effect between gender diversity and team creativity. In response to rapid change and fierce competition, creativity is an imperative factor to develop and implement innovation. Hence, most firms have pursued diverse strategies to promote individual and team creativity in the workplace. Shared leadership is a voluntarily, informally emergent structure beyond vertical leadership. A team is composed of individual members, and shared leadership and demographic diversity exist within the team, influencing team creativity. In this respect, we introduced shared leadership as a social network perspective as well as diversity into a team creativity model. In sum, we examined the influence of shared leadership and diversity on knowledge sharing and the subsequent effects on team creativity. Our results showed that role diversity directly influences team creativity, with shared leadership and knowledge sharing positively contributing to team creativity. Thus, knowledge sharing had a partially mediating role between shared leadership and team creativity. Apart from our hypotheses, the present results implied that if gender diversity (as a differentiated factor) is not a minority status, knowledge sharing may have a fully mediating effect between gender diversity and team creativity.


title: Parallel Counters

abstract: Multiple-input circuits that count the number of their inputs that are in a given state (normally logic ONE) are called parallel counters. In this paper three separate types of counters are described, analyzed, and compared. The first counter consists of a network of full adders. The second counter uses a combination of full adders and fast adders (that may be realized with READ-ONLY memories), while the third type of counter uses quasi-digital (i.e., analog current summing) techniques to generate an analog signal proportional to the count which is then digitized.


title: Validity of Self-Reported Hyperthyroidism and Hypothyroidism: Comparison of Self-Reported Questionnaire Data with Medical Record Review

abstract: Studies that aim at identifying genes or environmental factors contributing to the development of autoimmune thyroid disease (AITD) demand that several hundred patients and control subjects be assessed. In these large studies, the laboratory methodology is often described in detail whereas little attention is given to an accurate description of the study population. Usually, a diagnosis of AITD in the control group is based on self-reported disease status. Although such studies have been criticized for diagnostic inaccuracy, no study has evaluated the validity of self-reported hyperthyroidism and hypothyroidism in detail. We have assessed the validity of self-reported hyperthyroidism and hypothyroidism in 401 twin pairs from among 6,628 same gender pairs, ages 18-41 years who participated in a nationwide questionnaire survey in 1994. The self-reported questionnaire data were compared with information from medical records and the 1/κ coefficient, sensitivity, and specificity were determined. Overall, there...


title: Writing a narrative biomedical review: considerations for authors, peer reviewers, and editors

abstract: Review articles comprehensively covering a specific topic are crucial for successful research and academic projects. Most editors consider review articles for special and regular issues of journals. Writing a review requires deep knowledge and understanding of a field. The aim of this review is to analyze the main steps in writing a narrative biomedical review and to consider points that may increase the chances of success. We performed a comprehensive search through MEDLINE, EMBASE, Scopus, and Web of Science using the following keywords: review of the literature, narrative review, title, abstract, authorship, ethics, peer review, research methods, medical writing, scientific writing, and writing standards. Opinions expressed in the review are also based on personal experience as authors, peer reviewers, and editors.


title: Detection of eating and drinking arm gestures using inertial body-worn sensors

abstract: We propose a two-stage recognition system for detecting arm gestures related to human meal intake. Information retrieved from such a system can be used for automatic dietary monitoring in the domain of behavioural medicine. We demonstrate that arm gestures can be clustered and detected using inertial sensors. To validate our method, experimental results including 384 gestures from two subjects are presented. Using isolated discrimination based on HMMs an accuracy of 94% can be achieved. When spotting the gestures in continuous movement data, an accuracy of up to 87% is reached.


title: Microstrip antenna for 5G broadband communications: Overview of design issues

abstract: In this contribution we present two practical implementation cases of single patch antennas at 28GHz and 60GHz. We describe the design challenge of conciliating the antenna performance specifications and fabrication restrictions. Over this basic element of an array antenna we discuss the influence of three different categories of design issues.


Zika: the origin and spread of a mosquito-borne virus


title: Advanced Therapeutic Dressings for Effective Wound Healing--A Review.

abstract: Advanced therapeutic dressings that take active part in wound healing to achieve rapid and complete healing of chronic wounds is of current research interest. There is a desire for novel strategies to achieve expeditious wound healing because of the enormous financial burden worldwide. This paper reviews the current state of wound healing and wound management products, with emphasis on the demand for more advanced forms of wound therapy and some of the current challenges and driving forces behind this demand. The paper reviews information mainly from peer-reviewed literature and other publicly available sources such as the US FDA. A major focus is the treatment of chronic wounds including amputations, diabetic and leg ulcers, pressure sores, and surgical and traumatic wounds (e.g., accidents and burns) where patient immunity is low and the risk of infections and complications are high. The main dressings include medicated moist dressings, tissue-engineered substitutes, biomaterials-based biological dressings, biological and naturally derived dressings, medicated sutures, and various combinations of the above classes. Finally, the review briefly discusses possible prospects of advanced wound healing including some of the emerging physical approaches such as hyperbaric oxygen, negative pressure wound therapy and laser wound healing, in routine clinical care.


title: Strategy, Resource Orchestration and E-commerce Enabled Social Innovation in Rural China

abstract: There is a fit between strategy, resource orchestration and social innovation.E-commerce enabled social innovation types: incubator, independent and clone.Specify resource orchestration in e-commerce enabled social innovation. E-commerce enabled social innovation is becoming increasingly important as a way of reducing poverty in developing countries and yet has not been studied much by the IS community. We utilize the concept of resource orchestration as a theoretical lens to develop a fit model that explicates how resources are orchestrated under the guidance of either an indigenous, exogenous or collaborative strategy to achieve e-commerce enabled social innovation. The findings show how resources are orchestrated through the following specific resource-focused actions (collaborating, linking and enriching), which are influenced by the types of strategies applied. Our study also identifies different resource portfolios that influence the type of e-commerce enabled social innovation that can be achieved. This research benefits both academics and practitioners by contributing to cumulative theoretical developments related to e-commerce enabled social innovation and the resource orchestration perspective, and by offering corresponding practical insights to achieve fit between strategy, resource orchestration and social innovation.


title: R-C4.5 decision tree model and its applications to health care dataset

abstract: In this paper, a robust and practical decision tree improved model R-C4.5 and its simplified version are introduced. This model is based on C4.5 and improved efficiently on attribution selection and partitioning methods. R-C4.5 decision tree model avoids the appearance of fragmentation by uniting the branches which have poor classified effect. The simplified version of R-C4.5 model is implemented in data preprocessing. The experiments show that R-C4.5 and the simplified version enhance the interpretability of splitting attribute selection, reduce the numbers of insignificant or empty branches and avoid the appearance of over fitting. This paper focuses on applying the improved R-C4.5 decision tree model to the research on health care to predict inpatient length of stay. The result can be understood and accepted better by managers. It can also help health care organizations to arrange and make full use of hospital resources.


title: Top 10 Lessons Learned from Electronic Medical Record Implementation in a Large Academic Medical Center.

abstract: Electronic medical record (EMR) implementation efforts face many challenges, including individual and organizational barriers and concerns about loss of productivity during the process. These issues may be particularly complex in large and diverse settings with multiple specialties providing inpatient and outpatient care. This case report provides an example of a successful EMR implementation that emphasizes the importance of flexibility and adaptability on the part of the implementation team. It also presents the top 10 lessons learned from this EMR implementation in a large midwestern academic medical center. Included are five overarching lessons related to leadership, initial approach, training, support, and optimization as well as five lessons related to the EMR system itself that are particularly important elements of a successful implementation.


title: Four Experiments on the Perception of Bar Charts

abstract: Bar charts are one of the most common visualization types. In a classic graphical perception paper, Cleveland & McGill studied how different bar chart designs impact the accuracy with which viewers can complete simple perceptual tasks. They found that people perform substantially worse on stacked bar charts than on aligned bar charts, and that comparisons between adjacent bars are more accurate than between widely separated bars. However, the study did not explore why these differences occur. In this paper, we describe a series of follow-up experiments to further explore and explain their results. While our results generally confirm Cleveland & McGill's ranking of various bar chart configurations, we provide additional insight into the bar chart reading task and the sources of participants' errors. We use our results to propose new hypotheses on the perception of bar charts.


MRJS : A JavaScript MapReduce Framework for Web Browsers


title: Unconventional Phased Array Architectures and Design Methodologies—A Review

abstract: The proliferation of wireless services is driving innovative phased array solutions that are able to provide better cost/performance tradeoffs. In this context, the use of irregular array architectures provides a viable solution. This paper reviews and highlights some of the most recent advances in this field, including clustered, thinned, sparse, and time-modulated arrays, and their proposed design methodologies.


title: A vision-based intelligent path following control of a four-wheel differentially driven skid steer mobile robot

abstract: In this paper a Fuzzy Logic Controller (FLC) for path following of a four-wheel differentially skid steer mobile robot is presented. Fuzzy velocity and fuzzy torque control of the mobile robot is compared with classical controllers. To assess controllers robot kinematics and dynamics are simulated with parameters of P2-AT mobile robot. Results demonstrate the better performance of fuzzy logic controllers in following a predefined path.


title: Towards Automated Classification of Firmware Images and Identification of Embedded Devices

abstract: Embedded systems, as opposed to traditional computers, bring an incredible diversity. The number of devices manufactured is constantly increasing and each has a dedicated software, commonly known as firmware. Full firmware images are often delivered as multiple releases, correcting bugs and vulnerabilities, or adding new features. Unfortunately, there is no centralized or standardized firmware distribution mechanism. It is therefore difficult to track which vendor or device a firmware package belongs to, or to identify which firmware version is used in deployed embedded devices. At the same time, discovering devices that run vulnerable firmware packages on public and private networks is crucial to the security of those networks. In this paper, we address these problems with two different, yet complementary approaches: firmware classification and embedded web interface fingerprinting. We use supervised Machine Learning on a database subset of real world firmware files. For this, we first tell apart firmware images from other kind of files and then we classify firmware images per vendor or device type. Next, we fingerprint embedded web interfaces of both physical and emulated devices. This allows recognition of web-enabled devices connected to the network. In some cases, this complementary approach allows to logically link web-enabled online devices with the corresponding firmware package that is running on the devices. Finally, we test the firmware classification approach on 215 images with an accuracy of 93.5%, and the device fingerprinting approach on 31 web interfaces with 89.4% accuracy.


title: MapReduce as a programming model for association rules algorithm on Hadoop

abstract: As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance inefficient. Recently the development of network and distributed technology makes cloud computing a reality in the implementation of association rules algorithm. In this paper we describe the improved Apriori algorithm based on MapReduce mode, which can handle massive datasets with a large number of nodes on Hadoop platform.


title: Real-time identification and localization of body parts from depth images

abstract: We deal with the problem of detecting and identifying body parts in depth images at video frame rates. Our solution involves a novel interest point detector for mesh and range data that is particularly well suited for analyzing human shape. The interest points, which are based on identifying geodesic extrema on the surface mesh, coincide with salient points of the body, which can be classified as, e.g., hand, foot or head using local shape descriptors. Our approach also provides a natural way of estimating a 3D orientation vector for a given interest point. This can be used to normalize the local shape descriptors to simplify the classification problem as well as to directly estimate the orientation of body parts in space. Experiments involving ground truth labels acquired via an active motion capture system show that our interest points in conjunction with a boosted patch classifier are significantly better in detecting body parts in depth images than state-of-the-art sliding-window based detectors.


title: Priming the Secure Attachment Schema: Effects on Emotion Information Processing

abstract: The present study examined the effects of secure schema activation on selective attention towards attachment-related and emotional information. Seventy two participants were randomly allocated into two conditions–subliminal priming of mental representations of supportive attachment figures (a Picasso sketch of a mother holding a baby and looking into his eyes) and a no priming condition followed by an administration of a dot probe task that included positive and negative attachment-related and emotion words. The results showed that the activation of the secure prime in conjunction with chronic attachment orientations affected the processing of positive and negative attachment-unrelated emotional information. Results highlight relationships between higher-order processes of the attachment system (attachment schema activation) with early stage information processing (selective attention) as assessed by the dot probe task. Methodological issues are discussed with reference to the priming method used and the traditional version of the dot-probe task.


title: Soft switching interleaved PWM buck converter with one auxiliary switch

abstract: This paper proposes an interleaved zero voltage transition (ZVT) PWM buck converter. The proposed converter consists of two identical buck converter modules and an auxiliary circuit. The auxiliary circuit provides zero voltage switching condition for the main switches. Also, zero current switching condition for the auxiliary switch and diodes is achieved. The interleaved buck converter with the proposed auxiliary switch is analyzed. The simulation results of a 300W ZVT interleaved buck converter operating at 100 KHz are presented to justify the validity of theoretical analysis.


title: Screaming Fast Galois Field Arithmetic Using Intel SIMD Instructions

abstract: Galois Field arithmetic forms the basis of Reed-Solomon and other erasure coding techniques to protect storage systems from failures. Most implementations of Galois Field arithmetic rely on multiplication tables or discrete logarithms to perform this operation. However, the advent of 128-bit instructions, such as Intel's Streaming SIMD Extensions, allows us to perform Galois Field arithmetic much faster. This short paper details how to leverage these instructions for various field sizes, and demonstrates the significant performance improvements on commodity microprocessors. The techniques that we describe are available as open source software.


title: Need for speed: CORA scheduler for optimizing completion-times in the cloud

abstract: There is an increasing need for cloud service performance that can be tailored to customer requirements. In the context of jobs submitted to cloud computing clusters, a crucial requirement is the specification of job completion-times. A natural way to model this specification, is through client/job utility functions that are dependent on job completion-times. We present a method to allocate and schedule heterogeneous resources to jointly optimize the utilities of jobs in a cloud. Specifically: (i) we formulate a completion-time optimal resource allocation (CORA) problem to apportion cluster resources across the jobs that enforces max-min fairness among job utilities, and (ii) starting with an integer programming problem, we perform a series of steps to transform it into an equivalent linear programming problem, and (iii) we implement the proposed framework as a utility-aware resource scheduler in the widely used Hadoop data processing framework, and finally (iv) through extensive experiments with real-world datasets, we show that our prototype achieves significant performance improvement over existing resource-allocation policies.


title: Customer Efficiency, Channel Usage, and Firm Performance in Retail Banking

abstract: Innovations in technology and service design have increasingly enabled firms to incorporate self-service technology to augment or substitute for “traditional” employee-provided service channels. Although it is clear that self-service can reduce cost, less is known about how customers utilize self-service channels in a multichannel service delivery system and the resulting impact on firm performance. An important aspect of service operations is that customers are coproducers of the service. Thus, the performance of the delivery system and customers' use of service channels can be affected by customers' own efficiency or productivity in service coproduction (customer efficiency). In this paper, we utilize prior theoretical frameworks in service operations and economics to hypothesize relationships among customer characteristics (especially coproduction efficiency), channel utilization, and firm performance. We then test these hypotheses using panel data from a large retail bank. Overall, we find that higher customer efficiency in self-service channels is associated with greater profitability and has a complex relationship with customer retention and product utilization.


title: Data Quality: From Theory to Practice

abstract: Data quantity and data quality, like two sides of a coin, are equally important to data management. This paper provides an overview of recent advances in the study of data quality, from theory to practice. We also address challenges introduced by big data to data quality management.


title: Black-out test vs. UV camera for corona inspection of HV motor stator endwindings

abstract: As part of a factory acceptance test program for high voltage motors, end users sometimes specify a black-out test. This is a traditional offline inspection where the stator is placed in complete darkness, each phase is energized to 115% of rated line-to-neutral voltage, and both ends of the stator are observed to determine the presence, location and severity of endwinding surface partial discharges (PD). The setup for the test may be complex and risky due to the need for observers to be standing in darkness close to the energized parts of the stator. The test results are qualitative and strongly depend on the observer's eyesight and individual perception. A safer and more accurate alternative is to use an ultraviolet (UV) camera or viewer. The observed PD activity may be observed in ambient lighting, recorded, and quantified through simultaneous offline PD measurements. This paper describes the two inspection techniques, and presents experimental validation of the UV corona camera inspection method as a suitable replacement for a black-out test. Sample 13.8-kV coils were wound in a fixture simulating their relationship in a stator winding, and subjected to high-potential tests while observed under black-out conditions and with a UV corona camera in ambient lighting. The stator windings from two high voltage compressor motors were inspected using the same camera. Recorded images of the observed discharges and measured PD activity in the sample coils and stator winding were used to compare the evaluation by each test method.


title: Majority Vote of Diverse Classifiers for Late Fusion

abstract: In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle this problem, we investigate a recent and elegant well-founded quadratic program named MinCq coming from the machine learning PAC-Bayesian theory. MinCq looks for the weighted combination, over a set of real-valued functions seen as voters, leading to the lowest misclassification rate, while maximizing the voters' diversity. We propose an extension of MinCq tailored to multimedia indexing. Our method is based on an order-preserving pairwise loss adapted to ranking that allows us to improve Mean Averaged Precision measure while taking into account the diversity of the voters that we want to fuse. We provide evidence that this method is naturally adapted to late fusion procedures and confirm the good behavior of our approach on the challenging PASCAL VOC'07 benchmark.


title: Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections

abstract: Detecting healthcare-associated infections pose a major challenge in healthcare. Using natural language processing and machine learning applied on electronic patient records is one approach that has been shown to work. However the results indicate that there was room for improvement and therefore we have applied deep learning methods. Specifically we implemented a network of stacked sparse auto encoders and a network of stacked restricted Boltzmann machines. Our best results were obtained using the stacked restricted Boltzmann machines with a precision of 0.79 and a recall of 0.88.


title: Binary Phase Detector Gain in Bang-Bang Phase-Locked Loops With DCO Jitter

abstract: Bang-bang phase-locked loops are hard nonlinear systems due to the nonlinearity introduced by the binary phase detector (BPD). In the presence of jitter, the nonlinear loop is typically analyzed by linearizing the BPD and applying linear transfer functions in the analysis. In contrast to a linear phase detector, the linearized gain of a BPD depends on the rms jitter and the type of jitter (either nonaccumulative or accumulative). Previous works considered the case of nonaccumulative reference clock jitter and showed that the BPD gain is inversely proportional to the rms jitter when the latter is small or large. In this brief, we consider the case of accumulative digitally-controlled oscillator (DCO) jitter and derive an asymptotic closed-form expression for the BPD gain, which becomes exact in the limit of small and large jitter. Contrary to the reference clock jitter case, the BPD gain is constant for small DCO jitter and is inversely proportional to the square of jitter for large DCO jitter; in the latter case, the timing jitter has a normal Laplace distribution.


title: MAGIC—Memristor-Aided Logic

abstract: Memristors are passive components with a varying resistance that depends on the previous voltage applied across the device. While memristors are naturally used as memory, memristors can also be used for other applications, including logic circuits. In this brief, a memristor-only logic family, i.e., memristor-aided logic (MAGIC), is presented. In each MAGIC logic gate, memristors serve as an input with previously stored data, and an additional memristor serves as an output. The topology of a MAGIC nor gate is similar to the structure of a common memristor-based crossbar memory array. A MAGIC nor gate can therefore be placed within memory, providing opportunities for novel non-von Neumann computer architectures. Other MAGIC gates also exist (e.g., and , or , not , and nand ) and are described in this brief.


title: A reference architecture for educational data mining

abstract: In this paper we present a reference architecture for ETL stages of EDM and LA that works with different data formats and different extraction sites, ensuring privacy and making easier for new participants to enter into the process without demanding more computing power. Considering scenarios with a multitude of virtual environments hosting educational activities, accessible through a common infrastructure, we devised a reference model where data generated from interaction between users and among users and the environment itself, are selected, organized and stored in local “baskets”. Local baskets are then collected and grouped in a global basket. Organization resources like item modeling are used in both levels of basket construction. Using this reference upon a client-server architectural style, a reference architecture was developed and has been used to carry out a project for an official foundation linked to Brazilian Ministry of Education, involving educational data mining and sharing of 100+ higher education institutions and their respective virtual environments. In this architecture, a client-collector inside each virtual environment collects information from database and event logs. This information along with definitions obtained from item models are used to build local baskets. A synchronization protocol keeps all item models synced with client-collectors and server-collectors generating global baskets. This approach has shown improvements on ETL like: parallel processing of items, economy on storage space and bandwidth, privacy assurance, better tenacity, and good scalability.


title: OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases

abstract: Benchmarking is an essential aspect of any database management system (DBMS) effort. Despite several recent advancements, such as pre-configured cloud database images and database-as-a-service (DBaaS) offerings, the deployment of a comprehensive testing platform with a diverse set of datasets and workloads is still far from being trivial. In many cases, researchers and developers are limited to a small number of workloads to evaluate the performance characteristics of their work. This is due to the lack of a universal benchmarking infrastructure, and to the difficulty of gaining access to real data and workloads. This results in lots of unnecessary engineering efforts and makes the performance evaluation results difficult to compare. To remedy these problems, we present OLTP-Bench, an extensible "batteries included" DBMS benchmarking testbed. The key contributions of OLTP-Bench are its ease of use and extensibility, support for tight control of transaction mixtures, request rates, and access distributions over time, as well as the ability to support all major DBMSs and DBaaS platforms. Moreover, it is bundled with fifteen workloads that all differ in complexity and system demands, including four synthetic workloads, eight workloads from popular benchmarks, and three workloads that are derived from real-world applications. We demonstrate through a comprehensive set of experiments conducted on popular DBMS and DBaaS offerings the different features provided by OLTP-Bench and the effectiveness of our testbed in characterizing the performance of database services.


title: Multi-modal and Multi-spectral Registration for Natural Images

abstract: Images now come in different forms - color, near-infrared, depth, etc. - due to the development of special and powerful cameras in computer vision and computational photography. Their cross-modal correspondence establishment is however left behind. We address this challenging dense matching problem considering structure variation pos- sibly existing in these image sets and introduce new model and solution. Our main contribution includes designing the descriptor named robust selective normalized cross correlation (RSNCC) to establish dense pixel correspondence in input images and proposing its mathematical param- eterization to make optimization tractable. A computationally robust framework including global and local matching phases is also established. We build a multi-modal dataset including natural images with labeled sparse correspondence. Our method will benefit image and vision appli- cations that require accurate image alignment.


title: Araucaria: Software for argument analysis, diagramming and representation

abstract: Argumentation theory involves the analysis of naturally occurring argument, and one key tool employed to this end both in the academic community and in teaching critical thinking skills to undergraduates is argument diagramming. By identifying the structure of an argument in terms of its constituents and the relationships between them, it becomes easier to critically evaluate each part of an argument in turn. The task of analysis and diagramming, however, is labor intensive and often idiosyncratic, which can make academic exchange difficult. The Araucaria system provides an interface which supports the diagramming process, and then saves the result using AML, an open standard, designed in XML, for describing argument structure. Araucaria aims to be of use not only in pedagogical situations, but also in support of research activity. As a result, it has been designed from the outset to handle more advanced argumentation theoretic concepts such as schemes, which capture stereotypical patterns of reasoning. The software is also designed to be compatible with a number of applications under development, including dialogic interaction and online corpus provision. Together, these features, combined with its platform independence and ease of use, have the potential to make Araucaria a valuable resource for the academic community.


title: Parallel Poisson disk sampling with spectrum analysis on surfaces

abstract: The ability to place surface samples with Poisson disk distribution can benefit a variety of graphics applications. Such a distribution satisfies the blue noise property, i.e. lack of low frequency noise and structural bias in the Fourier power spectrum. While many techniques are available for sampling the plane, challenges remain for sampling arbitrary surfaces. In this paper, we present new methods for Poisson disk sampling with spectrum analysis on arbitrary manifold surfaces. Our first contribution is a parallel dart throwing algorithm that generates high-quality surface samples at interactive rates. It is flexible and can be extended to adaptive sampling given a user-specified radius field. Our second contribution is a new method for analyzing the spectral quality of surface samples. Using the spectral mesh basis derived from the discrete mesh Laplacian operator, we extend standard concepts in power spectrum analysis such as radial means and anisotropy to arbitrary manifold surfaces. This provides a way to directly evaluate the spectral distribution quality of surface samples without requiring mesh parameterization. Finally, we implement our Poisson disk sampling algorithm on the GPU, and demonstrate practical applications involving interactive sampling and texturing on arbitrary surfaces.


title: Factors Affecting the Adoption of Internet Banking AmongstIIUMÃ¢ÂÂ students: A Structural Equation Modeling Approach (SEM)

abstract: This article examines the factors that determine the internet banking adoption amongst International Islamic University Malaysia (IIUM) and its causal effects using a theoretical model based on the Technology Acceptance Model (TAM). The research model consists of four exogenous latent constructs, namely, awareness, perceived usefulness, trust and perceived risk and endogenous latent construct namely Internet banking adoption. Data relating to constructs were collected from 200 university’s students in (IIUM) and subjected to structural Equation Modeling (SEM) analysis. Confirmatory Factor Analysis (CFA) was performed to examine the reliability, construct validity, convergent validity and goodness of fit of structural models and measurement models. The hypothesized structural model fits the data well. The results show that the significant factor that leads to the adoption of internet banking is perceived usefulness but awareness, trust and risk have negative significant towards the use of internet banking.


Managing Evolving Workflow Specifications With Schema Versioning and Migration Rules


title: A forensic acquisition and analysis system for IaaS

abstract: Cloud computing is a promising next-generation computing paradigm that offers significant economic benefits to both commercial and public entities. Furthermore, cloud computing provides accessibility, simplicity, and portability for its customers. Due to the unique combination of characteristics that cloud computing introduces (including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service), digital investigations face various technical, legal, and organizational challenges to keep up with current developments in the field of cloud computing. There are a wide variety of issues that need to be resolved in order to perform a proper digital investigation in the cloud environment. This paper examines the challenges in cloud forensics that are identified in the current research literature, alongside exploring the existing proposals and technical solutions addressed in the respective research. The open problems that need further effort are highlighted. As a result of the analysis of literature, it is found that it would be difficult, if not impossible, to perform an investigation and discovery in the cloud environment without relying on cloud service providers (CSPs). Therefore, dependence on the CSPs is ranked as the greatest challenge when investigators need to acquire evidence in a timely yet forensically sound manner from cloud systems. Thus, a fully independent model requires no intervention or cooperation from the cloud provider is proposed. This model provides a different approach to a forensic acquisition and analysis system (FAAS) in an Infrastructure as a Service model. FAAS seeks to provide a richer and more complete set of admissible evidences than what current CSPs provide, with no requirement for CSP involvement or modification to the CSP's underlying architecture.


title: Fuzzy deep belief networks for semi-supervised sentiment classification

abstract: By embedding prior knowledge into the learning structure, this paper presents a two-step semi-supervised learning method called fuzzy deep belief networks (FDBN) for sentiment classification. First, we train the general deep belief networks (DBN) by the semi-supervised learning taken on training dataset. Then, we design a fuzzy membership function for each class of reviews based on the learned deep architecture. Since the training of DBN maps each review into the DBN output space, the distribution of all training samples in the space is treated as prior knowledge and is encoded by a series of fuzzy membership functions. Second, based on the fuzzy membership functions and the DBN obtained in the first step, a novel FDBN architecture is constructed and the supervised learning stage is applied to improve the classification performance of the FDBN. FDBN not only inherits the powerful abstraction ability of DBN, but also demonstrates the attractive fuzzy classification ability for handling sentiment data. To inherit the advantages of both active learning and FDBN, we also propose an active FDBN (AFD) semi-supervised learning method. The empirical validation on five sentiment classification datasets demonstrates the effectiveness of FDBN and AFD methods.


title: k-Anonymously Private Search over Encrypted Data

abstract: In this paper we compare the performance of various homomorphic encryption methods on a private search scheme that can achieve $k$-anonymity privacy. To make our benchmarking fair, we use open sourced cryptographic libraries which are written by experts and well scrutinized. We find that Goldwasser-Micali encryption achieves good enough performance for practical use, whereas fully homomorphic encryptions are much slower than partial ones like Goldwasser-Micali and Paillier.


title: Using Argument-based Features to Predict and Analyse Review Helpfulness

abstract: We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.


title: Multimodal Multi-task Learning for Dimensional and Continuous Emotion Recognition

abstract: Automatic emotion recognition is a challenging task which can make great impact on improving natural human computer interactions. In this paper, we present our effort for the Affect Subtask in the Audio/Visual Emotion Challenge (AVEC) 2017, which requires participants to perform continuous emotion prediction on three affective dimensions: Arousal, Valence and Likability based on the audiovisual signals. We highlight three aspects of our solutions: 1) we explore and fuse different hand-crafted and deep learned features from all available modalities including acoustic, visual, and textual modalities, and we further consider the interlocutor influence for the acoustic features; 2) we compare the effectiveness of non-temporal model SVR and temporal model LSTM-RNN and show that the LSTM-RNN can not only alleviate the feature engineering efforts such as construction of contextual features and feature delay, but also improve the recognition performance significantly; 3) we apply multi-task learning strategy for collaborative prediction of multiple emotion dimensions with shared representations according to the fact that different emotion dimensions are correlated with each other. Our solutions achieve the CCC of 0.675, 0.756 and 0.509 on arousal, valence, and likability respectively on the challenge testing set, which outperforms the baseline system with corresponding CCC of 0.375, 0.466, and 0.246 on arousal, valence, and likability.


title: A conceptual privacy framework for privacy-aware IoT health applications

abstract: Internet of things (IoT) is intensely gaining reputation due to its necessity and efficiency in the computer realm.The support of wireless ::: connectivity as well as the emergence of gadgets alleviates its usage essentially in governing systems in various fields.Though these systems are ::: ubiquitous, pervasive and seamless, an issue concerning consumers’ privacy remains debatable. This is most evident in the health sector, as there is an immaculate rise in terms of awareness amongst patients where data privacy is concerned. In this paper, we propose a framework modelling the privacy requirements for IoT-based health applications.We have reviewed several privacy frameworks to derive at the essential principles required to develop privacy-aware IoT health applications.The proposed framework presents ::: important privacy requirements to be addressed in the development of novel IoT health applications.


title: A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)

abstract: The recent advance in neural network architecture and training algorithms has shown the effectiveness of representation learning. The neural-network-based models generate better representation than the traditional ones. They have the ability to automatically learn the distributed representation for sentences and documents. To this end, we proposed a novel model that addresses several issues that are not adequately modeled by the previously proposed models, such as the memory problem and incorporating the knowledge of document structure. Our model uses a hierarchical structured self-attention mechanism to create the sentence and document embeddings. This architecture mirrors the hierarchical structure of the document and in turn enables us to obtain better feature representation. The attention mechanism provides extra source of information to guide the summary extraction. The new model treated the summarization task as a classification problem in which the model computes the respective probabilities of sentence–summary membership. The model predictions are broken up by several features such as information content, salience, novelty, and positional representation. The proposed model was evaluated on two well-known datasets, the CNN/Daily Mail and DUC 2002. The experimental results show that our model outperforms the current extractive state of the art by a considerable margin.


title: 13.56 MHz class E power amplifier with 94.6% efficiency and 31 watts output power for RF heating applications

abstract: Radio frequency power amplifiers (RF PA) are widely used for several applications e.g. wireless communication, wireless power transmission (WPT) and radio frequency heating. In general, the choice of power amplifier's operating class is based on requirements regarding linearity and power efficiency. For applications in which linearity is not a critical issue whereas high efficiency is desired, switched-mode amplifier e.g. class E is suitable. Switched-mode operation of class E offers high efficiency with a simple topology, leading to optimal costs of production and system operation. This work presents design and implementation of a high power and high efficiency 13.56 MHz class E power amplifier with parallel load circuit. As a power device, LDMOS was chosen. The fabricated class E PA provides Power-Added Efficiency (PAE) of 94.6% with 21.92 power gain when maximum output power of 44.92 dBm (31 W) is delivered to the load. Industrial, Scientific and medical (ISM) bands Class E PA with parallel load circuit.


title: Evidence Regarding the Need for a Diagnostic Category for a Coercive Paraphilia

abstract: Evidence relevant to a potential diagnosis of Paraphilic Coercive Disorder is reviewed. Salient cues indicating that their partner is feeling coerced normally at least partially inhibit male sexual arousal while cues indicating mutual interest heighten arousal. However, for a minority of males, this pattern reverses with salient coercion cues leading to heightened arousal. This unusual pattern of arousal and fantasy is associated with a self-reported willingness to rape among non-convicted samples and is more common among convicted rapists than in other offender groups. It is inconsistently associated with Sadism as defined by the DSM-IV-TR and only weakly associated with psychopathy or general criminality. Evidence for it as an abnormal and persistent sexual interest comes from behavioral patterns, self-reported sexual fantasy, and laboratory tests. Two possible ways of incorporating it into a future version of the DSM are outlined.


Evaluating the quality of the LOD cloud: An empirical investigation


title: An update on the perioperative management of children with upper respiratory tract infections

abstract: Purpose of reviewThis review summarizes the current evidence for the management of children with recent upper respiratory tract infections (URTIs). Furthermore, the review includes management guidelines for children with URTIs.Recent findingsGood history and clinical examination is sufficient in mos


title: The discone antenna in a BPSK direct-sequence indoor UWB communication system

abstract: The current ultra-wideband (UWB) channel models proposed by different research groups are generally based on measurements in which the effect of the antennas used are inevitably embedded. There is potentially significant waveform distortion by UWB antennas, and it is difficult to distinguish between antenna and real propagation path effects in many of the reported channel measurements. However, understanding these effects is important for overall system performance prediction. The discone antenna is known for its broadband characteristics and has been widely used for channel measurements. In this paper, we address the importance of the antenna and indoor propagation effects as applied to a UWB system analysis. This is achieved by use of a true time-domain three-dimensional ray-tracing propagation model coupled with accurate simulation of the time-domain performance of the discone antenna. A DS-UWB system approach was adopted for detailed study and its performance is evaluated under the channel model with and without considering the antenna pattern effect. Both line-of-sight and non-line-of-sight cases are considered. The antenna effect can be seen to be significant, leading to the conclusion that interpretation of practical system measurements must allow for the antennas being used in the measurement system.


title: Electromagnetic Induction Spectroscopy for Clearing

abstract: An estimated 110 million landmines, mostly antipersonnel mines laid in over 60 countries, kill or maim over 26000 people a year. One of the dilemmas for removing landmines is the amount of false alarms in a typical minefield. Broadband electromagnetic induction spectroscopy (EMIS) is a promising technology that can both detect and identify buried objects as landmines. By reducing the number of false alarms, this approach significantly reduces costs associated with landmine removal. Combining the EMIS technology and a broadband EMI sensor, the scientific phenomenology that has potential applications for identifying landmines, unexploded ordnance, and hidden weapons at security checkpoints can now be explored.


title: Genes, Evolution and Intelligence

abstract: I argue that the g factor meets the fundamental criteria of a scientific construct more fully than any other conception of intelligence. I briefly discuss the evidence regarding the relationship of brain size to intelligence. A review of a large body of evidence demonstrates that there is a g factor in a wide range of species and that, in the species studied, it relates to brain size and is heritable. These findings suggest that many species have evolved a general-purpose mechanism (a general biological intelli- gence) for dealing with the environments in which they evolved. In spite of numerous studies with considerable statistical power, we know of very few genes that influence g and the effects are very small. Nevertheless, g appears to be highly polygenic. Given the complexity of the human brain, it is not surprising that that one of its primary fac- ulties—intelligence—is best explained by the near infini- tesimal model of quantitative genetics.


Outpatient appointment scheduling given individual day-dependent no-show predictions


title: Surface shape and curvature scales

abstract: The classical surface curvature measures, such as the Gaussian and the mean curvature at a point of a surface, are not very indicative of local shape. The two principal curvatures (taken as a pair) are more informative, but one would prefer a single shape indicator rather than a pair of numbers. Moreover, the shape indicator should preferably be independent of the size i.e. the amount of curvature, as distinct from the type of curvature. We propose two novel measures of local shape, the ‘curvedness’ and the ‘shape index’. The curvedness is a positive number that specifies the amount of curvature, whereas the shape index is a number in the range [−1, +1] and is scale invariant. The shape index captures the intuitive notion of ‘local shape’ particularly well. The shape index can be mapped upon an intuitively natural colour scale. Two complementary shapes (like stamp and mould) map to complementary hues. The symmetrical saddle (which is very special because it is self-complementary) maps to white. When a surface is tinted according to this colour scheme, this induces an immediate perceptual segmentation of convex, concave, and hyperbolic areas. We propose it as a useful tool in graphics representation of 3D shape.


title: Fields of Experts

abstract: We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach provides a practical method for learning high-order Markov random field (MRF) models with potential functions that extend over large pixel neighborhoods. These clique potentials are modeled using the Product-of-Experts framework that uses non-linear functions of many linear filter responses. In contrast to previous MRF approaches all parameters, including the linear filters themselves, are learned from training data. We demonstrate the capabilities of this Field-of-Experts model with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the model is trained on a generic image database and is not tuned toward a specific application, we obtain results that compete with specialized techniques.


title: Fiber orientation-dependent white matter contrast in gradient echo MRI

abstract: Recent studies have shown that there is a direct link between the orientation of the nerve fibers in white matter (WM) and the contrast observed in magnitude and phase images acquired using gradient echo MRI. Understanding the origin of this link is of great interest because it could offer access to a new diagnostic tool for investigating tissue microstructure. Since it has been suggested that myelin is the dominant source of this contrast, creating an accurate model for characterizing the effect of the myelin sheath on the evolution of the NMR signal is an essential step toward fully understanding WM contrast. In this study, we show by comparison of the results of simulations and experiments carried out on human subjects at 7T, that the magnitude and phase of signals acquired from WM in vivo can be accurately characterized by (i) modeling the myelin sheath as a hollow cylinder composed of material having an anisotropic magnetic susceptibility that is described by a tensor with a radially oriented principal axis, and (ii) adopting a two-pool model in which the water in the sheath has a reduced T2 relaxation time and spin density relative to its surroundings, and also undergoes exchange. The accuracy and intrinsic simplicity of the hollow cylinder model provides a versatile framework for future exploitation of the effect of WM microstructure on gradient echo contrast in clinical MRI.


title: Comparison of Digital Water Marking methods

abstract: In Digital watermarking, image or video is embedded information data within an insensible form for human visual system but in a way that protects from attacks such as common image processing techniques. Spatial domain(Least significant bit(LSB)) and transform domain (Discrete Cosine Transform(DCT) and Discrete Wavelet Transform(DWT)) methods are used. DWT is best method because of using embedded zero tree wavelet image compression scheme and high frequency sub bands. Keyword: Digital watermarking, Leas significant bit, Discrete Cosine Transform, Discrete Wavelet Transform


title: A Low-Energy Crystal-Less Double-FSK Sensor Node Transceiver for Wireless Body-Area Network

abstract: An energy-efficient crystal-less double-FSK transceiver for wireless body-area-network (WBAN) sensor nodes is implemented in 0.18-μm CMOS technology with a 1-V supply. The injection-locking digitally controlled oscillator (IL-DCO) replaces the crystal oscillator (XO), which leads to significantly reduce the energy consumption and system cost. With the proposed calibration method using an injection-locking detector (IL-detector), the frequency drift of DCO can be calibrated within 100-kHz accuracy over 100° temperature variation. For the full satisfaction to the WBAN requirements, such as wide range of quality of service in terms of data rate, bit error rate, and network coexistence, we adopt a scalable double-FSK modulation scheme with divider-based transmitter by a power-efficient switching modulator. As a result, the fabricated crystal-less double-FSK WBAN compatible sensor node transceiver consumes 1 and 2 mW in calibrating and transmitting modes, respectively, at a data rate of up to 10 Mb/s, providing an 80 MHz reference source with 100-kHz accuracy by auto-calibrated DCO.


title: Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms

abstract: There is growing evidence that consumers are influenced by Internet-based opinion forums before making a variety of purchase decisions. Firms whose products are being discussed in such forums are therefore tempted to manipulate consumer perceptions by posting costly anonymous messages that praise their products. This paper offers a theoretical analysis of the impact of such behavior on firm profits and consumer surplus. There are three main results. First, if every firm's manipulation strategy is a monotonically increasing (decreasing) function of that firm's true quality, strategic manipulation of online forums increases (decreases) the information value of a forum to consumers. This result implies the existence of settings where online forum manipulation benefits consumers. Second, equilibria where strategies are monotonically increasing (decreasing) functions of a firm's true quality exist in settings where the firm's net payoff function, inclusive of the cost of manipulation, is supermodular (submodular) in the firm's quality and manipulation action. Third, in a broad class of settings, if the precision of honest consumer opinions that firms manipulate is sufficiently high, firms of all types, as well as society, would be strictly better off if manipulation of online forums was not possible. Nonetheless, firms are locked into a “rat race” and forced to spend resources on such profit-reducing activities; if they don't, consumer perceptions will be biased against them. The social cost of online manipulation can be reduced by developing “filtering” technologies that make it costlier for firms to manipulate. Interestingly, as the amount of user-contributed online content increases, it is firms, and not consumers, that have most to gain from the development of such technologies.


title: A qualitative study of nursing student experiences of clinical practice

abstract: Background ::: Nursing student's experiences of their clinical practice provide greater insight to develop an effective clinical teaching strategy in nursing education. The main objective of this study was to investigate student nurses' experience about their clinical practice.


title: Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment

abstract: Abstract Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation system in the future. Many studies have been made to improve AVs’ ability of environment recognition and vehicle control, while the attention paid to decision making is not enough and the existing decision algorithms are very preliminary. Therefore, a framework of the decision-making training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning (DRL) training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following (CF), is trained within this framework. In addition, theoretical analysis and experiments were conducted to evaluate the proposed reward functions for accelerating training using DRL. The results show that on the premise of driving comfort, the efficiency of the trained AV increases 7.9% and 3.8% respectively compared to the classical adaptive cruise control models, intelligent driver model and constant-time headway policy. Moreover, on a more complex three-lane section, we trained an integrated model combining both CF and lane-changing behavior, with the average speed further growing 2.4%. It indicates that our framework is effective for AV’s decision-making learning.


title: Circumlocution in diagnostic medical queries

abstract: Circumlocution is when many words are used to describe what could be said with fewer, e.g., "a machine that takes moisture out of the air" instead of "dehumidifier." Web search is a perfect backdrop for circumlocution where people struggle to name what they seek. In some domains, not knowing the correct term can have a significant impact on the search results that are retrieved. We study the medical domain, where professional medical terms are not commonly known and where the consequence of not knowing the correct term can impact the accuracy of surfaced information, as well as escalation of anxiety, and ultimately the medical care sought. Given a free-form colloquial health search query, our objective is to find the underlying professional medical term. The problem is complicated by the fact that people issue quite varied queries to describe what they have. Machine-learning algorithms can be brought to bear on the problem, but there are two key complexities: creating high-quality training data and identifying predictive features. To our knowledge, no prior work has been able to crack this important problem due to the lack of training data. We give novel solutions and demonstrate their efficacy via extensive experiments, greatly improving over the prior art.


title: Ratio estimators for the population variance in simple and stratified random sampling

abstract: We propose some ratio-type variance estimators using ratio estimators for the population mean in literature. We obtain mean square error (MSE) equations of proposed estimators and show that proposed estimators are more efficient than the traditional ratio estimator, suggested by [C.T. Isaki, Variance estimation using auxiliary information, Journal of the American Statistical Association 78 (1983) 117-123], under certain conditions. We also adapt the proposed estimators in the simple random sampling to the stratified random sampling. In addition, we support the theoretical results with the aid of numerical examples.


title: Optimization of Preventive Maintenance scheduling in semiconductor manufacturing models using a simulation-based Approximate Dynamic Programming approach

abstract: This paper presents initial results on the application of a simulation-based Approximate Dynamic Programming (ADP) approach for the optimization of Preventive Maintenance (PM) scheduling decisions in semiconductor manufacturing systems. In particular, the so-called Intel Mini-Fab benchmark is used as an illustrative example. Our approach is based on an actor-critic architecture in which the critic corresponds to a parametric estimation of the optimal differential cost for an infinite horizon average cost criterion-based optimization model. The actor is defined using post-decision state variables and a heuristic approach. Our algorithm also utilizes a temporal-difference learning algorithm with a gradient descent approach to tune a linear parametric structure that approximates the optimal differential cost function. Simulation experiments validated the applicability of our algorithm in the Intel Mini-Fab by showing a significant reduction in average cycle time when compared with a series of fixed baseline PM schedules.


title: Chronic Expanding Hematoma in the Popliteal Fossa after Pseudoaneurysm Surgery because of Nail Puncture

abstract: Hematomas caused by surgery or trauma that persist and expand slowly for more than a month are defined as chronic expanding hematomas (CEH). Magnetic resonance imaging (MRI) is useful for the diagnosis. Total excision with the pseudocapsule is the treatment method. Pseudoaneurysms result from arterial wall disruptions and can be mistaken for CEH. We present a rare case report of a 45-year-old man with a large, painful swelling in his left popliteal fossa. He had a puncture wound by a nail 11 years ago and a gradually expanding mass occurred in his popliteal fossa. A pseudoaneurysm was detected and operated a year later. After surgery, a gradually expanding mass recurred in his popliteal fossa. On the arteriography, the popliteal artery was occluded and the blood flow was maintained with collateral vessels. On MRI, an enormous swelling of 115 × 107 × 196 cm in diameter was seen. It was diagnosed as CEH and was excised completely protecting the collateral vessels and there was no recurrence after a year from the surgery.


title: Characterizing Cloud Applications on a Google Data Center

abstract: In this paper, we characterize Google applications, based on a one-month Google trace with over 650k jobs running across over 12000 heterogeneous hosts from a Google data center. On one hand, we carefully compute the valuable statistics about task events and resource utilization for Google applications, based on various types of resources (such as CPU, memory) and execution types (e.g., whether they can run batch tasks or not). Resource utilization per application is observed with an extremely typical Pareto principle. On the other hand, we classify applications via a K-means clustering algorithm with optimized number of sets, based on task events and resource usage. The number of applications in the K-means clustering sets follows a Pareto-similar distribution. We believe our work is very interesting and valuable for the further investigation of Cloud environment.


title: W- and G-Band Solid State Power Combining

abstract: Recent advances in semiconductor technologies have enabled the design and fabrication of mmW MMIC amplifiers with unprecedented gain, output power, and power added efficiency. However, individual mmW MMIC performance is ultimately limited by transistor size and on-die power combining losses. Therefore, in order to achieve significant solid state mmW amplifier power levels, low-loss off-die power combining is obviously necessary. This paper examines trade-offs between on-die, waveguide, and spatial power combining approaches. Several examples of mmW power combined amplifiers are discussed including 10W+ W-band GaN and 1W-class EHF InP amplifiers.


title: 60 GHz patch antenna array on low cost Liquid-Crystal Polymer (LCP) substrate

abstract: Millimeter wave communications promises to enable the very high data rates demanded of 5G networks, but there are several hurdles that must be negotiated first. One of these hurdles is the small energy collecting capability of the small aperture antennas at these wavelengths. High gain antennas are required to overcome this limitation. The 60 GHz band is of particular interest due to the large 7-9 GHz of unlicensed spectrum available. The often cited atmospheric absorption in the band (~13dB/km) is easily tolerated in the 100-200m links envisioned for 5G networks. In order to test the feasibility of a 60GHz network, a platform is being constructed and a low cost, high gain antenna is required. For a low cost and small size solution, a low profile microstrip phased array antenna has been designed and fabricated on low-loss, two-layer Liquid Crystal Polymer (LCP) substrate. 4×4, 8×8 antenna array prototypes have been characterized and the results are described.


title: The heat treatment response of semisolid formed Al–5Fe–4Cu alloy

abstract: AbstractThe effect of heat treatment on both microstructures and performances of semisolid Al–5Fe–4Cu alloy was investigated with scanning electron microscopy (SEM), transmission electron microscopy (TEM), differential scanning calorimetry (DSC) and X-ray diffraction (XRD) and tensile test. The results showed that the semi-solid Al–5Fe–4Cu alloy’s response to the solution heat treatment (SHT) is abnormal compared with the conventional semi-solid Al alloys, i.e. the tensile properties of the semi-solid Al–5Fe–4Cu alloy decreased significantly after SHT. Nevertheless, the semi-solid Al–5Fe–4Cu alloy subject to direct ageing without SHT could exhibit conspicuous improvement of the tensile properties. And, the reason of the abnormal results has been analysed and discussed in detail.


title: Scribble-Supervised Segmentation of Aerial Building Footprints Using Adversarial Learning

abstract: Aerial image segmentation usually requires a large amount of pixel-level masks in order to achieve quality performance. Obtaining these annotations can be both costly and time-consuming, limiting the amount of data available for training. In this paper, we present an approach for learning to segment aerial building footprints in the absence of fully annotated label masks. Instead, we exploit cheap and efficient scribble annotations to supervise deep convolutional neural networks for segmentation. Our proposed model is based on an adversarial architecture that jointly trains two networks to produce building footprint segmentations that resemble synthetic label masks. We present competitive segmentation results on the Massachusetts Buildings data set by using only scribble supervision signals. Further experiments show that our method effectively alleviates building instance separation issue and displays strong robustness towards different scribble instance levels. We believe our cost-effective approach has the potential to be adapted for other aerial image interpretation tasks.


An Interface for Annotating Science Questions


title: A Beginner’s Guide to Analyzing and Visualizing Mass Cytometry Data

abstract: Mass cytometry has revolutionized the study of cellular and phenotypic diversity, significantly expanding the number of phenotypic and functional characteristics that can be measured at the single-cell level. This high-dimensional analysis platform has necessitated the development of new data analysis approaches. Many of these algorithms circumvent traditional approaches used in flow cytometric analysis, fundamentally changing the way these data are analyzed and interpreted. For the beginner, however, the large number of algorithms that have been developed, as well as the lack of consensus on best practices for analyzing these data, raise multiple questions: Which algorithm is the best for analyzing a dataset? How do different algorithms compare? How can one move beyond data visualization to gain new biological insights? In this article, we describe our experiences as recent adopters of mass cytometry. By analyzing a single dataset using five cytometry by time-of-flight analysis platforms (viSNE, SPADE, X-shift, PhenoGraph, and Citrus), we identify important considerations and challenges that users should be aware of when using these different methods and common and unique insights that can be revealed by these different methods. By providing annotated workflow and figures, these analyses present a practical guide for investigators analyzing high-dimensional datasets. In total, these analyses emphasize the benefits of integrating multiple cytometry by time-of-flight analysis algorithms to gain complementary insights into these high-dimensional datasets.


CPDScorer: Modeling and Evaluating Developer Programming Ability across Software Communities.


title: DecentID: Decentralized and Privacy-Preserving Identity Storage System Using Smart Contracts

abstract: Many Internet services require the registration of an account before permitting use of their services. Over time, many Internet users end up with a multitude of accounts with separated identities. A solution to this problem is offered by single-sign-on (SSO) providers, where a user can create a single identity and use this identity for multiple services. However it requires the user to trust the SSO provider. When the provider blocks access to the identities the users lose access to their subscribed services. To avoid this problem, we propose DecentID, a completely decentralized identity storage system that does not require a centralized trusted third party. Instead, a public blockchain is used as trust anchor. Identities can be created and used for different services. Each service can only read the identity attributes disclosed for it without being able to read attributes the user wants to keep secret.


title: LDA/SVM driven nearest neighbor classification

abstract: Nearest neighbor classification relies on the assumption that class conditional probabilities are locally constant. This assumption becomes false in high dimensions with finite samples due to the curse of dimensionality. The nearest neighbor rule introduces severe bias under these conditions. We propose a locally adaptive neighborhood morphing classification method to try to minimize bias. We use local support vector machine teaming to estimate an effective metric for producing neighborhoods that are elongated along less discriminant feature dimensions and constricted along most discriminant ones. As a result, the class conditional probabilities can be expected to be approximately constant in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other competing techniques using a number of data sets.


title: Programs as Pencils: Investigating Form Generation

abstract: This paper reviews two projects undertaken in a CAD programming course that demonstrate to the students how programs could be developed to investigate possible architectural forms. The projects highlight a very sequential approach to form investigation in using both common geometries and the introduction of randomness to control design rules. This approach stressed the development of rules and evaluating their results as a method to determine the next step to investigate. Equal importance was placed on the anticipated, as well as, the unexpected.


Control Barrier Function Based Quadratic Programs with Application to Automotive Safety Systems


title: Segmentation of Time Series in Improving Dynamic Time Warping

abstract: Since its introduction to the computer science community, the Dynamic Time Warping (DTW) algorithm has demonstrated good performance with time series data. While this elastic measure is known for its effectiveness with time series sequence comparisons, the possibility of pathological warping paths weakens the algorithms potential considerably. Techniques centering on pruning off impossible mappings or lowering data dimensions such as windowing, slope weighting, step pattern, and approximation have been proposed over the years to reduce the possibility of pathological warping paths with Dynamic Time Warping. However, because the current DTW improvement techniques are mostly global methods, they are either limited in effect or limit the warping path excessively. We believe segmenting time series at significant feature points will alleviate some of the pathological warpings, and at the same time allowing us to obtain more intuitive warpings. Our heuristic approaches the problem from the human perspective of sequence comparison: by identifying global similarity before local similarities. We use easily identifiable peaks as the significant feature. The final distance is the DTW distance sum of all segments of time series. In this paper, we explore the impact of different peak identification parameters on Dynamic Time Warping and demonstrate how segmentation can help to avoid pathological warpings.


title: An Interface to Heterogeneous Data Sources Based on the Mediator/Wrapper Architecture in the Hadoop Ecosystem

abstract: Heterogeneity of data sources is a challenge for Big Data applications. This contribution presents an approach based on the conceptual foundation of the mediator/wrapper architecture where source data is transformed into a global schema and transferred to one or more data sinks with Apache Kafka. Given appropriate wrappers, arbitrary data sources and sinks can be connected with one another in a flexible fashion. The design and implementation of a prototype for a data set from a metagenomics study of are described.


Planning, design and environmentally sustainable housing in a compact environment: public rental housing in HongKong


Wither Childhood? Child Trafficking in India


title: Working Paper Series, Paper No. 06-13 Economic Determinants of Participation in Physical Activity and Sport

abstract: This paper examines the economic determinants of participation in physical activity by developing and analyzing a consumer choice model of participation and by testing the predictions of this model using data drawn from the Behavioral Risk Factor Surveillance Survey (BRFSS). Both emphasize that individuals face two distinct decisions: (1) should I participate in sport?; and (2) how much time should I spend participating in sport? The evidence highlights the importance of selectivity. The economic factors that affect these two decisions work in opposite directions; factors that increase the likelihood of participation generally decrease the amount of time spent participating.


title: Enhancing cluster labeling using wikipedia

abstract: This work investigates cluster labeling enhancement by utilizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candidate labels from Wikipedia in addition to important terms that are extracted directly from the text. The "labeling quality" of each candidate is then evaluated by several independent judges and the top evaluated candidates are recommended for labeling. Our experimental results reveal that the Wikipedia labels agree with manual labels associated by humans to a cluster, much more than with significant terms that are extracted directly from the text. We show that in most cases even when human's associated label appears in the text, pure statistical methods have difficulty in identifying them as good descriptors. Furthermore, our experiments show that for more than 85% of the clusters in our test collection, the manual label (or an inflection, or a synonym of it) appears in the top five labels recommended by our system.


title: Educational data mining: A survey from 1995 to 2005

abstract: Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.


title: Valuation of Early Stage High-Tech Start-Up Companies

abstract: Valuating start-ups, especially at early stages, is a challenge given the lack of historical data and many uncertain factors about the future. This article presents a methodology for the valuation of early stage start-ups that we have proven in practice. The risk linked to a start-up is expressed through an individual beta coefficient as important component of the discounting factor within a discounted cash flow (DCF) valuation based on the data in a business plan. Core of this methodology is the development of an evaluation framework for the individual adjustment of the beta coefficient that is applicable to early stage start-ups. This was shown by applying the methodology to 16 early stage start-ups in the fields of biotechnology, nanotechnology, medical technology and clean technology, which provided an easy-to-handle and comprehensible comparison of different investment options for early stage investors.


title: Force-Directed Edge Bundling for Graph Visualization

abstract: Graphs depicted as node-link diagrams are widely used to show relationships between entities. However, nodelink diagrams comprised of a large number of nodes and edges often suffer from visual clutter. The use of edge bundling remedies this and reveals high-level edge patterns. Previous methods require the graph to contain a hierarchy for this, or they construct a control mesh to guide the edge bundling process, which often results in bundles that show considerable variation in curvature along the overall bundle direction. We present a new edge bundling method that uses a self-organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. In contrast to previous methods, no hierarchy is used and no control mesh. The resulting bundled graphs show significant clutter reduction and clearly visible high-level edge patterns. Curvature variation is furthermore minimized, resulting in smooth bundles that are easy to follow. Finally, we present a rendering technique that can be used to emphasize the bundling.


Disconnected Recurrent Neural Networks for Text Categorization


title: Enabling a Smart City Application Ecosystem: Requirements and Architectural Aspects

abstract: Cities are becoming increasingly "smart," but they need an underlying foundation that handles and manages all the lower-level complexities. In this article, the authors discuss relevant challenges and building blocks of a Smart City Application Ecosystem (SCALE).


title: Habits—A Repeat Performance

abstract: Habits are response dispositions that are activated automatically by the context cues that co-occurred with responses during past performance. Experience-sampling diary studies indicate that much of everyday action is characterized by habitual repetition. We consider various mechanisms that could underlie the habitual control of action, and we conclude that direct cuing and motivated contexts best account for the characteristic features of habit responding—in particular, for the rigid repetition of action that can be initiated without intention and that runs to completion with minimal conscious control. We explain the utility of contemporary habit research for issues central to psychology, especially for behavior prediction, behavior change, and self-regulation.


title: Event Stream Processing with Out-of-Order Data Arrival

abstract: Complex event processing has become increasingly important in modern applications, ranging from supply chain management for RFID tracking to real-time intrusion detection. The goal is to extract patterns from such event streams in order to make informed decisions in real-time. However, networking latencies and even machine failure may cause events to arrive out-of-order at the event stream processing engine. In this work, we address the problem of processing event pattern queries specified over event streams that may contain out-of-order data. First, we analyze the problems state-of-the-art event stream processing technology would experience when faced with out-of-order data arrival. We then propose a new solution of physical implementation strategies for the core stream algebra operators such as sequence scan and pattern construction, including stack- based data structures and associated purge algorithms. Optimizations for sequence scan and construction as well as state purging to minimize CPU cost and memory consumption are also introduced. Lastly, we conduct an experimental study demonstrating the effectiveness of our approach.


Analytical solution of non-linear boundary value problem for MHD flow