title: Perception of risk

abstract: Decision Processes, Rationality and Adjustment to Natural Hazards * Cognitive Processes and Societal Risk Taking * Preference for Insuring Against Probable Small Losses: Insurance Implications * Accident Probabilities and Seat Belt Usage: A Psychological Perspective * How Safe Is Safe Enough? A Psychometric Study of Attitudes Toward Technological Risks and Benefits * Rating the Risks * Weighing the Risks: Which Risks are Acceptable? * Facts and Fears: Understanding Perceived Risk * Response Mode, Framing and Information-processing Effects in Risk Assessment * The Nature of Technological Hazard * Informing and Educating the Public about Risk * Perception of Risk from Automobile Safety Defects * Perception of Risk * The Social Amplification of Risk: A Conceptual Framework * The Perception and Management of Therapeutic Risk * Perception of Risk from Radiation * Perceived Risk, Trust and the Politics of Nuclear Waste * Intuitive Toxicology: Expert and Lay Judgments of Chemical Risks * Perceived Risk, Trust and Democracy * Adolescent Health-threatening and Health-enhancing Behaviors: A Study of Word Association and Imagery * Technological Stigma * Probability, Danger and Coercion: A Study of Risk Perception and Decision-making in Mental Health Law * Do Adolescent Smokers Know the Risks? * Insensitivity to the Value of a Human Life: A Study of Psychophysical Numbing * Trust, Emotion, Sex, Politics and Science: Surveying the Risk-assessment Battlefield * The Affect Heuristic in Judgments of Risks and Benefits*


title: Lessons learned from three interorganizational health care information systems

abstract: Abstract The airline, financial, retail, consumer goods and cotton industries are not alone in their increased use of interorganizational information systems (IOSs). Health care organizations are investing and participating in a growing number of IOSs, such as community health information networks (CHINs) and integrated delivery systems (IDS). This paper examines the experiences of three CHIN systems located in the US — Midwest. Multiple data sources, including executive interviews, memoranda, internal documentation and system demonstrations, provided data for this research. The history of each case provides a better understanding of systems implementation and the underlying determinants that emerged as having significant impact. Not only do these findings provide guidelines and learning tools for practitioners, but they also hold implications to the field and its current position on bipolar streams in the literature. Lastly, our findings suggest that large health care infrastructures will continue to be confronted by the presence of the World Wide Web and electronic commerce.


title: Patch-Based Progressive 3D Point Set Upsampling

abstract: We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.


Wireless Mesh Networking with XBee


title: Kalman Filters, Factor Graphs, and Electrical Networks

abstract: Abstract Factor graphs are graphical models with origins in coding theory. It is well known that Kalman filtering is an instance of the generic sum(mary)-product algorithm on the corresponding factor graph. In this paper, a one-to-one correspondence between such factor graphs and a class of electrical networks is presented. The electrical network “computes” correct Bayesian estimates even for factor graphs with cycles.


title: Magnetless Nonreciprocal Metamaterial (MNM) Technology: Application to Microwave Components

abstract: A magnetless nonreciprocal metamaterial (MNM), consisting of traveling-wave resonant ring particles loaded by transistor and exhibiting the gyromagnetic properties as ferrites, without their size, weight, cost, and monolithic microwave integrated circuit incompatibility drawbacks, was recently introduced in 2011 by Kodera et al. This paper presents the first extensive investigation of the applicability of MNM technology to nonreciprocal microwave components. It recalls the key principle of the MNM, provides basic MNM design guidelines, explains coupling mechanism between a microstrip line and MNM rings, and demonstrates two nonreciprocal MNM components based on a microstrip-ring configuration, an isolator, and a circulator. Although these components have not been fully optimized, they already exhibit attractive performance and provide a proof-of-concept that MNM technology has a potential for microwave nonreciprocal microwave components with substantial benefits compared to their ferrite and active-circuit counterparts.


title: Zero-Shot Transfer Learning for Event Extraction

abstract: Most previous event extraction studies have relied heavily on features derived from annotated event mentions, thus cannot be applied to new event types without annotation effort. In this work, we take a fresh look at event extraction and model it as a grounding problem. We design a transferable neural architecture, mapping event mentions and types jointly into a shared semantic space using structural and compositional neural networks, where the type of each event mention can be determined by the closest of all candidate types . By leveraging (1)~available manual annotations for a small set of existing event types and (2)~existing event ontologies, our framework applies to new event types without requiring additional annotation. Experiments on both existing event types (e.g., ACE, ERE) and new event types (e.g., FrameNet) demonstrate the effectiveness of our approach. \textit{Without any manual annotations} for 23 new event types, our zero-shot framework achieved performance comparable to a state-of-the-art supervised model which is trained from the annotations of 500 event mentions.


title: An information-theoretic approach to curiosity-driven reinforcement learning

abstract: We provide a fresh look at the problem of exploration in reinforcement learning, drawing on ideas from information theory. First, we show that Boltzmann-style exploration, one of the main exploration methods used in reinforcement learning, is optimal from an information-theoretic point of view, in that it optimally trades expected return for the coding cost of the policy. Second, we address the problem of curiosity-driven learning. We propose that, in addition to maximizing the expected return, a learner should choose a policy that also maximizes the learner's predictive power. This makes the world both interesting and exploitable. Optimal policies then have the form of Boltzmann-style exploration with a bonus, containing a novel exploration-exploitation trade-off which emerges naturally from the proposed optimization principle. Importantly, this exploration-exploitation trade-off persists in the optimal deterministic policy, i.e., when there is no exploration due to randomness. As a result, exploration is understood as an emerging behavior that optimizes information gain, rather than being modeled as pure randomization of action choices.


title: Encoding and decoding the knowledge of association rules over SVM classification trees

abstract: This paper presents a constructive method for association rule extraction, where the knowledge of data is encoded into an SVM classification tree (SVMT), and linguistic association rule is extracted by decoding of the trained SVMT. The method of rule extraction over the SVMT (SVMT-rule), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the decision-tree structure of SVMT. Thus, the obtained rules from SVMT-rule have the better comprehensibility of decision-tree rule, meanwhile retains the good classification accuracy of SVM. Moreover, profiting from the super generalization ability of SVMT owing to the aggregation of a group of SVMs, the SVMT-rule is capable of performing a very robust classification on such datasets that have seriously, even overwhelmingly, class-imbalanced data distribution. Experiments with a Gaussian synthetic data, seven benchmark cancers diagnosis, and one application of cell-phone fraud detection have highlighted the utility of SVMT and SVMT-rule on comprehensible and effective knowledge discovery, as well as the superior properties of SVMT-rule as compared to a purely support-vector based rule extraction. (A version of SVMT Matlab software is available online at http://kcir.kedri.info)


title: ProxImaL: efficient image optimization using proximal algorithms

abstract: Computational photography systems are becoming increasingly diverse, while computational resources---for example on mobile platforms---are rapidly increasing. As diverse as these camera systems may be, slightly different variants of the underlying image processing tasks, such as demosaicking, deconvolution, denoising, inpainting, image fusion, and alignment, are shared between all of these systems. Formal optimization methods have recently been demonstrated to achieve state-of-the-art quality for many of these applications. Unfortunately, different combinations of natural image priors and optimization algorithms may be optimal for different problems, and implementing and testing each combination is currently a time-consuming and error-prone process. ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline, deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate highly efficient solvers that achieve state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.


title: Speech-based Diagnosis of Autism Spectrum Condition by Generative Adversarial Network Representations

abstract: Machine learning paradigms based on child vocalisations show great promise as an objective marker of developmental disorders such as Autism. In conventional detection systems, hand-crafted acoustic features are usually fed into a discriminative classifier (e.g, Support Vector Machines); however it is well known that the accuracy and robustness of such a system is limited by the size of the associated training data. This paper explores, for the first time, the use of feature representations learnt using a deep Generative Adversarial Network (GAN) for classifying children's speech affected by developmental disorders. A comparative evaluation of our proposed system with different acoustic feature sets is performed on the Child Pathological and Emotional Speech database. Key experimental results presented demonstrate that GAN based methods exhibit competitive performance with the conventional paradigms in terms of the unweighted average recall metric.


title: Affivir: An affect-based Internet video recommendation system

abstract: Abstract In this paper, we present Affivir, a video browing system that recommends Internet videos that match a user's affective preference. Affivir models a user's watching behavior as sessions, and dynamically adjusts session parameters to cater to the user's current mood. In each session, Affivir discovers a user's affective preference through user interactions, such as watching or skipping videos. Affivir uses video affective features (motion, shot change rate, sound energy, and audio pitch average) to retrieve videos that have similar affective responses. To efficiently search videos of interest from our video repository, all videos in the repository are pre-processed and clustered. Our experimental results show that Affivir has made a significant improvement in user satisfaction and enjoyment, compared with several other popular baseline approaches.


title: Fermionic Linear Optics and Matchgates

abstract: Fermionic linear optics is efficiently classically simulatable. Here it is shown that the set of states achievable with fermionic linear optics and particle measurements is the closure of a low dimensional Lie group. The weakness of fermionic linear optics and measurements can therefore be explained and contrasted with the strength of bosonic linear optics with particle measurements. An analysis of fermionic linear optics is used to show that the two-qubit matchgates and the simulatable matchcircuits introduced by Valiant generate a monoid of extended fermionic linear optics operators. A useful interpretation of efficient classical simulations such as this one is as a simulation of a model of non-deterministic quantum computation. Problem areas for future investigations are suggested.


title: Concepts for 3D Printing-Based Self-Replicating Robot Command and Coordination Techniques

abstract: Self-replicating robots represent a new area for prospective advancement in robotics. A self-replicating robot can identify when additional robots are needed to solve a problem or meet user needs, and create them in response to this identified need. This allows robotic systems to respond to changing (or non-predicted) mission needs. Being able to modify the physical system component provides an additional tool for optimizing robotic system performance. This paper begins the process of developing a command and coordination system that makes decisions with the consideration of replication, repair, and retooling capabilities. A high-level algorithm is proposed and qualitatively assessed.


title: Digital Archaeological Fieldwork and the Jezreel Valley Regional Project, Israel

abstract: The application of digital technologies to enhance archaeological documentation has long been a priority of researchers around the world. However, progress on this front has been generally restricted to only a few categories of technology (e.g. total stations, simple databases, etc.) due to the costs involved in deploying new technology and the need for highly trained specialists to implement them. Fortunately, the last few years have seen a significant increase in the accessibility of cutting edge techniques and technology. Since its inception in 2010, the Jezreel Valley Regional Project (JVRP) has sought to develop innovative digital archaeological technologies that on one hand are cost-effective and on the other are scalable to the site-level and regional-level research the project is engaged in. In order to effectively address the JVRP's research goals, the project has developed workflows for new and existing technologies such as three-dimensional modeling, X-ray fluorescence, reflectance transformati...


title: Advances in Autism

abstract: Autism is a common childhood neurodevelopmental disorder with strong genetic liability. It is not a unitary entity but a clinical syndrome, with variable deficits in social behavior and language, restrictive interests, and repetitive behaviors. Recent advances in the genetics of autism emphasize its etiological heterogeneity, with each genetic susceptibility locus accounting for only a small fraction of cases or having a small effect. Therefore, it is not surprising that no unifying structural or neuropathological features have been conclusively identified. Given the heterogeneity of autism spectrum disorder (ASD), approaches based on studying heritable components of the disorder, or endophenotypes, such as language or social cognition, provide promising avenues for genetic and neurobiological investigations. Early intensive behavioral and cognitive interventions are efficacious in many cases, but autism does not remit in the majority of children. Therefore, development of targeted therapies based on pathophysiologically and etiologically defined subtypes of ASD remains an important and achievable goal of current research.


title: The use of the interactive whiteboard for creative teaching and learning in literacy and mathematics: a case study.

abstract: This paper considers the ways in which the interactive whiteboard may support and enhance pedagogic practice through whole-class teaching within literacy and numeracy. Data collected from observations of whole-class lessons, alongside individual interviews and focus group discussions with class teachers and Initial Teacher Education students, has provided opportunities to consider the potential of such technology to facilitate a more creative approach to whole-class teaching. The data suggests that, in the first instance, the special features of information and communications technology such as interactivity, ‘provisionality,’ speed, capacity and range enhance the delivery and pace of the session. This research seems to indicate that it is the skill and the professional knowledge of the teacher who mediates the interaction, and facilitates the development of pupils' creative responses at the interface of technology, which is critical to the enhancement of the whole-class teaching and learning processes.


Employee Retention Through Employee Engagement-A Study At An Indian International Airport


title: Quantifying the Spatiotemporal Trends of Canopy Layer Heat Island (CLHI) and Its Driving Factors over Wuhan, China with Satellite Remote Sensing

abstract: Canopy layer heat islands (CLHIs) in urban areas are a growing problem. In recent decades, the key issues have been how to monitor CLHIs at a large scale, and how to optimize the urban landscape to mitigate CLHIs. Taking the city of Wuhan as a case study, we examine the spatiotemporal trends of the CLHI along urban-rural gradients, including the intensity and footprint, based on satellite observations and ground weather station data. The results show that CLHI intensity (CLHII) decays exponentially and significantly along the urban-rural gradients, and the CLHI footprint varies substantially and especially in winter. We then quantify the driving factors of the CLHI by establishing multiple linear regression (MLR) models with the assistance of ZY-3 satellite data (with a spatial resolution of 2.5 m), and obtain five main findings: (1) built-up area had a significant positive effect on daily mean CLHII in summer and a negative effect in winter; (2) vegetation had significant inhibiting effects on daily mean CLHII in both summer and winter; (3) absolute humidity has a significant inhibiting effect on daily mean CLHII in summer and a positive effect in winter; (4) anthropogenic heat emissions exacerbated the daily mean CLHII by about 0.19 °C (90% confidence interval −0.06–0.44 °C) on 17 September 2013 and by about 0.06 °C (−0.06–0.19 °C) on 23 January 2014; and (5) if most of the urban area is transformed into roads (i.e., an extreme case), we estimate that the daily mean CLHII would reach 1.41 °C (0.38–2.44 °C) on 17 September 2013 and 0.14 °C (0.08–0.2 °C) on 23 January 2014 in Wuhan metropolitan area. Overall, the results provide new insights into quantifying the CLHI and its driving factors, to enhance our understanding of urban heat islands.


title: 360-degree video stitching for dual-fisheye lens cameras based on rigid moving least squares

abstract: Dual-fisheye lens cameras are becoming popular for 360-degree video capture, especially for User-generated content (UGC), since they are affordable and portable. Images generated by the dual-fisheye cameras have limited overlap and hence require non-conventional stitching techniques to produce high-quality 360×180-degree panoramas. This paper introduces a novel method to align these images using interpolation grids based on rigid moving least squares. Furthermore, jitter is the critical issue arising when one applies the image-based stitching algorithms to video. It stems from the unconstrained movement of stitching boundary from one frame to another. Therefore, we also propose a new algorithm to maintain the temporal coherence of stitching boundary to provide jitter-free 360-degree videos. Results show that the method proposed in this paper can produce higher quality stitched images and videos than prior work.


title: The right generations

abstract: The Standard Model has three generations of fermions and although it does not contain any explicit reason for this, the existence of additional generations is now very constrained by experiment. Present measurements are saturating perturbative unitarity limits. The main idea of this work is to show that those restrictions can be relaxed if the new generations experience different interactions. This new setup leads to the presence of additional stable degrees of freedom that give rise to a very rich phenomenology for cosmology, astrophysics and particle physics. The stability is a consequence of the conservation of new accidental baryon and lepton numbers. We present an explicit example by introducing a fourth generation charged under a new $SU(2)_R$ gauge interaction instead of the standard $SU(2)_L$. The simplest implementations lead to models that contain stable quarks, leptons and neutrinos. We show that these new particles can have a wide range of masses within a non-standard cosmological set-up. Indeed, the new neutrinos (and {\it neutral leptons}) constitute viable dark matter candidates if they are the lightest of these new particles.


title: Automatic generation of personalized human avatars from multi-view video

abstract: In multi-user virtual environments real-world people interact via digital avatars. In order to make the step from the real world onto the virtual stage convincing the digital equivalent of the user has to be personalized. It should reflect the shape and proportions, the kinematic properties, as well as the textural appearance of its real-world equivalent. In this paper, we present a novel spatio-temporal approach to create a personalized avatar from multi-view video data of a moving person. The avatar's geometry is generated by shape-adapting a template human body model. Its surface texture is assembled from multi-view video frames showing arbitrary different body poses. consistent surface texture for the model is generated using multi-view video frames from different camera views and different body poses. With our proposed method photo-realistic human avatars can be robustly generated.


title: Deep fraud. A fraud intention recognition framework in public transport context using a deep-learning approach

abstract: In this paper, we present a framework for fraud intention recognition of public transport bus operators based on a deep learning approach using a stack of denoising and sparse autoencoders. Bus operator's fraud is a common problem in passenger transport organizations with cash payment method and tickets as proof of passenger on board. Fraud detection could be considered as an anomaly detection problem, and several techniques in this area have been applied to it. Our approach is an architecture based on a sort of Stacked Autoencoders with a Softmax Classification Layer and a normalization pre-phase using word2vec and a Denoising Autoencoder. This framework permits to recognize the fraud intention in the operators delivers account time, based on date, route, count of passengers on board, among other features. In this framework, the fraud intention is modeled as a binary classification problem, there is fraud intention, or there isn't. We compare our approach with another nondeep state of the art classification approaches like Logistic Regression, SVM, Decision Trees, KNN and Adaboost ensemble method, obtaining a superior performance on intention recognition, with an accuracy of 87.3 percent and an F1 score of 0.752.


title: Development of forming and product properties of copper wire in a linear coil winding process

abstract: Since electric motors are becoming more important in many application fields, e. g. hybrid electric vehicles, the optimization of the linear coil winding process is an important contribution to a higher productivity and flexibility. For the investigation of the forming behavior of the winding wire the material behavior is characterized in different experimental setups using wire diameters of 0.63 mm–3.35 mm. By comparing the results of tensile tests and compression tests, a tension-compression anisotropy of the wire behavior can be noticed. The Young's Modulus, measured in cyclic tensile tests from 99–116 GPa (dependent on the amount of strain), is used for the characterization of the elastic behavior of the copper wire. Subsequently, numerical investigations of the linear winding process in a case study for a rectangular bobbin are carried out in order to analyze the influence forming parameters have on the resulting properties of the wound coil. The wire tensile force represents a key parameter concerning the geometrical properties of the wound wire and the clearance between wire and bobbin. Numerical results show that the wire cross-section decreases through bending already at standard wire tensile forces. Additionally, the clearance between wire and bobbin increases with larger wire diameters. The aforementioned results serve as fundamentals for a comprehensive modeling of the linear winding process of noncircular coil bobbins with copper wire.


title: Toward a more practical unsupervised anomaly detection system

abstract: During the last decade, various machine learning and data mining techniques have been applied to Intrusion Detection Systems (IDSs) which have played an important role in defending critical computer systems and networks from cyber attacks. Unsupervised anomaly detection techniques have received a particularly great amount of attention because they enable construction of intrusion detection models without using labeled training data (i.e., with instances preclassified as being or not being an attack) in an automated manner and offer intrinsic ability to detect unknown attacks; i.e., 0-day attacks. Despite the advantages, it is still not easy to deploy them into a real network environment because they require several parameters during their building process, and thus IDS operators and managers suffer from tuning and optimizing the required parameters based on changes of their network characteristics. In this paper, we propose a new anomaly detection method by which we can automatically tune and optimize the values of parameters without predefining them. We evaluated the proposed method over real traffic data obtained from Kyoto University honeypots. The experimental results show that the performance of the proposed method is superior to that of the previous one.


title: Mental images and the Brain

abstract: One theory of visual mental imagery posits that early visual cortex is also used to support representations during imagery. This claim is important because it bears on the "imagery debate": Early visual cortex supports depictive representations during perception, not descriptive ones. Thus, if such cortex also plays a functional role in imagery, this is strong evidence that imagery does not rely exclusively on the same sorts of representations that underlie language. The present article first outlines the nature of a processing system in which such a dual use of early visual cortex (in perception and in imagery) makes sense. Following this, literature bearing on the claim that early visual cortex is used in visual mental imagery is reviewed, and key issues are discussed.


title: MARSA: A Marketplace for Realtime Human Sensing Data

abstract: This article introduces a dynamic cloud-based marketplace of near-realtime human sensing data (MARSA) for different stakeholders to sell and buy near-realtime data. MARSA is designed for environments where information technology (IT) infrastructures are not well developed but the need to gather and sell near-realtime data is great. To this end, we present techniques for selecting data types and managing data contracts based on different cost models, quality of data, and data rights. We design our MARSA platform by leveraging different data transferring solutions to enable an open and scalable communication mechanism between sellers (data providers) and buyers (data consumers). To evaluate MARSA, we carry out several experiments with the near-realtime transportation data provided by people in Ho Chi Minh City, Vietnam, and simulated scenarios in multicloud environments.


title: Why johnny can’t browse in peace: On the uniqueness of web browsing history pattern

abstract: We present the results of the first large-scale study of the uniqueness of Web browsing histories, gathered from a total of 368; 284 Internet users who visited a history detection demonstration website. Our results show that for a majority of users (69%), the browsing history is unique and that users for whom we could detect at least 4 visited websites were uniquely identified by their histories in 97% of cases. We observe a significant rate of stability in browser history fingerprints: for repeat visitors, 38% of fingerprints are identical over time, and differing ones were correlated with original history contents, indicating static browsing preferences (for history subvectors of size 50). We report a striking result that it is enough to test for a small number of pages in order to both enumerate users' interests and perform an efficient and unique behavioral fingerprint; we show that testing 50 web pages is enough to fingerprint 42% of users in our database, increasing to 70% with 500 web pages. Finally, we show that indirect history data, such as information about categories of visited websites can also be effective in fingerprinting users, and that similar fingerprinting can be performed by common script providers such as Google or Facebook.


title: Touché: enhancing touch interaction on humans, screens, liquids, and everyday objects

abstract: Touche proposes a novel Swept Frequency Capacitive Sensing technique that can not only detect a touch event, but also recognize complex configurations of the human hands and body. Such contextual information significantly enhances touch interaction in a broad range of applications, from conventional touchscreens to unique contexts and materials. For example, in our explorations we add touch and gesture sensitivity to the human body and liquids. We demonstrate the rich capabilities of Touche with five example setups from different application domains and conduct experimental studies that show gesture classification accuracies of 99% are achievable with our technology.


title: Video game addiction in emerging adulthood: Cross-sectional evidence of pathology in video game addicts as compared to matched healthy controls.

abstract: Abstract Background The Internet Gaming Disorder Scale (IGDS) is a widely used measure of video game addiction, a pathology affecting a small percentage of all people who play video games. Emerging adult males are significantly more likely to be video game addicts. Few researchers have examined how people who qualify as video game addicts based on the IGDS compared to matched controls based on age, gender, race, and marital status. Method The current study compared IGDS video game addicts to matched non-addicts in terms of their mental, physical, social-emotional health using self-report, survey methods. Results Addicts had poorer mental health and cognitive functioning including poorer impulse control and ADHD symptoms compared to controls. Additionally, addicts displayed increased emotional difficulties including increased depression and anxiety, felt more socially isolated, and were more likely to display internet pornography pathological use symptoms. Female video game addicts were at unique risk for negative outcomes. Limitations The sample for this study was undergraduate college students and self-report measures were used. Conclusions Participants who met the IGDS criteria for video game addiction displayed poorer emotional, physical, mental, and social health, adding to the growing evidence that video game addictions are a valid phenomenon.


title: Energy Consumption and Proximity Accuracy of BLE Beacons for Internet of Things Applications

abstract: In the Internet of Things (IoT) era, with millions of connected devices to the internet, indoor location services regarding room discovery and resource identification/tracking are among the most popular applications for smart homes and smart buildings. Bluetooth Low Energy (BLE) beacons are a promising solution to improve the scalability and accuracy of indoor localization applications. They are low cost, configurable, small transmitters designed to attract attention to a specific location. In this paper, we investigate three popular BLE beacon devices available on the market and compare them in terms of energy consumption and proximity accuracy for indoor localization services. In addition, two state-estimation filters are developed for the Android mobile platform in order to improve the proximity accuracy when using smartphone devices. Specifically, a static Kalman filter and Gaussian filter are implemented.


title: Trickle: Rate Limiting YouTube Video Streaming

abstract: YouTube traffic is bursty. These bursts trigger packet losses and stress router queues, causing TCP's congestion-control algorithm to kick in. In this paper, we introduce Trickle, a server-side mechanism that uses TCP to rate limit YouTube video streaming. Trickle paces the video stream by placing an upper bound on TCP's congestion window as a function of the streaming rate and the round-trip time. We evaluated Trickle on YouTube production data centers in Europe and India and analyzed its impact on losses, bandwidth, RTT, and video buffer under-run events. The results show that Trickle reduces the average TCP loss rate by up to 43% and the average RTT by up to 28% while maintaining the streaming rate requested by the application.


title: Method development of Swab sampling for cleaning validation of a residual active pharmaceutical ingredient

abstract: A swab-sampling method was developed for cleaning validation of a residual active pharmaceutical ingredient in samples collected after cleaning the sampling suite. A summary of the strategies and results of the method development is presented. The developed extraction method produced an acceptable level of recovery and precision.


title: --> Europe's Journal of Psychology ejop.psychopen.eu | 1841-0413 Theoretical Contributions Humor and Resiliency: Towards a Process Model of Coping and Growth

abstract: This article considers how humor may fit within a resiliency perspective. Following a brief overview of resiliency approaches, including selected work on positive psychology, several lines of research that provide initial support for resiliency effects of humor on stress and trauma are highlighted. This work ranges from anecdotal case report descriptions of facilitative humor use in extremely traumatic situations (e.g., paramedics), to more rigorous studies examining moderator and cognitive appraisal effects of humor on psychological well-being. Although these initial findings are quite promising, it is noted that some resiliency-based approaches to humor are limited by a sole focus on humor as a positive attribute. As such, a humor styles model, which acknowledges both the adaptive and maladaptive aspects of humor, is used to describe broader avenues of research within a resiliency perspective. This process orientation to humor use also highlights the importance of both negative and positive emotion regulation in modulating coping and growth. This model is then used to comment on limitations and potential extensions of current resiliency perspectives on humor, including programs and exercises that attempt to train humor use in a facilitative manner.


title: Reputation-based pricing of P2P services

abstract: In the future peer-to-peer service oriented computing systems, maintaining a cooperative equilibrium is a non-trivial task. In the absence of Trusted Third Parties (TTP's) or verification authorities, rational service providers minimize their costs by providing ever degrading service quality levels. Anticipating this, rational clients are willing to pay only the minimum amounts (often zero) which leads to the collapse of the market.In this paper, we show how a simple reputation mechanism can be used to overcome this moral hazard problem. The mechanism does not act by social exclusion (i.e. exclude providers that cheat) but rather by allowing flexible service level agreements in which quality can be traded for the price. We show that such a mechanism can drive service providers of different types to exert the social efficient effort levels.


title: On the Transitivity of Hypernym-Hyponym Relations in Data-Driven Lexical Taxonomies

abstract: Taxonomy is indispensable in understanding natural language. A variety of large scale, usage-based, data-driven lexical taxonomies have been constructed in recent years.Hypernym-hyponym relationship, which is considered as the backbone of lexical taxonomies can not only be used to categorize the data but also enables generalization. In particular, we focus on one of the most prominent properties of the hypernym-hyponym relationship, namely, transitivity, which has a significant implication for many applications. We show that, unlike human crafted ontologies and taxonomies, transitivity does not always hold in data-drivenlexical taxonomies. We introduce a supervised approach to detect whether transitivity holds for any given pair of hypernym-hyponym relationships. Besides solving the inferencing problem, we also use the transitivity to derive new hypernym-hyponym relationships for data-driven lexical taxonomies. We conduct extensive experiments to show the effectiveness of our approach.


title: A Mobile Cloud Computing Middleware for Low Latency Offloading of Big Data

abstract: Recent years have witnessed an explosive growth of mobile applications. Thanks to improved network connectivity, it becomes a promising enabling solution to offload computation-intensive applications to the resource abundant public cloud to further augment the capacity of resource-constrained devices. As mobile applications usually have QoS requirements, it is critical to provide low latency services to the mobile users while maintain low leasing cost of cloud resources. However, the resources offered by cloud vendors are usually charged based on a time quanta while the offloading demand for heavy-lifting computation may occur infrequently on mobile devices. This mismatch would demotivate users to resort to public cloud for computation offloading. In this paper, we design a computation offloading middleware which bridges the aforementioned gap between cloud vendors and mobile clients, providing offloading service to multiple users with low cost and delay. The proposed middleware has two key components: Task Scheduler and Instance Manager. The Task Scheduler dispatches the received offloading tasks to execute in the instances reserved by the Instance Manager. Based on the arrival pattern of offloading tasks, the Instance Manager dynamically changes the number of instances to ensure certain service grade of mobile users. Our proposed mechanisms are validated through numerical results. It is shown that a lower average delay can be achieved through proposed scheduling heuristic, and the number of reserved instances well adapts to the offloading demands.


title: WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia

abstract: We present WIKIREADING, a large-scale natural language understanding task and publicly-available dataset with 18 million instances. The task is to predict textual values from the structured knowledge base Wikidata by reading the text of the corresponding Wikipedia articles. The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs). We compare various state-of-the-art DNNbased architectures for document classification, information extraction, and question answering. We find that models supporting a rich answer space, such as word or character sequences, perform best. Our best-performing model, a word-level sequence to sequence model with a mechanism to copy out-of-vocabulary words, obtains an accuracy of 71.8%.


title: The amyloid cascade hypothesis for Alzheimer's disease: an appraisal for the development of therapeutics

abstract: The amyloid cascade hypothesis — which posits that the deposition of the amyloid-β peptide in the brain is a central event in Alzheimer's disease (AD) pathology — has strongly influenced recent AD drug discovery efforts. However, so far all agents targeting amyloid-β have failed in Phase III trials. Here, Karran and colleagues re-evaluate the amyloid cascade hypothesis and explore the future potential of amyloid-β-targeting therapeutics.


title: Battery Model Parameters Estimation with the Sigma Point Kalman Filter

abstract: Accurate estimation of the State of Charge (SOC) of the battery is one of the key problems to the battery management system. The SOC should be obtained indirectly according to some algorithms under a mathematical model, along with some measurable quantities. A Sigma Point Kalman Filter based battery model parameters estimation method is proposed. The parameters can be estimated accurately while efficiently with the proposed method. Compared to the classical least squares method, the proposed method consumes much less memory and calculation time, which makes it suitable for embedded applications.


title: From Neural Networks to Deep Learning: zeroing in on the human brain

abstract: Pondering the brain with the help of machine learning expert Andrew Ng and researcher-turned-author-turned-entrepreneur Jeff Hawkins.


title: Experiences with rapid mobile game development using unity engine

abstract: In this work, we describe our experiences with FunCopter, a casual game we have designed and implemented using Unity engine, suitable for portable devices. We emphasize some general principles, particularly with respect to rapid game development and constrained graphics. In addition to that, we present the main activities realized in each software engineering phase and the lessons we learned during its development. These descriptions include details from the initial concept to the fully realized, playable game. This work will share general but important experiences with Unity, particularly interesting for game developers in beginner's level.


title: ShiverPaD: A Glass Haptic Surface That Produces Shear Force on a Bare Finger

abstract: We discuss the design and performance of a new haptic surface capable of controlling shear force on a bare finger. At the heart of the ShiverPaD is the TPaD variable friction device. It modulates the friction of a glass surface by using 39 kHz out-of-plane vibrations to reduce friction. To generate shear forces, the TPaD is oscillated in-plane (i.e., “shivered”) while alternating between low and high friction within each cycle. In previous research, the ShiverPaD produced shear forces using in-plane vibrations below 100 Hz. In this research, we develop a new ShiverPaD that produces force using 854 Hz vibrations, where human sensitivity to vibration is diminished. The new device is used to display a virtual toggle switch and a variety of virtual edges. A human subject study is conducted to demonstrate that users can easily trace virtual edges displayed on the surface of the ShiverPaD.


title: A Survey of Image Security in Wireless Sensor Networks

abstract: Wireless sensor networks are increasingly gaining attention. In recent years, a great deal of monitoring, control and tracking applications have been designed for different scenarios. For such networks, camera-enabled sensors can retrieve visual data from a monitored field, providing valuable information for many applications. In general, those networks have resource constraints of processing, memory, energy and transmission bandwidth, imposing many design challenges. Nevertheless, a group of applications may also have security requirements, which bring additional complexity to be handled. Most traditional security mechanisms for popular networks, like the Internet, are not suitable for wireless sensor networks, demanding proper investigation in this area. In this paper, we survey recent developments in encryption and privacy in wireless sensor networks deployed for transmissions of image snapshots, reviewing innovative approaches to provide different levels of security. Promising research directions are also discussed.


title: Features of enterprise information systems integration: A systemic analysis

abstract: This paper strives to review the contribution of systems theory to enterprise architecture and integration, make a summary of methods or tools at systems level and probe into some crucial concepts and thinking of systems theory applied in the enterprise integration activities. As the summary to this paper, some new prospects in enterprise architecture and integration are presented to tackle the increasing complexity and new requirements on modern enterprises.


title: A Bibliometric Study on Culture Research in International Business

abstract: National cultures and cultural differences provide a crucial component of the context of international business (IB) research. We conducted a bibliometric study of the articles published in seven leading IB journals, over a period of three decades, to analyze how “national culture” has been impacting in IB research. Co-citation mappings permit us to identify the ties binding works dealing with culture and cultural issues in IB. We identify two main clusters of research each comprising two sub-clusters, with Hofstede’s (1980) work setting much of the conceptual and empirical approach on culture-related studies. One main cluster entails works on the conceptualization of culture and its dimensions and other cluster on cultural distance. This conceptual framework captures the extant IB research incorporating culture-related concepts and influences.


title: Hermes: Providing Tight Control over High-Performance SDN Switches

abstract: SDN controllers demand tight performance guarantees over the control plane actions performed by switches. For example, traffic engineering techniques that frequently reconfigure the network require guarantees on the speed of reconfiguring the network. Initial experiments show that poor performance of Ternary Content-Addressable Memory (TCAM) control actions (e.g., rule insertion) can inflate application performance by a factor of 2x! Yet, modern switches provide no guarantees for these important control plane actions -- inserting, modifying, or deleting rules. In this paper, we present the design and evaluation of Hermes, a practical and immediately deployable framework that offers a novel method for partitioning and optimizing switch TCAM to enable performance guarantees. Hermes builds on recent studies on switch performance and provides guarantees by trading-off a nominal amount of TCAM space for assured performance. We evaluated Hermes using large-scale simulations. Our evaluations show that with less than 5% overheads, Hermes provides 5ms insertion guarantees that translates into an improvement of application level metrics by up to 80%. Hermes is more than 50% better than existing state of the art techniques and provides significant improvement for traditional networks running BGP.


title: Effectiveness of cheek and jaw support to improve feeding performance of preterm infants

abstract: OBJECTIVE. We examined the effects of cheek and jaw support on the feeding ability of inefficient feeders born prematurely. ::: ::: METHOD. Twenty preterm infants served as their own controls. Each infant received either intervention (feeding with oral support) or control (feeding without oral support) for 2 consecutive feedings per day on 2 consecutive days. ::: ::: RESULTS. Infants displayed a greater intake rate during the intervention feedings, both during the first 5 min (p = .046) and throughout the entire feeding (p = .023). The percentage of leakage during the first 5-min feeding was smaller in the intervention condition than in the control condition (p = .040). No significant differences were found between the two conditions in the sucking, physiological, and alertness variables. ::: ::: CONCLUSION. Findings confirm oral support as a safe and effective strategy to improve the feeding performance of preterm infants who are poor feeders


title: Metamodels for Computer-based Engineering Design: Survey and recommendations

abstract: The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of today’s engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper, we review several of these techniques, including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning and kriging. We survey their existing application in engineering design, and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations, and how common pitfalls can be avoided.


title: Towards Neural Machine Translation with Latent Tree Attention

abstract: Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing. We introduce such a model for the task of machine translation, pairing a recurrent neural network grammar encoder with a novel attentional RNNG decoder and applying policy gradient reinforcement learning to induce unsupervised tree structures on both the source and target. When trained on character-level datasets with no explicit segmentation or parse annotation, the model learns a plausible segmentation and shallow parse, obtaining performance close to an attentional baseline.


title: Use of different thresholds in learning and recognition

abstract: Abstract The neural networks' ability to robustly recognize patterns is influenced by the selectivity of feature-extracting cells in the networks. This selectivity can be controlled by the threshold values of the cells. This paper proposes to use different threshold values for feature-extracting cells in the learning and recognition phases, when an unsupervised learning with a winner-take-all process is used to train the network. During the recognition phase, better performance is achieved when the thresholds are set low enough to maintain the generalization ability. The thresholds in the learning phase, however, should be kept higher than in the recognition phase. If the thresholds in the learning phase are made as low as in the recognition phase, a sufficient number of feature-extracting cells cannot be generated in the network because of the competition among the cells. The effectiveness of adopting two different threshold values is demonstrated by computer simulation, taking the ‘neocognitron’ as an example.


title: Real-time robot path planning around complex obstacle patterns through learning and transferring options

abstract: We consider the problem of path planning in an initially unknown environment where a robot does not have an a priori map of its environment but has access to prior information accumulated by itself from navigation in similar but not identical environments. To address the navigation problem, we propose a novel, machine learning-based algorithm called Semi-Markov Decision Process with Unawareness and Transfer (SMDPU-T) where a robot records a sequence of its actions around obstacles as action sequences called options which are then reused by it to learn suitable, collision-free maneuvers around more complex obstacles in future. Our results illustrate that SMDPU-T takes 24% planning time and 39% total time to solve same navigation tasks as compared to a recent, sampling-based path planner


title: See No Evil: The Effect of Communication Medium and Motivation on Deception Detection

abstract: The present study reports an experiment that examines the role of communication medium and liar motivation on deception detection. Participants were randomly assigned to one of two dyadic communication conditions, text-based, computer-mediated environment or face-to-face, and to one of two motivation conditions, high or low. Participants engaged in a discussion of four topics, in which one participant was deceptive during two topics and truthful during the other two. No main effect of communication medium or motivation level was observed. However, an interaction effect suggests that highly motivated liars interacting in a text-based, computer- mediated environment were the most successful in deceiving their partners. The implications of these results are discussed both in terms of the elimination of non- verbal cues, as well as the potential advantages to the motivated liar offered by text-based media.


title: Two-Sided versus One-Sided Celebrity Endorsements: The Impact on Advertising Effectiveness and Credibility

abstract: Abstract This study examines celebrity endorsements in advertising using a two-sided framework, in terms of the internalization and identification processes of social influence as discussed by Kelman (1961). The two-sided execution was designed to increase a viewer's perception of advertiser credibility by including a discussion of a limitation of the advertised service. Results show that when compared to a traditional one-sided celebrity endorsement, the two-sided communication elicited significantly higher advertising credibility and effectiveness ratings, higher evaluation of the sponsor in terms of perceived overall quality of service, as well as a significantly greater intention to use the advertised service. These findings suggest that the use of a celebrity appeal in a two-sided form is an effective advertising strategy.


title: A self-boost charge pump topology for a gate drive high-side power supply

abstract: A self-boost charge pump topology is presented for a floating high-side gate drive power supply that features high voltage and current capabilities for use in integrated power electronic modules (IPEMs). The transformerless topology uses a small capacitor to transfer energy to the high-side switch from a single power supply referred to the negative rail. Unlike conventional bootstrap power supplies, no switching of the main phase-leg switches is required to provide power continuously to the high-side gate drive, even if the high-side switch is permanently on. Additional advantages include a low parts count and simple control requirements. A piecewise linear model of the self-boost charge pump is derived and the circuit's operating characteristics are analyzed. Simulation and experimental results are provided to verify the desired operation of the new charge pump circuit.


title: Color-based segmentation and feature detection for ball and goal post on mobile soccer robot game field

abstract: This study presents the real time implementation of object detection and tracking algorithm on mobile soccer robot. Object detection is considered as one of the most important task because ball and goal post are the main component in soccer. The system uses the combination of color-based segmentation and feature detection to detect the color and also the shape feature of the object used in the soccer robot game. The color segmentation uses thresholding method in Hue, Saturation, and Value (HSV) color space to differentiate the ball and goal post color from other objects in the field. Then, morphological operation is applied to the thersholded image to minimize the error. After that, Hough line transform is applied to detect the feature of the goal post. Then, ellipse detection is also applied to find the ball feature. This step is used so the desired object is correctly detected, not other object that have the same color. The final step is to calculate the image moments to determine the centroid of the objects and tracking it. Object's color, feature, and coordinate are obtained from this purposed method. In the implementation, the robot has successfully detect ball, goal post, and its position in a real time manner.


title: Coordinated control of second-order multi-agent systems with quantized-observer

abstract: In this paper, we consider distributed coordination of discrete-time second-order multi-agent systems with limited communication data-rate. The position of each agent is measurable, while the velocity is not. A quantized-observer based encoding-decoding scheme is proposed, which integrates the state observation with channel coding/decoding. A distributed coordinated control law is proposed based on the states and outputs of the encoders and decoders. By converting the closed-loop nonlinear system into a linear system with disturbance, sufficient conditions on the control gains and network topology for the existence of finite-level quantizers to ensure the closed-loop convergence are given, and it is shown that these conditions are also necessary in some sense. It is proved that, by selecting the number of quantization levels (data-rate) properly, the asymptotic synchronization of the positions and velocities can be achieved.


title: Time perspective in Internet and Facebook addiction

abstract: Time perspective is a predictor of addictive behaviors. As the number of Internet and Facebook users is increasing, it is worth investigating the role of time perspective in maladaptive types of usage. In this study, we examined the potential relationship of time perspective with Internet addiction and Facebook intrusion. The participants were 756 Internet users with Facebook accounts. We used the Zimbardo Time Perspective Inventory, the Facebook Intrusion Questionnaire, the Facebook Intensity Scale, and the Internet Addiction Test. We analyzed similar associations between time perspective and the two types of addiction. Age and daily time spent online were predictors of Internet addiction, Facebook intensity, and Facebook intrusion. Past Negative and Present Fatalistic orientations were positive predictors for both types of addiction, whereas Future time perspective was a negative predictor. Present Hedonistic orientation was a negative predictor only of Internet addiction. The findings of this study may contribute to the development of health promotion interventions and workshops aimed at preventing maladaptive online behaviors. Past Negative and Present Fatalistic were positive predictors for both types of addiction and Facebook intensity.Future was a negative predictor of these two types of addictions.Present Hedonistic was a negative predictor only of Internet addiction.


title: Procedural level generation with answer set programming for general Video Game playing

abstract: This paper proposes an automatic way of evolving level generators for arbitrary 2D games, which are described in the Video Game Description Language (VGDL). The process works as follows: a game described in VGDL is interpreted and transformed in a set of rules defined in Answer Set Programming (ASP), along with other general and customizable rules. Although a set of rules described in ASP can generate multiple levels, not all of them will be playable or well designed. Therefore, an evolutionary process is run to determine the values of the parameters of those customizable rules. The different level generators are evaluated with general video game playing agents, which are able to play any game and level in the framework. The aim is to maximize the difference between their performance in the levels generated, under the assumption that levels are better designed if good skilled players play better than poor agents. This work presents some initial experiments that suggest that it is possible to evolve interesting level generators using this technique, and outlines some lines of future work.


Membership Inference Attack against Differentially Private Deep Learning Model


title: Safety Verification of Deep Neural Networks

abstract: Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers for self-driving cars, this raises concerns about their safety. We develop a novel automated verification framework for feed-forward multi-layer neural networks based on Satisfiability Modulo Theory (SMT). We focus on safety of image classification decisions with respect to image manipulations, such as scratches or changes to camera angle or lighting conditions that would result in the same class being assigned by a human, and define safety for an individual decision in terms of invariance of the classification within a small neighbourhood of the original image. We enable exhaustive search of the region by employing discretisation, and propagate the analysis layer by layer. Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations. If found, adversarial examples can be shown to human testers and/or used to fine-tune the network. We implement the techniques using Z3 and evaluate them on state-of-the-art networks, including regularised and deep learning networks. We also compare against existing techniques to search for adversarial examples and estimate network robustness.


title: Distribution-Balanced Stratified Cross-Validation for Accuracy Estimation

abstract: Cross-validation has often been applied in machine learning research for estimating the accuracies of classifiers. In this work, we propose an extension to this method, called distribution-balanced stratified cross-validation (DBSCV), which improves the estimation quality by providing balanced intraclass distributions when partitioning a data set into multiple folds. We have tested DBSCV on nine real-world and three artificial domains using the C4.5 decision trees classifier. The results show that DBSCV performs better (has smaller biases) than the regular stratified crossvalidationin most cases, especially when the number of folds is small. The analysis and experiments based on three artificial data sets also reveal that DBSCV is particularly effective when multiple intraclass clusters exist in a data set.


title: Design of filter to reject motion artifact of pulse oximetry

abstract: Abstract The performance of the pulse oximeter, an instrument which non-invasively measures oxygen saturation in blood, is influenced by a patient's respiration or movement. Generally, the frequency band of the motion artifact signal generated from the patient's movement is overlapped with that of the patient's pulse wave which is measured by the received light sensor. Accordingly, it is difficult to filter the motion artifact of the pulse wave by using a filter which has a cut-off frequency that is fixed. In this study, the motion artifact is removed using the filter bank and the matched filter. Compared with traditional adaptive filter methods, the ratio variation is 50% lower than that of the moving average filter, allowing more stable measurement of oxygen saturation despite the patient's movement.


title: Linear prediction: A tutorial review

abstract: This paper gives an exposition of linear prediction in the analysis of discrete signals. The signal is modeled as a linear combination of its past values and present and past values of a hypothetical input to a system whose output is the given signal. In the frequency domain, this is equivalent to modeling the signal spectrum by a pole-zero spectrum. The major part of the paper is devoted to all-pole models. The model parameters are obtained by a least squares analysis in the time domain. Two methods result, depending on whether the signal is assumed to be stationary or nonstationary. The same results are then derived in the frequency domain. The resulting spectral matching formulation allows for the modeling of selected portions of a spectrum, for arbitrary spectral shaping in the frequency domain, and for the modeling of continuous as well as discrete spectra. This also leads to a discussion of the advantages and disadvantages of the least squares error criterion. A spectral interpretation is given to the normalized minimum prediction error. Applications of the normalized error are given, including the determination of an "optimal" number of poles. The use of linear prediction in data compression is reviewed. For purposes of transmission, particular attention is given to the quantization and encoding of the reflection (or partial correlation) coefficients. Finally, a brief introduction to pole-zero modeling is given.


title: Absence of vancomycin-resistance genes in Staphylococcus aureus isolated from potable water

abstract: The aim of this study was to investigate the carriage of vancomycin-resistance genes by Staphylococcus aureus found in drinking water supplies. A total of 100 samples (potable water and faucet swabs) were analyzed for the presence of S. aureus and their carriage of vancomycin-resistance genes. Mannitol salt agar was used for the isolation of staphylococci, and confirmation of S. aureus was carried out by means of Gram staining technique, growth on blood agar, production of catalase and coagulase. Antibiotic susceptibility testing was performed by disk diffusion method, and polymerase chain reaction (PCR) was used to investigate the presence of vancomycin-resistance genes (vanA, vanB, vanC, vanS, vanY). Staphylococci were recovered from 25% of water samples (n = 75), of which seven samples were positive for S. aureus, on the other hand 76% (n = 25) of the faucet swabs yielded typical staphylococcal colonies, with 53% being positive for S. aureus. The disk diffusion method showed that all S. aureus resistance to penicillin G, and about 66.6% (n = 51 isolates) were resistance to oxacillin. Of all 51 S. aureus isolates, only 14% were resistant to vancomycin by disk diffusion method, however, the DNA extracted from all confirmed S. aureus did not yield any PCR products with all the primers used for detecting vancomycin-resistance genes. Vancomycin-resistant S. aureus remains rare. The disk diffusion method may give false-resistance with vancomycin, therefore, caution is required with the investigation and interpretation of vancomycin susceptibility testing by agar diffusion method.


title: Towards a Generalized Map Algebra: Principles and Data Types

abstract: Map Algebra is a collection of functions for handling continuous spatial data, which allows modeling of different problems and getting new information from the existing data. There is an established set of map algebra functions in the GIS literature, originally proposed by Dana Tomlin. However, the question whether his proposal is complete is still an open problem in GIScience. This paper describes the design of a map algebra that generalizes Tomlin’s map algebra by incorporating topological and directional spatial predicates. Our proposal enables operations that are not directly expressible by Tomlin’s proposal. One of the important results of our paper is to show that Tomlin’s Map Algebra can be defined as an application of topological predicates to coverages. This paper points to a convergence between these two approaches and shows that it is possible to develop a foundational theory for GIScience where topological predicates are the heart of both object-based algebras and field-based algebras.


title: Data-Driven Phonetic Comparison and Conversion Between South African, British and American English Pronunciations

abstract: We analyse pronunciations in American, British and South African English pronunciation dictionaries. Three analyses are perfomed. First the accuracy is determined with which decision tree based grapheme-to-phoneme (G2P) conversion can be applied to each accent. It is found that there is little difference between the accents in this regard. Secondly, pronunciations are compared by performing pairwise alignments between the accents. Here we find that South African English pronunciation most closely matches British English. Finally, we apply decision trees to the conversion of pronunciations from one accent to another. We find that pronunciations of unknown words can be more accurately determined from a known pronunciation in a different accent than by means of G2P methods. This has important implications for the development of pronunciation dictionaries in less-resourced varieties of English, and hence also for the development of ASR systems.


title: The Contradictory Influence of Social Media Affordances on Online Communal Knowledge Sharing

abstract: The use of social media creates the opportunity to turn organization-wide knowledge sharing in the workplace from an intermittent, centralized knowledge management process to a continuous online knowledge conversation of strangers, unexpected interpretations and re-uses, and dynamic emergence. We theorize four affordances of social media representing different ways to engage in this publicly visible knowledge conversations: metavoicing, triggered attending, network-informed associating, and generative role-taking. We further theorize mechanisms that affect how people engage in the knowledge conversation, finding that some mechanisms, when activated, will have positive effects on moving the knowledge conversation forward, but others will have adverse consequences not intended by the organization. These emergent tensions become the basis for the implications we draw.


title: ESPEN Guideline Clinical Nutrition in Neurology

abstract: Summary Neurological diseases are frequently associated with swallowing disorders and malnutrition. Moreover, patients with neurological diseases are at increased risk of micronutrient deficiency and dehydration. On the other hand, nutritional factors may be involved in the pathogenesis of neurological diseases. Multiple causes for the development of malnutrition in patients with neurological diseases are known including oropharyngeal dysphagia, impaired consciousness, perception deficits, cognitive dysfunction, and increased needs. The present evidence- and consensus-based guideline addresses clinical questions on best medical nutrition therapy in patients with neurological diseases. Among them, management of oropharyngeal dysphagia plays a pivotal role. The guideline has been written by a multidisciplinary team and offers 88 recommendations for use in clinical practice for amyotrophic lateral sclerosis, Parkinson's disease, stroke and multiple sclerosis.


title: A Database Management System for the Federal Courts

abstract: A judicial systems laboratory has been established and several large-scale information management systems projects have been undertaken within the Federal Judicial Center in Washington, D.C. The newness of the court application area, together with the experimental nature of the initial prototypes, required that the system building tools be as flexible and efficient as possible for effective software design and development. The size of the databases, the expected transaction volumes, and the long-term value of the court records required a data manipulation system capable of providing high performance and integrity. The resulting design criteria, the programming capabilities developed, and their use in system construction are described herein. This database programming facility has been especially designed as a technical management tool for the database administrator, while providing the applications programmer with a flexible database software interface for high productivity. Specifically, a network-type database management system using SAIL as the data manipulation host language is described. Generic data manipulation verb formats using SAIL's macro facilities and dynamic data structuring facilities allowing in-core database representations have been developed to achieve a level of flexibility not usually attained in conventional database systems.


title: A Survey on Near Field Communication (NFC) Technology

abstract: Near Field Communication (NFC) as a promising short range wireless communication technology facilitates mobile phone usage of billions of people throughout the world that offers diverse services ranging from payment and loyalty applications to access keys for offices and houses. Eventually NFC technology integrates all such services into one single mobile phone. NFC technology has emerged lately, and consequently not much academic source is available yet. On the contrary, due to its promising business case options, there will be an increasing amount of work to be studied in the very close future. This paper presents the concept of NFC technology in a holistic approach with different perspectives, including communication essentials with standards, ecosystem and business issues, applications, and security issues. Open research areas and further recommended studies in terms of academic and business point of view are also explored and discussed at the end of each major subject's subsection. This comprehensive survey will be a valuable guide for researchers and academicians as well as for business world interested in NFC technology.


title: Voltage-Sag Tolerance of DFIG Wind Turbine With a Series Grid Side Passive-Impedance Network

abstract: Due to the increase of the number of wind turbines connected directly to the electric utility grid, new regulator codes have been issued that require low-voltage ride-through capability for wind turbines so that they can remain online and support the electric grid during voltage sags. Conventional ride-through techniques for the doubly fed induction generator (DFIG) architecture result in compromised control of the turbine shaft and grid current during fault events. In this paper, a series passive-impedance network at the stator side of a DFIG wind turbine is presented. It is easy to control, capable of off-line operation for high efficiency, and low cost for manufacturing and maintenance. The balanced and unbalanced fault responses of a DFIG wind turbine with a series grid side passive-impedance network are examined using computer simulations and hardware experiments.


title: Efficient domain adaptation for painting theme recognition

abstract: In this paper we approach the problem of scene recognition in paintings. We tackle this task with the aid of Convolutional Neural Networks and a large database consisting of around 80,000 paintings. The main purpose is to identify an efficient method to enlarge the database by domain transfer from photographic content to artistic content. Thus, we discuss the practical capabilities of a recent method of domain transfer from photographs to paintings while augmenting the employed database and aid the learning of difficult styles. We propose a set of improvements to increase the feasibility of the domain transfer in the context of large databases.


title: Classification of text documents

abstract: We investigate four different classification methods for document classification: the naive Bayes classifier, nearest neighbor classifier, decision tree classifier, and subspace method. The classifiers were applied to seven-class Yahoo newsgroups individually and in combination. We study three classifier combination approaches: simple voting, dynamic classifier selection, and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classification methods on our data sets. Combinations of multiple classifiers did not always improve classification accuracy. Among the three different combination approaches, the adaptive classifier combination method proposed here performed the best.


title: The PI3K/Akt/mTOR pathway in ovarian cancer: therapeutic opportunities and challenges

abstract: The phosphatidylinositol 3 kinase (PI3K) pathway is frequently altered in cancer, including ovarian cancer (OC). Unfortunately, despite a sound biological rationale and encouraging activity in preclinical models, trials of first-generation inhibitors of mammalian target of rapamycin (mTOR) in OC have demonstrated negative results. The lack of patient selection as well as resistance to selective mTOR complex-1 (mTORC1) inhibitors could explain the disappointing results thus far. Nonetheless, a number of novel agents are being investigated, including dual mTORC1/mTORC2, Akt, and PI3K inhibitors. Although it is likely that inhibition of the PI3K/Akt/mTOR pathway may have little effect in unselected OC patients, certain histological types, such as clear cell or endometrioid OC with frequent phosphatidylinositol-4,5-biphosphate 3-kinase, catalytic subunit alpha (PIK3CA) and/or phosphatase and tensin homolog (PTEN) alterations, may be particularly suited to this approach. Given the complexity and redundancy of the PI3K signaling network, PI3K pathway inhibition may be most useful in combination with either chemotherapy or other targeted therapies, such as MEK inhibitors, anti-angiogenic therapy, and hormonal therapy, in appropriately selected OC patients. Here, we discuss the relevance of the PI3K pathway in OC and provide an up-to-date review of clinical trials of novel PI3K inhibitors alone or in combination with cytotoxics and novel therapies in OC. In addition, the challenges of drug resistance and predictive biomarkers are addressed.


title: Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications

abstract: The authors present an approach for designing self-monitoring technology called "semi-automated tracking," which combines both manual and automated data collection methods. Through this approach, they aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve their self-monitoring goals. They first specify three design considerations for semi-automated tracking: data capture feasibility, the purpose of self-monitoring, and the motivation level. They then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies they developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations.


title: Emotion circuits in the brain.

abstract: The field of neuroscience has, after a long period of looking the other way, again embraced emotion as an important research area. Much of the progress has come from studies of fear, and especially fear conditioning. This work has pin- pointed the amygdala as an important component of the system involved in the acqui- sition, storage, and expression of fear memory and has elucidated in detail how stimuli enter, travel through, and exit the amygdala. Some progress has also been made in understanding the cellular and molecular mechanisms that underlie fear conditioning, and recent studies have also shown that the findings from experimental animals apply to the human brain. It is important to remember why this work on emotion succeeded where past efforts failed. It focused on a psychologically well-defined aspect of emo- tion, avoided vague and poorly defined concepts such as "affect," "hedonic tone," or "emotional feelings," and used a simple and straightforward experimental approach. With so much research being done in this area today, it is important that the mistakes of the past not be made again. It is also time to expand from this foundation into broader aspects of mind and behavior


title: Deep Convolutional Neural Networks for Efficient Pose Estimation in Gesture Videos

abstract: Our objective is to efficiently and accurately estimate the upper body pose of humans in gesture videos. To this end, we build on the recent successful applications of deep convolutional neural networks (ConvNets). Our novelties are: (i) our method is the first to our knowledge to use ConvNets for estimating human pose in videos; (ii) a new network that exploits temporal information from multiple frames, leading to better performance; (iii) showing that pre-segmenting the foreground of the video improves performance; and (iv) demonstrating that even without foreground segmentations, the network learns to abstract away from the background and can estimate the pose even in the presence of a complex, varying background.


Impact of Yoga and Meditation on Cellular Aging in Apparently Healthy Individuals: A Prospective, Open-Label Single-Arm Exploratory Study


title: Nutrient movement into soils beneath eucalypt and exotic conifer forests in southern central Victoria

abstract: Above-ground nutrient return to the soil was estimated by studying forest floor leachates for up to 2 years in two eucalypt forests (Eucalyptus obliqua and E. regnans) and two nearby conifer plantations (Pinus radiata and Pseudotsuga menziesii) near Narbethong in southern central Victoria. Forest floor leachate volumes were recorded approximately once a month and samples were analysed for pH and potassium, sodium, magnesium, and calcium concentrations. ::: ::: ::: ::: The volume of forest floor leachate was greater in the eucalypts than in the conifers. This was attributed to greater interception losses of water by the conifers. ::: ::: ::: ::: Forest floor leachate chemistry underwent regular seasonal fluctuations which were similar for each of the four forest types. Acidity was highest in spring and autumn and lowest in summer. Cation concentrations were highest in autumn and lowest in spring. This behaviour was attributed to changes in climate and in rates of litter decomposition. ::: ::: ::: ::: In contrast to cation concentrations, cation quantities entering the soil in forest floor leachate showed no pronounced seasonal variations although they tended to be high in winter. The quantities of cations added to the soil in forest floor leachate varied with forest type, and were significantly higher in E. obliqua than in the comparable P. radiata stand. Greater quantities of acid were added to the soil under P. radiata. ::: ::: ::: ::: The P. radiata soil contained lower quantities of exchangeable cations than the E. obliqua soil. This was consistent with the observed differences in forest floor leachate chemistry. Unlike the P. radiata soil, the P. menziesii soil did not appear to be depleted in exchangeable cations. ::: ::: ::: ::: It was concluded that P. radiata had depleted the exchangeable cation reserves in the soil studied but that the full significance of this is still unknown.


title: Rehabilitation of symptomatic atraumatic degenerative rotator cuff tears: A clinical commentary on assessment and management

abstract: STUDY DESIGN ::: Clinical Commentary. ::: ::: ::: INTRODUCTION ::: Atraumatic rotator cuff (RC) disease, is one of the most common cause of shoulder pain, which encompasses a continuum from tendinopathy to full thickness cuff tears. Extrinsic, intrinsic and environmental factors have been implicated in the pathophysiology of this disorder, affecting the clinical presentation of symptoms including pain and irritability. Successful rehabilitation of symptomatic atraumatic degenerative rotator cuff (SADRC) tears must address the underlying mechanisms causing dysfunction and correct modifiable factors. ::: ::: ::: PURPOSE OF THE STUDY ::: The purpose of this paper is to review the shoulder complex anatomy, introduce atraumatic degenerative RC pathology, differentiate between symptomatic and asymptomatic degenerative RC tears, propose an assessment and introduce the Rotator Cuff Protocol 1 (RCP1) designed by the clinical reasoning of one of the lead authors (LW) as a rehabilitation management approach for those clients who present with SADRC tears. ::: ::: ::: METHODS/RESULTS/DISCUSSION ::: N/A for clinical commentary. ::: ::: ::: CONCLUSIONS ::: The ability to identify SADRC tears should consider shoulder anatomy, extrinsic, intrinsic and environmental factors, and the consideration for the natural history of atraumatic partial and full thickness tears in the general population. A thorough clinical history and examination, which includes shoulder symptom modification tests, allows the examiner to determine at what phase the patient may start their exercise program. The RCP1 is a program that has been used clinically by many therapists and clients over the years and research is underway to test this protocol in atraumatic rotator cuff disease including SADRC tears. ::: ::: ::: LEVEL OF EVIDENCE ::: 5.


title: A trust model-based Bayesian decision theory in large scale Internet of Things

abstract: In addressing the growing problem of security of Internet of Things, we present, from a statistical decision point of view, a naval approach for trust-based access control using Bayesian decision theory. We build a trust model, TrustBayes which represents a trust level for identity management in IoT. TrustBayes model is be applied to address access control on uncertainty environment where identities are not known in advance. The model consists of EX (Experience), KN (Knowledge) and RC (Recommendation) values which is be obtained in measurement while a IoT device requests to access a resource. A decision will be taken based model parameters and be computed using Bayesian decision rules. To evaluate our a trust model, we do a statistical analysis and simulate it using OMNeT++ to investigate battery usage. The simulation result shows that the Bayesian decision theory approach for trust based access control guarantees scalability and it is energy efficient as increasing number of devices and not affecting the functioning and performance.


title: Developmental Neurocircuitry of Motivation in Adolescence : A Critical Period of Addiction Vulnerability

abstract: Objective: Epidemiological studies indicate that experimentation with addictive drugs and onset of addictive disorders is primarily concentrated in adolescence and young adulthood. The authors describe basic and clinical data supporting adolescent neurodevelopment as a biologically critical period of greater vulnerability for experimentation with substances and acquisition of substance use disorders. Method: The authors reviewed recent literature regarding neurocircuitry underlying motivation, impulsivity, and addiction, with a focus on studies investigating adolescent neurodevelopment. Results: Adolescent neurodevelopment occurs in brain regions associated with motivation, impulsivity, and addiction. Adolescent impulsivity and/or novelty seeking as a transitional trait behavior can be explained in part by maturational changes in frontal cortical and subcortical monoaminergic systems. These developmental processes may advantageously promote learning drives for adaptation to adult roles but may also confer greater vulnerability to the addictive actions of drugs. Conclusions: An exploration of developmental changes in neurocircuitry involved in impulse control has significant implications for understanding adolescent behavior, addiction vulnerability, and the prevention of addiction in adolescence and adulthood.


title: Learning View Priors for Single-View 3D Reconstruction

abstract: There is some ambiguity in the 3D shape of an object when the number of observed views is small. Because of this ambiguity, although a 3D object reconstructor can be trained using a single view or a few views per object, reconstructed shapes only fit the observed views and appear incorrect from the unobserved viewpoints. To reconstruct shapes that look reasonable from any viewpoint, we propose to train a discriminator that learns prior knowledge regarding possible views. The discriminator is trained to distinguish the reconstructed views of the observed viewpoints from those of the unobserved viewpoints. The reconstructor is trained to correct unobserved views by fooling the discriminator. Our method outperforms current state-of-the-art methods on both synthetic and natural image datasets; this validates the effectiveness of our method.


NONCONVEX MODEL FOR FACTORING NONNEGATIVE MATRICES


title: Insecure attachments: Attachment, emotional regulation, sexting and condomless sex among women in relationships

abstract: Associations have been found among sexting, attachment insecurity, emotional dysregulation, and sexual and risky sexual behavior in young adults. In a sample of 92 young adult women in romantic relationships, this study aimed to examine whether emotional regulation might constitute an indirect pathway linking attachment and sexting, and the potential implications for engaging in condomless sex. Findings revealed that in young adult women in romantic relationships, attachment anxiety was associated with sexting through difficulty controlling impulses during moments of emotional distress. Also, sexting directly predicted condomless sex. Attachment avoidance and anxiety were both associated with sexting in the bivariate correlations. Findings highlight the role of digital technology in understanding how people's negotiation of needs for attachment and emotional regulation might lead to risky sexual behavior. Interventions targeting the interplay of attachment, emotional regulation and technology use may positively impact relational and behavioral health outcomes. Difficulty controlling impulses mediated the association between attachment anxiety and sexting.Sexting was associated with condomless sex.Attachment anxiety and avoidance were associated with sexting in bivariate correlations.Interventions targeting attachment and sexting might influence risky sexual behavior.


title: Offline geometric parameters for automatic signature verification using fixed-point arithmetic

abstract: This paper presents a set of geometric signature features for offline automatic signature verification based on the description of the signature envelope and the interior stroke distribution in polar and Cartesian coordinates. The features have been calculated using 16 bits fixed-point arithmetic and tested with different classifiers, such as hidden Markov models, support vector machines, and Euclidean distance classifier. The experiments have shown promising results in the task of discriminating random and simple forgeries.


title: Multimodal Pivots for Image Caption Translation

abstract: We present an approach to improve statistical machine translation of image descriptions by multimodal pivots defined in visual space. The key idea is to perform image retrieval over a database of images that are captioned in the target language, and use the captions of the most similar images for crosslingual reranking of translation outputs. Our approach does not depend on the availability of large amounts of in-domain parallel data, but only relies on available large datasets of monolingually captioned images, and on state-of-the-art convolutional neural networks to compute image similarities. Our experimental evaluation shows improvements of 1 BLEU point over strong baselines.


Towards Seamless Integration of Data Analytics into Existing HPC Infrastructures


title: Rendering the darkness: glimpse on the LEGO Batman movie

abstract: The technical and creative challenges of The LEGO Batman Movie motivated many changes to rendering at Animal Logic. The project was the first feature animation to be entirely rendered with the studio's proprietary path-tracer, Glimpse. Brick-based modelling, animation and destruction techniques taken to the extents of Gotham City required extraordinary scalability and control. The desire to separate complexity from artistic intent led to the development of a novel material composition system. Lensing and lighting choices also drove technical development for efficient in-render lens distortion, depth-of-field effects and accelerated handling of thousands of city and interior lights.


title: Emotion detection and recognition using HRV features derived from photoplethysmogram signals

abstract: Detection of true human emotions has attracted a lot of interest in the recent years. The applications range from e-retail to health-care for developing effective companion systems with reliable emotion recognition. This paper proposes heart rate variability (HRV) features extracted from photoplethysmogram (PPG) signal obtained from a cost-effective PPG device such as Pulse Oximeter for detecting and recognizing the emotions on the basis of the physiological signals. The HRV features obtained from both time and frequency domain are used as features for classification of emotions. These features are extracted from the entire PPG signal obtained during emotion elicitation and baseline neutral phase. For analyzing emotion recognition, using the proposed HRV features, standard video stimuli are used. We have considered three emotions namely, happy, sad and neutral or null emotions. Support vector machines are used for developing the models and features are explored to achieve average emotion recognition of 83.8% for the above model and listed features.


title: Towards bipolar linguistic summaries: a novel fuzzy bipolar querying based approach

abstract: We study the possibility to extend the concept of linguistic data summaries employing the notion of bipolarity. Yager's linguistic summaries may be derived using a fuzzy linguistic querying interface. We look for a similar analogy between bipolar queries and the extended form of linguistic summaries. The general concept of bipolar query, and its special interpretation are recalled, which turns out to be applicable to accomplish our goal. Some preliminary results are presented and possible directions of further research are pointed out.


title: Understanding the Properties of the BitTorrent Overlay

abstract: In this paper, we conduct extensive simulations to understand the properties of the overlay generated by BitTorrent. We start by analyzing how the overlay properties impact the efficiency of BitTorrent. We focus on the average peer set size (i.e., average number of neighbors), the time for a peer to reach its maximum peer set size, and the diameter of the overlay. In particular, we show that the later a peer arrives in a torrent, the longer it takes to reach its maximum peer set size. Then, we evaluate the impact of the maximum peer set size, the maximum number of outgoing connections per peer, and the number of NATed peers on the overlay properties. We show that BitTorrent generates a robust overlay, but that this overlay is not a random graph. In particular, the connectivity of a peer to its neighbors depends on its arriving order in the torrent. We also show that a large number of NATed peers significantly compromise the robustness of the overlay to attacks. Finally, we evaluate the impact of peer exchange on the overlay properties, and we show that it generates a chain-like overlay with a large diameter, which will adversely impact the efficiency of large torrents.


title: A near-infrared image based face recognition system

abstract: In this paper, we present a near infrared (NIR) image based face recognition system. Firstly, we describe a design of NIR image capture device which minimizes influence of environmental lighting on face images. Both face and facial feature localization and face recognition are performed using local features with AdaBoost learning. An evaluation in real-world user scenario shows that the system achieves excellent accuracy, speed and usability.


title: On building phonetically and prosodically rich speech corpus for text-to-speech synthesis

abstract: This paper proposes a way of preparing and recording a speech corpus for unit selection text-to-speech speech synthesis driven by symbolic prosody. The research is focused on a phonetically and prosodically rich sentence selection algorithm. Symbolic description on a deep prosody level is used to enrich the phonetic representation of sentences (by respecting the prosodeme types phones appear in). The resulting algorithm then selects sentences with respect to both phonetic and prosodic criteria. To cover supra-sentential prosody phenomena, paragraphs were selected at random and recorded as well. The new speech corpus can be utilised in unit selection speech synthesis and also for training a data-driven prosodic parser.


title: On Text Preprocessing for Opinion Mining Outside of Laboratory Environments

abstract: Opinion mining deals with scientific methods in order to find, extract and systematically analyze subjective information. When performing opinion mining to analyze content on the Web, challenges arise that usually do not occur in laboratory environments where prepared and preprocessed texts are used. This paper discusses preprocessing approaches that help coping with the emerging problems of sentiment analysis in real world situations. After outlining the identified shortcomings and presenting a general process model for opinion mining, promising solutions for language identification, content extraction and dealing with Internet slang are discussed.