title: NaturaSketch: Modeling from Images and Natural Sketches

abstract: Sketching on paper is a quick and easy way to communicate ideas. However, many sketch-based systems require people to draw in contrived ways instead of sketching freely as they would on paper. NaturaSketch affords a more natural interface through multiple strokes that overlap, cross, and connect. It also features a meshing algorithm to support multiple strokes of different classifications, which lets users design complex 3D shapes from sketches drawn over existing images. To provide a familiar workflow for object design, a set of sketch annotations can also specify modeling and editing operations. NaturaSketch empowers designers to produce a variety of models quickly and easily.


title: Teaching to the rating: School accountability and the distribution of student achievement

abstract: This paper examines whether minimum competency school accountability systems, such as those created under No Child Left Behind, influence the distribution of student achievement. Because school ratings in these systems only incorporate students' test scores via pass rates, this type of system increases incentives for schools to improve the performance of students who are on the margin of passing but does not increase short-run incentives for schools to improve other students' performance. Using student-level, panel data from Texas during the 1990's, I explicitly calculate schools' short-run incentives to improve various students' expected performance, and I find that schools do respond to these incentives. Students perform better than expected when their test score is particularly important for their schools' accountability rating. Also, low achieving students perform better than expected in math when many of their classmates' math scores are important for the schools' rating, while relatively high achieving students do not perform better. Distributional effects appear to be related to broad changes in resources or instruction, as well as narrowly tailored attempts to improve the performance of specific students.


title: Detecting and Characterizing Mental Health Related Self-Disclosure in Social Media

abstract: Self-disclosure is an important element facilitating improved psychological wellbeing in individuals with mental illness. As social media is increasingly adopted in health related discourse, we examine how these new platforms might be allowing honest and candid expression of thoughts, experiences and beliefs. Specifically, we seek to detect levels of self-disclosure manifested in posts shared on different mental health forums on Reddit. We develop a classifier for the purpose based on content features. The classifier is able to characterize a Reddit post to be of high, low, or no self-disclosure with 78% accuracy. Applying this classifier to general mental health discourse on Reddit, we find that bulk of such discourse is characterized by high self-disclosure, and that the community responds distinctively to posts that disclose less or more. We conclude with the potential of harnessing our proposed self-disclosure detection algorithm in psychological therapy via social media. We also discuss design considerations for improved community moderation and support in these vulnerable self-disclosing communities.


Factors Affecting Jordanian Consumers ’ Adoption of Mobile Banking Services


title: Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation

abstract: The integration of visual and inertial sensors for human motion tracking has attracted significant attention recently, due to its robust performance and wide potential application. This paper introduces a real-time hybrid solution to articulated 3D arm motion tracking for home-based rehabilitation by combining visual and inertial sensors. Data fusion is a key issue in this hybrid system and two different data fusion methods are proposed. The first is a deterministic method based on arm structure and geometry information, which is suitable for simple rehabilitation motions. The second is a probabilistic method based on an Extended Kalman Filter (EKF) in which data from two sensors is fused in a predict-correct manner in order to deal with sensor noise and model inaccuracy. Experimental results are presented and compared with commercial marker-based systems, CODA and Qualysis. They show good performance for the proposed solution.


Predictable Shared Memory Resources for Multi-Core Real-Time Systems


title: Optical Sensors for Real-Time Measurement of Motorcycle Tilt Angle

abstract: This paper addresses the analysis and design of an optical sensor for the real-time measurement of the tilt angle in hypersport motorcycles. The aim of this paper is to design a compact, reliable, and low-cost optical triangulator that is capable of accurate in-field measurements in the harsh environment of sport motorcycles. An analytical computation of the required system sensitivity and achievable accuracy is carried out. The detrimental effects of solar interference are also described and discussed. The proposed instrumentation, which is realized with ad hoc laser emitters, is shown to have superior performance with respect to a previous solution based on light-emitting diode (LED) emitters. Such novel triangulators are shown to provide good and reliable performances for the proposed application to maintain low costs and small sizes, overcoming the problem of solar interference. The performance of the proposed sensor is assessed by experiments on an instrumented motorbike in a racetrack.


title: DynamicGEM: A Library for Dynamic Graph Embedding Methods

abstract: DynamicGEM is an open-source Python library for learning node representations of dynamic graphs. It consists of state-of-the-art algorithms for defining embeddings of nodes whose connections evolve over time. The library also contains the evaluation framework for four downstream tasks on the network: graph reconstruction, static and temporal link prediction, node classification, and temporal visualization. We have implemented various metrics to evaluate the state-of-the-art methods, and examples of evolving networks from various domains. We have easy-to-use functions to call and evaluate the methods and have extensive usage documentation. Furthermore, DynamicGEM provides a template to add new algorithms with ease to facilitate further research on the topic.


title: EVALUATION OF BANK'S PERFORMANCE BY USING BALANCED SCORE CARD: PRACTICAL STUDY IN LIBYAN ENVIRONMENT

abstract: The purpose of this study is to contribute to the understanding of how BSC is developed and applied in evaluating the performance of The Jumhouria Bank (JB) in Libya. Using the concepts of Kaplan and Norton, and the data made available from the bank, a BSC was derived to measure the performance of the bank between 2007-2010. The analysis assisted the cause-effect relationships between the non-financial, and the financial dimensions of the BSC. Due to lack of research work, in this area, in the banking sector in Libya, this study shall contribute to the knowledge on how banks in Libya may apply the BSC to evaluate their performance, and how they might turn strategic vision into potential performance. The author proposed some future research needs required in this area. The use of the BSC developed here is limited to the bank studied; however, the approach could trigger off reflections among policy makers and other banks to start using the BSC.


title: Ask the Right Questions: Active Question Reformulation with Reinforcement Learning

abstract: We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer. The reformulation system is trained end-to-end to maximize answer quality using policy gradient. We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!. The agent outperforms a state-of-the-art base model, playing the role of the environment, and other benchmarks. We also analyze the language that the agent has learned while interacting with the question answering system. We find that successful question reformulations look quite different from natural language paraphrases. The agent is able to discover non-trivial reformulation strategies that resemble classic information retrieval techniques such as term re-weighting (tf-idf) and stemming.


title: Socioeconomic position, resilience, and health behaviour among elderly people

abstract: OBJECTIVES ::: Healthy psychological functioning, the ability to respond rapidly to environmental changes, has been associated with better health outcomes. Less work has examined the association with health behaviour. This study explores whether resilience (a specific expression of healthy psychological functioning) is positively associated with health behaviour in an elderly population aged ≥65 years and whether this association differs in different socioeconomic groups. ::: ::: ::: METHODS ::: Resilience was measured in 3,942 elderly participating in a population-based cohort study (KORA-Age study) in Germany through a short version of the Resilience Scale developed by Wagnild and Young. Regression analyses were performed by socioeconomic position (low/high educational level or income) for two outcome variables, i.e. high consumption of fruit and vegetables and high/moderate physical activity. ::: ::: ::: RESULTS ::: Resilient people were more likely to consume ≥5 servings of fruit and vegetables a day and to perform high/moderate physical activity as compared to non-resilient people (ORs ranging from 1.5 to 2.2), irrespective of socioeconomic position. ::: ::: ::: CONCLUSIONS ::: Resilience could provide an important starting point for health promotion strategies, addressing resources rather than deficits and risk factors.


title: Detecting patterns of crime with series finder

abstract: Many crimes can happen every day in a major city, and figuring out which ones are committed by the same individual or group is an important and difficult data mining challenge. To do this, we propose a pattern detection algorithm called Series Finder, that grows a pattern of discovered crimes from within a database, starting from a "seed" of a few crimes. Series Finder incorporates both the common characteristics of all patterns and the unique aspects of each specific pattern. We compared Series Finder with classic clustering and classification models applied to crime analysis. It has promising results on a decade's worth of crime pattern data from the Cambridge Police Department.


title: A platform for wearable physiological computing

abstract: Abstract As computers emerge, from the desktop and palm top, into everyday life, and on to our bodies there are opportunities to aggregate and present data and to realize and envision applications that have never before been possible. Tracking the physiological state of individuals, at resolutions measured in thousandths of a second instead of in visits per year, now makes it possible to ascertain caloric intake and expenditure, patterns of sleep, contextual activities such as working-out and driving, even parameters of mental state and health. An award wining multi-channel wearable physiological sensor has enabled the collection of data in natural settings from thousands of subjects engaged in diverse activities. The resulting corpus of physiological data from 4 years of aggregation has yielded over 30 million minutes of physiological data. Data modeling efforts are resulting in applications that enable real-time presentation of meaningful and actionable information to users and their designated collaborators (physicians, family members, counselors, coaches, etc.) The SenseWear system, its design and a summery of the experimental results and ongoing research initiatives will be presented. This discussion will show how the design and research efforts of ubiquitous, pervasive, and collaborative computing are converging to manifest the future of computing as: wearable, personal, and sympathetic.


title: Field test of autonomous loading operation by wheel loader

abstract: The authors have been conducting research on an autonomous system for loading operation by wheel loader. Experimental results at a field test site using full-size model (length: 6.1m) will be described in this paper. Basic structure of system consists of three sub systems: measuring and modeling of environment, task planning and motion control. The experimental operation includes four cycles of scooping and loading to dump truck. The experimental results prove that the developed system performed the autonomous operation smoothly and completed the mission.


title: Wideband Dual-Polarized Patch Antenna With Broadband Baluns

abstract: A pair of novel 180deg broadband microstrip baluns are used to feed a dual-polarized patch antenna. The 180deg broadband balun delivers equal amplitude power division and consistent 180deg (plusmn5deg) phase shifting over a wide bandwidth (>50%). We demonstrate that for a dual-polarized quadruple L-probe square patch antenna, the use of the proposed 180deg broadband balun pair, in place of a conventional 180deg narrowband balun pair, provides improved input port isolation and reduced H-plane cross-polarization levels over a wider frequency range, while maintaining low E-plane cross-polarization levels and stable E- and H-plane co-polarization patterns throughout the impedance passband


title: AutoGait: A mobile platform that accurately estimates the distance walked

abstract: AutoGait is a mobile platform that autonomously discovers a user's walking profile and accurately estimates the distance walked. The discovery is made by utilizing the GPS in the user's mobile device when the user is walking outdoors. This profile can then be used both indoors and outdoors to estimate the distance walked. To model the person's walking profile, we take advantage of the fact that a linear relationship exists between step frequency and stride length, which is unique to individuals and applies to everyone regardless of age. Autonomous calibration invisible to users allows the system to maintain a high level of accuracy under changing conditions. AutoGait can be integrated into any pedometer or indoor navigation software on handheld devices as long as they are equipped with GPS. The main contribution of this paper is two fold: (1) we propose an auto-calibration method that trains a person's walking profile by effectively processing noisy GPS readings, and (2) we build a prototype system and validate its performance by performing extensive experiments. Our experimental results confirm that the proposed auto-calibration method can accurately estimate a person's walking profile and thus significantly reduce the error rate.


title: Experimental evaluation of material identification methods with CdTe X-rays spectrometric detector

abstract: This paper presents multi dimensional approaches for material identification applied on experimental X-ray measurements obtained with CdTe spectrometric detector. LETI has been developing a 16 channels fast read-out electronics capable to perform spectrometric measurements at count rates above 107 counts/s. The read-out electronics was coupled to CdTe linear array pixel detectors. A multidimensional analysis of the measured spectra has been developed to perform material identification. The results show a decrease of false detection rate by a factor up to 2 compared to an optimised dual counting approach applied on the same spectrometric raw data. Compared to a sandwich detector, the false detection rate is decreasing by a factor higher than 3 for thin objects. Generally, the paper demonstrates quantitatively the continuous performance and robustness increase with the number of detector energy channels.


title: Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

abstract: Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation) and a social event (Pope Francis’ visit to the US) in the New York City—Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information), this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information.


title: Hybrid-based Arabic single-document text summarization approach using genatic algorithm

abstract: Text summarization is one of the most paramount applications of the field of natural language processing. This paper presents a Hybrid-based Single-document Extractive Arabic Text Summarization approach based on Genetic Algorithm. The proposed summarization approach was evaluated using EASC corpus, and ROUGE evaluation method to determine the accuracy of the proposed approach. The result showed that our method outperformed many state-of-the art methods.


title: Differential Privacy in Metric Spaces: Numerical, Categorical and Functional Data Under the One Roof

abstract: We study differential privacy in the abstract setting of probability on metric spaces. Numerical, categorical and functional data can be handled in a uniform manner in this setting. We demonstrate how mechanisms based on data sanitisation and those that rely on adding noise to query responses fit within this framework. We prove that once the sanitisation is differentially private, then so is the query response for any query. We show how to construct sanitisations for high-dimensional databases using simple 1-dimensional mechanisms. We also provide lower bounds on the expected error for differentially private sanitisations in the general metric space setting. Finally, we consider the question of sufficient sets for differential privacy and show that for relaxed differential privacy, any algebra generating the Borel ?-algebra is a sufficient set for relaxed differential privacy.


title: Intrinsically Motivated Learning in Natural and Artificial Systems

abstract: It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intelligence, and we know that higher mammals engage in exploratory activities that are not directed to pursue goals of immediate relevance for survival and reproduction but are instead driven by intrinsic motivations such as curiosity, interest in novel stimuli or surprising events, and interest in learning new behaviours. The adaptive value of such intrinsically motivated activities lies in the fact that they allow the cumulative acquisition of knowledge and skills that can be used later to accomplish tness-enhancing goals. Intrinsic motivations continue during adulthood, and in humans they underlie lifelong learning, artistic creativity, and scientific discovery, while they are also the basis for processes that strongly affect human well-being, such as the sense of competence, self-determination, and self-esteem. This book has two aims: to present the state of the art in research on intrinsically motivated learning, and to identify the related scientific and technological open challenges and most promising research directions. The book introduces the concept of intrinsic motivation in artificial systems, reviews the relevant literature, offers insights from the neural and behavioural sciences, and presents novel tools for research. The book is organized into six parts: the chapters in Part I give general overviews on the concept of intrinsic motivations, their function, and possible mechanisms for implementing them; Parts II, III, and IV focus on three classes of intrinsic motivation mechanisms, those based on predictors, on novelty, and on competence; Part V discusses mechanisms that are complementary to intrinsic motivations; and Part VI introduces tools and experimental frameworks for investigating intrinsic motivations.The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots. The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots.


title: UWB-based localization in large indoor scenarios: optimized placement of anchor nodes

abstract: In this paper, we consider the problem of locating a target node (TN) moving along a corridor in a large industrial environment by means of ultrawide band signaling from fixed anchor nodes (ANs) uniformly positioned at the same height on both sides of the corridor. For a representative geometry of a large indoor (industrial) scenario, we formulate an analytical approach to the optimized placement (in terms of internode distance) of ANs using the criterion of minimizing the average mean square error (MSE) in the time-difference-of-arrival-based estimated positions of the TN. Under the assumption of a fixed variance of the range estimation error, we derive a simple closed-form expression for the optimal inter-AN distance in terms of the corridor width and the height of the ANs. The effectiveness of the analytical approach is confirmed by simulations. We also show that the proposed approach allows the MSE in the TN position estimates to reach the Cramer Rao lower bound.


title: Query graphs, implementing trees, and freely-reorderable outerjoins

abstract: We determine when a join/outerjoin query can be expressed unambiguously as a query graph, without an explicit specification of the order of evaluation. To do so, we first characterize the set of expression trees that implement a given join/outerjoin query graph, and investigate the existence of transformations among the various trees. Our main theorem is that a join/outerjoin query is freely reorderable if the query graph derived from it falls within a particular class, every tree that “implements” such a graph evaluates to the same result. The result has applications to language design and query optimization. Languages that generate queries within such a class do not require the user to indicate priority among join operations, and hence may present a simplified syntax. And it is unnecessary to add extensive analyses to a conventional query optimizer in order to generate legal reorderings for a freely-reorderable language.


Object recognition


title: AR typing interface for mobile devices

abstract: We propose a new user interface system for mobile devices. By using augmented reality (AR) technology, the system overlays virtual objects on real images captured by a camera attached to the back of a mobile device, and the user can operate the mobile device by manipulating the virtual objects with his/her hand in the space behind the mobile device. This system allows the user to operate the device in a wide three-dimensional space and to select small objects easily. Also, the AR technology provides the user with a sense of reality in operating the device. We developed a typing application using our system and verified the effectiveness by user studies. The results showed that more than half of the subjects felt that the operation area of the proposed system is larger than that of a smartphone and that both AR and unfixed key-plane are effective for improving typing speed.


title: Adapt-UI: an IDE supporting model-driven development of self-adaptive UIs

abstract: Self-adaptive UIs (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. The development of SAUIs is a complex task since self-adaptivity and context management aspects have to be incorporated in the UI development process. In this paper, we present an integrated development environment (IDE) for model-driven development of SAUIs. This IDE, named Adapt-UI, provides integrated views for UI, context and adaptation modeling. Based on the specified models, final UI code and context as well as adaptation services are generated and integrated in an overall UI framework. This allows runtime UI adaptation realized by an automatic reaction to context-of-use changes. The benefit of our approach is demonstrated by a case study, showing the development of self-adaptive UIs for a university library application, utilizing the Angular 2 JavaScript framework.


title: Analysis of clickjacking attacks and an effective defense scheme for Android devices

abstract: Smartphones bring users lots of convenience by integrating all useful functions people may need. While users are spending more time on their phones, have they ever questioned of being spoofed by the phone they are interacting with? This paper conducts a thorough study of the mobile clickjacking attacks. We first present how the clickjacking attack works and the key points to remain undiscovered. Then, we evaluate its potential threats by exploring the feasibility of launching clickjacking attacks on various UIs, including system app windows, 3rd-party app windows, and other system UIs. Finally, we propose a system-level defense scheme against clickjacking attacks on Android platform, which requires no user or developer effort and is compatible with existing apps. The performance of the countermeasure is evaluated with extensive experiments. The results show that our scheme can effectively prevent clickjacking attacks with only a minor impact to the system.


title: Slicing: A New Approach to Privacy Preserving Data Publishing

abstract: Several anonymity techniques, such as generalization and bucketization, have been designed for privacy preserving micro data publishing. Recent work has shown that generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called overlapping slicing, which partitions the data both horizontally and vertically. We show that overlapping slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of overlapping slicing is that it can handle high-dimensional data storage. We show how overlapping slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the '-diversity requirement. Our workload experiment confirm that overlapping slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that overlapping slicing can be used to prevent membership disclosure. We consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. We consider a new type of "insider attack" by colluding data providers who may use their own data records (a subset of the overall data) in addition to the external background knowledge to infer the data records contributed by other data providers. The paper addresses this new threat and makes several contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymity data satisfies a given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic algorithms exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering techniques for efficiently checking m- privacy given a set of records. Finally, we present a data provider-aware anonymity algorithm with adaptive m- privacy checking strategies to ensure high utility and m- privacy of anonymity data with efficiency. Experiments on real-life datasets suggest that our approach achieves better or comparable utility and efficiency than existing and baseline algorithms while providing m-privacy guarantee.


title: Analysis of end user security behaviors

abstract: Many information security specialists believe that promoting good end user behaviors and constraining bad end user behaviors provide one important method for making information security effective within organizations. Because of the important of end user security-related behaviors, having a systematic viewpoint on the different kinds of behavior that end users enact could provide helpful benefits for managers, auditors, information technologists, and others with an interest in assessing and/or influencing end user behavior. In the present article, we describe our efforts to work with subject matter experts to develop a taxonomy of end user security-related behaviors, test the consistency of that taxonomy, and use behaviors from that taxonomy to conduct a U.S. survey of an important set of end user behaviors. We interviewed 110 individuals who possessed knowledge of end user security-related behaviors, conducted a behavior rating exercise with 49 information technology subject matter experts, and ran a U.S. survey of 1167 end users to obtain self-reports of their password-related behaviors. Results suggested that six categories of end user security-related behaviors appeared to fit well on a two-dimensional map where one dimension captured the level of technical knowledge needed to enact the behavior and another dimension captured the intentionality of the behavior (including malicious, neutral, and benevolent intentions). Our U.S. survey of non-malicious, low technical knowledge behaviors related to password creation and sharing showed that password ''hygiene'' was generally poor but varied substantially across different organization types (e.g., military organizations versus telecommunications companies). Further, we documented evidence that good password hygiene was related to training, awareness, monitoring, and motivation.


title: Short Text Entity Linking with Fine-grained Topics

abstract: A wide range of web corpora are in the form of short text, such as QA queries, search queries and news titles. Entity linking for these short texts is quite important. Most of supervised approaches are not effective for short text entity linking. The training data for supervised approaches are not suitable for short text and insufficient for low-resourced languages. Previous unsupervised methods are incapable of handling the sparsity and noisy problem of short text. We try to solve the problem by mapping the sparse short text to a topic space. We notice that the concepts of entities have rich topic information and characterize entities in a very fine-grained granularity. Hence, we use the concepts of entities as topics to explicitly represent the context, which helps improve the performance of entity linking for short text. We leverage our linking approach to segment the short text semantically, and build a system for short entity text recognition and linking. Our entity linking approach exhibits the state-of-the-art performance on several datasets for the realistic short text entity linking problem.


title: Robust 3D Head Tracking Using Camera Pose Estimation

abstract: In this paper we present a robust method to recover 3D position and orientation (pose) of a moving head using a single stationary camera. Head pose is recovered via a camera pose estimation formulation. 3D feature points (artificial or natural occurring) are acquired from the head prior to tracking and used as a model. Pose is estimated by solving a robust version of "Perspective n Point" problem. The proposed algorithm can handle self occlusions, outliers and recover from tracking failures. Results were validated by simultaneous tracking using our system and an accurate magnetic field 3D measuring device. Our contribution is a system that is not restricted to track only human heads, and is accurate enough to be used as a measuring device. To demonstrate the applicability of our method, three types of heads (human, barn owl, chameleon) were tracked in a series of biological experiments.


title: Optically resonant dielectric nanostructures

abstract: The resonant modes of plasmonic nanoparticle structures made of gold or silver endow them with an ability to manipulate light at the nanoscale. However, owing to the high light losses caused by metals at optical wavelengths, only a small fraction of plasmonics applications have been realized. Kuznetsov et al. review how high-index dielectric nanoparticles can offer a substitute for these metals, providing a highly flexible and low-loss route to the manipulation of light at the nanoscale. ::: ::: Science , this issue p. [10.1126/science.aag2472][1] ::: ::: [1]: /lookup/doi/10.1126/science.aag2472


title: Learning decision trees for named-entity recognition and classification

abstract: We propose the use of decision tree induction as a solution to the problem of customising a named-entity recognition and classification (NERC) system to a specific domain. A NERC system assigns semantic tags to phrases that correspond to named entities, e.g. persons, locations and organisations. Typically, such a system makes use of two language resources: a recognition grammar and a lexicon of known names, classified by the corresponding named-entity types. NERC systems have been shown to achieve good results when the domain of application is very specific. However, the construction of the grammar and the lexicon for a new domain is a hard and time-consuming process. We propose the use of decision trees as NERC “grammars” and the construction of these trees using machine learning. In order to validate our approach, we tested C4.5 on the identification of person and organisation names involved in management succession events, using data from the sixth Message Understanding Conference. The results of the evaluation are very encouraging showing that the induced tree can outperform a grammar that was constructed manually.


title: A comparison of fingerprint enhancement algorithms for poor quality fingerprint images

abstract: Image enhancement is a common step in most fingerprint feature extraction algorithms. Unfortunately, many fingerprints are poor in quality, which makes extracting reliable minutiae difficult. There are many factors why this may be the case. There might be too much noise or ghosting on the image, or damages (such as scars or creases) on the fingers itself caused by the person's line of work, such as secretaries who deal with a lot of paper in their job, or people who perform manual labor as their occupation. This research attempts to evaluate the effectiveness of image enhancement by comparing three different algorithms, including the use of power transformation in the frequency domain, smoothing on the spatial domain and contextual filtering using Gabor Filters. The experimental results definitely showed improvements after enhancing poor quality fingerprint images, especially when the image is processed in the frequency domain. Contextual filtering also works well in enhancing images based on data in the local context, but in order for it to be more effective and be able to construct better enhanced fingerprint image results, it should also be accompanied with data in the global context.


title: Discrete Extended Kalman Filter on Lie groups

abstract: In this paper, we generalize the Discrete Extended Kalman Filter (D-EKF) to the case where the state and the observations evolve on Lie group manifolds. We propose a new filter called Discrete Extended Kalman Filter on Lie Groups (D-LG-EKF). It assumes that the posterior distribution of the state is a concentrated Gaussian distribution on Lie groups. Our formalism yields closed-form equations for both nonlinear discrete propagation and update of the distribution parameters based on the likelihood. We also show that the D-LG-EKF reduces to the traditional D-EKF if the state evolves on an Euclidean space. Our approach leads to a systematic methodology for the design of filters, which is illustrated by the application to a camera pose estimation problem. Results show that the D-LG-EKF outperforms both a constrained D-EKF and a D-EKF applied on the Lie algebra of the Lie group.


title: Preliminary research in dynamic-BIM (D-BIM) workbench development

abstract: Past and ongoing research efforts toward seamless integration of building design and analysis have established a strong foothold in the building community. Yet, there is lack of seamless connectivity between Building Information Modeling (BIM) and building performance tools. D-BIM Workbench provides an essential framework to conduct integrated building performance assessments within BIM, an environment familiar to all stakeholders. With tighter tool integration within BIM, this open-source Workbench can be tailored to specific analysis such as energy, environmental, and economic impact of buildings. The Workbench, currently under development, will enable on-the-fly simulations of building performance tools to design, operate, and maintain a low / Net Zero Energy (NZE) built environment and beyond. This paper discusses the preliminary research in D-BIM Workbench development such as the Workbench architecture, its open-source environment, and other efforts currently under progress including integration of 3D heat transfer in the Workbench.


title: Structural Equation Modeling in the Communication Sciences, 1995–2000

abstract: Structural equation modeling (SEM) is a viable multivariate tool used by communication researchers for the past quarter century. Building off Cappella (1975) as well as McPhee and Babrow (1987), this study summarizes the use of this technique from 1995–2000 in 37 communication-based academic journals. We identify and critically assess 3 unique methods for testing structural relationships via SEM in terms of the specification, estimation, and evaluation of their respective structural equation models. We provide general guidelines for the use of SEM and make recommendations concerning latent variable models, sample size, reporting parameter estimates, model fit statistics, cross-sectional data, univariate normality, cross-validation, nonrecursive modeling, and the decomposition of effects (direct, indirect, and total).


title: The primate basal ganglia: parallel and integrative networks

abstract: The basal ganglia and frontal cortex operate together to execute goal directed behaviors. This requires not only the execution of motor plans, but also the behaviors that lead to this execution, including emotions and motivation that drive behaviors, cognition that organizes and plans the general strategy, motor planning, and finally, the execution of that plan. The components of the frontal cortex that mediate these behaviors, are reflected in the organization, physiology, and connections between areas of frontal cortex and in their projections through basal ganglia circuits. This comprises a series of parallel pathways. However, this model does not address how information flows between circuits thereby developing new learned behaviors (or actions) from a combination of inputs from emotional, cognitive, and motor cortical areas. Recent anatomical evidence from primates demonstrates that the neuro-networks within basal ganglia pathways are in a position to move information across functional circuits. Two networks are: the striato-nigral-striatal network and the thalamo-cortical-thalamic network. Within each of these sets of connected structures, there are both reciprocal connections linking up regions associated with similar functions and non-reciprocal connections linking up regions that are associated with different cortical basal ganglia circuits. Each component of information (from limbic to motor outcome) sends both feedback connection, and also a feedforward connection, allowing the transfer of information. Information is channeled from limbic, to cognitive, to motor circuits. Action decision-making processes are thus influenced by motivation and cognitive inputs, allowing the animal to respond appropriate to environmental cues.


title: Skolemization for Weighted First-Order Model Counting

abstract: First-order model counting emerged recently as a novel reasoning task, at the core of efficient algorithms for probabilistic logics. We present a Skolemization algorithm for model counting problems that eliminates existential quantifiers from a first-order logic theory without changing its weighted model count. For certain subsets of first-order logic, lifted model counters were shown to run in time polynomial in the number of objects in the domain of discourse, where propositional model counters require exponential time. However, these guarantees apply only to Skolem normal form theories (i.e., no existential quantifiers) as the presence of existential quantifiers reduces lifted model counters to propositional ones. Since textbook Skolemization is not sound for model counting, these restrictions precluded efficient model counting for directed models, such as probabilistic logic programs, which rely on existential quantification. Our Skolemization procedure extends the applicability of first-order model counters to these representations. Moreover, it simplifies the design of lifted model counting algorithms.


title: Intelligent Shopping Cart

abstract: An innovative product with societal acceptance is the one that aids the comfort, convenience and efficiency in everyday life. Purchasing and shopping at big malls is becoming daily activity in metro cities. We can see big rush at these malls on holidays and weekends. People purchase different items and put them in trolley. After completion of purchases, one needs to go to billing counter for payments. At billing counter the cashier prepare the bill using bar code reader which is very time consuming process and results in long queue at billing counter. In this paper, we discuss a product "Intelligent Shopping Cart" being developed to assist a person in everyday shopping in terms of reduced time spent while purchasing. The main objective of proposed system is to provide a technology oriented, low-cost, easily scalable, and rugged system for assisting shopping in person. The developed system consists of 3 key components/modules (a) Server Communication component (SCC) (b) User Interface and display component (UIDC), and (c) Automatic billing component (ABC). SCC establishes and maintains the connection of the shopping cart with the main server. UIDC provides the user interface and ABC handles the billing in association with the SCC. These 3 modules are integrated into an embedded system and are tested to satisfy the functionality. The prototype developed is ready for commercial deployment with proper attention to security and network issues as discussed.


title: Brain source localization of MMN and P300 ERPs in mild cognitive impairment and Alzheimer's disease: a high-density EEG approach

abstract: Alzheimer's disease is the most common neurodegenerative disease of Western societies, suggesting the need for early diagnosis, even in preclinical stages. In this vein, the localization of neuronal generators of event-related potential (ERP) components, that is, the mismatch negativity and the P300, based on high-density electroencephalogram data, was explored as a means to enhance their sensitivity as markers of preclinical Alzheimer's disease (AD). A 2-tone oddball experiment was conducted, involving 21 healthy elderly, 21 mild cognitive impairment, and 21 mild AD patients, while high-density electroencephalogram data were recorded. The results revealed longer latencies of both mismatch negativity and P300 and slower and far less accurate responses as neurodegeneration progressed. Standardized low-resolution electromagnetic tomography revealed that source differences between healthy and mild cognitive impairment and healthy and AD patients for both ERP components were present in the same Brodmann area independently of the ERP and the stage of cognitive decline. This finding indicates an early change of source activation related to cognitive performance and may be used to improve the diagnostic and prognostic value of ERPs.


title: A comparison of five methodological variants of emoji questionnaires for measuring product elicited emotional associations: An application with seafood among Chinese consumers.

abstract: Product insights beyond hedonic responses are increasingly sought and include emotional associations. Various word-based questionnaires for direct measurement exist and an emoji variant was recently proposed. Herein, emotion words are replaced with emoji conveying a range of emotions. Further assessment of emoji questionnaires is needed to establish their relevance in food-related consumer research. Methodological research contributes hereto and in the present research the effects of question wording and response format are considered. Specifically, a web study was conducted with Chinese consumers (n=750) using four seafood names as stimuli (mussels, lobster, squid and abalone). Emotional associations were elicited using 33 facial emoji. Explicit reference to "how would you feel?" in the question wording changed product emoji profiles minimally. Consumers selected only a few emoji per stimulus when using CATA (check-all-that-apply) questions, and layout of the CATA question had only a small impact on responses. A comparison of CATA questions with forced yes/no questions and RATA (rate-all-that-apply) questions revealed an increase in frequency of emoji use for yes/no questions, but not a corresponding improvement in sample discrimination. For the stimuli in this research, which elicited similar emotional associations, RATA was probably the best methodological choice, with 8.5 emoji being used per stimulus, on average, and increased sample discrimination relative to CATA (12% vs. 6-8%). The research provided additional support for the potential of emoji surveys as a method for measurement of emotional associations to foods and beverages and began contributing to development of guidelines for implementation.


title: A spherical multi-camera system with real-time omnidirectional video acquisition capability

abstract: A multi-camera system inspired from the visual system of flying insects is introduced which is referred to as the Panoptic camera. In the Panoptic system each camera is mounted over a hemispherical geometry and has its own vision of surrounding and distinct focal direction. The Panoptic device finds application in the area of omnidirectional vision and 3D imaging. A systematic approach is presented for the coverage analysis and design guidelines of the Panoptic device. The presented approach enables the construction of arbitrary multi-camera systems based on the Panoptic principle. An implemented hardware architecture based on FPGA is introduced which is capable of real-time omnidirectional vision reconstruction. The implemented hardware is capable of live video streaming at a rate of 6.25M pixels per second and a maximum image resolution of 32M pixels per FPGA board.


title: Resource-Efficient Byzantine Fault Tolerance

abstract: One of the main reasons why Byzantine fault-tolerant (BFT) systems are currently not widely used lies in their high resource consumption: $3f+1$ replicas are required to tolerate only $f$ faults. Recent works have been able to reduce the minimum number of replicas to $2f+1$ by relying on trusted subsystems that prevent a faulty replica from making conflicting statements to other replicas without being detected. Nevertheless, having been designed with the focus on fault handling, during normal-case operation these systems still use more resources than actually necessary to make progress in the absence of faults. This paper presents Resource-efficient Byzantine Fault Tolerance ( ReBFT ), an approach that minimizes the resource usage of a BFT system during normal-case operation by keeping $f$ replicas in a passive mode. In contrast to active replicas, passive replicas neither participate in the agreement protocol nor execute client requests; instead, they are brought up to speed by verified state updates provided by active replicas. In case of suspected or detected faults, passive replicas are activated in a consistent manner. To underline the flexibility of our approach, we apply ReBFT to two existing BFT systems: PBFT and MinBFT.


title: Analysis of questioned documents: a review.

abstract: During the last years (2000-2014), many publications concerning the forensic analysis of questioned documents have been published, and new techniques and methodologies are nowadays employed to overcome forensic caseworks. This article reviews a comprehensive collection of the works focused on this issue, including dating studies, the analysis of inks from pens and printers, the analysis of paper, the analysis of other samples related to questioned documents and studies on intersecting lines. These sections highlight the most relevant analytical studies by a wide range of analytical techniques. Separation and spectrometric techniques are critically discussed and compared, emphasizing the advantages and disadvantages of each one. Finally, concluding remarks on the research published are included.


title: Gender identity and lexical variation in social media

abstract: We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female/male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population-level language statistics. We view these clusters as a more accurate reflection of the multifaceted nature of gendered language styles. Previous corpus-based work has also had little to say about individuals whose linguistic styles defy population-level gender patterns. To identify such individuals, we train a statistical classifier, and measure the classifier confidence for each individual in the dataset. Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections and that, in general, social network homophily is correlated with the use of same-gender language markers. Pairing computational methods and social theory thus offers a new perspective on how gender emerges as individuals position themselves relative to audiences, topics, and mainstream gender norms.


title: A review of the detection methodologies of multiple human tracking & action recognition in a real time video surveillance

abstract: Human tracking & action recognition is an important research area in computer vision or what is called as real time video processing along with potential applications in many other fields such as augment reality, human-machine interaction, and advanced driver assistance systems and so on. Despite a lot of progress in the field, visual tracking remains a difficult problem due to many challenges. In this survey paper, a brief review of the research work that is being be done in the field of multiple human tracking & action recognition is being presented along with some of their drawbacks.


title: Extracting Opinion Features in Sentiment Patterns

abstract: Due to the increasing amount of opinions and reviews on the Internet, opinion mining has become a hot topic in data mining, in which extracting opinion features is a key step. Most of existing methods utilize a rule-based mechanism or statistics to extract opinion features, but they ignore the structure characteristics of reviews. The performance has hence not been promising. A new approach of OFESP (Opinion Feature Extraction based on Sentiment Patterns) is proposed in this paper, which takes into account the structure characteristics of reviews for higher values of precision and recall. With a self-constructed database of sentiment patterns, OFESP matches each review sentence to obtain its features, and then filters redundant features regarding relevance of the domain, statistics and semantic similarity. Experimental studies on real-world data demonstrate that as compared with the traditional method based on a window mechanism, OFESP outperforms it on precision, recall and F-score. Meanwhile, in comparison to the approach based on syntactic analysis, OFESP performs better on recall and F-score.


title: Retrograde amnesia and remote memory impairment

abstract: Abstract The status of remote memory in case N.A., patients receiving bilateral ECT, and patients with Korsakoff syndrome was assessed using seven different tests. The extent and pattern of remote memory impairment depended upon the etiology of amnesia, appearing as a relatively brief retrograde amnesia for N.A. and patients receiving ECT and as an extensive impairment covering many decades for patients with Korsakoff syndrome. Differences in remote memory impairment could not be explained by differences in the severity of anterograde amnesia, indicating that amnesia is not a unitary disorder. We suggest that brief retrograde amnesia is typically present whenever amnesia occurs and that extensive remote memory impairment is a distinct entity related to superimposed cognitive deficits. These ideas and a review of previous studies lead to a comprehensive proposal for understanding retrograde amnesia and remote memory impairment.


An RNN Model of Text Normalization


title: A single-chip 24 GHz SiGe BiCMOS transceiver for FMCW automotive radars

abstract: The design and measured results of a highly integrated FMCW radar transceiver are presented. The transceiver includes a transmitter with +7dBm output power and −82 dBc/Hz phase noise at 100 kHz, dual I/Q receivers with 10dB NF and 18 dB gain, PLL, 15-bit DAC, instrumentation amplifiers, LDO regulators, and a serial programming interface - all designed to operate from −40 to +125°C at 3.5V, 275 mA. Fabricated using Jazz Semiconductor's 0.18 µm SiGe BiCMOS process and packaged in a 32-pin 5 mm × 5 mm QFN, this RFIC has the highest level of integration at 24 GHz known to the authors.


title: Reliability of GaN High-Electron-Mobility Transistors: State of the Art and Perspectives

abstract: Failure modes and mechanisms of AlGaN/GaN high-electron-mobility transistors are reviewed. Data from three de-accelerated tests are presented, which demonstrate a close correlation between failure modes and bias point. Maximum degradation was found in "semi-on" conditions, close to the maximum of hot-electron generation which was detected with the aid of electroluminescence (EL) measurements. This suggests a contribution of hot-electron effects to device degradation, at least at moderate drain bias (VDS 30-50 V), new failure mechanisms are triggered, which induce an increase of gate leakage current. The latter is possibly related with the inverse piezoelectric effect leading to defect generation due to strain relaxation, and/or to localized permanent breakdown of the AlGaN barrier layer. Results are compared with literature data throughout the text.


title: Moving-Target Defenses for Computer Networks

abstract: One of the criticisms of traditional security approaches is that they present a static target for attackers. Critics state, with good justification, that by allowing the attacker to reconnoiter a system at leisure to plan an attack, defenders are immediately disadvantaged. To address this, the concept of moving-target defense (MTD) has recently emerged as a new paradigm for protecting computer networks and systems.


A cross-sectional study of sexual function and fertility status in adults with congenital genitourinary abnormalities


title: A reconfigurable graphene transmission array for the generation of vortex THz waves

abstract: In this paper, a reconfigurable graphene transmission array is proposed for the generation of vortex radio waves at THz. By perforating the substrate with holes of various diameters, different values of effective permittivity are obtained. The transmission coefficient can be adjusted by changing the chemical potential of graphene. With the excitation of planar wave, the phase-compensation of corresponding regions will create multimode orbital angular momentum (OAM) waves. Numerical example shows that the proposed graphene transmission array can be reconfigured to generate and transform the 0, 1, 2 modes of vortex radio wave at 2.0 THz.


title: Effect of Penile‐Extender Device in Increasing Penile Size in Men with Shortened Penis: Preliminary Results

abstract: INTRODUCTION ::: It has been suggested that the application of penile-extender devices increases penile length and circumference. However, there are a few scientific studies in this field. ::: ::: ::: AIMS ::: The aim of this study was to assess the efficacy of a penile-extender (Golden Erect(®) , Ronas Tajhiz Teb, Tehran, Iran) in increasing penile size. ::: ::: ::: METHODS ::: This prospective study was performed on subjects complaining about "short penis" who were presented to our clinic between September 15, 2008 and December 15, 2008. After measuring the penile length in flaccid and stretched forms and penile circumference, patients were instructed to wear Golden Erect(®) , 4-6 hours per day during the first 2 weeks and then 9 hours per day until the end of the third month. The subjects were also trained how to increase the force of the device during determined intervals. The patients were visited at the end of the first and third months, and penile length and circumference were measured and compared with baseline. ::: ::: ::: MAIN OUTCOME MEASURES ::: The primary end point of the study was changes in flaccid and stretched penile lengths compared with the baseline size during the 3 months follow-up. ::: ::: ::: RESULTS ::: Twenty-three cases with a mean age of 26.5 ± 8.1 years entered the study. The mean flaccid penile length increased from 8.8 ± 1.2 cm to 10.1 ± 1.2 cm and 10.5 ± 1.2 cm, respectively, in the first and third months of follow-up, which was statistically significant (P < 0.05). Mean stretched penile length also significantly increased from 11.5 ± 1.0 cm to, respectively, 12.4 ± 1.3 cm and 13.2 ± 1.4 cm during the first and second follow-up (P < 0.05). No significant difference was found regarding proximal penile girth. However, it was not the same regarding the circumference of the glans penis (9.3 ± 0.86 cm vs. 8.8 ± 0.66 cm, P < 0.05). ::: ::: ::: CONCLUSION ::: Our findings supported the efficacy of the device in increasing penile length. Our result also suggested the possibility of glans penis girth enhancement using penile extender. Performing more studies is recommended.


title: Comparison of effects of amphotericin B deoxycholate infused over 4 or 24 hours: randomised controlled trial

abstract: Abstract Objective: To test the hypothesis that amphotericin B deoxycholate is less toxic when given by continuous infusion than by conventional rapid infusion. Design: Randomised, controlled, non-blinded, single centre study. Setting: University hospital providing tertiary clinical care. Patients: 80 mostly neutropenic patients with refractory fever and suspected or proved invasive fungal infections. Intervention: Patients were randomised to receive 0.97 mg/kg amphotericin B by continuous infusion over 24 hours or 0.95 mg/kg by rapid infusion over four hours. Main outcome measures: Patients were evaluated for side effects related to infusion, nephrotoxicity, and mortality up to three months after treatment. Analysis was on an intention to treat basis. Results: Patients in the continuous infusion group had fewer side effects and significantly reduced nephrotoxicity than those in the rapid infusion group. Overall mortality was higher during treatment and after three months9 follow up in the rapid infusion than in the continuous infusion group. Conclusion: Continuous infusions of amphotericin B reduce nephrotoxicity and side effects related to infusion without increasing mortality


title: Searching for the IoT Resources: Fundamentals, Requirements, Comprehensive Review, and Future Directions

abstract: Internet of Things (IoT) paradigm links physical objects in the real world to cyber world and enables the creation of smart environments and applications. A physical object is the fundamental building block of the IoT, known as a Smart Device , that can monitor the environment. These devices can communicate with each other and have data processing abilities. When deployed, smart devices collect real-time data and publish the gathered data on the Web. The functionality of smart devices can be abstracted as a service and an IoT application can be built by combining the smart devices with these services that help to address challenges of day-to-day activities. The IoT comprises billions of these intelligent communicating devices that generate enormous amount of data, and hence performing analysis on this data is a significant task. Using search techniques, the size and extent of data can be reduced and limited, so that an application can choose just the most important and valuable data items as per its necessities. It is, however, a tedious task to effectively seek and select a proper device and/or its data among a large number of available devices for a specific application. Search techniques are fundamental to IoT and poses various challenges like a large number of devices, dynamic availability, restrictions on resource utilization, real time data in various types and formats, past and historical monitoring. In the recent past, various methods and techniques have been developed by the research community to address these issues. In this paper, we present a review of the state-of-the-art search methods for the IoT, classifying them according to their design principle and search approaches as: IoT data and IoT object-based techniques. Under each classification, we describe the method adopted, their advantages and disadvantages. Finally, we identify and discuss key challenges and future research directions that will allow the next generation search techniques to recognize and respond to user queries and satisfy the information needs of users.


title: A 3D Fax Machine based on Claytronics

abstract: This paper presents a novel application of modular robotic technology. Many researchers expect manufacturing technology will allow robot modules to be built at smaller and smaller scales, but movement and actuation are increasingly difficult as dimensions shrink. We describe an application ? a 3D fax machine ? which exploits inter-module communication and computation without requiring self-reconfiguration. As a result, this application may be feasible sooner than applications which depend upon modules being able to move themselves. In our new approach to 3D faxing, a large number of submillimeter robot modules form an intelligent "clay" which can be reshaped via the external application of mechanical forces. This clay can act as a novel input device, using intermodule localization techniques to acquire the shape of a 3D object by casting. We describe software for such digital clay. We also describe how, when equipped with simple inter-module latches, such clay can be used as a 3D output device. Finally, we evaluate results from simulations which test how well our approach can replicate particular objects.


title: A study on peer startup process and initial offset placement in P2P live streaming systems

abstract: In this paper, we measure and study the peer startup process in PPLive, a popular commercial P2P streaming system, and focus on a fundamental issue in this aspect: how a peer initializes its buffer when it joins a channel, i.e., initial offset placement of peers' buffers in the startup stage. We build a general model of peer startup process in chunk-based P2P streaming systems and present an initial offset placement scheme we inferred from the measurement results, i.e., proportional placement (PP) scheme. With FP scheme, the initial buffer offset is set to the offset of the reference neighbor peer plus an advance proportional to the reference neighbor peer's offset lag or buffer width. We evaluate the performance of PP scheme and find it is stable when the placement is based on offset lag, but will be unstable when it is based on buffer width if the chunk fetching strategy and neighbor peer selection mechanism are not properly designed. We finally report our detailed measurement results of the peer startup process and initial offset placement algorithms used in PPLive. Our models and measurement results could be useful for guiding the analysis and design of buffering protocols for a real P2P live streaming system.


Obfuscating Gender in Social Media Writing


title: SCENE: a scalable two-stage personalized news recommendation system

abstract: Recommending news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Traditional news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. However, the latent relationships among different news items, and the special properties of new articles, such as short shelf lives and value of immediacy, render the previous approaches inefficient. In this paper, we propose a scalable two-stage personalized news recommendation approach with a two-level representation, which considers the exclusive characteristics (e.g., news content, access patterns, named entities, popularity and recency) of news items when performing recommendation. Also, a principled framework for news selection based on the intrinsic property of user interest is presented, with a good balance between the novelty and diversity of the recommended result. Extensive empirical experiments on a collection of news articles obtained from various news websites demonstrate the efficacy and efficiency of our approach.


title: A hybrid network intrusion detection technique using random forests

abstract: Intrusion detection is important in network security. Most current network intrusion detection systems (NIDSs) employ either misuse detection or anomaly detection. However, misuse detection cannot detect unknown intrusions, and anomaly detection usually has high false positive rate. To overcome the limitations of both techniques, we incorporate both anomaly and misuse detection into the NIDS. In this paper, we present our framework of the hybrid system. The system combines the misuse detection and anomaly detection components in which the random forests algorithm is applied. We discuss the advantages of the framework and also report our experimental results over the KDD'99 dataset. The results show that the proposed approach can improve the detection performance of the NIDSs, where only anomaly or misuse detection technique is used.


title: Part and Attribute Discovery from Relative Annotations

abstract: Part and attribute based representations are widely used to support high-level search and retrieval applications. However, learning computer vision models for automatically extracting these from images requires significant effort in the form of part and attribute labels and annotations. We propose an annotation framework based on comparisons between pairs of instances within a set, which aims to reduce the overhead in manually specifying the set of part and attribute labels. Our comparisons are based on intuitive properties such as correspondences and differences, which are applicable to a wide range of categories. Moreover, they require few category specific instructions and lead to simple annotation interfaces compared to traditional approaches. On a number of visual categories we show that our framework can use noisy annotations collected via "crowdsourcing" to discover semantic parts useful for detection and parsing, as well as attributes suitable for fine-grained recognition.


title: Dysfunction of ventral striatal reward prediction in schizophrenia

abstract: Abstract Background: Negative symptoms may be associated with dysfunction of the brain reward system in schizophrenia. We used functional magnetic resonance imaging (fMRI) to assess the BOLD response in the ventral striatum of unmedicated schizophrenics during presentation of reward-indicating and loss-indicating stimuli. Methods: A total of 10 schizophrenic men (7 never medicated, 3 unmedicated for at least 2 years) and 10 age-matched healthy male volunteers participated in an incentive monetary delay task, in which visual cues predicted that a rapid response to a subsequent target stimulus would result either in monetary gain or loss or would have no consequence. Results: Compared to healthy controls, unmedicated schizophrenics showed reduced ventral striatal activation during the presentation of reward-indicating cues. Decreased activation of the left ventral striatum was inversely correlated with the severity of negative (and trendwise positive) symptoms. Discussion: Reduced activation in one of the central areas of the brain reward system, the ventral striatum, was correlated with the severity of negative symptoms in medication-free schizophrenics. In unmedicated schizophrenic patients, a high striatal dopamine turnover may increase the “noise” in the reward system, thus interfering with the neuronal processing of reward-predicting cues by phasic dopamine release. This, in turn, may contribute to negative symptoms as such as anhedonia, apathy, and loss of drive and motivation.


title: A High Performance Packet Core for Next Generation Cellular Networks

abstract: Cellular traffic continues to grow rapidly making the scalability of the cellular infrastructure a critical issue. However, there is mounting evidence that the current Evolved Packet Core (EPC) is ill-suited to meet these scaling demands: EPC solutions based on specialized appliances are expensive to scale and recent software EPCs perform poorly, particularly with increasing numbers of devices or signaling traffic. In this paper, we design and evaluate a new system architecture for a software EPC that achieves high and scalable performance. We postulate that the poor scaling of existing EPC systems stems from the manner in which the system is decomposed which leads to device state being duplicated across multiple components which in turn results in frequent interactions between the different components. We propose an alternate approach in which state for a single device is consolidated in one location and EPC functions are (re)organized for efficient access to this consolidated state. In effect, our design "slices" the EPC by user. We prototype and evaluate PEPC, a software EPC that implements the key components of our design. We show that PEPC achieves 3-7x higher throughput than comparable software EPCs that have been implemented in industry and over 10x higher throughput than a popular open-source implementation (OpenAirInterface). Compared to the industrial EPC implementations, PEPC sustains high data throughput for 10-100x more users devices per core, and a 10x higher ratio of signaling-to-data traffic. In addition to high performance, PEPC's by-user organization enables efficient state migration and customization of processing pipelines. We implement user migration in PEPC and show that state can be migrated with little disruption, e.g., migration adds only up to 4μs of latency to median per packet latencies.


title: AN APPROACH FOR TEXT SUMMARIZATION USING DEEP LEARNING ALGORITHM

abstract: Now days many research is going on for text summarization. Because of increasing information in the internet, these kind of research are gaining more a nd more attention among the researchers. Extractive text summarization generates a brief summary by extracting proper set of sentences from a document or multi ple documents by deep learning. The whole concept is to reduce or minimize the important information prese nt in the documents. The procedure is manipulated by Restricted Boltzmann Machine (RBM) algorithm for better efficiency by removing redundant sentences. The restricted Boltzmann machine is a graphical model for binary random variables. It consist of three layers input, hidden and output layer. The input data uni formly distributed in the hidden layer for operation. The experimentation is carried out and the summary is g enerated for three different document set from different kno wledge domain. The f-measure value is the identifie r to the performance of the proposed text summarization method. The top responses of the three different knowle dge domain in accordance with the f-measure are 0.85, 1 .42 and 1.97 respectively for the three document se t.


title: A layered approach to cognitive radio network security: A survey

abstract: Cognitive radios have been identified as a solution to the crowded spectrum issue. With the realization of cognitive radio networks came the recognition that both new and old security threats are relevant. The cognitive radio network is still vulnerable to many of the denial of service, wormhole, routing, and jamming attacks that plague other wireless technologies. In addition, the cognitive radio network is vulnerable to new attacks based on cognitive radio innovations, such as spectrum sharing, spectrum sensing, cognitive capability, and radio reconfigurability. The scope of this survey is to present an overview of security threats and challenges to the cognitive radio network, especially focusing on new solutions from 2012 and the first half of 2013. Included are prior mitigation techniques that are adaptive to the new technology, as well as new mitigation techniques specifically targeted at new cognitive radio vulnerabilities. The threats provided are organized according to the protocol layer at which the attack is targeted.


title: Genetic-based random key generator (GRKG): a new method for generating more-random keys for one-time pad cryptosystem

abstract: In this paper, a new algorithm for generating more-randomized keys for symmetrical cipher one-time pad (OTP) according to the linear congruential (LCG) method based on the idea of genetic algorithm is proposed. The method, genetic-based random key generator, is proposed for generating keys for the OTP method with a high degree of key randomness; this adds more strength to the OTP method against breaking this cryptosystem. This algorithm is composed of two parts. Initially, the first population is being generated by LCG method, and then, genetic operators for generating the next populations are being used. Generating random keys with the presented method requires seven-parameter key that increases the security of communication between the transceivers.


title: Chip-to-rectangular waveguide transition realized in embedded Wafer Level Ball Grid Array (eWLB) package

abstract: In this paper, we present for the first time the realization of a 77 GHz chip-to-rectangular waveguide transition realized in an embedded Wafer Level Ball Grid Array (eWLB) package. The chip is contacted with a coplanar waveguide (CPW). For the transformation of the transverse electromagnetic (TEM) mode of the CPW line to the transverse electric (TE) mode of the rectangular waveguide an insert is used in the eWLB package. This insert is based on radio-frequency (RF) printed circuit board (PCB) technology. Micro vias formed in the insert are used to realize the sidewalls of the rectangular waveguide structure in the fan-out area of the eWLB package. The redistribution layers (RDLs) on the top and bottom surface of the package form the top and bottom wall, respectively. We present two possible variants of transforming the TEM mode to the TE mode. The first variant uses a via realized in the rectangular waveguide structure. The second variant uses only the RDLs of the eWLB package for mode conversion. We present simulation and measurement results of both variants. We obtain an insertion loss of 1.5 dB and return loss better than 10 dB. The presented results show that this approach is an attractive candidate for future low loss and highly integrated RF systems.


title: Effect of potassium oxalate on liver function and kidney tissue of dogs (beagles)

abstract: Calcium oxalate crystalluria is a problem of growing concern in dogs. A few ::: reports have discussed acute kidney injury by oxalates in dogs, describing ::: ultrastructural findings in particular. We evaluated the possibility of ::: deposition of calcium oxalate crystals in renal tissue and its probable ::: consequences. Six dogs were intravenously injected with 0.5 M potassium ::: oxalate (KOx) for seven consecutive days. By the end of the experiment, ::: ultrasonography revealed a significant increase in the renal mass and renal ::: parenchymal echogenicity. Serum creatinine and blood urea nitrogen levels ::: were gradually increased. The histopathological features of the kidneys were ::: assessed by both light and electron microscopy, which showed CaOx crystal ::: deposition accompanied by morphological changes in the renal tissue of KOx ::: injected dogs. Canine renal oxalosis provides a good model to study the ::: biological and pathological changes induced upon damage of renal tissue by ::: KOx injection. This article has been retracted. Link to the retraction 10.2298/ABS150615077E


title: A voxel-based parallel collision detection algorithm

abstract: Two physical objects cannot occupy the same space at the same time.Simulated physical objects do not naturally obey this constraint.Instead, we must detect when two objects have collided---we mustperform collision detection. This work presents a simplevoxel-based collision detection algorithm, an efficient parallelimplementation of the algorithm, and performance results.


title: Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

abstract: Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow. With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs. However, the performance of most existing surveillance systems is susceptible to challenging complex traffic scenes (e.g., object occlusion, pose variation, and cluttered background). Moreover, existing related research is mainly on a single video sensor node, which is incapable of addressing the surveillance of traffic road networks. Accordingly, we present a review of the literature on the video-based vehicle surveillance systems in ITSs. We analyze the existing challenges in video-based surveillance systems for the vehicle and present a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques. Then, different methods are reviewed and discussed with respect to each module. Applications and future developments are discussed to provide future needs of ITS services.


title: Combining intrusion detection and recovery for enhancing system dependability

abstract: Current cyber defenses are reactive and cannot protect against customized malware and other zero day attacks which persist for many weeks. Using Receiver Operating Characteristic curve analysis and damage cost models, we trade-off the true positive rate and false positive rate to compare alternative architectures. This analysis provides optimal value(s) of Probability of Detection by evaluating the potential damage from a missed intrusion and costs of processing false positives. In this paper, we propose an approach which involves determining the influencing factors of each strategy and studying the impact of their variations within the context of an integrated intrusion defense strategy. Our goal is to manage the intrusion risks by proactively scheduling recovery for dependable networks.


title: Ranking with Fairness Constraints

abstract: The problem of ranking a set of items is fundamental in today's data-driven world. Ranking algorithms lie at the core of applications such as search engines, news feeds, and recommendation systems. However, recent events have pointed to the fact that algorithmic bias in rankings, which results in decreased fairness or diversity in the type of content presented, can promote stereotypes and propagate injustices. Motivated by such applications, we initiate the study of the traditional ranking problem with additional fairness constraints. Given a collection of items along with 1) the value of placing an item at a particular position, 2) the collection of possibly non-disjoint attributes (e.g., gender, race) of each item and 3) a collection of fairness constraints that upper bound the number of items with each attribute that are allowed to appear in the top positions of the ranking, the goal is to output a ranking that maximizes value while respecting the constraints. This problem encapsulates various well-studied problems related to matching as special cases and turns out to be hard to approximate even with simple constraints. Our main technical contributions are exact and approximation algorithms and hardness results that, together, come close to settling the approximability of the constrained ranking maximization problem. Technically, our main results rely on novel insights about the standard linear programming relaxation for the constrained matching problem when the objective function satisfies certain properties that appear in common ranking metrics such as DCG, Spearman's rho or Bradley-Terry, along with the structure of fairness constraints. Overall, our results contribute to the growing set of algorithms that can counter algorithmic bias, and the structural insights obtained may find use in other algorithmic contexts related to ranking problems.


Probable sexual transmission of Zika virus from a vasectomised man


title: Evolutionary Anthropology 12:264–274 (2003) ARTICLES Signaling, Solidarity, and the Sacred: The Evolution of Religious Behavior

abstract: Anthropologists have repeatedly noted that there has been little theoretical progress in the anthropology of religion over the past fifty years.1–7 By the 1960s, Geertz2 had pronounced the field dead. Recently, however, evolutionary researchers have turned their attention toward understanding the selective pressures that have shaped the human capacity for religious thoughts and behaviors, and appear to be resurrecting this long-dormant but important area of research.8–19 This work, which focuses on ultimate evolutionary explanations, is being complemented by advances in neuropsychology and a growing interest among neuroscientists in how ritual, trance, meditation, and other altered states affect brain functioning and development.20–26 This latter research is providing critical insights into the evolution of the proximate mechanisms responsible for religious behavior. Here we review these literatures and examine both the proximate mechanisms and ultimate evolutionary processes essential for developing a comprehensive evolutionary explanation of religion.


title: Chapter 20 Trust and Recommendations

abstract: Recommendation technologies and trust metrics constitute the two pillars of trust-enhanced recommender systems. We discuss and illustrate the basic trust concepts such as trust and distrust modeling, propagation and aggregation. These concepts are needed to fully grasp the rationale behind the trust-enhanced recommender techniques that are discussed in the central part of the chapter, which focuses on the application of trust metrics and their operators in recommender systems. We explain the benefits of using trust in recommender algorithms and give an overview of state-of-the-art approaches for trust-enhanced recommender systems. Furthermore, we explain the details of three well-known trust-based systems and provide a comparative analysis of their performance. We conclude with a discussion of some recent developments and open challenges, such as visualizing trust relationships in a recommender system, alleviating the cold start problem in a trust network of a recommender system, studying the effect of involving distrust in the recommendation process, and investigating the potential of other types of social relationships.


title: Encoding Source Language with Convolutional Neural Network for Machine Translation

abstract: The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more systematic treatment by summarizing the relevant source information through a convolutional architecture guided by the target information. With different guiding signals during decoding, our specifically designed convolution+gating architectures can pinpoint the parts of a source sentence that are relevant to predicting a target word, and fuse them with the context of entire source sentence to form a unified representation. This representation, together with target language words, are fed to a deep neural network (DNN) to form a stronger NNJM. Experiments on two NIST Chinese-English translation tasks show that the proposed model can achieve significant improvements over the previous NNJM by up to +1.08 BLEU points on average


title: Learning the Latent Topics for Question Retrieval in Community QA

abstract: Community-based Question Answering (cQA) is a popular online service where users can ask and answer questions on any topics. This paper is concerned with the problem of question retrieval. Question retrieval in cQA aims to find historical questions that are semantically equivalent or relevant to the queried questions. Although the translation-based language model (Xue et al., 2008) has gained the state-of-the-art performance for question retrieval, they ignore the latent topic information in calculating the semantic similarity between questions. In this paper, we propose a topic model incorporated with the category information into the process of discovering the latent topics in the content of questions. Then we combine the semantic similarity based latent topics with the translation-based language model into a unified framework for question retrieval. Experiments are carried out on a real world cQA data set from Yahoo! Answers. The results show that our proposed method can significantly improve the question retrieval performance of translation-based language model.


title: Individual differences in emotion processing

abstract: Abstract Recent functional brain imaging studies of the neurobiology of emotion have investigated how individual differences among subjects modulate neural responses during emotion processing. Differences in personality, dispositional affect, biological sex, and genotype can all substantially modulate the neural bases of emotion processing in prefrontal, limbic, and other brain regions, across a variety of domains including emotional reactions, emotional memory, and emotion perception. Analysis of individual differences provides a new window into the neurobiology of emotion processing that complements traditional approaches.


AN AUTOMATED VIRUS CLASSIFICATION SYSTEM GHEORGHESCU AN AUTOMATED VIRUS CLASSIFICATION SYSTEM


title: An integrated machine learning approach to stroke prediction

abstract: Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early intervention and treatment. In this study, we compare the Cox proportional hazards model with a machine learning approach for stroke prediction on the Cardiovascular Health Study (CHS) dataset. Specifically, we consider the common problems of data imputation, feature selection, and prediction in medical datasets. We propose a novel automatic feature selection algorithm that selects robust features based on our proposed heuristic: conservative mean. Combined with Support Vector Machines (SVMs), our proposed feature selection algorithm achieves a greater area under the ROC curve (AUC) as compared to the Cox proportional hazards model and L1 regularized Cox feature selection algorithm. Furthermore, we present a margin-based censored regression algorithm that combines the concept of margin-based classifiers with censored regression to achieve a better concordance index than the Cox model. Overall, our approach outperforms the current state-of-the-art in both metrics of AUC and concordance index. In addition, our work has also identified potential risk factors that have not been discovered by traditional approaches. Our method can be applied to clinical prediction of other diseases, where missing data are common and risk factors are not well understood.


title: Sensor observation streams within cloud-based IoT platforms: Challenges and directions

abstract: Observation streams can be considered as a special case of data streams produced by sensors. With the growth of the Internet of Things (IoT), more and more connected sensors will produce unbounded observation streams. In order to bridge the gap between sensors and observation consumers, we have witnessed the design and the development of Cloud-based IoT platforms. Such systems raise new research challenges, in particular regarding observation collection, processing and consumption. These new research challenges are related to observation streams and should be addressed from the implementation phase by developers to build platforms able to meet other non-functional requirements later. Unlike existing surveys, this paper is intended for developers that would like to design and implement a Cloud-based IoT platform capable of handling sensor observation streams. It provides a comprehensive way to understand main observation-related challenges, as well as non-functional requirements of IoT platforms such as platform adaptation, scalability and availability. Last but not the least, it gives recommendations and compares some relevant open-source software that can speed up the development process.


title: Documenting the Hungarian Heritage of the U.S.: Efforts at Home and Abroad

abstract: Resources on the history of Hungarians in the U.S. have been an object of extraordinary interest among information professionals in Hungary in recent years. The article categorizes the major collections of Hungarica in the United States and efforts within Hungary to document those collections.


title: Hyaluronic acid gel ( Juvéderm™) preparations in the treatment of facial wrinkles and folds

abstract: Soft tissue augmentation with temporary dermal fillers is a continuously growing field, supported by the ongoing development and advances in technology and biocompatibility of the products marketed. The longer lasting, less immunogenic and thus more convenient hyaluronic acid (HA) fillers are encompassing by far the biggest share of the temporary dermal filler market. Since the approval of the first HA filler, Restylane, there are at least 10 HA fillers that have been approved by the FDA. Not all of the approved HA fillers are available on the market, and many more are coming. The Juvéderm product line (Allergan, Irvine, CA), consisting of Juvéderm Plus and Juvéderm Ultra Plus, was approved by the FDA in 2006. Juvéderm is a bacterium-derived nonanimal stabilized HA. Juvéderm Ultra and Ultra Plus are smooth, malleable gels with a homologous consistency that use a new technology called "Hylacross technology". They have a high concentration of cross-linked HAs, which accounts for its longevity. Juvéderm Ultra Plus is used for volumizing and correcting deeper folds, whereas Juvéderm Ultra is best for contouring and volumizing medium depth facial wrinkles and lip augmentation. Various studies have shown the superiority of the HA filler products compared with collagen fillers for duration, volume needed, and patient satisfaction. Restylane, Perlane, and Juvéderm are currently the most popular dermal fillers used in the United States.


title: A Boolean Map Theory of Visual Attention

abstract: A theory is presented that attempts to answer two questions. What visual contents can an observer consciously access at one moment? Answer: only one feature value (e.g., green) per dimension, but those feature values can be associated (as a group) with multiple spatially precise locations (comprising a single labeled Boolean map). How can an observer voluntarily select what to access? Answer: in one of two ways: (a) by selecting one feature value in one dimension (e.g., selecting the color red) or (b) by iteratively combining the output of (a) with a preexisting Boolean map via the Boolean operations of intersection and union. Boolean map theory offers a unified interpretation of a wide variety of visual attention phenomena usually treated in separate literatures. In so doing, it also illuminates the neglected phenomena of attention to structure.


title: Cross-Language Document Summarization Based on Machine Translation Quality Prediction

abstract: Cross-language document summarization is a task of producing a summary in one language for a document set in a different language. Existing methods simply use machine translation for document translation or summary translation. However, current machine translation services are far from satisfactory, which results in that the quality of the cross-language summary is usually very poor, both in readability and content. In this paper, we propose to consider the translation quality of each sentence in the English-to-Chinese cross-language summarization process. First, the translation quality of each English sentence in the document set is predicted with the SVM regression method, and then the quality score of each sentence is incorporated into the summarization process. Finally, the English sentences with high translation quality and high informative-ness are selected and translated to form the Chinese summary. Experimental results demonstrate the effectiveness and usefulness of the proposed approach.


title: Moving and making strange: An embodied approach to movement-based interaction design

abstract: There is growing interest in designing for movement-based interactions with technology, now that various sensing technologies are available enabling a range of movement possibilities from gestural to whole-body interactions. We present a design methodology of Moving and Making Strange, an approach to movement-based interaction design that recognizes the central role of the body and movement in lived cognition. The methodology was developed through a series of empirical projects, each focusing on different conceptions of movement available within motion-sensing interactive, immersive spaces. The methodology offers designers a set of principles, perspectives, methods, and tools for exploring and testing movement-related design concepts. It is innovative for the inclusion of the perspective of the mover, together with the traditional perspectives of the observer and the machine. Making strange is put forward as an important tactic for rethinking how to approach the design of movement-based interaction.


title: Event-based Camera Pose Tracking using a Generative Event Model

abstract: Event-based vision sensors mimic the operation of biological retina and they represent a major paradigm shift from traditional cameras. Instead of providing frames of intensity measurements synchronously, at artificially chosen rates, event-based cameras provide information on brightness changes asynchronously, when they occur. Such non-redundant pieces of information are called "events". These sensors overcome some of the limitations of traditional cameras (response time, bandwidth and dynamic range) but require new methods to deal with the data they output. We tackle the problem of event-based camera localization in a known environment, without additional sensing, using a probabilistic generative event model in a Bayesian filtering framework. Our main contribution is the design of the likelihood function used in the filter to process the observed events. Based on the physical characteristics of the sensor and on empirical evidence of the Gaussian-like distribution of spiked events with respect to the brightness change, we propose to use the contrast residual as a measure of how well the estimated pose of the event-based camera and the environment explain the observed events. The filter allows for localization in the general case of six degrees-of-freedom motions.


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