title: A Novel Weighted Centroid Localization Algorithm Based on RSSI for an Outdoor Environment

abstract: Location information is one of the most important services in the field of wireless sensor networks (WSNs). Compared to other proposed non-interactive localization algorithms, the weighted centroid localization scheme only uses the received signal strength indication (RSSI), which makes it simple to implement and robust to variations in the propagation environment. In this paper, we propose a weight-compensated weighted centroid localization algorithm based on RSSI for an outdoor environment. By theoretically analyzing, the proposed algorithm has the advantage of lower complexity, little prior information and lower power consumption. The simulation results show that the proposed algorithm is better than AMWCL-RSSI (Anchor_optimized Modified Weighted Centroid Localization based on RSSI) and WCL (Weighted Centroid Localization) in terms of the localization accuracy. Our real experimental results also show that WCWCL-RSSI (Weight-Compensated Weighted Centroid Localization Based on RSSI) is better than WCL in terms of the localization accuracy. 


title: Characterizing Variation in Crowd-Sourced Data for Training Neural Language Generators to Produce Stylistically Varied Outputs

abstract: One of the biggest challenges of end-to-end language generation from meaning representations in dialogue systems is making the outputs more natural and varied. Here we take a large corpus of 50K crowd-sourced utterances in the restaurant domain and develop text analysis methods that systematically characterize types of sentences in the training data. We then automatically label the training data to allow us to conduct two kinds of experiments with a neural generator. First, we test the effect of training the system with different stylistic partitions and quantify the effect of smaller, but more stylistically controlled training data. Second, we propose a method of labeling the style variants during training, and show that we can modify the style of the generated utterances using our stylistic labels. We contrast and compare these methods that can be used with any existing large corpus, showing how they vary in terms of semantic quality and stylistic control.


title: Apk2vec: Semi-Supervised Multi-view Representation Learning for Profiling Android Applications

abstract: Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semisupervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for large-scale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.


title: Slow-Time Coding for Mutual Interference Mitigation

abstract: The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates. To address the interference mitigation problems in similar radar systems, we propose herein two slow-time coding schemes to modulate the pulses within a coherent processing interval (CPI). Specifically, the first coding scheme is designed through Doppler shifting and the second is devised via an optimization method. The proposed coding schemes are very easy to implement in practice and the incorporation of the coding schemes only requires slight modification of the existing systems. Our numerical examples indicate that the proposed coding schemes can reduce the interference power level in a desired area of the cross-ambiguity function significantly.


title: Two High Slew Rate Buffers Based on Flipped Voltage Follower

abstract: Two Voltage followers (VF) suited to low-voltage operation and able to operate with a high-slew rate and low-output impedance are presented. The buffer core is based on the flipped voltage follower (FVF) which is previously presented in literature. A detailed comparison of proposed buffers and other voltage followers is presented. These buffers have been simulated using 0.18 µm CMOS technology models provided by TSMC. The buffers consume 10 µA from 1.2 V supply and have a power consumption of 25 µw. The first and second proposed buffers have slew rate of 13 V/µs and 32 V/µs, respectively, with output impedance of about 50 Ω.


title: Automated Detection and Classification for Packed Android Applications

abstract: Android packing services provide significant benefits in code protection by hiding original executable code, which help app developers to protect their code against reverse engineering. However, adversaries take the advantage of packers to hide their malicious code. A number of unpacking approaches have been proposed to defend against malicious packed apps. Unfortunately, most of the unpacking approaches work only for a limited time or for a particular type of packers. The analysis for different packers often requires specific domain knowledge and a significant amount of manual effort. In this paper, we conducted analyses of known Android packers appeared in recent years and propose to design an automatic detection and classification framework. The framework is capable of identifying packed apps, extracting the execution behavioral pattern of packers, and categorizing packed apps into groups. The variants of packer families share typical behavioral patterns reflecting their activities and packing techniques. The behavioral patterns obtained dynamically can be exploited to detect and classify unknown packers, which shed light on new directions for security researchers.


title: Trends in distributed artificial intelligence

abstract: Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented, based on the social abilities of an individual agent, the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally, the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.


title: Internet Addiction and Relationships with Insomnia, Anxiety, Depression, Stress and Self-Esteem in University Students: A Cross-Sectional Designed Study

abstract: Background and Aims ::: Internet addiction (IA) could be a major concern in university medical students aiming to develop into health professionals. The implications of this addiction as well as its association with sleep, mood disorders and self-esteem can hinder their studies, impact their long-term career goals and have wide and detrimental consequences for society as a whole. The objectives of this study were to: 1) Assess potential IA in university medical students, as well as factors associated with it; 2) Assess the relationships between potential IA, insomnia, depression, anxiety, stress and self-esteem. ::: ::: ::: Methods ::: Our study was a cross-sectional questionnaire-based survey conducted among 600 students of three faculties: medicine, dentistry and pharmacy at Saint-Joseph University. Four validated and reliable questionnaires were used: the Young Internet Addiction Test, the Insomnia Severity Index, the Depression Anxiety Stress Scales (DASS 21), and the Rosenberg Self Esteem Scale (RSES). ::: ::: ::: Results ::: The average YIAT score was 30 ± 18.474; Potential IA prevalence rate was 16.8% (95% confidence interval: 13.81–19.79%) and it was significantly different between males and females (p-value = 0.003), with a higher prevalence in males (23.6% versus 13.9%). Significant correlations were found between potential IA and insomnia, stress, anxiety, depression and self-esteem (p-value < 0.001); ISI and DASS sub-scores were higher and self-esteem lower in students with potential IA. ::: ::: ::: Conclusions ::: Identifying students with potential IA is important because this addiction often coexists with other psychological problems. Therefore, interventions should include not only IA management but also associated psychosocial stressors such as insomnia, anxiety, depression, stress, and self-esteem.


title: Comparative study of electromagnetic and electronic ballasts - an assessment on harmonic emission

abstract: In this paper, several high power lighting networks based on high pressures sodium (HPS) lamps with rated lamp powers of 70, 150 and 250 W have been set up. Those lamps have been studied with both electromagnetic and dimmable electronic ballasts, which can dim the lamp output smoothly and uniformly. Lamps connected to electronic equipment have been tested with different levels of power using dimming for a 220 V power supply. HPS lamps have been chosen because they are used in public lighting. This paper focuses on the harmonic characterization of modern HPS lamps connected to both types of devices. Harmonics under full and reduced power for several HPS lamps have been measured. It presents an investigation on the attenuation effect and a proposal of an index called the equivalent lamp index to characterize this effect on HPS lamps. Also it is concluded that using a combination of HPS lamps can result in a reduction of the current distortion. Streszczenie. Analizowano system oświetleniowy wykorzystujący lampy sodowe. Badano lampy obciązone ukladami elektronicznymi i elektromagnetycznymi do sterowania jasnością. Zmierzono zawartośc harmonicznych przy pelnej i ograniczonej mocy. Zaproponowano metody redukcji znieksztalcenia prądu. (Studium porownawcze obciązania lamp sodowych ukladami regulującymi oświetlenie - ocena zawartości harmonicznych)


title: A formal model of services

abstract: Service-oriented software systems rapidly gain importance across application domains: They emphasize functionality (services), rather structural entities (components), as the basic building block for system composition. More specifically, services coordinate the interplay of components to accomplish specific tasks. In this article, we establish a foundation of service orientation: Based on the Focus theory of distributed systems (see Broy and Stolen [2001]), we introduce a theory and formal model of services. In Focus, systems are composed of interacting components. A component is a total behavior. We introduce a formal model of services where, in contrast, a service is a partial behavior. For services and components, we work out foundational specification techniques and outline methodological development steps. We show how services can be structured and how software architectures can be composed of services and components. Although our emphasis is on a theoretical foundation of the notion of services, we demonstrate utility of the concepts we introduce by means of a running example from the automotive domain.


Solving the Prerequisites : Improving Question Answering on the bAbI Dataset


title: A Survey of Attribute-based Access Control with User Revocation in Cloud Data Storage

abstract: Cloud storage service is one of cloud services where cloud service provider can provide storage space to customers. Because cloud storage service has many advantages which include convenience, high computation and capacity, it attracts the user to outsource data in the cloud. However, the user outsources data directly in cloud storage service that is unsafe when outsourcing data is sensitive for the user. Therefore, ciphertext-policy attribute-based encryption is a promising cryptographic solution in cloud environment, which can be drawn up for access control by the data owner to define access policy. Unfortunately, an outsourced architecture applied with the attribute-based encryption introduces many challenges in which one of the challenges is revocation. The issue is a threat to data security in the data owner. In this paper, we survey related studies in cloud data storage with revocation and define their requirements. Then we explain and analyze four representative approaches. Finally, we provide some topics for future research


title: Acoustic modeling for sea-mine CAD/CAC development

abstract: Computer aided detection/classification (CAD/ CAC) algorithms have been developed to help mine warfare operators correctly identify targets within vast quantities of synthetic aperture sonar/side look sonar (SAS/SLS) imagery. Typically, large, and thus expensive, sets of target and clutter exemplars are required for the evaluation and training of these algorithms. To expedite the assessment and preliminary training of CAD/CAC algorithms for systems under development, physics-based acoustic models can be utilized to generate synthetic data-sets. Model-based recognition algorithms that predict exemplars for a given search hypothesis may use acoustic models to reduce dependence on exemplar databases. Acoustic models predicting the scatter from proud and buried targets are developed and described in this paper. A rigid-target model, using boundary integral equations for the target and a sediment-scatter model, is applied for proud and partially buried targets. This model is suitable for high-frequency, narrowband sonars. An elastic-target model using a spheroidal T matrix approach is employed for buried targets. This model captures the responses appropriate for systems that scan the sediment volume. The synthetic, acoustic datasets produced are processed into SAS images and subsequently used to investigate candidate feature sets and for CAD/ CAC algorithm development.


title: A ferrite permanent magnet axial gap motor with segmented rotor structure for the next generation hybrid vehicle

abstract: Permanent magnet synchronous motors (PMSMs) are a key part of the current hybrid electric vehicle (HEV) drive system, and the sophisticated performance such as small size, high power density, and high efficiency is required to the PMSMs in the HEV drive system. Therefore, rare earth permanent magnet is generally employed in the PMSMs in order to satisfy those demands. However, rare earth materials contained in the rare earth permanent magnet have several concerns caused by uneven distribution, because the rare earth materials such as neodymium and dysprosium depend on China for 90% or more of the world share. Accordingly, the rare earth materials have the undeniable possibility of sudden rise of price or reinforcement of export restriction. On the other hand, the HEVs tend to be promoted rapidly because of growing concern about environmental problems. Therefore, it is very important to develop rare-earth-free motors for the next generation HEV drive system. This paper proposes a ferrite permanent magnet axial gap motor without the rare earth materials for the HEVs. In addition, the proposed motor structure and the analytical results with the 3D-FEA are introduced.


title: Psychology Teaching Resources in the MERLOT Digital Learning Objects Catalog

abstract: MERLOT (Multimedia Educational Resource for Learning and Online Teaching) is a free multidisciplinary catalog of digital learning materials, peer reviews, learning assignments, and member comments designed to facilitate faculty instruction. The catalog's goal is to expand the quantity and quality of peer-reviewed online teaching materials. We present an overview of MERLOT, describe the peer reviewing of materials, and highlight the psychology resources found in its catalog. The MERLOT Psychology Portal provides instructors with a wide range of teaching materials, resources, and tools as well as guidelines for the creation of digital learning objects. MERLOT offers an organized, streamlined, and timesaving way to find good digital materials for teaching psychology. ********** Many instructors in psychology are developing and using digital and online supplements for their courses (Brink, 2001; Buzhardt & Semb, 2005). As in other disciplines, psychologists may have trouble finding or developing high-quality and relevant Web-based teaching materials that are appropriate for their students. For many instructors, the time and effort required to search for and sift through possible teaching resources on the Internet is daunting. Developing new learning objects can be very time-consuming, especially if one is not familiar with existing and emerging instructional technologies. Much time and effort could be saved by instructors if there were places they could go where someone has already done the work for them. Recently, several places have appeared. A leading example of such digital learning object repositories is MERLOT (Multimedia Educational Resource for Learning and Online Teaching; www.merlot.org). MERLOT is a continually growing free catalog of digital learning materials, peer reviews, learning assignments, and member comments designed to help faculty enhance their instruction (for more specifics about the general nature and structure of MERLOT, see Malloy & Hanley, 2001, as well as the MERLOT Web page). Its mission is to expand the quantity and quality of peer-reviewed online materials that faculty can incorporate into their courses. The catalog is organized into discipline-specific communities, including psychology. In this article, we present a brief overview of MERLOT, describe the peer reviewing of materials, and highlight the psychology resources found in the MERLOT catalog. We believe that by highlighting the structure, organization, and contents of MERLOT, we can provide psychology instructors with useful information and ideas for teaching as well as for the creation of digital learning objects. The MERLOT Catalog and Member Services Since its inception in 1997, MERLOT has enabled the formation of discipline communities engaged in building shared knowledge bases of reusable digital learning materials. At the time of this writing, there were over 61,000 members (approximately three-quarters of whom are faculty or students) and over 20,000 learning materials in MERLOT. Instead of storing the actual material, MERLOT stores information about it; the URL for its actual location; and peer reviews, member comments, and assignments pertaining to that material. MERLOT is not responsible for the content or upkeep of the learning material in the catalog. Whereas anyone can access the materials, members who join MERLOT (for free) can create a personal profile, contribute learning materials for evaluation, make comments on the materials, submit assignments, and create personal collections. Nonmembers may access the catalog, but they cannot participate in these other options without joining. When people join MERLOT, they complete a personal profile. This option includes contact information, membership history, and skills and interests. In addition, MERLOT automatically tracks each member's activities and contributions (i.e., authored materials, submitted materials, assignments created, and member comments made). …


A Survey on the Major Security Issues in VANET


title: A Technique for Ultrahigh‐Precision CCD Photometry

abstract: We present techniques for wide-field ultrahigh-precision time-sampled CCD photometry. Representing a survey of field stars, our data consist of UBVRI photometry and a V-band time series of exposures on 5 consecutive nights covering 11,500 stars in a ∼1 deg 2 field. The resulting light curves reach precisions of 0.0020 mag per exposure in the brightest stars ( ). Light curves binned and averaged over 4.5 hr reach V ∼ 14 precisions of 0.00019 mag for the brightest stars. We present example light curves and discuss one application for surveys of this type: the search for transits by extrasolar planets.


title: SPOT: A tool for set-based prediction of traffic participants

abstract: Predicting the movement of other traffic participants is an integral part in the motion planning of most automated road vehicles. While simple predictions, e.g. based on assuming constant velocity, may suffice for deciding a driving strategy, predicting the set of all possible behaviors is required to ensure safe motion plans. In this work, we propose a novel tool for the latter problem based on reachability analysis: Set-Based Prediction Of Traffic Participants (SPOT). Our tool can predict the future occupancy of other traffic participants, including all possible maneuvers (e.g. full acceleration, full braking, and arbitrary lane changes), by considering physical constraints and assuming that the traffic participants abide by the traffic rules. However, we remove assumptions for each traffic participant individually as soon as a violation of a traffic rule is detected. Removal of assumptions automatically results in larger occupancies and thus a smaller drivable area for the ego vehicle, ensuring that the ego vehicle does not cause a collision during the time horizon of the prediction. Experimental results show that we obtain the set of future occupancies within a fraction of the prediction horizon. Our tool is available at spot.in.tum.de.


title: Corona and breakdown characteristics between sphere electrode with a protrusion and plane electrode under negative pulse voltage in N/sub 2/ gas

abstract: Corona and breakdown characteristics for a configuration of a sphere with a needle and a plane in N/sub 2/ gas are studied. The protrusion was used to simulate a metallic particle attached to a high voltage conductor surface in power apparatus. The corona and breakdown characteristics depend on the vertical distance between the needle tip and the bottom sphere. It was observed that the breakdown path changed from needle-to-plane to sphere-to-plane while increasing the vertical distance of the needle from the plane. Although corona was generated at the needle-tip at first and developed in the direction of the plane, both the needle-plane and the sphere-plane breakdown occurred at some short vertical distances. The sphere-plane breakdown in that distance was triggered by corona from the needle.


title: Zombie-cookies: Case studies and mitigation

abstract: Putting the user in charge of their online identity is a necessary precondition for privacy on the web. This requires the ability to manage identifying information that remote parties store in data repositories on the user's device. We performed a crawl of thousands of popular websites with the intention of assessing the prevalence of various problematic patterns of clientside data storage. The focus of this paper is on techniques that make use of the browser cache, especially when they impede active identity management by the user. From these results, we derive a novel privacy-enhancing policy for client-side data storage - self-destructing identifiers - improving the current defacto standard. This policy has already seen some adoption in practice in the form of a Firefox add-on that we developed as a proof of concept.


title: Global Convergence of Online Limited Memory BFGS

abstract: Global convergence of an online (stochastic) limited memory version of the Broyden-Fletcher- Goldfarb-Shanno (BFGS) quasi-Newton method for solving optimization problems with stochastic objectives that arise in large scale machine learning is established. Lower and upper bounds on the Hessian eigenvalues of the sample functions are shown to suffice to guarantee that the curvature approximation matrices have bounded determinants and traces, which, in turn, permits establishing convergence to optimal arguments with probability 1. Numerical experiments on support vector machines with synthetic data showcase reductions in convergence time relative to stochastic gradient descent algorithms as well as reductions in storage and computation relative to other online quasi-Newton methods. Experimental evaluation on a search engine advertising problem corroborates that these advantages also manifest in practical applications.


title: Direct visibility of point sets

abstract: This paper proposes a simple and fast operator, the "Hidden" Point Removal operator, which determines the visible points in a point cloud, as viewed from a given viewpoint. Visibility is determined without reconstructing a surface or estimating normals. It is shown that extracting the points that reside on the convex hull of a transformed point cloud, amounts to determining the visible points. This operator is general - it can be applied to point clouds at various dimensions, on both sparse and dense point clouds, and on viewpoints internal as well as external to the cloud. It is demonstrated that the operator is useful in visualizing point clouds, in view-dependent reconstruction and in shadow casting.


title: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

abstract: Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency.


title: Noncontiguous I/O accesses through MPI-IO

abstract: I/O performance remains a weakness of parallel computing systems today. While this weakness is partly attributed to rapid advances in other system components, I/O interfaces available to programmers and the I/O methods supported by file systems have traditionally not matched efficiently with the types of I/O operations that scientific applications perform, particularly noncontiguous accesses. The MPI-IO interface allows for rich descriptions of the I/O patterns desired for scientific applications and implementations such as ROMIO have taken advantage of this ability while remaining limited by underlying file system methods. A method of noncontiguous data access, list I/O, was recently implemented in the Parallel Virtual File System (PVFS). We implement support for this interface in the ROMIO MPI-IO implementation. Through a suite of noncontiguous I/O tests we compared ROMIO list I/O to current methods of ROMIO noncontiguous access and found that the list I/O interface provides performance benefits in many noncontiguous cases.


title: Ubiquitous monitoring of pedestrian dynamics: Exploring wireless ad hoc network of multi-sensor technologies

abstract: In this paper we incorporate various automatic sensor technologies in Ad Hoc network to monitor pedestrian activities at the entire facility level. A case study is demonstrated during a large street festival in Montreal covering more than ten street blocks. We used WiFi technology, computer vision tools, and infrared counters to develop the detailed estimates on attendance, movement, and activity patterns of pedestrians over time and space, which can be used for future planning, operations, and financing.


Risk Factors for Speech-Language Pathologies in Children


title: Design of an Early Warning System for Patients with Cardiovascular Diseases Under Mobile Environment

abstract: We proposed a framework for early warning of Cardiovascular diseases (CVDs) under mobile environment, which is a real-time and personalized problem involving various complex knowledge. Specially, the principles and methods for early warning are proposed, as well as the selection and collection of key indicators. The system integrates patients and families, physicians, hospitals and community clinics, which consists of two tiers. The mobile device tier could detect the abnormal situations of the patients, and the service center will discover and refine the knowledge of personal situation which could increase the effect and efficiency of CVD's early warning.


title: [Advance in microRNAs and EGFR-TKIs secondary resistance research in non-small cell lung cancer].

abstract: In recent years, in non-small cell lung cancer (NSCLC) targeted therapy, especially in patients with epidermal growth factor receptor (EGFR) mutations, EGFR-tyrosine kinase inhibitors (TKI) more and more come into the clinical treatment, but EGFR-TKI resistance not only influence the drug sensitivity, appear even disease progression, become the main bottleneck of its curative effect. MicroRNAs (miRNAs) is a non coding RNA and protein involved in regulating gene expression in the transcription level. Recent studies found that miRNAs involved in EGFR-TKIs resistance, which affect the sensitivity of tumor cells to treatment. In this paper, we reviewed briefly advance in miRNAs and EGFR-TKIs secondary resistance research in NSCLC. ::: ::: DOI: 10.3779/j.issn.1009-3419.2014.12.07


A Hierarchical Neural Model for Learning Sequences of Dialogue Acts


title: An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks

abstract: Abstract Many real world, complex phenomena have underlying structures of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Recent work has suggested that the incorporation of topological features and node attributes can improve link prediction. We provide an approach to predicting future links by applying the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine a large dynamic social network with over 106 nodes (Twitter reciprocal reply networks), both as a test of our general method and as a problem of scientific interest in itself. Our method exhibits fast convergence and high levels of precision for the top twenty predicted links. Based on our findings, we suggest possible factors which may be driving the evolution of Twitter reciprocal reply networks.


title: Super-resolution: a comprehensive survey

abstract: Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.


Weighting Features


title: The Promise of Nanomagnetics and Spintronics for Future Logic and Universal Memory

abstract: This paper is both a review of some recent developments in the utilization of magnetism for applications to logic and memory and a description of some new innovations in nanomagnetics and spintronics. Nanomagnetics is primarily based on the magnetic interactions, while spintronics is primarily concerned with devices that utilize spin polarized currents. With the end of complementary metal-oxide-semiconductor (CMOS) in sight, nanomagnetics can provide a new paradigm for information process using the principles of magnetic quantum cellular automata (MQCA). This paper will review and describe these principles and then introduce a new nonlithographic method of producing reconfigurable arrays of MQCAs and/or storage bits that can be configured electrically. Furthermore, this paper will provide a brief description of magnetoresistive random access memory (MRAM), the first mainstream spintronic nonvolatile random access memory and project how far its successor spin transfer torque random access memory (STT-RAM) can go to provide a truly universal memory that can in principle replace most, if not all, semiconductor memories in the near future. For completeness, a description of an all-metal logic architecture based on magnetoresistive structures (transpinnor) will be described as well as some approaches to logic using magnetic tunnel junctions (MTJs).


title: Real-time non-rigid reconstruction using an RGB-D camera

abstract: We present a combined hardware and software solution for markerless reconstruction of non-rigidly deforming physical objects with arbitrary shape in real-time. Our system uses a single self-contained stereo camera unit built from off-the-shelf components and consumer graphics hardware to generate spatio-temporally coherent 3D models at 30 Hz. A new stereo matching algorithm estimates real-time RGB-D data. We start by scanning a smooth template model of the subject as they move rigidly. This geometric surface prior avoids strong scene assumptions, such as a kinematic human skeleton or a parametric shape model. Next, a novel GPU pipeline performs non-rigid registration of live RGB-D data to the smooth template using an extended non-linear as-rigid-as-possible (ARAP) framework. High-frequency details are fused onto the final mesh using a linear deformation model. The system is an order of magnitude faster than state-of-the-art methods, while matching the quality and robustness of many offline algorithms. We show precise real-time reconstructions of diverse scenes, including: large deformations of users' heads, hands, and upper bodies; fine-scale wrinkles and folds of skin and clothing; and non-rigid interactions performed by users on flexible objects such as toys. We demonstrate how acquired models can be used for many interactive scenarios, including re-texturing, online performance capture and preview, and real-time shape and motion re-targeting.


title: Broad-band high-power amplifier using spatial power-combining technique

abstract: High power, broad bandwidth, high linearity, and low noise are among the most important features in amplifier design. The broad-band spatial power-combining technique addresses all these issues by combining the output power of a large quantity of microwave monolithic integrated circuit (MMIC) amplifiers in a broad-band coaxial waveguide environment, while maintaining good linearity and improving phase noise of the MMIC amplifiers. A coaxial waveguide was used as the host of the combining circuits for broader bandwidth and better uniformity by equally distributing the input power to each element. A new compact coaxial combiner with much smaller size is investigated. Broad-band slotline to microstrip-line transition is integrated for better compatibility with commercial MMIC amplifiers. Thermal simulations are performed and an improved thermal management scheme over previous designs is employed to improve the heat sinking in high-power application. A high-power amplifier using the compact combiner design is built and demonstrated to have a bandwidth from 6 to 17 GHz with 44-W maximum output power. Linearity measurement has shown a high third-order intercept point of 52 dBm. Analysis shows the amplifier has the ability to extend spurious-free dynamic range by N/sup 2/3/ times. The amplifier also has shown a residual phase floor close to -140 dBc at 10-kHz offset from the carrier with 5-6-dB reductions compared to a single MMIC amplifier it integrates.


title: The bibliometric analysis of scholarly production: How great is the impact?

abstract: Bibliometric methods or “analysis” are now firmly established as scientific specialties and are an integral part of research evaluation methodology especially within the scientific and applied fields. The methods are used increasingly when studying various aspects of science and also in the way institutions and universities are ranked worldwide. A sufficient number of studies have been completed, and with the resulting literature, it is now possible to analyse the bibliometric method by using its own methodology. The bibliometric literature in this study, which was extracted from Web of Science, is divided into two parts using a method comparable to the method of Jonkers et al. (Characteristics of bibliometrics articles in library and information sciences (LIS) and other journals, pp. 449–551, 2012: The publications either lie within the Information and Library Science (ILS) category or within the non-ILS category which includes more applied, “subject” based studies. The impact in the different groupings is judged by means of citation analysis using normalized data and an almost linear increase can be observed from 1994 onwards in the non-ILS category. The implication for the dissemination and use of the bibliometric methods in the different contexts is discussed. A keyword analysis identifies the most popular subjects covered by bibliometric analysis, and multidisciplinary articles are shown to have the highest impact. A noticeable shift is observed in those countries which contribute to the pool of bibliometric analysis, as well as a self-perpetuating effect in giving and taking references.


title: Topology and Geometry of Half-Rectified Network Optimization

abstract: The loss surface of deep neural networks has recently attracted interest ::: in the optimization and machine learning communities as a prime example of ::: high-dimensional non-convex problem. Some insights were recently gained using spin glass ::: models and mean-field approximations, but at the expense of strongly simplifying the nonlinear nature of the model. ::: ::: In this work, we do not make any such approximation and study conditions ::: on the data distribution and model architecture that prevent the existence ::: of bad local minima. Our theoretical work quantifies and formalizes two ::: important folklore facts: (i) the landscape of deep linear networks has a radically different topology ::: from that of deep half-rectified ones, and (ii) that the energy landscape ::: in the non-linear case is fundamentally controlled by the interplay between the smoothness of the data distribution and model over-parametrization. Our main theoretical contribution is to prove that half-rectified single layer networks are asymptotically connected, and we provide explicit bounds that reveal the aforementioned interplay. ::: ::: The conditioning of gradient descent is the next challenge we address. ::: We study this question through the geometry of the level sets, and we introduce ::: an algorithm to efficiently estimate the regularity of such sets on large-scale networks. ::: Our empirical results show that these level sets remain connected throughout ::: all the learning phase, suggesting a near convex behavior, but they become ::: exponentially more curvy as the energy level decays, in accordance to what is observed in practice with ::: very low curvature attractors.


title: Fuzzy sets and probability: misunderstandings, bridges and gaps

abstract: One of the most controversial issues in uncertainty modeling and information sciences is the relationship between probability theory and fuzzy sets. The literature pertaining to this debate is surveyed. The authors address some classical misunderstandings between fuzzy sets and probabilities. They consider probabilistic interpretations of membership functions that may help in membership function assessment. Nonprobabilistic interpretations of fuzzy sets are identified. The literature on possibility-probability transformations is examined, and some lurking controversies on that topic are clarified. Several subfields of fuzzy set research where fuzzy sets and probability are conjointly used are discussed. >


title: The Devil is in the Tails: Fine-grained Classification in the Wild

abstract: The world is long-tailed. What does this mean for computer vision and visual recognition? The main two implications are (1) the number of categories we need to consider in applications can be very large, and (2) the number of training examples for most categories can be very small. Current visual recognition algorithms have achieved excellent classification accuracy. However, they require many training examples to reach peak performance, which suggests that long-tailed distributions will not be dealt with well. We analyze this question in the context of eBird, a large fine-grained classification dataset, and a state-of-the-art deep network classification algorithm. We find that (a) peak classification performance on well-represented categories is excellent, (b) given enough data, classification performance suffers only minimally from an increase in the number of classes, (c) classification performance decays precipitously as the number of training examples decreases, (d) surprisingly, transfer learning is virtually absent in current methods. Our findings suggest that our community should come to grips with the question of long tails.


title: Iterative machine and deep learning approach for aviation delay prediction

abstract: In the aviation industry, flight arrival delays cause approximately 18 billion of loss to customers as stated in the literature. So, it becomes inevitable on the part of the aviation authorities to predict such delays and take necessary action to fix this loss for customer satisfaction. In this paper, an approach based on machine learning techniques is proposed that predicts the flight arrival delays considering input parameters ranging from distance to their corresponding weather details to make a decision of whether the specific flight is delayed or not. It makes use of neural networks and deep learning concepts to estimate flight delay. The proposed approach is tested on real world flight big dataset that gives an accuracy of 77% using deep nets and 89% using neural nets. This approach can achieve reliable prediction with respect to if flight arrival delay is to be expected or not, moving forward the use of such a model can come in handy not only for airline administrators but also the passengers who can rearrange their schedules and arrange accommodation.


title: Exploring Community Structures for Influence Maximization in Social Networks

abstract: Since the surge of the popularity of social network, recently, there has been a tremendous wave of interest in the investigation of influence maximization problem. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. Nowadays, due to the dramatic size growing of social network, the efficiency and scalability of algorithms for influence maximization become more and more crucial. Although many recent studies have focused on the problem of influence maximization, these works, in general, are time consuming when a large-scale social network is given. In this paper, by utilizing community structures, we develop two efficient algorithms CDHKcut and CDH-SHRINK (standing for Community and Degree Heuristic with Kcut / SHRINK) that significantly decrease the number of candidate influential nodes. The experimental results on real datasets indicate that our algorithms not only significantly outperform state-of-the-art algorithms in efficiency but also possess graceful scalability.


title: Mapping Virtual Machines onto Physical Machines in Cloud Computing: A Survey

abstract: Cloud computing enables users to provision resources on demand and execute applications in a way that meets their requirements by choosing virtual resources that fit their application resource needs. Then, it becomes the task of cloud resource providers to accommodate these virtual resources onto physical resources. This problem is a fundamental challenge in cloud computing as resource providers need to map virtual resources onto physical resources in a way that takes into account the providers’ optimization objectives. This article surveys the relevant body of literature that deals with this mapping problem and how it can be addressed in different scenarios and through different objectives and optimization techniques. The evaluation aspects of different solutions are also considered. The article aims at both identifying and classifying research done in the area adopting a categorization that can enhance understanding of the problem.


title: Motivating User Contributions in Online Knowledge Communities: Virtual Rewards and Reputation

abstract: User contribution determines the success of online knowledge communities. As user contributions are voluntary, many online communities failed due to declining user contributions. Hence, it becomes critical to understand and design mechanisms that are effective to motivate user contributions. This paper studies the effects of two mechanisms, virtual rewards and reputation, on the quantity and quality of user contributions in online community. We analyze user-level panel data from a representative knowledge sharing platform Stack Exchange.com, and investigate whether these two mechanisms improve the quantity and quality of user contributions. Surprisingly, we find that a user's reputation does not directly motivate contribution. It is the relative reputation (ranking) that serves as the motivation. This finding highlights the importance of peer effect and social comparison in the incentive design of open collaboration platforms. Our study provides implications for platform designers on how to motivate community participants and build sustainable online knowledge communities.


title: Wind-biogas and controlled grid based hybrid power system for rural electrification

abstract: This paper presents a scheme of rural electrification (RE) using wind, biogas and grid supply. The novel scheme of limited grid operation for defined load share is developed in this work. The system frequency and voltage (magnitude and angle) are controlled using ISE tuned PI controller. The change in system load is supplied by the renewable energy sources (RES) and the grid shares constant load. The permanent magnet synchronous generator (PMSG) with full rating voltage source converter (VSC) is used as wind generator. The biogas engine with electronic governor, using synchronous generator (SG) and automatic voltage regulator (AVR). The system is simulated in MATLAB/Simulink and the performance is analyzed for variable load and input wind power.


title: Automated camera dysfunctions detection

abstract: Surveillance systems depend greatly on the robustness and availability of the video streams. The cameras must deliver reliable streams from an angle corresponding to the correct viewpoint. In other words, the field of view and video quality must remain unchanged after the initial installation of a surveillance camera. The paper proposes an approach to detect changes (such as displacement, out of focus, obstruction) automatically in a difficult environment with illumination variations and dynamic background and foreground objects.


title: Understanding the data-processing challenges in Intelligent Vehicular Systems

abstract: Vehicular sensors able to perceive and measure the environment, ranging from in-vehicle sensors to speed cameras, are revolutionizing how technology can interact with our daily lives, enabling Intelligent Vehicular Systems (IVSs). These sensors generate large volumes of data which can reveal useful information for enhancing the sustainable development (through improved utilization of resources), as well as the safety and functionality of the system.


title: HCI and Society: Towards a Typology of Universal Design Principles

abstract: The task of this article is to focus on aspects of design from a theoretical perspective that works out a general framework of techno-social design that is grounded in the notion of the participatory, cooperative, sustainable information society. The approach is normative in that it is based on the judgment that not just any information society is needed, but one that has specific qualities that this article tries to identify. Methodologically, the article works out a conceptual framework that synthesizes general social theory and human–computer interaction. Design is seen as a social process that shapes society and techno-social systems. First, a model of society that is based on the dialectical interaction of economic, political, and cultural subsystems is introduced. Then, the notion of the the participatory, cooperative, sustainable information society is introduced. This theoretical definition is then used for classifying design principles of techno-social information systems from a social theory perspective.


title: On the Interpolation of Data with Normally Distributed Uncertainty for Visualization

abstract: In many fields of science or engineering, we are confronted with uncertain data. For that reason, the visualization of uncertainty received a lot of attention, especially in recent years. In the majority of cases, Gaussian distributions are used to describe uncertain behavior, because they are able to model many phenomena encountered in science. Therefore, in most applications uncertain data is (or is assumed to be) Gaussian distributed. If such uncertain data is given on fixed positions, the question of interpolation arises for many visualization approaches. In this paper, we analyze the effects of the usual linear interpolation schemes for visualization of Gaussian distributed data. In addition, we demonstrate that methods known in geostatistics and machine learning have favorable properties for visualization purposes in this case.


title: Extending kalibr: Calibrating the extrinsics of multiple IMUs and of individual axes

abstract: An increasing number of robotic systems feature multiple inertial measurement units (IMUs). Due to competing objectives—either desired vicinity to the center of gravity when used in controls, or an unobstructed field of view when integrated in a sensor setup with an exteroceptive sensor for ego-motion estimation—individual IMUs are often mounted at considerable distance. As a result, they sense different accelerations when the platform is subjected to rotational motions. In this work, we derive a method for spatially calibrating multiple IMUs in a single estimator based on the open-source camera/IMU calibration toolbox kalibr. We further extend the toolbox to determine IMU intrinsics, enabling accurate calibration of low-cost IMUs. The results suggest that the extended estimator is capable of precisely determining these intrinsics and even of localizing individual accelerometer axes inside a commercial grade IMU to millimeter precision.


title: A cost-efficient partially-parallel irregular LDPC decoder based on sum-delta message passing algorithm

abstract: A partially-parallel decoder architecture for irregular LDPC code targeting high throughput and low cost applications is proposed. The design is based on a novel sum-delta message passing algorithm that facilitates the decoding throughput by removing redundant computations and decreases the hardware cost by optimizing the storage. Techniques such as binary sorting, parallel column operation, high performance pipelining are used to further speed up the message passing procedure. The synthesis result in TSMC 0.18 CMOS technology demonstrates that for (648,324) irregular LDPC code, our decoder achieves 7.5X improvement in throughput, which reaches 402 Mbps at the frequency of 200MHz, with 11% area reduction.


title: Feature reduction and selection for EMG signal classification

abstract: Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.


title: Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay

abstract: This paper considers the problem of observer-based adaptive neural network (NN) control for a class of single-input single-output strict-feedback nonlinear stochastic systems with unknown time delays. Dynamic surface control is used to avoid the so-called explosion of complexity in the backstepping design process. Radial basis function NNs are directly utilized to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. The proposed adaptive NN output feedback controller can guarantee all the signals in the closed-loop system to be mean square semi-globally uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness of the proposed methods.


title: Investigating the relationship between language model perplexity and IR precision-recall measures

abstract: An empirical study has been conducted investigating the relationship between the performance of an aspect based language model in terms of perplexity and the corresponding information retrieval performance obtained. It is observed, on the corpora considered, that the perplexity of the language model has a systematic relationship with the achievable precision recall performance though it is not statistically significant.


title: Imitation Learning of Agenda-based Semantic Parsers

abstract: Semantic parsers conventionally construct logical forms bottom-up in a fixed order, resulting in the generation of many extraneous partial logical forms. In this paper, we combine ideas from imitation learning and agenda-based parsing to train a semantic parser that searches partial logical forms in a more strategic order. Empirically, our parser reduces the number of constructed partial logical forms by an order of magnitude, and obtains a 6x-9x speedup over fixed-order parsing, while maintaining comparable accuracy.


title: On-Line Building Energy Optimization Using Deep Reinforcement Learning

abstract: Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power systems and to help customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using deep reinforcement learning, a hybrid type of methods that combines reinforcement learning with deep learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and deep policy gradient, both of which have been extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly dimensional database includes information about photovoltaic power generation, electric vehicles and buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.


title: Variational inference for the nested Chinese restaurant process

abstract: The nested Chinese restaurant process (nCRP) is a powerful nonparametric Bayesian model for learning tree-based hierarchies from data. Since its posterior distribution is intractable, current inference methods have all relied on MCMC sampling. In this paper, we develop an alternative inference technique based on variational methods. To employ variational methods, we derive a tree-based stick-breaking construction of the nCRP mixture model, and a novel variational algorithm that efficiently explores a posterior over a large set of combinatorial structures. We demonstrate the use of this approach for text and hand written digits modeling, where we show we can adapt the nCRP to continuous data as well.


title: Analyzing Factors Affecting Users' Behavior Intention to Use Social Media: Twitter Case

abstract: Advancement of technology and the Internet proliferation have visible effects in the world. One of the most important effects is the increased social media usage among the Internet users. For this purpose, factors having impacts on users’ behavior intention to social media usage are investigated within the scope of the study. A research model based on technology acceptance model 2 is proposed and revised by the taking social media platforms into consideration. The effects of perceived ease of use, perceived usefulness, social influence, facilitating conditions, playfulness, and trust are measured. Data collected from 462 respondents are analyzed by structural equation modeling technique. According to results of the measurement and structural model validities, a fit and acceptable model is achieved to measure the related effects of variables. The results of study reveal the both direct and indirect positive impacts of the factors on users’ behavior intention to use social media.


title: Linearization Technologies for Power Amplifiers of Cellular Base Stations

abstract: This paper presents a historical evolution of linearization technologies for power amplifiers of cellular base stations, which has three aspects. One is to reduce the saturation output power level of power amplifiers in multi-carrier base stations in second generation mobile communication systems. Second is to reduce the power consumption of power amplifiers in wideband modulated carrier base stations in third and fourth generation mobile communication systems. Third is to reduce the gain and phase deviations of power amplifiers in Massive MIMO base stations in fifth generation mobile communication systems. This paper shows our research activities of power amplifiers employing the linearization technologies for cellular base stations with regard to the historical evolution, while the linearization technologies will continuously provide a new solution for realizing the cellular base stations in new era.


Popular Culture in South Africa: The Limits of Black Identity in "Drum" Magazine


title: THE MEANING OF MONEY: AN INDIVIDUAL-DIFFERENCE PERSPECTIVE.

abstract: We discuss individual differences in the meanings people attach to money. We briefly review the management theory and research that describe money as a motivator and how attitudes about pay influence behavior. Following this is a section on money as an individual-difference construct, how it is defined and measured, and to what it is related. We conclude the article with a discussion of how an understanding of this individual-difference variable can further our theory, research, and practice in the areas of human resource management and organizational behavior.


title: SMART SEMANTIC MIDDLEWARE FOR THE INTERNET OF THINGS

abstract: As ubiquitous systems become increasingly complex, traditional solutions to manage and control them reach their limits and pose a need for self-manageability. Also, heterogeneity of the ubiquitous components, standards, data formats, etc, creates significant obstacles for interoperability in such complex systems. The promising technologies to tackle these problems are the Semantic technologies, for interoperability, and the Agent technologies for management of complex systems. This paper describes our vision of a middleware for the Internet of Things, which will allow creation of self-managed complex systems, in particular industrial ones, consisting of distributed and heterogeneous components of different nature. We also present an analysis of issues to be resolved to realize such a middleware.


title: Power Limitations and Dark Silicon Challenge the Future of Multicore

abstract: Since 2004, processor designers have increased core counts to exploit Moore’s Law scaling, rather than focusing on single-core performance. The failure of Dennard scaling, to which the shift to multicore parts is partially a response, may soon limit multicore scaling just as single-core scaling has been curtailed. This paper models multicore scaling limits by combining device scaling, single-core scaling, and multicore scaling to measure the speedup potential for a set of parallel workloads for the next five technology generations. For device scaling, we use both the ITRS projections and a set of more conservative device scaling parameters. To model single-core scaling, we combine measurements from over 150 processors to derive Pareto-optimal frontiers for area/performance and power/performance. Finally, to model multicore scaling, we build a detailed performance model of upper-bound performance and lower-bound core power. The multicore designs we study include single-threaded CPU-like and massively threaded GPU-like multicore chip organizations with symmetric, asymmetric, dynamic, and composed topologies. The study shows that regardless of chip organization and topology, multicore scaling is power limited to a degree not widely appreciated by the computing community. Even at 22 nm (just one year from now), 21p of a fixed-size chip must be powered off, and at 8 nm, this number grows to more than 50p. Through 2024, only 7.9× average speedup is possible across commonly used parallel workloads for the topologies we study, leaving a nearly 24-fold gap from a target of doubled performance per generation.


title: The application of co-simulation based on AMESim and Matlab in electro-hydraulic servo system

abstract: The structure and operating principle of Electro-hydraulic servo system were described, and the Co-Simulation based on AMESim and Matlab was performed. Furthermore, aiming at the problem that construction of an accurate model of an Electro-hydraulic servo system based on traditional linear methods remains a difficult task due to its nonlinear characteristics including flow/pressure relation, etc, the paper proposed a data-driven control method based on the model-free adaptive control. The simulation results show that the model-free adaptive control method satisfies the performance of servo control system, and provides the electro-hydraulic servo control system with new ideas and new methods.


High-Performance Deep Neural Network-Based Tomato Plant Diseases and Pests Diagnosis System With Refinement Filter Bank


title: An outbreak of malignant catarrhal fever in young rusa deer (Cervus timorensis)

abstract: On the basis of clinical signs and histological findings eight 9-month-old male rusa deer (Cervus timorensis) were diagnosed with sheep associated-malignant catarrhal fever. Following a variable course involving rectal temperatures around 40.5 degrees C, depression, inappetence, diarrhoea, corneal opacity and hypopyon all animals died or were euthanased over a 5-week period. Severe multifocal vasculitis, mainly periglomerular and in the arcuate vessels were consistent histological findings which in the past have been adequate to confirm clinical diagnosis of sheep associated-malignant catarrhal fever. A nested polymerase chain reaction test has been used to detect a sheep associated-malignant catarrhal fever PRC product, 238 base-pairs in size, in DNA extracted from lymphocyte preparations. The result supported the diagnosis of sheep associated-malignant catarrhal fever in these deer.


title: A Novel 1.5 Quadruple Antenna for Tri-Band GPS Applications

abstract: A new global positioning system (GPS) antenna is proposed to cover the three GPS bands (L1, L2, and L5, namely 1575, 1227, and 1176 MHz) with the L5 band to be added after 2006. The developed antenna size is only 1.5" x 1.5" in aperture corresponding to lambda/7 x lambda/7 (lambda = free space wavelength) and lambda/13 thick. Quadrature feeding is employed to ensure right-hand circular polarized (RHCP) radiation. The final miniature antenna exhibits a gain greater than 2 dBi, and to our knowledge this is the smallest such size for circular polarized (CP) operation covering all three bands. Detailed parametric simulations leading to the best design along with measurements for the constructed antenna are presented.


title: Narcissistic personality disorder in DSM-5.

abstract: The criteria for personality disorders in Section II of DSM-5 have not changed from those in DSM–IV. Therefore, the diagnosis of Section II narcissistic personality disorder (NPD) will perpetuate all of the well-enumerated shortcomings associated with the diagnosis since DSM–III. In this article, we will briefly review problems associated with Section II NPD and then discuss the evolution of a new model of personality disorder and the place in the model of pathological narcissism and NPD. The new model was intended to be the official approach to the diagnosis of personality pathology in DSM-5, but was ultimately placed as an alternative in Section III for further study. The new model is a categorical-dimensional hybrid based on the assessment of core elements of personality functioning and of pathological personality traits. The specific criteria for NPD were intended to rectify some of the shortcomings of the DSM–IV representation by acknowledging both grandiose and vulnerable aspects, overt and covert presentations, and the dimensionality of narcissism. In addition, criteria were assigned and diagnostic thresholds set based on empirical data. The Section III representation of narcissistic phenomena using dimensions of self and interpersonal functioning and relevant traits offers a significant improvement over Section II NPD.


title: Summarizing visual data using bidirectional similarity

abstract: We propose a principled approach to summarization of visual data (images or video) based on optimization of a well-defined similarity measure. The problem we consider is re-targeting (or summarization) of image/video data into smaller sizes. A good ldquovisual summaryrdquo should satisfy two properties: (1) it should contain as much as possible visual information from the input data; (2) it should introduce as few as possible new visual artifacts that were not in the input data (i.e., preserve visual coherence). We propose a bi-directional similarity measure which quantitatively captures these two requirements: Two signals S and T are considered visually similar if all patches of S (at multiple scales) are contained in T, and vice versa. The problem of summarization/re-targeting is posed as an optimization problem of this bi-directional similarity measure. We show summarization results for image and video data. We further show that the same approach can be used to address a variety of other problems, including automatic cropping, completion and synthesis of visual data, image collage, object removal, photo reshuffling and more.


title: Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models

abstract: Textual-visual cross-modal retrieval has been a hot research topic in both computer vision and natural language processing communities. Learning appropriate representations for multi-modal data is crucial for the cross-modal retrieval performance. Unlike existing image-text retrieval approaches that embed image-text pairs as single feature vectors in a common representational space, we propose to incorporate generative processes into the cross-modal feature embedding, through which we are able to learn not only the global abstract features but also the local grounded features. Extensive experiments show that our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset.


title: A pose graph-based localization system for long-term navigation in CAD floor plans

abstract: Abstract Accurate localization is an essential technology for flexible automation. Industrial applications require mobile platforms to be precisely localized in complex environments, often subject to continuous changes and reconfiguration. Most of the approaches use precomputed maps both for localization and for interfacing robots with workers and operators. This results in increased deployment time and costs as mapping experts are required to setup the robotic systems in factory facilities. Moreover, such maps need to be updated whenever significant changes in the environment occur in order to be usable within commanding tools. To overcome those limitations, in this work we present a robust and highly accurate method for long-term LiDAR-based indoor localization that uses CAD-based architectural floor plans. The system leverages a combination of graph-based mapping techniques and Bayes filtering to maintain a sparse and up-to-date globally consistent map that represents the latest configuration of the environment. This map is aligned to the CAD drawing using prior constraints and is exploited for relative localization, thus allowing the robot to estimate its current pose with respect to the global reference frame of the floor plan. Furthermore, the map helps in limiting the disturbances caused by structures and clutter not represented in the drawing. Several long-term experiments in changing real-world environments show that our system outperforms common state-of-the-art localization methods in terms of accuracy and robustness while remaining memory and computationally efficient.


title: Examining Motivation in Online Distance Learning Environments: Complex, Multifaceted, and Situation-Dependent.

abstract: Existing research into motivation in online environments has tended to use one of two approaches. The first adopts a trait-like model that views motivation as a relatively stable, personal characteristic of the learner. Research from this perspective has contributed to the notion that online learners are, on the whole, intrinsically motivated. The alternative view concentrates on the design of online learning environments to encourage optimal learner motivation. Neither approach acknowledges a contemporary view of motivation that emphasises the situated, mutually constitutive relationship of the learner and the learning environment. Using self-determination theory (SDT) as a framework, this paper explores the motivation to learn of preservice teachers in two online distance-learning contexts. In this study, learners were found to be not primarily intrinsically motivated. Instead, student motivation was found to be complex, multifaceted, and sensitive to situational conditions.


title: A Bandwidth-enhanced millimeter-wave microstrip comb-line array antenna with parasitic elements on liquid crystal polymer substrate

abstract: In this paper, a microstrip comb-line array antenna with parasitic elements for bandwidth enhancement at 60 GHz band is proposed. The antenna is fabricated on liquid crystal polymer (LCP) substrate due to its low dielectric loss. The antenna consists of a waveguide-to-microstrip mode transition, 16 radiation elements which construct an one-dimensional array and a terminated element. Two side-coupled parasitic elements are set symmetrically on both sides of each radiation element and an end-coupled parasitic element is set at the edge of the element for the sake of bandwidth enhancement. The antenna can be made consistently for the emerging market in the near future. Good agreement between simulated and measured results is obtained. Around 5% impedance bandwidth for |S 11 | less than -10 dB and 10% gain bandwidth for maximum gain over 10 dBi are finally obtained.


Analysis of Bitcoin Network Dataset for Fraud


title: Kernel codebooks for scene categorization

abstract: This paper introduces a method for scene categorization by modeling ambiguity in the popular codebook approach. The codebook approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization. There are two drawbacks to the traditional codebook model: codeword uncertainty and codeword plausibility. Both of these drawbacks stem from the hard assignment of visual features to a single codeword. We show that allowing a degree of ambiguity in assigning codewords improves categorization performance for three state-of-the-art datasets.


title: Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion

abstract: Reflection photoplethysmography (PPG) using 530 nm (green) wavelength light has the potential to be a superior method for monitoring heart rate (HR) during normal daily life due to its relative freedom from artifacts. However, little is known about the accuracy of pulse rate (PR) measured by 530 nm light PPG during motion. Therefore, we compared the HR measured by electrocadiography (ECG) as a reference with PR measured by 530, 645 (red), and 470 nm (blue) wavelength light PPG during baseline and while performing hand waving in 12 participants. In addition, we examined the change of signal-to-noise ratio (SNR) by motion for each of the three wavelengths used for the PPG. The results showed that the limit of agreement in Bland-Altman plots between the HR measured by ECG and PR measured by 530 nm light PPG (±0.61 bpm) was smaller than that achieved when using 645 and 470 nm light PPG (±3.20 bpm and ±2.23 bpm, respectively). The ΔSNR (the difference between baseline and task values) of 530 and 470nm light PPG was significantly smaller than ΔSNR for red light PPG. In conclusion, 530 nm light PPG could be a more suitable method than 645 and 470nm light PPG for monitoring HR in normal daily life.


Separately Excited DC Motor Parametric Control Using Electronic Workbench


title: IN-SPACE CALIBRATION OF A GYRO QUADRUPLET

abstract: This work presents a new approach to gyro calibration where, in addition to being used for computing attitude that is needed in the calibration process, the gyro outputs are also used as measurements in a Kalman filter. This work also presents an algorithm for calibrating a quadruplet rather than the customary triad gyro set. In particular, a new misalignment error model is derived for this case. The new calibration algorithm is applied to the EOS-AQUA satellite gyros. The effectiveness of the new algorithm is demonstrated through simulations.


title: Modeling POI Transition Network of Human Mobility

abstract: Point-of-interest (POI) denotes the category of a location people visit, reflecting the daily life and personal interests. Through a massive dataset of check-in records, this paper discloses that the POI visiting pattern of human spatial mobility falling into both Zipf's law and Heaps' law. It is found that people always have three potential choices when selecting a particular POI as their next travel destination, namely, exploring a new POI, returning to a visited one, or staying at the current one. We calculate the probabilities of these scenarios and propose a statistical model based on random walk to represent such phenomena. Simulation results show that our model can well reproduce the heterogeneous Zipf's law and Heaps' law of human mobility.


title: Improved Face Detection and Alignment using Cascade Deep Convolutional Network

abstract: Real-world face detection and alignment demand an advanced discriminative model to address challenges by pose, lighting and expression. Illuminated by the deep learning algorithm, some convolutional neural networks based face detection and alignment methods have been proposed. Recent studies have utilized the relation between face detection and alignment to make models computationally efficiency, however they ignore the connection between each cascade CNNs. In this paper, we propose an structure to propose higher quality training data for End-to-End cascade network training, which give computers more space to automatic adjust weight parameter and accelerate convergence. Experiments demonstrate considerable improvement over existing detection and alignment models.


title: Water Main Burst Event Detection and Localization

abstract: In this paper we present a technique for detecting and locating burst events in pipelines. The proposed method uses wavelet analysis of the high-rate pressure data to detect pipe burst events. Multiscale wavelet analysis of the pressure signal will be shown to be robust to impulsive noise encountered in the physical phenomena under observation. The wavelet coefficients also allow us to obtain additional information about the nature of the signal feature detected, which can used for further feature classification. A local search method is also proposed to accurately determine the arrival time of the pressure front associated with the burst event. The detection performance of these algorithms is verified through leak-off experiments performed on the WaterWiSe@SG test bed deployed on the water distribution system in Singapore. We also propose a graph-based search algorithm which uses the arrival times of the pressure front at different locations within the water distribution system to determine the actual location of the pipe burst event.


title: A Survey of Automatic Protocol Reverse Engineering Tools

abstract: Computer network protocols define the rules in which two entities communicate over a network of unique hosts. Many protocol specifications are unknown, unavailable, or minimally documented, which prevents thorough analysis of the protocol for security purposes. For example, modern botnets often use undocumented and unique application-layer communication protocols to maintain command and control over numerous distributed hosts. Inferring the specification of closed protocols has numerous advantages, such as intelligent deep packet inspection, enhanced intrusion detection system algorithms for communications, and integration with legacy software packages. The multitude of closed protocols coupled with existing time-intensive reverse engineering methodologies has spawned investigation into automated approaches for reverse engineering of closed protocols. This article summarizes and organizes previously presented automatic protocol reverse engineering tools by approach. Approaches that focus on reverse engineering the finite state machine of a target protocol are separated from those that focus on reverse engineering the protocol format.


title: Network Traffic Obfuscation and Automated Internet Censorship

abstract: Internet censors seek ways to identify and block internet access to information they deem objectionable. Increasingly, censors deploy advanced networking tools such as deep-packet inspection (DPI) to identify such connections. In response, activists and academic researchers have developed and deployed network traffic obfuscation mechanisms. These apply specialized cryptographic tools to attempt to hide from DPI the true nature and content of connections. ::: In this survey, we give an overview of network traffic obfuscation and its role in circumventing Internet censorship. We provide historical and technical background that motivates the need for obfuscation tools, and give an overview of approaches to obfuscation used by state of the art tools. We discuss the latest research on how censors might detect these efforts. We also describe the current challenges to censorship circumvention research and identify concrete ways for the community to address these challenges.


title: Mutual coupling modeling of NFC antennas by using open-source CAD/FEM tools

abstract: Near Field Communication (NFC) antennas are not comparable to standard communication antennas used for traditional transmission services. The geometries are complex, narrow conductor cross-sections and long loops located close to materials sensitive to electromagnetic interaction, when integrated, make the application of analytical coupling formulations inapplicable, even when it comes down to non-linear relations forced by ferrite shielding. Commercial finite element (FEM) solvers are powerful, but they do not serve the needs of NFC applications due to complexity, inadequateness and code obscuration. An intensive open source code investigation has identified one alternative solver which is capable to deal with all NFC RFID related coupling effects one can imagine. Automated scripts, the tool-chain, and geometry macros have been developed for rapid prototyping of such. Amongst other open source tools listed, the ElmerFEM solver is the most promising solver for linear and non-linear quasi-static electro-magnetic (EM) NFC problems. The solver has a powerful interface and delivers results close to reality even when computational complexity is a trade of physical resolution and memory capacity of standard commercial workstations.


Manual Evaluation of the Pelvic Floor : State of the Art and Evaluative Hypothesis


title: Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice

abstract: Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.


title: Smart vehicle accident detection and alarming system using a smartphone

abstract: Vehicle accident is the paramount thread for the people's life which causes a serious wound or even dead. The automotive companies have made lots of progress in alleviating this thread, but still the probability of detrimental effect due to an accident is not reduced. Infringement of spFieed is one of the elementary reasons for a vehicle accident. Therewithal, external pressure and change of tilt angle with road surface blameworthy for this mishap. As soon as the emergency service could divulge about an accident, the more the effect would be mitigated. For this purpose, we developed an Android based application that detects an accidental situation and sends emergency alert message to the nearest police station and health care center. This application is integrated with an external pressure sensor to extract the outward force of the vehicle body. It measures speed and change of tilt angle with GPS and accelerometer sensors respectively on Android phone. By checking conditions, this application also capable of reducing the rate of false alarm.


Information Technology Continuance Research : Current State and Future Directions *


title: Classifier cascades and trees for minimizing feature evaluation cost

abstract: Machine learning algorithms have successfully entered industry through many real-world applications (e.g., search engines and product recommendations). In these applications, the test-time CPU cost must be budgeted and accounted for. In this paper, we examine two main components of the test-time CPU cost, classifier evaluation cost and feature extraction cost, and show how to balance these costs with the classifier accuracy. Since the computation required for feature extraction dominates the test-time cost of a classifier in these settings, we develop two algorithms to efficiently balance the performance with the test-time cost. Our first contribution describes how to construct and optimize a tree of classifiers, through which test inputs traverse along individual paths. Each path extracts different features and is optimized for a specific sub-partition of the input space. Our second contribution is a natural reduction of the tree of classifiers into a cascade. The cascade is particularly useful for class-imbalanced data sets as the majority of instances can be early-exited out of the cascade when the algorithm is sufficiently confident in its prediction. Because both approaches only compute features for inputs that benefit from them the most, we find our trained classifiers lead to high accuracies at a small fraction of the computational cost.


title: The nude image identification with adaptive skin chromatic distribution matching scheme

abstract: This paper presents a new nude image identification system. We propose an adaptive chroma-distribution matching scheme based on face detection to on-line determine the image's skin chromatic distribution such that it can tolerate the color deviation coming from special lighting without increasing false alarm. The object detection based on machine learning approach is used to locate the face region in the test image. According to the color information of face, the matched skin chromatic distribution is selected to detect the skin objects in the test image. The texture feature, namely coarseness, is used to acquire accurate skin segmentation. The low-level but reliable geometrical constraints and the mug shot exclusion procedure are employed to further examine the skin regions. Experimental results that the overall detection rate is 86.3% show our method can achieve satisfactory performance for detecting nude images under special lighting conditions.


title: A review and future direction of agile, business intelligence, analytics and data science

abstract: We provide review of agile methodologies, business intelligence and data science.We explain the current trends and interplay between agile methodologies, business intelligence and data science.We develop lifecycle approaches for BI, analytics and data science.We discuss challenges and future directions for agile methodologies, business intelligence and data science. Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.


title: On the exploitation of a high-throughput SHA-256 FPGA design for HMAC

abstract: High-throughput and area-efficient designs of hash functions and corresponding mechanisms for Message Authentication Codes (MACs) are in high demand due to new security protocols that have arisen and call for security services in every transmitted data packet. For instance, IPv6 incorporates the IPSec protocol for secure data transmission. However, the IPSec's performance bottleneck is the HMAC mechanism which is responsible for authenticating the transmitted data. HMAC's performance bottleneck in its turn is the underlying hash function. In this article a high-throughput and small-size SHA-256 hash function FPGA design and the corresponding HMAC FPGA design is presented. Advanced optimization techniques have been deployed leading to a SHA-256 hashing core which performs more than 30p better, compared to the next better design. This improvement is achieved both in terms of throughput as well as in terms of throughput/area cost factor. It is the first reported SHA-256 hashing core that exceeds 11Gbps (after place and route in Xilinx Virtex 6 board).


title: Gamification: designing for motivation

abstract: Social Mediator is a forum exploring the ways that HCI research and principles interact---or might interact---with practices in the social media world. Joe McCarthy, Editor


title: Structural Couplings of Organizational Design and Organizational Engineering

abstract: The theory of autopoiesis, which is less known to both organizational design and organizational engineering (ODE) practice, is a theory of complex, non-linear, autonomous, and especially living systems that found its way from biology through the social sciences to organization theory and information systems (Schatten and Baca, 2010). Autopoiesis, a pseudo Greek word that comes from avro (auto — self), and noir\oic (poiesis — a “making,” the process of forming, creation, or production), was introduced by the Chilean biologists Humberto Maturana and Francisco Varela in 1973 (Maturana and Varela, 1973) to label the type of phenomenon which they had identified as the definitive characteristic of living systems (Whitaker, 2001). Autopoietic, or living in contrast to alopoietic, systems are systems that produce the network of processes that produced them.


title: TimeCrunch: Interpretable Dynamic Graph Summarization

abstract: How can we describe a large, dynamic graph over time? Is it random? If not, what are the most apparent deviations from randomness -- a dense block of actors that persists over time, or perhaps a star with many satellite nodes that appears with some fixed periodicity? In practice, these deviations indicate patterns -- for example, botnet attackers forming a bipartite core with their victims over the duration of an attack, family members bonding in a clique-like fashion over a difficult period of time, or research collaborations forming and fading away over the years. Which patterns exist in real-world dynamic graphs, and how can we find and rank them in terms of importance? These are exactly the problems we focus on in this work. Our main contributions are (a) formulation: we show how to formalize this problem as minimizing the encoding cost in a data compression paradigm, (b) algorithm: we propose TIMECRUNCH, an effective, scalable and parameter-free method for finding coherent, temporal patterns in dynamic graphs and (c) practicality: we apply our method to several large, diverse real-world datasets with up to 36 million edges and 6.3 million nodes. We show that TIMECRUNCH is able to compress these graphs by summarizing important temporal structures and finds patterns that agree with intuition.


title: An automated fine-grain pipelining using domino style asynchronous library

abstract: Register transfer level (RTL) synthesis model which simplified the design of clocked circuits allowed design automation boost and VLSI progress for more than a decade. Shrinking technology and progressive increase in clock frequency are bringing clock to its physical limits. Asynchronous circuits, which are believed to replace globally clocked designs in the future, remain out of the competition due to the design complexity of some automated approaches and poor results of other techniques. Successful asynchronous designs are known but they are primarily custom. This work sketches an automated approach for automatically re-implementing conventional RTL designs as fine-grain pipelined asynchronous quasi-delay-insensitive (QDI) circuits and presents a framework for automated synthesis of such implementations from high-level behavior specifications. Experimental results are presented using our new dynamic asynchronous library.


title: Special Section on Advanced Displays: A survey on computational displays: Pushing the boundaries of optics, computation, and perception

abstract: Display technology has undergone great progress over the last few years. From higher contrast to better temporal resolution or more accurate color reproduction, modern displays are capable of showing images which are much closer to reality. In addition to this trend, we have recently seen the resurrection of stereo technology, which in turn fostered further interest on automultiscopic displays. These advances share the common objective of improving the viewing experience by means of a better reconstruction of the plenoptic function along any of its dimensions. In addition, one usual strategy is to leverage known aspects of the human visual system (HVS) to provide apparent enhancements, beyond the physical limits of the display. In this survey, we analyze these advances, categorize them along the dimensions of the plenoptic function, and present the relevant aspects of human perception on which they rely.


title: FairFuzz: Targeting Rare Branches to Rapidly Increase Greybox Fuzz Testing Coverage

abstract: In recent years, fuzz testing has proven itself to be one of the most effective techniques for finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing tool, American Fuzzy Lop or AFL, has become popular thanks to its ease-of-use and bug-finding power. However, AFL remains limited in the depth of program coverage it achieves, in particular because it does not consider which parts of program inputs should not be mutated in order to maintain deep program coverage. We propose an approach, FairFuzz, that helps alleviate this limitation in two key steps. First, FairFuzz automatically prioritizes inputs exercising rare parts of the program under test. Second, it automatically adjusts the mutation of inputs so that the mutated inputs are more likely to exercise these same rare parts of the program. We conduct evaluation on real-world programs against state-of-the-art versions of AFL, thoroughly repeating experiments to get good measures of variability. We find that on certain benchmarks FairFuzz shows significant coverage increases after 24 hours compared to state-of-the-art versions of AFL, while on others it achieves high program coverage at a significantly faster rate.