title: Massive MIMO in Sub-6 GHz and mmWave: Physical, Practical, and Use-Case Differences

abstract: The use of base stations (BSs) and access points (APs) with a large number of antennas, called Massive MIMO (multiple-input multiple-output), is a key technology for increasing the capacity of 5G networks and beyond. While originally conceived for conventional sub-6 GHz frequencies, Massive MIMO (mMIMO) is also ideal for frequency bands in the range 30-300 GHz, known as millimeter wave (mmWave). Despite conceptual similarities, the way in which mMIMO can be exploited in these bands is radically different, due to their specific propagation behaviors and hardware characteristics. This article reviews these differences and their implications, while dispelling common misunderstandings. Building on this foundation, we suggest appropriate signal processing schemes and use cases to efficiently exploit mMIMO in both frequency bands.


title: Information security model of block chain based on intrusion sensing in the IoT environment

abstract: Block chain is a decentralized core architecture, which is widely used in emerging digital encryption currencies. It has attracted much attention and has been researched with the gradual acceptance of bitcoin. Block chaining technology has the characteristics of centralization, block data, no tampering and trust, so it is sought after by enterprises, especially financial institutions. This paper expounds the core technology principle of block chain technology, discusses the application of block chain technology, the existing regulatory problems and security problems, so as to provide some help for the related research of block chain technology. Intrusion detection is an important way to protect the security of information systems. It has become the focus of security research in recent years. This paper introduces the history and current situation of intrusion detection system, expounds the classification of intrusion detection system and the framework of general intrusion detection, and discusses all kinds of intrusion detection technology in detail. Intrusion detection technology is a kind of security technology to protect network resources from hacker attack. IDS is a useful supplement to the firewall, which can help the network system to quickly detect attacks and improve the integrity of the information security infrastructure. In this paper, intrusion detection technology is applied to block chain information security model, and the results show that proposed model has higher detection efficiency and fault tolerance.


title: VisInfo: a digital library system for time series research data based on exploratory search—a user-centered design approach

abstract: To this day, data-driven science is a widely accepted concept in the digital library (DL) context (Hey et al. in The fourth paradigm: data-intensive scientific discovery. Microsoft Research, 2009). In the same way, domain knowledge from information visualization, visual analytics, and exploratory search has found its way into the DL workflow. This trend is expected to continue, considering future DL challenges such as content-based access to new document types, visual search, and exploration for information landscapes, or big data in general. To cope with these challenges, DL actors need to collaborate with external specialists from different domains to complement each other and succeed in given tasks such as making research data publicly available. Through these interdisciplinary approaches, the DL ecosystem may contribute to applications focused on data-driven science and digital scholarship. In this work, we present VisInfo (2014) , a web-based digital library system (DLS) with the goal to provide visual access to time series research data. Based on an exploratory search (ES) concept (White and Roth in Synth Lect Inf Concepts Retr Serv 1(1):1---98, 2009), VisInfo at first provides a content-based overview visualization of large amounts of time series research data. Further, the system enables the user to define visual queries by example or by sketch. Finally, VisInfo presents visual-interactive capability for the exploration of search results. The development process of VisInfo was based on the user-centered design principle. Experts from computer science, a scientific digital library, usability engineering, and scientists from the earth, and environmental sciences were involved in an interdisciplinary approach. We report on comprehensive user studies in the requirement analysis phase based on paper prototyping, user interviews, screen casts, and user questionnaires. Heuristic evaluations and two usability testing rounds were applied during the system implementation and the deployment phase and certify measurable improvements for our DLS. Based on the lessons learned in VisInfo, we suggest a generalized project workflow that may be applied in related, prospective approaches.


title: Why Evaluating Uncertainty Visualization is Error Prone

abstract: Evaluating a visualization that depicts uncertainty is fraught with challenges due to the complex psychology of uncertainty. However, relatively little attention is paid to selecting and motivating a chosen interpretation or elicitation method for subjective probabilities in the uncertainty visualization literature. I survey existing evaluation work in uncertainty visualization, and examine how research in judgment and decision-making that focuses on subjective uncertainty elicitation sheds light on common approaches in visualization. I propose suggestions for practice aimed at reducing errors and noise related to how ground truth is defined for subjective probability estimates, the choice of an elicitation method, and the strategies used by subjects making judgments with an uncertainty visualization.


title: CNN based semantic segmentation for urban traffic scenes using fisheye camera

abstract: Semantic segmentation is an important step of visual scene understanding for autonomous driving. Recently, Convolutional Neural Network (CNN) based methods have successfully applied in semantic segmentation using narrow-angle or even wide-angle pinhole camera. However, in urban traffic environments, autonomous vehicles need wider field of view to perceive surrounding things and stuff, especially at intersections. This paper describes a CNN-based semantic segmentation solution using fisheye camera which covers a large field of view. To handle the complex scene in the fisheye image, Overlapping Pyramid Pooling (OPP) module is proposed to explore local, global and pyramid local region context information. Based on the OPP module, a network structure called OPP-net is proposed for semantic segmentation. The net is trained and evaluated on a fisheye image dataset for semantic segmentation which is generated from an existing dataset of urban traffic scenes. In addition, zoom augmentation, a novel data augmentation policy specially designed for fisheye image, is proposed to improve the net's generalization performance. Experiments demonstrate the outstanding performance of the OPP-net for urban traffic scenes and the effectiveness of the zoom augmentation.


title: RemixNet: Generative Adversarial Networks for Mixing Multiple Inputs

abstract: Generative Adversarial Networks (GANs) are a class of Artificial Neural Network architectures for generating realistic data distributions from a small number of latent variables. The latent variables are typically drawn IID from a Normal distribution and fed as input. Empirically, it has been shown that GANs are able to produce realistic images from as few as 100 latent variables. While numerous studies have focused on modifying the original GAN architecture, few have explored the structure of the input latent variables. In this study we propose placing structure on the input for aiding GANs to converge faster, lead insight into the structure of the underlying data distribution, and produce potentially more creative outputs. To achieve a GAN with such constraints, we call our proposed architecture RemixNet. In RemixNet the latent variables are a result of multiple draws from our existing dataset and "remixed" into a new distribution. This idea of combining existing data into new data is inspired from the artistic and musical practice of collage making and remixes. For example, an artist may take an existing recording of music, modify each with a filter and combine to form a new composition. We show several experiments on the MNIST dataset to gain some intuition on various RemixNet architectures. We also compare empirically RemixNet with GAN and show that the RemixNet architecture converges significantly faster to realistic images.


title: Mathematical Features for Recognizing Preference in Sub-saharan African Traditional Rhythm Timelines

abstract: The heart of an African rhythm is the timeline, a beat that cyclically repeats thoughout a piece, and is often performed with an iron bell that all performers can hear. Such rhythms can be represented as sequences of points on a circular lattice, where the position of the points indicates the time in the cycle at which the instrument is struck. Whereas in theory there are thousands of possible choices for such timeline patterns, in practice only a few of these are ever used. This brings up the question of how these few patterns were selected over all the others, and of those selected, why some are preferred (have more widespread use) than others. Simha Arom discovered that the rhythms used in the traditional music of the Aka Pygmies of Central Africa possess what he calls the rhythmic oddity property. A rhythm has the rhythmic oddity property if it does not contain two onsets that partition the cycle into two half-cycles. Here a broader spectrum of rhythms from West, Central and South Africa are analysed. A mathematical property of rhythms is proposed, dubbed “Off-Beatness”, that is based on group theory, and it is argued that it is superior to the rhythmic oddity property as a measure of preference among Sub-Saharan African rhythm timelines. The “Off-Beatness” measure may also serve as a mathematical definition of syncopation, a feature for music recognition in general, and it is argued that it is superior to the mathematical syncopation measure proposed by Michael Keith.


Nonlinear Dynamic Modeling for High Performance Control of a Quadrotor


title: Implementation of medical image segmentation in CUDA

abstract: As the fast development of GPU, people tend to use it for more general purposes than its original graphic related work. The high parallel computation capabilities of GPU are welcomed by programmers who work at medical image processing which always have to deal with a large scale of voxel computation. The birth of NVIDIAreg CUDAtrade technology and CUDA-enabled GPUs brought a revolution in the general purpose GPU area. In this paper, we propose the implementation of several medical image segmentation algorithms using CUDA and CUDA-enabled GPUs, compare their performance and results to the previous implementation in old version of GPU and CPU, indicate the advantages of using CUDA technology and how to design algorithm to make full use of it.


title: A CMOS Energy Harvesting and Imaging (EHI) Active Pixel Sensor (APS) Imager for Retinal Prosthesis

abstract: A CMOS image sensor capable of imaging and energy harvesting on same focal plane is presented for retinal prosthesis. The energy harvesting and imaging (EHI) active pixel sensor (APS) imager was designed, fabricated, and tested in a standard 0.5 μm CMOS process. It has 54 × 50 array of 21 × 21 μm2 EHI pixels, 10-bit supply boosted (SB) SAR ADC, and charge pump circuits consuming only 14.25 μW from 1.2 V and running at 7.4 frames per second. The supply boosting technique (SBT) is used in an analog signal chain of the EHI imager. Harvested solar energy on focal plane is stored on an off-chip capacitor with the help of a charge pump circuit with better than 70% efficiency. Energy harvesting efficiency of the EHI pixel was measured at different light levels. It was 9.4% while producing 0.41 V open circuit voltage. The EHI imager delivers 3.35 μW of power was delivered to a resistive load at maximum power point operation. The measured pixel array figure of merit (FoM) was 1.32 pW/frame/pixel while imager figure of merit (iFoM) including whole chip power consumption was 696 fJ/pixel/code for the EHI imager.


title: Evidence for discontinuity patterns across the stages of change: A role for attitudinal ambivalence

abstract: Cross-sectional analyses of the transtheoretical ("stages of change") model (TTM) can provide valuable information concerning the variables that are likely to shift individuals from one stage to the next. The present study was designed to investigate the roles of theory of planned behaviour (TPB) variables and attitudinal ambivalence across the stages of change. Two samples derived from the general population completed measures of stage, TPB and attitudinal ambivalence with respect to consuming a low-fat diet (n = 148) and eating five portions of fruit and vegetables per day (n = 151). Consistent with previous research, the present study found linear increases in TPB variables across the stages of change. However, as predicted, attitudinal ambivalence showed discontinuity patterns, displaying a quadratic relationship with the stages of change, such that least ambivalence was experienced in the precontemplation and maintenance stages. Discussion of the findings focuses on implications for the development o...


title: A wideband dual circular polarization feed chain for satellite antennas at K/Ka bands

abstract: This paper presents the development of a wideband dual circular polarization feed chain applying for satellite antennas at K/Ka bands. The proposed feed chain mainly consists of a wide band common Tx-Rx corrugated horn, an orthogonal mode junction (OMJ), a turnstile junction, a K band polarizer, a Ka band polarizer and connecting waveguides. By optimizing the design parameters of these basic components utilizing different kinds of electromagnetic simulation software, the whole feed chain presents an excellent performance, and the wide range of frequency bands about 14.8% at Tx (10.1% at Rx) is obtained, which covers the operating bands of recent mainly K/Ka satellite antennas and the multiple beam satellite antennas (Tx: 17.7∼20.2 GHz; Rx: 27.5∼30.0GHz). Details of the feed chain design are described, and experimental results of the constructed prototypes are presented and discussed. Due to its high performance, the feed chain can be applying to the fixed satellite, broadcast satellite, and broadband satellite services.


title: Behavior A M

abstract: In today's scenario, cyber security is one of the major concerns in network security and malware pose a serious threat to cyber security. The foremost step to guard the cyber system is to have an in-depth knowledge of the existing malware, various types of malware, methods of detecting and bypassing the adverse effects of malware. In this work, machine learning approach to the fore-going static and dynamic analysis techniques is investigated and reported to discuss the most recent trends in cyber security. The study captures a wide variety of samples from various online sources. The peculiar details about the malware such as file details, signatures, and hosts involved, affected files, registry keys, mutexes, section details, imports, strings and results from different antivirus have been deeply analyzed to conclude origin and functionality of malware. This approach contributes to vital cyber situation awareness by combining different malware discovery techniques, for example, static examination, to alter the session of malware triage for cyber defense and decreases the count of false alarms. Current trends in warfare have been determined.


Systems biology 101—what you need to know


title: Can Instagram posts help characterize urban micro-events?

abstract: Social media content, from platforms such as Twitter and Foursquare, has enabled an exciting new field of “social sensing”, where participatory content generated by users has been used to identify unexpected emerging or trending events. In contrast to such text-based channels, we focus on image-sharing social applications (specifically Instagram), and investigate how such urban social sensing can leverage upon the additional multi-modal, multimedia content. Given the significantly higher fraction of geotagged content on Instagram, we aim to use such channels to go beyond identification of long-lived events (e.g., a marathon) to achieve finer-grained characterization of multiple micro-events (e.g., a person winning the marathon) that occur over the lifetime of the macro-event. Via empirical analysis from a corpus of Instagram data from 3 international marathons, we establish the need for novel data pre-processing as: (a) semantic annotation of image content indeed provides additional features distinct from text captions, and (b) an appreciable fraction of the posted images do not pertain to the event under consideration. We propose a framework, called EiM, that combines such preprocessing with clustering-based event detection. We show that our initial prototype of EiM shows promising results: it is able to identify many micro-events in the three marathons, with spatial and temporal resolution that is less than 1% and 10%, respectively, of the corresponding ranges for the macro-event.


title: Towards a Methodology to Assess Changes in it Business Value in Terms of Business Process Performance

abstract: The question of to what extent IT contributes to business success has repeatedly examined in both science and practice. In this context, literature strongly indicates that the IT business value is best observed on the level of business processes. However, the methodologies by which the contribution of IT can be systematically analyzed on a business processes level are currently insufficient. Therefore, this paper presents a methodology which, proceeding from a given status quo, analyzes and measures changes in IT usage in business processes. The proposed methodology combines the possibilities of the methods Data Envelopment Analysis and Business Process Simulation. An example of application will demonstrate how effects on Business Process Performance resulting from changes in IT usage can be analyzed and measured in a practical application by means of this methodology. The application of this methodology is demonstrated taking into account a loan application process. The results show that this methodology bears potential for analyzing scientific and practical research questions about the quantification of IT contribution to business value.


title: Small-Signal Model of Flyback Converter in Continuous-Conduction Mode With Peak-Current Control at Variable Switching Frequency

abstract: Flyback converter is widely used in low-power adaptor applications. To achieve high efficiency for the entire load range of operations, the variable-frequency peak-current-mode (VFPCM) control is often used in the flyback converter to reduce switching frequency at light load. Since there is no small-signal model being developed for the VFPCM control to overcome the unstable problem in a wide range of operations, the present study proposes a novel, accurate small-signal model for VFPCM control operated in continuous-conduction mode. Control behavior and design are analyzed and compared with constant-frequency peak-current-mode control to give engineers insight. A control IC and a flyback converter are built for the experimental validation of the proposed model. Experimental and simulation results verify the proposed model.


title: CIRCULAR POLARIZED PATCH ANTENNA GENERATING ORBITAL ANGULAR MOMENTUM

abstract: The recent extension of the orbital angular momentum (OAM) concept from optical to microwave frequencies has led some researchers to explore how well established antenna techniques can be used to radiate a non-zero OAM electromagnetic fleld. In this frame, the aim of the present paper is to propose a new approach to generate a non-zero OAM fleld through a single patch antenna. Using the cavity model, we flrst analyze the radiated fleld by a standard circular patch and show that a circular polarized (CP) TMnm mode excited by using two coaxial cables generates an electromagnetic fleld with an OAM of order §(ni1). Then, in order to obtain a simpler structure with a single feed, we design an elliptical patch antenna working on the right-handed (RH) CP TM21 mode. Using full-wave simulations and experiments on a fabricated prototype, we show that the proposed antenna efiectively radiates an electromagnetic fleld with a flrst order OAM. Such results prove that properly designed patch antennas can be used as compact and low-cost generators of electromagnetic flelds carrying OAM.


title: Intelligent management of intelligence agencies: Beyond accountability ping-pong

abstract: The intelligence community (IC) is asked to predict outcomes that may often be inherently unpredictable-and is blamed for the inevitable forecasting failures, be they false positives or false negatives. To move beyond blame games of accountability ping-pong that incentivize bureaucratic symbolism over substantive reform, it is necessary to reach bipartisan agreements on performance indicators that are transparent enough to reassure clashing elites (to whom the IC must answer) that estimates have not been politicized. Establishing such transideological credibility requires (a) developing accuracy metrics for decoupling probability and value judgments; (b) using the resulting metrics as criterion variables in validity tests of the IC's selection, training, and incentive systems; and (c) institutionalizing adversarial collaborations that conduct level-playing-field tests of clashing perspectives.


title: An Interpretable Knowledge Transfer Model for Knowledge Base Completion

abstract: Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, \emph{ITransF}, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets---WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.


title: Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework

abstract: This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines ε-dominance, a measure of convergence speed named ε-progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm's feasible parameterization space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.


A Survey on Mac Protocols for Wireless Sensor Networks


Couples Identified for Male Partner Violence Love as a Battlefield: Attachment and Relationship Dynamics in


title: Appearance Descriptors for Person Re-identification: a Comprehensive Review

abstract: In video-surveillance, person re-identification is the task of recognising whether an individual has already been observed over a network of cameras. Typically, this is achieved by exploiting the clothing appearance, as classical biometric traits like the face are impractical in real-world video surveillance scenarios. Clothing appearance is represented by means of low-level \textit{local} and/or \textit{global} features of the image, usually extracted according to some part-based body model to treat different body parts (e.g. torso and legs) independently. This paper provides a comprehensive review of current approaches to build appearance descriptors for person re-identification. The most relevant techniques are described in detail, and categorised according to the body models and features used. The aim of this work is to provide a structured body of knowledge and a starting point for researchers willing to conduct novel investigations on this challenging topic.


title: Journal of Communication ISSN 0021-9916 ORIGINAL ARTICLE Looking for Gender: Gender Roles and Behaviors Among Online Gamers

abstract: Several hypotheses regarding the importance of gender and relationships were tested by combining a large survey dataset with unobtrusive behavioral data from 1 year of play. Consistentwithexpectations,malesplayedforachievement-orientedreasonsandweremore aggressive, especially within romantic relationships where both partners played. Female players in such relationships had higher general happiness than their male counterparts. Contrary to stereotypes and current hypotheses, it was the female players who played the most. Female players were also healthier than male players or females in the general population. The findings have implications for gender theory and communication-oriented methods in games and online research—most notably for the use of self-reported time spent, which was systematically incorrect and different by gender. doi:10.1111/j.1460-2466.2009.01453.x Female video game players now comprise 40% of all players, and women over 18 make up more of the game-playing population than do males under 17 (Top 10 IndustryFacts, 2008). Yet, studies of men and women report both genders believe that computer games are a ‘‘particularly masculine pursuit’’ (Selwyn, 2007, p. 533). Are more women ignoring social sanctions for engaging in a supposedly masculine activity? Are men avoiding feminine actions while playing? To determine the answers, we must investigate how video game players are positioning their activity in relation to their gender, and better understand their reasons for playing games at all. Past media research on gaming has found that men and women perform in, and perceive, games differently (Blumberg & Sokol, 2004), but this research has not been updated since the mass adoption of large-scale online games. Likewise, social scientific research in the area of gender roles has not examined online game play, and how it may be changing gendered definitions of game play. At the same time, research covering a wide variety of social Internet activities has found women’s uses and considerations to be different


abstract: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies by D. Floreano and C. Mattiussi This is a book that bridges biological systems and computer science. For digital-based researchers, having this book which details the biological components of natural life and seamlessly integrates that knowledge into our digital realm is an essential asset. Each chapter is systematically introduces the reader to a biological system while easing them into the its computational counterpart. There are seven chapters covering evolution, cellular, neural, developmental, immune, behavioral, and collective systems. Chapter 1 introduces the fundamental concept of computational evolution as related to biological systems. This chapter starts with the basic concepts of evolutionary theory and progresses, covering everything from fitness functions to analog circuits. The following chapter presents the next logical step upwards in biology, cellular structures and systems. Again introducing the basics of life and progressing towards cellular automata. Chapter 3 covers Neural Networks by introducing the Biological Nervous System, then the Artificial Neural Network. The core concepts to Neural Networks are detailed in a systematic and common-sense manner, introducing unsupervised learning, supervised learning, and reinforce learning, then progressing onto neural hardware and hybrid systems. In Chapter 4, the authors detail developmental systems, explaining how nature utilizes the cellular structures to how engineers can mimic nature. This theme of progression from biological introduction to digital computation is reproduced as a single voice through out each chapter. The fundamentals of Bio-Inspired Artificial Intelligence are well demonstrated, allowing for a novice researcher in this area to develop the necessary skills and have a firm grasp on this topic. Once the reader has a solid grasp of the building blocks of life, the authors present chapters related to larger systems. Of particular interest to my research is the chapter on Immune Systems. This chapter provides a fundamental understanding of the Human Immune System, detailing the finer points of immunological cellular structures, while introducing a slightly more than generalized immune response concept. After a lengthy introduction of human immunology, we are introduced to the core of Artificial Immune Systems, the Negative Selection Algorithm and Clonal Selection Algorithm. Each one of these algorithms is covered enough so that the reader is capable of understanding each respective algorithms strengths and limitations. For new researchers to Artificial Immune Systems, days of reading journal articles is summarized in these sections, allowing for intelligent and efficient decision making in choosing your next step of research. Chapter 6 and 7 provides the audience with behavior systems and collective systems, respectively. The behavioral systems covered in this book relate to aspects of AI, robots, and some machine learning. Once behavior is understood, collective and cooperative systems are covered. Optimization techniques of particle swarms, ant colonies, and topics derived for robotics are detailed and well explained. While this is not a textbook, is does cover the fundamental concepts required to research Bio-Inspired Artificial Intelligence. For myself, the quality of this book can simply be noted by the publishers, MIT Press. Many of the best books I have encountered in my studies have been published by MIT, and here is another. Floreano and Mattiussi have not let me down in their quality, albeit I do have some complaints. First, while the topics cover a solid breadth, the depth on detailing the computation side is limited. I would like to have seen either more depth in each chapter or a broader look at each chapters algorithms, but the book falls somewhere in the middle. My current research involves Danger Signals and their relationship to preventing Epidemic Attacks, so I would have like to seen more detail about Polly Matzinger's Danger Theory rather than one short paragraph saying that it is not universally accepted. While Immunologists may debate Danger Theory, novel algorithms have been developed off of the concept of Danger Theory and deserve a place in this book. Yet to counter my own argument, the authors do finish off each chapter with a Suggested Readings section outlining a series of excellent supplement papers to the chapters topics that would eventually lead the reader to these novel topics. Overall, if you are interested in this field, buy this book. You can find it online at MIT Press for a discounted price. This book will make an excellent addition to any computer researchers library. Anthony Kulis, Department of Computer Science, Southern Illinois University


title: Research on low-speed gearless permanent magnet synchronous motor for elevator drive

abstract: Some key problems in designing low-speed gearless permanent magnet synchronous motor (PMSM) is discussed in this paper. Firstly, measures to reduce harmonic torque are proposed. Secondly, magnetic circuit structure of PMSM is surveyed to make corresponding selection. At last, finite element analysis of a low-speed gearless PMSM for elevator drive is proposed. A real PMSM product verified the design.


title: Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks

abstract: The determination of a coronary stenosis and its severity in current clinical workflow is typically accomplished manually via physician visual assessment (PVA) during invasive coronary angiography. While PVA has shown large inter-rater variability, the more reliable and accurate alternative of Quantitative Coronary Angiography (QCA) is challenging to perform in real-time due to the busy workflow in cardiac catheterization laboratories. We propose a deep learning approach based on Convolutional Neural Networks (CNN) that automatically characterizes and analyzes coronary stenoses in real-time by automating clinical tasks performed during QCA. Our deep learning methods for localization, segmentation and classification of stenosis in still-frame invasive coronary angiography (ICA) images of the right coronary artery (RCA) achieve performance of 72.7% localization accuracy, 0.704 dice coefficient and 0.825 C-statistic in each respective task. Integrated in an end-to-end approach, our model's performance shows statistically significant improvement in false discovery rate over the current standard in real-time clinical stenosis assessment, PVA. To the best of the authors' knowledge, this is the first time an automated machine learning system has been developed that can implement tasks performed in QCA, and the first time an automated machine learning system has demonstrated significant improvement over the current clinical standard for rapid RCA stenosis analysis.


title: Multilayer homogeneous dielectric filler for electromagnetic invisibility

abstract: In recent years, invisibility has become a research area of increasing interest due to the advances in material engineering. It may be possible to achieve invisibility through cloaking devices by coating the body using one or more layers of materials with the proper electromagnetic properties. By using techniques associated to plasmonic cloaking it is maybe possible to obtain also invisibility for small objects with several layers of homogeneous materials working from inside the object. We demonstrate numerically that it is, therefore, possible to achieve invisibility through an inner system based on scattering cancellation techniques.


title: Deep Textured 3D Reconstruction of Human Bodies

abstract: Recovering textured 3D models of non-rigid human body shapes is challenging due to self-occlusions caused by complex body poses and shapes, clothing obstructions, lack of surface texture, background clutter, sparse set of cameras with non-overlapping fields of view, etc. Further, a calibration-free environment adds additional complexity to both - reconstruction and texture recovery. In this paper, we propose a deep learning based solution for textured 3D reconstruction of human body shapes from a single view RGB image. This is achieved by first recovering the volumetric grid of the non-rigid human body given a single view RGB image followed by orthographic texture view synthesis using the respective depth projection of the reconstructed (volumetric) shape and input RGB image. We propose to co-learn the depth information readily available with affordable RGBD sensors (e.g., Kinect) while showing multiple views of the same object during the training phase. We show superior reconstruction performance in terms of quantitative and qualitative results, on both, publicly available datasets (by simulating the depth channel with virtual Kinect) as well as real RGBD data collected with our calibrated multi Kinect setup.


title: A 13.56 MHz high-efficiency current mode class-D amplifier using a transmission-line transformer and harmonic filter

abstract: This paper presents a high-efficiency current mode class-D (CMCD) amplifier using a Guanella's 1∶1 transmission-line transformer and a harmonic filtering technique. Low second and third harmonic levels are achieved by the harmonic filtering technique used in the output matching network. The implemented CMCD amplifier exhibited a power gain of 13.4 dB and a high PAE of 84.6 % at an output power of 44.4 dBm using a 13.56 MHz input CW signal. The second and third harmonic distortion levels were measured at −50.3 dBc and −46.5 dBc, respectively, at the same power level.


title: Recommender system for Persian blogs

abstract: With the rapid growth of the internet and the spread of the information contained therein, the volume of information available on the web is more than the ability of users to manage, capture and keep the information up to date. One solution to this problem are personalization and recommender systems. Recommender systems use the comments of the group of users so that, to help people in that group more effectively to identify their favorite items from a huge set of choices. In recent years, the web has seen very strong growth in the use of blogs. Considering the high volume of information in blogs, bloggers are in trouble to find the desired information and find blogs with similar thoughts and desires. Therefore, considering the mass of information for the blogs, a blog recommender system seems to be necessary. In this paper, by combining different methods of clustering and collaborative filtering, personalized recommender system for Persian blogs is suggested.


title: ChipLock: support for secure microarchitectures

abstract: The increasing need for security has caused system designers to consider placing some security support directly at the hardware level. In fact, this is starting to emerge as an important consideration in processor design, because the performance overhead of supporting security in hardware is usually significantly lower than a complete software solution. In this paper, we investigate integrating some security support into hardware. We show that security support can be added at some acceptable cost in area and performance. We propose a processor extension called ChipLock. It provides hardware security support for a mostly untrusted operating system to ensure the integrity and confidentiality of all computational results. ChipLock's modular design can be easily integrated into existing hardware platforms with only slight modification to the operating system. ChipLock includes a built-in hardware Key Manager that supports symmetric key assignment, and a read-only-memory, TrustROM, that executes secure hardware routines. The software required is a small trusted portion of the operating system called Trust-Code. We modeled ChipLock's architecture on a full system simulator and showed that, for SPEC2000 benchmarks, it adds about an average of 20% to the execution time, primarily from cryptographic and verification latencies. In addition, layout studies show an area cost of about 8 mm2 in 180 nm technology. This translates to an area overhead of 5% ~ 15% depending on the processor type.


title: Trajectories of resilience and dysfunction following potential trauma: A review and statistical evaluation

abstract: Abstract Given the rapid proliferation of trajectory-based approaches to study clinical consequences to stress and potentially traumatic events (PTEs), there is a need to evaluate emerging findings. This review examined convergence/divergences across 54 studies in the nature and prevalence of response trajectories, and determined potential sources of bias to improve future research. Of the 67 cases that emerged from the 54 studies, the most consistently observed trajectories following PTEs were resilience (observed in: n = 63 cases), recovery ( n = 49), chronic ( n = 47), and delayed onset ( n = 22). The resilience trajectory was the modal response across studies (average of 65.7% across populations, 95% CI [0.616, 0.698]), followed in prevalence by recovery (20.8% [0.162, 0.258]), chronicity (10.6%, [0.086, 0.127]), and delayed onset (8.9% [0.053, 0.133]). Sources of heterogeneity in estimates primarily resulted from substantive population differences rather than bias, which was observed when prospective data is lacking. Overall, prototypical trajectories have been identified across independent studies in relatively consistent proportions, with resilience being the modal response to adversity. Thus, trajectory models robustly identify clinically relevant patterns of response to potential trauma, and are important for studying determinants, consequences, and modifiers of course following potential trauma.


title: Happiness Runs in a Circular Motion: Evidence for a Positive Feedback Loop between Prosocial Spending and Happiness

abstract: We examine whether a positive feedback loop exists between spending money on others (i.e. prosocial spending) and happiness. Participants recalled a previous purchase made for either themselves or someone else and then reported their happiness. Afterward, participants chose whether to spend a monetary windfall on themselves or someone else. Participants assigned to recall a purchase made for someone else reported feeling significantly happier immediately after this recollection; most importantly, the happier participants felt, the more likely they were to choose to spend a windfall on someone else in the near future. Thus, by providing initial evidence for a positive feedback loop between prosocial spending and well-being, these data offer one potential path to sustainable happiness: prosocial spending increases happiness which in turn encourages prosocial spending.


title: Real time embedded system for accident prevention

abstract: This paper presents autonomous accident prevention with security enabling techniques, speed control and accident detection system. The main objective is to design an Atmega328P controller to monitor the zones, which can run on an embedded system and to automatically locate the site of accident and alert concerned people. It should be done automatically as the person involved in the accident may not be in a circumstance to send the information [7]. The proposed system is composed of two separate design units: transmitter unit and receiver unit. Just before the vehicle is in the transmitter zone, the vehicle speed is controlled by receiving the signal from the RF transmitter. For this, RF transmitter can be kept at a few meters before the zone. Security system includes alcohol sensor, eye sensor and smoke sensor. Accident detection system comprises of GPS and GSM in cell phones [13]. As collision occurs, piezoelectric sensor will detect the signal and sends it to ATmega328P microcontroller [6]. Then, the GPS available in the smart phone will start communicate with the satellite and get the latitude and longitude values and name of place of accident will be send to the previously set phone numbers of relatives, ambulance services etc [5].


title: Community-based question answering via heterogeneous social network learning

abstract: Community-based question answering (cQA) sites have accumulated vast amount of questions and corresponding crowdsourced answers over time. How to efficiently share the underlying information and knowledge from reliable (usually highly-reputable) answerers has become an increasingly popular research topic. A major challenge in cQA tasks is the accurate matching of high-quality answers w.r.t given questions. Many of traditional approaches likely recommend corresponding answers merely depending on the content similarity between questions and answers, therefore suffer from the sparsity bottleneck of cQA data. In this paper, we propose a novel framework which encodes not only the contents of question-answer(Q-A) but also the social interaction cues in the community to boost the cQA tasks. More specifically, our framework collaboratively utilizes the rich interaction among questions, answers and answerers to learn the relative quality rank of different answers w.r.t a same question. Moreover, the information in heterogeneous social networks is comprehensively employed to enhance the quality of question-answering (QA) matching by our deep random walk learning framework. Extensive experiments on a large-scale dataset from a real world cQA site show that leveraging the heterogeneous social information indeed achieves better performance than other state-of-the-art cQA methods.


title: On the Fractal Dimension of Isosurfaces

abstract: A (3D) scalar grid is a regular n1 x n2 x n3 grid of vertices where each vertex v is associated with some scalar value sv. Applying trilinear interpolation, the scalar grid determines a scalar function g where g(v) = sv for each grid vertex v. An isosurface with isovalue σ is a triangular mesh which approximates the level set g(-1)(σ). The fractal dimension of an isosurface represents the growth ;in the isosurface as the number of grid cubes increases. We define and discuss the fractal isosurface dimension. Plotting the fractal ;dimension as a function of the isovalues in a data set provides information about the isosurfaces determined by the data set. We present statistics on the average fractal dimension of 60 publicly available benchmark data sets. We also show the fractal dimension is highly correlated with topological noise in the benchmark data sets, measuring the topological noise by the number of connected components in the isosurface. Lastly, we present a formula predicting the fractal dimension as a function of noise and validate the formula with experimental results.


title: Learning to Detect Video Saliency With HEVC Features

abstract: Saliency detection has been widely studied to predict human fixations, with various applications in computer vision and image processing. For saliency detection, we argue in this paper that the state-of-the-art High Efficiency Video Coding (HEVC) standard can be used to generate the useful features in compressed domain. Therefore, this paper proposes to learn the video saliency model, with regard to HEVC features. First, we establish an eye tracking database for video saliency detection, which can be downloaded from https://github.com/remega/video_database . Through the statistical analysis on our eye tracking database, we find out that human fixations tend to fall into the regions with large-valued HEVC features on splitting depth, bit allocation, and motion vector (MV). In addition, three observations are obtained with the further analysis on our eye tracking database. Accordingly, several features in HEVC domain are proposed on the basis of splitting depth, bit allocation, and MV. Next, a kind of support vector machine is learned to integrate those HEVC features together, for video saliency detection. Since almost all video data are stored in the compressed form, our method is able to avoid both the computational cost on decoding and the storage cost on raw data. More importantly, experimental results show that the proposed method is superior to other state-of-the-art saliency detection methods, either in compressed or uncompressed domain.


title: Even Good Bots Fight: The Case of Wikipedia

abstract: In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other’s edits over the period 2001–2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these sterile “fights” may sometimes continue for years. Unlike humans on Wikipedia, bots’ interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively “dumb” bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles.


Elastic Actuation : Potential and Pitfalls ∗


title: An improved collision avoidance scheme using Artificial Potential Field with fuzzy logic

abstract: The Artificial Potential Field (APF), reputed for being one of the most prominent algorithm for traversing a path in the shortest distance possible, has a critical disability when confronted by obstacles classified by the state of being under local minima. The said state occurs when the APF algorithm yields an appreciable attractive force between the immediate position of the robot and the goal distance while, at the same time, yielding a considerable repulsive force due to the presence of obstacles that prevents it from determining its next position. The solution manifested by the researchers utilizes another reputed algorithm known as the Fuzzy Logic Inference (FLI) System by using membership functions that takes input from ultrasonic distance sensors and outputs left and right motor speed that implicitly controls the robot turn as well. Membership functions have a given pre-determined range of values that control the state of an input or an output. The fuzzy rule base, a linguistic set of IF-THEN statements, used in this FLI system follows a wall-following robot behaviour accessed if and only if the robot determines that it is under the state of local minima. Simulation and experiments of their algorithm shows good performance and ability to overcome the local minimum problem associated with potential field methods.


title: Mobile-Edge Computing Come Home Connecting things in future smart homes using LTE device-to-device communications

abstract: Future 5G cellular networks are expected to play a major role in supporting the Internet of Things (IoT) due to their ubiquitous coverage, plug-and-play configuration, and embedded security. Besides connectivity, however, the IoT will need computation and storage in proximity of sensors and actuators to support timecritical and opportunistic applications. Mobile-edge computing (MEC) is currently under standardization as a novel paradigm expected to enrich future broadband communication networks [1], [2]. With MEC, traditional networks will be empowered by placing cloud-computing-like capabilities within the radio access network, in an MEC server located in close proximity to end users. Such distributed computing and storage infrastructure will enable the deployment of applications and services at the edge of the network, allowing operators to offer a virtualized environment to enterprise customers and industries to implement applications and services close to end users.


title: Broker architecture for collaborative UAVs cloud computing

abstract: Unmanned Aerial Vehicles (UAVs) are used, in addition to the military, in various civilian applications. Some UAV applications may involve multiple UAVs working and collaborating together to achieve a common goal. Developing such applications is time and effort consuming due to the heterogeneity of UAVs and the complexity of the operations involved. These UAVs are usually monitored and controlled using peer-to-peer radio frequency communications. This requires either direct links between the UAVs and the ground station, or through multiple hops between them. This approach suffers from many limitations and it forces the user(s) to be at the mission location. In addition, it restricts the mission's area within the range of UAV communication. Overcome this issue, we propose a platform architecture that integrates the UAVs with the cloud computing paradigm, so that UAVs become as servers and part of the cloud. We modeled the UAVs in a Resources-Oriented Architecture (ROA); they provide their resources and services through the uniform interface RESTful HTTP. For distributed UAVs, we propose a broker architecture to separate the requester side from the provider side. The main purpose of this broker is to assign the requested tasks to the suitable available UAVs. The separation of responsibilities facilitates easier development of UAV applications on top of the platform.


A SURVEY OF UNDERWATER VEHICLE NAVIGATION: RECENT ADVANCES AND NEW CHALLENGES


title: Robust Traffic Sign Recognition Based on Color Global and Local Oriented Edge Magnitude Patterns

abstract: Most of the existing traffic sign recognition (TSR) systems make use of the inner region of the signs or the local features such as Haar, histograms of oriented gradients (HOG), and scale-invariant feature transform for recognition, whereas these features are still limited to deal with the rotation, illumination, and scale variations situations. A good feature of a traffic sign is desired to be discriminative and robust. In this paper, a novel Color Global and Local Oriented Edge Magnitude Pattern (Color Global LOEMP) is proposed. The Color Global LOEMP is a framework that is able to effectively combine color, global spatial structure, global direction structure, and local shape information and balance the two concerns of distinctiveness and robustness. The contributions of this paper are as follows: 1) color angular patterns are proposed to provide the color distinguishing information; 2) a context frame is established to provide global spatial information, due to the fact that the context frame is established by the shape of the traffic sign, thus allowing the cells to be aligned well with the inside part of the traffic sign even when rotation and scale variations occur; and 3) a LOEMP is proposed to represent each cell. In each cell, the distribution of the orientation patterns is described by the HOG feature, and then, each direction of HOG is represented in detail by the occurrence of local binary pattern histogram in this direction. Experiments are performed to validate the effectiveness of the proposed approach with TSR systems, and the experimental results are satisfying, even for images containing traffic signs that have been rotated, damaged, altered in color, or undergone affine transformations or images that were photographed under different weather or illumination conditions.


title: Does self-confidence link to motivation? A study in field hockey athletes

abstract: Sari, I., Ekici, S., Soyer, F., & Eskiler, E. (2015). Does Self-confidence Link to Motivation? A Study in Field Hockey Athletes. J. Hum. Sport Exerc., 10(1), pp.24-35. Previous research indicated some evidence of a positive relationship between motivation and self-efficacy beliefs/perceived competence/self-perceptions. Therefore, the relationship between self-confidence and motivation was investigated in sport context in this study. Participants of this study were 111 field hockey athletes. Sport Motivation Scale (Pelletier et al., 1995) and Self-confidence Scales (Tokinan, 2008) were used for data collection. SPSS.17 package program was used to analyse the data. Descriptive statistics techniques, t-test and Pearson’s correlation analysis were used. According to the results, self-confidence was found to be positively and significantly correlating with intrinsic motivation to know and to accomplish things, intrinsic motivation to experience stimulation, external regulation, identification, introjection, intrinsic motivation and extrinsic motivation (p<0,05). Self-confidence appeared to be negatively and significantly correlating with amotivation (p<0,05). In the light of the result it could be suggested that when athletes perceive high self- confidence and efficient self-perception and trust their abilities, their motivation to practice their sports enhances. Results were discussed in line with the relevant literature and some applications were proposed. Key words: SELFCONFIDENCE, MOTIVATION, ATHLETES, FIELD HOCKEY.


title: Semantic Representation and Recognition of Continued and Recursive Human Activities

abstract: This paper describes a methodology for automated recognition of complex human activities. The paper proposes a general framework which reliably recognizes high-level human actions and human-human interactions. Our approach is a description-based approach, which enables a user to encode the structure of a high-level human activity as a formal representation. Recognition of human activities is done by semantically matching constructed representations with actual observations. The methodology uses a context-free grammar (CFG) based representation scheme as a formal syntax for representing composite activities. Our CFG-based representation enables us to define complex human activities based on simpler activities or movements. Our system takes advantage of both statistical recognition techniques from computer vision and knowledge representation concepts from traditional artificial intelligence. In the low-level of the system, image sequences are processed to extract poses and gestures. Based on the recognition of gestures, the high-level of the system hierarchically recognizes composite actions and interactions occurring in a sequence of image frames. The concept of hallucinations and a probabilistic semantic-level recognition algorithm is introduced to cope with imperfect lower-layers. As a result, the system recognizes human activities including `fighting' and `assault', which are high-level activities that previous systems had difficulties. The experimental results show that our system reliably recognizes sequences of complex human activities with a high recognition rate.


title: Sustainable development of smart cities: a systematic review of the literature

abstract: This paper aims to analyse scientific studies focusing on both environmental sustainability and smart city concepts to understand the relationship between these two. ::: ::: In order to do so the study identifies information about researchers, models, frameworks and tools focused on the chosen themes. This research uses a qualitative methodology, through a systematic review of the literature, which examines the terms, ‘smart city’ and ‘sustainability’, aimed at sustainable development of smart cities. ::: ::: For this, three databases were used: Scopus, Science Direct, and Emerald Insight. This paper provides detailed information on the most recent scientific articles focusing on smart cities and sustainability issues. The paper can serve as a basis for researchers seeking background information for further investigations. ::: ::: The findings provide invaluable insights for scholars researching on the subject, and public managers considering applying those into practice in their cities.


title: Kinematics and design of a portable and wearable exoskeleton for hand rehabilitation

abstract: We present the kinematic design and actuation mechanics of a wearable exoskeleton for hand rehabilitation of post-stroke. Our design method is focused on achieving maximum safety, comfort and reliability in the interaction, and allowing different users to wear the device with no manual regulations. In particular, we propose a kinematic and actuation solution for the index finger flexion/extension, which leaves full movement freedom on the abduction-adduction plane. This paper presents a detailed kineto-static analysis of the system and a first prototype of the device. I. INTRODUCTION


Efficient Organized Point Cloud Segmentation with Connected Components


title: SHED: shape edit distance for fine-grained shape similarity

abstract: Computing similarities or distances between 3D shapes is a crucial building block for numerous tasks, including shape retrieval, exploration and classification. Current state-of-the-art distance measures mostly consider the overall appearance of the shapes and are less sensitive to fine changes in shape structure or geometry. We present shape edit distance (SHED) that measures the amount of effort needed to transform one shape into the other, in terms of re-arranging the parts of one shape to match the parts of the other shape, as well as possibly adding and removing parts. The shape edit distance takes into account both the similarity of the overall shape structure and the similarity of individual parts of the shapes. We show that SHED is favorable to state-of-the-art distance measures in a variety of applications and datasets, and is especially successful in scenarios where detecting fine details of the shapes is important, such as shape retrieval and exploration.


title: Time delay estimation in room acoustic environments: an overview

abstract: Time delay estimation has been a research topic of significant practical importance in many fields (radar, sonar, seismology, geophysics, ultrasonics, hands-free communications, etc.). It is a first stage that feeds into subsequent processing blocks for identifying, localizing, and tracking radiating sources. This area has made remarkable advances in the past few decades, and is continuing to progress, with an aim to create processors that are tolerant to both noise and reverberation. This paper presents a systematic overview of the state-of-the-art of time-delay-estimation algorithms ranging from the simple cross-correlation method to the advanced blind channel identification based techniques. We discuss the pros and cons of each individual algorithm, and outline their inherent relationships. We also provide experimental results to illustrate their performance differences in room acoustic environments where reverberation and noise are commonly encountered.


title: gSketch: On Query Estimation in Graph Streams

abstract: Many dynamic applications are built upon large network infrastructures, such as social networks, communication networks, biological networks and the Web. Such applications create data that can be naturally modeled as graph streams, in which edges of the underlying graph are received and updated sequentially in a form of a stream. It is often necessary and important to summarize the behavior of graph streams in order to enable effective query processing. However, the sheer size and dynamic nature of graph streams present an enormous challenge to existing graph management techniques. In this paper, we propose a new graph sketch method, gSketch, which combines well studied synopses for traditional data streams with a sketch partitioning technique, to estimate and optimize the responses to basic queries on graph streams. We consider two different scenarios for query estimation: (1) A graph stream sample is available; (2) Both a graph stream sample and a query workload sample are available. Algorithms for different scenarios are designed respectively by partitioning a global sketch to a group of localized sketches in order to optimize the query estimation accuracy. We perform extensive experimental studies on both real and synthetic data sets and demonstrate the power and robustness of gSketch in comparison with the state-of-the-art global sketch method.


title: An untethered jumping soft robot

abstract: Locomoting soft robots typically walk or crawl slowly relative to their rigid counterparts. In order to execute agile behaviors such as jumping, rapid actuation modes are required. Here we present an untethered soft-bodied robot that uses a combination of pneumatic and explosive actuators to execute directional jumping maneuvers. This robot can autonomously jump up to 0.6 meters laterally with an apex of up to 0.6 meters (7.5 times it's body height) and can achieve targeted jumping onto an object. The robot is able to execute these directed jumps while carrying the required fuel, pneumatics, control electronics, and battery. We also present a thermodynamic model for the combustion of butane used to power jumping, and calculate the theoretical maximum work output for the design. From experimental results, we find the mechanical efficiency of this prototype to be 0.8%.


title: The cognitive structure of Library and Information Science: Analysis of article title words

abstract: This study comprises a suite of analyses of words in article titles in order to reveal the cognitive structure of Library and Information Science (LIS). The use of title words to elucidate the cognitive structure of LIS has been relatively neglected. The present study addresses this gap by performing (a) co-word analysis and hierarchical clustering, (b) multidimensional scaling, and (c) determination of trends in usage of terms. The study is based on 10,344 articles published between 1988 and 2007 in 16 LIS journals. Methodologically, novel aspects of this study are: (a) its large scale, (b) removal of non-specific title words based on the “word concentration” measure (c) identification of the most frequent terms that include both single words and phrases, and (d) presentation of the relative frequencies of terms using “heatmaps”. Conceptually, our analysis reveals that LIS consists of three main branches: the traditionally recognized library-related and information-related branches, plus an equally distinct bibliometrics/scientometrics branch. The three branches focus on: libraries, information, and science, respectively. In addition, our study identifies substructures within each branch. We also tentatively identify “information seeking behavior” as a branch that is establishing itself separate from the three main branches. Furthermore, we find that cognitive concepts in LIS evolve continuously, with no stasis since 1992. The most rapid development occurred between 1998 and 2001, influenced by the increased focus on the Internet. The change in the cognitive landscape is found to be driven by the emergence of new information technologies, and the retirement of old ones. © 2011 Wiley Periodicals, Inc.


title: Ambient RF Energy Harvesting in Urban and Semi-Urban Environments

abstract: RF harvesting circuits have been demonstrated for more than 50 years, but only a few have been able to harvest energy from freely available ambient (i.e., non-dedicated) RF sources. In this paper, our objectives were to realize harvester operation at typical ambient RF power levels found within urban and semi-urban environments. To explore the potential for ambient RF energy harvesting, a city-wide RF spectral survey was undertaken from outside all of the 270 London Underground stations at street level. Using the results from this survey, four harvesters (comprising antenna, impedance-matching network, rectifier, maximum power point tracking interface, and storage element) were designed to cover four frequency bands from the largest RF contributors (DTV, GSM900, GSM1800, and 3G) within the ultrahigh frequency (0.3-3 GHz) part of the frequency spectrum. Prototypes were designed and fabricated for each band. The overall end-to-end efficiency of the prototypes using realistic input RF power sources is measured; with our first GSM900 prototype giving an efficiency of 40%. Approximately half of the London Underground stations were found to be suitable locations for harvesting ambient RF energy using our four prototypes. Furthermore, multiband array architectures were designed and fabricated to provide a broader freedom of operation. Finally, an output dc power density comparison was made between all the ambient RF energy harvesters, as well as alternative energy harvesting technologies, and for the first time, it is shown that ambient RF harvesting can be competitive with the other technologies.


title: Seeing Small Faces from Robust Anchor's Perspective

abstract: This paper introduces a novel anchor design to support anchor-based face detection for superior scale-invariant performance, especially on tiny faces. To achieve this, we explicitly address the problem that anchor-based detectors drop performance drastically on faces with tiny sizes, e.g. less than 16x16 pixels. In this paper, we investigate why this is the case. We discover that current anchor design cannot guarantee high overlaps between tiny faces and anchor boxes, which increases the difficulty of training. The new Expected Max Overlapping (EMO) score is proposed which can theoretically explain the low overlapping issue and inspire several effective strategies of new anchor design leading to higher face overlaps, including anchor stride reduction with new network architectures, extra shifted anchors, and stochastic face shifting. Comprehensive experiments show that our proposed method significantly outperforms the baseline anchor-based detector, while consistently achieving state-of-the-art results on challenging face detection datasets with competitive runtime speed.


title: An Ontology for Developing e-Business Models

abstract: In this paper we demonstrate why executives and academics should consider thinking about e-business models. We show that the business model concept is an interesting tool for understanding, designing, sharing, measuring, changing and even simulating businesses. Based on an extensive review of e-business and business model literature we develop an e-business model ontology. This ontology defines the concepts in e-business models and the relationships between them and shall be the foundation for a variety of management tools that facilitate business decisions. Our e-business model ontology outlines what value a company offers to which customer segments. It describes the architecture of the firm and its network of partners for creating, marketing and delivering value and relationship capital, in order to generate profitable and sustainable revenue streams.


title: Analysis of fMRI Time-Series Revisited

abstract: Abstract This paper presents a general approach to the analysis of functional MRI time-series from one or more subjects. The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing. This extension uses the effective degrees of freedom associated with the error term. The effective degrees of freedom are a simple function of the number of scans and the temporal autocorrelation function. A specific form for the latter can be assumed if the data are smoothed, in time, to accentuate hemodynamic responses with a neural basis. This assumption leads to an expedient implementation of a flexible statistical framework. The importance of this small extension is that, in contradistinction to our previous approach, any parametric statistical analysis can be implemented. We demonstrate this point using a multiple regression analysis that tests for effects of interest (activations due to word generation), while taking explicit account of some obvious confounds.


title: A survey of authentication methods in mobile cloud computing

abstract: Mobile Cloud Computing (MCC) is an emerging technology which attempts to combine the storage and processing resources of cloud environment with the dynamicity and accessibility of mobile devices. Security, particularly authentication, is fast evolving as a focal area in mobile cloud computing research. This paper comprehensively surveys the various authentication mechanisms proposed so far for mobile cloud computing. We propose a novel classification system for existing authentication methods in MCC. Further, the pros and cons of the various methods are discussed. We present a comparative analysis and recommends future research in improving the surveyed implicit authentication by establishing cryptographic security of stored usage context and actions.


title: Forensic analysis of ordered data structures on the example of JPEG files

abstract: JPEG file format standards define only a limited number of mandatory data structures and leave room for interpretation. Differences between implementations employed in digital cameras, image processing software, and software to edit metadata provide valuable clues for basic authentication of digital images. We show that there exists a realistic chance to fool state-of-the-art image file forensic methods using available software tools and introduce the analysis of ordered data structures on the example of JPEG file formats and the EXIF metadata format as countermeasure. The proposed analysis approach enables basic investigations of image authenticity and documents a much better trustworthiness of EXIF metadata than commonly accepted. Manipulations created with the renowned metadata editor ExifTool and various image processing software can be reliably detected. Analysing the sequence of elements in complex data structures is not limited to JPEG files and might be a general principle applicable to different multimedia formats.


title: Fine-Grained Sentiment Analysis for Movie Reviews in Bulgarian

abstract: We present a system for fine-grained sentiment analysis in Bulgarian movie reviews. As this is pioneering work for this combination of language and sentiment granularity, we create suitable, freely available resources: a dataset of movie reviews with fine-grained scores, and a sentiment polarity lexicon. We further compare experimentally the performance of classification, regression and ordinal regression in a 3-way, 5-way and 11-way classification setups, using as features not only the text from the reviews, but also contextual information in the form of metadata, e.g., movie length, director, actors, genre, country, and various scores: IMDB, Cinexio, and user-average. The results show that adding contextual information yields strong performance gains.


title: Real-Time and Noncontact Impulse Radio Radar System for $\mu \text{m}$ Movement Accuracy and Vital-Sign Monitoring Applications

abstract: In this paper, a real-time, contactless pulse-based ultra-wideband (UWB) radar sensor was used to precisely detect the movement of the robotic arm, heartbeats, and respiration activities. To increase the object tracking resolution, an algorithm using adaptive and digitalized post-calibration techniques were proposed and tested. Both the radiated and reflected analog pulses were converted to digital signals by a low-noise comparator and its buffer for the time interval and distance detection. Moreover, the dynamic and calibrated tracking mechanism was incorporated into the algorithm for improving tracking accuracy and to continuously localize an object’s vibration. The measured performance revealed that the proposed method successfully achieved $\mu \text{m}$ movement precision, thus providing its excellent tracking ability. We performed three different experimental tests to demonstrate the UWB radar’s viability. First, the radar was employed to detect the undesired structural vibration of a delta robotic arm, which was subjected to high acceleration and structural flexibility. The measured results indicated that the accuracy of $100~\mu \text{m}$ can be achieved at a 10 Hz vibration frequency. Next, the apnea alarm was tested, which included an embedded sensitive sensor in the smart mattress in order to execute long-term physiological monitoring. The apnea was mitigated by auto-adjusting the patient’s sleeping posture on the mattress when the UWB sensor detected the apnea condition. The final experiment using the radar sensor was to monitor human vital signs, such as heart and breathing rates, simultaneously. In comparison with a commercial electrocardiography instrument, this radar sensor measured 73.2 bpm with only 1.2% deviation. Our results shown that the contactless UWB sensor performs superbly and is well-suited for monitoring physiological parameters.


title: Goal-Driven Conceptual Blending: A Computational Approach for Creativity

abstract: Conceptual blending has been proposed as a creative cognitive process, but most theories focus on the analysis of existing blends rather than mechanisms for the efficient construction of novel blends. While conceptual blending is a powerful model for creativity, there are many challenges related to the computational application of blending. Inspired by recent theoretical research, we argue that contexts and context-induced goals provide insights into algorithm design for creative systems using conceptual blending. We present two case studies of creative systems that use goals and contexts to efficiently produce novel, creative artifacts in the domains of story generation and virtual characters engaged in pretend play respectively.


title: Implementing Semantic Document Search Using a Bounded Random Walk in a Probabilistic Graph

abstract: Given a set of documents and an input query that is expressed using natural language, the problem of document search is retrieving all relevant documents ordered by the degree of relevance. Semantic document search fetches not only documents that contain words from the input query, but also documents that are semantically relevant. For example, the query "friendly pets" will consider documents that contain the words "dog" and "cat", among others. One way to implement semantic search is to use a probabilistic graph in which the input query is connected to the documents through paths that contain semantically similar words and phrases, where we use WordNet to initially populate the graph. Each edge in the graph is labeled with the conditional probability that the destination node is relevant given that the source node is relevant. Our semantic document search algorithm works in two phases. In the first phase, we find all documents in the graph that are close to the input query and create a bounded subgraph that includes the query, the found documents, and the paths that connect them. In the second phase, we simulate multiple random walks. Each random walk starts at the input query and continues until a document is reached, a jump outside the bounding subgraph is made, or the number of allowed jumps is exhausted. This allows us to rank the documents based on the number of random walks that terminated in them. We experimentally validated the algorithm on the Cranfield benchmark that contains 1400 documents and 225 natural language queries. We show that we achieve higher value for the mean average precision (MAP) measure than a keywords-based search algorithm and a previously published algorithm that relies on a variation of the probabilistic graph.


title: Motivational Predictors of Weight Loss and Weight-Loss Maintenance

abstract: Self-determination theory proposes that behavior change will occur and persist if it is autonomously motivated. Autonomous motivation for a behavior is theorized to be a function both of individual differences in the autonomy orientation from the General Causality Orientations Scale and of the degree of autonomy supportiveness of relevant social contexts. We tested the theory with 128 patients in a 6-month, very-low-calorie weight-loss program with a 23-month follow-up. Analyses confirmed the predictions that (a) participants whose motivation for weight loss was more autonomous would attend the program more regularly, lose more weight during the program, and evidence greater maintained weight loss at follow-up, and (b) participants' autonomous motivation for weight loss would be predicted both by their autonomy orientation and by the perceived autonomy supportiveness of the interpersonal climate created by the health-care staff.


title: Applications of Open Split Ring Resonators and Open Complementary Split Ring Resonators to the Synthesis of Artificial Transmission Lines and Microwave Passive Components

abstract: This paper is focused on the application of open split ring resonators (OSRRs) and their dual counterparts, open complementary split ring resonators (OCSRRs), to the synthesis of composite right/left-handed transmission lines, that is, artificial lines exhibiting backward wave propagation at low frequencies and forward wave propagation at high frequencies. Due to the small dimensions of these resonators, the resulting lines are very compact. Several artificial lines, with different electrical characteristics and topologies, are reported as illustrative examples. It is shown that these artificial lines can be applied to the synthesis of dual-band components and bandpass filters, and two prototype device examples are designed and fabricated in coplanar waveguide technology: a dual-band impedance inverter applied to a dual-band power divider, and an order-3 wide-band bandpass filter. Finally, it is also demonstrated that OSRRs and OCSRRs can be combined for the synthesis of band pass filters in microstrip technology. Since OSRRs and OCSRRs are described by means of series and shunt resonant tanks, respectively, and they are electrically small, their potential to the design of semi lumped planar microwave devices is very high.


title: Artificial neural network method for solving the Navier–Stokes equations

abstract: In this paper, a new method based on neural network is developed for obtaining the solution of the Navier---Stokes equations in an analytical function form. The solution procedure is based upon forming a trial solution consisting of two parts. The first part directly satisfies the boundary conditions and therefore, contains no adjustable parameters. The second part is constructed such that the governing equation is satisfied inside the solution domain, while the boundary conditions remain untouched. This part involves a feed-forward neural network, containing adjustable parameters (the weights), which must be determined such that the resulting approximate error function is minimized. The details of the method are discussed, and the capabilities of the method are illustrated by solving Navier---Stokes problem with different boundary conditions. The performance of the method and the accuracy of the results are evaluated by comparing with the available numerical and analytical solutions.


title: Voyager 2: Augmenting Visual Analysis with Partial View Specifications

abstract: Visual data analysis involves both open-ended and focused exploration. Manual chart specification tools support question answering, but are often tedious for early-stage exploration where systematic data coverage is needed. Visualization recommenders can encourage broad coverage, but irrelevant suggestions may distract users once they commit to specific questions. We present Voyager 2, a mixed-initiative system that blends manual and automated chart specification to help analysts engage in both open-ended exploration and targeted question answering. We contribute two partial specification interfaces: wildcards let users specify multiple charts in parallel, while related views suggest visualizations relevant to the currently specified chart. We present our interface design and applications of the CompassQL visualization query language to enable these interfaces. In a controlled study we find that Voyager 2 leads to increased data field coverage compared to a traditional specification tool, while still allowing analysts to flexibly drill-down and answer specific questions.


title: The genome editing revolution: A CRISPR-Cas TALE off-target story

abstract: In the last 10 years, we have witnessed a blooming of targeted genome editing systems and applications. The area was revolutionized by the discovery and characterization of the transcription activator-like effector proteins, which are easier to engineer to target new DNA sequences than the previously available DNA binding templates, zinc fingers and meganucleases. Recently, the area experimented a quantum leap because of the introduction of the clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein (Cas) system (clustered regularly interspaced short palindromic sequence). This ribonucleoprotein complex protects bacteria from invading DNAs, and it was adapted to be used in genome editing. The CRISPR ribonucleic acid (RNA) molecule guides to the specific DNA site the Cas9 nuclease to cleave the DNA target. Two years and more than 1000 publications later, the CRISPR-Cas system has become the main tool for genome editing in many laboratories. Currently the targeted genome editing technology has been used in many fields and may be a possible approach for human gene therapy. Furthermore, it can also be used to modifying the genomes of model organisms for studying human pathways or to improve key organisms for biotechnological applications, such as plants, livestock genome as well as yeasts and bacterial strains.


title: On the Strategy and Behavior of Bitcoin Mining with N-attackers.

abstract: Selfish mining is a well-known mining attack strategy discovered by Eyal and Sirer in 2014. After that, the attackers' strategy has been further discussed by many other works, which analyze the strategy and behavior of a single attacker. The extension of the strategy research is greatly restricted by the assumption that there is only one attacker in the blockchain network, since, in many cases, a proof of work blockchain has multiple attackers. The attackers can be independent of others instead of sharing information and attacking the blockchain as a whole. In this paper, we will establish a new model to analyze the miners' behavior in a proof of work blockchain with multiple attackers. Based on our model, we extend the attackers' strategy by proposing a new strategy set publish-n. Meanwhile, we will also review other attacking strategies such as selfish mining and stubborn mining in our model to explore whether these strategies work or not when there are multiple attackers. The performances of different strategies are compared using relative stale block rate of the attackers. In a proof of work blockchain model with two attackers, strategy publish-n can beat selfish mining by up to 26.3%.


title: Full Attitude Control of a VTOL tailsitter UAV

abstract: This paper addresses the challenges of the design, development and control of a new convertible VTOL tailsitter unmanned aerial vehicle that combines the advantages of both fixed wing and rotary wing systems. Wind tunnel measurements are used to get an understanding of the control allocation and to model the static forces and moments acting on the system. Based on the derived model, a novel controller that operates in SO(3) and handles the dynamics of the vehicle at any attitude configuration, including the rotorcraft and fixed-wing regimes as well as their transitions, is presented. This unified controller allows the autonomous transition of the system without discontinuities of switching, as well as its overall high performance flight control. The capabilities and flying qualities of the platform and the controller are demonstrated and evaluated by means of extensive experimental studies.


title: Deep Learning Logo Detection with Data Expansion by Synthesising Context

abstract: Logo detection in unconstrained images is challenging, particularly when only very sparse labelled training images are accessible due to high labelling costs. In this work, we describe a model training image synthesising method capable of improving significantly logo detection performance when only a handful of (e.g., 10) labelled training images captured in realistic context are available, avoiding extensive manual labelling costs. Specifically, we design a novel algorithm for generating Synthetic Context Logo (SCL) training images to increase model robustness against unknown background clutters, resulting in superior logo detection performance. For benchmarking model performance, we introduce a new logo detection dataset TopLogo-10 collected from top 10 most popular clothing/wearable brandname logos captured in rich visual context. Extensive comparisons show the advantages of our proposed SCL model over the state-of-the-art alternatives for logo detection using two real-world logo benchmark datasets: FlickrLogo-32 and our new TopLogo-10.


title: Predictive Analytics in Information Systems Research

abstract: textabstractThis research essay highlights the need to integrate predictive analytics into information systems research and shows several concrete ways in which this goal can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. Extant IS literature relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models, and predictive power is assumed to follow automatically from the explanatory model. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show that predictive analytics and explanatory statistical modeling are fundamentally disparate, we show that they are different in each step of the modeling process. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure predictive model is best in terms of predictive power. We convert a well-known explanatory paper on TAM to a predictive context to illustrate these differences and show how predictive analytics can add theoretical and practical value to IS research.


title: Creating Healthy Food and Eating Environments: Policy and Environmental Approaches

abstract: Food and eating environments likely contribute to the increasing epidemic of obesity and chronic diseases, over and above individual factors such as knowledge, skills, and motivation. Environmental and policy interventions may be among the most effective strategies for creating population-wide improvements in eating. This review describes an ecological framework for conceptualizing the many food environments and conditions that influence food choices, with an emphasis on current knowledge regarding the home, child care, school, work site, retail store, and restaurant settings. Important issues of disparities in food access for low-income and minority groups and macrolevel issues are also reviewed. The status of measurement and evaluation of nutrition environments and the need for action to improve health are highlighted.


title: Predicting students performance in final examination using linear regression and multilayer perceptron

abstract: Currently, many educational institutions are highly oriented to improve the quality of education and students? learning achievement-examination result. To fulfil such intention, predicting students? performance by analyzing their learning behavior is one of the best way can be taken into account. Once the performance was predicted, it will be easy for teachers, school authority or other related parties to determine the appropriate policies on the issue. Relatedly, this paper aimed to provide the prediction of students? performance in final examination by applying linear regression and multilayer perceptron in WEKA- in terms of accuracy, performance and error rate- to compare their feasibility. The basis of data was derived from extraction and analysis of e-learning logged-post in discussion forum and attendance. Based on the result, it has been concluded that multilayer perceptron provides better prediction results of final examination than linear regression.


title: Indoor Visible Light Positioning with Angle Diversity Transmitter

abstract: A new concept for indoor positioning using visible light communication (VLC) is presented in this paper. The feasibility of uplink localization is verified so that the proposed system can work in conjunction with downlink positioning systems to improve overall localization accuracy. We propose to use an angle diversity transmitter (ADT) associated with accelerometers for uplink three-dimensional localization. Unlike other techniques using VLC for positioning, the proposed system can achieve indoor localization without making assumptions about the height or orientation angle of a mobile user. The received signal strength (RSS) method is used to minimize implementation costs while achieving satisfactory positioning accuracy. Simulation results show that an average localization error of less than $0.15$ $\rm m$ can be achieved even when the receiver is tilted by $45^{\circ}$. Also, compared with a single light emitting diode (LED), the ADT is found to be more robust to accelerometer measurement errors.


title: A Deep Learning Approach for Object Recognition with NAO Soccer Robots

abstract: The use of identical robots in the RoboCup Standard Platform League (SPL) made software development the key aspect to achieve good results in competitions. In particular, the visual detection process is crucial for extracting information about the environment. In this paper, we present a novel approach for object detection and classification based on Convolutional Neural Networks (CNN). The approach is designed to be used by NAO robots and is made of two stages: image region segmentation, for reducing the search space, and Deep Learning, for validation. The proposed method can be easily extended to deal with different objects and adapted to be used in other RoboCup leagues. Quantitative experiments have been conducted on a data set of annotated images captured in real conditions from NAO robots in action. The used data set is made available for the community.


title: Multi-task low-rank affinity pursuit for image segmentation

abstract: This paper investigates how to boost region-based image segmentation by pursuing a new solution to fuse multiple types of image features. A collaborative image segmentation framework, called multi-task low-rank affinity pursuit, is presented for such a purpose. Given an image described with multiple types of features, we aim at inferring a unified affinity matrix that implicitly encodes the segmentation of the image. This is achieved by seeking the sparsity-consistent low-rank affinities from the joint decompositions of multiple feature matrices into pairs of sparse and low-rank matrices, the latter of which is expressed as the production of the image feature matrix and its corresponding image affinity matrix. The inference process is formulated as a constrained nuclear norm and l 2;1 -norm minimization problem, which is convex and can be solved efficiently with the Augmented Lagrange Multiplier method. Compared to previous methods, which are usually based on a single type of features, the proposed method seamlessly integrates multiple types of features to jointly produce the affinity matrix within a single inference step, and produces more accurate and reliable segmentation results. Experiments on the MSRC dataset and Berkeley segmentation dataset well validate the superiority of using multiple features over single feature and also the superiority of our method over conventional methods for feature fusion. Moreover, our method is shown to be very competitive while comparing to other state-of-the-art methods.


title: A 5b 800MS/s 2mW asynchronous binary-search ADC in 65nm CMOS

abstract: Digital wireless communication applications such as UWB and WPAN necessitate low-power high-speed ADCs to convert RF/IF signals into digital form for subsequent baseband processing. Considering latency and conversion speed, flash ADCs are often the most preferred option. Generally, flash ADCs suffer from high power consumption and large area overhead. On the contrary, SAR ADCs have low power dissipation and occupy a small area. However, a SAR ADC needs several comparison cycles to complete one conversion, which limits its conversion speed. The highest single-channel operation speed of previously reported SAR ADCs is 625MS/s [1]. The ADC in [1] utilizes a 2b/step structure. For non-multi-bit/step SAR ADCs, the highest reported conversion rate is 300MS/s [2]. The structure of a comparator-based binary-search ADC is between that of flash and SAR ADCs [3]. Compared to a flash ADC (high speed, high power) and a SAR ADC (low speed, low power), a binary-search ADC achieves balance between operation speed and power consumption. This paper reports a 5b asynchronous binary-search ADC with reference-range prediction. The maximum conversion speed of this ADC is 800MS/s at a cost of 2mW power consumption.


title: Variation of velocity with effective stress in chalk:null result from North Sea well data

abstract: Wireline log data and pore pressure measurements from eight wells have been analysed to investigate how velocity in the Chalk of the Central Graben, North Sea depends on porosity and vertical effective stress. The main conclusion is that velocity shows no detectable dependence on vertical effective stress when porosity and vertical effective stress are treated as independent variables. This result contrasts with the behaviour of shales, which exhibit a reduction in velocity on unloading. The significance is that velocity in the Chalk cannot be used to detect the presence of any overpressure caused by unloading. It is suggested that the absence of an observable velocity reduction in unloaded Chalk is due to cementation.