title: Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment

abstract: A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.


title: Molecular Timetrees Reveal a Cambrian Colonization of Land and a New Scenario for Ecdysozoan Evolution

abstract: Ecdysozoans have been key components of ecosystems since the early Cambrian, when trilobites and soft-bodied Burgess Shale-type ecdysozoans dominated marine animal communities. Even today, the most abundant animals on Earth are either nematode worms or plankton-forming crustaceans, whereas the most diverse are the insects. Throughout geological time, several ecdysozoan lineages independently colonized land, shaping both marine and terrestrial ecosystems and providing an adequate environment for successive animal terrestrialization. The timing of these events is largely uncertain and has been investigated only partially using molecular data. Here we present a timescale of ecdysozoan evolution based on multiple molecular data sets, the most complete set of fossil calibrations to date, and a thorough series of validation analyses. Results converge on an Ediacaran origin of all major ecdysozoan lineages (∼587-543 million years ago [mya]), followed by a fast Cambrian radiation of the pancrustaceans (∼539-511 mya), a Cambro-Ordovician colonization of land of different arthropod lineages (∼510-471 mya), and a relatively recent radiation of extant nematodes, onychophorans, and tardigrades (∼442 mya). Arthropods colonized land nearly synchronously with land plants. Further diversification within flying insects, nematodes and onychophorans might be related to the evolution of vascular plants and forests.


title: Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain

abstract: Single-cell analysis of the adult human brain is facilitated by improved methods for RNA-seq and hypersensitive-site mapping.


A Dictionary of Nonsubsective Adjectives


title: PRI: Privacy Preserving Inspection of Encrypted Network Traffic

abstract: Traffic inspection is a fundamental building block of many security solutions today. For example, to prevent the leakage or exfiltration of confidential insider information, as well as to block malicious traffic from entering the network, most enterprises today operate intrusion detection and prevention systems that inspect traffic. However, the state-of-theart inspection systems do not reflect well the interests of the different involved autonomous roles. For example, employees in an enterprise, or a company outsourcing its network management to a specialized third party, may require that their traffic remains confidential, even from the system administrator. Moreover, the rules used by the intrusion detection system, or more generally the configuration of an online or offline anomaly detection engine, may be provided by a third party, e.g., a security research firm, and can hence constitute a critical business asset which should be kept confidential. Today, it is often believed that accounting for these additional requirements is impossible, as they contradict efficiency and effectiveness. We in this paper explore a novel approach, called Privacy Preserving Inspection (PRI), which provides a solution to this problem, by preserving privacy of traffic inspection and confidentiality of inspection rules and configurations, and e.g., also supports the flexible installation of additional Data Leak Prevention (DLP) rules specific to the company.


title: Learning Deep Features for Scene Recognition using Places Database

abstract: Scene recognition is one of the hallmark tasks of computer vision, allowing definition of a context for object recognition. Whereas the tremendous recent progress in object recognition tasks is due to the availability of large datasets like ImageNet and the rise of Convolutional Neural Networks (CNNs) for learning high-level features, performance at scene recognition has not attained the same level of success. This may be because current deep features trained from ImageNet are not competitive enough for such tasks. Here, we introduce a new scene-centric database called Places with over 7 million labeled pictures of scenes. We propose new methods to compare the density and diversity of image datasets and show that Places is as dense as other scene datasets and has more diversity. Using CNN, we learn deep features for scene recognition tasks, and establish new state-of-the-art results on several scene-centric datasets. A visualization of the CNN layers' responses allows us to show differences in the internal representations of object-centric and scene-centric networks.


The Book Of Wisdom


title: Cyber-Threat Intelligence Architecture for Smart-Grid Critical Infrastructures Protection

abstract: Critical infrastructures (CIs) are becoming increasingly sophisticated with embedded cyber-physical systems (CPSs) that provide managerial automation and autonomic controls. Yet these advances expo ...


title: Performance of polar codes on wireless communication channels

abstract: Polar codes are a class of codes proposed currently, which can achieve the capacity of binary symmetric channel with low encoding and decoding algorithm. By generalizing the definition of Bhattacharyya parameter in discrete memoryless channel, we discuss the performance of polar codes on wireless communication channels in this paper. We present the special expressions of the parameters for continuous channels (Gaussian and Rayleigh fading channels), including the recursive formulas and the initial values, and we discuss the construction of polar codes for Gaussian and Rayleigh fading channels. We analyze the application of polar codes with the defined parameter over Rayleigh fading channel by transmitting image signals. The simulation results show that polar codes have better performance than that of low density parity-check codes (LDPC) codes with the same simulation condition. It is shown that polar codes will be a good candidate for wireless communication channels.


title: Extracting meronyms for a biology knowledge base using distant supervision

abstract: Knowledge of objects and their parts, meronym relations, are at the heart of many question-answering systems, but manually encoding these facts is impractical. Past researchers have tried hand-written patterns, supervised learning, and bootstrapped methods, but achieving both high precision and recall has proven elusive. This paper reports on a thorough exploration of distant supervision to learn a meronym extractor for the domain of college biology. We introduce a novel algorithm, generalizing the ``at least one'' assumption of multi-instance learning to handle the case where a fixed (but unknown) percentage of bag members are positive examples. Detailed experiments compare strategies for mention detection, negative example generation, leveraging out-of-domain meronyms, and evaluate the benefit of our multi-instance percentage model.


title: Join operations in temporal databases

abstract: Joins are arguably the most important relational operators. Poor implementations are tantamount to computing the Cartesian product of the input relations. In a temporal database, the problem is more acute for two reasons. First, conventional techniques are designed for the evaluation of joins with equality predicates rather than the inequality predicates prevalent in valid-time queries. Second, the presence of temporally varying data dramatically increases the size of a database. These factors indicate that specialized techniques are needed to efficiently evaluate temporal joins.We address this need for efficient join evaluation in temporal databases. Our purpose is twofold. We first survey all previously proposed temporal join operators. While many temporal join operators have been defined in previous work, this work has been done largely in isolation from competing proposals, with little, if any, comparison of the various operators. We then address evaluation algorithms, comparing the applicability of various algorithms to the temporal join operators and describing a performance study involving algorithms for one important operator, the temporal equijoin. Our focus, with respect to implementation, is on non-index-based join algorithms. Such algorithms do not rely on auxiliary access paths but may exploit sort orderings to achieve efficiency.


title: CheapBFT: resource-efficient byzantine fault tolerance

abstract: One of the main reasons why Byzantine fault-tolerant (BFT) systems are not widely used lies in their high resource consumption: 3f+1 replicas are necessary to tolerate only f faults. Recent works have been able to reduce the minimum number of replicas to 2f+1 by relying on a trusted subsystem that prevents a replica from making conflicting statements to other replicas without being detected. Nevertheless, having been designed with the focus on fault handling, these systems still employ a majority of replicas during normal-case operation for seemingly redundant work. Furthermore, the trusted subsystems available trade off performance for security; that is, they either achieve high throughput or they come with a small trusted computing base. This paper presents CheapBFT, a BFT system that, for the first time, tolerates that all but one of the replicas active in normal-case operation become faulty. CheapBFT runs a composite agreement protocol and exploits passive replication to save resources; in the absence of faults, it requires that only f+1 replicas actively agree on client requests and execute them. In case of suspected faulty behavior, CheapBFT triggers a transition protocol that activates f extra passive replicas and brings all non-faulty replicas into a consistent state again. This approach, for example, allows the system to safely switch to another, more resilient agreement protocol. CheapBFT relies on an FPGA-based trusted subsystem for the authentication of protocol messages that provides high performance and comprises a small trusted computing base.


title: A conditional model of deduplication for multi-type relational data

abstract: Abstract : Record deduplication is the task of merging database records that refer to the same underlying entity. In relational databases, accurate deduplication for records of one type is often dependent on the merge decisions made for records of other types. Whereas nearly all previous approaches have merged records of different types independently, this work models these inter-dependencies explicitly to collectively deduplicate records of multiple types. We construct a conditional random field model of deduplication that captures these relational dependencies, and then employ a novel relational partitioning algorithm to jointly deduplicate records. We evaluate the system on two citation matching datasets, for which we deduplicate both papers and venues. We show that by collectively deduplicating paper and venue deduplication, and up to a 20% error reduction in paper deduplication over competing methods.


Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content


title: Mobbing and Psychological Terror at Workplaces

abstract: In recent years, the existence of a significant problem in workplaces has been documented in Sweden and other countries. It involves employees "ganging up" on a target employee and subjecting him or her to psychological harassment. This "mobbing" behavior results in severe psychological and occupational consequences for the victim. This phenomenon is described, its stages and consequences analyzed. An ongoing program of research and intervention that is currently being supported by the Swedish government is then considered. Language: en


A Computer Vision System for Chess Game Tracking


title: Ternary Content Addressable Memory Types And Matchline Schemes

abstract: Ternary Content Addressable Memory (TCAM) used in many application like network routers and packet classification. To reduce the power consumption of matchlines, the matchline partitioning scheme is used. Matchline is partitioned into NOR and NAND type matchline. If NAND (NOR) type TCAM cell is used then NAND (NOR) type matchline is used. NOR type TCAM has feature of high speed and high compare power. NAND type TCAM has low speed and low compare power. The NAND and NOR type matchlines are combined to form the pai-sigma matchlines. In pai segment the NAND type cells are connected in parallel to form the NAND type matchline. In sigma segment NOR type cells are connected in series to form the NOR type matchline. When compare operation is performed all NAND type matchlines are activated because switching power is low. Based on the match result of the NAND matchlines the NOR matchlines are activated because of high speed. The matchline incurs the problem of short circuit current due to mismatch and match result of NAND and NOR matchlines. NAND type matchline exist the problem of charge sharing when the search result of the NAND line is mismatches. This proposed TCAM has less compare (search) power compared to the NAND/NOR type TCAM cell. Key Words: Content Addressable Memories (CAM), Ternary CAM (TCAM), NAND TCAM, NOR TCAM, Matchline Power (MP), Ripple Precharge (RP), Internet Protocol (IP), Classless Inter-Domain Routing (CIDR), Match Line (ML).


title: THE ROLE OF MEDIA VIOLENCE IN VIOLENT BEHAVIOR

abstract: Media violence poses a threat to public health inasmuch as it leads to an increase in real-world violence and aggression. Research shows that fictional television and film violence contribute to both a short-term and a long-term increase in aggression and violence in young viewers. Television news violence also contributes to increased violence, principally in the form of imitative suicides and acts of aggression. Video games are clearly capable of producing an increase in aggression and violence in the short term, although no long-term longitudinal studies capable of demonstrating long-term effects have been conducted. The relationship between media violence and real-world violence and aggression is moderated by the nature of the media content and characteristics of and social influences on the individual exposed to that content. Still, the average overall size of the effect is large enough to place it in the category of known threats to public health.


title: On some common compressive sensing recovery algorithms and applications - Review paper

abstract: Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well.


title: Investigation of the four-gate action in G/sup 4/-FETs

abstract: The four-gate silicon-on-insulator transistor (G/sup 4/-FET) combines MOS and JFET actions in a single transistor to control the drain current. The various operation modes of the G/sup 4/-FET are analyzed, based on the measured current-voltage, transconductance and threshold characteristics. The main parameters (threshold voltage, swing, mobility) are extracted and shown to be optimized for particular combinations of gate biasing. Numerical simulations are used to clarify the role of volume or interface conduction mechanisms. Besides excellent performance (such as subthreshold swing and transconductance) and unchallenged flexibility, the new device has the unique feature to allow independent switching by its four separate gates, which inspires many innovative applications.


title: Distinguishable Brain Activation Networks for Short- and Long-Term Motor Skill Learning

abstract: The acquisition of a new motor skill is characterized first by a short-term, fast learning stage in which performance improves rapidly, and subsequently by a long-term, slower learning stage in which additional performance gains are incremental. Previous functional imaging studies have suggested that distinct brain networks mediate these two stages of learning, but direct comparisons using the same task have not been performed. Here we used a task in which subjects learn to track a continuous 8-s sequence demanding variable isometric force development between the fingers and thumb of the dominant, right hand. Learning-associated changes in brain activation were characterized using functional MRI (fMRI) during short-term learning of a novel sequence, during short-term learning after prior, brief exposure to the sequence, and over long-term (3 wk) training in the task. Short-term learning was associated with decreases in activity in the dorsolateral prefrontal, anterior cingulate, posterior parietal, primary motor, and cerebellar cortex, and with increased activation in the right cerebellar dentate nucleus, the left putamen, and left thalamus. Prefrontal, parietal, and cerebellar cortical changes were not apparent with short-term learning after prior exposure to the sequence. With long-term learning, increases in activity were found in the left primary somatosensory and motor cortex and in the right putamen. Our observations extend previous work suggesting that distinguishable networks are recruited during the different phases of motor learning. While short-term motor skill learning seems associated primarily with activation in a cortical network specific for the learned movements, long-term learning involves increased activation of a bihemispheric cortical-subcortical network in a pattern suggesting “plastic” development of new representations for both motor output and somatosensory afferent information.


title: Miniaturized Quasi-Lumped Coupled-Line Single-Section and Multisection Directional Couplers

abstract: A class of lumped-element coupled-line directional couplers has been investigated. The paper discusses the influence of the number of coupler's subsections on the overall couplers' performance. The design procedure for calculating elements of the coupler for the chosen coupling C and the number of coupler's subsections has been proposed. The design procedure has been experimentally verified by the measurements of a single-section directional coupler. Also, an asymmetric two-section coupler not reported in the literature has been designed using a lumped-element technique, and the experimental results are shown.


title: Interdisciplinary project-based learning: technology for improving student cognition

abstract: The article studies a way of enhancing student cognition by using interdisciplinary project-based learning (IPBL) in a higher education institution. IPBL is a creative pedagogic approach allowing students of one area of specialisation to develop projects for students with different academic profiles. The application of this approach in the Ural State University of Economics resulted in a computer-assisted learning system (CALS) designed by IT students. The CALS was used in an analytical chemistry course with students majoring in Commodities Management and Expertise (‘expert’ students). To test how effective the technology was, the control and experimental groups were formed. In the control group, learning was done with traditional methods. In the experimental group, it was reinforced by IPBL. A statistical analysis of the results, with an application of Pearson χ 2 test, showed that the cognitive levels in both IT and ‘expert’ experimental groups improved as compared with the control groups. The findings demonstrated that IPBL can significantly enhance learning. It can be implemented in any institution of higher or secondary education that promotes learning, including the CALS development and its use for solving problems in different subject areas. Keywords: higher education; learning technology; computer-assisted learning; cognitive level (Published: 17 November 2015) Citation: Research in Learning Technology 2015, 23 : 27577 - http://dx.doi.org/10.3402/rlt.v23.27577


title: Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN

abstract: Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of hyperbolic tangent and the sigmoid action functions results in gradient decay over layers. Consequently, construction of an efficiently trainable deep network is challenging. In addition, all the neurons in an RNN layer are entangled together and their behaviour is hard to interpret. To address these problems, a new type of RNN, referred to as independently recurrent neural network (IndRNN), is proposed in this paper, where neurons in the same layer are independent of each other and they are connected across layers. We have shown that an IndRNN can be easily regulated to prevent the gradient exploding and vanishing problems while allowing the network to learn long-term dependencies. Moreover, an IndRNN can work with non-saturated activation functions such as relu (rectified linear unit) and be still trained robustly. Multiple IndRNNs can be stacked to construct a network that is deeper than the existing RNNs. Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly. Better performances have been achieved on various tasks by using IndRNNs compared with the traditional RNN and LSTM.


title: Omnidirectional Circularly Polarized Antenna Combining Monopole and Loop Radiators

abstract: In this letter, a new omnidirectional circularly polarized (CP) antenna is proposed. By adopting a coaxial probe, in which a metal sleeve acts as a monopole, and printed spoke-like metal strips fabricated on two substrates, which act as a loop, this antenna exhibits an omnidirectional circularly polarized radiation in the azimuthal plane. Theory, design, simulation, and experiment of the antenna are presented. Measurement results show that the proposed antenna can achieve a -10-dB impedance bandwidth of 13.8% (ranges from 1.55 to 1.78 GHz) and a 3-dB axial-ratio (AR) bandwidth of 17.5% (ranges from 1.46 to 1.74 GHz) in the azimuthal plane. At the frequency of 1.575 GHz, the measured average axial ratio and right-hand CP (RHCP) gain are 1.91 dB and 0.86 dBi, respectively, in the azimuthal plane.


title: Acoustic feature prediction from semantic features for expressive speech using deep neural networks

abstract: The goal of the study is to predict acoustic features of expressive speech from semantic vector space representations. Though a lot of successful work was invested in expressiveness analysis and prediction, the results often involve manual labeling, or indirect prediction evaluation such as speech synthesis. The proposed analysis aims at direct acoustic feature prediction and comparison to original acoustic features from an audiobook. The audiobook is mapped in a semantic vector space. A set of acoustic features is extracted from the same utterances, involving iVectors trained on MFCC and F0 basis. Two regression models are trained with the semantic coordinates, DNNs and a baseline CART. Later, semantic and acoustic context features are combined for the prediction. The prediction is achieved successfully using the DNNs. A closer analysis shows that the prediction works best for larger utterances or utterances with specific contexts, and worst for general short utterances and proper names.


Hand-Eye Calibration of SCARA Robots


title: Nephrogenic diabetes insipidus in mice lacking aquaporin-3 water channels

abstract: Aquaporin-3 (AQP3) is a water channel expressed at the basolateral plasma membrane of kidney collecting-duct epithelial cells. The mouse AQP3 cDNA was isolated and encodes a 292-amino acid water/glycerol-transporting glycoprotein expressed in kidney, large airways, eye, urinary bladder, skin, and gastrointestinal tract. The mouse AQP3 gene was analyzed, and AQP3 null mice were generated by targeted gene disruption. The growth and phenotype of AQP3 null mice were grossly normal except for polyuria. AQP3 deletion had little effect on AQP1 or AQP4 protein expression but decreased AQP2 protein expression particularly in renal cortex. Fluid consumption in AQP3 null mice was more than 10-fold greater than that in wild-type litter mates, and urine osmolality ( 1,200 milliosmol). After 1-desamino-8-d-arginine-vasopressin administration or water deprivation, the AQP3 null mice were able to concentrate their urine partially to ≈30% of that in wild-type mice. Osmotic water permeability of cortical collecting-duct basolateral membrane, measured by a spatial filtering optics method, was >3-fold reduced by AQP3 deletion. To test the hypothesis that the residual concentrating ability of AQP3 null mice was due to the inner medullary collecting-duct water channel AQP4, AQP3/AQP4 double-knockout mice were generated. The double-knockout mice had greater impairment of urinary-concentrating ability than did the AQP3 single-knockout mice. Our findings establish a form of nephrogenic diabetes insipidus produced by impaired water permeability in collecting-duct basolateral membrane. Basolateral membrane aquaporins may thus provide blood-accessible targets for drug discovery of aquaretic inhibitors.


title: Evaluating the Actual and Perceived Exertion Provided by Virtual Reality Games

abstract: Virtual Reality games have shown potential to make exercise fun and engaging. This is because many virtual reality games, using devices like the HTC Vive, offer engaging experiences where interaction requires the player to physically move around, moving their arms and body. This paper presents the first work evaluating the actual and perceived exertion in four existing virtual reality games. Our key contribution is in the insights about the exertion provided by four diverse types of VR games, highlighting their potential value for improving exercise levels. Our work points to the need for standards to classify VR games in terms of both the cardiovascular level and the particular muscle groups


title: A Framework for Research in Gamified Mobile Guide Applications using Embodied Conversational Agents (ECAs)

abstract: Mobile Guides are mobile applications that provide players with local and location-based services (LBS), such as navigation assistance, where and when they need them most. Advances in mobile technologies in recent years have enabled the gamification of these applications, opening up new opportunities to transfer education and culture through game play. However, adding traditional game elements such as PBLs (points, badges, and leaderboards) alone cannot ensure that the intended learning outcomes will be met, as the player’s cognitive resources are shared between the application and the surrounding environment. This distribution of resources prevents players from easily immersing themselves into the educational scenario. Adding artificial conversational characters (ECAs) that simulate the social norms found in real-life human-to-human guide scenarios has the potential to address this problem and improve the player’s experience and learning of cultural narratives [1]. Although significant progress has been made towards creating game-like mobile guides with ECAs ([2], [3]), there is still a lack of a unified framework that enables researchers and practitioners to investigate the potential effects of such applications to players and how to approach the concepts of player experience, cognitive accessibility and usability in this context. This paper presents a theoretically-well supported research framework consisted of four key components: differences in players, different features of the gamified task, aspects of how the ECA looks, sound or behaves and different mobile environments. Furthermore, it provides based on this framework a working definition of what player experience, cognitive accessibility and usability are in the context of game-like mobile guide applications. Finally, a synthesis of the results of six empirical studies conducted within this research framework is discussed and a series of design guidelines for the effective gamification of mobile guide applications using ECAs are presented. Results show that an ECA can positively affect the quality of the player’s experience, but it did not elicit better player retention of cultural narratives and navigation of routes.


title: EXPERIMENTS OF ZERNIKE MOMENTS FOR LEAF IDENTIFICATION 1

abstract: So far, plant identification has challenges for sev eral researchers. Various methods and features have been proposed. However, there are still many approaches could be investigated to develop robust plant identification systems. This paper reports several experiments in using Zernike moments to build folia ge plant identification systems. In this case, Zernike moments were combined with other features: geometric features, color moments and gray-level co-occurrenc e matrix (GLCM). To implement the identifications systems, two approaches has been investigated. Firs t approach used a distance measure and the second u sed Probabilistic Neural Networks (PNN). The results sh ow that Zernike Moments have a prospect as features in leaf identification systems when they are combin ed with other features.


title: The mutual inductance of two thin coaxial disk coils in air

abstract: This paper presents an efficient method for computing the mutual inductance between two thin coaxial disk coils in air. The derived principal semi-analytical expressions involve complete elliptic integrals of the first and second kind, Heuman's Lambda function and three terms that have to be computed numerically. The presented method is compared to the filament method where both conductors are approximated by Maxwell's filamentary coils. The mutual inductance values (for a chosen case) computed by both methods are in good agreement. However, the method presented in this work exceeds by far the filament method in its precision and computational efficiency. In another example, the computed mutual inductance is validated by measurement.


title: Distracted walking: Cell phones increase injury risk for college pedestrians

abstract: Abstract Introduction Distraction on cell phones jeopardizes motor-vehicle driver safety, but few studies examine distracted walking. At particular risk are college students, who walk frequently in and near traffic, have increased pedestrian injury rates compared to other age groups, and frequently use cell phones. Method : Using an interactive and immersive virtual environment, two experiments studied the effect of cell phone conversation on distraction of college student pedestrians. In the first, we examined whether pedestrians would display riskier behavior when distracted by a naturalistic cell phone conversation than when undistracted. We also considered whether individual difference factors would moderate the effect of the distraction. In a second experiment, we examined the impact of three forms of distraction on pedestrian safety: (a) engaging in a cell phone conversation, (b) engaging in a cognitively challenging spatial task by phone, and (c) engaging in a cognitively challenging mental arithmetic task by phone. Results : Results revealed that cell phone conversations distracted college pedestrians considerably across all pedestrian safety variables measured, with just one exception. Attention to traffic was not affected by the naturalistic phone conversation in Experiment 1, but was altered by the cognitively-demanding content of some types of conversation in Experiment 2. The content of the conversation did not play a major role in distraction across other variables; both mundane and cognitively complex conversations distracted participants. Moreover, no significant associations between individual difference factors and susceptibility to distraction emerged. Impact on Industry : Results may inform researchers, policy makers, and pedestrians themselves. Educational campaigns might discourage telephone conversations in pedestrian environments.


title: On the fiber bundle structure of the space of belief functions

abstract: The study of finite non-additive measures or “belief functions” has been recently posed in connection with combinatorics and convex geometry. As a matter of fact, as belief functions are completely specified by the associated belief values on the events of the frame on which they are defined, they can be represented as points of a Cartesian space. The space of all belief functions \({\mathcal{B}}\) or “belief space” is a simplex whose vertices are BF focused on single events. In this paper, we present an alternative description of the space of belief functions in terms of differential geometric notions. The belief space possesses indeed a recursive bundle structure inherently related to the mass assignment mechanism, in which basic probability is recursively assigned to events of increasing size. A formal proof of the decomposition of \({\mathcal{B}}\) together with a characterization of its bases and fibers as simplices are provided.


Efficient content authentication in peer-to-peer networks (Extended Abstract)


title: Usability of Online Services: The Role of Technology Readiness and Context

abstract: An important prerequisite for the success of any online service is ensuring that customers' experience - via the interface - satisfies both sensory and functional needs. Developing interfaces that are responsive to customers' needs requires a perspective on interface design as well as a deep understanding of the customers themselves. Drawing upon research in consumer behavior concerning consumer beliefs about technology, we deploy an alternative way to describe customers based on psychographic characteristics. Technology readiness (TR), a multidimensional psychographic construct, offers a way to segment online customers based upon underlying positive and negative technology beliefs. The core premise of this study is that the beliefs form the foundation for expectations of how things should work and how specific online service interfaces are evaluated by customers. At the same time, usability evaluations of specific online services might be contingent on contextual factors, specifically the type of site (hedonic vs. utilitarian) and access method (Web vs. wirelessWeb). The aspects of usability examined here are those incorporated into the usability metric and instrument based on the Microsoft Usability Guidelines (MUG). The results of an empirical study with 160 participants indicate that (i) TR customer segments vary in usability requirements and (ii) usability evaluations of specific online service interfaces are influenced by complex interactions among site type, access method, and TR segment membership. As organizations continue to expand their online service offerings, managers must recognize that the interface exists to serve the customers, so their design must be matched to market needs and TR.


title: Consumer-Centred Validation for Runtime Adaptation in Service-Oriented System

abstract: Ensuring that service-oriented systems can adapt quickly and effectively to changes in service quality, business needs and their runtime environment is an increasingly important research problem. However, while considerable research has focused on developing runtime adaptation frameworks for service-oriented systems, there has been markedly less work on assessing the effectiveness of recommended adaptations. One way to address the problem is through validation. Validation not only allows us to assess how well a recommended adaptation addresses the concerns for which the system is reconfigured, it provides us with insights into the nature of problems for which different adaptations are suited. However, the dynamic nature of runtime adaptation and the changeable contexts in which service-oriented systems operate make it difficult to specify appropriate validation mechanisms in advance. This paper describes a novel consumer-centred approach for supporting runtime validation in self-adapting service-oriented systems. Our proposed solution uses machine learning to continuously assess and refine adaptation decisions.


title: Reducing Masking Effects in CombinatorialInteraction Testing: A Feedback DrivenAdaptive Approach

abstract: The configuration spaces of modern software systems are too large to test exhaustively. Combinatorial interaction testing (CIT) approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. The basic justification for CIT approaches is that they can cost-effectively exercise all system behaviors caused by the settings of t or fewer options. We conjecture, however, that in practice some of these behaviors are not actually tested because of unanticipated masking effects - test case failures that perturb system execution so as to prevent some behaviors from being exercised. While prior research has identified this problem, most solutions require knowing the masking effects a priori. In practice this is impractical, if not impossible. In this work, we reduce the harmful consequences of masking effects. First we define a novel interaction testing criterion, which aims to ensure that each test case has a fair chance to test all valid t-way combinations of option settings. We then introduce a feedback driven adaptive combinatorial testing process (FDA-CIT) to materialize this criterion in practice. At each iteration of FDA-CIT, we detect potential masking effects, heuristically isolate their likely causes (i.e., fault characterization), and then generate new samples that allow previously masked combinations to be tested in configurations that avoid the likely failure causes. The iterations end when the new interaction testing criterion has been satisfied. This paper compares two different fault characterization approaches - an integral part of the proposed approach, and empirically assesses their effectiveness and efficiency in removing masking effects on two widely used open source software systems. It also compares FDA-CIT against error locating arrays, a state of the art approach for detecting and locating failures. Furthermore, the scalability of the proposed approach is evaluated by comparing it with perfect test scenarios, in which all masking effects are known a priori. Our results suggest that masking effects do exist in practice, and that our approach provides a promising and efficient way to work around them, without requiring that masking effects be known a priori.


title: Playing online games against computer- vs. human-controlled opponents: Effects on presence, flow, and enjoyment

abstract: The purpose of this study was to examine whether playing online games against other users leads to different experiences in comparison with playing against computer-controlled opponents. Thereby, a one-factorial multivariate design was used (computer-controlled vs. human-controlled opponent). Dependent variables were the participants' feelings of presence and flow. Additionally, the amount of enjoyment was measured. The findings indicate that the type of opponent influences playing experiences: participants who played against a human-controlled opponent reported more experiences of presence, flow, and enjoyment, whereby the strongest effect refers to the experience of presence. Furthermore, strong relations between presence, flow, and enjoyment were observed. Further analyzes suggest that flow mediates the relationship between presence and enjoyment.


title: Deploying chains of virtual network functions: On the relation between link and server usage

abstract: Recently, Network Function Virtualization (NFV) has been proposed to transform from network hardware appliances to software middleboxes. Normally, a demand needs to invoke several Virtual Network Functions (VNFs) in a particular order following the service chain along a routing path. In this paper, we study the joint problem of VNF placement and path selection to better utilize the network. We discover that the relation between the link and server usage plays a crucial role in the problem. We first propose a systematic way to elastically tune the proper link and server usage of each demand based on network conditions and demand properties. In particular, we compute a proper routing path length, and decide, for each VNF in the service chain, whether to use additional server resources or to reuse resources provided by existing servers. We then propose a chain deployment algorithm to follow the guidance of this link and server usage. Via simulations, we show that our design effectively adapts resource usage to network dynamics, and, hence, serves more demands than other heuristics.


title: Consensus-ADMM for General Quadratically Constrained Quadratic Programming

abstract: Non-convex quadratically constrained quadratic programming (QCQP) problems have numerous applications in signal processing, machine learning, and wireless communications, albeit the general QCQP is NP-hard, and several interesting special cases are NP-hard as well. This paper proposes a new algorithm for general QCQP. The problem is first reformulated in consensus optimization form, to which the alternating direction method of multipliers (ADMM) can be applied. The reformulation is done in such a way that each of the sub-problems is a QCQP with only one constraint (QCQP-1), which is efficiently solvable irrespective of (non-)convexity. The core components are carefully designed to make the overall algorithm more scalable, including efficient methods for solving QCQP-1, memory efficient implementation, parallel/distributed implementation, and smart initialization. The proposed algorithm is then tested in two applications: multicast beamforming and phase retrieval. The results indicate superior performance over prior state-of-the-art methods.


title: The Vascular Hypothesis of Alzheimer's Disease: A Key to Preclinical Prediction of Dementia Using Neuroimaging.

abstract: The vascular hypothesis of Alzheimer's disease (VHAD) was proposed 24 years ago from observations made in our laboratory using aging rats subjected to chronic brain hypoperfusion. In recent years, VHAD has become a mother-lode to numerous neuroimaging studies targeting cerebral hemodynamic changes, particularly brain hypoperfusion in elderly patients at risk of developing Alzheimer's disease (AD). There is a growing consensus among neuroradiologists that brain hypoperfusion is likely involved in the pathogenesis of AD and that disturbed cerebral blood flow (CBF) can serve as a key biomarker for predicting conversion of mild cognitive impairment to AD. The use of cerebral hypoperfusion as a preclinical predictor of AD is becoming decisive in stratifying low and high risk patients that may develop cognitive decline and for assessing the effectiveness of therapeutic interventions. There is currently an international research drive from neuroimaging groups to seek new perspectives that can broaden our understanding of AD and improve lifestyle. Diverse neuroimaging methods are currently being used to monitor normal and dyscognitive brain activity. Some techniques are very powerful and can detect, diagnose, quantify, prognose, and predict cognitive decline before AD onset, even from a healthy cognitive state. Multimodal imaging offers new insights in the treatment and prevention of cognitive decline during advanced aging and better understanding of the functional and structural organization of the human brain. This review discusses the impact the VHAD and CBF are having on the neuroimaging technology that can usher practical strategies to help prevent AD.


title: UML distilled: applying the standard object modeling language

abstract: Today, a software designer or architect who seeks to represent the design of a software system can choose from a wide variety of notational languages, each aligned with a particular analysis and design methodology. Ironically, this wide variety of choice is one impediment to the significant benefits promised by software reuse. The emergence of the Unified Modeling Language (UML) -- created by the joint efforts of leading object technologists Grady Booch, Ivar Jacobson, and James Rumbaugh with contributions from many others in the object community -- represents one of the most significant developments in object technology. Supported by a broad base of industry-leading companies, the UML merges the best of the notations used by the three most popular analysis and design methodologies, Booch, OOSE (use-cases), and OMT, to produce a single, universal modeling language that can be used with any method.Written for those already grounded in object-oriented analysis and design, this concise overview introduces you to UML, highlighting the key elements of its notation, semantics, and processes. Included is a brief explanation of UML's history, development, and rationale, as well as discussions on how UML can be integrated into the object-oriented development process. In addition, the book profiles various modeling techniques associated with UML -- use cases, CRC cards, design by contract, dynamic classification, interfaces, and abstract classes -- along with concise descriptions of notation and semantics and numerous insightful tips for effective use based on the authors' experience. In addition, the authors offer the first look at the emerging Objectory Software Development Process derivedfrom the methodologies of Grady Booch, Ivar Jacobson, and James Rumbaugh. To give you a feel for the UML in action, the book includes a Java programming example that outlines the implementation of a UML-based design.You will come away with an excellent understanding of UML essentials, insight into how UML functions within the software development process, and a firm foundation upon which to expand and build your knowledge of the Unified Modeling Language.


title: HPLC of flavanones and chalcones in different species and clones of Salix.

abstract: The SPE-HPLC method was developed to determine an isosalipurposide (5) and its derivative, 6i- O-p-coumaroyl ester (6) in the bark of eight taxa (InVIII) belonging to three species of the genus Salix and originating from a natural habitat or cultivated for pharmaceutical purposes. The chalcones were separated by HPLC under gradient elution with the concentration of ACN increasing from 20% to 50% in 0.1% aqueous H3PO4 (tG 15 min). The content of both compounds was determined by an external standardization with the use of isoliquiritigenin (7) as a reference substance n a commercially available chalcone, and also isosalipurposide (5) and its derivative, 6i-O-p-coumaroyl ester (6). The latter compound was isolated from the bark of Salix daphnoides (IV) by a CC and semi-preparative HPLC and its structure was elucidated by MS and NMR spec- tra. It was stated that 6i-O-p-coumaroylisosalipurposide (6), in addition to isosalipurposide (5), is a character- istic flavonoid for the S. daphnoides species. Moreover, the presence of these two chalcones was confirmed in the bark of S. acutifolia (I). Differences were observed in the results obtained from a quantitative analysis due to the type of reference substance used. The content of chalcones was varied and dependent on the species selected for analysis, namely from 22.01/21.08 mg/g in S. daphnoides clone 1095 (III) to 2.47/2.44 mg/g in S. daphnoides (II), collected from a natural habitat. Isosalipurposide (5) was determined in all the investigated species and clones of Salix, besides a number of naringenin derivatives. Separation of all flavonoids: flavanones n naringenin (1), naringenin (+)-5-O-glucoside (2), (n)-5-O-glucoside (3), 7-O-glucoside (4) and chalcones (5 and 6) was performed under gradient elution with the same solvents and changes in ACN concentration from 2% to 37% (tG 60 min). The total amount of flavanones ranged from 4.69 mg/g in S. purpurea clone 1132 (VII) to 41.93 mg/g in S. purpurea (VIII) from Herbapol Wroc≥aw.


title: Unifying Terrain Awareness for the Visually Impaired through Real-Time Semantic Segmentation

abstract: Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework.


title: Forex trading prediction using linear regression line, artificial neural network and dynamic time warping algorithms

abstract: Forex prediction has become a challenging task in the Forex market since the late 1970s due to uncertainty movement of exchange rates.In this paper, we utilised linear regression equation to analyse the historical data and discover the trends patterns in Forex.These trends patterns are modeled and learned by Artificial Neural Network algorithm, and Dynamic Time Warping algorithm is used to predict the near future trends.Our experiment result shows a satisfactory result using the proposed approach.


Joint Learning of CNN and LSTM for Image Captioning


title: Time series forecasting using Artificial Neural Networks vs. evolving models

abstract: Time series forecasting plays an important role in many fields such as economics, finance, business intelligence, natural sciences, and the social sciences. This forecasting task can be achieved by using different techniques such as statistical methods or Artificial Neural Networks (ANN). In this paper, we present two different approaches to time series forecasting: evolving Takagi-Sugeno (eTS) fuzzy model and ANN. These two different methods will be compared taking into account the different characteristic of each approach.


title: Survey on High-Temperature Packaging Materials for SiC-Based Power Electronics Modules

abstract: High temperature SiC devices need the materials for device packages also capable of working at higher temperature than those for Si devices. This paper presents a selection of materials that are potentially suitable for use in high temperature package assembly, including die attach, substrate, interconnections, encapsulation, case, heat spreader and heat sink. The temperature under consideration is up to 250degC, corresponding to the need of many applications, including automobiles and aircraft.


title: Digital enablement of blockchain: Evidence from HNA group

abstract: Abstract Blockchain, the distributed ledger underlying bitcoin, has attracted much attention and stimulated rich discussions. However, extant discussions are mostly conceptual expositions, and empirical evidence of how to use the technology is limited. This case analysis fills this gap by conducting a study on Hainan Airlines (HNA) group, a large conglomerate, which has successfully implemented a blockchain-enabled E-commerce platform to offer employees flexible benefits. The case study unveils that blockchain of value in three ways: 1) issuing cryptocurrency, 2) protecting sensitive information, and 3) eliminating institutional intermediaries. These findings provide a reference point for IT and general managers who intend to use blockchain to digitally enable their organizations further.


title: Client-server computing in mobile environments

abstract: Recent advances in wireless data networking and portable information appliances have engendered a new paradigm of computing, called mobile computing , in which users carrying portable devices have access to data and information services regardless of their physical location or movement behavior. In the meantime, research addressing information access in mobile environments has proliferated. In this survey, we provide a concrete framework and categorization of the various ways of supporting mobile client-server computing for information access. We examine characteristics of mobility that distinguish mobile client-server computing from its traditional counterpart. We provide a comprehensive analysis of new paradigms and enabler concepts for mobile client-server computing, including mobile-aware adaptation, extended client-server model, and mobile data access. A comparative and detailed review of major research prototypes for mobile information access is also presented.


title: TCP ex machina: computer-generated congestion control

abstract: This paper describes a new approach to end-to-end congestion control on a multi-user network. Rather than manually formulate each endpoint's reaction to congestion signals, as in traditional protocols, we developed a program called Remy that generates congestion-control algorithms to run at the endpoints. In this approach, the protocol designer specifies their prior knowledge or assumptions about the network and an objective that the algorithm will try to achieve, e.g., high throughput and low queueing delay. Remy then produces a distributed algorithm---the control rules for the independent endpoints---that tries to achieve this objective. In simulations with ns-2, Remy-generated algorithms outperformed human-designed end-to-end techniques, including TCP Cubic, Compound, and Vegas. In many cases, Remy's algorithms also outperformed methods that require intrusive in-network changes, including XCP and Cubic-over-sfqCoDel (stochastic fair queueing with CoDel for active queue management). Remy can generate algorithms both for networks where some parameters are known tightly a priori, e.g. datacenters, and for networks where prior knowledge is less precise, such as cellular networks. We characterize the sensitivity of the resulting performance to the specificity of the prior knowledge, and the consequences when real-world conditions contradict the assumptions supplied at design-time.


title: Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods

abstract: Diabetic retinopathy is a complication of diabetes that is caused by changes in the blood vessels of the retina. The symptoms can blur or distort the patient's vision and are a main cause of blindness. Exudates are one of the primary signs of diabetic retinopathy. Detection of exudates by ophthalmologists normally requires pupil dilation using a chemical solution which takes time and affects patients. This paper investigates and proposes a set of optimally adjusted morphological operators to be used for exudate detection on diabetic retinopathy patients' non-dilated pupil and low-contrast images. These automatically detected exudates are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. The results are successful and the sensitivity and specificity for our exudate detection is 80% and 99.5%, respectively.


title: Playing Alone, Playing With Others: Differences in Player Experience and Indicators of Wellbeing

abstract: Video game play is becoming an increasingly social experience, yet we have little understanding of how social and solitary modes of play differ in terms of the player experience or interact with player wellbeing. An online survey (n = 446) collected data on players' current mode of play, their game play experience, social capital gained from game play and wellbeing. The results indicate that social and solitary players differ in terms of degree of autonomy, presence and relatedness experienced, while the different types of social play are associated with differences in relatedness and social capital experienced. Different predictors of wellbeing were also present across solitary and social player samples. People who play games on their own experience greater wellbeing when experiencing in-game autonomy. Social players experience greater wellbeing when playing with strangers, and when experiencing in-game bridging social capital. All players experienced increased wellbeing with age and decreased wellbeing with greater amounts of play.


title: Species and variety detection of fruits and vegetables from images

abstract: Efficient detection of ‘species and variety’ of fruits and vegetables from the images is one of the major challenges for the computers. In this paper, we introduce a framework for the fruit and vegetable classification problem which takes the images of fruits and vegetables as input and returns it is species and variety. The input image contains fruit or vegetable of single variety in arbitrary position and in any number. This paper also introduces a texture feature based on sum and difference of intensity values of the neighbouring pixels of the colour images. The experimental results show that the proposed texture feature supports accurate fruit and vegetable recognition and performs better than other state-of-the-art colour and texture features. The classification accuracy for the proposed ISADH texture feature is achieved up to 99%.


title: Comparing Convolutional Neural Networks to Traditional Models for Slot Filling

abstract: We address relation classification in the context of slot filling, the task of finding and evaluating fillers like “Steve Jobs” for the slot X in “X founded Apple”. We propose a convolutional neural network which splits the input sentence into three parts according to the relation arguments and compare it to state-ofthe-art and traditional approaches of relation classification. Finally, we combine different methods and show that the combination is better than individual approaches. We also analyze the effect of genre differences on performance.


title: Testing of Copper Pillar Bumps for Wafer Sort

abstract: Using a copper pillar interconnect in flip chip packaging provides a lead-free solution that is more reliable, and also scalable to very fine pitch. Vertical probe card technology, also called buckling beam technology, was used in characterization of wafer probe process and electrical contact on solder bumps and copper pillars at 150 μm pitch arrays. Probe contact was investigated by modeling the scrub, penetration or deformation on a bump under various conditions of wafer probe and experimentally tested on wafers on copper pillars, solder bumps of different metallurgies or sheet wafers. A single-probe contact test system was devised to study the contact behavior of a probe on a bump. Various probe tip geometries including wedge, pointed and flat, were studied. Probing procedures were investigated for achieving reliable electrical contact for large pitch area array bumps as well as fine pitch, 50 μm, copper pillar array bumps using two different wafer test technologies.


title: Clustering: A neural network approach

abstract: Clustering is a fundamental data analysis method. It is widely used for pattern recognition, feature extraction, vector quantization (VQ), image segmentation, function approximation, and data mining. As an unsupervised classification technique, clustering identifies some inherent structures present in a set of objects based on a similarity measure. Clustering methods can be based on statistical model identification (McLachlan & Basford, 1988) or competitive learning. In this paper, we give a comprehensive overview of competitive learning based clustering methods. Importance is attached to a number of competitive learning based clustering neural networks such as the self-organizing map (SOM), the learning vector quantization (LVQ), the neural gas, and the ART model, and clustering algorithms such as the C-means, mountain/subtractive clustering, and fuzzy C-means (FCM) algorithms. Associated topics such as the under-utilization problem, fuzzy clustering, robust clustering, clustering based on non-Euclidean distance measures, supervised clustering, hierarchical clustering as well as cluster validity are also described. Two examples are given to demonstrate the use of the clustering methods.


title: Energy-Efficient Resource Allocation for Device-to-Device Underlay Communication

abstract: Device-to-device (D2D) communication underlaying cellular networks is expected to bring significant benefits for utilizing resources, improving user throughput and extending battery life of user equipments. However, the allocation of radio and power resources to D2D communication needs elaborate coordination, as D2D communication can cause interference to cellular communication. In this paper, we study joint channel and power allocation to improve the energy efficiency of user equipments. To solve the problem efficiently, we introduce an iterative combinatorial auction algorithm, where the D2D users are considered as bidders that compete for channel resources, and the cellular network is treated as the auctioneer. We also analyze important properties of D2D underlay communication, and present numerical simulations to verify the proposed algorithm.


title: A Series Slot Array Antenna for 45 $^{\circ}$-Inclined Linear Polarization With SIW Technology

abstract: The design method for a standing-wave series slot array antenna with 45°-inclined linear polarization in the Ka-band is presented. The proposed 16 × 8 planar slot array antenna consisted of arrays with alternating reactance slot pairs that could achieve impedance matching and uniform field ex- citation, simultaneously. Furthermore, the grating lobes were effectively suppressed with the help of these radiating units due to the one-half guided wavelength slot spacing. An equivalent circuit analysis was done to evaluate the input impedances of the radiating and feeding lines with the impedance recursive formulas as well as the mode voltages of the radiating slot elements. In addition, a wideband transition with a 30.13% bandwidth under the criteria of less than VSWR 1.5:1 was presented to provide the necessary power with the minimal reflection and to prevent the distortion of the radiation patterns. The proposed planar slot array antenna occupied an aperture area of 57.4× 51.6 mm. The measured bandwidth, gain, and efficiency were 990 MHz (2.88%), 24.3 dBi, and 53.7%, respectively. Furthermore, the side lobe levels (SLLs) were verified for each cutting plane with -13.57 dB and -13.17 dB, respectively. The proposed antenna structure was achieved with a substrate integrated waveguide (SIW) with the low costs and lightweight features.


title: Control architecture for a tail-sitter unmanned air vehicle

abstract: This paper details the control and guidance architecture for the T-wing tail-sitter unmanned air vehicle, (UAV). The T-wing is a vertical take off and landing (VTOL) UAV that is capable of both wing-born horizontal flight and propeller born vertical mode flight including hover and descent. During low-speed vertical flight the T-wing uses propeller wash over its aerodynamic surfaces to effect control. At the lowest level, the vehicle uses a mixture of classical and LQR controllers for angular rate and translational velocity control. These low-level controllers are directed by a series of proportional guidance controllers for the vertical, horizontal and transition flight modes that allow the vehicle to achieve autonomous waypoint navigation. The control design for the T-wing is complicated by the large differences in vehicle dynamics between vertical and horizontal flight; the difficulty of accurately predicting the low-speed vehicle aerodynamics; and the basic instability of the vertical flight mode. This paper considers the control design problem for the T-wing in light of these factors. In particular it focuses on the integration of all the different types and levels of controllers into a full flight-vehicle control system.


title: Direct Discriminative Pattern Mining for Effective Classification

abstract: The application of frequent patterns in classification has demonstrated its power in recent studies. It often adopts a two-step approach: frequent pattern (or classification rule) mining followed by feature selection (or rule ranking). However, this two-step process could be computationally expensive, especially when the problem scale is large or the minimum support is low. It was observed that frequent pattern mining usually produces a huge number of "patterns" that could not only slow down the mining process but also make feature selection hard to complete. In this paper, we propose a direct discriminative pattern mining approach, DDPMine, to tackle the efficiency issue arising from the two-step approach. DDPMine performs a branch-and-bound search for directly mining discriminative patterns without generating the complete pattern set. Instead of selecting best patterns in a batch, we introduce a "feature-centered" mining approach that generates discriminative patterns sequentially on a progressively shrinking FP-tree by incrementally eliminating training instances. The instance elimination effectively reduces the problem size iteratively and expedites the mining process. Empirical results show that DDPMine achieves orders of magnitude speedup without any downgrade of classification accuracy. It outperforms the state-of-the-art associative classification methods in terms of both accuracy and efficiency.


title: A study on classification techniques in data mining

abstract: Data mining is a process of inferring knowledge from such huge data. Data Mining has three major components Clustering or Classification, Association Rules and Sequence Analysis. By simple definition, in classification/clustering analyze a set of data and generate a set of grouping rules which can be used to classify future data. Data mining is the process is to extract information from a data set and transform it into an understandable structure. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Data mining involves six common classes of tasks. Anomaly detection, Association rule learning, Clustering, Classification, Regression, Summarization. Classification is a major technique in data mining and widely used in various fields. Classification is a data mining (machine learning) technique used to predict group membership for data instances. In this paper, we present the basic classification techniques. Several major kinds of classification method including decision tree induction, Bayesian networks, k-nearest neighbor classifier, the goal of this study is to provide a comprehensive review of different classification techniques in data mining.


title: Optimized route network graph as map reference for autonomous cars operating on German autobahn

abstract: This paper describes several optimization techniques used to create an adequate route network graph for autonomous cars as a map reference for driving on German autobahn or similar highway tracks. We have taken the Route Network Definition File Format (RNDF) specified by DARPA and identified multiple flaws of the RNDF for creating digital maps for autonomous vehicles. Thus, we introduce various enhancements to it to form a digital map graph called RND-FGraph, which is well suited to map almost any urban transportation infrastructure. We will also outline and show results of fast optimizations to reduce the graph size. The RNDFGraph has been used for path-planning and trajectory evaluation by the behavior module of our two autonomous cars “Spirit of Berlin” and “MadeInGermany”. We have especially tuned the graph to map structured high speed environments such as autobahns where we have tested autonomously hundreds of kilometers under real traffic conditions.


title: Predicting Sufficient Annotation Strength for Interactive Foreground Segmentation

abstract: The mode of manual annotation used in an interactive segmentation algorithm affects both its accuracy and ease-of-use. For example, bounding boxes are fast to supply, yet may be too coarse to get good results on difficult images, freehand outlines are slower to supply and more specific, yet they may be overkill for simple images. Whereas existing methods assume a fixed form of input no matter the image, we propose to predict the tradeoff between accuracy and effort. Our approach learns whether a graph cuts segmentation will succeed if initialized with a given annotation mode, based on the image's visual separability and foreground uncertainty. Using these predictions, we optimize the mode of input requested on new images a user wants segmented. Whether given a single image that should be segmented as quickly as possible, or a batch of images that must be segmented within a specified time budget, we show how to select the easiest modality that will be sufficiently strong to yield high quality segmentations. Extensive results with real users and three datasets demonstrate the impact.


title: M^3RL: Mind-aware Multi-agent Management Reinforcement Learning

abstract: Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not wish to do. For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together. The objective of the manager is maximizing the overall productivity as well as minimizing payments made to the workers for ad-hoc worker teaming. To train the manager, we propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL), which consists of agent modeling and policy learning. We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents. The experimental results have validated the effectiveness of our approach in modeling worker agents' minds online, and in achieving optimal ad-hoc teaming with good generalization and fast adaptation.


title: Point Linking Network for Object Detection

abstract: Object detection is a core problem in computer vision. With the development of deep ConvNets, the performance of object detectors has been dramatically improved. The deep ConvNets based object detectors mainly focus on regressing the coordinates of bounding box, e.g., Faster-R-CNN, YOLO and SSD. Different from these methods that considering bounding box as a whole, we propose a novel object bounding box representation using points and links and implemented using deep ConvNets, termed as Point Linking Network (PLN). Specifically, we regress the corner/center points of bounding-box and their links using a fully convolutional network; then we map the corner points and their links back to multiple bounding boxes; finally an object detection result is obtained by fusing the multiple bounding boxes. PLN is naturally robust to object occlusion and flexible to object scale variation and aspect ratio variation. In the experiments, PLN with the Inception-v2 model achieves state-of-the-art single-model and single-scale results on the PASCAL VOC 2007, the PASCAL VOC 2012 and the COCO detection benchmarks without bells and whistles. The source code will be released.


title: An Efficient Features - Based License Plate Localization Method

abstract: This paper presents a feature-based license plate localization algorithm that copes with multi-object problem in different image capturing conditions. The proposed algorithm is robust against illumination, shadow, scale, rotation, and weather condition. It extracts license plate candidates using edge statistics and morphological operations and removes the incorrect candidates according to the determined features of license plates. We have formed a rather complete database of 269 images in different conditions. The proposed algorithm successfully detecteds the accurate location of the license plates in 96.5% cases, which outperforms the other available approaches in the literature.


UWB Bowtie Slot Antenna for Breast Cancer Detection


i TWO-WHEELED BALANCING ROBOT CONTROLLER


title: Deriving Verb Predicates By Clustering Verbs with Arguments

abstract: Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage. Verb classes automatically induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other hand, can give clusters with much larger coverage, and can be adapted to specific corpora such as Twitter. We present a method for clustering the outputs of VerbKB: verbs with their multiple argument types, e.g. "marry(person, person)", "feel(person, emotion)." We make use of a novel low-dimensional embedding of verbs and their arguments to produce high quality clusters in which the same verb can be in different clusters depending on its argument type. The resulting verb clusters do a better job than hand-built clusters of predicting sarcasm, sentiment, and locus of control in tweets.


title: A Production Oriented Approach for Vandalism Detection in Wikidata - The Buffaloberry Vandalism Detector at WSDM Cup 2017

abstract: Wikidata is a free and open knowledge base from the Wikimedia Foundation, that not only acts as a central storage of structured data for other projects of the organization, but also for a growing array of information systems, including search engines. Like Wikipedia, Wikidata's content can be created and edited by anyone; which is the main source of its strength, but also allows for malicious users to vandalize it, risking the spreading of misinformation through all the systems that rely on it as a source of structured facts. Our task at the WSDM Cup 2017 was to come up with a fast and reliable prediction system that narrows down suspicious edits for human revision. Elaborating on previous works by Heindorf et al. we were able to outperform all other contestants, while incorporating new interesting features, unifying the programming language used to only Python and refactoring the feature extractor into a simpler and more compact code base.


title: Random Interaction Matrix Ensembles in Mesoscopic Physics

abstract: We analyze several ground state related properties of mesoscopic systems using the random interaction matrix model EGOE(1+2)-$\cs$ (or RIMM) for many fermion systems with spin degree of freedom and the Hamiltonian containing pairing and exchange interactions in addition to the mean-field one-body and random two-body parts. RIMM reproduces the essential features of various properties: odd-even staggering in ground state energies as a function of particle number, delay in ground state magnetization and conductance peak spacing distributions. The analytical formula, we have derived, for the ensemble averaged spectral variances provides a simple understanding of some of these properties.


title: Dependency-based Discourse Parser for Single-Document Summarization

abstract: The current state-of-the-art singledocument summarization method generates a summary by solving a Tree Knapsack Problem (TKP), which is the problem of finding the optimal rooted subtree of the dependency-based discourse tree (DEP-DT) of a document. We can obtain a gold DEP-DT by transforming a gold Rhetorical Structure Theory-based discourse tree (RST-DT). However, there is still a large difference between the ROUGE scores of a system with a gold DEP-DT and a system with a DEP-DT obtained from an automatically parsed RST-DT. To improve the ROUGE score, we propose a novel discourse parser that directly generates the DEP-DT. The evaluation results showed that the TKP with our parser outperformed that with the state-of-the-art RST-DT parser, and achieved almost equivalent ROUGE scores to the TKP with the gold DEP-DT.


title: Railways: How they may contribute to a sustainable future

abstract: AbstractA combination of a rapidly increasing world population and an increasing energy-intensive lifestyle has led to doubts about the sufficiency of natural resources. The waste products of these economic activities have become large enough to change the climate of the globe. The demand for transport is characterized by an exponential increase and a tendency to choose faster modes. These facts give rise to concerns about the sustainability of present transport practices.While railways presently offer significant environmental advantages over other modes of transport, particularly the car, in many areas the gap is closing quickly. Cars are much safer, more energy efficient and less polluting than a decade ago. Trains in the future will have to contend with reducing hydrocarbon fuel supplies and much stricter legislation limiting emissions, particularly diesel particulates. Because the energy environmental performance of trains depends on the passenger load factor, the areas in which railways excel are hi...


title: Pairwise Matching of 3D Fragments Using Cluster Trees

abstract: We propose a novel and efficient surface matching approach for reassembling broken solids as well as for matching assembly components using cluster trees of oriented points. The method rapidly scans through the space of all possible contact poses of the fragments to be (re)assembled using a tree search strategy, which neither relies on any surface features nor requires an initial solution. The new method first decomposes each point set into a binary tree structure using a hierarchical clustering algorithm. Subsequently the fragments are matched pairwise by descending the cluster trees simultaneously in a depth-first fashion. In contrast to the reassemblage of pottery and thin walled artifacts, this paper addresses the problem of matching broken 3D solids on the basis of their 2.5D fracture surfaces, which are assumed to be reasonable large. Our proposed contact area maximization is a powerful common basis for most surface matching tasks, which can be adapted to numerous special applications. The suggested approach is very robust and offers an outstanding efficiency.


title: Manipulating the surface coating of ultra-small Gd2O3 nanoparticles for improved T1-weighted MR imaging.

abstract: Abstract In this report, monodispersed ultra-small Gd 2 O 3 nanoparticles capped with hydrophobic oleic acid (OA) were synthesized with average particle size of 2.9 nm. Two methods were introduced to modify the surface coating to hydrophilic for bio-applications. With a hydrophilic coating, the polyvinyl pyrrolidone (PVP) coated Gd 2 O 3 nanoparticles (Gd 2 O 3 -PVP) showed a reduced longitudinal T 1 relaxation time compared with OA and cetyltrimethylammonium bromide (CTAB) co-coated Gd 2 O 3 (Gd 2 O 3 -OA-CTAB) in the relaxation study. The Gd 2 O 3 -PVP was thus chosen for its further application study in MRI with an improved longitudinal relaxivity r 1 of 12.1 m m − 1 s −1 at 7 T, which is around 3 times as that of commercial contrast agent Magnevist ® . In vitro cell viability in HK-2 cell indicated negligible cytotoxicity of Gd 2 O 3 -PVP within preclinical dosage. In vivo MR imaging study of Gd 2 O 3 -PVP nanoparticles demonstrated considerable signal enhancement in the liver and kidney with a long blood circulation time. Notably, the OA capping agent was replaced by PVP through ligand exchange on the Gd 2 O 3 nanoparticle surface. The hydrophilic PVP grants the Gd 2 O 3 nanoparticles with a polar surface for bio-application, and the obtained Gd 2 O 3 -PVP could be used as an in vivo indicator of reticuloendothelial activity.


title: A Differential-Fed UWB Antenna Element With Unidirectional Radiation

abstract: A new differential-fed broadband antenna element with unidirectional radiation is proposed. This antenna is composed of a folded bowtie, a center-fed loop, and a box-shaped reflector. A pair of differential feeds is developed to excite the antenna and provide an ultrawideband (UWB) impedance matching. The box-shaped reflector is used for the reduction of the gain fluctuation across the operating frequency band. An antenna prototype for UWB applications is fabricated and measured, exhibiting an impedance bandwidth of 132% with standing wave ratio ≤ 2 from 2.48 to 12.12 GHz, over which the gain varies between 7.2 and 14.1 dBi at boresight. The proposed antenna radiates unidirectionally with low cross polarization and low back radiation. Furthermore, the time-domain characteristic of the proposed antenna is evaluated. In addition, a $2 \times 2$ element array using the proposed element is also investigated in this communication.


title: Cyclone /spl trade/: a low-cost, high-performance FPGA

abstract: This paper describes the Altera Cyclone/spl trade/ FPGA, an architecture specifically designed for low-cost, high-volume applications. An optimized routing architecture, simplified I/O structure, and carefully selected features combine to yield a 57% die size reduction relative to a Stratix/spl trade/ device of similar logic density, with a 7% reduction in performance.


title: Treatment of Soft Tissue Filler Complications: Expert Consensus Recommendations

abstract: Background ::: Dermal fillers have been increasingly used in minimally invasive facial esthetic procedures. This widespread use has led to a rise in reports of associated complications. The aim of this expert consensus report is to describe potential adverse events associated with dermal fillers and to provide guidance on their treatment and avoidance.


title: A systematic literature review of software quality cost research

abstract: Abstract: Software quality costs have not received as much attention from the research community as other economic aspects of software development. Over the last three decades, a number of articles on this topic have appeared in a range of journals, but comprehensive overviews of this body of research are not available. For the detailed review of software quality cost research presented in this article, we collect 87 articles published between 1980 and 2009 in 60 leading computing journals. We study the distribution of these articles across research disciplines and journals as well as over time. Moreover, we identify the predominant researchers in the software quality cost domain and the related research clusters. We also classify the articles according to three properties, namely, research topic, research scope, and research approach. This categorization enables us to identify aspects emphasized by previous research on software quality costs and to point out promising future research directions. Our review shows that prevention costs have gained the least attention, in spite of their big cost impact. It also reveals that only one article has targeted multiple companies. Further, we observe that many articles do not empirically validate their findings. This is especially true for those articles dealing with an entire firm.


title: An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities

abstract: The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to have a positive impact across all aspects of operations. In recent years, many initiatives and groups have been formed to advance smart manufacturing, with the most prominent being the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium. These initiatives comprise industry, academic and government partners, and contribute to the development of strategic policies, guidelines, and roadmaps relating to smart manufacturing adoption. In turn, many of these ::: recommendations may be implemented using data-centric technologies, such as Big Data, Machine Learning, Simulation, Internet of Things and Cyber Physical Systems, to realise smart operations in the factory. Given the importance of machine uptime and availability in smart manufacturing, this research centres on the application of data-driven analytics to industrial equipment maintenance. The main contributions of this research are a set of data and system requirements for implementing equipment maintenance applications in industrial environments, and an information system model that provides a scalable and fault tolerant big data pipeline for integrating, processing and analysing industrial equipment data. These contributions are considered in the context of highly regulated large-scale manufacturing environments, where legacy (e.g. automation controllers) and emerging instrumentation (e.g. internet-aware smart sensors) must be supported to facilitate initial smart manufacturing efforts.


title: Compact Circularly Polarized Meandered-Loop Antenna for UHF-Band RFID Tag

abstract: A planar loop antenna design for ultra-high frequency radio frequency identification (UHF RFID) operating in the 922-928 MHz band is investigated. To achieve compact size of 58.6 × 58.6 mm 2 for this loop antenna, the technique of using meandered-loop structure is applied, and by further introducing an open gap into this meandered-loop, good circularly polarized (CP) radiation is also attained. Desirable impedance matching between the meandered-loop antenna and RFID chip is achieved by applying a simple matching stub. Besides exhibiting satisfactory 3-dB axial-ratio (AR) bandwidth of 921-929 MHz and 10-dB impedance bandwidth of 915-942 MHz, good antenna gain of 1.73 dBic and long read range of up to 20.5 m are also demonstrated.


title: Pattern division multiple access (PDMA) for cellular future radio access

abstract: This paper presents a pattern division multiple access (PDMA) concept for cellular future radio access (FRA) towards the 2020s information society. Different from the current LTE radio access scheme (until Release 11), PDMA is a novel non-orthogonal multiple access technology based on the total optimization of multiple user communication system. It considers joint design from both transmitter and receiver. At the receiver, multiple users are detected by successive interference cancellation (SIC) detection method. Numerical results show that the PDMA system based on SIC improve the average sum rate of users over the orthogonal system with affordable complexity.


title: Root Cause Analysis of Zero-Rate Output Sources in an MEMS Gyroscope

abstract: This paper analyses the root causes of zero-rate output in MEMS vibratory gyroscopes. Zero-rate output is one of the major challenges for high performance gyroscopes. The knowledge of its causes is important to minimize zero-rate output and achieve a robust sensor design. In this paper, a new method to describe a MEMS gyroscope with a parametric state space model is introduced. The model is used to theoretically describe the behavioural influences. A new, more detailed and general gyroscope approximation is used to vary influence parameters, and to verify the method with simulations. The focus is on varying stiffness terms, and an extension of the model to other gyroscope approximations is also discussed.


title: A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

abstract: Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions. However, most of the existing dietary assessment methods rely on memory. With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment. However, enabling this future Internet of Things-based dietary assessment imposes several fundamental challenges on algorithm development and system design. In this paper, we set to address these issues from the following two aspects: (1) to develop novel deep learning-based visual food recognition algorithms to achieve the best-in-class recognition accuracy; (2) to design a food recognition system employing edge computing-based service computing paradigm to overcome some inherent problems of traditional mobile cloud computing paradigm, such as unacceptable system latency and low battery life of mobile devices. We have conducted extensive experiments with real-world data. Our results have shown that the proposed system achieved three objectives: (1) outperforming existing work in terms of food recognition accuracy; (2) reducing response time that is equivalent to the minimum of the existing approaches; and (3) lowering energy consumption which is close to the minimum of the state-of-the-art.


title: IoT Based Smart Charger: An ESP8266 Based Automatic Charger

abstract: The internet of things has many applications; one of those is Smart Power Monitoring and Control Systems. Energy efficient devices can be designed in IoT, which can reduce both, the power consumption and the human effort required to do so. This work has been designed in to implement a smart charging system that automatically controls its behaviour using the webserver and the phone which is being charged. The device being used is a ESP8266, the Webserver which will be stored on the ESP8266 module acts as the IoT platform. As the webserver is platform independent, it can be used on any device, like mobiles and laptops, these devices relay commands, and in turn the web server toggles the charger through the ESP8266 module. This project helps in automatic monitoring of the device and sends notifications to the phone, which is necessary if we want to reduce both the energy and time needed to maintain said devices. This system would be capable of turning on and off only when the device is connected and charge only when needed, and can be further improved to monitor other appliances. There are various modules available, which when paired up with the charging circuit will turn the charger into an IoT based device, like ESP8266, Particle Photon, and other Arduino based modules, but ESP8266 is the most cost effective and also has low power consumption.


title: Application of D-Q frame impedance-based stability criterion in power systems with multiple STATCOMs in proximity

abstract: Impedance-based stability criterion has been proven to be a powerful tool for small-signal stability analysis and design, derived from Nyquist stability criterion and first developed in dc-dc power electronics converter systems. Recently it has been extended to ac systems to address the issues and challenges coming with more and more power electronics based converters connected to the existing ac power grids, either in transmission or distribution level. With that, only terminal information is sufficient to judge small-signal stability, which can be from either impedance model or experimental results. Therefore, internal details of inverters are not necessary, facilitating the system operator to integrate. This paper specifically presents a case study with multiple STATCOMs in IEEE 14-bus system to illustrate the application of the d-q frame impedance-based stability criterion. It shows that the impedance-based stability criterion can predict the small-signal stability with accurate oscillatory frequency for STATCOMs' interactions, and a new instability pattern was found from the impedance point of view.


title: Quantized Convolutional Neural Networks for Mobile Devices

abstract: Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models due to the high computation complexity, which prohibits their further extensions. In this paper, we propose an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Both filter kernels in convolutional layers and weighting matrices in fully-connected layers are quantized, aiming at minimizing the estimation error of each layer's response. Extensive experiments on the ILSVRC-12 benchmark demonstrate 4 ~ 6× speed-up and 15 ~ 20× compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.


title: User's Social Media Profile as Predictor of Empathy

abstract: The use of social media, like Facebook, Twitter and LinkedIn, is nowadays very common and quite for sure each one of us has at least a digital profile on them. The information left of these platforms such as likes, posts, tweets and photos are very informative and can be used for deducting our preferences, tendencies and behaviors. The analysis of the social media footprints has become a relevant research topic in the last decade and many works have demonstrated how to extract some traits of the user's affective sphere. In this paper, we focus on the prediction of empathic tendencies of a subject as an index of the influence of emotions during decisional processes. This value can be included in the user profile and can be relevant in some scenarios, such as music and movie recommender systems, where the emotional component is strongly delineated. We propose an approach of empathy level prediction based on a linear regression algorithm over Facebook profiles. We use a word2vec representation of the textual contents of the user's time-line posts, a LDA and SVD vector representation of the user's likes and other general descriptive data. The evaluation performed has demonstrated the validity of the approach for predicting the empathy tendency and the results have showed some relevant correlations with some specific groups of user's descriptive features.