title: Study of Hidden Markov Model in credit card fraudulent detection

abstract: The most accepted payment mode is credit card for both online and offline in today's world, it provides cashless shopping at every shop in all countries. It will be the most convenient way to do online shopping, paying bills etc. Hence, risks of fraud transaction using credit card has also been increasing. In the existing credit card fraud detection business processing system, fraudulent transaction will be detected after transaction is done. It is difficult to find out fraudulent and regarding loses will be barred by issuing authorities. Hidden Markov Model is the statistical tools for engineer and scientists to solve various problems. In this paper, it is shown that credit card fraud can be detected using Hidden Markov Model during transactions. Hidden Markov Model helps to obtain a high fraud coverage combined with a low false alarm rate.


title: Circularly Polarized Crossed Dipole Antenna With Phase Delay Lines for RFID Handheld Reader

abstract: To produce circularly polarized (CP) radiation, a small, double-sided, crossed dipole antenna with two metal strips loaded for phase delay is proposed. The proposed antenna uses two orthogonal, unequal length dipoles, connected in parallel to a coaxial probe feed. It requires no matching network. The required power and phase relationships are obtained by proper choice of dipole lengths and the metal strips attached to them. The antenna also operates at its half wavelength orthogonal modes, for the UHF band. The phase delay metal strip generates the two resonant modes for CP radiation. Simulated and measured results indicate that the crossed dipole structure can produce CP radiation. An impedance bandwidth (VSWR ≤ 2) of about 11.8% and a 3-dB axial-ratio (AR) bandwidth of about 3.3% around the center frequency of 925 MHz were measured.


title: A Bregman Matrix and the Gradient of Mutual Information for Vector Poisson and Gaussian Channels

abstract: A generalization of Bregman divergence is developed and utilized to unify vector Poisson and Gaussian channel models, from the perspective of the gradient of mutual information. The gradient is with respect to the measurement matrix in a compressive-sensing setting, and mutual information is considered for signal recovery and classification. Existing gradient-of-mutual-information results for scalar Poisson models are recovered as special cases, as are known results for the vector Gaussian model. The Bregman-divergence generalization yields a Bregman matrix, and this matrix induces numerous matrix-valued metrics. The metrics associated with the Bregman matrix are detailed, as are its other properties. The Bregman matrix is also utilized to connect the relative entropy and mismatched minimum mean squared error. Two applications are considered: 1) compressive sensing with a Poisson measurement model and 2) compressive topic modeling for analysis of a document corpora (word-count data). In both of these settings, we use the developed theory to optimize the compressive measurement matrix, for signal recovery and classification.


title: National Institute of Mental Health Treatment of Depression Collaborative Research Program: General Effectiveness of Treatments

abstract: • We investigated the effectiveness of two brief psychotherapies, interpersonal psychotherapy and cognitive behavior therapy, for the treatment of outpatients with major depressive disorder diagnosed by Research Diagnostic Criteria. Two hundred fifty patients were randomly assigned to one of four 16-week treatment conditions: interpersonal psychotherapy, cognitive behavior therapy, imipramine hydrochloride plus clinical management (as a standard reference treatment), and placebo plus clinical management. Patients in all treatments showed signifi-cant reduction in depressive symptoms and improvement in functioning over the course of treatment. There was a consistent ordering of treatments at termination, with imipramine plus clinical management generally doing best, placebo plus clinical management worst, and the two psychotherapies in between but generally closer to imipramine plus clinical management. In analyses carried out on the total samples without regard to initial severity of illness (the primary analyses), there was no evidence of greater effectiveness of one of the psychotherapies as compared with the other and no evidence that either of the psychotherapies was significantly less effective than the standard reference treatment, imipramine plus clinical management. Comparing each of the psychotherapies with the placebo plus clinical management condition, there was limited evidence of the specific effectiveness of interpersonal psychotherapy and none for cognitive behavior therapy. Superior recovery rates were found for both interpersonal psychotherapy and imipramine plus clinical management, as compared with placebo plus clinical management. On mean scores, however, there were few significant differences in effectiveness among the four treatments in the primary analyses. Secondary analyses, in which patients were dichotomized on intial level of severity of depressive symptoms and impairment of functioning, helped to explain the relative lack of significant findings in the primary analyses. Significant differences among treatments were present only for the subgroup of patients who were more severely depressed and functionally impaired; here, there was some evidence of the effectiveness of interpersonal psychotherapy with these patients and strong evidence of the effectiveness of imipramine plus clinical management. In contrast, there were no significant differences among treatments, including placebo plus clinical management, for the less severely depressed and functionally impaired patients.


title: Hierarchical Solution of Markov Decision Processes using Macro-actions

abstract: We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and macro-actions and leave the state space unchanged, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macroactions as local policies that act in certain regions of state space, and by restricting states in the abstract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss several ways in which macro-actions can be generated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational overhead of macro-action generation.


title: Dual Band-Notched Square Monopole Antenna for Ultrawideband Applications

abstract: This letter proposes a novel printed monopole antenna for ultrawideband applications with dual band-notch function. The antenna consists of a square radiating patch with a pair of L-shaped slits, and an E-shaped slot and a ground plane with a V-shaped protruded strip, which provides a wide usable fractional bandwidth of more than 140% (2.89-17.83 GHz). In order to generate single band-notch characteristics, we use two L-shaped slits in the radiating patch. By adding an E-shaped slot in the center of the radiating patch, a dual band-notch function is achieved. Also, by inserting a V-shaped protruded in the ground plane, additional resonances are excited, and hence much wider impedance bandwidth can be produced, especially at the higher band. The measured results reveal that the presented dual band-notch monopole antenna offers a very wide bandwidth with two notched bands, covering all the 5.2/5.8-GHz WLAN, 3.5/5.5-GHz WiMAX and 4-GHz C-bands. The designed antenna has a small size area of 10 × 16 mm2, which has a size reduction of 35% with respect to the previous similar antenna.


title: An efficient handover decision making for heterogeneous wireless connectivity management

abstract: During vertical handover process, considering a network selection based on various Quality of Service (QoS) metrics which indicate the network access performance does not necessarily imply that the chosen network improves or satisfies the users' Perceived Quality of Service (PQoS). This paper not only investigates all kind of information that can constitute the context-aware during handover decision step but also it defines a handover decision algorithm based on network measurements with taking into account the PQoS. Our algorithm uses the sensitive context criteria which are the key required by each class of service and checks their impact on the performance of the chosen network. Here we develop a new context-aware vertical handover algorithm based on a Multi Attribute Decision Making (MADM) approach and we take into account the PQoS. A perform convergence toward the appropriate network is given by our proposed decision function algorithm which guarantees the end users' PQoS. Simulation results show that our algorithm reduces the number of criteria based on sensitivity for the context awareness. It also avoids the unnecessary executed handover generated by bad network selection which is computationally expensive and can impacts processing delay.


title: Review and Perspective for Distance-Based Clustering of Vehicle Trajectories

abstract: In this paper, we tackle the issue of clustering trajectories of geolocalized observations based on the distance between trajectories. We first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then, based on the limitations of these methods, we introduce a new distance: symmetrized segment-path distance (SSPD). We compare this new distance to the others according to their corresponding clustering results obtained using both the hierarchical clustering and affinity propagation methods. We finally present a python package: trajectory distance, which contains the methods for calculating the SSPD distance, and the other distances reviewed in this paper.


title: E-Government: Evolving relationship of citizens and government, domestic, and international development

abstract: The purpose of this study is to explore the emerging applications of E-Government, both in the United States and in the International Community. Information technology allows governments to service citizens in a more timely, effective, and cost-efficient method. E-Government implementation may meet initial citizen resistance, require cultural sensitivity, and change the way citizens and governments relate to each other. There is a focus on the domestic and international state of EGovernment and the importance of citizen acceptance of E-Government.


title: ProductChain: Scalable Blockchain Framework to Support Provenance in Supply Chains

abstract: An increased incidence of food mislabeling and handling in recent years has led to consumers demanding transparency in how food items are produced and handled. The current traceability solutions suffer from issues such as scattering of information across multiple silos and susceptibility in recording erroneous data and thus are often unable to produce reliable farm to fork stories of products. Blockchain (BC) is a promising technology that could play an important role in providing data transparency and integrity due to its salient features which include decentralisation, immutability and auditability. In this paper, we propose a permissioned blockchain framework which is governed by a consortium of key Food Supply Chain (FSC) entities including government and regulatory bodies to promote food provenance. We propose to use a sharded, three-tiered architecture which ensures availability of data to consumers, limits access to competitive partners and provides scalability for handling transaction load. We also propose a transaction vocabulary and access rights to manage read and write privileges to BC supported by the consortium. The framework, ProductChain, ensures that trade flows are kept confidential when provenance information is retrieved by consumers and stakeholders. Simulation results show that query time for a product ledger is of the order of a few milliseconds even when the information is collated from multiple shards. ProductChain is generalised and applicable to supply chains in diverse industries.


title: Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems

abstract: For nonlinear programming problems which are factorable, a computable procedure for obtaining tight underestimating convex programs is presented. This is used to exclude from consideration regions where the global minimizer cannot exist.


title: Ethics and consciousness in artificial agents

abstract: In what ways should we include future humanoid robots, and other kinds of artificial agents, in our moral universe? We consider the Organic view, which maintains that artificial humanoid agents, based on current computational technologies, could not count as full-blooded moral agents, nor as appropriate targets of intrinsic moral concern. On this view, artificial humanoids lack certain key properties of biological organisms, which preclude them from having full moral status. Computationally controlled systems, however advanced in their cognitive or informational capacities, are, it is proposed, unlikely to possess sentience and hence will fail to be able to exercise the kind of empathic rationality that is a prerequisite for being a moral agent. The organic view also argues that sentience and teleology require biologically based forms of self-organization and autonomous self-maintenance. The organic view may not be correct, but at least it needs to be taken seriously in the future development of the field of Machine Ethics.


title: Anti-forensics of digital image compression

abstract: As society has become increasingly reliant upon digital images to communicate visual information, a number of forensic techniques have been developed to verify the authenticity of digital images. Amongst the most successful of these are techniques that make use of an image's compression history and its associated compression fingerprints. Little consideration has been given, however, to anti-forensic techniques capable of fooling forensic algorithms. In this paper, we present a set of anti-forensic techniques designed to remove forensically significant indicators of compression from an image. We do this by first developing a generalized framework for the design of anti-forensic techniques to remove compression fingerprints from an image's transform coefficients. This framework operates by estimating the distribution of an image's transform coefficients before compression, then adding anti-forensic dither to the transform coefficients of a compressed image so that their distribution matches the estimated one. We then use this framework to develop anti-forensic techniques specifically targeted at erasing compression fingerprints left by both JPEG and wavelet-based coders. Additionally, we propose a technique to remove statistical traces of the blocking artifacts left by image compression algorithms that divide an image into segments during processing. Through a series of experiments, we demonstrate that our anti-forensic techniques are capable of removing forensically detectable traces of image compression without significantly impacting an image's visual quality. Furthermore, we show how these techniques can be used to render several forms of image tampering such as double JPEG compression, cut-and-paste image forgery, and image origin falsification undetectable through compression-history-based forensic means.


title: GUESS: a language and interface for graph exploration

abstract: As graph models are applied to more widely varying fields, researchers struggle with tools for exploring and analyzing these structures. We describe GUESS, a novel system for graph exploration that combines an interpreted language with a graphical front end that allows researchers to rapidly prototype and deploy new visualizations. GUESS also contains a novel, interactive interpreter that connects the language and interface in a way that facilities exploratory visualization tasks. Our language, Gython, is a domain-specific embedded language which provides all the advantages of Python with new, graph specific operators, primitives, and shortcuts. We highlight key aspects of the system in the context of a large user survey and specific, real-world, case studies ranging from social and knowledge networks to distributed computer network analysis.


title: From Depth Data to Head Pose Estimation: a Siamese approach

abstract: The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner. Traditional methods usually rely on visual facial features, such as facial landmarks or nose tip position. In contrast, we exploit a Convolutional Neural Network (CNN) to perform head pose estimation directly from depth data. We exploit a Siamese architecture and we propose a novel loss function to improve the learning of the regression network layer. The system has been tested on two public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported results demonstrate the improvement in accuracy with respect to current state-of-the-art approaches and the real time capabilities of the overall framework.


title: Compact microstrip bandstop filter using open stub and spurline

abstract: A compact wideband high-rejection microstrip bandstop filter (BSF) is presented. This filter combines two traditional BSFs: open-stub filter and spurline filter. Due to the inherently compact characteristics of the spurline, the proposed filter shows a better rejection performance than open-stub BSF without increasing the circuit size. From 3.7 to 5.4 GHz, the proposed BSF has a rejection of better than 20 dB and the maximum rejection level of 61 dB. Application of this BSF to suppress the second harmonic of an open-loop ring bandpass filter is also investigated with a 40-dB suppression improvement achieved.


title: Description and analysis of a bottom-up DFA minimization algorithm

abstract: We establish linear-time reductions between the minimization of a deterministic finite automaton (DFA) and the conjunction of 3 subproblems: the minimization of a strongly connected DFA, the isomorphism problem for a set of strongly connected minimized DFAs, and the minimization of a connected DFA consisting in two strongly connected components, both of which are minimized. We apply this procedure to minimize, in linear time, automata whose nontrivial strongly connected components are cycles.


title: Finding Competitive Network Architectures Within a Day Using UCT

abstract: The design of neural network architectures for a new data set is a laborious task which requires human deep learning expertise. In order to make deep learning available for a broader audience, automated methods for finding a neural network architecture are vital. Recently proposed methods can already achieve human expert level performances. However, these methods have run times of months or even years of GPU computing time, ignoring hardware constraints as faced by many researchers and companies. We propose the use of Monte Carlo planning in combination with two different UCT (upper confidence bound applied to trees) derivations to search for network architectures. We adapt the UCT algorithm to the needs of network architecture search by proposing two ways of sharing information between different branches of the search tree. In an empirical study we are able to demonstrate that this method is able to find competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day. Extending the search time to five GPU days, we are able to outperform human architectures and our competitors which consider the same types of layers.


title: Deep Learning Based Car Damage Classification

abstract: Image based vehicle insurance processing is an important area with large scope for automation. In this paper we consider the problem of car damage classification, where some of the categories can be fine-granular. We explore deep learning based techniques for this purpose. Initially, we try directly training a CNN. However, due to small set of labeled data, it does not work well. Then, we explore the effect of domain-specific pre-training followed by fine-tuning. Finally, we experiment with transfer learning and ensemble learning. Experimental results show that transfer learning works better than domain specific fine-tuning. We achieve accuracy of 89.5% with combination of transfer and ensemble learning.


title: Detecting Deception in Children: A Meta-Analysis

abstract: Although research reveals that children as young as 3 can use deception and will take steps to obscure truth, research concerning how well others detect children’s deceptive efforts remains unclear. Yet adults regularly assess whether children are telling the truth in a variety of contexts, including at school, in the home, and in legal settings, particularly in investigations of maltreatment. We conducted a meta-analysis to synthesize extant research concerning adults’ ability to detect deceptive statements produced by children. We included 45 experiments involving 7,893 adult judges and 1,858 children. Overall, adults could accurately discriminate truths/lies at an average rate of 54%, which is slightly but significantly above chance levels. The average rate at which true statements were correctly classified as honest was higher (63.8%), whereas the rate at which lies were classified as dishonest was not different from chance (47.5%). A small positive correlation emerged between judgment confidence and judgment accuracy. Professionals (e.g., social workers, police officers, teachers) slightly outperformed laypersons (e.g., college undergraduates). Finally, exploratory analyses revealed that the child’s age did not significantly affect the rate at which adults could discriminate truths/lies from chance. Future research aimed toward improving lie detection accuracy might focus more on individual differences in children’s lie-telling abilities in order to uncover any reliable indicators of deception.


title: Security challenges with network functions virtualization

abstract: Abstract The advent of network functions virtualization (NFV) has revolutionized numerous network-based applications due to its several benefits such as flexibility, manageability, scalability, and security. By the software-based virtualization of network functions on a single infrastructure, NFV provides users with a framework that dynamically provisions various network services in a flexible manner. However, NFV faces several security challenges (e.g., multi-tenancy and live migration) which make it vulnerable to some cybersecurity attacks (e.g., side-channel attacks and shared resource misuse attacks). In this paper, we provide an overview of NFV, discuss potentially serious security threats on NFV and introduce effective countermeasures to mitigate those threats. Finally, we suggest some practical solutions to provide a trustworthy platform for NFV.


title: AuthoRing: Wearable User-presence Authentication

abstract: A common log-in process at computers involves the entry of username and password; log out depends on the user to remember to log out, or a timeout to expire the user session. Once logged in, user sessions may be vulnerable to imposter attacks in which an impostor steps up to the user's unattended computer and inherits the user's access privilege. We propose a ring-based authentication system called "AuthoRing", which restricts the imposter attackers from generating new inputs at the computer's mouse and keyboard. During the log-in process, an eligible AuthoRing user wears a digital ring with accelerometers and wireless communication capability. When input is detected at the mouse or keyboard, the computer's AuthoRing system correlates hand-motion data received from the ring with the input data from the computer's window manager, and detects imposter attacks when these data are insufficiently correlated. We implemented the AuthoRing system and evaluated its security, efficiency, and usability; we found that imposter attacks can be effectively detected and the required operations happen quickly with negligible delays experienced by the user.


title: COVERT: Compositional Analysis of Android Inter-App Permission Leakage

abstract: Android is the most popular platform for mobile devices. It facilitates sharing of data and services among applications using a rich inter-app communication system. While access to resources can be controlled by the Android permission system, enforcing permissions is not sufficient to prevent security violations, as permissions may be mismanaged, intentionally or unintentionally. Android’s enforcement of the permissions is at the level of individual apps, allowing multiple malicious apps to collude and combine their permissions or to trick vulnerable apps to perform actions on their behalf that are beyond their individual privileges. In this paper, we present COVERT , a tool for compositional analysis of Android inter-app vulnerabilities. COVERT’s analysis is modular to enable incremental analysis of applications as they are installed, updated, and removed. It statically analyzes the reverse engineered source code of each individual app, and extracts relevant security specifications in a format suitable for formal verification. Given a collection of specifications extracted in this way, a formal analysis engine (e.g., model checker) is then used to verify whether it is safe for a combination of applications—holding certain permissions and potentially interacting with each other—to be installed together. Our experience with using COVERT to examine over 500 real-world apps corroborates its ability to find inter-app vulnerabilities in bundles of some of the most popular apps on the market.


Reactive Environments : Throwing Away Your Keyboard and Mouse


title: Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes

abstract: Order selection of autoregressive processes is an active research topic in time series analysis, and the development and evaluation of automatic order selection criteria remains a challenging task for domain experts. We propose a visual analytics approach, to guide the analysis and development of such criteria. A flexible synthetic model generator—combined with specialized responsive visualizations—allows comprehensive interactive evaluation. Our fast framework allows feedback-driven development and fine-tuning of new order selection criteria in real-time. We demonstrate the applicability of our approach in three use-cases for two general as well as a real-world example.


title: Social influence in social advertising: evidence from field experiments

abstract: Social advertising uses information about consumers' peers, including peer affiliations with a brand, product, organization, etc., to target ads and contextualize their display. This approach can increase ad efficacy for two main reasons: peers' affiliations reflect unobserved consumer characteristics, which are correlated along the social network; and the inclusion of social cues (i.e., peers' association with a brand) alongside ads affect responses via social influence processes. For these reasons, responses may be increased when multiple social signals are presented with ads, and when ads are affiliated with peers who are strong, rather than weak, ties. We conduct two very large field experiments that identify the effect of social cues on consumer responses to ads, measured in terms of ad clicks and the formation of connections with the advertised entity. In the first experiment, we randomize the number of social cues present in word-of-mouth advertising, and measure how responses increase as a function of the number of cues. The second experiment examines the effect of augmenting traditional ad units with a minimal social cue (i.e., displaying a peer's affiliation below an ad in light grey text). On average, this cue causes significant increases in ad performance. Using a measurement of tie strength based on the total amount of communication between subjects and their peers, we show that these influence effects are greatest for strong ties. Our work has implications for ad optimization, user interface design, and central questions in social science research.


title: Learning from the past: automated rule generation for complex event processing

abstract: Complex Event Processing (CEP) systems aim at processing large flows of events to discover situations of interest. In CEP, the processing takes place according to user-defined rules, which specify the (causal) relations between the observed events and the phenomena to be detected. We claim that the complexity of writing such rules is a limiting factor for the diffusion of CEP. In this paper, we tackle this problem by introducing iCEP, a novel framework that learns, from historical traces, the hidden causality between the received events and the situations to detect, and uses them to automatically generate CEP rules. The paper introduces three main contributions. It provides a precise definition for the problem of automated CEP rules generation. It dicusses a general approach to this research challenge that builds on three fundamental pillars: decomposition into subproblems, modularity of solutions, and ad-hoc learning algorithms. It provides a concrete implementation of this approach, the iCEP framework, and evaluates its precision in a broad range of situations, using both synthetic benchmarks and real traces from a traffic monitoring scenario.


title: Fast mode decision algorithm for intra prediction in HEVC

abstract: The High Efficiency Video Coding (HEVC) standard achieves much better efficiency than previous video coding standards. One contributor to this improvement is the large set of intra prediction modes that it supports. While resulting in the better Rate-Distortion (R-D) performance compared to the H.264/AVC equivalent predictor, it suffers from heavier computation load. In this paper, a fast intra mode decision algorithm is proposed to speed up the original intra coding in HEVC. By analysing the gradient information of the current prediction unit (PU), only a small part of modes are selected as the candidate modes for the rate-distortion optimization (RDO) process. Two other performance tuning methods (SBS and SDBS) are also introduced in this paper. Experimental results show that, compared to the newest HEVC test model software HM 10.0, the proposed method provides a reduction of up to 42% in computational complexity with a small increase in the bit-rate and a negligible reduction in PSNR values.


title: A compositional exemplar-based model for hair segmentation

abstract: Hair is a very important part of human appearance. Robust and accurate hair segmentation is difficult because of challenging variation of hair color and shape. In this paper, we propose a novel Compositional Exemplar-based Model (CEM) for hair style segmentation. CEM generates an adaptive hair style (a probabilistic mask) for the input image automatically in the manner of Divide-and-Conquer, which can be divided into decomposition stage and composition stage naturally. For the decomposition stage, we learn a strong ranker based on a group of weak similarity functions emphasizing the Semantic Layout similarity (SLS) effectively; in the composition stage, we introduce the Neighbor Label Consistency (NLC) Constraint to reduce the ambiguity between data representation and semantic meaning and then recompose the hair style using alpha-expansion algorithm. Final segmentation result is obtained by Dual-Level Conditional Random Fields. Experiment results on face images from Labeled Faces in the Wild data set show its effectiveness.


title: 3D printed horn antenna with PCB microstrip feed for UWB radar applications

abstract: In this paper, a 3D printed Double Ridge Horn Antenna (DRHA) for mobile snow cover monitoring is presented. The DRHA is designed for high bandwidth and high gain as required for the snow monitoring pulsed radar system. The bandwidth is between 2.4 and 5.1 GHz with an in-band gain between 12 to 14 dBi. The antenna parts are fabricated with a standard 3D printer using light-weighted plastic material. To add conductivity the surface is coated with silver-copper spray paint. The antenna feed is a PCB plate is inserted into the 3D printed plastic waveguides replacing traditional coaxial probe. The antenna has a total weight of less than 400g and the total cost is about $60. Both cost and weight is significantly lower than commercially available DRHAs adding fast and easy prototyping. Low weight, high gain and large bandwidth makes this antenna suitable for mobile systems like snow cover analysis.


title: Electron Scattering and Electrical Conductance in Polycrystalline Metallic Films and Wires: Impact of Grain Boundary Scattering Related to Melting Point

abstract: For electrical conductance in polycrystalline metallic films and wires, the reflection coefficient of electrons at grain boundaries is explored and found to be proportional to the square root of the melting points of metals. As validated by available experimental results, this exploration enables classical models to take an essential role in theoretically predicting the electrical conductance of low-dimensional metals. One thus sees that the mechanism dominating the suppression of electrical conductance is transformed from the surface scattering into the grain boundary scattering as the ratio of film thickness (or wire diameter) to grain size rises. Furthermore, the impact of grain boundary scattering becomes less important for metals with lower melting points.


title: Simultaneous calibration, localization, and mapping

abstract: The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on environmental changes or on the wear of the devices. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the platform parameters. The proposed approach performs on-line estimation of the parameters and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real world data using different types of robotic platforms.


title: Performance Evaluation of Priority Queues for Fine-Grained Parallel Tasks on GPUs

abstract: Graphics processing units (GPUs) are increasingly applied to accelerate tasks such as graph problems and discreteevent simulation that are characterized by irregularity, i.e., a strong dependence of the control flow and memory accesses on the input. The core data structure in many of these irregular tasks are priority queues that guide the progress of the computations and which can easily become the bottleneck of an application. To our knowledge, currently no systematic comparison of priority queue implementations on GPUs exists in the literature. We close this gap by a performance evaluation of GPU-based priority queue implementations for two applications: discrete-event simulation and parallel A* path searches on grids. We focus on scenarios requiring large numbers of priority queues holding up to a few thousand items each. We present performance measurements covering linear queue designs, implicit binary heaps, splay trees, and a GPU-specific proposal from the literature. The measurement results show that up to about 500 items per queue, circular buffers frequently outperform tree-based queues for the considered applications, particularly under a simple parallelization of individual item enqueue operations. We analyze profiling metrics to explore classical queue designs in light of the importance of high hardware utilization as well as homogeneous computations and memory accesses across GPU threads.


title: Emotions in human-computer interaction : the role of nonverbal behavior in interactive systems

abstract: Until recently, the area of human-computer interaction was based on a traditional, cognitive approach, which separated the study of usability from that of emotions. The recent research has shown that emotions play an important role in our life, which led to focusing on the need of studying the emotions in the domain of interactive design. This paper underlines the role of emotions as part of the interactive human-computer process, reinstating the importance of nonverbal communication in this domain. The main issues of this paper are concerned with aspects such as: emotional design approach, the importance of nonverbal as an instrument of usability evaluation and the role of emotions in human-computer interaction.


title: Internal Uniplanar Antenna for LTE/GSM/UMTS Operation in a Tablet Computer

abstract: With the use of an internal printed loop matching circuit, a small-size uniplanar antenna with eight-band LTE/GSM/UMTS operation in the tablet computer is proposed. The obtained impedance bandwidth across the operating bands can reach about 262/1610 MHz for the LTE/GSM/UMTS bands, respectively. Only with the antenna size of $40\times 15\times 0.8\ {\rm mm}^{3}$ , the proposed planar antenna has the compact operation with more than 30% antenna size reduction. Furthermore, the measured peak gains and antenna efficiencies are about 2.81/4.97 dBi and 79/87% for the LTE/GSM/UMTS bands, respectively.


title: Emergence of Invariance and Disentangling in Deep Representations

abstract: Using classical notions of statistical decision and information theory, we show that invariance in a deep neural network is equivalent to minimality of the representation it computes, and can be achieved by stacking layers and injecting noise in the computation, under realistic and empirically validated assumptions. We use an Information Decomposition of the empirical loss to show that overfitting can be reduced by limiting the information content stored in the weights. We then present a sharp inequality that relates the information content in the weights -- which are a representation of the training set and inferred by generic optimization agnostic of invariance and disentanglement -- and the minimality and total correlation of the activation functions, which are a representation of the test datum. This allows us to tackle recent puzzles concerning the generalization properties of deep networks and their relation to the geometry of the optimization residual.


title: Approximate nearest neighbor queries in fixed dimensions

abstract: Given a set of n points in d-dimensional Euclidean space, S ⊂ E, and a query point q ∈ E, we wish to determine the nearest neighbor of q, that is, the point of S whose Euclidean distance to q is minimum. The goal is to preprocess the point set S, such that queries can be answered as efficiently as possible. We assume that the dimension d is a constant independent of n. Although reasonably good solutions to this problem exist when d is small, as d increases the performance of these algorithms degrades rapidly. We present a randomized algorithm for approximate nearest neighbor searching. Given any set of n points S ⊂ E, and a constant ǫ > 0, we produce a data structure, such that given any query point, a point of S will be reported whose distance from the query point is at most a factor of (1 + ǫ) from that of the true nearest neighbor. Our algorithm runs in O(log n) expected time and requires O(n log n) space. The data structure can be built in O(n) expected time. The constant factors depend on d and ǫ. Because of the practical importance of nearest neighbor searching in higher dimensions, we have implemented a practical variant of this algorithm, and show empirically that for many point distributions this variant of the algorithm finds the nearest neighbor in moderately large dimension significantly faster than existing practical approaches.


title: The connectedness to nature scale: A measure of individuals’ feeling in community with nature

abstract: Abstract Five studies assessed the validity and reliability of the connectedness to nature scale (CNS), a new measure of individuals’ trait levels of feeling emotionally connected to the natural world. Data from two community and three college samples demonstrated that the CNS has good psychometric properties, correlates with related variables (the new environmental paradigm scale, identity as an environmentalist), and is uncorrelated with potential confounds (verbal ability, social desirability). This paper supports ecopsychologists’ contention that connection to nature is an important predictor of ecological behavior and subjective well-being. It also extends social psychological research on self–other overlap, perspective taking, and altruistic behavior to the overlap between self and nature. The CNS promises to be a useful empirical tool for research on the relationship between humans and the natural world.


title: The evolution of China's mobile telecommunications industry: past, present and future

abstract: This paper studies the evolutionary direction of the mobile telecommunications industry in China. Different stages of the industry development process with policy intervention and market competition are examined. The interaction of different players like the Chinese government, incumbent operator, and competitive operator within an actor-network is studied. A process-mapping technique is used to visualise the key drivers of the evolution trajectory of this giant industry. In the meantime, an analytic framework is developed. The future road toward 3G network in China is also analysed.


title: Photon counting and direct ToF camera prototype based on CMOS SPADs

abstract: This paper presents a camera prototype for 2D/3D image capture in low illumination conditions based on single-photon avalanche-diode (SPAD) image sensor for direct time-offlight (d-ToF). The imager is a 64×64 array with in-pixel TDC for high frame rate acquisition. Circuit design techniques are combined to ensure successful 3D image capturing under low sensitivity conditions and high level of uncorrelated noise such as dark count and background illumination. Among them an innovative time gated front-end for the SPAD detector, a reverse start-stop scheme and real-time image reconstruction at Ikfps are incorporated by the imager. To the best of our knowledge, this is the first ToF camera based on a SPAD sensor fabricated and proved for 3D image reconstruction in a standard CMOS process without any opto-flavor or high voltage option. It has a depth resolution of 1cm at an illumination power from less than 6nW/mm2 down to 0.1nW/mm2.


title: Epidemiologic Background of Hand Hygiene and Evaluation of the Most Important Agents for Scrubs and Rubs

abstract: The etiology of nosocomial infections, the frequency of contaminated hands with the different nosocomial pathogens, and the role of health care workers' hands during outbreaks suggest that a hand hygiene preparation should at least have activity against bacteria, yeasts, and coated viruses. The importance of efficacy in choosing the right hand hygiene product is reflected in the new Centers for Disease Control and Prevention guideline on hand hygiene (J. M. Boyce and D. Pittet, Morb. Mortal. Wkly. Rep. 51:1-45, 2002). The best antimicrobial efficacy can be achieved with ethanol (60 to 85%), isopropanol (60 to 80%), and n-propanol (60 to 80%). The activity is broad and immediate. Ethanol at high concentrations (e.g., 95%) is the most effective treatment against naked viruses, whereas n-propanol seems to be more effective against the resident bacterial flora. The combination of alcohols may have a synergistic effect. The antimicrobial efficacy of chlorhexidine (2 to 4%) and triclosan (1 to 2%) is both lower and slower. Additionally, both agents have a risk of bacterial resistance, which is higher for chlorhexidine than triclosan. Their activity is often supported by the mechanical removal of pathogens during hand washing. Taking the antimicrobial efficacy and the mechanical removal together, they are still less effective than the alcohols. Plain soap and water has the lowest efficacy of all. In the new Centers for Disease Control and Prevention guideline, promotion of alcohol-based hand rubs containing various emollients instead of irritating soaps and detergents is one strategy to reduce skin damage, dryness, and irritation. Irritant contact dermatitis is highest with preparations containing 4% chlorhexidine gluconate, less frequent with nonantimicrobial soaps and preparations containing lower concentrations of chlorhexidine gluconate, and lowest with well-formulated alcohol-based hand rubs containing emollients and other skin conditioners. Too few published data from comparative trials are available to reliably rank triclosan. Personnel should be reminded that it is neither necessary nor recommended to routinely wash hands after each application of an alcohol-based hand rub. Long-lasting improvement of compliance with hand hygiene protocols can be successful if an effective and accessible alcohol-based hand rub with a proven dermal tolerance and an excellent user acceptability is supplied, accompanied by education of health care workers and promotion of the use of the product.


title: Home Location Identification of Twitter Users

abstract: We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone or geographic region, using the content of users tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.


title: Robust Global Translations with 1DSfM

abstract: We present a simple, effective method for solving structure from motion problems by averaging epipolar geometries. Based on recent successes in solving for global camera rotations using averaging schemes, we focus on the problem of solving for 3D camera translations given a network of noisy pairwise camera translation directions (or 3D point observations). To do this well, we have two main insights. First, we propose a method for removing outliers from problem instances by solving simpler low-dimensional subproblems, which we refer to as 1DSfM problems. Second, we present a simple, principled averaging scheme. We demonstrate this new method in the wild on Internet photo collections.


title: The Emerging Genre of Data Comics

abstract: As we increasingly rely on data to understand our world, and as problems require global solutions, we need to effectively communicate that data to help people make informed decisions. The special Art on Graphics article explores the potential of data comics and their unique ability to communicate both data and context via compelling visual storytelling.


title: A Comparison Among ARIMA, BP-NN, and MOGA-NN for Software Clone Evolution Prediction

abstract: Software evolution continues throughout the life cycle of the software. During the evolution of software system, it has been observed that the developers have a tendency to copy the modules completely or partially and modify them. This practice gives rise to identical or very similar code fragments called software clones. This paper examines the evolution of clone components by using advanced time series analysis. In the first phase, software clone components are extracted from the source repository of the software application by using the abstract syntax tree approach. Then, the evolution of software clone components is analyzed. In this paper, three models, Autoregressive Integrated Moving Average, back propagation neural network, and multi-objective genetic algorithm-based neural network, have been compared for the prediction of the evolution of software clone components. Evaluation is performed on the large open-source software application, ArgoUML. The ability to predict the clones helps the software developer to reduce the effort during software maintenance activities.


title: Functional analysis of enterprise resource planning systems

abstract: An analysis of the general functions of the Enterprise Resource Planning (ERP) systems is performed. Historical development of ERP is given. Analysis and comparisons of three open source ERP are provided.


title: Inverse Reinforcement Learning for Marketing

abstract: Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control. We suggest an alternative approach to study dynamic consumer demand, based on Inverse Reinforcement Learning (IRL). We develop a version of the Maximum Entropy IRL that leads to a highly tractable model formulation that amounts to low-dimensional convex optimization in the search for optimal model parameters. Using simulations of consumer demand, we show that observational noise for identical customers can be easily confused with an apparent consumer heterogeneity.


title: Cartograph: Unlocking Spatial Visualization Through Semantic Enhancement

abstract: This paper introduces Cartograph, a visualization system that harnesses the vast amount of world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. While these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph's map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original data sets, but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered, and we present findings from an exploratory user study


title: Fast and High Resolution 3D Face Scanning

abstract: In this work, we present a framework to capture 3D models of faces in high resolutions with low computational load. The system captures only two pictures of the face, one illuminated with a colored stripe pattern and one with regular white light. The former is needed for the depth calculation, the latter is used as texture. Having these two images a combination of specialized algorithms is applied to generate a 3D model. The results are shown in different views: simple surface, wire grid respective polygon mesh or textured 3D surface.


title: 3, 4-dihydroxy-L-phenylalanine-derived melanin from Yarrowia lipolytica mediates the synthesis of silver and gold nanostructures

abstract: Nanobiotechnology applies the capabilities of biological systems in generating a variety of nano-sized structures. Plants, algae, fungi and bacteria are some systems mediating such reactions. In fungi, the synthesis of melanin is an important strategy for cell-survival under metal-stressed conditions. Yarrowia lipolytica, the biotechnologically significant yeast also produces melanin that sequesters heavy metal ions. The content of this cell-associated melanin is often low and precursors such as L-tyrosine or 3, 4-dihydroxy-L-phenylalanine (L-DOPA) can enhance its production. The induced melanin has not been exploited for the synthesis of nanostructures. In this investigation, we have employed L-DOPA-melanin for the facile synthesis of silver and gold nanostructures. The former have been used for the development of anti-fungal paints. Yarrowia lipolytica NCIM 3590 cells were incubated with L-DOPA for 18 h and the resultant dark pigment was subjected to physical and chemical analysis. This biopolymer was used as a reducing and stabilizing agent for the synthesis of silver and gold nanostructures. These nanoparticles were characterized by UV-Visible spectra, X-ray diffraction (XRD) studies, and electron microscopy. Silver nanoparticles were evaluated for anti-fungal activity. The pigment isolated from Y. lipolytica was identified as melanin. The induced pigment reduced silver nitrate and chloroauric acid to silver and gold nanostructures, respectively. The silver nanoparticles were smaller in size (7 nm) and displayed excellent anti-fungal properties towards an Aspergillus sp. isolated from a wall surface. An application of these nanoparticles as effective paint-additives has been demonstrated. The yeast mediated enhanced production of the metal-ion-reducing pigment, melanin. A simple and rapid method for the extracellular synthesis of nanoparticles with paint-additive-application was developed.


THE GREAT SPANISH DELUSION


title: The first 10,000 Adult Attachment Interviews: distributions of adult attachment representations in clinical and non-clinical groups

abstract: More than 200 adult attachment representation studies, presenting more than 10,500 Adult Attachment Interview (AAI; George, Kaplan, & Main, 1985) classifications, have been conducted in the past 25 years. In a series of analyses on the distributions of the AAI classifications in various cultural and age groups, fathers, and high-risk and clinical samples, we used the distribution of the combined samples of North American non-clinical mothers (23% dismissing, 58% secure, 19% preoccupied attachment representations, and 18% additionally coded for unresolved loss or other trauma) to examine deviations from this normative pattern, through multinomial tests and analyses of correspondence. The analyses were restricted to AAI classifications coded according to the Main, Goldwyn, and Hesse (2003) system. We did not find gender differences in the use of dismissing versus preoccupied attachment strategies, and the AAI distributions were largely independent of language and country of origin. Clinical subjects showed more insecure and unresolved attachment representations than the norm groups. Disorders with an internalizing dimension (e.g., borderline personality disorders) were associated with more preoccupied and unresolved attachments, whereas disorders with an externalizing dimension (e.g., antisocial personality disorders) displayed more dismissing as well as preoccupied attachments. Depressive symptomatology was associated with insecurity but not with unresolved loss or trauma, whereas adults with abuse experiences or PTSD were mostly unresolved. In order to find more reliable associations with clinical symptoms and disorders, future AAI studies may make more fruitful use of continuous AAI scales in addition to the conventionally used categorical classifications.


title: Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising

abstract: Many estimation tasks come in groups and hierarchies of related problems. In this paper we propose a hierarchical model and a scalable algorithm to perform inference for multitask learning. It infers task correlation and subtask structure in a joint sparse setting. Implementation is achieved by a distributed subgradient oracle and the successive application of prox-operators pertaining to groups and subgroups of variables. We apply this algorithm to conversion optimization in display advertising. Experimental results on over 1TB data for up to 1 billion observations and 1 million attributes show that the algorithm provides significantly better prediction accuracy while simultaneously beingefficiently scalable by distributed parameter synchronization.


title: Markov logic networks

abstract: We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.


title: NeuroTrainer: An Intelligent Memory Module for Deep Learning Training

abstract: This paper presents, NeuroTrainer, an intelligent memory module with in-memory accelerators that forms the building block of a scalable architecture for energy efficient training for deep neural networks. The proposed architecture is based on integration of a homogeneous computing substrate composed of multiple processing engines in the logic layer of a 3D memory module. NeuroTrainer utilizes a programmable data flow based execution model to optimize memory mapping and data re-use during different phases of training operation. A programming model and supporting architecture utilizes the flexible data flow to efficiently accelerate training of various types of DNNs. The cycle level simulation and synthesized design in 15nm FinFET showspower efficiency of 500 GFLOPS/W, and almost similar throughput for a wide range of DNNs including convolutional, recurrent, multi-layer-perceptron, and mixed (CNN+RNN) networks


title: A fail-operational truck platooning architecture

abstract: Truck platooning for which multiple trucks follow at a short distance is considered a near-term truck automation opportunity, with the potential to reduce fuel consumption. Short following distances and increasing automation make it hard for a driver to be the backup if the system fails. The EcoTwin consortium successfully demonstrated a two truck platooning system with trucks following at 20 meters distance at the public road, in which the driver is the backup. The ambition of the consortium is to increase the truck automation and to reduce the following distance, which requires a new fail-operational truck platooning architecture. This paper presents a level 2+ platooning system architecture, which is fail-operational for a single failure, and the corresponding process to obtain it. First insights in the existing two truck platooning system are obtained by analyzing its key aspects, being utilization, latency, reliability, and safety. Using these insights, candidate level 2+ platooning system architectures are defined from which the most suitable truck platooning architecture is selected. Future work is the design and implementation of a prototype, based on the presented level 2+ platooning system architecture.


title: Social media and mobiles as political mobilization forces for young adults: Examining the moderating role of online political expression in political participation:

abstract: A web survey of college students was conducted to examine whether online political expression moderates the effects of political media use on political participation. Results showed that online political expression enhanced the effects of political mobile apps, traditional offline and online media, and social media on political participation. Implications are discussed for a mobilizing role of online media in the democratic process for young adults.


MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning


Dependency Parsing with Partial Annotations: An Empirical Comparison


title: Impaired Face Processing in Autism: Fact or Artifact?

abstract: Within the last 10 years, there has been an upsurge of interest in face processing abilities in autism which has generated a proliferation of new empirical demonstrations employing a variety of measuring techniques. Observably atypical social behaviors early in the development of children with autism have led to the contention that autism is a condition where the processing of social information, particularly faces, is impaired. While several empirical sources of evidence lend support to this hypothesis, others suggest that there are conditions under which autistic individuals do not differ from typically developing persons. The present paper reviews this bulk of empirical evidence, and concludes that the versatility and abilities of face processing in persons with autism have been underestimated.


title: Advanced digital beamforming concepts for future SAR systems

abstract: This paper reviews advanced multi-channel SAR system concepts for the imaging of wide swaths with high resolution. Several novel system architectures employing both direct radiating arrays and reflector antennas fed by a digital array are introduced and compared to each other with regard to their imaging performance. In addition, innovative SAR imaging modes are proposed which enable the mapping of ultra-wide swaths with high azimuth resolution. The new techniques and technologies have the potential to enhance the imaging performance of future SAR systems by one order of magnitude if compared to state of the art SAR sensors like TerraSAR-X, ALOS, Radarsat-2 or Sentinel-1.


title: Feature Representation in Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) are powerful models that achieve impressive results for image classification. In addition, pre-trained CNNs are also useful for other computer vision tasks as generic feature extractors. This paper aims to gain insight into the feature aspect of CNN and demonstrate other uses of CNN features. Our results show that CNN feature maps can be used with Random Forests and SVM to yield classification results that outperforms the original CNN. A CNN that is less than optimal (e.g. not fully trained or overfitting) can also extract features for Random Forest/SVM that yield competitive classification accuracy. In contrast to the literature which uses the top-layer activations as feature representation of images for other tasks, using lower-layer features can yield better results for classification.


title: Overhead transmission line sag, tension and length calculation using affine arithmetic

abstract: Transmission line sag, tension and conductor length varies with the variation of temperature due to thermal expansion and elastic elongation. Beside thermal effect, wind pressure and ice accumulation creates a horizontal and vertical loading on the conductor respectively. Such changes make the calculated data uncertain and require an uncertainty model. A novel affine arithmetic (AA) based transmission line sag, tension and conductor length calculation for parabolic curve is proposed and the proposed method is tested for different test cases. The results are compared with Monte Carlo (MC) and interval arithmetic (IA) methods. The AA based result gives a more conservative bound than MC and IA method in all the cases.


title: Morphology of Heschl's gyrus reflects enhanced activation in the auditory cortex of musicians

abstract: Using magnetoencephalography (MEG), we compared the processing of sinusoidal tones in the auditory cortex of 12 non-musicians, 12 professional musicians and 13 amateur musicians. We found neurophysiological and anatomical differences between groups. In professional musicians as compared to non-musicians, the activity evoked in primary auditory cortex 19–30 ms after stimulus onset was 102% larger, and the gray matter volume of the anteromedial portion of Heschl's gyrus was 130% larger. Both quantities were highly correlated with musical aptitude, as measured by psychometric evaluation. These results indicate that both the morphology and neurophysiology of Heschl's gyrus have an essential impact on musical aptitude.


title: Broadband Circularly Polarized Crossed-Dipole Antenna With a Single Asymmetrical Cross-Loop

abstract: This letter presents a broadband cavity-backed circularly polarized (CP) crossed-dipole antenna encircled by a novel asymmetrical cross-loop. By using the single parasitic cross-loop, the antenna has a simple structure and a compact configuration. More importantly, it produces additional two CP modes. The suitable combination of the crossed dipole and the cross-loop provides broadband CP operation. The measured results show that the proposed antenna with an overall dimension of $1.1{\lambda _0} \times 1.1{\lambda _0} \times 0.28{\lambda _0}$ (in terms of the CP center frequency) features broadband CP characteristics of an impedance bandwidth with $S_{11}$ < –10 dB from 3.14 to 6.34 GHz (67.5%) and a 3 dB axial-ratio bandwidth from 3.83 to 6.62 GHz (53.4%). A unidirectional right-hand circular-polarized radiation pattern with a peak gain of 10.62 dBi is achieved over the CP operating band.


title: Sustainable education for bridge engineers

abstract: Abstract This article examines the way we educate future bridge engineers from the perspective of sustainability. The term sustainability is used in its original sense, namely, a quality of a practice that can be continued long into the future because the benefits it creates outweigh its costs. The focus is on the relation between the way we educate future designers of bridges and the value created by the works they design. It is demonstrated that there are several significant flaws in the current curriculum, and recent efforts to inject more “design” into the curriculum are unlikely to bring about needed change. The article proposes incorporating specific elements of knowledge, skills, and values into the curriculum to increase its capacity to produce engineers who are competent and creative. The most important aspect of knowledge to be added to the curriculum is the knowledge obtained from the critical study of good completed bridges. The most important skills to be added are the use of drawing as a tool for enhancing creativity and the use of scientific principles as tools for validating new ideas. The most important values to be taught relate to the duty of the engineering profession to create value for society by designing every bridge to be an increment in a continual process of improvement on existing technology.


title: The ability of Zea mays ears (Young Corn) powder in enhancing nutritional composition and changing textural properties and sensory acceptability of yeast bread

abstract: Consumption of dietary fibre-rich food has been associated with various beneficial physiological properties and health effects. Presently, different types of natural fibre-rich ingredients are added into variety of bakery-based products to improve it fibre content for health promotional purposes. However, majority of these products are frequently added with imported dietary fibre ingredients. In the present study, bread samples were prepared with Zea mays ear (young corn) powder at the levels of either 2%, 4% or 6%. The bread samples were analyzed for nutritional composition, textural properties and sensory acceptance. Addition of young corn powder (YCP) at the level of 6% increases total dietary fibre (from 3.48% to 5.26%), moisture (from 25.64% to 26.55%), fat (from 4.35% to 4.61%) and protein content (from 9.13% to 9.51%) significantly. However, with the increasing of YCP levels in the bread, the carbohydrate content was decreased significantly (from 59.93% to 58.34%) while the ash content (from 0.95% to 0.99%) was not significantly affected. Results of texture profile analysis indicated that addition of YCP up to 6% not significantly affected the springiness (1.01-1.00) but significantly decreased cohesiveness (0.95-0.82). However, the addition of YCP up to 6% has increased hardness (0.18kg-0.57kg), gumminess (0.17kg-0.47kg) and chewiness (0.18kg0.47kg) attributes of bread samples. On the other result, the sensory evaluation shows that the flavour score was not significantly affected by addition of YCP up to 4% (4.82-4.52) while the tenderness (4.53-4.42), elasticity (4.75-4.58), aroma (4.40-4.47), colour (4.93-4.55) and overall acceptance (4.80-4.35) scores were not significant affected up to 6%. In summary, breads with 4% addition of YCP were considered to be acceptable and potentially used in improving nutritional composition without changing sensory score.


title: Edinburgh's Phrase-based Machine Translation Systems for WMT-14

abstract: This paper describes the University of Edinburgh’s (UEDIN) phrase-based submissions to the translation and medical translation shared tasks of the 2014 Workshop on Statistical Machine Translation (WMT). We participated in all language pairs. We have improved upon our 2013 system by i) using generalized representations, specifically automatic word clusters for translations out of English, ii) using unsupervised character-based models to translate unknown words in RussianEnglish and Hindi-English pairs, iii) synthesizing Hindi data from closely-related Urdu data, and iv) building huge language on the common crawl corpus.


title: Diabetic neuropathy—a review

abstract: Diabetic neuropathy—the most common neuropathy in industrialized countries—encompasses a broad range of clinical manifestations. In this Review, Gerard Said discusses the clinical, pathological and therapeutic aspects of various forms of diabetic neuropathy, including length-dependent diabetic polyneuropathy and focal diabetic neuropathies. The author also addresses the diagnosis of nondiabetic neuropathies in patients with diabetes.


title: A Combined Wye-Delta Connection to Increase the Performance of Axial-Flux PM Machines With Concentrated Windings

abstract: In this paper, a combined wye-delta connection is introduced and compared with a conventional wye-connection of a concentrated winding. Because the combined wye-delta connection has a higher fundamental winding factor, the output torque is higher for the same current density when a sinusoidal current is imposed. As the combined wye-delta connection has only a minor influence on the losses in the machine, the efficiency of the machine is also increased. The combined wye-delta connection is illustrated in detail for an axial-flux permanent-magnet synchronous machine with a rated power of 4 kW at a fixed speed of 2500 r/min, using finite element computation and measurements on a prototype machine.


Hepatoprotective Effect of Ginseng, Green Tea, Cinnamon Their Combination against Acetamiprid - induced Oxidative Stress in Rats


title: iCrowd: Near-Optimal Task Allocation for Piggyback Crowdsensing

abstract: This paper first defines a novel spatial-temporal coverage metric, $k$ -depth coverage, for mobile crowdsensing (MCS) problems. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings collected in each covered subarea. Then iCrowd , a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and $k$ -depth coverage objectives/constraints. iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals—i.e., Goal. 1 near-maximal $k$ -depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined $k$ -depth coverage goal. We evaluated iCrowd extensively using a large-scale real-world dataset for these two data collection goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher $k$ -depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same $k$ -depth coverage constraint.


title: A solid state 100kV long pulse generator for Klystrons power supply

abstract: A solid state pulsed power generator rated at 100kV, 2MWpeak, 0.8ms/2Hz suited for klystrons cathode power supply (klystron modulator) is proposed as an alternative to traditional topologies, requiring thyratrons and PFN's, for long pulse width applications (ms range). Some technological issues and design criteria of the proposed circuit main sub-systems (solid state switch, pulse transformer, undershoot network, droop compensation bouncer) will be presented. The principle of the klystron arc detection electronics and protection strategy is described. An optimization will be developed allowing the achievement of the required pulse flat-top precision (1%) with minimum volume and costs. Finally, experimental results obtained on a resistive dummy load and on a 1.3 MW klystron will be presented and discussed.


title: Clinimetric properties of the alberta infant motor scale in infants born preterm.

abstract: PURPOSE ::: The Alberta Infant Motor Scale (AIMS) is a standardized motor assessment for young infants. This study aimed to examine the reliability of the AIMS in a group of infants born at or before 29 weeks of gestation. ::: ::: ::: METHODS ::: Fifty-nine infants born preterm were recruited. Two experienced pediatric physical therapists participated in this reliability study. Infants were assessed at 4, 8, 12, and 18 months corrected age (CA). ::: ::: ::: RESULTS ::: Intrarater reliability was high (intraclass correlation coefficient [ICC] > or =0.99). The ICC for interrater reliability varied from 0.85 to 0.97. The ICC was low at 4 and 18 months CA. ::: ::: ::: CONCLUSIONS ::: The AIMS is reliable in evaluating motor development in infants born preterm. Clinicians should be cautious about using the AIMS in infants at very young ages and those approaching independent ambulation. Accurate placement of the window on a movement repertoire is crucial. Attention is required when using the AIMS in infants developing atypically.


title: Hybrid computing using a neural network with dynamic external memory

abstract: A ‘differentiable neural computer’ is introduced that combines the learning capabilities of a neural network with an external memory analogous to the random-access memory in a conventional computer.


title: Predict Stock Price with Financial News Based on Recurrent Convolutional Neural Networks

abstract: People have been interested in making profits from financial stock market prediction. However, stock market forecast has always been a challenging problem because of its uncertainty and volatility. We take a different approach by a model called recurrent convolutional neural networks (RCN) that combines the advantages of convolutions, sequence modeling, word embedding for stock price analysis and information extraction from financial news. We then combine RCN with technical analysis indicators to predict stock price. The results show that the technical analysis model combining with RCN performs better than the technical analysis alone. Besides, the prediction error of RCN is lower than that of Long-short term memory networks.


title: Comparative study for various DNA based steganography techniques with the essential conclusions about the future research

abstract: Information security and confidentiality has become of prime concern and importance as a result of the notifiable and explosive growth of the internet. Also, with the growth of information technology and communication techniques, unauthorized access for sensitive data increases daily. There are a lot of widely used techniques in the communication and computer security fields like; cryptography and steganography to protect sensitive data from attackers. New fields of DNA based cryptography and steganography are emerging to provide data security using DNA as a carrier by exploiting its bio-molecular computational abilities. In this paper, the authors compare various DNA based steganography by using important security parameters with explaining the main DNA based steganography strategies that each one is considered as a building block for most of the other steganographic schemes, which are: The substitution, insertion and complementary pair based algorithms. Finally, on this base some suggestions are given to help future researchers to design or improve the DNA storage techniques for secure data storage through more efficient, reliable, high capacity and biologically preserved DNA steganography techniques.


title: Metaphor as structure mapping: The relational shift

abstract: GENTNER, DEDRE. Metaphor as Structure Mapping: The Relational Shift. CHILD DEVELOPMENT, 1988, 59, 47-59. The goal of this research is to clarify the development of metaphor by using structure-mapping theory to make distinctions among kinds of metaphors. In particular, it is proposed that children can understand metaphors based on shared object attributes before those based on shared relational structure. This predicts (1) early ability to interpret metaphors based on shared attributes, (2) a developmental increase in ability to interpret metaphors based on shared relational structure, and (3) a shift from primarily attributional to primarily relational interpretations for metaphors that can be understood in either way. 2 experiments were performed to test these claims. There were 3 kinds of metaphors, varying in whether the shared information forming the basis for the interpretation was attributional, relational, or both. In Experiment 1, children aged 5-6 and 910 and adults produced interpretations of the 3 types of metaphors. The attributionality and relationality of their interpretations were scored by independent judges. In Experiment 2, children aged 45 and 7-8 and adults chose which of 2 interpretations-relational or attributional-of a metaphor they preferred. In both experiments, relational responding increased significantly with age, but attributional responding did not. These results indicate a developmental shift toward a focus on relational structure in metaphor interpretation.


title: Opaque: an oblivious and encrypted distributed analytics platform

abstract: Many systems run rich analytics on sensitive data in the cloud, but are prone to data breaches. Hardware enclaves promise data confidentiality and secure execution of arbitrary computation, yet still suffer from access pattern leakage. We propose Opaque, a distributed data analytics platform supporting a wide range of queries while providing strong security guarantees. Opaque introduces new distributed oblivious relational operators that hide access patterns, and new query planning techniques to optimize these new operators. Opaque is implemented on Spark SQL with few changes to the underlying system. Opaque provides data encryption, authentication and computation verification with a performance ranging from 52% faster to 3.3x slower as compared to vanilla Spark SQL; obliviousness comes with a 1.6-46x overhead. Opaque provides an improvement of three orders of magnitude over state-of-the-art oblivious protocols, and our query optimization techniques improve performance by 2-5x.


title: Whole Test Suite Generation

abstract: Not all bugs lead to program crashes, and not always is there a formal specification to check the correctness of a software test's outcome. A common scenario in software testing is therefore that test data are generated, and a tester manually adds test oracles. As this is a difficult task, it is important to produce small yet representative test sets, and this representativeness is typically measured using code coverage. There is, however, a fundamental problem with the common approach of targeting one coverage goal at a time: Coverage goals are not independent, not equally difficult, and sometimes infeasible-the result of test generation is therefore dependent on the order of coverage goals and how many of them are feasible. To overcome this problem, we propose a novel paradigm in which whole test suites are evolved with the aim of covering all coverage goals at the same time while keeping the total size as small as possible. This approach has several advantages, as for example, its effectiveness is not affected by the number of infeasible targets in the code. We have implemented this novel approach in the EvoSuite tool, and compared it to the common approach of addressing one goal at a time. Evaluated on open source libraries and an industrial case study for a total of 1,741 classes, we show that EvoSuite achieved up to 188 times the branch coverage of a traditional approach targeting single branches, with up to 62 percent smaller test suites.


title: Maintenance of Automated Test Suites in Industry: An Empirical study on Visual GUI Testing

abstract: Context: Verification and validation (V&V) activities make up 20-50% of the total development costs of a software system in practice. Test automation is proposed to lower these V&V costs but available research only provides limited empirical data from industrial practice about the maintenance costs of automated tests and what factors affect these costs. In particular, these costs and factors are unknown for automated GUI-based testing.Objective: This paper addresses this lack of knowledge through analysis of the costs and factors associated with the maintenance of automated GUI-based tests in industrial practice.Method: An empirical study at two companies, Siemens and Saab, is reported where interviews about, and empirical work with, Visual GUI Testing is performed to acquire data about the technique's maintenance costs and feasibility.Results: 13 factors are observed that affect maintenance, e.g. tester knowledge/experience and test case complexity. Further, statistical analysis shows that developing new test scripts is costlier than maintenance but also that frequent maintenance is less costly than infrequent, big bang maintenance. In addition a cost model, based on previous work, is presented that estimates the time to positive return on investment (ROI) of test automation compared to manual testing.Conclusions: It is concluded that test automation can lower overall software development costs of a project while also having positive effects on software quality. However, maintenance costs can still be considerable and the less time a company currently spends on manual testing, the more time is required before positive, economic, ROI is reached after automation.


8 A 236 nW-56 . 5 dBm-Sensitivity Bluetooth Low-Energy Wakeup Receiver with Energy Harvesting in 65 nm CMOS


title: A start-up boosting circuit with 133× speed gain for 2-transistor voltage reference

abstract: This work presents a start-up boosting circuit designed for fast stabilization of a 2-transistor voltage reference. A clock injection method is used to induce a large bias on the 2-transistor voltage reference resulting in a fast output voltage settling which is critical to reducing initialization time for analog components, reducing energy consumption. The fast stabilization technique is implemented in 180nm CMOS process and uses 0.404mm2 of area. Measurement from test chips shows 50.4μW power consumption during start-up phase with 133× speed gain.


title: Learning in Implicit Generative Models

abstract: Generative adversarial networks (GANs) provide an algorithmic framework for constructing generative models with several appealing properties: they do not require a likelihood function to be specified, only a generating procedure; they provide samples that are sharp and compelling; and they allow us to harness our knowledge of building highly accurate neural network classifiers. Here, we develop our understanding of GANs with the aim of forming a rich view of this growing area of machine learning---to build connections to the diverse set of statistical thinking on this topic, of which much can be gained by a mutual exchange of ideas. We frame GANs within the wider landscape of algorithms for learning in implicit generative models---models that only specify a stochastic procedure with which to generate data---and relate these ideas to modelling problems in related fields, such as econometrics and approximate Bayesian computation. We develop likelihood-free inference methods and highlight hypothesis testing as a principle for learning in implicit generative models, using which we are able to derive the objective function used by GANs, and many other related objectives. The testing viewpoint directs our focus to the general problem of density ratio estimation. There are four approaches for density ratio estimation, one of which is a solution using classifiers to distinguish real from generated data. Other approaches such as divergence minimisation and moment matching have also been explored in the GAN literature, and we synthesise these views to form an understanding in terms of the relationships between them and the wider literature, highlighting avenues for future exploration and cross-pollination.


title: Causal Discovery for Manufacturing Domains

abstract: Yield and quality improvement is of paramount importance to any manufacturing company. One of the ways of improving yield is through discovery of the root causal factors affecting yield. We propose the use of data-driven interpretable causal models to identify key factors affecting yield. We focus on factors that are measured in different stages of production and testing in the manufacturing cycle of a product. We apply causal structure learning techniques on real data collected from this line. Specifically, the goal of this work is to learn interpretable causal models from observational data produced by manufacturing lines. ::: Emphasis has been given to the interpretability of the models to make them actionable in the field of manufacturing. We highlight the challenges presented by assembly line data and propose ways to alleviate them.We also identify unique characteristics of data originating from assembly lines and how to leverage them in order to improve causal discovery. Standard evaluation techniques for causal structure learning shows that the learned causal models seem to closely represent the underlying latent causal relationship between different factors in the production process. These results were also validated by manufacturing domain experts who found them promising. This work demonstrates how data mining and knowledge discovery can be used for root cause analysis in the domain of manufacturing and connected industry.


title: Reusing historical interaction data for faster online learning to rank for IR

abstract: Online learning to rank for information retrieval (IR) holds promise for allowing the development of "self-learning" search engines that can automatically adjust to their users. With the large amount of e.g., click data that can be collected in web search settings, such techniques could enable highly scalable ranking optimization. However, feedback obtained from user interactions is noisy, and developing approaches that can learn from this feedback quickly and reliably is a major challenge. ::: In this paper we investigate whether and how previously collected (historical) interaction data can be used to speed up learning in online learning to rank for IR. We devise the first two methods that can utilize historical data (1) to make feedback available during learning more reliable and (2) to preselect candidate ranking functions to be evaluated in interactions with users of the retrieval system. We evaluate both approaches on 9 learning to rank data sets and find that historical data can speed up learning, leading to substantially and significantly higher online performance. In particular, our pre-selection method proves highly effective at compensating for noise in user feedback. Our results show that historical data can be used to make online learning to rank for IR much more effective than previously possible, especially when feedback is noisy.


title: Design and construction of a hydro-pneumatic mock circulation loop that emulates the systemic circuit of the circulatory system

abstract: An inexpensive and simple mock circulation loop was developed with the purpose of emulating the systemic circuit of the cardiovascular system, and which must be able to function under physiological conditions found on an average healthy adult person. This mock circulation loop contains a pulsatile left ventricle and a passive left atrium coupled with both, arterial and venous, resistances and compliances. A fluid, complying with blood's density and viscosity, was developed to emulate the human cardiovascular system behavior in the mock circulation loop. Also, a data acquisition system was developed and implemented to control the system's heart rate. This data acquisition system also contains a GUI used to control the parameters that have direct implications in the function of the mock circulation loop and is used to present the gathered values to the user.


title: OCRoRACT: A Sequence Learning OCR System Trained on Isolated Characters

abstract: Digitizing historical documents is crucial in preserving the literary heritage. With the availability of low cost capturing devices, libraries and institutes all over the world have old literature preserved in the form of scanned documents. However, searching through these scanned images is still a tedious job as one is unable to search through them. Contemporary machine learning approaches have been applied successfully to recognize text in both printed and handwriting form, however, these approaches require a lot of transcribed training data in order to obtain satisfactory performance. Transcribing the documents manually is a laborious and costly task, requiring many man-hours and language-specific expertise. This paper presents a generic iterative training framework to address this issue. The proposed framework is not only applicable to historical documents, but for present-day documents as well, where manually transcribed training data is unavailable. Starting with the minimal information available, the proposed approach iteratively corrects the training and generalization errors. Specifically, we have used a segmentation-based OCR method to train on individual symbols and then use the semi-corrected recognized text lines as the ground-truth data for segmentation-free sequence learning, which learns to correct the errors in the ground-truth by incorporating context-aware processing. The proposed approach is applied to a collection of 15th century Latin documents. The iterative procedure using segmentation-free OCR was able to reduce the initial character error of about 23% (obtained from segmentation-based OCR) to less than 7% in few iterations.


title: Dynamic movement primitives in latent space of time-dependent variational autoencoders

abstract: Dynamic movement primitives (DMPs) are powerful for the generalization of movements from demonstration. However, high dimensional movements, as they are found in robotics, make finding efficient DMP representations difficult. Typically, they are either used in configuration or Cartesian space, but both approaches do not generalize well. Additionally, limiting DMPs to single demonstrations restricts their generalization capabilities. In this paper, we explore a method that embeds DMPs into the latent space of a time-dependent variational autoencoder framework. Our method enables the representation of high-dimensional movements in a low-dimensional latent space. Experimental results show that our framework has excellent generalization in the latent space, e.g., switching between movements or changing goals. Also, it generates optimal movements when reproducing the movements.


title: Current distribution control design for paralleled DC/DC converters using sliding-mode control

abstract: This paper shows the analysis and design of a parallel-connected converter system using sliding mode control techniques. The design is particularised for a system that consists of N boost converters and a current feedback loop based on a proportional-integral compensator of the output voltage error. The paper emphasises the advantages of the sliding-mode control over the classic design method based on small-signal models, thus providing an effective and robust means of controlling nonlinear multi-input converters. The design is based on the Utkin conditions, which permit us to know the regions under which a sliding mode exists. This fact allows us to design the compensator and to introduce some modifications in the control loop that avoids input-current overshoots during the system startup. Simple design expressions are obtained and verified with simulation and experimental results, thus showing the improvements achieved with the proposed modifications.


title: Circularly Polarized Conical-Beam Antenna With Wide Bandwidth and Low Profile

abstract: This work introduces a circularly polarized (CP) conical-beam antenna with low profile and wide impedance & axial ratio (AR) bandwidths. The antenna structure simply consists of a wideband monopolar patch antenna and eight parasitic loop stubs for generating vertically polarized electric field Eθ and horizontally polarized field Eφ, respectively. The proposed antenna is low profile but wideband because of merging the TM01 and TM02 modes at around the center frequency. In order to obtain the wideband CP radiation, the eight parasitic loop stubs are printed and placed sequentially along the radiating edge of the patch. With the resultant radiations from both the patch antenna and the loop stubs, the CP conical-beam radiation pattern can be obtained. In addition, a parametric study of the shorting vias effect, number of loop stub elements, and loop widths are demonstrated how to design this wideband CP antenna. The antenna exhibits the wide impedance bandwidth of 28% from 4.9 to 6.5 GHz (for the reflection coefficient ≤ -10 dB) and the 3-dB AR bandwidth of 14.4% from 5.8 to 6.7 GHz. The thickness of this antenna is only 0.06λ in terms of the free-space wavelength at its lowest operation frequency. Stable omni-directional conical-beam radiation patterns are observed over the operating bandwidth with the peak gain of 4.9 dBic. The antenna can cover the uplink of standard C band (5.85 to 6.425 GHz) for satellite communication.


title: Footprint: Detecting Sybil Attacks in Urban Vehicular Networks

abstract: In urban vehicular networks, where privacy, especially the location privacy of anonymous vehicles is highly concerned, anonymous verification of vehicles is indispensable. Consequently, an attacker who succeeds in forging multiple hostile identifies can easily launch a Sybil attack, gaining a disproportionately large influence. In this paper, we propose a novel Sybil attack detection mechanism, Footprint, using the trajectories of vehicles for identification while still preserving their location privacy. More specifically, when a vehicle approaches a road-side unit (RSU), it actively demands an authorized message from the RSU as the proof of the appearance time at this RSU. We design a location-hidden authorized message generation scheme for two objectives: first, RSU signatures on messages are signer ambiguous so that the RSU location information is concealed from the resulted authorized message; second, two authorized messages signed by the same RSU within the same given period of time (temporarily linkable) are recognizable so that they can be used for identification. With the temporal limitation on the linkability of two authorized messages, authorized messages used for long-term identification are prohibited. With this scheme, vehicles can generate a location-hidden trajectory for location-privacy-preserved identification by collecting a consecutive series of authorized messages. Utilizing social relationship among trajectories according to the similarity definition of two trajectories, Footprint can recognize and therefore dismiss “communities” of Sybil trajectories. Rigorous security analysis and extensive trace-driven simulations demonstrate the efficacy of Footprint.


title: Augmented Reality Games for Upper-Limb Stroke Rehabilitation

abstract: Stroke is the number one cause of severe physical disability in the UK. Recent studies have shown that technologies such as virtual reality and imaging can provide an engaging and motivating tool for physical rehabilitation. In this paper we summarize previous work in our group using virtual reality technology and webcam-based games. We then present early work we are conducting in experimenting with desktop augmented reality (AR) for rehabilitation. AR allows the user to use real objects to interact with computer-generated environments. Markers attached to the real objects enable the system (via a webcam) to track the position and orientation of each object as it is moved. The system can then augment the captured image of the real environment with computer-generated graphics to present a variety of game or task-driven scenarios to the user. We discuss the development of rehabilitation prototypes using available AR libraries and express our thoughts on the potential of AR technology.


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