title: Low-cost 360 stereo photography and video capture

abstract: A number of consumer-grade spherical cameras have recently appeared, enabling affordable monoscopic VR content creation in the form of full 360° X 180° spherical panoramic photos and videos. While monoscopic content is certainly engaging, it fails to leverage a main aspect of VR HMDs, namely stereoscopic display. Recent stereoscopic capture rigs involve placing many cameras in a ring and synthesizing an omni-directional stereo panorama enabling a user to look around to explore the scene in stereo. In this work, we describe a method that takes images from two 360° spherical cameras and synthesizes an omni-directional stereo panorama with stereo in all directions. Our proposed method has a lower equipment cost than camera-ring alternatives, can be assembled with currently available off-the-shelf equipment, and is relatively small and light-weight compared to the alternatives. We validate our method by generating both stills and videos. We have conducted a user study to better understand what kinds of geometric processing are necessary for a pleasant viewing experience. We also discuss several algorithmic variations, each with their own time and quality trade-offs.


title: The Vivostat® application system: A comparison with conventional fibrin sealant application systems

abstract: The method of applying fibrin sealants may affect their physical properties and surgical performance. In this study, the characteristics of a novel fibrin-sealant application system (Vivostat®) were compared with conventional fibrin sealant spray systems. Delivery rates of Vivostat were highly reproducible in both high and low delivery modes (coefficient of variation [CV] < 1.0%) Airflow rates of Vivostat were 578 ml/min (6.6% (6.5% were lower and less variable than conventional systems, which ranged from 5,000 to 10,000 ml/min depending on the pressure. Spray forces of Vivostat were conseqently lower than other systems and the deposited fibrin films formed were of more consistent thickness. Unlike other systems, blockages with Vivostat were rare on intermittent use. The Vivostat system offers the surgeon a controlled, precise and efficient means of fibrin sealant application with potential performance advantages over conventional spray application systems.


title: An online auction framework for dynamic resource provisioning in cloud computing

abstract: Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and resource allocation in cloud computing. This work, to the authors' knowledge, represents the first online combinatorial auction designed in the cloud computing paradigm, which is general and expressive enough to both (a) optimize system efficiency across the temporal domain instead of at an isolated time point, and (b) model dynamic provisioning of heterogeneous Virtual Machine (VM) types in practice. The final result is an online auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ~ e+ 1 over e-1 ~ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: (1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with an additive loss of 1 over e-1 in competitive ratio, (2) a randomized auction sub-framework that applies primal-dual optimization for translating a centralized co-operative social welfare approximation algorithm into an auction mechanism, retaining a similar approximation ratio while adding truthfulness, and (3) a primal-dual update plus dual fitting algorithm for approximating the one-shot optimization with a ratio λ close to e. The efficacy of the online auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework, as well as its three independent modules, can be instructive in auction design for other related problems.


title: Adversarial Regression with Multiple Learners

abstract: Despite the considerable success enjoyed by machine learning techniques in practice, numerous studies demonstrated that many approaches are vulnerable to attacks. An important class of such attacks involves adversaries changing features at test time to cause incorrect predictions. Previous investigations of this problem pit a single learner against an adversary. However, in many situations an adversary's decision is aimed at a collection of learners, rather than specifically targeted at each independently. We study the problem of adversarial linear regression with multiple learners. We approximate the resulting game by exhibiting an upper bound on learner loss functions, and show that the resulting game has a unique symmetric equilibrium. We present an algorithm for computing this equilibrium, and show through extensive experiments that equilibrium models are significantly more robust than conventional regularized linear regression.


title: HSP-F Simulation of a Constructed Wetland Stormwater BMP for Urban Highway Runoff.

abstract: This chapter describes the use of the HSP-F program methodology to model and design best management practices (BMPS) for a proposed freeway interchange in Hami…


title: Schema Independent Relational Learning

abstract: Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions.


title: A Fast Algorithm for Approximate Quantiles in High Speed Data Streams

abstract: We present a fast algorithm for computing approximate quantiles in high speed data streams with deterministic error bounds. For data streams of size N where N is unknown in advance, our algorithm partitions the stream into sub-streams of exponentially increasing size as they arrive. For each sub-stream which has a fixed size, we compute and maintain a multi-level summary structure using a novel algorithm. In order to achieve high speed performance, the algorithm uses simple block-wise merge and sample operations. Overall, our algorithms for fixed-size streams and arbitrary-size streams have a computational cost of O(N log(1/epsivlogepsivN)) and an average per-element update cost of O(log logN) if epsiv is fixed.


title: QR Code Security

abstract: This paper examines QR Codes and how they can be used to attack both human interaction and automated systems. As the encoded information is intended to be machine readable only, a human cannot distinguish between a valid and a maliciously manipulated QR code. While humans might fall for phishing attacks, automated readers are most likely vulnerable to SQL injections and command injections. Our contribution consists of an analysis of the QR Code as an attack vector, showing different attack strategies from the attackers point of view and exploring their possible consequences.


title: Performance comparison of fractional slot concentrated winding spoke type synchronous motors with different slot-pole combinations

abstract: Fractional Slot Concentrated Winding (FSCW) Interior Permanent Magnet (IPM) synchronous motors are nowadays an attractive solution in automotive applications due to their important advantages in terms of high performance and manufacturing simplicity. Among the different topologies, the spoke type is an effective configuration when high torque density is required. On the other hand, the motor performance are heavily related to the selected slot-pole combination. This is a critical issue when there are high constraints in terms of torque quality, flux weakening performance and noise/vibration/harshness. This paper deals with the design and analysis of FSCW IPM spoke type motors, comparing eight optimal slot-pole combinations. Two different low cost PM typologies are considered. Design and optimization procedures, based upon a finite element analysis, are presented. The torque density, torque quality, electromechanical performance and deformation of the machines are compared, highlighting the advantages and the differences among the solutions.


title: SnabbSwitch user space virtual switch benchmark and performance optimization for NFV

abstract: New paradigms in networking industry, such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV), require the hypervisors to enable the execution of Virtual Network Functions in virtual machines (VMs). In this context, the virtual switch function is critical to achieve carrier grade performance, hardware independence, advanced features and programmability. SnabbSwitch is a virtual switch designed to run in user space with carrier grade performance targets, based on an efficient architecture which has driven the development of vhost-user (now also adopted by OVS-DPDK, the user space implementation of OVS based on Intel DPDK), easy to deploy and to program through its Lua scripting layer. This paper presents the SnabbSwitch virtual switch implementation along with its novelties (the vhost-user implementation and the usage of a trace compiler) and code optimizations, which have been merged in the mainline project repository. Extensive benchmarking activities, whose results are included in this paper, have been carried on to compare SnabbSwitch with other virtual switching solutions (i.e., OVS, OVS-DPDK, Linux Bridge, VFIO and SR-IOV). These results show that SnabbSwitch performs as well as hardware based solutions, such as SR-IOV and VFIO, while allowing for additional functional and flexible operation; they show also that SnabbSwitch is faster than the vhost-user based version (user space) of OVS-DPDK.


ESTABLISHING DOSE REFERENCE LEVEL FOR COMPUTED TOMOGRAPHY ( CT ) EXAMINATIONS IN MALAYSIA


title: A privacy-preserving framework for outsourcing location-based services to the cloud

abstract: Thanks to the popularity of mobile devices numerous location-based services (LBS) have emerged. While several privacy-preserving solutions for LBS have been proposed, most of these solutions do not consider the fact that LBS are typically cloud-based nowadays. Outsourcing data and computation to the cloud raises a number of significant challenges related to data confidentiality, user identity and query privacy, fine-grained access control, and query expressiveness. In this work, we propose a privacy-preserving framework for outsourcing LBS to the cloud. The framework supports multi-location queries with fine-grained access control, and search by location attributes, while providing semantic security. In particular, the framework implements a new model that allows the user to govern the trade-off between precision and privacy on a dynamic per-query basis.We also provide a security analysis to show that the proposed scheme preserves privacy in the presence of different threats. We also show the viability of our proposed solution and scalability with the number of locations through an experimental evaluation, using a real-life OpenStreetMap dataset.


title: Among friends ? An examination of friendship and the self-serving bias

abstract: Do friends bound each other's self-enhancement tendencies? Do friends display the self-serving bias (SSB; i.e. taking individual credit for success but blaming a partner for failure)? Dyads consisting of either friends or strangers engaged in an interdependent-outcomes creativity test, received bogus success or failure feedback at the dyadic level, and made responsibility attributions for the joint test performance. Strangers displayed the SSB. Friends, in contrast, refrained from the SSB: they shared responsibility for both successful and unsuccessful test outcomes. Friendship does place boundaries on self-enhancement.


title: Tweet Segmentation and Its Application to Named Entity Recognition

abstract: Twitter has attracted millions of users to share and disseminate most up-to-date information, resulting in large volumes of data produced everyday. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer severely from the noisy and short nature of tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. By splitting tweets into meaningful segments, the semantic or context information is well preserved and easily extracted by the downstream applications. HybridSeg finds the optimal segmentation of a tweet by maximizing the sum of the stickiness scores of its candidate segments. The stickiness score considers the probability of a segment being a phrase in English (i.e., global context) and the probability of a segment being a phrase within the batch of tweets (i.e., local context). For the latter, we propose and evaluate two models to derive local context by considering the linguistic features and term-dependency in a batch of tweets, respectively. HybridSeg is also designed to iteratively learn from confident segments as pseudo feedback. Experiments on two tweet data sets show that tweet segmentation quality is significantly improved by learning both global and local contexts compared with using global context alone. Through analysis and comparison, we show that local linguistic features are more reliable for learning local context compared with term-dependency. As an application, we show that high accuracy is achieved in named entity recognition by applying segment-based part-of-speech (POS) tagging.


title: Universal coding, information, prediction, and estimation

abstract: A connection between universal codes and the problems of prediction and statistical estimation is established. A known lower bound for the mean length of universal codes is sharpened and generalized, and optimum universal codes constructed. The bound is defined to give the information in strings relative to the considered class of processes. The earlier derived minimum description length criterion for estimation of parameters, including their number, is given a fundamental information, theoretic justification by showing that its estimators achieve the information in the strings. It is also shown that one cannot do prediction in Gaussian autoregressive moving average (ARMA) processes below a bound, which is determined by the information in the data.


title: Nanocomposite rubber elastomer with piezoresistive detection for flexible tactile sense application

abstract: This work presents flexible tactile sensor using piezoresistive nanocomposite rubber elastomers (C-PDMS): Carbon-black (C) powders filled into polymethylsiloxane (PDMS) using solvent-wetting methods with n-Hexane to produce well-dispersed conductive polymeric nanocomposites. The C-PDMS nanocomposites are utilized to measure resistance changes. In application, the C-PDMS is casted and stacked into PDMS substrate and gold films deposited with 3-mercaptopropyltrimethoxysilane (MPTMS) adhesion layers as bond pads to implement the flexible polymer-based devices. Typical normal load tests show resistance changes of C-PDMS piezoresistors on flat surface are ranging 2.92%∼4.45% (ΔR/R 0 /mN). Moreover, resistance changes of C-PDMS piezoresistors on curved surface are ranging 0.92%∼0.98% (ΔR/R 0 /mN).


title: Deep Convolutional Neural Network for Natural Image Matting Using Initial Alpha Mattes

abstract: We propose a deep convolutional neural network (CNN) method for natural image matting. Our method takes multiple initial alpha mattes of the previous methods and normalized RGB color images as inputs, and directly learns an end-to-end mapping between the inputs and reconstructed alpha mattes. Among the various existing methods, we focus on using two simple methods as initial alpha mattes: the closed-form matting and KNN matting. They are complementary to each other in terms of local and nonlocal principles. A major benefit of our method is that it can “recognize” different local image structures and then combine the results of local (closed-form matting) and nonlocal (KNN matting) mattings effectively to achieve higher quality alpha mattes than both of the inputs. Furthermore, we verify extendability of the proposed network to different combinations of initial alpha mattes from more advanced techniques such as KL divergence matting and information-flow matting. On the top of deep CNN matting, we build an RGB guided JPEG artifacts removal network to handle JPEG block artifacts in alpha matting. Extensive experiments demonstrate that our proposed deep CNN matting produces visually and quantitatively high-quality alpha mattes. We perform deeper experiments including studies to evaluate the importance of balancing training data and to measure the effects of initial alpha mattes and also consider results from variant versions of the proposed network to analyze our proposed DCNN matting. In addition, our method achieved high ranking in the public alpha matting evaluation dataset in terms of the sum of absolute differences, mean squared errors, and gradient errors. Also, our RGB guided JPEG artifacts removal network restores the damaged alpha mattes from compressed images in JPEG format.


title: Alterations of lesions of mycosis fungoides lymphoma by direct imposition of delayed hypersensitivity reactions.

abstract: Plaque lesions of mycosis fungoides lymphoma in six patients sensitized to both 2-4 dinitrochlorobenzene (DNCB) and S, 2-aminoethylisothiuronium (AET) were treated topically with varying doses of the chemicals to evoke delayed hypersensitivity reactions. Complete or partial clearing of lesions, lasting several months, was observed in 22 of 31 plaques treated. No clinical benefit was observed following applications of a cutaneous irritant, sodium lauryl sulfate, nor with four chemical congeners of DNCB not cross-antigenic with DNCB. Cutaneous lymphoma lesions of one patient anergic to DNCB did not regress following large topical doses of the chemical. This evidence indicates that lesions of the lymphoma mycosis fungoides may be altered significantly by delayed hypersitivity responses, an event of seeming importance to understanding the pathogenesis of this disease.


title: Network monitoring: Present and future

abstract: Network monitoring guides network operators in understanding the current behavior of a network. Therefore, accurate and efficient monitoring is vital to ensure that the network operates according to the intended behavior and then to troubleshoot any deviations. However, the current practice of network-monitoring largely depends on manual operations, and thus enterprises spend a significant portion of their budgets on the workforce that monitor their networks. We analyze present network-monitoring technologies, identify open problems, and suggest future directions. In particular, our findings are based on two different analyses. The first analysis assesses how well present technologies integrate with the entire cycle of network-management operations: design, deployment, and monitoring. Network operators first design network configurations, given a set of requirements, then they deploy the new design, and finally they verify it by continuously monitoring the network’s behavior. One of our observations is that the efficiency of this cycle can be greatly improved by automated deployment of pre-designed configurations, in response to changes in monitored network behavior. Our second analysis focuses on network-monitoring technologies and group issues in these technologies into five categories. Such grouping leads to the identification of major problem groups in network monitoring, e.g., efficient management of increasing amounts of measurements for storage, analysis, and presentation. We argue that continuous effort is needed in improving network-monitoring since the presented problems will become even more serious in the future, as networks grow in size and carry more data.


Anthropomorphism in Human–Robot Co-evolution


title: Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero

abstract: This paper examines factors that influence prices of most common five cryptocurrencies such Bitcoin, Ethereum, Dash, Litecoin, and Monero over 2010-2018 using weekly data. The study employs ARDL technique and documents several findings. First, cryptomarket-related factors such as market beta, trading volume, and volatility appear to be significant determinant for all five cryptocurrencies both in short- and long-run. Second, attractiveness of cryptocurrencies also matters in terms of their price determination, but only in long-run. This indicates that formation (recognition) of the attractiveness of cryptocurrencies are subjected to time factor. In other words, it travels slowly within the market. Third, SP500 index seems to have weak positive long-run impact on Bitcoin, Ethereum, and Litcoin, while its sign turns to negative losing significance in short-run, except Bitcoin that generates an estimate of -0.20 at 10% significance level. Lastly, error-correction models for Bitcoin, Etherem, Dash, Litcoin, and Monero show that cointegrated series cannot drift too far apart, and converge to a long-run equilibrium at a speed of 23.68%, 12.76%, 10.20%, 22.91%, and 14.27% respectively.


title: How to measure baroreflex sensitivity: from the cardiovascular laboratory to daily life

abstract: Arterial baroreflex function in humans is commonly assessed through a number of laboratory tests based on quantification of the reflex responses in heart rate or blood pressure to external stimuli applied to the cardiovascular system. Evidence is available that these laboratory estimates of baroreflex sensitivity have both pathophysiological and clinical relevance. Indeed, a number of studies have shown that the sensitivity of the baroreceptor-heart rate reflex may have a prognostic value in myocardial infarction, heart failure and diabetic patients, where mortality seems to be inversely related to the sensitivity of cardiac baroreflex modulation. A deeper insight into the features of daily-life baroreflex cardiovascular control has been offered more recently by techniques based on computer analysis of spontaneous blood pressure and heart rate fluctuations. This innovative approach allows spontaneous baroreflex sensitivity to be assessed in real life conditions, with no need for external stimulation of the patient as required by the older laboratory techniques. This review will briefly survey the methods most widely used to assess baroreflex function in humans, in the laboratory and in daily life.


title: The Extreme Right Filter Bubble

abstract: Due to its status as the most popular video sharing platform, YouTube plays an important role in the online strategy of extreme right groups, where it is often used to host associated content such as music and other propaganda. In this paper, we develop a categorization suitable for the analysis of extreme right channels found on YouTube. By combining this with an NMF-based topic modelling method, we categorize channels originating from links propagated by extreme right Twitter accounts. This method is also used to categorize related channels, which are determined using results returned by YouTube’s related video service. We identify the existence of a \lter bubble", whereby users who access an extreme right YouTube video are highly likely to be recommended further extreme right content.


title: Extensible-Link Kinematic Model for Determining Motion Characteristics of Compliant Mechanisms

abstract: We present an extensible-link kinematic model for characterizing the motion trajectory of an arbitrary planar compliant mechanism. This is accomplished by creating an analogous kinematic model consisting of links that change length over the course of actuation to represent elastic deformation of the compliant mechanism. Within the model, the motion trajectory is represented as an analytical function. By Taylor series expansion, the trajectory is expressed in a parametric formulation composed of load-independent and load-dependent terms. Here, the load-independent terms are entirely defined by the shape of the undeformed compliant mechanism topology, and all load-geometry interdependencies are captured by the load-dependent terms. This formulation adds insight to the process for designing compliant mechanisms for high accuracy motion applications because: (1) inspection of the load-independent terms enables determination of specific topology modifications for improving the accuracy of the motion trajectory; and (2) the load-dependent terms reveal the polynomial orders of principally uncorrectable error components of the motion trajectory. The error components in the trajectory simply represent the deviation of the actual motion trajectory provided by the compliant mechanism compared to the ideally desired one. We develop the generalized model framework, and then demonstrate its utility by designing a compliant micro-gripper with straight-line parallel jaw motion. We use the model to analytically determine all topology modifications for optimizing the jaw trajectory, and to predict the polynomial order of the uncorrectable trajectory components. The jaw trajectory is then optimized by iterative finite elements (FE) simulation until the polynomial order of the uncorrectable trajectory component becomes apparent.


title: Point Cloud Collision Detection

abstract: In the past few years, many efficient rendering and surface reconstruction algorithms for point clouds have been developed. However, collision detection of point clouds has not been considered until now, although this is a prerequisite to use them for interactive or animated 3D graphics. We present a novel approach for time-critical collision detection of point clouds. Based solely on the point representation, it can detect intersections of the underlying implicit surfaces. The surfaces do not need to be closed. We construct a point hierarchy where each node stores a sufficient sample of the points plus a sphere covering of a part of the surface. These are used to derive criteria that guide our hierarchy traversal so as to increase convergence. One of them can be used to prune pairs of nodes, the other one is used to prioritize still to be visited pairs of nodes. At the leaves we efficiently determine an intersection by estimating the smallest distance. We have tested our implementation for several large point cloud models. The results show that a very fast and precise answer to collision detection queries can always be given.


title: Protecting Moving Trajectories with Dummies

abstract: Dummy-based anonymization techniques for protecting location privacy of mobile users have been proposed in the literature. By generating dummies that move in humanlike trajectories, shows that location privacy of mobile users can be preserved. However, by monitoring long-term movement patterns of users, the trajectories of mobile users can still be exposed. We argue that, once the trajectory of a user is identified, locations of the user is exposed. Thus, it's critical to protect the moving trajectories of mobile users in order to preserve user location privacy. We propose two schemes that generate consistent movement patterns in a long run. Guided by three parameters in user specified privacy profile, namely, short- term disclosure, long-term disclosure and distance deviation, the proposed schemes derive movement trajectories for dummies. A preliminary performance study shows that our approach is more effective than existing work in protecting moving trajectories of mobile users and their location privacy.


title: A model of shared grasp affordances from demonstration

abstract: This paper presents a hierarchical, statistical topic model for representing the grasp preshapes of a set of objects. Observations provided by teleoperation are clustered into latent affordances shared among all objects. Each affordance defines a joint distribution over position and orientation of the hand relative to the object and conditioned on visual appearance. The parameters of the model are learned using a Gibbs sampling method. After training, the model can be used to compute grasp preshapes for a novel object based on its visual appearance. The model is evaluated experimentally on a set of objects for its ability to generate grasp preshapes that lead to successful grasps, and compared to a baseline approach.


title: Hardware Trojan Insertion by Direct Modification of FPGA Configuration Bitstream

abstract: In this work, we have demonstrated the feasibility of hardware Trojan insertion in circuits mapped on FPGAs by direct modification of the FPGA configuration bitstream. The main challenge of this attack proved to be the lack of sufficient information in the public domain about the bitstream format and the internal architecture and configurability of the FPGA. Nevertheless, we were able to show that under certain constraints on the functionality, size and placement of the Trojan on the FPGA, it is possible to modify the configuration bitstream by a software program to insert a hardware Trojan in the design. The main strength of the attack lies in the fact that since the modification is at the configuration bitstream level, it bypasses all predeployment design validation mechanisms. We also propose some techniques to prevent the demonstrated attack. We hope that this work will raise awareness among FPGA users about the potency of the threat posed by this relatively simple attack and its improved variants. .


title: PRADA: prioritizing android devices for apps by mining large-scale usage data

abstract: Selecting and prioritizing major device models are critical for mobile app developers to select testbeds and optimize resources such as marketing and quality-assurance resources. The heavily fragmented distribution of Android devices makes it challenging to select a few major device models out of thousands of models available on the market. Currently app developers usually rely on some reported or estimated general market share of device models. However, these estimates can be quite inaccurate, and more problematically, can be irrelevant to the particular app under consideration. To address this issue, we propose PRADA, the first approach to prioritizing Android device models for individual apps, based on mining large-scale usage data. PRADA adapts the concept of operational profiling (popularly used in software reliability engineering) for mobile apps -- the usage of an app on a specific device model reflects the importance of that device model for the app. PRADA includes a collaborative filtering technique to predict the usage of an app on different device models, even if the app is entirely new (without its actual usage in the market yet), based on the usage data of a large collection of apps. We empirically demonstrate the effectiveness of PRADA over two popular app categories, i.e., Game and Media, covering over 3.86 million users and 14,000 device models collected through a leading Android management app in China.


Latent semantic indexing: a probabilistic analysis


title: wUbi-Pen: windows graphical user interface interacting with haptic feedback stylus

abstract: In this work, we designed stylus type haptic interfaces interacting with a touch screen. There are two kinds of haptic styli termed wUbi-Pen. The type I has functions of providing vibration, impact, texture and sound and it is a stand alone system including own battery and wireless communication module. The type II does not include the texture display module and it is a miniaturized version. We present a new interaction scheme on the Windows graphical user interface based on pointer movement haptic feedback events such as clicking, drag, drop, moving and etc. In addition, a simple interactive digital sketchbook has been implemented, which is providing haptic and auditory feedback while drawing and touching objects. We also introduce a tactile image display method on a touch screen with the wUbi-Pen and a simple fitting puzzle utilizing haptic feedback events.


title: Neural simulation of action: A unifying mechanism for motor cognition

abstract: Abstract Paradigms drawn from cognitive psychology have provided new insight into covert stages of action. These states include not only intending actions that will eventually be executed, but also imagining actions, recognizing tools, learning by observation, or even understanding the behavior of other people. Studies using techniques for mapping brain activity, probing cortical excitability, or measuring the activity of peripheral effectors in normal human subjects and in patients all provide evidence of a subliminal activation of the motor system during these cognitive states. The hypothesis that the motor system is part of a simulation network that is activated under a variety of conditions in relation to action, either self-intended or observed from other individuals, will be developed. The function of this process of simulation would be not only to shape the motor system in anticipation to execution, but also to provide the self with information on the feasibility and the meaning of potential actions.


title: Research Article Fixed-Point Algorithms for the Blind Separation of Arbitrary Complex-Valued Non-Gaussian Signal Mixtures

abstract: We derive new fixed-point algorithms for the blind separation of complex-valued mixtures of independent, noncircularly symmetric, and non-Gaussian source signals. Leveraging recently developed results on the separability of complex-valued signal mixtures, we systematically construct iterative procedures on a kurtosis-based contrast whose evolutionary characteristics are identical to those of the FastICA algorithm of Hyvarinen and Oja in the real-valued mixture case. Thus, our methods inherit the fast convergence properties, computational simplicity, and ease of use of the FastICA algorithm while at the same time extending this class of techniques to complex signal mixtures. For extracting multiple sources, symmetric and asymmetric signal deflation procedures can be employed. Simulations for both noiseless and noisy mixtures indicate that the proposed algorithms have superior finite-sample performance in data-starved scenarios as compared to existing complex ICA methods while performing about as well as the best of these techniques for larger data-record lengths.


title: Ultra-wideband pulse generator with picosecond fall time and tunable amplitude

abstract: Generating ultra-wideband pulse is a key technique for subsurface ground penetrating radar. By sharpening the transition time of pulse, the high frequency component can be improved, which contributes to the increase of bandwidth. In this paper, a compact pulse generator based on avalanche transistor and step recovery diode (SRD) is studied. Load resistor in the Gaussian pulse circuit is optimized to increase the pulse amplitude. In addition, the performance of output pulse is studied for different stage of shaping circuit. Experiment results show that transition time of the pulse is reduced by increasing stage of shaping circuit properly. As a result, we demonstrate a pulse with transition time of 200ps, full width at half maximum of 500ps, and amplitude of 43V.


title: Chinese Couplet Generation with Neural Network Structures

abstract: Part of the unique cultural heritage of China is the Chinese couplet. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Moreover, a special phenomenon is that corresponding characters from the same position in the two clauses match each other by following certain constraints on semantic and/or syntactic relatedness. Automatic couplet generation by computer is viewed as a difficult problem and has not been fully explored. In this paper, we formulate the task as a natural language generation problem using neural network structures. Given the issued antecedent clause, the system generates the subsequent clause via sequential language modeling. To satisfy special characteristics of couplets, we incorporate the attention mechanism and polishing schema into the encoding-decoding process. The couplet is generated incrementally and iteratively. A comprehensive evaluation, using perplexity and BLEU measurements as well as human judgments, has demonstrated the effectiveness of our proposed approach.


title: An Efficient Active Learning Framework for New Relation Types

abstract: Relation extraction is a fundamental task in information extraction. Different methods have been studied for building a relation extraction system. Supervised training of models for this task has yielded good performance, but at substantial cost for the annotation of large training corpora (About 40K same-sentence entity pairs). Semi-supervised methods can only require a seed set, but the performance is very limited when the seed set is very small, which is not very satisfactory for real relation extraction applications. The trade-off of annotation and performance is also hard to decide in practice. Active learning strategies allow users to gradually improve the model and to achieve comparable performance to supervised methods with limited annotation. Recent study shows active learning on this task needs much fewer labels for each type to build a useful relation extraction application. We feel active learning is a good direction to do relation extraction and presents a more efficient active learning framework. This framework starts from a better balance between positive and negative samples, and boosts by interleaving self-training and co-testing. We also studied the reduction of annotation cost by enforcing argument type constraints. Experiments show a substantial speed-up by comparison to previous state-of-the-art pure co-testing active learning framework. We obtain reasonable performance with only a hundred labels for individual ACE 2004 relation types. We also developed a GUI tool for real human-in-the-loop active learning trials. The goal of building relation extraction systems in a very short time seems to be promising.


title: Stroke-based stylization learning and rendering with inverse reinforcement learning

abstract: Among various traditional art forms, brush stroke drawing is one of the widely used styles in modern computer graphic tools such as GIMP, Photoshop and Painter. In this paper, we develop an AI-aided art authoring (A4) system of non-photorealistic rendering that allows users to automatically generate brush stroke paintings in a specific artist's style. Within the reinforcement learning framework of brush stroke generation proposed by Xie et al. [Xie et al., 2012], our contribution in this paper is to learn artists' drawing styles from video-captured stroke data by inverse reinforcement learning. Through experiments, we demonstrate that our system can successfully learn artists' styles and render pictures with consistent and smooth brush strokes.


Management Systems in Higher Education You can control a Web site ’ s look and feel while letting the owners of information control content


title: Dynamic mean-variance asset allocation

abstract: Mean-variance criteria remain prevalent in multi-period problems, and yet not much is known about their dynamically optimal policies. We provide a fully analytical characterization of the optimal dynamic mean-variance portfolios within a general incomplete-market economy, and recover a simple structure that also inherits several conventional properties of static models. We also identify a probability measure that incorporates intertemporal hedging demands and facilitates much tractability in the explicit computation of portfolios. We solve the problem by explicitly recognizing the time-inconsistency of the mean-variance criterion and deriving a recursive representation for it, which makes dynamic programming applicable. We further show that our time-consistent solution is generically different from the pre-commitment solutions in the extant literature, which maximize the mean-variance criterion at an initial date and which the investor commits to follow despite incentives to deviate. We illustrate the usefulness of our analysis by explicitly computing dynamic mean-variance portfolios under various stochastic investment opportunities in a straightforward way, which does not involve solving a Hamilton-Jacobi-Bellman differential equation. A calibration exercise shows that the mean-variance hedging demands may comprise a significant fraction of the investor's total risky asset demand.


title: Metadata Extraction from PDF Papers for Digital Library Ingest

abstract: In this paper we analyze our recent research on the use of document analysis techniques for metadata extraction from PDF papers. We describe a package that is designed to extract basic metadata from these documents. The package is used in combination with a digital library software suite to easily build personal digital libraries. The proposed software is based on a suitable combination of several techniques that include PDF parsing, low level document image processing, and layout analysis. In addition, we use the information gathered from a widely known citation database (DBLP) to assist the tool in the difficult task of author identification. The system is tested on some paper collections selected from recent conference proceedings.


title: Cyber-physical system integration for industry 4.0: Modelling and simulation of an induction heating process for aluminium-steel molds in footwear soles manufacturing

abstract: In recent years, the Cyber-Physical Systems (CPSs), have become a new trend to increase and to enrich the interactions between physical and virtual systems with the goal to create a truly connected world in which smart objects interact and exchange data with each other. The CPS is the core of the new industrial revolution called “Industry 4.0”, which promotes the computerization of manufacturing to make decentralized decisions. Within the modular structured smart factories, Cyber-Physical Systems monitor physical processes, create a virtual copy of the physical world, simulate parts of process and implement sophisticated control policies in order to take optimized decisions. This research proposes the modelling and simulation of an induction heating process for aluminium-steel mold, which is used in the production of footwear soles. The modelling supports the simulation of a CPS model related to the use of a multi-use LGV (Laser Guided Vehicle) which transports aluminum-steel molds from a mechanized warehouse to the final rotary production line, used for the soles foaming. In detail, a thermal model and an induction heating electronic circuit model have been studied to describe the whole mold heating system and they have been simulated using Simulink/MATLAB. In addition, two types of controllers, an induction preheating control technique based on a Model Predictive Controller (MPC), and another one based on PID, have been developed in order to analyse the different behaviour of the system.


title: Energy production from biomass. (Part 1): Overview of biomass

abstract: The use of renewable energy sources is becoming increasingly necessary, if we are to achieve the changes required to address the impacts of global warming. Biomass is the most common form of renewable energy, widely used in the third world but until recently, less so in the Western world. Latterly much attention has been focused on identifying suitable biomass species, which can provide high-energy outputs, to replace conventional fossil fuel energy sources. The type of biomass required is largely determined by the energy conversion process and the form in which the energy is required. In the first of three papers, the background to biomass production (in a European climate) and plant properties is examined. In the second paper, energy conversion technologies are reviewed, with emphasis on the production of a gaseous fuel to supplement the gas derived from the landfilling of organic wastes (landfill gas) and used in gas engines to generate electricity. The potential of a restored landfill site to act as a biomass source, providing fuel to supplement landfill gas-fuelled power stations, is examined, together with a comparison of the economics of power production from purpose-grown biomass versus waste-biomass. The third paper considers particular gasification technologies and their potential for biomass gasification.


title: Helping Novices Avoid the Hazards of Data: Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources

abstract: The infrastructure and tools necessary for large-scale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data-miner or domain expert can rightly be expected to exercise caution when required (for example, around fallacious conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge which gives reason to trust a narrower set of hypotheses. This article adopts the solution of using higher-level knowledge to allow this sort of domain knowledge to be used automatically, selecting relevant input attributes, and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.


title: Studying Synchronization to a Musical Beat in Nonhuman Animals

abstract: The recent discovery of spontaneous synchronization to music in a nonhuman animal (the sulphur-crested cockatoo Cacatua galerita eleonora) raises several questions. How does this behavior differ from nonmusical synchronization abilities in other species, such as synchronized frog calls or firefly flashes? What significance does the behavior have for debates over the evolution of human music? What kinds of animals can synchronize to musical rhythms, and what are the key methodological issues for research in this area? This paper addresses these questions and proposes some refinements to the “vocal learning and rhythmic synchronization hypothesis.”


title: Protein and Lysine as Factors in the Cariogenicity of a Cereal Diet

abstract: A method for forming an electronically active biaxially textured article includes the steps of providing a substrate having a single crystal metal or metal alloy surface, deforming the substrate to form an elongated substrate surface having biaxial texture and depositing an epitaxial electronically active layer on the biaxially textured surface. The method can include at least one annealing step after the deforming step to produce the biaxially textured substrate surface. The invention can be used to form improved biaxially textured articles, such as superconducting wire and tape articles having improved Jc values.


title: MyiFogSim: A Simulator for Virtual Machine Migration in Fog Computing

abstract: Low latency in IT applications is an important aspect of improving the quality of the user's experience. Frequently, applications are run in a virtual machine in the cloud. Because cloud providers are datacentre facilities that are often distant from users, unacceptably high latencies are experienced in some applications. Fog computing can be seen as a cloud computing extension, namely cloudlets, located in access points at the edge of the network and hence able to provide lower latencies than the cloud. However, as mobile devices and applications become more popular, users' computing and data capacities should be maintained close to the user to keep latencies as low as possible. This paper discusses resource allocation in fog computing in the face of users' mobility and introduces MyiFogSim, an extension of iFogSim to support mobility through migration of virtual machines between cloudlets. Moreover, a migration policy is proposed, and MyiFogSim is used to analyze the policy impact on application quality of service. Results suggest that the policy can promote lower latencies when compared to a scenario without the migration policy.


title: Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks-with a focus on Propagation Models

abstract: This paper provides an overview of the features of fifth generation (5G) wireless communication systems now being developed for use in the millimeter wave (mmWave) frequency bands. Early results and key concepts of 5G networks are presented, and the channel modeling efforts of many international groups for both licensed and unlicensed applications are described here. Propagation parameters and channel models for understanding mmWave propagation, such as line-of-sight (LOS) probabilities, large-scale path loss, and building penetration loss, as modeled by various standardization bodies, are compared over the 0.5–100 GHz range.


title: [Juvenile gigantomastia. Case report and literature survey].

abstract: A juvenile gigantomastia with wet tender ulceration in a 13-year-old premenarchal girl (155 cm, 51 kg) developed in only 6 months. Treatment consisted of a bilateral reduction mammoplasty with free transplantation of the areolae and nipples and the removal of 4.2 kg of breast tissue. Macroscopically the resections consisted of "white and solid" tissue. Histologically a fibrous-cystic mastopathia was confirmed. It is thus suggested that the etiology of this disease might be related to a local hypersensitivity to estrogen. Mediactions (Tamoxifen) are mostly ineffective. The treatment consisted of reduction plasty.


title: Comparative Metagenomics Reveals Host Specific Metavirulomes and Horizontal Gene Transfer Elements in the Chicken Cecum Microbiome

abstract: BACKGROUND ::: The complex microbiome of the ceca of chickens plays an important role in nutrient utilization, growth and well-being of these animals. Since we have a very limited understanding of the capabilities of most species present in the cecum, we investigated the role of the microbiome by comparative analyses of both the microbial community structure and functional gene content using random sample pyrosequencing. The overall goal of this study was to characterize the chicken cecal microbiome using a pathogen-free chicken and one that had been challenged with Campylobacter jejuni. ::: ::: ::: METHODOLOGY/PRINCIPAL FINDINGS ::: Comparative metagenomic pyrosequencing was used to generate 55,364,266 bases of random sampled pyrosequence data from two chicken cecal samples. SSU rDNA gene tags and environmental gene tags (EGTs) were identified using SEED subsystems-based annotations. The distribution of phylotypes and EGTs detected within each cecal sample were primarily from the Firmicutes, Bacteroidetes and Proteobacteria, consistent with previous SSU rDNA libraries of the chicken cecum. Carbohydrate metabolism and virulence genes are major components of the EGT content of both of these microbiomes. A comparison of the twelve major pathways in the SEED Virulence Subsystem (metavirulome) represented in the chicken cecum, mouse cecum and human fecal microbiomes showed that the metavirulomes differed between these microbiomes and the metavirulomes clustered by host environment. The chicken cecum microbiomes had the broadest range of EGTs within the SEED Conjugative Transposon Subsystem, however the mouse cecum microbiomes showed a greater abundance of EGTs in this subsystem. Gene assemblies (32 contigs) from one microbiome sample were predominately from the Bacteroidetes, and seven of these showed sequence similarity to transposases, whereas the remaining sequences were most similar to those from catabolic gene families. ::: ::: ::: CONCLUSION/SIGNIFICANCE ::: This analysis has demonstrated that mobile DNA elements are a major functional component of cecal microbiomes, thus contributing to horizontal gene transfer and functional microbiome evolution. Moreover, the metavirulomes of these microbiomes appear to associate by host environment. These data have implications for defining core and variable microbiome content in a host species. Furthermore, this suggests that the evolution of host specific metavirulomes is a contributing factor in disease resistance to zoonotic pathogens.


title: Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

abstract: Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map ), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors’ take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?


title: Decreased functional connectivity between ventral tegmental area and nucleus accumbens in Internet gaming disorder: evidence from resting state functional magnetic resonance imaging

abstract: BackgroundInternet gaming disorder (IGD) has become an increasing mental health problem worldwide. Decreased resting-state functional connectivity (rsFC) between the ventral tegmental area (VTA) and the nucleus accumbens (NAcc) has been found in substance use and is thought to play an important role in the development of substance addiction. However, rsFC between the VTA and NAcc in a non-substance addiction, such as IGD, has not been assessed previously. The current study aimed to investigate: (1) if individuals with IGD exhibit alterations in VTA-NAcc functional connectivity; and (2) whether VTA-NAcc functional connectivity is associated with subjective Internet craving.MethodsThirty-five male participants with IGD and 24 healthy control (HC) individuals participated in resting-state functional magnetic resonance imaging. Regions of interest (left NAcc, right NAcc and VTA) were selected based on the literature and were defined by placing spheres centered on Talairach Daemon coordinates.ResultsIn comparison with HCs, individuals with IGD had significantly decreased rsFC between the VTA and right NAcc. Resting-state functional connectivity strength between the VTA and right NAcc was negatively correlated with self-reported subjective craving for the Internet.ConclusionsThese results suggest possible neural functional similarities between individuals with IGD and individuals with substance addictions.


title: Document dewarping via text-line based optimization

abstract: This paper presents a new document image dewarping method that removes geometric distortions in camera-captured document images. The proposed method does not directly use the text-line which has been the most widely used feature for the document dewarping. Instead, we use the discrete representation of text-lines and text-blocks which are the sets of connected components. Also, we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively. With these distortion models and the discrete representation of the features, we design a cost function whose minimization yields the parameters of the distortion model. In the cost function, we encode the properties of the pages such as text-block alignment, line-spacing, and the straightness of text-lines. By describing the text features using the sets of discrete points, the cost function can be easily defined and efficiently solved by Levenberg-Marquadt algorithm. Experiments show that the proposed method works well for the various layouts and curved surfaces, and compares favorably with the conventional methods on the standard dataset. HighlightsDocument dewarping is an important problem in camera-based OCR.We formulate the dewarping as an optimization problem.The proposed method performs dewarping in a fully automatic manner.The proposed method can handle various layouts of documents.Our method yields the improved OCR performances compared with other methods.


title: A 100GHz phase-locked loop in 0.13µm SiGe BiCMOS process

abstract: A fully integrated 100GHz phase-locked loop (PLL) is demonstrated in 0.13µm SiGe BiCMOS process. The PLL employs a fundamental-frequency differential Colpitts voltage-controlled oscillator (VCO) with 8.3% tuning range, which achieves a phase noise of −124.5dBc/Hz at 10MHz offset, and a single-ended output power of 3dBm. The FoM of this VCO is the best among 90–100GHz VCOs. A Miller divider, operating from 50GHz up to 130GHz, is designed and the Gilbert-mixer phase detector is used to attenuate reference spurs. The total lock range of the PLL is from 92.7 to 100.2GHz, the phase noise is −102dBc/Hz at 1MHz offset, and reference spurs are not observable. The PLL dissipates 570mW and occupies 1.21mm2.


title: Understanding variable moment arms for the index finger MCP joints through the ACT hand

abstract: Human levels of dexterity has not been duplicated in a robotic form to date. Dexterity is achieved in part due to the biomechanical structure, and in part due to the neural control of movement. An anatomically correct test-bed (ACT) hand has been constructed to investigate the importance and behavioral consequences of anatomical features and neural control strategies of the human hand. This paper focused on the role of the human handpsilas variable moment arm. System identification was conducted on the ACT index fingerpsilas two degrees of freedom at the metacarpal-phalange (MCP) joint to provide an understanding of, for the first time, how the moment arms vary with multiple joints moving simultaneously. The specific combination of nonlinear moment arms results in an increased ability to produce force at the fingertip for the same neural input when the fingerpsilas flexion and adduction angles increase (that is toward the middle of the hand). This preliminary work will lead to answering what biomechanical and neural functions are required to construct fully dexterous robotic and prosthetic hands in the future.


A Triband Folded Dipole Antenna for GSM Mobile Communication Applications


title: Hyponym/Hypernym Detection in Science and Technology Thesauri from Bibliographic Datasets

abstract: Thesauri for science and technology information are increasingly used in bibliometrics and scientometrics. However, the manual construction and maintenance of thesauri is costly and time consuming, thus, methods for semi-automatic construction and maintenance are being actively studied. We propose a method that expands an existing thesaurus with specified terms extracted from the abstracts of articles. Specifically, we assign the terms to specified subcategories by clustering a word vector space, then determine the hyponyms and hypernyms based on their relations with terms in the sub-categories. The word vectors are constructed from 177,000 IEEE articles archived from 2012 to 2014 in the Scopus dataset. In experiments, the terms were correctly classified into the Japan Science and Technology thesaurus with 70.8% precision and 75.4% recall. In future, we will develop a semiautomatic thesaurus maintenance system that recommends new terms in their proper relative positions.


title: The nonverbal expression of pride: Evidence for cross-cultural recognition. Manuscript submitted for publication

abstract: The present research tests whether recognition for the nonverbal expression of pride generalizes across cultures. Study 1 provided the first evidence for cross-cultural recognition of pride, demonstrating that the expression generalizes across Italy and the United States. Study 2 found that the pride expression generalizes beyond Western cultures; individuals from a preliterate, highly isolated tribe in Burkina Faso, West Africa, reliably recognized pride, regardless of whether it was displayed by African or American targets. These Burkinabe participants were unlikely to have learned the pride expression through cross-cultural transmission, so their recognition suggests that pride may be a human universal. Studies 3 and 4 used drawn figures to systematically manipulate the ethnicity and gender of targets showing the expression, and demonstrated that pride recognition generalizes across male and female targets of African, Asian, and Caucasian descent. Discussion focuses on the implications of the findings for the universality of the pride expression.


title: Errors in MET Estimates of Physical Activities Using 3.5 ml·kg−1·min−1as the Baseline Oxygen Consumption

abstract: PURPOSE ::: To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml x kg(-1) x min(-1) (standMET) and corrected METs [corrMET = mean standMET x (3.5 / Harris-Benedict RMR)] in subgroups. ::: ::: ::: METHODS ::: RMR was measured for 252 subjects following a 4-hr fast and before completion of 11 activities. VO2 was measured during activity using indirect calorimetry (n = 2555 activities). Subjects were classified by BMI category (normal-weight or overweight/obese), sex, age (decade 20, 30, 40, or 50 y), and fitness quintiles (low to high). Activities were classified into low, moderate, and vigorous intensity categories. ::: ::: ::: RESULTS ::: The (mean +/- SD) measMET was 6.1 +/- 2.64 METs. StandMET [mean (95% CI)] was (0.51(0.42, 0.59) METs) less than measMET. CorrMET was not statistically different from measMET (-0.02 (-0.11, 0.06) METs). 12.2% of the activities were misclassified using standMETs compared with an 8.6% misclassification rate for METs based on predicted RMR (P < .0001). StandMET differences and misclassification rates were highest for low fit, overweight, and older individuals while there were no differences when corrMETs were used. ::: ::: ::: CONCLUSION ::: Using 3.5 ml x kg(-1) x min(-1) to calculate activity METs causes higher misclassification of activities and inaccurate point estimates of METs than a corrected baseline which considers individual height, weight, and age. These errors disproportionally impact subgroups of the population with the lowest activity levels.


title: Phonetic based SoundEx&ShapeEx algorithm for Sindhi Spell Checker System

abstract: This paper presents a novel combinational phonetic algorithm for Sindhi Language, to be used in developing Sindhi Spell Checker which has yet not been developed prior to this work. The compound textual forms and glyphs of Sindhi language presents a substantial challenge for developing Sindhi spell checker system and generating similar suggestion list for misspelled words. In order to implement such a system, phonetic based Sindhi language rules and patterns must be considered into account for increasing the accuracy and efficiency. The proposed system is developed with a blend between Phonetic based SoundEx algorithm and ShapeEx algorithm for pattern or glyph matching, generating accurate and efficient suggestion list for incorrect or misspelled Sindhi words. A table of phonetically similar sounding Sindhi characters for SoundEx algorithm is also generated along with another table containing similar glyph or shape based character groups for ShapeEx algorithm. Both these are first ever attempt of any such type of categorization and representation for Sindhi Language.


title: Dysphagia in the elderly.

abstract: Elderly patients are inherently predisposed to dysphagia predominately because of comorbid health conditions. With the aging of the population in the United States, along with the increased prevalence of obesity and gastroesophageal reflux disease, healthcare providers will increasingly encounter older patients with either oropharyngeal or esophageal disease and complaints of dysphagia. Useful tests to evaluate dysphagia include the videofluoroscopic swallowing study and the fiberoptic endoscopic evaluation of swallowing. Swallow rehabilitation is useful to help patients compensate for swallowing difficulty and ultimately help strengthen the neuromusculature involved in swallowing.


title: Operational profiles in software-reliability engineering

abstract: A systematic approach to organizing the process of determining the operational profile for guiding software development is presented. The operational profile is a quantitative characterization of how a system will be used that shows how to increase productivity and reliability and speed development by allocating development resources to function on the basis of use. Using an operational profile to guide testing ensures that if testing is terminated and the software is shipped because of schedule constraints, the most-used operations will have received the most testing and the reliability level will be the maximum that is practically achievable for the given test time. For guiding regression testing, it efficiently allocates test cases in accordance with use, so the faults most likely to be found, of those introduced by changes, are the ones that have the most effect on reliability. >


title: The Representation and Matching of Pictorial Structures

abstract: The primary problem dealt with in this paper is the following. Given some description of a visual object, find that object in an actual photograph. Part of the solution to this problem is the specification of a descriptive scheme, and a metric on which to base the decision of "goodness" of matching or detection.


title: Group Deviation Detection Methods: A Survey

abstract: Pointwise anomaly detection and change detection focus on the study of individual data instances; however, an emerging area of research involves groups or collections of observations. From applications of high-energy particle physics to health care collusion, group deviation detection techniques result in novel research discoveries, mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights. In particular, static group anomaly detection is the process of identifying groups that are not consistent with regular group patterns, while dynamic group change detection assesses significant differences in the state of a group over a period of time. Since both group anomaly detection and group change detection share fundamental ideas, this survey article provides a clearer and deeper understanding of group deviation detection research in static and dynamic situations.


Training Utterance-level Embedding Networks for Speaker Identification and Verification


title: Classifier chains for multi-label classification

abstract: The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.


title: Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

abstract: This paper presents a methodology for the feature estimation of a new fault indicator focused on detecting gear mechanical degradation under different operating conditions. Preprocessing of acoustic emission signal is performed by applying chromatic transformation to highlight characteristic patterns of the mechanical degradation. In this study, chromaticity based on the computation of the hue, light, and saturation transformation of the main acoustic emission intrinsic mode functions is performed. Then, a topology preservation approach is carried out to describe the chromatic signature of the healthy gear condition. Thus, the detection index can be estimated. It must be noted that the applied chromatic monitoring process only requires the characterization of the healthy gear condition, being applicable to a wide range of operating conditions of the gear. Performance of the proposed system is validated experimentally. According to the obtained results, the proposed methodology is reliable and feasible for monitoring gear mechanical degradation in industrial applications.


title: Culture and the categorization of emotions.

abstract: Some writers assume-and others deny-that all human beings distinguish emotions from non-emotions and divide the emotions into happiness, anger, fear, and so on. A review of ethnographic and cross-cultural studies on (a) emotion lexicons, (b) the emotions inferred from facial expressions, and (c) dimensions implicit in comparative judgments of emotions indicated both similarities and differences in how the emotions are categorized in different languages and cultures


title: A case study of topographical disorientation: behavioural intervention for achieving independent navigation

abstract: ABSTRACTThis study introduces an intervention that enabled a man (LH) with acquired topographical disorientation (TD) to travel independently without fear of getting lost. Adapting an errorless method, LH learned to use a smartphone to find his routes accurately and reliably. A time-series design (A1-B1-A2-B2) was used: In all phases, LH was given a printed map on which city locations were indicated. He had to walk to the indicated locations while naturalistic outcomes were recorded. In Phases A, he navigated without his smartphone, and in Phases B, with it. In Phases A, LH made numerous surplus direction changes, and openly expressed his frustration. In Phases B, he did not have surplus direction changes and could calmly find his routes. Before intervention, LH and his wife were frustrated and worried about his way-finding. They rated their confidence in his navigational ability and his actual ability in way-finding to various locations as low. After intervention, they were more confident that LH could t...


title: Image retrieval using scene graphs

abstract: This paper develops a novel framework for semantic image retrieval based on the notion of a scene graph. Our scene graphs represent objects (“man”, “boat”), attributes of objects (“boat is white”) and relationships between objects (“man standing on boat”). We use these scene graphs as queries to retrieve semantically related images. To this end, we design a conditional random field model that reasons about possible groundings of scene graphs to test images. The likelihoods of these groundings are used as ranking scores for retrieval. We introduce a novel dataset of 5,000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval. In particular, we evaluate retrieval using full scene graphs and small scene subgraphs, and show that our method outperforms retrieval methods that use only objects or low-level image features. In addition, we show that our full model can be used to improve object localization compared to baseline methods.


title: Circadian preference, sleep and daytime behaviour in adolescence

abstract: Summary ::: The aim of this study was to determine the relationship between circadian preferences, regularity of sleep patterns, sleep problems, daytime sleepiness and daytime behaviour. As a part of an epidemiological survey on sleep in a representative sample of Italian high-school students, a total of 6631 adolescents, aged 14.1–18.6 years, completed the School Sleep Habits Survey, a comprehensive questionnaire including items regarding sleep, sleepiness, substance use, anxiety and depressed mood, use of sleeping pills, school attendance and a morningness/eveningness scale. The sample consisted of 742 evening-types (315 males and 427 females; mean age 17.1 years) and 1005 morning-types (451 males and 554 females; mean age 16.8 years). No significant sex differences were found for morningness/eveningness score. Eveningness was associated with later bedtime and wake-up time, especially on weekends, shorter time in bed during the week, longer weekend time in bed, irregular sleep–wake schedule, subjective poor sleep. Moreover, evening types used to nap more frequently during school days, complained of daytime sleepiness, referred more attention problems, poor school achievement, more injuries and were more emotionally upset than the other chronotype. They referred also greater caffeine-containing beverages and substances to promote sleep consumption. Our results suggest that circadian preference might be related not only to sleep pattern, but also to other adolescent behaviours.


title: Design of Low-Power High-Performance 2–4 and 4–16 Mixed-Logic Line Decoders

abstract: This brief introduces a mixed-logic design method for line decoders, combining transmission gate logic, pass transistor dual-value logic, and static complementary metal-oxide semiconductor (CMOS). Two novel topologies are presented for the 2-4 decoder: a 14-transistor topology aiming on minimizing transistor count and power dissipation and a 15-transistor topology aiming on high power-delay performance. Both normal and inverting decoders are implemented in each case, yielding a total of four new designs. Furthermore, four new 4-16 decoders are designed by using mixed-logic 2-4 predecoders combined with standard CMOS postdecoder. All proposed decoders have full-swinging capability and reduced transistor count compared to their conventional CMOS counterparts. Finally, a variety of comparative spice simulations at 32 nm shows that the proposed circuits present a significant improvement in power and delay, outperforming CMOS in almost all cases.


A Survey on Internet Traffic Archival Systems


title: Using Text and Data Mining Techniques to extract Stock Market Sentiment from Live News Streams

abstract: Analyzing stock market trends and sentiment is an interdisciplinary area of research being undertaken by many disciplines such as Finance, Computer Science, Statistics, and Economics. It has been well established that real time news plays a strong role in the movement of stock prices. With the advent of electronic and online news sources, analysts have to deal with enormous amounts of real-time, unstructured streaming data. In this paper, we present an automated text mining based approach to aggregate news stories from diverse sources and create a News Corpus. The Corpus is filtered down to relevant sentences and analyzed using Natural Language Processing (NLP) techniques. A sentiment metric, called NewsSentiment, utilizing the count of positive and negative polarity words is proposed as a measure of the sentiment of the overall news corpus. We have used various open source packages and tools to develop the news collection and aggregation engine as well as the sentiment evaluation engine. Extensive experimentation has been done using news stories about various stocks. The time variation of NewsSentiment shows a very strong correlation with the actual stock price movement. Our proposed metric has many applications in analyzing current news stories and predicting stock trends for specific companies and sectors of the economy.


title: Project Managers' Skills and Project Success in IT Outsourcing

abstract: What skills do project managers (PMs) need, and how do these skills impact project success in IT outsourcing? In this study, we seek to identify what factors impact IT project outcomes, such as costs and client satisfaction, given the project characteristics and PM’s hard and soft skills. We examine data collected from a field study conducted at a major IT service provider in India. Our results suggest that while hard skills such as technical or domain expertise may be essential in a PM, soft skills, such as tacit knowledge of organizational culture and clients, are more important for project success. The results are robust to different specifications.


title: Tumour evolution inferred by single-cell sequencing

abstract: Although it is known that tumours are genetically heterogeneous it has so far been difficult to dissect this heterogeneity at a single cell level. This paper combines whole-genome amplification and next-generation sequencing of flow-sorted nuclei from breast tumours to investigate their population structure and evolution. In contrast to gradual models of tumour progression, the results indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.


title: PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions

abstract: Given a set D of tuples defined on a domain Omega, we study differentially private algorithms for constructing a histogram over Omega to approximate the tuple distribution in D. Existing solutions for the problem mostly adopt a hierarchical decomposition approach, which recursively splits Omega into sub-domains and computes a noisy tuple count for each sub-domain, until all noisy counts are below a certain threshold. This approach, however, requires that we (i) impose a limit h on the recursion depth in the splitting of Omega and (ii) set the noise in each count to be proportional to h. The choice of h is a serious dilemma: a small h makes the resulting histogram too coarse-grained, while a large h leads to excessive noise in the tuple counts used in deciding whether sub-domains should be split. Furthermore, h cannot be directly tuned based on D; otherwise, the choice of h itself reveals private information and violates differential privacy. To remedy the deficiency of existing solutions, we present PrivTree, a histogram construction algorithm that adopts hierarchical decomposition but completely eliminates the dependency on a pre-defined h. The core of PrivTree is a novel mechanism that (i) exploits a new analysis on the Laplace distribution and (ii) enables us to use only a constant amount of noise in deciding whether a sub-domain should be split, without worrying about the recursion depth of splitting. We demonstrate the application of PrivTree in modelling spatial data, and show that it can be extended to handle sequence data (where the decision in sub-domain splitting is not based on tuple counts but a more sophisticated measure). Our experiments on a variety of real datasets show that PrivTree considerably outperforms the states of the art in terms of data utility.


title: A Probabilistic Graphical Model-based Approach for Minimizing Energy Under Performance Constraints

abstract: In many deployments, computer systems are underutilized -- meaning that applications have performance requirements that demand less than full system capacity. Ideally, we would take advantage of this under-utilization by allocating system resources so that the performance requirements are met and energy is minimized. This optimization problem is complicated by the fact that the performance and power consumption of various system configurations are often application -- or even input -- dependent. Thus, practically, minimizing energy for a performance constraint requires fast, accurate estimations of application-dependent performance and power tradeoffs. This paper investigates machine learning techniques that enable energy savings by learning Pareto-optimal power and performance tradeoffs. Specifically, we propose LEO, a probabilistic graphical model-based learning system that provides accurate online estimates of an application's power and performance as a function of system configuration. We compare LEO to (1) offline learning, (2) online learning, (3) a heuristic approach, and (4) the true optimal solution. We find that LEO produces the most accurate estimates and near optimal energy savings.


title: A new method to detect inter-turn short-circuit in induction motors

abstract: It is well known that stator winding faults such the inter-turn short circuit are the most frequent source of breakdowns in induction motors. Early detection of any small inter-turn short circuit during motor operation would eliminate some subsequent damage to adjacent coils and stator core, reducing then the repair cost. To achieve this purpose, the present paper proposes a new method, called Park-Hilbert (P-H), to detect stator faults in induction motors using a combination between the Hilbert transform and the Extended Park's Vector Approach. The P-H approach is based on the spectral analysis, via the FFT, of the PSVM P-H that represents the Park's Square Vector Modulus computed starting from the amplitudes of the three-phase current's analytical signals obtained by Hilbert Transform. The theoretical bases of the P-H method are presented, and the experimental tests which are carried out on a special 3kW three-phase induction motor confirm the efficiency of the proposed method and corroborate that this fault doesn't have new specific spectral signatures, but it causes important increases on the amplitudes of all harmonics that were present in the PSVM P-H spectrum, even those related to other faults.


title: Kinesio Taping reduces disability and pain slightly in chronic non-specific low back pain: a randomised trial.

abstract: QUESTION ::: Does Kinesio Taping reduce disability, pain, and kinesiophobia in people with chronic non-specific low back pain? ::: ::: ::: DESIGN ::: Randomised trial, with concealed allocation, assessor blinding, and intention-to-treat analysis. ::: ::: ::: PARTICIPANTS ::: Sixty adults with chronic non-specific low back pain. ::: ::: ::: INTERVENTION ::: The experimental intervention was Kinesio Taping over the lumbar spine for one week; the control intervention was sham taping. ::: ::: ::: OUTCOME MEASURES ::: The following outcomes were measured at baseline, immediately after the week with the tape in situ, and four weeks later: Oswestry Disability Index, Roland- Morris Low Back Pain and Disability Questionnaire, pain on a 10-cm visual analogue scale, Tampa kinesiophobia scale, trunk flexion range of motion, and the McQuade test of trunk muscle endurance. ::: ::: ::: RESULTS ::: At one week, the experimental group had significantly greater improvement in disability, by 4 points (95% CI 2 to 6) on the Oswestry score and by 1.2 points (95% CI 0.4 to 2.0) on the Roland-Morris score. However, these effects were not significant four weeks later. The experimental group also had a greater decrease in pain than the control group immediately after treatment (mean between-group difference 1.1cm, 95% CI 0.3 to 1.9), which was maintained four weeks later (1.0cm, 95% CI 0.2 to 1.7). Similarly trunk muscle endurance was significantly better at one week (by 23 sec, 95% CI 14 to 32) and four weeks later (by 18 sec, 95% CI 9 to 26). Other outcomes were not significantly affected. ::: ::: ::: CONCLUSION ::: Kinesio Taping reduced disability and pain in people with chronic non-specific low back pain, but these effects may be too small to be clinically worthwhile. ::: ::: ::: TRIAL REGISTRATION ::: ACTRN12612000402842.


title: Planar Differential Antenna for Short-Range UWB Pulse Radar Sensor

abstract: A novel planar differential ultrawideband (UWB) antenna was designed and implemented on low-cost FR4 substrate and characterized experimentally. The dedicated design was motivated by the implementation of a UWB pulse radar sensor obtained by co-integrating a system-on-a-chip UWB pulse radar packaged in QFN32 package with the proposed antenna, one for the transmitter and one for the receiver. The experimental results confirm the predictions obtained by simulations, and the effectiveness of the novel antenna design for the implementation of low-cost short-range pulse radar sensor was validated by field operational tests.


title: Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS)

abstract: During the last years, hybrid models have proven to be a promising approach for the design of classification systems for the forecasting of bankruptcy. In the present research we propose a hybrid system which combines fuzzy clustering and MARS. Both models are especially suitable for the bankruptcy prediction problem, due to their theoretical advantages when the information used for the forecasting is drawn from company financial statements. We test the accuracy of our approach in a real setting consisting of a database made up of 59,336 non-bankrupt Spanish companies and 138 distressed firms which went bankrupt during 2007. As benchmarking techniques we used discriminant analysis, MARS and a feed-forward neural network. Our results show that the hybrid model outperforms the other systems, both in terms of the percentage of correct classifications and in terms of the profit generated by the lending decisions.


title: An Evolutionary Algorithm for Trajectory Based Gait Generation of Biped Robot

abstract: The evolution of a dynamic walking gait of biped robot is presented in this paper. The gait is generated for a biped robot to walk on flat ground and climb up stairs. For the trajectory based gait generation, various parameters satisfy ZMP criterion and can realize continuous walking. The evolutionary algorithm is used to choose the parameter combinations. Simulation studies show that the algorithm successfully achieves desired performance in dynamic walking. The RoboSapien, a 17 DOF biped robot is built to apply and verify the walking algorithm.


title: Capital Market Imperfections and the Sensitivity of Investment to Stock Prices

abstract: Prior studies argue that investment by undervalued firms that require external equity is particularly sensitive to stock prices in irrational capital markets. We present a model in which investment can appear to be more sensitive to stock prices when capital markets are rational, but subject to imperfections such as debt overhang, information asymmetries, and financial distress costs. Our empirical tests support the rational (but imperfect) capital markets view. Specifically, investment–stock price sensitivity is related to firm leverage, financial slack, and probability of financial distress, but is not related to proxies for firm undervaluation. Because, in our model, stock prices reflect the net present values (NPVs) of investment opportunities, our results are consistent with rational capital markets improving the allocation of capital by channeling more funds to firms with positive NPV projects.


Massively Parallel Individually Selecting Configuration Interaction


Deceptive Opinion Spam Detection Using Neural Network


title: Metacognitive mechanisms underlying lucid dreaming

abstract: Lucid dreaming is a state of awareness that one is dreaming, without leaving the sleep state. Dream reports show that self-reflection and volitional control are more pronounced in lucid compared with nonlucid dreams. Mostly on these grounds, lucid dreaming has been associated with metacognition. However, the link to lucid dreaming at the neural level has not yet been explored. We sought for relationships between the neural correlates of lucid dreaming and thought monitoring. Human participants completed a questionnaire assessing lucid dreaming ability, and underwent structural and functional MRI. We split participants based on their reported dream lucidity. Participants in the high-lucidity group showed greater gray matter volume in the frontopolar cortex (BA9/10) compared with those in the low-lucidity group. Further, differences in brain structure were mirrored by differences in brain function. The BA9/10 regions identified through structural analyses showed increases in blood oxygen level-dependent signal during thought monitoring in both groups, and more strongly in the high-lucidity group. Our results reveal shared neural systems between lucid dreaming and metacognitive function, in particular in the domain of thought monitoring. This finding contributes to our understanding of the mechanisms enabling higher-order consciousness in dreams.


title: Electromagnetic interference in CMOS circuits

abstract: A model for predicting the radiofrequency interference (RFI) susceptibility of CMOS circuits at the design stage, using SPICE, is reported on. The circuit investigated is the RCA CD4007A, which contains three CMOS pairs of n-MOSFETs and p-MOSFETs, input protection circuits, output protection circuits, and parasitic pn-junctions, which behave as parasitic capacitors. The model was verified in both the presence and absence of RFI. The SPICE simulations correctly predict the frequency dependences of the RFI responses. >


title: D.J.: Health Monitoring and Assistance to Support Aging

abstract: To many people, home is a sanctuary. For those people who need special medical care, they may need to be pulled out of their home to meet their medical needs. As the population ages, the percentage of people in this group is increasing and the effects are expensive as well as unsatisfying. We hypothesize that many people with disabilities can lead independent lives in their own homes with the aid of at- home automated assistance and health monitoring. In order to accomplish this, robust methods must be developed to collect relevant data and process it dynamically and adaptively to detect and/or predict threatening long-term trends or immediate crises. The main objective of this paper is to investigate techniques for using agent-based smart home technologies to provide this at-home health monitoring and assistance. To this end, we have developed novel inhabitant modeling and automation algorithms that provide remote health monitoring for caregivers. Specifically, we address the following technological challenges: 1) identifying lifestyle trends, 2) detecting anomalies in current data, and 3) designing a reminder assistance system. Our solution approaches are being tested in simulation and with volunteers at the UTA's MavHome site, an agent-based smart home project.


title: Proximal Dehaze-Net: A Prior Learning-Based Deep Network for Single Image Dehazing

abstract: Photos taken in hazy weather are usually covered with white masks and often lose important details. In this paper, we propose a novel deep learning approach for single image dehazing by learning dark channel and transmission priors. First, we build an energy model for dehazing using dark channel and transmission priors and design an iterative optimization algorithm using proximal operators for these two priors. Second, we unfold the iterative algorithm to be a deep network, dubbed as proximal dehaze-net, by learning the proximal operators using convolutional neural networks. Our network combines the advantages of traditional prior-based dehazing methods and deep learning methods by incorporating haze-related prior learning into deep network. Experiments show that our method achieves state-of-the-art performance for single image dehazing.


title: www.elsevier.com/locate/compedu Student–instructor communication: The role of email

abstract: We report on the use of email lists as a supplement to teaching. We argue that email lists can provide a valuable students-instructor communication channel and describe the process of setting up and managing such lists. A case study of email messages exchanged in an introductory operations research course is also included. The case illustrates how a 'strategic' use of email leads to a richer learning experience, by providing an extra medium for communication, and offers a valuable feedback database that can, among other things, be used to improve future editions of a course.


title: Detection of suicide-related posts in Twitter data streams

abstract: Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.


title: Deep Compositional Question Answering with Neural Module Networks

abstract: Visual question answering is fundamentally compositional in nature---a question like "where is the dog?" shares substructure with questions like "what color is the dog?" and "where is the cat?" This paper seeks to simultaneously exploit the representational capacity of deep networks and the compositional linguistic structure of questions. We describe a procedure for constructing and learning *neural module networks*, which compose collections of jointly-trained neural "modules" into deep networks for question answering. Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.). The resulting compound networks are jointly trained. We evaluate our approach on two challenging datasets for visual question answering, achieving state-of-the-art results on both the VQA natural image dataset and a new dataset of complex questions about abstract shapes.


title: A Hybrid Approach for Detection and Classification of the Defects on Printed Circuit Board

abstract: Printed Circuit Board (PCB) is the most important component of the electronic industry and thus, the electronic massproduction facilities make an attempt to achieve Printed Circuit Board with 100% quality assurance. To achieve Printed Circuit Board with 100% quality assurance i.e. to produce zero-defect Printed Circuit Board, inspection method is the vital process during manufacturing of PCB. This paper presents the Hybrid approach which helps in producing zerodefect PCB by detecting the defects. This approach not only detects the defect but also classifies and locates the defects. The hybrid approach uses referential and non-referential methods to analyze the Printed Circuit Board and this approach proposed an algorithm which involves Image Representation, Image Comparison, and Image Segmentation. The algorithm was tested with mass number of images of Printed Circuit Board. The experimental result shows that higher quality assurance is obtained than the previously implemented algorithms. General Terms Defect Detection, Defect Classification, Defect Localization