title: Visual analysis of pressure in football

abstract: Modern movement tracking technologies enable acquisition of high quality data about movements of the players and the ball in the course of a football match. However, there is a big difference between the raw data and the insights into team behaviors that analysts would like to gain. To enable such insights, it is necessary first to establish relationships between the concepts characterizing behaviors and what can be extracted from data. This task is challenging since the concepts are not strictly defined. We propose a computational approach to detecting and quantifying the relationships of pressure emerging during a game. Pressure is exerted by defending players upon the ball and the opponents. Pressing behavior of a team consists of multiple instances of pressure exerted by the team members. The extracted pressure relationships can be analyzed in detailed and summarized forms with the use of static and dynamic visualizations and interactive query tools. To support examination of team tactics in different situations, we have designed and implemented a novel interactive visual tool “time mask”. It enables selection of multiple disjoint time intervals in which given conditions are fulfilled. Thus, it is possible to select game situations according to ball possession, ball distance to the goal, time that has passed since the last ball possession change or remaining time before the next change, density of players’ positions, or various other conditions. In response to a query, the analyst receives visual and statistical summaries of the set of selected situations and can thus perform joint analysis of these situations. We give examples of applying the proposed combination of computational, visual, and interactive techniques to real data from games in the German Bundesliga, where the teams actively used pressing in their defense tactics.


title: DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model

abstract: The Android platform is becoming increasingly popular and various organizations have developed a variety of applications (App) to cater to market trends. Due to the characteristics of the Android platform, such as supporting the unofficial App stores, open source policy and the great tolerance for App verification, it is inevitable that it faces serious problems of malicious software intrusion. In order to protect the users from the serious damages caused by Android malware, we propose a low-cost and high-efficient method to extract permissions, sensitive APIs, monitoring system events and permission-rate as key features, and employ the ensemble Rotation Forest (RF) to construct a model to detect whether an Android App is malicious or not. Specifically, a dataset containing 2,130 samples is used to verify the performance of the proposed method. The experimental results show that the proposed method achieves an high accuracy of 88.26% with 88.40% sensitivity at the precision of 88.16%. To further evaluate the performance of the proposed model, we also compare it with the state-of-the-art Support Vector Machine (SVM) model under the same experimental conditions, and the comparison results demonstrate that the proposed method improves the accuracy by 3.33% compared to SVM. The experimental results show that the proposed model is extremely promising and could provide a cost-effective alternative for Android malware detection.


title: Video Frame Interpolation via Adaptive Convolution

abstract: Video frame interpolation typically involves two steps: motion estimation and pixel synthesis. Such a two-step approach heavily depends on the quality of motion estimation. This paper presents a robust video frame interpolation method that combines these two steps into a single process. Specifically, our method considers pixel synthesis for the interpolated frame as local convolution over two input frames. The convolution kernel captures both the local motion between the input frames and the coefficients for pixel synthesis. Our method employs a deep fully convolutional neural network to estimate a spatially-adaptive convolution kernel for each pixel. This deep neural network can be directly trained end to end using widely available video data without any difficult-to-obtain ground-truth data like optical flow. Our experiments show that the formulation of video interpolation as a single convolution process allows our method to gracefully handle challenges like occlusion, blur, and abrupt brightness change and enables high-quality video frame interpolation.


title: The Role of Expressive Writing in Math Anxiety

abstract: Math anxiety is a negative affective reaction to situations involving math. Previous work demonstrates that math anxiety can negatively impact math problem solving by creating performance-related worries that disrupt the working memory needed for the task at hand. By leveraging knowledge about the mechanism underlying the math anxiety–performance relationship, we tested the effectiveness of a short expressive writing intervention that has been shown to reduce intrusive thoughts and improve working memory availability. Students (N 80) varying in math anxiety were asked to sit quietly (control group) prior to completing difficulty-matched math and word problems or to write about their thoughts and feelings regarding the exam they were about to take (expressive writing group). For the control group, high math-anxious individuals (HMAs) performed significantly worse on the math problems than low math-anxious students (LMAs). In the expressive writing group, however, this difference in math performance across HMAs and LMAs was significantly reduced. Among HMAs, the use of words related to anxiety, cause, and insight in their writing was positively related to math performance. Expressive writing boosts the performance of anxious students in math-testing situations.


title: “Evaluation and optimization of innovative production systems of goods and services” MODELLING SPARE PARTS ' DEMAND: AN EMPIRICAL INVESTIGATION

abstract: Intermittent demand patterns are very difficult to forecast and they are, most commonly, associated with spare parts' requirements. Most research in this area focuses on the control of inventories assuming that an appropriate estimator is in place to forecast demand and that the demand is represented by one of the standard statistical distributions. The choice of a demand distribution has an important impact on the stock control performance. In this paper, after a critical review of the literature dealing with the demand distributions considered for stock control of intermittent demand, an empirical investigation is conducted, using a real data set of approximately 13,000 Stock Keeping Units (SKUs) coming from the military sector (Royal Air Force, RAF UK and US Defense Logistics Agency, DLA) and the Electronics industry. The empirical investigation aims at testing statistical significance of various theoretical distributions. The potential linkage between demand distributional assumptions and demand classification schemes is also explored followed by an agenda for further research in this area.


title: A 300khz-13.5ghz directional bridge

abstract: This paper presents a wideband directional bridge with a range of operating frequencies from 300 kHz to 13.5 GHz. The original topology of the directional bridge was designed, using the multilayer printed circuit board (PCB) technology, with the top layer of the laminated microwave dielectric Rogers RO4350, as resistive elements surface mounted (SMD) components are used. The circuit is designed for a nominal value of 16 dB coupling and an insertion loss of 1.6 dB.


title: Inpainting by flexible Haar wavelet shrinkage

abstract: We present novel wavelet-based inpainting algorithms. Applying ideas from anisotropic regularization and diffusion, our models can better handle degraded pixels at edges. We interpret our algorithms within the framework of forward-backward splitting methods in convex analysis and prove that the conditions for ensuring their convergence are fulfilled. Numerical examples illustrate the good performance of our algorithms.


title: Volitional personality trait change: Can people choose to change their personality traits?

abstract: Previous research has found that most people want to change their personality traits. But can people actually change their personalities just because they want to? To answer this question, we conducted 2, 16-week intensive longitudinal randomized experiments. Across both studies, people who expressed goals to increase with respect to any Big Five personality trait at Time 1 tended to experience actual increases in their self-reports of that trait-as well as trait-relevant daily behavior-over the subsequent 16 weeks. Furthermore, we tested 2 randomized interventions designed to help participants attain desired trait changes. Although 1 of the interventions was inefficacious, a second intervention that trained participants to generate implementation intentions catalyzed their ability to attain trait changes. We also tested several theoretical processes through which volitional changes might occur. These studies suggest that people may be able to change their self-reported personality traits through volitional means, and represent a first step toward understanding the processes that enable people to do so. (PsycINFO Database Record (c) 2015 APA, all rights reserved)


title: Dog bites to the head and neck in children; an increasing problem in the UK

abstract: There is growing concern regarding the number of dog related injuries in children. Hospital Episode Statistics data in England indicates dog related injuries presenting to hospitals increased 63% between 1998 and 2008 and is continuing to rise(1) . In 2010 there were over 6000 admissions to hospital due to dog related injuries and the cost to the NHS was £3.3 million(2) . © 2013 Blackwell Publishing Ltd. Language: en


Training Structured Prediction Energy Networks with Indirect Supervision


title: Autonomous underwater grasping using multi-view laser reconstruction

abstract: Autonomous manipulation in underwater scenarios is a highly complex task which is still poorly studied, but with a growing interest in the last years. Two of the main phases of this problem are the detection and characterization of the object which is going to be manipulated, and the planning of this manipulation. This paper presents, on the one hand, the results of a multi-view laser reconstruction approach used in a real system. This approach consists in attaching a laser emitter and a camera in the forearm of a robotic arm. Then, moving the arm, the scene is scanned and reconstructed. This configuration allows the user to reconstruct autonomously a scene from different points of view and with a high precision. On the other hand, a grasp planning algorithm has been described. This method makes possible to autonomously detect the object of interest from a scene with multiple objects, and to choose which is the best arm configuration in order to manipulate it.


title: Global Inference for Bridging Anaphora Resolution

abstract: We present the first work on antecedent selection for bridging resolution without restrictions on anaphor or relation types. Our model integrates global constraints on top of a rich local feature set in the framework of Markov logic networks. The global model improves over the local one and both strongly outperform a reimplementation of prior work.


title: The Use and Effectiveness of User Stories in Practice

abstract: [Context and motivation] User stories are an increasingly popular textual notation to capture requirements in agile software development. [Question/Problem] To date there is no scientific evidence on the effectiveness of user stories. The goal of this paper is to explore how practicioners perceive this artifact in the context of requirements engineering. [Principal ideas/results] We explore perceived effectiveness of user stories by reporting on a survey with 182 responses from practitioners and 21 follow-up semi-structured interviews. The data shows that practitioners agree that using user stories, a user story template and quality guidelines such as the INVEST mnemonic improve their productivity and the quality of their work deliverables. [Contribution] By combining the survey data with 21 semi-structured follow-up interviews, we present 12 findings on the usage and perception of user stories by practitioners that employ user stories in their everyday work environment.


title: Measurement study of Netflix, Hulu, and a tale of three CDNs

abstract: Netflix and Hulu are leading Over-the-Top (OTT) content service providers in the US and Canada. Netflix alone accounts for 29.7% of the peak downstream traffic in the US in 2011. Understanding the system architectures and performance of Netflix and Hulu can shed light on the design of such large-scale video streaming platforms, and help improving the design of future systems. In this paper, we perform extensive measurement study to uncover their architectures and service strategies. Netflix and Hulu bear many similarities. Both Netflix and Hulu video streaming platforms rely heavily on the third-party infrastructures, with Netflix migrating that majority of its functions to the Amazon cloud, while Hulu hosts its services out of Akamai. Both service providers employ the same set of three content distribution networks (CDNs) in delivering the video contents. Using active measurement study, we dissect several key aspects of OTT streaming platforms of Netflix and Hulu, e.g., employed streaming protocols, CDN selection strategy, user experience reporting, etc. We discover that both platforms assign the CDN to a video request without considering the network conditions and optimizing the user-perceived video quality. We further conduct the performance measurement studies of the three CDNs employed by Netflix and Hulu. We show that the available bandwidths on all three CDNs vary significantly over the time and over the geographic locations. We propose a measurement-based adaptive CDN selection strategy and a multiple-CDN-based video delivery strategy that can significantly increase users' average available bandwidth.


title: A Practical and Highly Optimized Convolutional Neural Network for Classifying Traffic Signs in Real-Time

abstract: Classifying traffic signs is an indispensable part of Advanced Driver Assistant Systems. This strictly requires that the traffic sign classification model accurately classifies the images and consumes as few CPU cycles as possible to immediately release the CPU for other tasks. In this paper, we first propose a new ConvNet architecture. Then, we propose a new method for creating an optimal ensemble of ConvNets with highest possible accuracy and lowest number of ConvNets. Our experiments show that the ensemble of our proposed ConvNets (the ensemble is also constructed using our method) reduces the number of arithmetic operations 88 and $$73\,\%$$73% compared with two state-of-art ensemble of ConvNets. In addition, our ensemble is $$0.1\,\%$$0.1% more accurate than one of the state-of-art ensembles and it is only $$0.04\,\%$$0.04% less accurate than the other state-of-art ensemble when tested on the same dataset. Moreover, ensemble of our compact ConvNets reduces the number of the multiplications 95 and $$88\,\%$$88%, yet, the classification accuracy drops only 0.2 and $$0.4\,\%$$0.4% compared with these two ensembles. Besides, we also evaluate the cross-dataset performance of our ConvNet and analyze its transferability power in different layers. We show that our network is easily scalable to new datasets with much more number of traffic sign classes and it only needs to fine-tune the weights starting from the last convolution layer. We also assess our ConvNet through different visualization techniques. Besides, we propose a new method for finding the minimum additive noise which causes the network to incorrectly classify the image by minimum difference compared with the highest score in the loss vector.


title: Directional binary code with application to . . .

abstract: This paper introduces the establishment of PolyU near-infrared face database (PolyU-NIRFD) and presents a new coding scheme for face recognition. The PolyU-NIRFD contains images from 350 subjects, each contributing about 100 samples with variations of pose, expression, focus, scale, time, etc. In total, 35,000 samples were collected in the database. The PolyU-NIRFD provides a platform for researchers to develop and evaluate various near-infrared face recognition techniques under large scale, controlled and uncontrolled conditions. A new coding scheme, namely directional binary code (DBC), is then proposed for near-infrared face recognition. Finally, we provide three protocols to evaluate and compare the proposed DBC method with baseline face recognition methods, including Gabor based Eigenface, Fisherface and LBP (local binary pattern) on the PolyU-NIRFD database. In addition, we also conduct experiments on the visible light band FERET database to further validate the proposed DBC scheme.


title: A systematic review of the effects of euthanasia and occupational stress in personnel working with animals in animal shelters, veterinary clinics, and biomedical research facilities

abstract: Background—The study of occupational stress and compassion fatigue in personnel working in animal-related occupations has gained momentum over the last decade. However, there remains incongruence in understanding what is currently termed compassion fatigue and the associated unique contributory factors. Furthermore, there is minimal established evidence of the likely influence of these conditions on the health and well-being of individuals working in various animal-related occupations. Objective—To assess currently available evidence and terminology regarding occupational stress and compassion fatigue in personnel working in animal shelters, veterinary clinics, and biomedical research facilities. Data Sources—Studies were identified by searching the following electronic databases with no publication date restrictions: ProQuest Research Library, ProQuest Social Science Journals, PsycARTICLES, Web of Science, Science Direct, Scopus, PsychINFO databases, and Google Scholar. Search terms included (euthanasia ...


title: A Case Study on Quality Function Deployment (QFD)

abstract: Quality Function Deployment (QFD) was conceived in Japan in the late 1960's, and introduced to America and Europe in 1983. This paper will provide a general overview of the QFD methodology and approach to product development. Once familiarity with the tool is established, a real-life application of the technique will be provided in a case study. The case study will illustrate how QFD was used to develop a new tape product and provide counsel to those that may want to implement the QFD process. Quality function deployment (QFD) is a "method to transform user demands into design quality, to deploy the functions forming quality, and to deploy methods for achieving the design quality into subsystems and component parts, and ultimately to specific elements of the manufacturing process."


title: A system model and inversion for synthetic aperture radar imaging

abstract: A system model and its corresponding inversion for synthetic aperture radar imaging are presented. The system model incorporates the spherical nature of a radar's radiation pattern at far field. The inverse method based on this model integrates the recorded signals at various coordinates of a translational radar (linear array) via a spatial Fourier transform. The transformed data are shown to provide samples of the spatial Fourier transform of the target's reflectivity function. The inverse method can be modified to incorporate deviations of the radar's motion from its prescribed straight-line path. These principles are also applicable in inverse synthetic aperture imaging of a rotational target (circular array). >


title: Evaluation of palatal donor site haemostasis and wound healing after free gingival graft surgery

abstract: AIM ::: Evaluating effectiveness of a medicinal plant extract (MPE) in achieving haemostasis and early wound healing at free gingival graft (FGG) donor site in a randomized controlled fashion. ::: ::: ::: METHODS ::: Forty patients requiring FGG at lower anterior area were randomly assigned into two groups. FGG was performed to all patients and following graft procurement; wet gauze (WG) was applied alone (control: WG group) or with MPE (test: MPE + WG group) for haemostasis. Donor site working time, bleeding (BLE), colour match (CM), pain, epithelization (EP) and sensation loss (SL) were evaluated. ::: ::: ::: RESULTS ::: Thirty-three participants completed a 6-month period study. In the test group, primary BLE was shorter (p < 0.001) and fewer individuals showed secondary BLE during 3 days (p < 0.001). During the 6 days, pain scores were higher in WG patients (p < 0.05). Later on, no inter-group difference was observed. EP was relatively faster (p < 0.001) and CM was slightly better (p < 0.05) in MPE + WG group. ::: ::: ::: CONCLUSION ::: MPE provided faster and continuous haemostasis that made a positive contribution to the early soft tissue healing to some extent but due to limitations; further trials are needed to demonstrate the efficiency of this material.


title: mTOR Signaling in Growth, Metabolism, and Disease

abstract: The mechanistic target of rapamycin (mTOR) coordinates eukaryotic cell growth and metabolism with environmental inputs, including nutrients and growth factors. Extensive research over the past two decades has established a central role for mTOR in regulating many fundamental cell processes, from protein synthesis to autophagy, and deregulated mTOR signaling is implicated in the progression of cancer and diabetes, as well as the aging process. Here, we review recent advances in our understanding of mTOR function, regulation, and importance in mammalian physiology. We also highlight how the mTOR signaling network contributes to human disease and discuss the current and future prospects for therapeutically targeting mTOR in the clinic.


title: Diagnosis of hair disorders.

abstract: Hair disorders include hair loss, increased hair growth, and hair structure defects with increased breakage, as well as unacceptable cosmetic appearance, such as reduced shine, strength, curliness, and elasticity. It is the task of the dermatologist to choose the right diagnostic tool depending on the suspected clinical diagnosis. Moreover, certain tools are best suited for diagnosis in private practice, whereas others can only be used to monitor hair growth under treatment in clinical studies. The techniques can be classified as either invasive (eg, biopsies in scarring alopecia), semi-invasive (trichogram, unit area trichogram), or noninvasive (eg, global hair counts, phototrichogram, electron microscopy, laser scanning microscopy) methods. Further, one must differentiate between subjective and objective techniques. For the practicing dermatologist, body and scalp hair distribution by use of different grading systems, the hair pull test, and dermoscopy belong in the category of basic diagnostic tools. Basic techniques may be extended by computer-assisted phototrichogram and, in selected cases, by use of the trichogram and/or scalp biopsies. For research purposes optical coherent tomography, electron microscopy, biochemical methods, atomic force microscopy, and confocal laser scanning microscopy are optional tools. For clinical studies global photographs (global expert panel), hair weighing, phototrichogram, and different clinical scoring systems have proven to be objective tools for documentation and evaluation of hair growth and hair quality.


SCTCMG 2018 International Scientific Conference «Social and Cultural Transformations in the Context of Modern Globalism» NEWSPAPER NARRATIVE AS COMMUNICATIVE ACTION: NEOGRICE APPROACH


title: Unveiling the ISCAS-85 benchmarks: a case study in reverse engineering

abstract: Designing at higher levels of abstraction is key to managing the complexity of today's VLSI chips. The authors show how they reverse-engineered the ISCAS-85 benchmarks to add a useful, new high-level tool to the designer's arsenal.


title: Predicting source code changes by mining change history

abstract: Software developers are often faced with modification tasks that involve source which is spread across a code base. Some dependencies between source code, such as those between source code written in different languages, are difficult to determine using existing static and dynamic analyses. To augment existing analyses and to help developers identify relevant source code during a modification task, we have developed an approach that applies data mining techniques to determine change patterns - sets of files that were changed together frequently in the past - from the change history of the code base. Our hypothesis is that the change patterns can be used to recommend potentially relevant source code to a developer performing a modification task. We show that this approach can reveal valuable dependencies by applying the approach to the Eclipse and Mozilla open source projects and by evaluating the predictability and interestingness of the recommendations produced for actual modification tasks on these systems.


title: Challenges on Large Scale Surveillance Video Analysis

abstract: Large scale surveillance video analysis is one of the most important components in the future artificial intelligent city. It is a very challenging but practical system, consists of multiple functionalities such as object detection, tracking, identification and behavior analysis. In this paper, we try to address three tasks hosted in NVIDIA AI City Challenge contest. First, a system that transforming the image coordinate to world coordinate has been proposed, which is useful to estimate the vehicle speed on the road. Second, anomalies like car crash event and stalled vehicles can be found by the proposed anomaly detector framework. Third, multiple camera vehicle re-identification problem has been investigated and a matching algorithm is explained. All these tasks are based on our proposed online single camera multiple object tracking (MOT) system, which has been evaluated on the widely used MOT16 challenge benchmark. We show that it achieves the best performance compared to the state-of-the-art methods. Besides of MOT, we evaluate the proposed vehicle re-identification model on VeRi-776 dataset and it outperforms all other methods with a large margin.


title: Reducing Data Stream Complexity by Applying Count-Min Algorithm and Discretization Procedure

abstract: This paper is a preliminary report on the efficiency of two strategies of data reduction in a data preprocessing stage. In the first experiment, we apply the Count-Min sketching algorithm, while in the second experiment we discretize our data prior to applying the Count-Min algorithm. By conducting a discretization before sketching, the need for the increased number of buckets in sketching is reduced. This preliminary attempt of combining two methods with the same purpose has shown potential. In our experiments, we use sensor data collected to study the environmental fluctuation and its impact on the quality of fresh peaches and nectarines in cold chain.


title: Long-Span Program Behavior Modeling and Attack Detection

abstract: Intertwined developments between program attacks and defenses witness the evolution of program anomaly detection methods. Emerging categories of program attacks, e.g., non-control data attacks and data-oriented programming, are able to comply with normal trace patterns at local views. This article points out the deficiency of existing program anomaly detection models against new attacks and presents long-span behavior anomaly detection (LAD), a model based on mildly context-sensitive grammar verification. The key feature of LAD is its reasoning of correlations among arbitrary events that occurred in long program traces. It extends existing correlation analysis between events at a stack snapshot, e.g., paired call and ret, to correlation analysis among events that historically occurred during the execution. The proposed method leverages specialized machine learning techniques to probe normal program behavior boundaries in vast high-dimensional detection space. Its two-stage modeling/detection design analyzes event correlation at both binary and quantitative levels. Our prototype successfully detects all reproduced real-world attacks against sshd, libpcre, and sendmail. The detection procedure incurs 0.1 ms to 1.3 ms overhead to profile and analyze a single behavior instance that consists of tens of thousands of function call or system call events.


title: A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

abstract: Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing [1], [2]. Blind HU aims at identifying materials present in a captured scene, as well as their compositions, by using high spectral resolution of hyperspectral images. It is a blind source separation (BSS) problem from a SP viewpoint. Research on this topic started in the 1990s in geoscience and remote sensing [3]-[7], enabled by technological advances in hyperspectral sensing at the time. In recent years, blind HU has attracted much interest from other fields such as SP, machine learning, and optimization, and the subsequent cross-disciplinary research activities have made blind HU a vibrant topic. The resulting impact is not just on remote sensing - blind HU has provided a unique problem scenario that inspired researchers from different fields to devise novel blind SP methods. In fact, one may say that blind HU has established a new branch of BSS approaches not seen in classical BSS studies. In particular, the convex geometry concepts - discovered by early remote sensing researchers through empirical observations [3]-[7] and refined by later research - are elegant and very different from statistical independence-based BSS approaches established in the SP field. Moreover, the latest research on blind HU is rapidly adopting advanced techniques, such as those in sparse SP and optimization. The present development of blind HU seems to be converging to a point where the lines between remote sensing-originated ideas and advanced SP and optimization concepts are no longer clear, and insights from both sides would be used to establish better methods.


title: Photon-gated intensity effect upon holographic storage for thin layers of methyl-orange-dye-doped polyvinyl alcohol

abstract: The photon-gated intensity effect upon holographic storage for thin layers of polyvinyl alcohol matrices doped methyl orange dyes is investigated. The dynamic curves for grating growing and decay are given for different gating intensity. The time that grating gets to saturation becomes short. The maximum diffraction efficiency of the grating gets little with the rise of gating light intensity. The proper intensity Ar-ion laser can erase grating. The erasing velocity is proportional to erasing light intensity. The modulation transfer function is measured for writing beams having different polarization states.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Novelty detection: a review - part 2: : neural network based approaches

abstract: Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. In this paper we focus on neural network-based approaches for novelty detection. Statistical approaches are covered in Part 1 paper.


title: Improving the intrinsic calibration of a Velodyne LiDAR sensor

abstract: LiDAR (Light detection and ranging) sensors are widely used in research and development. As such, they build the base for the evaluation of newly developed ADAS (Advanced Driver Assistance Systems) functions in the automotive field where they are used for ground truth establishment. However, the factory calibration provided for the sensors is not able to satisfy the high accuracy requirements by such applications. In this paper we propose a concept to easily improve the existing calibration of a Velodyne LiDAR sensor without the need for special calibration setups which can even be used to enhance already recorded data.


title: Aspect-based opinion mining from product reviews using conditional random fields

abstract: Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.


title: A Robust Single-Phase PLL System With Stable and Fast Tracking

abstract: Phase, frequency, and amplitude of single-phase voltages are the most important and basic information required for single-phase grid-connected applications. This paper proposes a method for instantly and robustly estimating the phase, frequency, and amplitude of frequency-varying single-phase signals for such applications, which is a phase-locked loop (PLL) method based on a structure. The proposed method has the following attractive features: 1) the estimation system results in a nonlinear system, but it can be stabilized; 2) all subsystems constructing the system can be easily designed; 3) "two-phase signal generator"; can autotune a single system parameter in response to varying frequency of the injected signal; 4) high-order "PLL-Controllers" allowing fast tracking can be stably used; and 5) even in hostile envelopments, favorable instant estimates can be obtained. This paper proposes the estimation method and verifies its usefulness by extensive numerical experiments.


title: Recognition of Isolated Words using Features based on LPC, MFCC, ZCR and STE, with Neural Network Classifiers

abstract: This paper proposes an approach to recognize English words corresponding to digits Zero to Nine spoken in an isolated way by different male and female speakers. A set of features consisting of a combination of Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC), Zero Crossing Rate (ZCR), and Short Time Energy (STE) of the audio signal, is used to generate a 63-element feature vector, which is subsequently used for discrimination. Classification is done using artificial neural networks (ANN) with feedforward back-propagation architectures. An accuracy of 85% is obtained by the combination of features, when the proposed approach is tested using a dataset of 280 speech samples, which is more than those obtained by using the features singly.


title: Coarse-to-Fine Learning for Single-Image Super-Resolution

abstract: This paper develops a coarse-to-fine framework for single-image super-resolution (SR) reconstruction. The coarse-to-fine approach achieves high-quality SR recovery based on the complementary properties of both example learning-and reconstruction-based algorithms: example learning-based SR approaches are useful for generating plausible details from external exemplars but poor at suppressing aliasing artifacts, while reconstruction-based SR methods are propitious for preserving sharp edges yet fail to generate fine details. In the coarse stage of the method, we use a set of simple yet effective mapping functions, learned via correlative neighbor regression of grouped low-resolution (LR) to high-resolution (HR) dictionary atoms, to synthesize an initial SR estimate with particularly low computational cost. In the fine stage, we devise an effective regularization term that seamlessly integrates the properties of local structural regularity, nonlocal self-similarity, and collaborative representation over relevant atoms in a learned HR dictionary, to further improve the visual quality of the initial SR estimation obtained in the coarse stage. The experimental results indicate that our method outperforms other state-of-the-art methods for producing high-quality images despite that both the initial SR estimation and the followed enhancement are cheap to implement.


title: BigSim: a parallel simulator for performance prediction of extremely large parallel machines

abstract: Summary form only given. We present a parallel simulator - BigSim - for predicting performance of machines with a very large number of processors. The simulator provides the ability to make performance predictions for machines such as BlueGene/L, based on actual execution of real applications. We present this capability using case-studies of some application benchmarks. Such a simulator is useful to evaluate the performance of specific applications on such machines even before they are built. A sequential simulator may be too slow or infeasible. However, a parallel simulator faces problems of causality violations. We describe our scheme based on ideas from parallel discrete event simulation and utilize inherent determinacy of many parallel applications. We also explore techniques for optimizing such parallel simulations of machines with large number of processors on existing machines with fewer number of processors.


title: Face recognition based on facial landmark detection

abstract: This paper presents a novel technique for face recognition based on facial landmarks extracted automatically. Our landmarks are those associated with eyes mouth and nose. With the extracted landmarks, the area triplets and the associated geometric invariance are formed. We opt to use area and triangle confined within the triangle as the invariance. To bypass the perspective constraints, we take the face image with high focal length and at the farther distance. Orthogonal projection and Euclidean transformation are then assumed. As area is relative invariance under Euclidean transformation, the absolute area ratios between consecutive area triples are applied. Our purposed algorithm is tested successfully to identify person and could be a promising technique for facial recognition.


title: On the Learning of Timing Behavior for Anomaly Detection in Cyber-Physical Production Systems

abstract: Model-based anomaly detection approaches by now have established themselves in the field of engineering sciences. Algorithms from the field of artificial intelligence and machine learning are used to identify a model automatically based on observations. Many algorithms have been developed to manage different tasks such as monitoring and diagnosis. However, the usage of the factor of time in modeling formalisms has not yet been duly investigated, though many systems are dependent on time. In this paper, we evaluate the requirements of the factor of time on the modeling formalisms and the suitability for automatic identification. Based on these features, which classify the timing modeling formalisms, we classify the formalisms concerning their suitability for automatic identification and the use of the identified models for the diagnosis in Cyber-Physical Production Systems (CPPS). We argue the reasons for choosing timed automata for this task and propose a new timing learning method, which differs from existing approaches and we proof the enhanced calculation runtime. The presentation of a use case in a real plant set up completes this paper.


title: Guided-wave and leakage characteristics of substrate integrated waveguide

abstract: The substrate integrated waveguide (SIW) technique makes it possible that a complete circuit including planar circuitry, transitions, and rectangular waveguides are fabricated in planar form using a standard printed circuit board or other planar processing techniques. In this paper, guided wave and modes characteristics of such an SIW periodic structure are studied in detail for the first time. A numerical multimode calibration procedure is proposed and developed with a commercial software package on the basis of a full-wave finite-element method for the accurate extraction of complex propagation constants of the SIW structure. Two different lengths of the SIW are numerically simulated under multimode excitation. By means of our proposed technique, the complex propagation constant of each SIW mode can accurately be extracted and the electromagnetic bandstop phenomena of periodic structures are also investigated. Experiments are made to validate our proposed technique. Simple design rules are provided and discussed.


title: Real-time human activity classification by accelerometer embedded wearable devices

abstract: Human activity information can be used in a lot of applications such as fitness monitoring. In this paper an activity classification wearable device system is designed which can provide the activity information of the users via a three-axis kinematic sensor. The features in time domain and frequency domain of acceleration data are extracted, and Decision Tree algorithm is applied. The training module is performed once off line to generate the classification model, while the classification can be executed real time on the STM32L low-power microcontroller. The wearable device was worn in watch style in experiment and it offered activity information in acceptable accuracy.


title: Probing the Biology of Alzheimer's Disease in Mice

abstract: Alzheimer's disease (AD), the most common cause of dementia among the elderly, may either represent the far end of a continuum that begins with age-related memory decline or a distinct pathobiological process. Although mice that faithfully model all aspects of AD do not yet exist, current mouse models have provided valuable insights into specific aspects of AD pathogenesis. We will argue that transgenic mice expressing amyloid precursor protein should be considered models of accelerated brain aging or asymptomatic AD, and the results of interventional studies in these mice should be considered in the context of primary prevention. Studies in mice have pointed to the roles of soluble beta-amyloid (Abeta) oligomers and soluble tau in disease pathogenesis and support a model in which soluble Abeta oligomers trigger synaptic dysfunction, but formation of abnormal tau species leads to neuron death and cognitive decline severe enough to warrant a dementia diagnosis.


title: Three Ages of FPGAs: A Retrospective on the First Thirty Years of FPGA Technology

abstract: Since their introduction, field programmable gate arrays (FPGAs) have grown in capacity by more than a factor of 10 $\thinspace$ 000 and in performance by a factor of 100. Cost and energy per operation have both decreased by more than a factor of 1000. These advances have been fueled by process technology scaling, but the FPGA story is much more complex than simple technology scaling. Quantitative effects of Moore's Law have driven qualitative changes in FPGA architecture, applications and tools. As a consequence, FPGAs have passed through several distinct phases of development. These phases, termed “Ages” in this paper, are The Age of Invention, The Age of Expansion and The Age of Accumulation. This paper summarizes each and discusses their driving pressures and fundamental characteristics. The paper concludes with a vision of the upcoming Age of FPGAs.


title: Healthcare reform literacy among academic dermatologists: What we know and don’t know about the Affordable Care Act

abstract: Importance: There have been no attempts to determinethe understanding of the Affordable Care Act (ACA)among dermatologists. Objective: Assess knowledgeof the ACA among academic dermatologists. Design:A 21-item survey was administered to membersof the Association of Professors of Dermatology(APD). The items assessed knowledge and opinionsof the ACA, and demographics of the participants.Multivariate regressions were conducted todetermine associations between variables.Participants: Members of the Association of Professorsof Dermatology. Main Measure: Knowledge of theACA. Results: One hundred and eight out of 368 APDmembers completed the survey (29.3%). Ninetysixpercent of responders practice in an academicsetting. Only 36 (33.3%) rated their knowledge of theACA as good/great, whereas 67 (62%) believed thatthe ACA will change their practice. The mean numberof knowledge questions correctly answered was 4.5out of 7 (64.3%). Dermatologists who reported thattheir knowledge of the bill was good/great (p=0.04)and procedural dermatologists (p= 0.04) answeredmore questions correctly. Compared to proceduraldermatologists, general dermatologists were morelikely to support the ACA (p=0.003) but less likely toanswer questions correctly. Conclusion: There appearsto be knowledge gaps among dermatologists aboutthe ACA and discordance between support for andknowledge about the ACA. The need for educatingdermatologists on health care policies may warrantfurther investigation.


iPCA : An Interactive System for PCA-based Visual Analytics


title: Fractional Order PID Controller Based on Particle Swarm Optimization Implemented with FPGA

abstract: According to parameters adjustment problems of fractional order PID controller applied in time-varying???nonlinear???real-time demanding higher systems, an improved particle swarm optimization (PSO) algorithm is adopted to optimize fractional order PID controller parameters. Because of high complexity of PSO and long tuning time, the paper adopts FPGA and combined with external circuit to implement fractional order PID controller. The results show that the improved PSO improves the parameters precision of the fractional order PID controller and adaptive capacity of control systems, and the real-time of systems is well met.


title: SAR ADC architecture with 98% reduction in switching energy over conventional scheme

abstract: A high energy-efficiency switching scheme for a successive approximation register (SAR) analogue-to-digital converter (ADC) is presented. The proposed method can achieve 98.4% savings in switching energy when compared to a conventional SAR. The proposed technique also achieves a 4 × reduction in total capacitance used in the digital-to-analogue converter (DAC) compared to the conventional DAC.


title: InstanceCut: From Edges to Instances with MultiCut

abstract: This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches. Our approach, we term InstanceCut, represents the problem by two output modalities: (i) an instance-agnostic semantic segmentation and (ii) all instance-boundaries. The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instance-aware edge detection model. To reason globally about the optimal partitioning of an image into instances, we combine these two modalities into a novel MultiCut formulation. We evaluate our approach on the challenging CityScapes dataset. Despite the conceptual simplicity of our approach, we achieve the best result among all published methods, and perform particularly well for rare object classes.


The Treatment of Evolutionary Psychology in Social Psychology Textbooks


title: Applications of cytotoxicity assays and pre-lethal mechanistic assays for assessment of human hepatotoxicity potential.

abstract: While drug toxicity (especially hepatotoxicity) is the most frequent reason cited for withdrawal of an approved drug, no simple solution exists to adequately predict such adverse events. Simple cytotoxicity assays in HepG2 cells are relatively insensitive to human hepatotoxic drugs in a retrospective analysis of marketed pharmaceuticals. In comparison, a panel of pre-lethal mechanistic cellular assays hold the promise to deliver a more sensitive approach to detect endpoint-specific drug toxicities. The panel of assays covered by this review includes steatosis, cholestasis, phospholipidosis, reactive intermediates, mitochondria membrane function, oxidative stress, and drug interactions. In addition, the use of metabolically competent cells or the introduction of major human hepatocytes in these in vitro studies allow a more complete picture of potential drug side effect. Since inter-individual therapeutic index (TI) may differ from patient to patient, the rational use of one or more of these cellular assay and targeted in vivo exposure data may allow pharmaceutical scientists to select drug candidates with a higher TI potential in the drug discovery phase.


title: A change in perspective – Teacher education as an open system

abstract: Teacher education is the environment for the learning and instruction of prospective teachers. Its structure, components, and contents shape the development of relevant competences which enable prospective teachers to be effective in the classroom. But its relevance is questioned because respective research, characterised by inconclusive results, does not offer explanations about the reasons why certain teacher education programmes are more effective than others in the development of relevant competences. One reason for the lack of explanations can be found in the way research assesses the effectiveness of teacher education. This might be due to problems regarding the conceptualisations of teacher education, as well as to the inherent selection and non-random allocation problems in research on the relation between teacher education and student achievement. In this paper we respond to claims for an organisational perspective on teacher education and develop such a new perspective. Accordingly, we provide these claims with an adequate theoretical foundation and develop an organisational model of teacher education based on Open Systems Theory. Besides being one of the first integrative organisational models of teacher education, it is among the first models which illustrate the relations and interdependencies of systems, its different parts, and its different levels, and enables researchers to investigate these interdependencies. The development of this model is further based on an alteration of the input variables of the concept of teacher quality. Moreover, the model has consequences for the notion of teacher education effectiveness. We illustrate these changes, and discuss them and the model with respect to possible areas of further research.


title: Regulation and dysregulation of esophageal peristalsis by the integrated function of circular and longitudinal muscle layers in health and disease

abstract: Muscularis propria throughout the entire gastrointestinal tract including the esophagus is comprised of circular and longitudinal muscle layers. Based on the studies conducted in the colon and the small intestine, for more than a century, it has been debated whether the two muscle layers contract synchronously or reciprocally during the ascending contraction and descending relaxation of the peristaltic reflex. Recent studies in the esophagus and colon prove that the two muscle layers indeed contract and relax together in almost perfect synchrony during ascending contraction and descending relaxation of the peristaltic reflex, respectively. Studies in patients with various types of esophageal motor disorders reveal temporal disassociation between the circular and longitudinal muscle layers. We suggest that the discoordination between the two muscle layers plays a role in the genesis of esophageal symptoms, i.e., dysphagia and esophageal pain. Certain pathologies may selectively target one and not the other muscle layer, e.g., in eosinophilic esophagitis there is a selective dysfunction of the longitudinal muscle layer. In achalasia esophagus, swallows are accompanied by the strong contraction of the longitudinal muscle without circular muscle contraction. The possibility that the discoordination between two muscle layers plays a role in the genesis of esophageal symptoms, i.e., dysphagia and esophageal pain are discussed. The purpose of this review is to summarize the regulation and dysregulation of peristalsis by the coordinated and discoordinated function of circular and longitudinal muscle layers in health and diseased states.


title: An application of classification models to predict learner progression in tertiary education

abstract: This paper reports on an application of classification models to identify college students at risk of failing in the first year of study. Data was gathered from three student cohorts in the academic years 2010 through 2012. Students within the cohorts were sampled from a range of academic disciplines (n=1074), and were diverse in their academic backgrounds and abilities. Metrics used included data that are typically available to colleges such as age, gender and prior academic performance. The study also considered psychometric indicators that can be assessed in the early stages after enrolment, specifically, personality, motivation and learning strategies. Six classification algorithms were considered. Model accuracy was assessed using cross validation and was compared to outcomes when models were applied to a subsequent academic year. It was found that mature students were more complex to model than younger students. Furthermore, 10-fold cross validation accurately estimated model performance when modeling younger students only, but over-estimated model accuracy when modeling mature students.


title: Accelerating LINPACK with MPI-OpenCL on Clusters of Multi-GPU Nodes

abstract: OpenCL is an open standard to write parallel applications for heterogeneous computing systems. Since its usage is restricted to a single operating system instance, programmers need to use a mix of OpenCL and MPI to program a heterogeneous cluster. In this paper, we introduce an MPI-OpenCL implementation of the LINPACK benchmark for a cluster with multi-GPU nodes. The LINPACK benchmark is one of the most widely used benchmark applications for evaluating high performance computing systems. Our implementation is based on High Performance LINPACK (HPL) and uses the blocked LU decomposition algorithm. We address that optimizations aimed at reducing the overhead of CPUs are necessary to overcome the performance gap between the CPUs and the multiple GPUs. Our LINPACK implementation achieves 93.69 Tflops (46 percent of the theoretical peak) on the target cluster with 49 nodes, each node containing two eight-core CPUs and four GPUs.


title: Humanoid motion planning for dual-arm manipulation and re-grasping tasks

abstract: In this paper, we present efficient solutions for planning motions of dual-arm manipulation and re-grasping tasks. Motion planning for such tasks on humanoid robots with a high number of degrees of freedom (DoF) requires computationally efficient approaches to determine the robot's full joint configuration at a given grasping position, i.e. solving the Inverse Kinematics (IK) problem for one or both hands of the robot. In this context, we investigate solving the inverse kinematics problem and motion planning for dual-arm manipulation and re-grasping tasks by combining a gradient-descent approach in the robot's pre-computed reachability space with random sampling of free parameters. This strategy provides feasible IK solutions at a low computation cost without resorting to iterative methods which could be trapped by joint-limits. We apply this strategy to dual-arm motion planning tasks in which the robot is holding an object with one hand in order to generate whole-body robot configurations suitable for grasping the object with both hands. In addition, we present two probabilistically complete RRT-based motion planning algorithms (J+−RRT and IK-RRT) that interleave the search for an IK solution with the search for a collision-free trajectory and the extension of these planners to solving re-grasping problems. The capabilities of combining IK methods and planners are shown both in simulation and on the humanoid robot ARMAR-III performing dual-arm tasks in a kitchen environment.


title: Automatic Face Image Quality Prediction

abstract: Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcher-independent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.


title: Age of first bilingual language exposure as a new window into bilingual reading development

abstract: How does age of first bilingual language exposure affect reading development in children learning to read in both of their languages? Is there a reading advantage for monolingual English children who are educated in bilingual schools? We studied children (grades 2–3, ages 7–9) in bilingual Spanish–English schools who were either from Spanish-speaking homes (new to English) or English-speaking homes (new to Spanish), as compared with English-speaking children in monolingual English schools. An early age of first bilingual language exposure had a positive effect on reading, phonological awareness, and language competence in both languages: early bilinguals (age of first exposure 0–3 years) outperformed other bilingual groups (age of first exposure 3–6 years). Remarkably, schooling in two languages afforded children from monolingual English homes an advantage in phoneme awareness skills. Early bilingual exposure is best for dual language reading development, and it may afford such a powerful positive impact on reading and language development that it may possibly ameliorate the negative effect of low SES on literacy. Further, age of first bilingual exposure provides a new tool for evaluating whether a young bilingual has a reading problem versus whether he or she is a typically-developing dual-language learner.


title: Semantic image segmentation using visible and near-infrared channels

abstract: Recent progress in computational photography has shown that we can acquire physical information beyond visible (RGB) image representations. In particular, we can acquire near-infrared (NIR) cues with only slight modification to any standard digital camera. In this paper, we study whether this extra channel can improve semantic image segmentation. Based on a state-of-the-art segmentation framework and a novel manually segmented image database that contains 4-channel images (RGB+NIR), we study how to best incorporate the specific characteristics of the NIR response. We show that it leads to improved performances for 7 classes out of 10 in the proposed dataset and discuss the results with respect to the physical properties of the NIR response.


title: Privacy-Preserving Publishing of Hierarchical Data

abstract: Many applications today rely on storage and management of semi-structured information, for example, XML databases and document-oriented databases. These data often have to be shared with untrusted third parties, which makes individuals’ privacy a fundamental problem. In this article, we propose anonymization techniques for privacy-preserving publishing of hierarchical data. We show that the problem of anonymizing hierarchical data poses unique challenges that cannot be readily solved by existing mechanisms. We extend two standards for privacy protection in tabular data (k-anonymity and e-diversity) and apply them to hierarchical data. We present utility-aware algorithms that enforce these definitions of privacy using generalizations and suppressions of data values. To evaluate our algorithms and their heuristics, we experiment on synthetic and real datasets obtained from two universities. Our experiments show that we significantly outperform related methods that provide comparable privacy guarantees.


Two Paradigms Of Linguistics: The Semiotic Versus Non-Semiotic Paradigm


title: Video-based face recognition using adaptive hidden Markov models

abstract: While traditional face recognition is typically based on still images, face recognition from video sequences has become popular. In this paper, we propose to use adaptive hidden Markov models (HMM) to perform video-based face recognition. During the training process, the statistics of training video sequences of each subject, and the temporal dynamics, are learned by an HMM. During the recognition process, the temporal characteristics of the test video sequence are analyzed over time by the HMM corresponding to each subject. The likelihood scores provided by the HMMs are compared, and the highest score provides the identity of the test video sequence. Furthermore, with unsupervised learning, each HMM is adapted with the test video sequence, which results in better modeling over time. Based on extensive experiments with various databases, we show that the proposed algorithm results in better performance than using majority voting of image-based recognition results.


title: Transfer of Information Technology to the Arab World: A Test of Cultural Influence Modeling

abstract: The complex societal beliefs and values of the Arab world provide a rich setting to examine the hypothesized influence of culture on information technology transfer (ITT). Two research questions arise in this context: (1) Do cultural beliefs and values affect the transference of information technology in the Arab world? and (2) Does contact with technologically advanced societies impact ITT and systems outcomes? The present study addresses these research questions by conceptualizing and testing a cultural influence model of ITT. In this model, cultural beliefs and values are one major construct while a counterbalancing variable is the external influence of technologically advanced societies. These constructs along with the variable â€œnational IT developmentâ€ form the conceptual basis for the model. This study is the second part of a program of research investigating ITT. The setting of the study was Arab society, which allowed us to test our â€œcultural influenceâ€ model in, perhaps, one of the more complex cultural and social systems in the world. The program of research took place in several phases. In the early phases, Arab-American businessmen and women as well as Arabs studying in American universities were studied. In the latter phases, the cross-disciplinary research team gathered primary data in the Arab cultures of Jordan, Egypt, Saudi Arabia, Lebanon, and the Sudan. Both quantitative and qualitative techniques were used to explore the phenomenon of ITT. This paper reports quantitative findings from the latter phase. Findings suggest that the model has explanatory power. Arab cultural beliefs were a very strong predictor of resistance to systems and thus ITT; technological culturation was also a factor. These results have implications for future theory-testing and for technology policy-setting by responsible Arab leaders. Additionally, there are implications for transnational firms and managers charged with introducing IT in foreign ports, subsidiaries, offices, and plants.


title: PERSPECTIVES ON TERRORISM Volume 6, Issues 4-‐5 I. Articles Terrorist Decision-Making: Insights from Economics and Political

abstract: Terrorist groups repeatedly take actions that are ultimately politically counter-productive. Sometimes these are the result of deliberate calculations that happen to be mistaken - Al-Qaeda’s decision to conduct the 9/11 attacks is the most obvious example of an ultimately self-defeating operation. Sometimes they reflect the challenges groups face in controlling their operatives: AlQaeda in Iraq’s excessive public violence against other Sunni organisations stand out. At other times they appear to steer difficult political waters quite effectively despite of deep internal divisions—Hamas is the exemplar here. This article reviews recent developments in the literature on terrorist decision-making in economics and political science. Overall, tremendous advances have been made in the last 10 years, but much work remains to be done. In particular, it is argued that the literature needs to do better at testing its theories in inferentially credible ways and at considering terrorism as one tactical option among many for opposition political groups.


title: A Short History of Computational Complexity

abstract: It all started with a machine. In 1936, Turing developed his theoretical computational model. He based his model on how he perceived mathematicians think. As digital computers were developed in the 40’s and 50’s, the Turing machine proved itself as the right theoretical model for computation. Quickly though we discovered that the basic Turing machine model fails to account for the amount of time or memory needed by a computer, a critical issue today but even more so in those early days of computing. The key idea to measure time and space as a function of the length of the input came in the early 1960’s by Hartmanis and Stearns. And thus computational complexity was born. In the early days of complexity, researchers just tried understanding these new measures and how they related to each other. We saw the first notion of efficient computation by using time polynomial in the input size. This led to complexity’s most important concept, NP-completeness, and its most fundamental question, whether P = NP. The work of Cook and Karp in the early 70’s showed a large number of combinatorial and logical problems were NP-complete, i.e., as hard as any problem computable in nondeterministic polynomial time. The P = NP question is equivalent to an efficient solution of any of these problems. In the thirty years hence this problem has become one of the outstanding open questions in computer science and indeed all of mathematics. In the 70’s we saw the growth of complexity classes as researchers tried to encompass different models of computations. One of those models, probabilistic computation, started with a probabilistic test for primality, led to probabilistic complexity classes and a new kind of interactive proof system that itself led to hardness results for approximating certain NP-complete problems. We have also seen strong evidence that we can remove the randomness from computations and most recently a deterministic algorithm for the original primality problem. In the 80’s we saw the rise of finite models like circuits that capture computation in an inherently different way. A new approach to problems like P = NP arose from these circuits and though they have had limited success in separating complexity classes, this approach brought combinatorial techniques into the area and led to a much better understanding of the limits of these devices. ∗URL: http://www.neci.nj.nec.com/homepages/fortnow. Email: fortnow@research.nj.nec.com. †URL: http://www.cs.bu.edu/faculty/homer. Email: homer@cs.bu.edu. Supported in part by the NSF under grant NSF-CCR-998310 and by the ARO under grant DAAD19-02-1-0058.


title: Efficient hardware architecture of softmax layer in deep neural network

abstract: Deep neural network (DNN) has emerged as a very important machine learning and pattern recognition technique in the big data era. Targeting to different types of training and inference tasks, the structure of DNN varies with flexible choices of different component layers, such as fully connection layer, convolutional layer, pooling layer and softmax layer. Deviated from other layers that only require simple operations like addition or multiplication, the softmax layer contains expensive exponentiation and division, thereby causing the hardware design of softmax layer suffering from high complexity, long critical path delay and overflow problems. This paper, for the first time, presents efficient hardware architecture of softmax layer in DNN. By utilizing the domain transformation technique and down-scaling approach, the proposed hardware architecture avoids the aforementioned problems. Analysis shows that the proposed hardware architecture achieves reduced hardware complexity and critical path delay.


title: Learning Networking by Reproducing Research Results

abstract: In the past five years, the graduate networking course at Stanford has assigned over 200 students the task of reproducing results from over 40 networking papers. We began the project as a means of teaching both engineering rigor and critical thinking, qualities that are necessary for careers in networking research and industry. We have observed that reproducing research can simultaneously be a tool for education and a means for students to contribute to the networking community. Through this editorial we describe our project in reproducing network research and show through anecdotal evidence that this project is important for both the classroom and the networking community at large, and we hope to encourage other institutions to host similar class projects.


title: The Multidimensional Role of Trust in Enabling Creativity within Virtual Communities of Practice: A Theoretical Model Integrating Swift, Knowledge-Based, Institution-Based, and Organizational Trust

abstract: Virtual communities of practice are gaining attention among scholars and practitioners as managers and high-level executives look for ways to adapt to an ever-increasing rate of environmental change. These communities have been recognized as a tool for generating new ideas by accessing geographically distributed expertise. As most expert knowledge is tacit and tacit knowledge exchange is what leads to creativity, an understanding of antecedents to tacit knowledge exchange is needed. This manuscript responds by examining the role of trust, a well-known antecedent of creativity, within the context of virtual communities of practice. An interdisciplinary approach led to the development of a multi-level, multi-dimensional model of trust. The authors propose that different dimensions of trust serve as both an antecedent and outcome of creativity in virtual communities of practice, by taking on various forms and roles in the creative process based on tenure of its members.


title: A Bayes filter based adaptive floor segmentation with homography and appearance cues

abstract: This paper proposes a robust approach for image based floor detection and segmentation from sequence of images or video. In contrast to many previous approaches, which uses a priori knowledge of the surroundings, our method uses combination of modified sparse optical flow and planar homography for ground plane detection which is then combined with graph based segmentation for extraction of floor from images. We also propose a probabilistic framework which makes our method adaptive to the changes in the surroundings. We tested our algorithm on several common indoor environment scenarios and were able to extract floor even under challenging circumstances. We obtained extremely satisfactory results in various practical scenarios such as where the floor and non floor areas are of same color, in presence of textured flooring, and where illumination changes are steep.


title: An Optimization Model for Human Activity Recognition Inspired by Information on Human-Object Interaction

abstract: Human activity categories can be mainly divided into two types: body motion (e.g. "dancing" and "jumping") and human-object interaction (e.g. "playing guitar" and "riding bike"). In this paper, we propose a model that uses this insight to combine spatiotemporal features with information on human-object interaction to predict human activity categories in realistic videos. In this paper, we (1) improve on prior work by proposing a model to provide long-term prediction based on spatiotemporal features extracted by a deep 3-dimensional convolutional network and (2) extend the spatiotemporal features by combining with information on human-object interaction generated from an object detection model. Our model achieves 92.5% accuracy on UCF101 dataset that outperforms any other state-of-the-art methods. In addition, our approach has high computing efficiency and achieves real-time processing.


title: Grasping Without Squeezing: Design and Modeling of Shear-Activated Grippers

abstract: Grasping objects that are too large to envelop is traditionally achieved using friction that is activated by squeezing. We present a family of shear-activated grippers that can grasp such objects without the need to squeeze. When a shear force is applied to the gecko-inspired material in our grippers, adhesion is turned on; this adhesion in turn results in adhesion-controlled friction, a friction force that depends on adhesion rather than a squeezing normal force. Removal of the shear force eliminates adhesion, allowing easy release of an object. A compliant shear-activated gripper without active sensing and control can use the same light touch to lift objects that are soft, brittle, fragile, light, or very heavy. We present three grippers, the first two designed for curved objects, and the third for nearly any shape. Simple models describe the grasping process, and empirical results verify the models. The grippers are demonstrated on objects with a variety of shapes, materials, sizes, and weights.


title: Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data

abstract: 3D object detection and pose estimation from a single image are two inherently ambiguous problems. Oftentimes, objects appear similar from different viewpoints due to shape symmetries, occlusion and repetitive textures. This ambiguity in both detection and pose estimation means that an object instance can be perfectly described by several different poses and even classes. In this work we propose to explicitly deal with these ambiguities. For each object instance we predict multiple 6D pose outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures. The distribution collapses to a single outcome when the visual appearance uniquely identifies just one valid pose. We show the benefits of our approach which provides not only a better explanation for pose ambiguity, but also a higher accuracy in terms of pose estimation.


title: An efficient hierarchical graph based image segmentation

abstract: Hierarchical image segmentation provides region-oriented scalespace, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy, and for which the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenzwalb and Huttenlocher. We illustrate with both real and synthetic images, showing efficiency, ease of use, and robustness of our method. IndexTerms— Hierarchical image segmentation, Edge-weighted graph, Saliency map


E-commerce, WTO and Developing Countries


title: Topic-sensitive PageRank

abstract: In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.


How Social Media Reduces Mass Political Polarization . Evidence from Germany , Spain , and the U . S .


title: Dynamic Spectrum Access in Cognitive Radio Networks with RF Energy Harvesting

abstract: Spectrum efficiency and energy efficiency are two critical issues in designing wireless networks. Through dynamic spectrum access, cognitive radios can improve the spectrum efficiency and capacity of wireless networks. On the other hand radio frequency (RF) energy harvesting has emerged as a promising technique to supply energy to wireless networks and thereby increase their energy efficiency. Therefore, to achieve both spectrum and energy efficiencies, the secondary users in a cognitive radio network (CRN) can be equipped with the RF energy harvesting capability, and such a network can be referred to as an RF-powered cognitive radio network. In this article we provide an overview of the RF-powered CRNs and discuss the challenges that arise for dynamic spectrum access in these networks. Focusing on the trade-off among spectrum sensing, data transmission, and RF energy harvesting, we then discuss the dynamic channel selection problem in a multi-channel RF-powered CRN. In the RF-powered CRN a secondary user can adaptively select a channel to transmit data when the channel is not occupied by any primary user. Alternatively, the secondary user can harvest RF energy for data transmission if the channel is occupied. The optimal channel selection policy of the secondary user can be obtained by formulating a Markov decision process (MDP) problem. We present some numerical results obtained by solving this MDP problem.


title: A new high efficient transformerless inverter for single phase grid-tied photovoltaic system with reactive power control

abstract: There has been an increasing interest in transformerless inverter for grid-tied photovoltaic (PV) system due to low cost, high efficiency, less weight, etc. Therefore, many transformerless topologies have been proposed and verified with real power injection only. Recently, almost every international regulation has imposed that a definite amount of reactive power should be handled by the grid-tied PV inverter. According to the standard VDE-AR-N 4105, grid-tied PV inverter of power rating below 3.68kVA, should attain power factor (PF) from 0.95 leading to 0.95 lagging. In this paper, a new high efficient transformerless topology is proposed for grid-tied PV system. The new topology structure and detail operation principle with reactive power flow is described. The proposed circuit structure does not lead itself to the reverse recovery issues which allow utilizing MOSFET switches to increase the overall efficiency. Finally, to validate the proposed topology, a 1kW laboratory prototype is built and tested. The experimental results show that the proposed topology can inject reactive power into utility grid without any additional current distortion and leakage current. The maximum efficiency and European efficiency of the proposed topology is measured and found to be 98.54% and 98.27%, respectively.


Connective tissue growth factor production by activated pancreatic stellate cells in mouse alcoholic chronic pancreatitis


title: Diseases as network perturbations.

abstract: The tremendous amount of the data obtained from the study of complex biological systems changes our view on the pathogenesis of human diseases. Instead of looking at individual components of biological processes, we focus our attention more on the interaction and dynamics of biological systems. A network representation and analysis of the physiology and pathophysiology of biological systems is an effective way to study their complex behavior. Specific perturbations can trigger cascades of failures, which lead to the malfunctioning of cellular networks and as a result to the development of specific diseases. In this review we discuss recent developments in the field of disease network analysis and highlight some of the topics and views that we think are important for understanding network-based disease mechanisms.


title: Apprenticeship learning for motion planning with application to parking lot navigation

abstract: Motion and path-planning algorithms often use complex cost functions for both global navigation and local smoothing of trajectories. Obtaining good results typically requires carefully hand-engineering the trade-offs between different terms in the cost function. In practice, it is often much easier to demonstrate a few good trajectories. In this paper, we describe an efficient algorithm which - when given access to a few trajectory demonstrations - can automatically infer good trade-offs between the different costs. In our experiments, we apply our algorithm to the problem of navigating a robotic car in a parking lot.


title: Bounding and Counting Linear Regions of Deep Neural Networks

abstract: We investigate the complexity of deep neural networks (DNN) that represent piecewise linear (PWL) functions. In particular, we study the number of linear regions, i.e. pieces, that a PWL function represented by a DNN can attain, both theoretically and empirically. We present (i) tighter upper and lower bounds for the maximum number of linear regions on rectifier networks, which are exact for inputs of dimension one; (ii) a first upper bound for multi-layer maxout networks; and (iii) a first method to perform exact enumeration or counting of the number of regions by modeling the DNN with a mixed-integer linear formulation. These bounds come from leveraging the dimension of the space defining each linear region. The results also indicate that a deep rectifier network can only have more linear regions than every shallow counterpart with same number of neurons if that number exceeds the dimension of the input.


title: Sleep, but not other daily routines, mediates the association between maternal employment and BMI for preschool children.

abstract: BACKGROUND ::: It has been established that the more time mothers spend working outside of the home, the more likely their preschool-aged children are to be overweight. However, the mechanisms explaining this relationship are not well understood. Our objective was to explore child sleep, dietary habits, TV time, and family mealtime routines as mediators of the relationship between maternal employment status (full-time, part-time, and no or minimal employment) and child body mass index (BMI) percentile. ::: ::: ::: METHODS ::: Data were drawn from waves 1 and 2 of STRONG Kids, a prospective panel study examining childhood obesity among parent-preschooler dyads (n = 247). Mothers reported their own work hours, their child's hours of nighttime sleep, dietary habits, TV time, and mealtime routines. Trained staff measured child height and weight. ::: ::: ::: RESULTS ::: Compared to working 0-19 h/week, both full-time (>35 h/week) and part-time (20-34 h/week) employment predicted higher child BMI percentile 1 year later. Hours of child nighttime sleep partially mediated the association between maternal full-time employment and child BMI percentile. Adjusting for individual and family characteristics, children whose mothers were employed full time were less likely to sleep longer hours than children whose mothers were employed 0-19 h/week (b = -0.49, p < 0.04). Shorter child nighttime sleep was associated with higher BMI percentile (b = -7.31, p < 0.001). None of the other mediation pathways tested were significant. ::: ::: ::: CONCLUSIONS ::: These findings add to the growing literature on the importance of adequate sleep for young children's health.


title: An Empirical Study of Online Word of Mouth as a Predictor for Multi-product Category e-Commerce Sales

abstract: The ability to exchange opinions and experiences online is known as online word of mouth (WOM) and has been shown in the literature to have the potential to impact e-commerce sales. The purpose of this paper is to expand previous findings by empirically evaluating the impact of online WOM attributes and other related factors (e.g. product views, promotion, and category) on e-commerce sales using real data from a multi-product retail e-commerce firm. Research has previously shown that the introduction of online WOM on a retail e-commerce site can positively impact product sales. We propose and validate a conceptual model of online WOM and its impact on product sales and the impact of moderator variables such as promotion, product category and product views. It is our conclusion that previous research on online WOM has been limited as our research empirically demonstrates the conclusion that it is the interaction of product category, volume and product views, and the interaction of product views and product category which are statistically significant in explaining changes in unit product sales. Pure increase in volume or number of reviewer comments has no significant effect on sales. These conclusions have critical implications for the practical use of online WOM in e-commerce and for internet marketing.


title: CapCodes: Capacitive 3D Printable Identification and On-screen Tracking for Tangible Interaction

abstract: Electronic markers can be used to link physical representations and virtual content for tangible interaction, such as visual markers commonly used for tabletops. Another possibility is to leverage capacitive touch inputs of smartphones, tablets and notebooks. However, existing approaches either do not couple physical and virtual representations or require significant post-processing. This paper presents and evaluates a novel approach using a coding scheme for the automatic identification of tangibles by touch inputs when they are touched and shifted. The codes can be generated automatically and integrated into a great variety of existing 3D models from the internet. The resulting models can then be printed completely in one cycle by off-the-shelf 3D printers; post processing is not needed. Besides the identification, the object's position and orientation can be tracked by touch devices. Our evaluation examined multiple variables and showed that the CapCodes can be integrated into existing 3D models and the approach could also be applied to untouched use for larger tangibles.


title: P-N learning: Bootstrapping binary classifiers by structural constraints

abstract: This paper shows that the performance of a binary classifier can be significantly improved by the processing of structured unlabeled data, i.e. data are structured if knowing the label of one example restricts the labeling of the others. We propose a novel paradigm for training a binary classifier from labeled and unlabeled examples that we call P-N learning. The learning process is guided by positive (P) and negative (N) constraints which restrict the labeling of the unlabeled set. P-N learning evaluates the classifier on the unlabeled data, identifies examples that have been classified in contradiction with structural constraints and augments the training set with the corrected samples in an iterative process. We propose a theory that formulates the conditions under which P-N learning guarantees improvement of the initial classifier and validate it on synthetic and real data. P-N learning is applied to the problem of on-line learning of object detector during tracking. We show that an accurate object detector can be learned from a single example and an unlabeled video sequence where the object may occur. The algorithm is compared with related approaches and state-of-the-art is achieved on a variety of objects (faces, pedestrians, cars, motorbikes and animals).


title: Multi‐Resolution Cloth Simulation

abstract: We propose a novel, multi-resolution method to efficiently perform large-scale cloth simulation. Our cloth simulation method is based on a triangle-based energy model constructed from a cloth mesh. We identify that solutions of the linear system of cloth simulation are smooth in certain regions of the cloth mesh and solve the linear system on those regions in a reduced solution space. Then we reconstruct the original solutions by performing a simple interpolation from solutions computed in the reduced space. In order to identify regions where solutions are smooth, we propose simplification metrics that consider stretching, shear, and bending forces, as well as geometric collisions. Our multi-resolution method can be applied to many existing cloth simulation methods, since our method works on a general linear system. In order to demonstrate benefits of our method, we apply our method into four large-scale cloth benchmarks that consist of tens or hundreds of thousands of triangles. Because of the reduced computations, we achieve a performance improvement by a factor of up to one order of magnitude, with a little loss of simulation quality.


title: VDC Planner: Dynamic migration-aware Virtual Data Center embedding for clouds

abstract: Cloud computing promises to provide computing resources to a large number of service applications in an on demand manner. Traditionally, cloud providers such as Amazon only provide guaranteed allocation for compute and storage resources, and fail to support bandwidth requirements and performance isolation among these applications. To address this limitation, recently, a number of proposals advocate providing both guaranteed server and network resources in the form of Virtual Data Centers (VDCs). This raises the problem of optimally allocating both servers and data center networks to multiple VDCs in order to maximize the total revenue, while minimizing the total energy consumption in the data center. However, despite recent studies on this problem, none of the existing solutions have considered the possibility of using VM migration to dynamically adjust the resource allocation, in order to meet the fluctuating resource demand of VDCs. In this paper, we propose VDC Planner, a migration-aware dynamic virtual data center embedding framework that aims at achieving high revenue while minimizing the total energy cost over-time. Our framework supports various usage scenarios, including VDC embedding, VDC scaling as well as dynamic VDC consolidation. Through experiments using realistic workload traces, we show our proposed approach achieves both higher revenue and lower average scheduling delay compared to existing migration-oblivious solutions.


title: Does simile comprehension differ from metaphor comprehension? A functional MRI study

abstract: Abstract Since Aristotle, people have believed that metaphors and similes express the same type of figurative meaning, despite the fact that they are expressed with different sentence patterns. In contrast, recent psycholinguistic models have suggested that metaphors and similes may promote different comprehension processes. In this study, we investigated the neural substrates involved in the comprehension of metaphor and simile using functional magnetic resonance imaging (fMRI) to evaluate whether simile comprehension differs from metaphor comprehension or not. In the metaphor and simile sentence conditions, higher activation was seen in the left inferior frontal gyrus. This result suggests that the activation in both metaphor and simile conditions indicates similar patterns in the left frontal region. The results also suggest that similes elicit higher levels of activation in the medial frontal region which might be related to inference processes, whereas metaphors elicit more right-sided prefrontal activation which might be related to figurative language comprehension.


title: Design and implementation of real time transformer health monitoring system using GSM technology

abstract: With the progress and development of national economy as well as power system, reliability and safety issues of power system have been more important. Development of distribution Transformer Health Monitoring System (THMS) has been done in that reason. Distribution transformer is the most vital asset in any electrical distribution network and therefore it needs special care and attention. This THMS can monitor the health status of the distribution transformer in real time aspect. As a large number of transformers are distributed over a wide area in present electric systems, it's difficult to monitor the condition manually of every single transformer. So automatic data acquisition and transformer condition monitoring has been an important issue. This project presents design and implementation of a mobile embedded system to monitor load currents, over voltage, transformer oil level and oil temperature. The implementation on-line monitoring system integrates Global Service Mobile (GSM) Modem, with single chip microcontroller and sensors. It is installed at the distribution transformer site. The output values of sensors are processed and recorded in the system memory. System programmed with some predefined instructions to check abnormal conditions. If there is any abnormality on the system, the GSM module will send SMS (Short Message Service) messages to designated mobile telephones containing information about the abnormality according to the aforesaid predefined instructions. This mobile system will help the utilities to optimally utilize transformers and identify problems before any catastrophic failure occurs. This system will be an advanced step to the automation by diminishing human dependency. As it is a wireless communicating system, there is no need of large cables which are of high cost. Thus THMS offers a more improved transformer monitoring.


title: Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals

abstract: Recognition of a human's continuous emotional states in real time plays an important role in machine emotional intelligence and human-machine interaction. Existing real-time emotion recognition systems use stimuli with low ecological validity (e.g., picture, sound) to elicit emotions and to recognise only valence and arousal. To overcome these limitations, in this paper, we construct a standardised database of 16 emotional film clips that were selected from over one thousand film excerpts. Based on emotional categories that are induced by these film clips, we propose a real-time movie-induced emotion recognition system for identifying an individual's emotional states through the analysis of brain waves. Thirty participants took part in this study and watched 16 standardised film clips that characterise real-life emotional experiences and target seven discrete emotions and neutrality. Our system uses a 2-s window and a 50 percent overlap between two consecutive windows to segment the EEG signals. Emotional states, including not only the valence and arousal dimensions but also similar discrete emotions in the valence-arousal coordinate space, are predicted in each window. Our real-time system achieves an overall accuracy of 92.26 percent in recognising high-arousal and valenced emotions from neutrality and 86.63 percent in recognising positive from negative emotions. Moreover, our system classifies three positive emotions (joy, amusement, tenderness) with an average of 86.43 percent accuracy and four negative emotions (anger, disgust, fear, sadness) with an average of 65.09 percent accuracy. These results demonstrate the advantage over the existing state-of-the-art real-time emotion recognition systems from EEG signals in terms of classification accuracy and the ability to recognise similar discrete emotions that are close in the valence-arousal coordinate space.


title: Accelerated variational dirichlet process mixtures

abstract: Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are unfortunately unsuitable for large scale data-mining applications. We propose a class of deterministic accelerated DP mixture models that can routinely handle millions of data-cases. The speedup is achieved by incorporating kd-trees into a variational Bayesian algorithm for DP mixtures in the stick-breaking representation, similar to that of Blei and Jordan (2005). Our algorithm differs in the use of kd-trees and in the way we handle truncation: we only assume that the variational distributions are fixed at their priors after a certain level. Experiments show that speedups relative to the standard variational algorithm can be significant.


The Effect of Metrics and Workloads on the Evaluation of Computer Systems