title: Evaluating Factors Influencing Consumer Satisfaction towards Online Shopping in China

abstract: With the development of Internet, online shopping is developing rapidly in China as a new way for shopping. Therefore, it is important for this research paper to identify the determinants of consumer satisfaction towards online shopping in China. A total of 350 online shoppers in China had participated in this research. The findings revealed that website design, security, information quality, payment method, e-service quality, product quality, product variety and delivery service are positively related to consumer satisfaction towards online shopping in China.


title: Congenital and Acquired Brain Injury. 1. Epidemiology, Pathophysiology, Prognostication, Innovative Treatments, and Prevention

abstract: Abstract Brown AW, Elovic EP, Kothari S, Flanagan SR. Kwasnica C. Congenital and acquired brain injury. 1. Epidemiology, pathophysiology, prognostication, innovative treatments, and prevention. This self-directed learning module reviews the current epidemiology of traumatic brain injury (TBI), its pathophysiology, prognostication after injury, currently available innovative early approaches to diagnosis and treatment, and effective methods of prevention. It is intended to provide the rehabilitation clinician with current knowledge to accurately inform patients, families, significant others, referring physicians, and payers and to aid in clinical decision making while caring for patients after TBI. Overall Article Objective To describe current knowledge in traumatic brain injury epidemiology, pathophysiology, prognostication, acute treatment, and prevention.


title: Investigating Factors That Influence Developers' Experience in Mobile Software Ecosystems

abstract: Software organizations (keystones) that maintain a Mobile Software Ecosystem (MSECO) must provide and manage mechanisms to attract, engage and retain mobile application developers. In this scenario, there is the involvement of expectations, perceptions and feelings of the developer in his/her interaction with various artifacts and other developers, named, Developer Experience (DX). Studying the factors that influence DX in the context of ecosystems is important for keystones that need mechanisms to keep developers accountable for productivity, robustness, and niche creation. In this paper, we applied the Snowballing methodology and thematic analysis to an initial identification of DX influence factors in MSECO. Eleven papers were analyzed from the viewpoint of Software Ecosystems. Initially, we identified 20 influence factors related to DX. Such factors can help to understand the DX in MSECO.


title: Reduce, You Say: What NoSQL Can Do for Data Aggregation and BI in Large Repositories

abstract: Data aggregation is one of the key features used in databases, especially for Business Intelligence (e.g., ETL, OLAP) and analytics/data mining. When considering SQL databases, aggregation is used to prepare and visualize data for deeper analyses. However, these operations are often impossible on very large volumes of data regarding memory-and-time-consumption. In this paper, we show how NoSQL databases such as MongoDB and its key-value stores, thanks to the native MapReduce algorithm, can provide an efficient framework to aggregate large volumes of data. We provide basic material about the MapReduce algorithm, the different NoSQL databases (read intensive vs. write intensive). We investigate how to efficiently modelize the data framework for BI and analytics. For this purpose, we focus on read intensive NoSQL databases using MongoDB and we show how NoSQL and MapReduce can help handling large volumes of data.


title: vector-controlled PWM current-source-inverter-fed induction motor drive with a new stator current control method

abstract: In this paper, the control of the pulsewidth-modulated current-source-inverter-fed induction motor drive is discussed. The vector control system of the induction motor is realized in a rotor-flux-oriented reference frame, where only the measured angular rotor speed and the dc-link current are needed for motor control. A new damping method for stator current oscillations is introduced. The method operates in an open-loop manner and is very suitable for microcontroller implementation, since the calculation power demand is low. Also, the stator current phase error caused by the load filter is compensated without measurement of any electrical variable. With the proposed control methods the motor current sensors can be totally eliminated since the stator current measurements are not needed either for protection in the current-source-inverter-fed drives. The proposed control methods are realized using a single-chip Motorola MC68HC916Y1 microcontroller. The experimental tests show excellent performance in both steady-state and transient conditions.


title: Efficient encoding of quasi-cyclic low-density parity-check codes

abstract: This paper presents methods for efficient encoding of quasi-cyclic LDPC codes. Based on these methods, encoding of quasi-cyclic LDPC codes can be implemented using simple shift-registers with complexity linearly proportional to the number of parity-check bits of a code for serial encoding and to the length of a code for parallel encoding. Various encoding circuits are devised and they provide a range of trade-offs between encoding complexity and speed.


title: Artifacts in PAPA camera images

abstract: The PAPA detector uses optical masks to decode the location of a photon event on the output phosphor of a high-gain image intensifier. Its use of Gray-coded masks creates image artifacts that are unique to this detector. The artifacts can arise from errors in mask alignment, incorrect discriminator settings, vignetting in the optics, and faults relating to the performance of the image intensifier. The symptoms and causes of the different image artifacts are discussed, and methods are presented to identify and correct these errors.


title: The cognitive neuroscience of visual attention

abstract: Abstract In current conceptualizations of visual attention, selection takes place through integrated competition between recurrently connected visual processing networks. Selection, which facilitates the emergence of a ‘winner’ from among many potential targets, can be associated with particular spatial locations or object properties, and it can be modulated by both stimulus-driven and goal-driven factors. Recent neurobiological data support this account, revealing the activation of striate and extrastriate brain regions during conditions of competition. In addition, parietal and temporal cortices play a role in selection, biasing the ultimate outcome of the competition.


title: Deterministic Policy Gradient Algorithms

abstract: In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the expected gradient of the action-value function. This simple form means that the deterministic policy gradient can be estimated much more efficiently than the usual stochastic policy gradient. To ensure adequate exploration, we introduce an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy. We demonstrate that deterministic policy gradient algorithms can significantly outperform their stochastic counterparts in high-dimensional action spaces.


title: The case of classroom robots: teachers’ deliberations on the ethical tensions

abstract: Robots are increasingly being studied for use in education. It is expected that robots will have the potential to facilitate children’s learning and function autonomously within real classrooms in the near future. Previous research has raised the importance of designing acceptable robots for different practices. In parallel, scholars have raised ethical concerns surrounding children interacting with robots. Drawing on a Responsible Research and Innovation perspective, our goal is to move away from research concerned with designing features that will render robots more socially acceptable by end users toward a reflective dialogue whose goal is to consider the key ethical issues and long-term consequences of implementing classroom robots for teachers and children in primary education. This paper presents the results from several focus groups conducted with teachers in three European countries. Through a thematic analysis, we provide a theoretical account of teachers’ perspectives on classroom robots pertaining to privacy, robot role, effects on children and responsibility. Implications for the field of educational robotics are discussed.


title: Psychometric evaluation of a new questionnaire measuring treatment satisfaction in hypothyroidism: the ThyTSQ.

abstract: OBJECTIVES ::: There is a clinical impression of dissatisfaction with treatment for hypothyroidism among some patients. Psychometric properties of the new ThyTSQ questionnaire are evaluated. The questionnaire, measuring patients' satisfaction with their treatment for hypothyroidism, has two parts: the seven-item ThyTSQ-Present and four-item ThyTSQ-Past, measuring satisfaction with present and past treatment, respectively, on scales from 6 (very satisfied) to 0 (very dissatisfied). ::: ::: ::: METHODS ::: The questionnaire was completed once by 103 adults with hypothyroidism, age (mean [SD]) 55.2 [14.4], range 23-84 years (all treated with thyroxine). ::: ::: ::: RESULTS ::: Completion rates were very high. Internal consistency reliability was excellent for both ThyTSQ-Present and ThyTSQ-Past (Cronbach's alpha = 0.91 and 0.90, respectively [N = 102 and 103]). Principal components analyses indicated that the seven items of the ThyTSQ-Present and the four items of the ThyTSQ-Past could be summed into separate Present Satisfaction and Past Satisfaction total scores. Mean Present Satisfaction was 32.5 (7.8), maximum range 0-42, and mean Past Satisfaction was 17.5 (6.1), maximum range 0-24, indicating considerable room for improvement. Patients were least satisfied with their present understanding of their condition, mean 4.2 (1.7) (maximum range 0-6), and with information provided about hypothyroidism around the time of diagnosis, mean 3.9 (1.8) (maximum range 0-6). ::: ::: ::: CONCLUSIONS ::: The ThyTSQ is highly acceptable to patients with hypothyroidism (excellent completion rates), and has established internal consistency reliability. It will assist health professionals in considering psychological outcomes when treating people with hypothyroidism, and is suitable for clinical trials and routine clinical monitoring.


title: Formation of e-satisfaction and repurchase intention : Moderating roles of computer self-efficacy and computer anxiety

abstract: The exploration of online customer satisfaction (called e-satisfaction hereafter) and repurchase becomes increasingly important in e-commerce. Yet, the process of e-satisfaction and repurchase formation and the context under which this process may vary have not been investigated rigorously. In order to fill this knowledge gap, this paper attempts to develop a conceptual model to decipher how e-satisfaction is formed. Furthermore, it investigates how computer-related individual differences such as computer self-efficacy and computer anxiety moderate this formation. The analysis results from a survey of 274 online buyers confirm that our antecedents play key roles in forming e-satisfaction and repurchase and the proposed moderators are important.


title: Projection-based Augmented Reality in Engineering Applications

abstract: Augmented Reality (AR) superimposes computer-generated graphics onto the user's view of the real world. Today, AR applications can occasionally be found in the late phases of the product development process (PDP), such as in training and in maintenance. Head-mounted displays (HMDs) are typically used for AR applications. HMDs still show deficiencies in terms of ergonomics and image quality. Looking at the development of VR technology, it can be stated that VR began its triumphal procession only after replacing HMDs by immersive projection technology (IPT) such as PowerWalls, CAVEs, Virtual Tables. Projection-based Augmented Reality enhances such environments towards augmented reality applications. We believe that this concept opens new application possibilities for AR – especially in the early phases of the product development process. In this paper we will describe the concept of Projection-based Augmented Reality (PBAR), introduce current hardware prototypes and discuss applications of PBAR configurations within the following areas: augmented design review, hybrid assembling simulation, hybrid modeling/sketching and visual inspection of augmented physical parts.


title: Factors preventing Malaysian teachers from using Information and Communication Technology (ICT) in teaching mathematics

abstract: This article discusses the factors which prevent secondary school teachers from using Information and Communication Technology (ICT) during their teaching of Mathematics in schools. Five factors have been identified based on literature reviews; 1) Self-confidence, 2) Time constraint, 3) Programs and training, 4) Facilities and resources, 5) Teachers' self-perception and attitude. A set of questionnaire consists of the factors was then developed and distributed to 178 mathematics teachers. In the context of Malaysia, the factor with the highest mean is time constraint (x=3.53), followed by facilities and resources (x=3.48), supportive assistance (x=3.14) and, programs and trainings (x=3.12). In addition, self-confidence and attitude recorded the mean of less than 3. Based on the findings, it is hoped that appropriate actions ad strategies can be made by the education ministry, teachers, students, parents and administration in schools in order to benefit from the use of ICT in the teaching and learning of Mathematics.


title: Adversarial domain adaptive subspace clustering

abstract: We propose a novel method for clustering a collection of data that comes from several domains. Since members of the same class might look very different across different domains and because in a clustering problem we have no side information such as labels, the main challenge in a domain adaptive clustering problem is to group the data into different clusters regardless of their domain. We approach this problem by finding mappings that can transfer the data points between the domains. We use adversarial networks to approximate these mapping functions, and form a paired representation at each domain by mapping the data onto their counter domains. Finally, we employ a multimodal subspace clustering type algorithm to cluster the paired representations with respect to their subspaces. Various experiments on datasets with domain shifts show that our method performs significantly better than many competitive domain adaptive subspace clustering methods.


title: Aspect-based Opinion Summarization with Convolutional Neural Networks

abstract: This paper considers Aspect-based Opinion Summarization (AOS) of reviews on particular products. To enable real applications, an AOS system needs to address two core subtasks, aspect extraction and sentiment classification. Most existing approaches to aspect extraction, which use linguistic analysis or topic modeling, are general across different products but not precise enough or suitable for particular products. Instead we take a less general but more precise scheme, directly mapping each review sentence into pre-defined aspects. To tackle aspect mapping and sentiment classification, we propose two Convolutional Neural Network (CNN) based methods, cascaded CNN and multitask CNN. Cascaded CNN contains two levels of convolutional networks. Multiple CNNs at level 1 deal with aspect mapping task, and a single CNN at level 2 deals with sentiment classification. Multitask CNN also contains multiple aspect CNNs and a sentiment CNN, but different networks share the same word embeddings. Experimental results indicate that both cascaded and multitask CNNs outperform SVM-based methods by large margins. Multitask CNN generally performs better than cascaded CNN.


title: On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization

abstract: Some recent works in machine learning and computer vision involve the solution of a bi-level optimization problem. Here the solution of a parameterized lower-level problem binds variables that appear in the objective of an upper-level problem. The lower-level problem typically appears as an argmin or argmax optimization problem. Many techniques have been proposed to solve bi-level optimization problems, including gradient descent, which is popular with current end-to-end learning approaches. In this technical report we collect some results on differentiating argmin and argmax optimization problems with and without constraints and provide some insightful motivating examples.


title: The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic review

abstract: Summary Insufficient sleep, poor sleep quality and sleepiness are common problems in children and adolescents being related to learning, memory and school performance. The associations between sleep quality ( k =16 studies, N =13,631), sleep duration ( k =17 studies, N =15,199), sleepiness ( k =17, N =19,530) and school performance were examined in three separate meta-analyses including influential factors (e.g., gender, age, parameter assessment) as moderators. All three sleep variables were significantly but modestly related to school performance. Sleepiness showed the strongest relation to school performance ( r =−0.133), followed by sleep quality ( r =0.096) and sleep duration ( r =0.069). Effect sizes were larger for studies including younger participants which can be explained by dramatic prefrontal cortex changes during (early) adolescence. Concerning the relationship between sleep duration and school performance age effects were even larger in studies that included more boys than in studies that included more girls, demonstrating the importance of differential pubertal development of boys and girls. Longitudinal and experimental studies are recommended in order to gain more insight into the different relationships and to develop programs that can improve school performance by changing individuals' sleep patterns.


title: An Approach to Biometric Identification by Using Low-Frequency Eye Tracker

abstract: Biometric identification is one of the most secure existing identification methods. Algorithms based on eye tracking techniques are being studied now by many authors as eye trackers had become more accessible during the last few years. This paper is related to a biometric identification approach and a problem of providing accurate identification results by using low-frequency eye tracker. This paper presents a review of the related works in the field as well as a general classification of different identification types. We stated a formal description of a saccade and an analyzed fragment of the gaze trajectory containing a saccade, which is based on the finite differences method, and revealed features of the eye movements that can be used for the identification purpose. Two classifiers are proposed and compared based on the experimental results obtained for them.


title: PRISM: A tool for automatic verification of probabilistic systems

abstract: Probabilistic model checking is an automatic formal verification technique for analysing quantitative properties of systems which exhibit stochastic behaviour. PRISM is a probabilistic model checking tool which has already been successfully deployed in a wide range of application domains, from real-time communication protocols to biological signalling pathways. The tool has recently undergone a significant amount of development. Major additions include facilities to manually explore models, Monte-Carlo discrete-event simulation techniques for approximate model analysis (including support for distributed simulation) and the ability to compute cost- and reward-based measures, e.g. the expected energy consumption of the system before the first failure occurs. This paper presents an overview of all the main features of PRISM. More information can be found on the website: www.cs.bham.ac.uk/∼dxp/prism.


title: A sliced spherical Luneburg lens

abstract: The Luneburg lens (LL) represents a very attractive candidate for many applications such as multibeam antennas, multifrequency scanning, and spatial scanning, due to its focusing properties. Indeed, it is a dielectric sphere on which each surface point is a frequency-independent perfect focusing point. This is produced by its index governing law n, which follows the radial distribution n/sup 2/=2-r/sup 2/, where r is the normalized radial position. Practically, an LL is manufactured as a finite number of concentric homogeneous dielectric shells - this is called a discrete LL. The inaccuracies in the curved shell manufacturing process produce intershell air gaps, which degrade the performance of the lens. Furthermore, this requires different materials whose relative dielectric constant covers the range 1-2. The paper proposes a new LL manufacturing process to avoid these drawbacks. The paper describe the theoretical background and the performance of the obtained lens.


Higher LSB Layer Based Audio Steganography Technique


title: Large-Margin Classification in Hyperbolic Space

abstract: Representing data in hyperbolic space can effectively capture latent hierarchical relationships. To enable accurate classification of points in hyperbolic space while respecting their hyperbolic geometry, we introduce hyperbolic SVM, a hyperbolic formulation of support vector machine classifiers, and describe its theoretical connection to the Euclidean counterpart. We also generalize Euclidean kernel SVM to hyperbolic space, allowing nonlinear hyperbolic decision boundaries and providing a geometric interpretation for a certain class of indefinite kernels. Hyperbolic SVM improves classification accuracy in simulation and in real-world problems involving complex networks and word embeddings. Our work enables end-to-end analyses based on the inherent hyperbolic geometry of the data without resorting to ill-fitting tools developed for Euclidean space.


title: Deep beamforming networks for multi-channel speech recognition

abstract: Despite the significant progress in speech recognition enabled by deep neural networks, poor performance persists in some scenarios. In this work, we focus on far-field speech recognition which remains challenging due to high levels of noise and reverberation in the captured speech signals. We propose to represent the stages of acoustic processing including beamforming, feature extraction, and acoustic modeling, as three components of a single unified computational network. The parameters of a frequency-domain beam-former are first estimated by a network based on features derived from the microphone channels. These filter coefficients are then applied to the array signals to form an enhanced signal. Conventional features are then extracted from this signal and passed to a second network that performs acoustic modeling for classification. The parameters of both the beamforming and acoustic modeling networks are trained jointly using back-propagation with a common cross-entropy objective function. In experiments on the AMI meeting corpus, we observed improvements by pre-training each sub-network with a network-specific objective function before joint training of both networks. The proposed method obtained a 3.2% absolute word error rate reduction compared to a conventional pipeline of independent processing stages.


title: Online Footsteps to Purchase: Exploring Consumer Behaviors on Online Shopping Sites

abstract: As an important part of the Internet economy, online markets have gained much interest in research community as well as industry. Researchers have studied various aspects of online markets including motivations of consumer behaviors on online markets. However, due to the lack of log data of consumers' online behaviors including their purchase, it has not been thoroughly investigated or validated on what drives consumers to purchase products on online markets. Our research moves forward from prior studies by analyzing consumers' actual online behaviors that lead to actual purchases, and using datasets from multiple online shopping sites that can provide comparisons across different types of online shopping sites. We analyzed consumers' buying process and constructed consumers' behavior trajectory to gain deeper understanding of consumer behaviors on online markets. We find that a substantial portion (24%) of consumers in a general-purpose marketplace (like eBay) discover items from external sources (e.g., price comparison sites), while most (>95%) of consumers in a special-purpose shopping site directly access items from the site itself. We also reveal that item browsing patterns and cart usage patterns are the important predictors of the actual purchases. Using behavioral features identified by our analysis, we developed a prediction model to infer whether a consumer purchases item(s). Our prediction model of purchases achieved over 80% accuracy across four different online shopping sites.


title: 3D-to-2D mapping for user interactive segmentation of human leg muscles from MRI data

abstract: The automated computation of muscle volume from MRI of human legs is an open problem in the biomedical imaging community. Such automation has the potential to provide an objective measure of effectiveness of pre- and post-surgery treatments. In this paper, we take a step toward automation by proposing a framework for user interactive segmentation of MRI of human leg muscles. Our framework is built upon the strategy of bootstrapping: after the first few tedious segmentation results are achieved, and once a small database of segmented muscles is built, user interaction is reduced. Further, as the database of segmented muscles grows, the user interaction becomes more efficient. At the heart of this proposed framework is a simple, computationally attractive 3D representation of muscles. By a generalized cylinder model, we represent a 3D human leg muscle by two smooth 2D images, which enables application of 2D image processing and analysis methods in this complex multi-segment 3D problem. The smoothness of a leg muscle is modeled by the smoothness of the 2D images. Interdependence and relative positions of leg muscles are represented by a linear combination (basically, convolutions) of such 2D images. We demonstrate that fitting and editing of these models during user interactive segmentation of MRI data are computationally efficient, because our linear interaction model can be represented in the Fourier domain.


title: Partisan sharing: facebook evidence and societal consequences

abstract: The hypothesis of selective exposure assumes that people seek out information that supports their views and eschew information that conflicts with their beliefs, and that has negative consequences on our society. Few researchers have recently found counter evidence of selective exposure in social media: users are exposed to politically diverse articles. No work has looked at what happens after exposure, particularly how individuals react to such exposure, though. Users might well be exposed to diverse articles but share only the partisan ones. To test this, we study partisan sharing on Facebook: the tendency for users to predominantly share like-minded news articles and avoid conflicting ones. We verified four main hypotheses. That is, whether partisan sharing: 1) exists at all; 2) changes across individuals (e.g., depending on their interest in politics); 3) changes over time (e.g., around elections); and 4) changes depending on perceived importance of topics. We indeed find strong evidence for partisan sharing. To test whether it has any consequence in the real world, we built a web application for BBC viewers of a popular political program, resulting in a controlled experiment involving more than 70 individuals. Based on what they share and on survey data, we find that partisan sharing has negative consequences: distorted perception of reality. However, we do also find positive aspects of partisan sharing: it is associated with people who are more knowledgeable about politics and engage more with it as they are more likely to vote in the general elections.


title: Deep Interest Network for Click-Through Rate Prediction

abstract: Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic.


title: Looking ahead in pervasive computing : Challenges and opportunities in the era of cyber – physical convergence

abstract: The physical environment is becoming more and more saturated with computing and communication entities that interact among themselves, as well as with users: virtually everything will be enabled to source information and respond to appropriate stimuli. In this technology-rich scenario, real-world components interact with cyberspace via sensing, computing and communication elements, thus driving towards what is called the Cyber-Physical World (CPW) convergence. Information flows from the physical to the cyber world, and vice-versa, adapting the converged world to human behavior and social dynamics. Indeed humans are at the center of this converged world since information about the context in which they operate is the key element to adapt the CPW applications and services. Alongside, a new wave of (human) social networks and structures are emerging as important drivers for the development of novel communication and computing paradigms. In this article we present some of the research issues, challenges and opportunities in the convergence between the cyber and physical worlds. This article is not a comprehensive survey of all aspects of the CPW convergence. Instead, it presents some exciting research challenges and opportunities identified by members of the journal's editorial board with a goal to stimulate new research activities in the emerging areas of CPW convergence.


title: Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data

abstract: Motivation: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results. Results: We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues, and other normal tissues. The dataset consists of expression experiment results for 97 802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification of tissues is achieved, but not with high confidence. We identify and analyse a subset of genes from the ovarian dataset whose expression is highly differentiated between the types of tissues. To show robustness of the SVM method, two previously published datasets from other types of tissues or cells are analysed. The results are comparable to those previously obtained. We show that other machine learning methods also perform comparably to the SVM on many of those datasets. Availability: The SVM software is available at http:// www. cs.columbia.edu/ ∼bgrundy/ svm.


title: Real-time digital forensics and triage

abstract: There are two main reasons the processing speed of current generation digital forensic tools is inadequate for the average case: a) users have failed to formulate explicit performance requirements; and b) developers have failed to put performance, specifically latency, as a top-level concern in line with reliability and correctness. In this work, we formulate forensic triage as a real-time computation problem with specific technical requirements, and we use these requirements to evaluate the suitability of different forensic methods for triage purposes. Further, we generalize our discussion to show that the complete digital forensics process should be viewed as a (soft) real-time computation with well-defined performance requirements. We propose and validate a new approach to target acquisition that enables file-centric processing without disrupting optimal data throughput from the raw device. We evaluate core forensic processing functions with respect to processing rates and show their intrinsic limitations in both desktop and server scenarios. Our results suggest that, with current software, keeping up with a commodity SATA HDD at 120 MB/s requires 120-200 cores.


TrumpBot : Seq 2 Seq with Pointer Sentinel Model


title: Motion Analysis and Control of the Ball Operation for Dribbling Action in RoboCup Soccer Robot

abstract: RoboCup is a platform designed to promote the fields such as Artificial Intelligent (AI) and robotics for researchers. As a team "Hibikino-Musashi" participating for RoboCup Middle-Size League soccer competition, we continuously upgrade the technology of our robots. In this paper, motion analysis and control of the ball operation at the time of ball dribbling is discussed. A ball handling mechanism is proposed based on the geometrical information of the robot and analysis is given in detail. A mathematical explanation of the control system is provided along with simulation studies performed in MATAB environment. Results have been evaluated to demonstrate the capability of controlling the motion of the ball and expected control could be achieved without prior calibration of the ball handling mechanism.


title: A novel SVPWM overmodulation technique for three-level NPC VSI

abstract: This paper proposes a simple space vector pulse width modulation (SVPWM) scheme for operating a three-phase three-level neutral-point clamped (NPC) voltage source inverter (VSI) at higher modulation indexes, including overmodulation region. Generally, the overmodulation techniques for SVPWM inverter need to store lots of data in the controller beforehand, which produces a huge memory while the control resolution is not perfect. Based on the study of existing overmodulation techniques published in literatures, a novel two-mode strategy and single-mode feed-forward carrier-based simplified SVPWM overmodulation algorithms are proposed which can manage smooth transition from the linear control range to six-step operation. The proposed overmodulation techniques make continuous control of the output voltage up to the maximum voltage with a smooth linear transition characteristics and minimum distortions. The strategy is simple and suitable for practical digital implementation. Simulation results demonstrate the effectiveness of the proposed SVPWM overmodulation algorithm.


title: Interactive Principal Component Analysis

abstract: Principal Component Analysis (PCA) is an established and efficient method for finding structure in a multidimensional data set. PCA is based on orthogonal transformations that convert a set of multidimensional values into linearly uncorrelated variables called principal components.The main disadvantage to the PCA approach is that the procedure and outcome are often difficult to understand. The connection between input and output can be puzzling, a small change in input can yield a completely different output, and the user may often wonder if the PCA is doing the right thing.We introduce a user interface that makes the procedure and result easier to understand. We have implemented an interactive PCA view in our text visualization tool called Text Variation Explorer. It allows the user to interactively study the result of PCA, and provides a better understanding of the process.We believe that although we are addressing the problem of interactive principal component analysis in the context of text visualization, these ideas should be useful in other contexts as well.


title: Navigated percutaneous screw fixation of a periprosthetic acetabular fracture.

abstract: Periprosthetic fractures are severe complications of total hip arthroplasty with increasing incidence. Most fractures are localized around the femoral component of prosthesis, whereas periacetabular fractures are rare and their management is difficult. In most cases, an operative procedure with revision and exchange of the acetabular cup is necessary. The approaches are demanding and an increased risk of complications is reported. We present the case of a female patient with a cementless total hip arthroplasty due to severe arthritis, suffering of an infratectal transverse periprosthetic fracture. After an initial conservative treatment attempt, we performed a minimally invasive screw fixation using navigated 2-dimensional fluoroscopic guidance. In this report, the literature for treatment recommendations is reviewed and the chosen navigation technique discussed.


title: Targeting Nuclear Factor-Kappa B to Overcome Resistance to Chemotherapy

abstract: Intrinsic or acquired resistance to chemotherapeutic agents is a common phenomenon and a major challenge to the treatment of cancer patients, especially those with progressive disease. Chemoresistance is defined by a complex network of factors including multi-drug resistance proteins, reduced cellular uptake of the drug, enhanced DNA repair, intracellular drug inactivation and evasion of apoptosis. Preclinical models have demonstrated activation of the NF-κB pathway by several chemotherapy drugs, including platinum based agents, anthracyclines and taxanes. NF-κB is a key transcription factor, playing a role in the development and progression of cancer as well as chemoresistance through the activation of anti-apoptotic genes. Consequently, NF-κB has emerged as a promising anticancer target. Here, we describe the role of NF-κB in the cancer cell and in resistance to chemotherapeutic agents, particularly cisplatin. Additionally, the potential benefits and disadvantages of targeting NF-κB signalling by pharmacological intervention will be addressed.


title: High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

abstract: This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements. The results are applicable to a wide range of machine learning problems and thus provide a solid ground for tackling numerous Big Data challenges. The included toolbox is targeted at enabling the full potential of ELMs to the widest range of users.


title: A data-reuse aware accelerator for large-scale convolutional networks

abstract: This paper presents a clustered SIMD accelerator template for Convolutional Networks. These networks significantly outperform other methods in detection and classification tasks in the vision domain. Due to the excessive compute and data transfer requirements these applications benefit a lot from a dedicated accelerator. The proposed accelerator reduces memory traffic by loop transformations such as tiling and fusion to merge successive layers. Although fusion can introduce redundant computations it often reduces the data transfer, and therefore can remove performance bottlenecks. The SIMD cluster is mapped to a Xilinx Zynq FPGA, which can achieve 6.4 Gops performance with a small amount of resources. The performance can be scaled by using multiple clusters.


LOYALTY AS MEDIATOR ON THE RELATIONSHIP AMONG SERVICE QUALITY , SATISFACTION , CORPORATE IMAGE AND BEHAVIORAL INTENTION OF HEALTH TOURISM CUSTOMERS IN UPPER NORTHERN REGION OF THAILAND


title: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

abstract: In this paper, we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% from support vector machine. We observed that the classification rate is high for a support vector machine classifier compared to self-organizing map-based approach.


title: Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

abstract: In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.


title: Control and analysis of multi-level type multi-phase resonant converter for wireless EV charging

abstract: In this study, a multi-level type multi-phase resonant converter is presented for high power wireless EV charging applications. As an alternative to the traditional frequency and phase shift control methods, a hybrid phase-frequency control strategy is implemented to improve the system efficiency. In order to confirm the proposed converter and control technique, a laboratory prototype wireless EV charger is designed using 8 inches air gap coreless transformer and rectifier. The proposed control is compared with the conventional control methods for various load conditions at the different power levels. The experimental results show that the proposed converter is within the desired frequency range while regulating output from 0 to 15 kW with 750 V input DC bus voltage.


title: Framework for learning agents in quantum environments

abstract: In this paper we provide a broad framework for describing learning agents in general quantum environments. We analyze the types of classically specified environments which allow for quantum enhancements in learning, by contrasting environments to quantum oracles. We show that whether or not quantum improvements are at all possible depends on the internal structure of the quantum environment. If the environments are constructed and the internal structure is appropriately chosen, or if the agent has limited capacities to influence the internal states of the environment, we show that improvements in learning times are possible in a broad range of scenarios. Such scenarios we call luck-favoring settings. The case of constructed environments is particularly relevant for the class of model-based learning agents, where our results imply a near-generic improvement.


title: High-speed, miniaturized fluorescence microscopy in freely moving mice

abstract: A central goal in biomedicine is to explain organismic behavior in terms of causal cellular processes. However, concurrent observation of mammalian behavior and underlying cellular dynamics has been a longstanding challenge. We describe a miniaturized (1.1 g mass) epifluorescence microscope for cellular-level brain imaging in freely moving mice, and its application to imaging microcirculation and neuronal Ca(2+) dynamics.


title: Zebra: An East-West Control Framework for SDN Controllers

abstract: Traditional networks are surprisingly fragile and difficult to manage. Software Defined Networking (SDN) gained significant attention from both academia and industry, as if simplify network management through centralized configuration. Existing work primarily focuses on networks of limited scope such as data-centers and enterprises, which makes the development of SDN hindered when it comes to large-scale network environments. One way of enabling communication between data-centers, enterprises and ISPs in a large-scale network is to establish a standard communication mechanism between these entities. In this paper, we propose Zebra, a framework for enabling communication between different SDN domains. Zebra has two modules: Heterogeneous Controller Management (HCM) module and Domain Relationships Management (DRM) module. HCM collects network information from a group of controllers with no interconnection and generate a domain-wide network view. DRM collects network information from other domains to generate a global-wide network view. Moreover, HCM supports different SDN controllers, such as floodlight, maestro and so on. To test this framework, we develop a prototype system, and give some experimental results.


title: Facebook friends, subjective well-being, social support, and personality

abstract: Offline social capital in the form of interpersonal networks is known to be associated with subjective well-being (SWB). In two studies run in the US (N?=?153) and Germany (N?=?187), we initially investigated whether the size of an individual's Facebook social network was associated with SWB and perceived social support. Objectively measured Facebook network size was positively associated with several measures of both self- and informant rated SWB but not with perceived social support. More pertinent to the present research, we next investigated whether the observed associations between network size and SWB were, in fact, an artifact of personality - trait Extraversion in particular. Indeed, self- and informant-rated Extraversion was associated with both Facebook social network size and with self- and informant-rated SWB. Importantly, controlling for Extraversion rendered the associations between Facebook social network size and SWB weak and statistically insignificant. We discuss the importance of social relationships on Facebook for well-being, as well as the implications of our results for research on the relationship between SWB, social network size, and personality. Facebook (FB) network size was related to Life satisfaction and Happiness.The results replicated across US and German samples.The results replicated across various self- and other-ratings of well-being.Number of FB friends was not associated with social support.FB Network size could not predict well-being when Extraversion was controlled for.


title: Negative probabilities in probabilistic logic programs

abstract: Negative probabilities are essential for representing some distributions by PLPs.Negative probabilities are a more expressive alternative to negations in PLP.A representation for probabilistic values gives additive algebra for combining rules.A canonical rule representation of CPDs using negative probabilities. We consider probabilistic logic programs (PLPs) for non-extreme distributions. We show that in the relational case with fixed populations, PLPs cannot represent many non-extreme distributions, even using negations. We introduce negative rule probabilities in PLPs, and show they make the language strictly more expressive. In addition, they render negations unnecessary: negations in PLPs can be translated to rules with negative probabilities, thus avoiding the problem of logical inconsistency. Furthermore, this translation keeps the PLP size compact (assuming the number of negations per rule is small). This translation algorithm also allows algorithms for exact inference that do not support negations to be applicable to PLPs with negations.The noise probabilities for non-exclusive rules are difficult to interpret and unintuitive to manipulate. To alleviate this we define probability-strengths, an alternative representation for probabilistic values, which results in an intuitive additive algebra for combining rules.For acyclic propositional PLPs we prove what constraints on the strengths allow for proper distributions on the non-noise variables and allow for all non-extreme distributions to be represented. We show how arbitrary CPDs can be converted into this form in a canonical way.


Reference in Robotics : a Givenness Hierarchy Theoretic Approach


title: An Extensible Framework for Verification of Numerical Claims

abstract: In this paper we present our automated fact checking system demonstration which we developed in order to participate in the ::: Fast and Furious Fact Check challenge. We focused on simple numerical claims such as “population of Germany in 2015 was 80 million” which comprised a quarter of the test instances in the challenge, achieving 68% accuracy. Our system extends previous work on semantic parsing and claim identification to handle tempo- ::: ral expressions and knowledge bases consisting of multiple tables, while relying solely on automatically generated training data. We demonstrate the extensible nature of our system by evaluating it on relations used in previous work. We make our system publicly available so that it can be used and extended by the community.


title: Finger-Vein Image Enhancement Based on Combination of Gray-Level Grouping and Circular Gabor Filter

abstract: tion. Generally, finger-vein images have low contrast and uneven illumination due to finger-vein imaging manner and finger-shape variation. So, finger-vein enhancement is indispensable for reliable finger-vein network extraction. This paper proposes a new method based on combination of Gray-Level Grouping (GLG) and Circular Gabor Filter (CGF) for finger-vein image enhancement. First, GLG is used to reduce illumination fluctuation and improve the contrast of finger-vein images. Then a circular Gabor filter is used to further strengthen vein ridges in images. The experimental results show that this proposed method is capable of enhancing finger-vein image effectively.


title: Nonlinear material design using principal stretches

abstract: The Finite Element Method is widely used for solid deformable object simulation in film, computer games, virtual reality and medicine. Previous applications of nonlinear solid elasticity employed materials from a few standard families such as linear corotational, nonlinear St.Venant-Kirchhoff, Neo-Hookean, Ogden or Mooney-Rivlin materials. However, the spaces of all nonlinear isotropic and anisotropic materials are infinite-dimensional and much broader than these standard materials. In this paper, we demonstrate how to intuitively explore the space of isotropic and anisotropic nonlinear materials, for design of animations in computer graphics and related fields. In order to do so, we first formulate the internal elastic forces and tangent stiffness matrices in the space of the principal stretches of the material. We then demonstrate how to design new isotropic materials by editing a single stress-strain curve, using a spline interface. Similarly, anisotropic (orthotropic) materials can be designed by editing three curves, one for each material direction. We demonstrate that modifying these curves using our proposed interface has an intuitive, visual, effect on the simulation. Our materials accelerate simulation design and enable visual effects that are difficult or impossible to achieve with standard nonlinear materials.


title: Adaptive Input Representations for Neural Language Modeling

abstract: We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. On the WikiText-103 benchmark we achieve 18.7 perplexity, an improvement of 10.5 perplexity compared to the previously best published result and on the Billion Word benchmark, we achieve 23.02 perplexity.


title: Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution

abstract: The performance of single image super-resolution has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). The features in deep CNN contain different types of information which make different contributions to image reconstruction. However, most CNN-based models lack discriminative ability for different types of information and deal with them equally, which results in the representational capacity of the models being limited. On the other hand, as the depth of neural networks grows, the long-term information coming from preceding layers is easy to be weaken or lost in late layers, which is adverse to super-resolving image. To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation memory (FMM) modules is cascaded with a densely connected structure to transform low-resolution features to high informative features. In each FMM module, we construct a set of channel-wise and spatial attention residual (CSAR) blocks and stack them in a chain structure to dynamically modulate multi-level features in a global-and-local manner. This feature modulation strategy enables the high contribution information to be enhanced and the redundant information to be suppressed. Meanwhile, for long-term information persistence, a gated fusion (GF) node is attached at the end of the FMM module to adaptively fuse hierarchical features and distill more effective information via the dense skip connections and the gating mechanism. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over the state-of-the-art methods.


title: Grassmannian beamforming for multiple-input multiple-output wireless systems

abstract: Transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple-input multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector; both are hard to realize. In this article, a quantized maximum signal-to-noise ratio (SNR) beamforming technique is proposed where the receiver only sends the label of the best beamforming vector in a predetermined codebook to the transmitter. By using the distribution of the optimal beamforming vector in independent and identically distributed Rayleigh fading matrix channels, the codebook design problem is solved and related to the problem of Grassmannian line packing. The proposed design criterion is flexible enough to allow for side constraints on the codebook vectors. Bounds on the codebook size are derived to guarantee full diversity order. Results on the density of Grassmannian line packings are derived and used to develop bounds on the codebook size given a capacity or SNR loss. Monte Carlo simulations are presented that compare the probability of error for different quantization strategies.


title: Software and Hardware Architectures in Cooperative Aerial and Ground Robots for Agricultural Disease Detection

abstract: Currently, disease detection tasks performed on strawberry crops are conducted by human scouts. This qualitative approach is susceptible to error and relies on the visibility of symptoms which are discernable only after the disease has progressed to a certain stage. Precision agriculture is a thriving new discipline that promises to integrate new autonomous machine technology with agronomy. In this research, the cooperation of dedicated ground and aerial vehicles is proposed to undertake the challenging task of early stage disease detection for commercial farms. The focus of this paper is the architectures of the communication, software, and hardware in such a network.


title: MuVR: A Multi-User Virtual Reality platform

abstract: Consumer adoption of virtual reality technology has historically been held back by poor accessibility, the lack of intuitive multi-user capabilities, dependence on external infrastructure for rendering and tracking, and the amount of time and effort required to enter virtual reality systems. This poster presents the current status of our work creating MuVR, a Multi-User Virtual Reality platform that seeks to overcome these hindrances. The MuVR project comprises four main goals: scalable and easy to use multi-user capabilities, portable and self-contained hardware, a rapidly deployable system, and ready accessibility to others. We provide a description of the platform we developed to address these goals and discuss potential directions for future work.


title: Architecting Cyber Defense: A Survey of the Leading Cyber Reference Architectures and Frameworks

abstract: The rapid development of cyber threats and intelligence challenges the traditional design of static cyber defense platforms. This paper discusses the need for an agile structure to inform the development of cybersecurity solutions that are not only widely adaptable to unknown threats, specific business practices, and technical requirements, but are also efficiently translatable to products. It employs a systems engineering approach in the evaluation of several Reference Architectures for cyber defense that were gathered from the both the public and private sector. The Northrop Grumman Cyber Defense Reference Architecture is introduced in this paper to go beyond basic cyber hygiene by focusing on cognitive tasks through functional implementations of advanced analytics and automation. The limitations of frameworks, design patterns, and security control checklists in comparison to reference architectures are also discussed.


title: Security Attacks and Solutions in Vehicular Ad Hoc Networks: A Survey

abstract: Vehicular Ad hoc Networks (VANETs) have emerged recently as one of the most attractive topics for researchers and automotive industries due to their tremendous potential to improve traffic safety, efficiency and other added services. However, VANETs are themselves vulnerable against attacks that can directly lead to the corruption of networks and then possibly provoke big losses of time, money, and even lives. This paper presents a survey of VANETs attacks and solutions in carefully considering other similar works as well as updating new attacks and categorizing them into different classes.


title: Three-way decisions with probabilistic rough sets

abstract: The rough set theory approximates a concept by three regions, namely, the positive, boundary and negative regions. Rules constructed from the three regions are associated with different actions and decisions, which immediately leads to the notion of three-way decision rules. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rule makes a decision of abstaining. This paper provides an analysis of three-way decision rules in the classical rough set model and the decision-theoretic rough set model. The results enrich the rough set theory by ideas from Bayesian decision theory and hypothesis testing in statistics. The connections established between the levels of tolerance for errors and costs of incorrect decisions make the rough set theory practical in applications.


title: SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data

abstract: Neural networks exhibit good generalization behavior in the over-parameterized regime, where the number of network parameters exceeds the number of observations. Nonetheless, current generalization bounds for neural networks fail to explain this phenomenon. In an attempt to bridge this gap, we study the problem of learning a two-layer over-parameterized neural network, when the data is generated by a linearly separable function. In the case where the network has Leaky ReLU activations, we provide both optimization and generalization guarantees for over-parameterized networks. Specifically, we prove convergence rates of SGD to a global minimum and provide generalization guarantees for this global minimum that are independent of the network size. Therefore, our result clearly shows that the use of SGD for optimization both finds a global minimum, and avoids overfitting despite the high capacity of the model. This is the first theoretical demonstration that SGD can avoid overfitting, when learning over-specified neural network classifiers.


title: 4-D tomographic inference : application to SPECT and MR-driven PET

abstract: Emission tomographic imaging is framed in the Bayesian and information theoretic framework. The first part of the thesis is inspired by the new possibilities offered by PET-MR systems, formulating models and algorithms for 4-D tomography and for the integration of information from multiple imaging modalities. The second part of the thesis extends the models described in the first part, focusing on the imaging hardware. Three key aspects for the design of new imaging systems are investigated: criteria and efficient algorithms for the optimisation and real-time adaptation of the parameters of the imaging hardware; learning the characteristics of the imaging hardware; exploiting the rich information provided by depthof- interaction (DOI) and energy resolving devices. The document concludes with the description of the NiftyRec software toolkit, developed to enable 4-D multi-modal tomographic inference.


title: Single-Phase Seven-Level Grid-Connected Inverter for Photovoltaic System

abstract: This paper proposes a single-phase seven-level inverter for grid-connected photovoltaic systems, with a novel pulsewidth-modulated (PWM) control scheme. Three reference signals that are identical to each other with an offset that is equivalent to the amplitude of the triangular carrier signal were used to generate the PWM signals. The inverter is capable of producing seven levels of output-voltage levels (Vdc, 2Vdc/3, Vdc/3, 0, -Vdc, -2Vdc/3, -Vdc/3) from the dc supply voltage. A digital proportional-integral current-control algorithm was implemented in a TMS320F2812 DSP to keep the current injected into the grid sinusoidal. The proposed system was verified through simulation and implemented in a prototype.


title: Short-Circuit Current of Wind Turbines With Doubly Fed Induction Generator

abstract: The short-circuit current contribution of wind turbines has not received much attention so far. This paper considers the short-circuit behavior, especially the short-circuit current of wind turbines with a doubly fed induction generator. Mostly, these wind turbines have a crowbar to protect the power electronic converter that is connected to the rotor windings of the induction generator. First, the maximum value of the short-circuit current of a conventional induction machine is determined. The differences between a crowbar-protected doubly fed induction generator and a conventional induction generator are highlighted and approximate equations for the maximum short-circuit current of a doubly fed induction generator are determined. The values obtained in this way are compared to the values obtained from time domain simulations. The differences are less then 15%


title: Integrated Optimization of Battery Sizing, Charging, and Power Management in Plug-In Hybrid Electric Vehicles

abstract: This brief presents an integrated optimization framework for battery sizing, charging, and on-road power management in plug-in hybrid electric vehicles. This framework utilizes convex programming to assess interactions between the three optimal design/control tasks. The objective is to minimize carbon dioxide (CO2) emissions, from the on-board internal combustion engine and grid generation plants providing electrical recharge power. The impacts of varying daily grid CO2 trajectories on both the optimal battery size and charging/power management algorithms are analyzed. We find that the level of grid CO2 emissions can significantly impact the nature of emission-optimal on-road power management. We also observe that the on-road power management strategy is the most important design task for minimizing emissions, through a variety of comparative studies.


LibN3L: A Lightweight Package for Neural NLP.


title: MAXIMUM LIKELIHOOD ESTIMATION OF THE APARCH MODEL WITH SKEW DISTRIBUTIONS FOR THE INNOVATION PROCESS

abstract: A method normally used in empirical financial studies to estimate the parameters of a general autoregressive conditional heteroskedasticity model is the quasi-maximum likelihood, which maximizes the likelihood function assuming conditional normality, also if it can be a false assumption. When it is possible to assume a nonnormal distribution of errors for this kind of models, it has been shown that there is a loss of efficiency of quasi-maximum likelihood estimators in finite samples with respect to maximum likelihood estimators. In this paper we study, with an empirical application to the daily returns of NASDAQ stock market index, the maximum likelihood estimates of the parameters of the asymmetric power ARCH model, a generalization of the general autoregressive conditional heteroskedasticity model, with skew distributions for the innovation process. The distributions considered are the Student-t, the exponential power and the generalized secant hyperbolic distributions, with reparametrization of the densities which adds inverse scale factors in positive and negative orthants in order to take the skewness into account. For comparison, we have analyzed the daily returns also with the quasi-maximum and the semiparametric maximum likelihood estimation procedures. We have used a quasi-Newton algorithm to optimize the average log-likelihood functions, in which analytical derivatives of the


title: Velocity Estimation for Ultralightweight Tendon-Driven Series Elastic Robots

abstract: Accurate velocity estimation is an important basis for robot control, but especially challenging for highly elastically driven robots. These robots show large swing or oscillation effects if they are not damped appropriately during the performed motion. In this letter, we consider an ultralightweight tendon-driven series elastic robot arm equipped with low-resolution joint position encoders. We propose an adaptive Kalman filter for velocity estimation that is suitable for these kinds of robots with a large range of possible velocities and oscillation frequencies. Based on an analysis of the parameter characteristics of the measurement noise variance, an update rule based on the filter position error is developed that is easy to adjust for use with different sensors. Evaluation of the filter both in simulation and in robot experiments shows a smooth and accurate performance, well suited for control purposes.


title: MATATABI: Multi-layer Threat Analysis Platform with Hadoop

abstract: Threat detection and analysis are indispensable processes in today's cyberspace, but current state of the art threat detection is still limited to specific aspects of modern malicious activities due to the lack of information to analyze. By measuring and collecting various types of data, from traffic information to human behavior, at different vantage points for a long duration, the viewpoint seems to be helpful to deeply inspect threats, but faces scalability issues as the amount of collected data grows, since more computational resources are required for the analysis. In this paper, we report our experience from operating the Hadoop platform, called MATATABI, for threat detections, and present the micro-benchmarks with four different backends of data processing in typical use cases such as log data and packet trace analysis. The benchmarks demonstrate the advantages of distributed computation in terms of performance. Our extensive use cases of analysis modules showcase the potential benefit of deploying our threat analysis platform.


title: Hardware-Software Partitioning at the Knowledge Level

abstract: Hardware-software co-design addresses the development of complex heterogeneous systems looking for the best tradeoffs among the different solutions. The basic idea is to combine the hardware and software design cycles. This article shows how knowledge-based techniques can be used to solve the hardware-software partitioning problem, the co-design task that makes the decision on the best implementation of the different components of a digital system. In particular, a fuzzy-logic-based expert system, SHAPES, has been developed based on the CommonKADS methodology. This tool takes advantage of two important artificial intelligence bases: the use of an expert‘s knowledge in the decision-making process and the possibility of dealing with imprecise and usually uncertain values by the definition of fuzzy magnitudes. ::: ::: Expert system construction has adopted a knowledge modeling approach, following the knowledge level and knowledge separation principles. This expertise model is the center of the knowledge-based system development. It is based in the problem-solving method Propose and Revise with a previous heuristic classification.


title: Study of photodegradation kinetics of melatonin by multivariate curve resolution (MCR) with estimation of feasible band boundaries.

abstract: Multivariate curve resolution—alternating least squares (MCR-ALS) has been applied to data collected from UV spectrophotometric analysis of melatonin samples exposed to light with varying irradiance power. MCR-ALS was able to explain the degradation kinetics of this drug, deducing the pure spectra and concentration changes of the different species present throughout the entire process. Possible rotational ambiguities associated with MCR solutions were investigated and their extent was evaluated. The extent of the rotation ambiguity was estimated from the band boundaries of feasible solutions calculated using the MCR-BANDS procedure. The use of a non-linear fitting routine allowed improving kinetic information and provided a method of evaluation of the rate constants of the degradation process. The degradation pathway was found to follow a first-order reaction model, in which melatonin underwent photo-oxidation of the indole ring to give a formylamine group. Kinetics of the reaction was shown to be dependent on irradiation conditions, with an increase of the rate constants when light power also increased. Copyright © 2011 John Wiley & Sons, Ltd.


title: ConPredictor: Concurrency Defect Prediction in Real-World Applications

abstract: Concurrent programs are difficult to test due to their inherent non-determinism. To address this problem, testing often requires the exploration of thread schedules of a program; this can be time-consuming when applied to real-world programs. Software defect prediction has been used to help developers find faults and prioritize their testing efforts. Prior studies have used machine learning to build such predicting models based on designed features that encode the characteristics of programs. However, research has focused on sequential programs; to date, no work has considered defect prediction for concurrent programs, with program characteristics distinguished from sequential programs. In this paper, we present ConPredictor, an approach to predict defects specific to concurrent programs by combining both static and dynamic program metrics. Specifically, we propose a set of novel static code metrics based on the unique properties of concurrent programs. We also leverage additional guidance from dynamic metrics constructed based on mutation analysis. Our evaluation on four large open source projects shows that ConPredictor improved both within-project defect prediction and cross-project defect prediction compared to traditional features.


title: A Multiple Criteria Decision Analysis (MCDA) Software Selection Framework

abstract: With the gaining popularity of multiple criteria decision analysis (MCDA) among researchers and practitioners, a variety of software that implements sophisticated MCDA methods and techniques is now available. To address the issue of the missing methodological approach in MCDA software selection, especially the mismatch between the decision making situation (DMS) structuring and the preference structuring employed by the tools, we propose a framework to enable the decision maker to choose relevant MCDA software. This is the first attempt to formally model the MCDA software selection process. A comprehensive set of MCDA methods meta-data for software selection is identified and demonstrated using the example of a specific DMS.


title: Distributed Protocols for Leader Election: A Game-Theoretic Perspective

abstract: We do a game-theoretic analysis of leader election, under the assumption that each agent prefers to have some leader than no leader at all. We show that it is possible to obtain a fair Nash equilibrium, where each agent has an equal probability of being elected leader, in a completely connected network, in a bidirectional ring, and a unidirectional ring, in the synchronous setting. In the asynchronous setting, Nash equilibrium is not quite the right solution concept. Rather, we must consider ex post Nash equilibrium; this means that we have a Nash equilibrium no matter what a scheduling adversary does. We show that ex post Nash equilibrium is attainable in the asynchronous setting in all the networks we consider, using a protocol with bounded running time. However, in the asynchronous setting, we require that n > 2. We show that we can get a fair ex post e-Nash equilibrium if ne2 in the asynchronous setting under some cryptographic assumptions (specifically, the existence of a one-way functions), using a commitment protocol. We then generalize these results to a setting where we can have deviations by a coalition of size k. In this case, we can get what we call a fair k-resilient equilibrium in a completely connected network if n> 2k; under the same cryptographic assumptions, we can a get a k-resilient equilibrium in a completely connected network, unidirectional ring, or bidirectional ring if n > k. Finally, we show that under minimal assumptions, not only do our protocols give a Nash equilibrium, they also give a sequential equilibrium, so players even play optimally off the equilibrium path.


title: KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

abstract: We present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contributions, of independent interest. ::: ::: The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time |E|1/2+o(1) with high probability, obtaining a significant speedup with respect to the Θ(|E|) worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well. ::: ::: The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the k most central nodes. Furthermore, our analysis is general, and it might be extended to other settings.


title: Supervised Dictionary Learning

abstract: It is now well established that sparse signal models are well suited for restoration tasks and can be effectively learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This paper proposes a new step in that direction, with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and discriminative class models. The linear version of the proposed model admits a simple probabilistic interpretation, while its most general variant admits an interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experimental results on standard handwritten digit and texture classification tasks.


title: Single-stage Flyback converter for constant current output LED driver with power factor correction

abstract: This paper presents a LED driver circuit based on single-stage Flyback converter with power factor correction. Voltage following control and Nonlinear carrier control are applied to constant current mode Flyback converter which is combined with constant current driver technique. By designing the outer loop to stabilize the output voltage and the inner loop to shape input current waveform, that makes the system achieve unity power factor. The circuit doesn't need an expensive multiplier as classical multiplier control. To verify the performance, the proposed circuit was confirmed by simulation and experiment. From the results, the proposed system shows high power factor with 0.96 and constant current output.


title: The contourlet transform: an efficient directional multiresolution image representation

abstract: The limitations of commonly used separable extensions of one-dimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches, such as curvelets, that first develop a transform in the continuous domain and then discretize for sampled data, our approach starts with a discrete-domain construction and then studies its convergence to an expansion in the continuous domain. Specifically, we construct a discrete-domain multiresolution and multidirection expansion using nonseparable filter banks, in much the same way that wavelets were derived from filter banks. This construction results in a flexible multiresolution, local, and directional image expansion using contour segments, and, thus, it is named the contourlet transform. The discrete contourlet transform has a fast iterated filter bank algorithm that requires an order N operations for N-pixel images. Furthermore, we establish a precise link between the developed filter bank and the associated continuous-domain contourlet expansion via a directional multiresolution analysis framework. We show that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing applications.


title: Reliability optimization of topology communication network design using an improved ant colony optimization

abstract: Network design problem is a well-known NP-hard problem which involves the selection of a subset of possible links or a network topology in order to minimize the network cost subjected to the reliability constraint. To overcome the problem, this paper proposes a new efficiency algorithm based on the conventional ant colony optimization (ACO) to solve the communication network design when considering both economics and reliability. The proposed method is called improved ant colony optimizations (IACO) which introduces two addition techniques in order to improve the search process, i.e. neighborhood search and re-initialization process. To show its efficiency, IACO is applied to test with three different topology network systems and its results are compared with those obtained results from the conventional approaches, i.e. genetic algorithm (GA), tabu search algorithm (TSA) and ACO. Simulation results, obtained these test problems with various constraints, shown that the proposed approach is superior to the conventional algorithms both solution quality and computational time.


title: Wireless hacking - a WiFi hack by cracking WEP

abstract: Wireless Local Area Networks frequently referred to as WLANs or Wi-Fi networks are all the vehemence in recent times. People are installing these in houses, institutions, offices and hotels etc, without any vain. In search of fulfilling the wireless demands, Wi-Fi product vendors and service contributors are exploding up as quickly as possible. Wireless networks offer handiness, mobility, and can even be less expensive to put into practice than wired networks in many cases. With the consumer demand, vendor solutions and industry standards, wireless network technology is factual and is here to stay. But how far this technology is going provide a protected environment in terms of privacy is again an anonymous issue. Realizing the miscellaneous threats and vulnerabilities associated with 802.11-based wireless networks and ethically hacking them to make them more secure is what this paper is all about. On this segment, we'll seize a look at common threats, vulnerabilities related with wireless networks. And also we have discussed the entire process of cracking WEP (Wired Equivalent Privacy) encryption of WiFi, focusing the necessity to become familiar with scanning tools like Cain, NetStumbler, Kismet and MiniStumbler to help survey the area and tests we should run so as to strengthen our air signals.


International Journal of Electrical and Computer Engineering 2:9 2007 Design Optimization Methodology of CMOS Active Mixers for Multi-Standard Receivers


Real-time Encrypted Traffic Identification using Machine Learning


Learning to Rank: Online Learning, Statistical Theory and Applications.


title: On persistence in mutual fund performance

abstract: This paper analyzes how mutual fund performance relates to past performance. These tests are based on a multiple portfolio benchmark that was formed on the basis of securities characteristics. The authors find evidence that differences in performance between funds persist over time and that this persistence is consistent with the ability of fund managers to earn abnormal returns. Copyright 1992 by American Finance Association.


Cryptanalysis of The A5/2 Algorithm


title: Wireless body area networks: challenges, trends and emerging technologies

abstract: We provide a comprehensive review of the challenges and emerging technologies for WBANs: Wireless Body Area Networks. We describe leading applications and the tiers of the WBAN architectures, itemizing their major design areas. A succinct review of solutions, progress and possible future research in the areas of sensors, power and radio hardware components, the network stack, localization and mobility techniques, security and privacy, and of certification and standardization follows. We finally make the connection between WBANs and the fast growing research field of Cognitive Radios, which help establish efficient wireless transmissions in an increasingly crowded radio spectrum


title: UUV Simulator: A Gazebo-based package for underwater intervention and multi-robot simulation

abstract: This paper describes the Unmanned Underwater Vehicle (UUV) Simulator, an extension of the open-source robotics simulator Gazebo to underwater scenarios, that can simulate multiple underwater robots and intervention tasks using robotic manipulators. This is achieved mainly through a set of newly implemented plugins that model underwater hydrostatic and hydrodynamic effects, thrusters, sensors, and external disturbances. In contrast to existing solutions, it reuses and extends a general-purpose robotics simulation platform to underwater environments.


title: Analysis of triple band artificial magnetic conductor (AMC) band conditions to wideband antenna performance

abstract: This paper presents the design of wideband antenna integrated with triple band artificial magnetic conductor (AMC) ground plane. The wideband antenna that acts as Reference Antenna (RA) operates from 2.69 GHz up to 6.27 GHz while the triple band AMC operates at 2.3 GHz, 5.8 GHz and 8.3 GHz. The investigation is carried out to observe the capability of triple band AMC to excite at least three new narrowband resonances which are in band (IB), slightly out Of band (SOB) and totally out of band (TOB) with respect to the bandwidth of RA. The antennas performance parameter such as return loss, gain variation, and radiation pattern are presented.


English Resource Semantics


title: Establishing Hypertext for User Education: one library's experience

abstract: A Nene College research project to investigate the potential for hypertext use in libraries involved the design and implementation of an interactive hypertext guide to the College library and its resources. The design constraints, the development of the project and the factors taken into consideration are described as are the facilities provided by the system.


Mining Association Rules between Sets of Items in Large Databases


title: Text mining approaches for stock market prediction

abstract: Stock market prediction is an attractive research problem to be investigated. News contents are one of the most important factors that have influence on market. Considering the news impact in analyzing the stock market behavior, leads to more precise predictions and as a result more profitable trades. So far various prototypes have been developed which consider the impact of news in stock market prediction. In this paper, the main components of such forecasting systems have been introduced. In addition, different developed prototypes have been introduced and the way whereby the main components are implemented compared. Based on studied attempts, the potential future research activities have been suggested.


title: Scattering Points in Parallel Coordinates

abstract: In this paper, we present a novel parallel coordinates design integrated with points (scattering points in parallel coordinates, SPPC), by taking advantage of both parallel coordinates and scatterplots. Different from most multiple views visualization frameworks involving parallel coordinates where each visualization type occupies an individual window, we convert two selected neighboring coordinate axes into a scatterplot directly. Multidimensional scaling is adopted to allow converting multiple axes into a single subplot. The transition between two visual types is designed in a seamless way. In our work, a series of interaction tools has been developed. Uniform brushing functionality is implemented to allow the user to perform data selection on both points and parallel coordinate polylines without explicitly switching tools. A GPU accelerated dimensional incremental multidimensional scaling (DIMDS) has been developed to significantly improve the system performance. Our case study shows that our scheme is more efficient than traditional multi-view methods in performing visual analysis tasks.


title: Favorable effect of rehabilitation on balance in ankylosing spondylitis: a quasi-randomized controlled clinical trial

abstract: Balance impairment is a frequent and undertreated manifestation in ankylosing spondylitis, leading to increased risk of falls and lower quality of life. Our aim was to assess supervised training and home-based rehabilitation efficacy on balance improvement in ankylosing spondylitis subjects on biologic agents. This was a single-blinded, quasi-randomized parallel study in a single outpatient Rehabilitation Clinic of a tertiary referral center. Subjects with ankylosing spondylitis on biologic agents were assigned either to supervised training and home-based rehabilitation program (rehabilitation group) plus educational-behavioral therapy, or to educational-behavioral therapy alone (educational groups). The same therapist provided therapy. Outcome measures were assessed at baseline (T0), end of treatment (T1) and at 7-month follow-up (T2). Rheumatologic outcomes were Bath Ankylosing Spondylitis Metrology Index, Bath Ankylosing Spondylitis Functional Index and Bath Ankylosing Spondylitis Disease Activity Index. Balance parameters (anterior-posterior oscillation, latero-lateral oscillation, sway area, sway density and sway path) were evaluated by stabilometry in a condition of open and closed eyes. Forty-six subjects (36 M, 10 F) were enrolled. Demographic data and clinical status at baseline were comparable between the two groups (22 rehabilitation group, 20 educational group). Primary outcome was sway density that improved both at T1 (SDy: open eyes p = 0.003, closed eyes p = 0.004) and at T2 (SDx: open eyes p = 0.0015, closed eyes p = 0.032). A trend toward improvement in the rehabilitation group rather than in the educational group emerged for balance parameters, especially those measured with closed eyes (0.004 < p < 0.048 at T1 and 0.004 < p < 0.036 at T2). Supervised training and home exercise lead to balance improvement in people with ankylosing spondylitis. Eyes-closed trials show a more marked trend toward improvement, and this may suggest a positive effect of rehabilitation on proprioception.


title: Bioactive Carbohydrates and Peptides in Foods: An Overview of Sources, Downstream Processing Steps and Associated Bioactivities

abstract: Bioactive peptides and carbohydrates are sourced from a myriad of plant, animal and insects and have huge potential for use as food ingredients and pharmaceuticals. However, downstream processing bottlenecks hinder the potential use of these natural bioactive compounds and add cost to production processes. This review discusses the health benefits and bioactivities associated with peptides and carbohydrates of natural origin and downstream processing methodologies and novel processes which may be used to overcome these.


title: Horse race analysis in credit card fraud—deep learning, logistic regression, and Gradient Boosted Tree

abstract: Fraud detection is an industry where incremental gains in predictive accuracy can have large benefits for banks and customers. Banks adapt models to the novel ways in which “fraudsters” commit credit card fraud. They collect data and engineer new features in order to increase predictive power. This research compares the algorithmic impact on the predictive power across three supervised classification models: logistic regression, gradient boosted trees, and deep learning. This research also explores the benefits of creating features using domain expertise and feature engineering using an autoencoder—an unsupervised feature engineering method. These two methods of feature engineering combined with the direct mapping of the original variables create six different feature sets. Across these feature sets this research compares the aforementioned models. This research concludes that creating features using domain expertise offers a notable improvement in predictive power. Additionally, the autoencoder offers a way to reduce the dimensionality of the data and slightly boost predictive power.


A Phenomenological Approach to Artificial Jazz Improvisation PICS


title: The FERET evaluation methodology for face-recognition algorithms

abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1199 individuals are included in the FERET database, which is divided into development and sequestered portions. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to (1) assess the state of the art, (2) identify future areas of research, and (3) measure algorithm performance on large databases.


title: Learning to Segment Instances in Videos with Spatial Propagation Network

abstract: We propose a deep learning-based framework for instance-level object segmentation. Our method mainly consists of three steps. First, We train a generic model based on ResNet-101 for foreground/background segmentations. Second, based on this generic model, we fine-tune it to learn instance-level models and segment individual objects by using augmented object annotations in first frames of test videos. To distinguish different instances in the same video, we compute a pixel-level score map for each object from these instance-level models. Each score map indicates the objectness likelihood and is only computed within the foreground mask obtained in the first step. To further refine this per frame score map, we learn a spatial propagation network. This network aims to learn how to propagate a coarse segmentation mask spatially based on the pairwise similarities in each frame. In addition, we apply a filter on the refined score map that aims to recognize the best connected region using spatial and temporal consistencies in the video. Finally, we decide the instance-level object segmentation in each video by comparing score maps of different instances.


title: A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving

abstract: We consider the task of accurately controlling a complex system, such as autonomously sliding a car sideways into a parking spot. Although certain regions of this domain are extremely hard to model (i.e., the dynamics of the car while skidding), we observe that in practice such systems are often remarkably deterministic over short periods of time, even in difficult-to-model regions. Motivated by this intuition, we develop a probabilistic method for combining closed-loop control in the well-modeled regions and open-loop control in the difficult-to-model regions. In particular, we show that by combining 1) an inaccurate model of the system and 2) a demonstration of the desired behavior, our approach can accurately and robustly control highly challenging systems, without the need to explicitly model the dynamics in the most complex regions and without the need to hand-tune the switching control law. We apply our approach to the task of autonomous sideways sliding into a parking spot, and show that we can repeatedly and accurately control the system, placing the car within about 2 feet of the desired location; to the best of our knowledge, this represents the state of the art in terms of accurately controlling a vehicle in such a maneuver.


title: Speaker independent emotion recognition based on SVM/HMMS fusion system

abstract: Speech emotion recognition as a significant part has become a challenge to artificial emotion. It is particularly difficult to recognize emotion independent of the person concentrating on the speech channel. In the paper, an integrated system of hidden Markov model (HMM) and support vector machine (SVM), which combining advantages on capability to dynamic time warping of HMM and pattern recognition of SVM, has been proposed to implement speaker independent emotion classification. Firstly, all emotions are divided into two groups by SVM. Then, HMMs are used to discriminate emotions from each group. For a more robust estimation, we also combine four HMMs classifiers into a system. The recognition result of the fusion system has been compared with the isolated HMMs using Mandarin database. Experimental results demonstrate that comparing with the method based on only HMMs, the proposed system is more effective and the average recognition rate reaches 76.1% when speaker is independent.