title: Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings

abstract: This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology.


title: EcoMark: evaluating models of vehicular environmental impact

abstract: The reduction of greenhouse gas (GHG) emissions from transportation is essential for achieving politically agreed upon emissions reduction targets that aim to combat global climate change. So-called eco-routing and eco-driving are able to substantially reduce GHG emissions caused by vehicular transportation. To enable these, it is necessary to be able to reliably quantify the emissions of vehicles as they travel in a spatial network. Thus, a number of models have been proposed that aim to quantify the emissions of a vehicle based on GPS data from the vehicle and a 3D model of the spatial network the vehicle travels in. We develop an evaluation framework, called EcoMark, for such environmental impact models. In addition, we survey all eleven state-of-the-art impact models known to us. To gain insight into the capabilities of the models and to understand the effectiveness of the EcoMark, we apply the framework to all models.


title: 75–85 GHz flip-chip phased array RFIC with simultaneous 8-transmit and 8-receive paths for automotive radar applications

abstract: This paper presents the first simultaneous 8-transmit and 8-receive paths 75-85 GHz phased array RFIC for FMCW automotive radars. The receive path has two separate I/Q mixers each connected to 4-element phased arrays for RF and digital beamforming. The chip also contains a build-in-self-test system (BIST) for the transmit and receive paths. Measurements on a flip-chip prototype show a gain >24 dB at 77 GHz, -25 dB coupling between adjacent channels in the transmit and receive paths (<;-45 dB between non-adjacent channels), and <;-50 dB coupling between the transmit and receive portions of the chip.


title: Personality Types Classification for Indonesian Text in Partners Searching Website Using Naïve Bayes Methods

abstract: The development of digital text information has been growing fast, but most of digital text is in unstructured form. Text mining analysis is needed in dealing with such unstructured text. One of the activities important in text mining is text classification or categorization. Text categorization itself currently has a variety of approaches such as probabilistic approaches, support vector machines, and artificial neural network or decision tree classification. Naive Bayes probabilistic method has several advantages of simplicity in computing. Naive Bayes method is a good method in machine learning based on training data using conditional probability as the basic. This experiments use text mining with Naive Bayes method to classify the personality type of user and use the type to find their couples based on the compatibility of their personality type.


title: Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks

abstract: With the development of Earth observation programs, more and more multi-temporal synthetic aperture radar (SAR) data are available from remote sensing platforms. Therefore, it is demanding to develop unsupervised methods for SAR image change detection. Recently, deep learning-based methods have displayed promising performance for remote sensing image analysis. However, these methods can only provide excellent performance when the number of training samples is sufficiently large. In this paper, a novel simple method for SAR image change detection is proposed. The proposed method uses two singular value decomposition (SVD) analyses to learn the non-linear relations between multi-temporal images. By this means, the proposed method can generate more representative feature expressions with fewer samples. Therefore, it provides a simple yet effective way to be designed and trained easily. Firstly, deep semi-nonnegative matrix factorization (Deep Semi-NMF) is utilized to select pixels that have a high probability of being changed or unchanged as samples. Next, image patches centered at these sample pixels are generated from the input multi-temporal SAR images. Then, we build SVD networks, which are comprised of two SVD convolutional layers and one histogram feature generation layer. Finally, pixels in both multi-temporal SAR images are classified by the SVD networks, and then the final change map can be obtained. The experimental results of three SAR datasets have demonstrated the effectiveness and robustness of the proposed method.


title: A new monotonic, clone-independent, reversal symmetric, and condorcet-consistent single-winner election method

abstract: In recent years, the Pirate Party of Sweden, the Wikimedia Foundation, the Debian project, the “Software in the Public Interest” project, the Gentoo project, and many other private organizations adopted a new single-winner election method for internal elections and referendums. In this article, we will introduce this method, demonstrate that it satisfies, e.g., resolvability, Condorcet, Pareto, reversal symmetry, monotonicity, and independence of clones and present an O(C^3) algorithm to calculate the winner, where C is the number of alternatives.


Principal Component Analysis


title: Survey on Reliability of Power Electronic Systems

abstract: With wide-spread application of power electronic systems across many different industries, their reliability is being studied extensively. This paper presents a comprehensive review of reliability assessment and improvement of power electronic systems from three levels: 1) metrics and methodologies of reliability assessment of existing system; 2) reliability improvement of existing system by means of algorithmic solutions without change of the hardware; and 3) reliability-oriented design solutions that are based on fault-tolerant operation of the overall systems. The intent of this review is to provide a clear picture of the landscape of reliability research in power electronics. The limitations of the current research have been identified and the direction for future research is suggested.


title: Implementing open innovation concept in social business

abstract: Social business is an emerging concept which requires additional research especially in terms of priority management tools that can be used. The paper aims to evaluate how closed and open innovation concepts are implemented by social enterprises in the emerging information economy. The paper also studies social enterprises from the point of view of joint efficiency and innovation development concept evaluation to define best practices used by social entrepreneurs nowadays. The paper provides comparative analysis of two types of social business models: the ones based on open innovation and the ones based on formal closed innovative process. Research question is to estimate how implementation of open innovation influences social businesses’ outcomes, and to define the way open innovation influences social enterprises’ efficiency.


title: Competitive on-line coverage of grid environments by a mobile robot

abstract: We describe in this paper two on-line algorithms for covering planar areas by a square-shaped tool attached to a mobile robot. Let D be the tool size. The algorithms, called Spanning Tree Covering (STC) algorithms, incrementally subdivide the planar area into a grid of D-size cells, while following a spanning tree of a grid graph whose nodes are 2D-size cells. The two STC algorithms cover general planar grids. The first, Spiral-STC, employs uniform weights on the grid-graph edges and generates spiral-like covering patterns. The second, Scan-STC , assigns lower weights to edges aligned with a particular direction and generates scan-like covering patterns along this direction. Both algorithms cover any planar grid using a path whose length is at most (n + m)D, where n is the total number of D-size cells and m ≤ n is the number of boundary cells, defined as cells that share at least one point with the grid boundary. We also demonstrate that any on-line coverage algorithm generates a covering path whose length is at least (2 - e)lopt in worst case, where lopt is the length of the optimal off-line covering path. Since (n + m)D ≤ 2lopt, the bound is tight and the STC algorithms are worst-case optimal. Moreover, in practical environments m || n, and the STC algorithms generate close-to-optimal covering paths in such environments.


title: Achieving dependability in software-defined networking — A perspective

abstract: In this paper we take a closer look at the operation of software defined networking (SDN) in intra-domain networks. The focus is on the dependability issues related to interworking of SDN controllers, network OS (NOS), and forwarding in the data plane. Both the separation of the control and data planes, and the (virtually) centralized control processes, are challenging from a dependability perspective. In particular, consistency in operation and information is a challenge, both between the control and data planes, but also within a given plane. To ensure the necessary level of consistency there could be a conflict with the strict realtime requirements given by the per-flow operation of the SDN controller. A principle system model is introduced to discuss the consistency challenge, and to point out undesirable cyclic dependencies between functions that are necessary to configure and operate SDN. The separation of control processing and forwarding do also introduce structural vulnerabilities, which are exemplified.


title: MPC for tracking piecewise constant references for constrained linear systems

abstract: In this paper, a novel model predictive control (MPC) for constrained (non-square) linear systems to track piecewise constant references is presented. This controller ensures constraint satisfaction and asymptotic evolution of the system to any target which is an admissible steady-state. Therefore, any sequence of piecewise admissible setpoints can be tracked without error. If the target steady state is not admissible, the controller steers the system to the closest admissible steady state. These objectives are achieved by: (i) adding an artificial steady state and input as decision variables, (ii) using a modified cost function to penalize the distance from the artificial to the target steady state (iii) considering an extended terminal constraint based on the notion of invariant set for tracking. The control law is derived from the solution of a single quadratic programming problem which is feasible for any target. Furthermore, the proposed controller provides a larger domain of attraction (for a given control horizon) than the standard MPC and can be explicitly computed by means of multiparametric programming tools. On the other hand, the extra degrees of freedom added to the MPC may cause a loss of optimality that can be arbitrarily reduced by an appropriate weighting of the offset cost term.


title: An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks

abstract: In this paper, we propose a novel Intrusion Detection System (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid Intrusion Detection System architecture consists of an anomaly detection module, a misuse detection module and a decision support system combining the results of these two detection modules. The proposed anomaly detection module uses a Self-Organizing Map (SOM) structure to model normal behavior. Deviation from the normal behavior is classified as an attack. The proposed misuse detection module uses J.48 decision tree algorithm to classify various types of attacks. The principle interest of this work is to benchmark the performance of the proposed hybrid IDS architecture by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. A rule-based Decision Support System (DSS) is also developed for interpreting the results of both anomaly and misuse detection modules. Simulation results of both anomaly and misuse detection modules based on the KDD 99 Data Set are given. It is observed that the proposed hybrid approach gives better performance over individual approaches.


title: Adversarial Deep Structural Networks for Mammographic Mass Segmentation

abstract: Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model potential function, followed by a CRF to perform structural learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with position priori for the task. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.


title: Genetic Algorithm Performance with Different Selection Strategies in Solving TSP

abstract: A genetic algorithm (GA) has several genetic operators that can be modified to improve the performance of particular implementations. These operators include parent selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection. This paper presents the comparison of GA performance in solving travelling salesman problem (TSP) using different parent selection strategy. Several TSP instances were tested and the results show that tournament selection strategy outperformed proportional roulette wheel and rank- based roulette wheel selections, achieving best solution quality with low computing times. Results also reveal that tournament and proportional roulette wheel can be superior to the rank- based roulette wheel selection for smaller problems only and become susceptible to premature convergence as problem size increases.


title: First-Order Theorem Proving and Vampire

abstract: In this paper we give a short introduction in first-order theorem proving and the use of the theorem prover Vampire. We discuss the superposition calculus and explain the key concepts of saturation and redundancy elimination, present saturation algorithms and preprocessing, and demonstrate how these concepts are implemented in Vampire. Further, we also cover more recent topics and features of Vampire designed for advanced applications, including satisfiability checking, theory reasoning, interpolation, consequence elimination, and program analysis.


title: Low-Volume PFC Rectifier Based on Nonsymmetric Multilevel Boost Converter

abstract: This paper introduces a digitally controlled single-phase rectifier with power factor correction (PFC) based on a modified three-level boost converter topology. In comparison with the conventional boost-based systems, the new PFC rectifier has about three times smaller inductor and significantly lower switching losses. The improvements are achieved by replacing the output capacitor of the boost converter with a nonsymmetric active capacitive divider, with a 3:1 division ratio, and by utilizing the downstream converter stage for the capacitive divider's center-tap voltage regulation. The nonsymmetric voltage divider and applied switching sequence effectively provide four-level converter behavior using the same number of components as three-level converters. As a result, a 66% reduction of the inductor compared to the conventional boost-based PFC and a 33% compared to the standard three-level solutions operating at the same effective switching frequencies are also achieved. Experimental results obtained with a 400 W, 200 kHz, universal input voltage (85 V ::: rms ::: -265 V ::: rms ::: ) PFC prototype demonstrate three times smaller inductor current ripple than that of the conventional boost converter allowing for the same inductor reduction. Efficiency improvements of up to a 6% are also demonstrated.


title: Learning to Rank Microblog Posts for Real-Time Ad-Hoc Search

abstract: Microblogging websites have emerged to the center of information production and diffusion, on which people can get useful information from other users' microblog posts. In the era of Big Data, we are overwhelmed by the large amount of microblog posts. To make good use of these informative data, an effective search tool is required specialized for microblog posts. However, it is not trivial to do microblog search due to the following reasons: 1 microblog posts are noisy and time-sensitive rendering general information retrieval models ineffective. 2 Conventional IR models are not designed to consider microblog-specific features. In this paper, we propose to utilize learning to rank model for microblog search. We combine content-based, microblog-specific and temporal features into learning to rank models, which are found to model microblog posts effectively. To study the performance of learning to rank models, we evaluate our models using tweet data set provided by TERC 2011 and TREC 2012 microblogs track with the comparison of three state-of-the-art information retrieval baselines, vector space model, language model, BM25 model. Extensive experimental studies demonstrate the effectiveness of learning to rank models and the usefulness to integrate microblog-specific and temporal information for microblog search task.


title: GPS-free positioning in mobile ad-hoc networks

abstract: We consider the problem of node positioning in ad-hoc networks. We propose a distributed, infrastructure-free positioning algorithm that does not rely on Global Positioning System (GPS). The algorithm uses the distances between the nodes to build a relative coordinate system in which the node positions are computed in two dimensions. The main contribution of this work is to define and compute relative positions of the nodes in an ad-hoc network without using GPS. We further explain how the proposed approach can be applied to wide area ad-hoc networks.


title: 2.4-GHz 0.18-µm CMOS highly linear Power Amplifier

abstract: A Class-AB Power Amplifier (PA) integrated circuit for 2.4GHz is presented. It is designed in SMIC 0.18µm RF CMOS process. The PA adopts two-stage differential structure. The driver-stage uses cascode structure. In the output-stage common-source structure is employed. The proposed PA provides 24.8dBm output power with a power-added efficiency (PAE) of 21.2% at 1dB compression point. It has a small signal gain of 20.6dB. The simulation results show that under a single 3.3V supply voltage, the maximum output power reaches to 26.2dBm. It can be used in IEEE 802.11b/g WLAN. The layout size is 1.4×0.75mm2.


title: A review of the application of data mining techniques for decision making in agriculture

abstract: This paper provides a review of research on the application of data mining techniques for decision making in agriculture. The paper reports the application of a number of data mining techniques including artificial neural networks, Bayesian networks and support vector machines. The review has outlined a number of promising techniques that have been used to understand the relationships of various climate and other factors on crop production. This review proposes that further investigations are needed to understand how these techniques can be used with complex agricultural datasets for crop yield prediction integrating seasonal and spatial factors by using GIS technologies.


title: Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

abstract: A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we close the problem of computationally and sample efficient learning in stochastic combinatorial semi-bandits. In particular, we analyze a UCB-like algorithm for solving the problem, which is known to be computationally efficient; and prove $O(K L (1 / \Delta) \log n)$ and $O(\sqrt{K L n \log n})$ upper bounds on its $n$-step regret, where $L$ is the number of ground items, $K$ is the maximum number of chosen items, and $\Delta$ is the gap between the expected returns of the optimal and best suboptimal solutions. The gap-dependent bound is tight up to a constant factor and the gap-free bound is tight up to a polylogarithmic factor.


Word embedding evaluation and combination


title: Expanding computer science education in schools: understanding teacher experiences and challenges

abstract: AbstractThe increased push for teaching computer science (CS) in schools in the United States requires training a large number of new K-12 teachers. The current efforts to increase the number of CS teachers have predominantly focused on training teachers from other content areas. In order to support these beginning CS teachers, we need to better understand their experiences and challenges encountered in the classroom. This study investigated U.S. CS teachers’ perspectives on the demands of teaching computer science and support needed to ensure quality teaching. Results suggested that teachers face a number of challenges, including isolation, lack of adequate computer science background, and limited professional development resources.


title: A New View of Predictive State Methods for Dynamical System Learning

abstract: Recently there has been substantial interest in predictive state methods for learning dynamical systems: these algorithms are popular since they often offer a good tradeoff between computational speed and statistical efficiency. Despite their desirable properties, though, predictive state methods can sometimes be difficult to use in practice. E.g., in contrast to the rich literature on supervised learning methods, which allows us to choose from an extensive menu of models and algorithms to suit the prior beliefs we have about properties of the function to be learned, predictive state dynamical system learning methods are comparatively inflexible: it is as if we were restricted to use only linear regression instead of being allowed to choose decision trees, nonparametric regression, or the lasso. To address this problem, we propose a new view of predictive state methods in terms of instrumentalvariable regression. This view allows us to construct a wide variety of dynamical system learners simply by swapping in different supervised learning methods. We demonstrate the effectiveness of our proposed methods by experimenting with non-linear regression to learn a hidden Markov model, showing that the resulting algorithm outperforms its linear counterpart; the correctness of this algorithm follows directly from our general analysis.


title: Brain structural concomitants of resting state heart rate variability in the young and old: evidence from two independent samples

abstract: Previous research has shown associations between brain structure and resting state high-frequency heart rate variability (HF HRV). Age affects both brain structure and HF HRV. Therefore, we sought to examine the relationship between brain structure and HF HRV as a function of age. Data from two independent studies were used for the present analysis. Study 1 included 19 older adults (10 males, age range 62–78 years) and 19 younger adults (12 males, age range 19–37). Study 2 included 23 older adults (12 males; age range 55–75) and 27 younger adults (17 males; age range 18–34). The root-mean-square of successive R–R-interval differences (RMSSD) from ECG recordings was used as time-domain measure of HF HRV. MRI scans were performed on a 3.0-T Siemens Magnetom Trio scanner. Cortical reconstruction and volumetric segmentation were performed with the Freesurfer image analysis suite, including 12 regions as regions of interests (ROI). Zero-order and partial correlations were used to assess the correlation of RMSSD with cortical thickness in selected ROIs. Lateral orbitofrontal cortex (OFC) cortical thickness was significantly associated with RMSSD. Further, both studies, in line with previous research, showed correlations between RMSSD and anterior cingulate cortex (ACC) cortical thickness. Meta-analysis on adjusted correlation coefficients from individual studies confirmed an association of RMSSD with the left rostral ACC and the left lateral OFC. Future longitudinal studies are necessary to trace individual trajectories in the association of HRV and brain structure across aging.


title: Evaluation of Japanese Agricultural Policy Reforms Under the WTO Agreement on Agriculture

abstract: Until 2009, Japan’s attitude towards domestic agricultural policy reforms had been one of compliance with the guidelines provided by the WTO, which requests that member countries reduce their aggregate measures of support by trimming trade-distorting (amber box) support and/or transforming traditional agricultural subsidies to decoupled ones. However, in 2010, Japan repealed the 2007 reforms and implemented a new direct payment program called the Income Compensation Program, which has led to obvious trade-distorting effects. The present paper provides a comprehensive picture of how and why Japanese agricultural policy has changed in recent decades.


title: Defend Deep Neural Networks Against Adversarial Examples via Fixed and Dynamic Quantized Activation Functions

abstract: Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks. To this end, many defense approaches that attempt to improve the robustness of DNNs have been proposed. In a separate and yet related area, recent works have explored to quantize neural network weights and activation functions into low bit-width to compress model size and reduce computational complexity. In this work, we find that these two different tracks, namely the pursuit of network compactness and robustness, can be merged into one and give rise to networks of both advantages. To the best of our knowledge, this is the first work that uses quantization of activation functions to defend against adversarial examples. We also propose to train robust neural networks by using adaptive quantization techniques for the activation functions. Our proposed Dynamic Quantized Activation (DQA) is verified through a wide range of experiments with the MNIST and CIFAR-10 datasets under different white-box attack methods, including FGSM, PGD, and C&W attacks. Furthermore, Zeroth Order Optimization and substitute model-based black-box attacks are also considered in this work. The experimental results clearly show that the robustness of DNNs could be greatly improved using the proposed DQA.


title: ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

abstract: In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of $1/30th$ of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation is edge-preserving and smooths the loss function, which is key to allow the network to reach compelling results. Finally we show how the task of predicting invalid regions, such as occlusions, can be trained end-to-end without ground-truth. This component is crucial to reduce blur and particularly improves predictions along depth discontinuities. Extensive quantitatively and qualitatively evaluations on real and synthetic data demonstrate state of the art results in many challenging scenes.


title: CloudRanger: Root Cause Identification for Cloud Native Systems

abstract: As more and more systems are migrating to cloud environment, the cloud native system becomes a trend. This paper presents the challenges and implications when diagnosing root causes for cloud native systems by analyzing some real incidents occurred in IBM Bluemix (a large commercial cloud). To tackle these challenges, we propose CloudRanger, a novel system dedicated for cloud native systems. To make our system more general, we propose a dynamic causal relationship analysis approach to construct impact graphs amongst applications without given the topology. A heuristic investigation algorithm based on second-order random walk is proposed to identify the culprit services which are responsible for cloud incidents. Experimental results in both simulation environment and IBM Bluemix platform show that CloudRanger outperforms some state-of-the-art approaches with a 10% improvement in accuracy. It offers a fast identification of culprit services when an anomaly occurs. Moreover, this system can be deployed rapidly and easily in multiple kinds of cloud native systems without any predefined knowledge.


CORPORATE GOVERNANCE AND CORPORATE SOCIAL RESPONSIB ILITY : A CRITICAL REVIEW


title: Microgrooves and fluid flows provide preferential passageways for sperm over pathogenTritrichomonas foetus

abstract: Successful mammalian reproduction requires that sperm migrate through a long and convoluted female reproductive tract before reaching oocytes. For many years, fertility studies have focused on biochemical and physiological requirements of sperm. Here we show that the biophysical environment of the female reproductive tract critically guides sperm migration, while at the same time preventing the invasion of sexually transmitted pathogens. Using a microfluidic model, we demonstrate that a gentle fluid flow and microgrooves, typically found in the female reproductive tract, synergistically facilitate bull sperm migration toward the site of fertilization. In contrast, a flagellated sexually transmitted bovine pathogen, Tritrichomonas foetus, is swept downstream under the same conditions. We attribute the differential ability of sperm and T. foetus to swim against flow to the distinct motility types of sperm and T. foetus; specifically, sperm swim using a posterior flagellum and are near-surface swimmers, whereas T. foetus swims primarily via three anterior flagella and demonstrates much lower attraction to surfaces. This work highlights the importance of biophysical cues within the female reproductive tract in the reproductive process and provides insight into coevolution of males and females to promote fertilization while suppressing infection. Furthermore, the results provide previously unidentified directions for the development of in vitro fertilization devices and contraceptives.


title: Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

abstract: We consider the problem of improving the efficiency of randomized Fourier feature maps to accelerate training and testing speed of kernel methods on large datasets. These approximate feature maps arise as Monte Carlo approximations to integral representations of shift-invariant kernel functions (e.g., Gaussian kernel). In this paper, we propose to use Quasi-Monte Carlo (QMC) approximations instead, where the relevant integrands are evaluated on a low-discrepancy sequence of points as opposed to random point sets as in the Monte Carlo approach. We derive a new discrepancy measure called box discrepancy based on theoretical characterizations of the integration error with respect to a given sequence. We then propose to learn QMC sequences adapted to our setting based on explicit box discrepancy minimization. Our theoretical analyses are complemented with empirical results that demonstrate the effectiveness of classical and adaptive QMC techniques for this problem.


The largest family of the Americas with dominant dystrophic epidermolysis bullosa pruriginosa: A 18-year longitudinal genotype–phenotype study


title: Real-time precise detection of regular grids and matrix codes

abstract: The traditional approach in detecting sets of concurrent and/or parallel lines is to first detect lines in the image and then find such groups of them which meet the concurrence condition. The Hough Transform can be used for detecting the lines and variants of HT such as the Cascaded Hough Transform can be used to detect the vanishing points. However, these approaches disregard much of the information actually accumulated to the Hough space. This article proposes using the Hough space as a 2D signal instead of just detecting the local maxima and processing them. On the example of QRcode detection, it is shown that this approach is computationally cheap, robust, and accurate. The proposed algorithm can be used for efficient and accurate detection and localization of matrix codes (QRcode, Aztec, DataMatrix, etc.) and chessboard-like calibration patterns.


title: On the Challenges of Building a BFT SCADA

abstract: In the last decade, Industrial Control Systems have been a frequent target of cyber attacks. As the current defenses sometimes fail to prevent more sophisticated threats, it is necessary to add advanced protection mechanisms to guarantee that correct operation is (always) maintained. In this work, we describe a Supervisory Control and Data Acquisition (SCADA) system enhanced with Byzantine fault-tolerant (BFT) techniques. We document the challenges of building such system from a ""traditional"" non-BFT solution. This effort resulted in a prototype that integrates the Eclipse NeoSCADA and the BFT-SMaRt open-source projects. We also present an evaluation comparing Eclipse NeoSCADA with our BFT solution. Although the results show a decrease in performance, our solution is still more than enough to accommodate realistic workloads.


A Comparison of Event Representations in DEFT


title: IT worker turnover: an empirical examination of intrinsic motivation

abstract: This study examined intrinsic motivation's influence on information technology (IT) workers' attitudes and intentions. Drawing on Human Resource management research (Eby & Freeman, 1999), we model Intrinsic motivation as mediating the influence of motivators (i.e., intrinsic job characteristics) and hygiene factors (i.e., pay and supervisory satisfaction) on workplace attitudes (i.e., job satisfaction and affective organizational commitment). In turn, workplace attitudes mediate the influence of intrinsic motivation on turnover intention. The model was tested using data collected from public sector IT workers in the Southeastern United States. Although intrinsic motivation did not fully mediate the influence of motivators and hygiene factors, findings suggest that intrinsic motivation positively influences workplace attitudes and has a mediated influence on turnover intent. Implications for research and practice are offered.


title: A Novel Feature-based Approach to Chinese Entity Relation Extraction

abstract: Relation extraction is the task of finding semantic relations between two entities from text. In this paper, we propose a novel feature-based Chinese relation extraction approach that explicitly defines and explores nine positional structures between two entities. We also suggest some correction and inference mechanisms based on relation hierarchy and co-reference information etc. The approach is effective when evaluated on the ACE 2005 Chinese data set.


title: Cannabis Pharmacology: The Usual Suspects and a Few Promising Leads.

abstract: The golden age of cannabis pharmacology began in the 1960s as Raphael Mechoulam and his colleagues in Israel isolated and synthesized cannabidiol, tetrahydrocannabinol, and other phytocannabinoids. Initially, THC garnered most research interest with sporadic attention to cannabidiol, which has only rekindled in the last 15 years through a demonstration of its remarkably versatile pharmacology and synergy with THC. Gradually a cognizance of the potential of other phytocannabinoids has developed. Contemporaneous assessment of cannabis pharmacology must be even far more inclusive. Medical and recreational consumers alike have long believed in unique attributes of certain cannabis chemovars despite their similarity in cannabinoid profiles. This has focused additional research on the pharmacological contributions of mono- and sesquiterpenoids to the effects of cannabis flower preparations. Investigation reveals these aromatic compounds to contribute modulatory and therapeutic roles in the cannabis entourage far beyond expectations considering their modest concentrations in the plant. Synergistic relationships of the terpenoids to cannabinoids will be highlighted and include many complementary roles to boost therapeutic efficacy in treatment of pain, psychiatric disorders, cancer, and numerous other areas. Additional parts of the cannabis plant provide a wide and distinct variety of other compounds of pharmacological interest, including the triterpenoid friedelin from the roots, canniprene from the fan leaves, cannabisin from seed coats, and cannflavin A from seed sprouts. This chapter will explore the unique attributes of these agents and demonstrate how cannabis may yet fulfil its potential as Mechoulam's professed "pharmacological treasure trove."


title: Phase unwrapping in three dimensions with application to InSAR time series

abstract: The problem of phase unwrapping in two dimensions has been studied extensively in the past two decades, but the three-dimensional (3D) problem has so far received relatively little attention. We develop here a theoretical framework for 3D phase unwrapping and also describe two algorithms for implementation, both of which can be applied to synthetic aperture radar interferometry (InSAR) time series. We test the algorithms on simulated data and find both give more accurate results than a two-dimensional algorithm. When applied to actual InSAR time series, we find good agreement both between the algorithms and with ground truth.


title: Comparing PMBOK and Agile Project Management software development processes

abstract: The objective of this article is to compare a generic set of project management processes as defined in Project Management Body of Knowledge (PMBOK) with a number of agile project management processes. PMBOK is developed by Project Management Institute and it is structured around five process groups (initiating, planning, execution, controlling and closure) and nine knowledge areas (integration management, scope management, time management, cost management, quality management, human resource management, communication management, risk management, procurement management). On the other hand, agile software project management is based on the following principles: embrace change, focus on customer value, deliver part of functionality incrementally, collaborate, reflect and learn continuously. The purpose of this comparison is to identify gaps, differences, discrepancies etc. The result is that, agile project management methodologies cannot be considered complete, from the traditional project management point of view, since a number of processes either are missing or not described explicitly.


title: Preserving Local and Global Information for Network Embedding

abstract: Networks such as social networks, airplane networks, and citation networks are ubiquitous. The adjacency matrix is often adopted to represent a network, which is usually high dimensional and sparse. However, to apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while completely neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. Hence, it also has potential to advance network embedding. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model {\bf LO}cal and {\bf G}lobal information. Experimental results demonstrate the ability to preserve global information of the proposed framework. Further experiments are conducted to demonstrate the effectiveness of learned representations of the proposed framework.


title: Dynamic Nonlinear Inverse-Model Based Control of a Twin Rotor System Using Adaptive Neuro-fuzzy Inference System

abstract: A dynamic control system design has been a great demand in the control engineering community, with many applications particularly in the field of flight control. This paper presents investigations into the development of a dynamic nonlinear inverse-model based control of a twin rotor multi-input multi-output system (TRMS). The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicle. A model inversion control with the developed adaptive model is applied to the system. An adaptive neuro-fuzzy inference system (ANFIS) is augmented with the control system to improve the control response. To demonstrate the applicability of the methods, a simulated hovering motion of the TRMS, derived from experimental data is considered in order to evaluate the tracking properties and robustness capacities of the inverse- model control technique.


title: Review: Dimensionality reduction based on rough set theory: A review

abstract: A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of decision system and it has been applied successfully in all the fields. It is used to identify the reduct set of the set of all attributes of the decision system. The reduct set is used as preprocessing technique for classification of the decision system in order to bring out the potential patterns or association rules or knowledge through data mining techniques. Several researchers have contributed variety of algorithms for computing the reduct sets by considering different cases like inconsistency, missing attribute values and multiple decision attributes of the decision system. This paper focuses on the review of the techniques for dimensionality reduction under rough set theory environment. Further, the rough sets hybridization with fuzzy sets, neural network and metaheuristic algorithms have also been reviewed. The performance analysis of the algorithms has been discussed in connection with the classification.


title: A Bayesian LDA-based model for semi-supervised part-of-speech tagging

abstract: We present a novel Bayesian model for semi-supervised part-of-speech tagging. Our model extends the Latent Dirichlet Allocation model and incorporates the intuition that words' distributions over tags, p(t|w), are sparse. In addition we introduce a model for determining the set of possible tags of a word which captures important dependencies in the ambiguity classes of words. Our model outperforms the best previously proposed model for this task on a standard dataset.


title: A Low-Power Low-Noise mm-Wave Subsampling PLL Using Dual-Step-Mixing ILFD and Tail-Coupling Quadrature Injection-Locked Oscillator for IEEE 802.11ad

abstract: This paper presents a low-power low-noise 60 GHz frequency synthesizer using a 20 GHz subsampling phase-locked loop (SS-PLL) and a 60 GHz tail-coupling quadrature injection-locked oscillator (QILO) which results in a lower in-band phase noise and out-of-band phase noise, respectively. To save battery life, dual-step-mixing injection-locked frequency divider (ILFD) enhances locking range for high division ratio. Moreover, tail cross-coupling technique in a QILO helps boost negative transconductance $({{-g}}_{m})$ of the 60 GHz QILO which allows the use of larger inductance for power reduction. Implemented in 65 nm CMOS, it can cover required channels from 58.32 to 64.80 GHz with quadrature outputs. It consumes 24.2 and 7.8 mW from 20 GHz SS-PLL and QILO, respectively. The proposed synthesizer achieves $-78.5\; \text{dBc/Hz}$ at 100 kHz offset, $-122\text{dBc/Hz}$ at 10 MHz offset, and a figure-of-merit (FoM) of $-236\text{dB}$ .


title: A Low-Power Subthreshold to Above-Threshold Voltage Level Shifter

abstract: This brief presents a power-efficient voltage level-shifter architecture that is capable of converting extremely low levels of input voltages to higher levels. In order to avoid the static power dissipation, the proposed structure uses a current generator that turns on only during the transition times, in which the logic level of the input signal is not corresponding to the output logic level. Moreover, the strength of the pull-up device is decreased when the pull-down device is pulling down the output node in order for the circuit to be functional even for the input voltage lower than the threshold voltage of a MOSFET. The operation of the proposed structure is also analytically investigated. Post-layout simulation results of the proposed structure in a 0.18-μm CMOS technology show that at the input low supply voltage of 0.4 V and the high supply voltage of 1.8 V, the level shifter has a propagation delay of 30 ns, a static power dissipation of 130 pW, and an energy per transition of 327 fJ for a 1-MHz input signal.


title: Camera calibration methods evaluation procedure for images rectification and 3D reconstruction

abstract: Camera calibration and images rectification are two necessary steps in most 3D reconstruction methods using image acquisition. This paper proposes an evaluation procedure for camera calibration methods for the case of 3D reconstruction using rectified multi-stereo images. The evaluation is based on the accuracy of the rectification and of the 3D reconstruction which are directly related to the calibration precision. Three methods are thus compared: Faugeras-Toscani, Zhang and a robust calibration algorithm. The procedure can be applied for computer vision systems with an arbitrary number of cameras and for any other calibration method. We show that, although the three methods provide significantly different intrinsic and stereo system parameter estimations, the rectified images of the planar target that we use for evaluation are relatively coherent and lead to close 3D reconstruction errors


title: Layer Recurrent Neural Networks

abstract: In this paper, we propose a Layer-RNN (L-RNN) module that is able to learn contextual information adaptively using within-layer recurrence. Our contributions are three-fold: ::: (i) we propose a hybrid neural network architecture that interleaves traditional convolutional layers with L-RNN module for learning long- range dependencies at multiple levels; ::: (ii) we show that a L-RNN module can be seamlessly inserted into any convolutional layer of a pre-trained CNN, and the entire network then fine-tuned, leading to a boost in performance; ::: (iii) we report experiments on the CIFAR-10 classification task, showing that a network with interleaved convolutional layers and L-RNN modules, achieves comparable results (5.39% top1 error) using only 15 layers and fewer parameters to ResNet-164 (5.46%); and on the PASCAL VOC2012 semantic segmentation task, we show that the performance of a pre-trained FCN network can be boosted by 5% (mean IOU) by simply inserting Layer-RNNs.


title: Reducing leakage flux in IPT systems by modifying pad ferrite structures

abstract: Recent multicoil polarised pads called Double D pads (DDP) and Bipolar Pads (BPP) show excellent promise when used in lumped charging due to having single sided fields and high native Q factors. However, improvements to field leakage are desired to enable higher power transfer while keeping the leakage flux within ICNIRP levels. This paper proposes a method to reduce the leakage flux which a lumped inductive power transfer (IPT) system exhibits by modifying the ferrite structure of its pads. The DDP and BPP pads ferrite structures are both modified by extending them past the ends of the coils in each pad with the intention of attracting only magnetic flux generated by the primary pad not coupled onto the secondary pad. Simulated improved ferrite structures are validated through practical measurements.


title: Data Collection Challenges and Recommendations for Early Career Researchers

abstract: Data collection is critical to the social research process. When implemented correctly, data collection enhances the quality of a social research study. However, doctoral students and early career researchers may encounter challenges with data collection. This article reports on the data collection challenges in dissertation research encountered by doctoral students enrolled in a public health program at a southeastern United States urban university. Each doctoral student shared at least one challenge and how it affected the data collection process. Additionally, the doctoral students shared how the identified challenges were addressed or suggested recommendations. Understanding these experiences of doctoral students is helpful for doctoral students and early career researchers conducting social research. The lessons learned may guide faculty in research mentoring and structuring research seminars for doctoral students. Keywords: Data Collection, Early Career Researchers, Doctoral Students, Qualitative MethodsThe authors of this manuscript completed a doctoral program in public health within the past 5 years. As part of the doctoral program, the authors were required to collect primary data while conducting dissertation research. In this manuscript, the authors share the identified challenges encountered during the data collection process of conducting dissertation research. Data collection is the first stage in the research process. Depending on the data collection method, researchers could encounter challenges with obtaining information from participants in a study. Although knowledgeable about data collection methods, theory, tips, and challenges, the authors encountered unanticipated real time situations that served as challenges. The authors also share recommendations to streamline the data collection process in an effort to inform doctoral students and early career professionals engaged in data collection.Limited information on the challenges of data collection is available in the public health and social research literature. A search of the literature on the topics of data collection challenges encountered by early career researchers yielded few studies (Ashton, 2014; Dearnley, 2005; Nicholl, 2010). Based on the dearth of studies in the literature, there is a need to share and report the data collection challenges of public health doctoral students. This knowledge can assist doctoral students and early career researchers when facing data collection challenges in the future. This topic is helpful and relevant given that many doctoral students collect data on limited budgets. The primary purpose of this manuscript is to describe data collection challenges encountered by public health doctoral students. The secondary purpose is to provide recommendations to doctoral students and early career researchers on strategies to address these identified data collection challenges.Data collection is the first stage in the research process. Data collection, usually occurring simultaneously with data analysis in qualitative research, is defined as the systematic gathering of data for a particular purpose from various sources, including, interviews, focus groups, observation, existing records, and electronic devices. Depending on the data collection method, researchers could encounter challenges with obtaining information from participants in a study. Early career researchers including doctoral students may face unanticipated challenges during the data collection phase of their dissertation research for numerous reasons. The challenges encountered during the data collection process can involve research with human subjects related to interviews or focus groups.Literature ReviewOur search of data collection challenges in the literature were organized into the following categories:1) researcher,2) participant,3) data collection environment, and4) interview guide design.Manuscripts were found in the nursing, public health, social science, and methodology literature (Bonevski et al. …


V Jornadas en Tecnología del Habla iATROS: A SPEECH AND HANDWRITING RECOGNITION SYSTEM


title: Cloud Computing Adoption by Higher Education Institutions in Saudi Arabia: Analysis Based on TOE

abstract: (1) Background, Motivation and Objective: Academic study of Cloud Computing within Saudi Arabia is an emerging research field. Saudi Arabia represents the largest economy in the Arabian Gulf region. This positions it as a potential market of cloud computing technologies. Adoption of new innovations should be preceded by analysis of the added value, challenges and adequacy from technological, organizational and environmental perspectives. (2) Statement of Contribution/Method: This cross-sectional exploratory empirical research is based on Technology, Organization and Environment model targeting higher education institutions. In this study, the factors that influence the adoption by higher education institutions were analyzed and tested using Partial Least Square. (3) Results, Discussion and Conclusions: Three factors were found significant in this context. Relative Advantage, Data Privacy and Complexity are the most significant factors. The model explained 43% of the total adoption measure variation. Significant differences in the areas of cloud computing compatibility, complexity, vendor lock-in and peer pressure between large and small institutions were revealed. Items for future cloud computing research were explored through open-ended questions. Adoption of cloud services by higher education institutions has been started. It was found that the adoption rate among large universities is higher than small higher education institutions. Improving the network and Internet Infrastructure in Saudi Arabia at an affordable cost is a pre-requisite for cloud computing adoption. Cloud service provider should address the privacy and complexity concerns raised by non-adopters. Future information systems that are potential for hosting in cloud were prioritized.


title: Abstracting log lines to log event types for mining software system logs

abstract: Log files contain valuable information about the execution of a system. This information is often used for debugging, operational profiling, finding anomalies, detecting security threats, measuring performance etc. The log files are usually too big for extracting this valuable information manually, even though manual perusal is still one of the more widely used techniques. Recently a variety of data mining and machine learning algorithms are being used to analyze the information in the log files. A major road block for the efficient use of these algorithms is the inherent variability present in every log line of a log file. Each log line is a combination of a static message type field and a variable parameter field. Even though both these fields are required, the analyses algorithm often requires that these be separated out, in order to find correlations in the repeating log event types. This disentangling of the message and parameter fields to find the event types is called abstraction of log lines. Each log line is abstracted to a unique ID or event type and the dynamic parameter value is extracted to give an insight on the current state of the system. In this paper we present a technique based on a clustering technique used in the Simple Log file Clustering Tool for log file abstraction. This solution is especially useful when we don't have access to the source code of the application or when the lines in the log file do not conform to a rigid structure. We evaluated our implementation on log files from the Virtual Computing Lab, a cloud computer management system at North Carolina State University, and abstracted it to 727 unique event types.


title: Ontology-based semantic similarity: A new feature-based approach

abstract: Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach's results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.


title: Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations

abstract: With unconstrained data acquisition scenarios widely prevalent, the ability to handle changes in data distribution across training and testing data sets becomes important. One way to approach this problem is through domain adaptation, and in this paper we primarily focus on the unsupervised scenario where the labeled source domain training data is accompanied by unlabeled target domain test data. We present a two-stage data-driven approach by generating intermediate data representations that could provide relevant information on the domain shift. Starting with a linear representation of domains in the form of generative subspaces of same dimensions for the source and target domains, we first utilize the underlying geometry of the space of these subspaces, the Grassmann manifold, to obtain a `shortest' geodesic path between the two domains. We then sample points along the geodesic to obtain intermediate cross-domain data representations, using which a discriminative classifier is learnt to estimate the labels of the target data. We subsequently incorporate non-linear representation of domains by considering a Reproducing Kernel Hilbert Space representation, and a low-dimensional manifold representation using Laplacian Eigenmaps, and also examine other domain adaptation settings such as (i) semi-supervised adaptation where the target domain is partially labeled, and (ii) multi-domain adaptation where there could be more than one domain in source and/or target data sets. Finally, we supplement our adaptation technique with (i) fine-grained reference domains that are created by blending samples from source and target data sets to provide some evidence on the actual domain shift, and (ii) a multi-class boosting analysis to obtain robustness to the choice of algorithm parameters. We evaluate our approach for object recognition problems and report competitive results on two widely used Office and Bing adaptation data sets.


title: Performance prediction of a wheeled vehicle on unknown terrain using identified soil parameters

abstract: This paper presents a novel technique for identifying soil parameters for a wheeled vehicle travelling on an unknown terrain. The identified soil parameters are required for predicting vehicle drawbar pull and wheel drive torque which can be employed for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The Newton Raphson method is used as the identification technique applied on the modified form of the wheel-soil interaction dynamics model using the composite Simpson's rule. This work focuses on identifying the internal friction angle, the shear deformation modulus, and the lumped pressure-sinkage coefficient. The fourth parameter, cohesion, does not influence the vehicle drawbar pull and is assigned an average value during the identification process. In an experimental study, the identified parameters are compared with known values, and shown to be in good agreement. Soil parameter identification can be carried out on-line and thus our approach is suitable for real-time applications. The robustness of the method is also shown to be relatively good. The identified soil parameters can be used to predict drawbar pull and wheel drive torque with good accuracy


title: Universality and complexity in cellular automata

abstract: Abstract Cellular automata are discrete dynamical systems with simple construction but complex self-organizing behaviour. Evidence is presented that all one-dimensional cellular automata fall into four distinct universality classes. Characterizations of the structures generated in these classes are discussed. Three classes exhibit behaviour analogous to limit points, limit cycles and chaotic attractors. The fourth class is probably capable of universal computation, so that properties of its infinite time behaviour are undecidable.


Sentiment Analysis : A Multi-Faceted Problem


title: Asymmetric biocatalysis of the nerve agent VX by human serum paraoxonase 1: molecular docking and reaction mechanism calculations

abstract: Organophosphorus compounds have been employed in agricultural activity for a long time, causing serious public health problems. Due to their toxic properties, these compounds have also been used as chemical weapons. In view of this scenario, the catalytic degradation and the development of bioremediation processes of organophosphorus compounds have been of wide interest. Among several enzymes capable of degrading organophosphorus compounds, the human serum paraoxonase 1 has shown good potential for this purpose. To evaluate the interaction mode between the human serum paraoxonase 1 (wild-type and mutants) enzymes and the VX compound, one of the most toxic organophosphorus compounds known, molecular docking calculations were conducted. In addition, seeking to analyze the reaction pathway and the stereochemistry preference by human serum paraoxonase 1 and the R p and S p enantiomers of VX, quantum mechanical/molecular mechanics calculations were performed. Our theoretical findings put in evidence that the wild-type and mutant human serum paraoxonase 1 enzymes strongly interact with VX. Moreover, with the quantum mechanical/molecular mechanics study, we observed that the human serum paraoxonase 1 preferentially degrades one enantiomer in relation to the other. The current results indicate key points for designing new, more efficient mutant human serum paraoxonase 1 enzymes for VX degradation.


ERP and Knowledge Management Integration: The Case of Malaysian Business Firms.


title: Blood and Violence: Exploring the Impact of Gore in Violent Video Games

abstract: Violence is a key element in video games and despite the extensive research in video game violence, there is still a debate on its psychophysiological effect. There is a lack of understanding on the elements of video game violence that influence aggression. The present pilot study examines the effect of blood and gore in a first-person shooter with participants playing in one of the two conditions, with or without blood and gore. To assess the effect of blood and gore, and thus violence, a number of elements were measured including physical arousal, individual differences, personality, and game experience. The results suggested that blood and gore had no effect on aggression-related associations and cognitive processes. The findings did suggest that previous game experience had a significant effect on increased physiological arousal when engaging in violent content featuring blood and gore. Furthermore, a personality trend emerged showing an effect on arousal and cognitive processes. As the study was preliminary, definitive conclusions cannot be drawn, however the findings warrant further investigation.


title: SAR Polarimetry to Observe Oil Spills

abstract: A study on sea oil spill observation by means of polarimetric synthetic aperture radar (SAR) data is accomplished. It is based on the use of a polarimetric constant false alarm rate filter to detect dark patches over SAR images. Then, the target decomposition theorem is exploited to distinguish oil spills and look-alikes. Experiments are conducted on polarimetric SAR data acquired during the SIR-C/X-SAR mission on October 1994. The data were processed and calibrated at the Jet Propulsion Laboratory, National Aeronautics and Space Administration. Results show that the new polarimetric approach is able to assist classification


title: Building a Virtual HPC Cluster with Auto Scaling by the Docker

abstract: Solving the software dependency issue under the HPC environment has always been a difficult task for both computing system administrators and application scientists. This work would like to tackle the issue by introducing the modern container technology, the Docker, to be specific. By integrating the auto-scaling feature of service discovery with the light-weight virtualization tool, the Docker, the construction of a virtual cluster on top of physical cluster hardware is attempted. Thus, through the isolation of computing environment, a remedy of software dependency of HPC environment is possible.


title: Electromagnetic shielding for WLAN using modified-Hilbert fractals

abstract: A novel modified-design of Hilbert curve is presented, which has stable frequency response for both TE and TM polarizations. Furthermore, a frequency selective surface (FSS) based on this modified-Hilbert curve is proposed for shielding against dual-band WLAN. It has an insertion loss of more than 15 dB with the bandwidths of 220 MHz at 2.45 GHz and 160 MHz at 5.78 GHz. The proposed FSS provides an excellent stable performance for both TE and TM polarizations under the incident angles of 0° through 60°. In addition, the size of unit-cell is only 0.082λ 0 ×0.082λ 0 , whereλ 0 represents the free-space wavelength.


The abc Group Building the abc AspectJ compiler with Polyglot and Soot abc Technical Report No . abc-2004-4


Acoustic Array Systems : Paper ICA 2016-352 A sound source localization algorithm using microphone array with rigid body


Detecting Linguistic Traces of Depression in Topic-Restricted Text: Attending to Self-Stigmatized Depression with NLP


Effective or Efficient? Effects of Beijing's Vehicle Lottery System on Fleet Composition and Environment


title: Changes in liver perfusion and function before and after percutaneous occlusion of spontaneous portosystemic shunt

abstract: Purpose ::: To evaluate changes in liver perfusion after occlusion of spontaneous portosystemic shunt and to analyze mechanisms of liver profile improvement.


title: Long-Term Effects of PECS on Social-Communicative Skills of Children with Autism Spectrum Disorders: A Follow-Up Study.

abstract: Background: The Picture Exchange Communication System (PECS) is a popular augmentative communication system frequently used with ‘nonverbal’ children with autism. Several studies suggested that PECS could represent an effective tool for promoting improvement of several social–communicative skills. Only sparse evidence is instead available on the long-term effectiveness of this treatment system. Aims: To test the long-term effects of PECS, for which a follow-up study was conducted by assessing social– communicative skills in nonverbal preschool children with autism after 12 months from treatment completion. Methods & Procedures: Two groups of children (N = 14) were assessed; one group had completed the PECS training and the other conventional language therapy (CLT). At follow-up all children received the same preand post-treatment assessment. Outcome measures were the following: Communication and Social domains of Autism Diagnostic Observation Schedule (ADOS); Language and Personal–Social subscales of the Griffiths’ Mental Developmental Scales (GMDS); Communication and Social Abilities domains of the Vineland Adaptive Behavior Scales (VABS); and several social–communicative variables coded in an unstructured setting. Outcomes & Results: The PECS group showed significant improvements compared with the CLT group on ADOS severity scores (Communication, Social and Total), on GMDS Social domain and on VABS Communication and Social domains. PECS-related gains on the VABS Social domain and on specific social–communicative measures coded during free-play, i.e. frequency of joint attention and initiation, and duration of cooperative play, were stable after 1-year follow-up. Cooperative play continued to improve on follow-up with respect to both post- and pre-treatment assessment. Conclusions & Implications: These findings demonstrated that PECS training can promote long-term enhancement of specific socio-communicative skills in children with autism.


title: A Structural Model of Health Behavior: A Pragmatic Approach to Explain and Influence Health Behaviors at the Population Level

abstract: Background. Behavior is influenced by individual-level attributes as well as by the conditions under which people live. Altering policies, practices, and the conditions of life can directly and indirectly influence individual behavior. This paper builds on existing ecological theories of health behavior by specifying structural mechanisms by which population-level factors effect change in individual health behaviors. ::: ::: Methods. This paper moves ecological theory from model building to a pragmatic characterization of structural interventions. We examined social and environmental factors beyond individual control and mechanisms as to how they influence behavior. ::: ::: Results. Four categories of structural factors are identified: (1) availability of protective or harmful consumer products, (2) physical structures (or physical characteristics of products), (3) social structures and policies, and (4) media and cultural messages. The first three can directly influence individuals through facilitating or constraining behavior. The fourth, media, operates by changing individual-level attitudes, beliefs, and cognitions, as well as group norms. ::: ::: Conclusion. Interventions that target the four identified structural factors are a means to provide conditions that not only reduce high-risk behavior but also prevent the adoption of high-risk behaviors. Structural interventions are important and underutilized approaches for improving our nation's health.


title: BRENDA in 2017: new perspectives and new tools in BRENDA

abstract: The BRENDA enzyme database (www.brenda-enzymes.org) has developed into the main enzyme and enzyme-ligand information system in its 30 years of existence. The information is manually extracted from primary literature and extended by text mining procedures, integration of external data and prediction algorithms. Approximately 3 million data from 83 000 enzymes and 137 000 literature references constitute the manually annotated core. Text mining procedures extend these data with information on occurrence, enzyme-disease relationships and kinetic data. Prediction algorithms contribute locations and genome annotations. External data and links complete the data with sequences and 3D structures. A total of 206 000 enzyme ligands provide functional and structural data. BRENDA offers a complex query tool engine allowing the users an efficient access to the data via different search methods and explorers. The new design of the BRENDA entry page and the enzyme summary pages improves the user access and the performance. New interactive and intuitive BRENDA pathway maps give an overview on biochemical processes and facilitate the visualization of enzyme, ligand and organism information in the biochemical context. SCOPe and CATH, databases for protein structure classification, are included. New online and video tutorials provide online training for the users. BRENDA is freely available for academic users.


title: A Data Quality in Use model for Big Data

abstract: Beyond the hype of Big Data, something within business intelligence projects is indeed changing. This is mainly because Big Data is not only about data, but also about a complete conceptual and technological stack including raw and processed data, storage, ways of managing data, processing and analytics. A challenge that becomes even trickier is the management of the quality of the data in Big Data environments. More than ever before the need for assessing the Quality-in-Use gains importance since the real contribution-business value-of data can be only estimated in its context of use. Although there exists different Data Quality models for assessing the quality of regular data, none of them has been adapted to Big Data. To fill this gap, we propose the "3As Data Quality-in-Use model", which is composed of three Data Quality characteristics for assessing the levels of Data Quality-in-Use in Big Data projects: Contextual Adequacy, Operational Adequacy and Temporal Adequacy. The model can be integrated into any sort of Big Data project, as it is independent of any pre-conditions or technologies. The paper shows the way to use the model with a working example. The model accomplishes every challenge related to Data Quality program aimed for Big Data. The main conclusion is that the model can be used as an appropriate way to obtain the Quality-in-Use levels of the input data of the Big Data analysis, and those levels can be understood as indicators of trustworthiness and soundness of the results of the Big Data analysis. Data Quality is basic to decide about the suitability of data for intended uses.A Data Quality-in-Use Model based on ISO/IEC 25012, 25024 is proposed for Big Data.The main concern when assessing the Data Quality-in-Use in Big Data is Adequacy.The model accomplishes all the challenges of a Data Quality program for Big Data.The results obtained must be understood in the context of each Big Data project.


title: Classification cost: An empirical comparison among traditional classifier, Cost-Sensitive Classifier, and MetaCost

abstract: Loan fraud is a critical factor in the insolvency of financial institutions, so companies make an effort to reduce the loss from fraud by building a model for proactive fraud prediction. However, there are still two critical problems to be resolved for the fraud detection: (1) the lack of cost sensitivity between type I error and type II error in most prediction models, and (2) highly skewed distribution of class in the dataset used for fraud detection because of sparse fraud-related data. The objective of this paper is to examine whether classification cost is affected both by the cost-sensitive approach and by skewed distribution of class. To that end, we compare the classification cost incurred by a traditional cost-insensitive classification approach and two cost-sensitive classification approaches, Cost-Sensitive Classifier (CSC) and MetaCost. Experiments were conducted with a credit loan dataset from a major financial institution in Korea, while varying the distribution of class in the dataset and the number of input variables. The experiments showed that the lowest classification cost was incurred when the MetaCost approach was used and when non-fraud data and fraud data were balanced. In addition, the dataset that includes all delinquency variables was shown to be most effective on reducing the classification cost.


title: Multi-tenant Database Access Control

abstract: Storing data in the cloud is a new multi-tenant database solution that has recently emerged to deliver database for multiple users, who can store and access their data over the internet. This multi-tenant database designed to be used by multiple tenants and each tenant may have multiple users. Therefore, this database type demands a special multi-tenant access control model, which provides an access control not only for multiple tenants, but also for multiple users per tenant. In this paper, we are proposing a multi-tenant access control model based on a multi-tenant database schema called Elastic Extension Tables (EET). In this model, we define access control data architecture, and the EET access grants which can be granted to tenants' users. Moreover, we propose an access control algorithm, which allows users to access the data granted to them based on a number of groups or roles assigned to these users.


title: Sentence Relations for Extractive Summarization with Deep Neural Networks

abstract: Sentence regression is a type of extractive summarization that achieves state-of-the-art performance and is commonly used in practical systems. The most challenging task within the sentence regression framework is to identify discriminative features to represent each sentence. In this article, we study the use of sentence relations, e.g., Contextual Sentence Relations (CSR), Title Sentence Relations (TSR), and Query Sentence Relations (QSR), so as to improve the performance of sentence regression. CSR, TSR, and QSR refer to the relations between a main body sentence and its local context, its document title, and a given query, respectively. ::: We propose a deep neural network model, Sentence Relation-based Summarization (SRSum), that consists of five sub-models, PriorSum, CSRSum, TSRSum, QSRSum, and SFSum. PriorSum encodes the latent semantic meaning of a sentence using a bi-gram convolutional neural network. SFSum encodes the surface information of a sentence, e.g., sentence length, sentence position, and so on. CSRSum, TSRSum, and QSRSum are three sentence relation sub-models corresponding to CSR, TSR, and QSR, respectively. CSRSum evaluates the ability of each sentence to summarize its local contexts. Specifically, CSRSum applies a CSR-based word-level and sentence-level attention mechanism to simulate the context-aware reading of a human reader, where words and sentences that have anaphoric relations or local summarization abilities are easily remembered and paid attention to. TSRSum evaluates the semantic closeness of each sentence with respect to its title, which usually reflects the main ideas of a document. TSRSum applies a TSR-based attention mechanism to simulate people’s reading ability with the main idea (title) in mind. QSRSum evaluates the relevance of each sentence with given queries for the query-focused summarization. QSRSum applies a QSR-based attention mechanism to simulate the attentive reading of a human reader with some queries in mind. The mechanism can recognize which parts of the given queries are more likely answered by a sentence under consideration. Finally as a whole, SRSum automatically learns useful latent features by jointly learning representations of query sentences, content sentences, and title sentences as well as their relations. ::: We conduct extensive experiments on six benchmark datasets, including generic multi-document summarization and query-focused multi-document summarization. On both tasks, SRSum achieves comparable or superior performance compared with state-of-the-art approaches in terms of multiple ROUGE metrics.


title: Experimental comparison of representation methods and distance measures for time series data

abstract: The previous decade has brought a remarkable increase of the interest in applications that deal with querying and mining of time series data. Many of the research efforts in this context have focused on introducing new representation methods for dimensionality reduction or novel similarity measures for the underlying data. In the vast majority of cases, each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive experimental study re-implementing eight different time series representations and nine similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this article, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. In addition to providing a unified validation of some of the existing achievements, our experiments also indicate that, in some cases, certain claims in the literature may be unduly optimistic.


title: Towards Automated Counter-Melody Generation for Monophonic Melodies

abstract: Algorithmic composition has focused on creating music from algorithms, stemming from the capacity to convert notes into numbers and vice versa, thus allowing simple to complex algorithmic manipulations. The focus of these studies has either been the creation of melodies, chords, accompaniments or entire songs. This study focuses on a relatively underexplored topic on the algorithmic generation of a counter-melody from a given melody. Using a method based on existing knowledge of generating chords and music theory on compatible notes and chord progressions, combined with concepts of machine learning and tree traversal techniques for generating chords, this study was able to generate 200 counter-melodies from 100 inputs, involving two generation techniques per input. The results show that counter-melodies were successfully generated based on chord progression generation and note selection approaches, and after subjecting the counter-melodies to proper subject evaluation, the average scores of 2.89 and 3.02 on a 5-point evaluation criteria reveal that the counter-melodies are musically fit for the original melodies they were based from.


title: MeditAid: a wearable adaptive neurofeedback-based system for training mindfulness state

abstract: Abstract ::: A recent interest in interaction design is towards the development of novel technologies emphasizing the value of mindfulness, monitoring, awareness, and self-regulation for both health and well-being. Whereas existing systems have focused mostly on relaxation and awareness of feelings, there has been little exploration on tools supporting the self-regulation of attention during mindfulness sitting meditation. This paper describes the design and initial evaluation of MeditAid, a wearable system integrating electroencephalography technology with an adaptive aural entrainment for real-time training of mindfulness state. The system identifies different meditative states and provides feedback to support users in deepening their meditation. We report on a study with 16 meditators about the perceived strengths and limitations of the MeditAid system. We demonstrate the benefits of binaural feedback in deepening meditative states, particularly for novice meditators.


title: Uncovering the relational web

abstract: World-Wide Web consists of a huge number of unstruc- tured hypertext documents, but it also contains structured data in the form of HTML tables. Many of these tables contain both relational-style data and a small "schema" of labeled and typed columns, making each such table a small structured database. The WebTables project is an effort to extract and make use of the huge number of these structured tables on the Web. A clean collection of relational-style ta- bles could be useful for improving web search, schema de- sign, and many other applications. This paper describes the first stage of the WebTables project. First, we give an in-depth study of the Web's HTML table corpus. For example, we extracted 14.1 billion HTML ta- bles from a several-billion-page portion of Google's general- purpose web crawl, and estimate that 154 million of these tables contain high-quality relational-style data. We also de- scribe the crawl's distribution of table sizes and data types. Second, we describe a system for performing relation recov- ery. The Web mixes relational and non-relational tables indiscriminately (often on the same page), so there is no simple way to distinguish the 1.1% of good relations from the remainder, nor to recover column label and type infor- mation. Our mix of hand-written detectors and statistical classifiers takes a raw Web crawl as input, and generates a collection of databases that is five orders of magnitude larger than any other collection we are aware of. Relation recovery achieves precision and recall that are comparable to other domain-independent information extraction systems.


Obstacles detection in foggy environment


title: Current control techniques for three-phase voltage-source PWM converters: a survey

abstract: The aim of this paper is to present a review of current control techniques for three-phase voltage-source pulsewidth modulated converters. Various techniques, different in concept, have been described in two main groups: linear and nonlinear. The first includes proportional integral (stationary and synchronous) and state feedback controllers, and predictive techniques with constant switching frequency. The second comprises bang-bang (hysteresis, delta modulation) controllers and predictive controllers with on-line optimization. New trends in current control-neural networks and fuzzy-logic-based controllers-are discussed, as well. Selected oscillograms accompany the presentation in order to illustrate properties of the described controller groups.


title: Privacy-aware location privacy preference recommendations

abstract: Location-Based Services have become increasingly popular due to the prevalence of smart devices and location-sharing applications such as Facebook and Foursquare. The protection of people's sensitive location data in such applications is an important requirement. Conventional location privacy protection methods, however, such as manually defining privacy rules or asking users to make decisions each time they enter a new location may be overly complex, intrusive or unwieldy. An alternative is to use machine learning to predict people's privacy preferences and automatically configure settings. Model-based machine learning classifiers may be too computationally complex to be used in real-world applications, or suffer from poor performance when training data are insufficient. In this paper we propose a location-privacy recommender that can provide people with recommendations of appropriate location privacy settings through user-user collaborative filtering. Using a real-world location-sharing dataset, we show that the prediction accuracy of our scheme (73.08%) is similar to the best performance of model-based classifiers (75.30%), and at the same time causes fewer privacy leaks (11.75% vs 12.70%). Our scheme further outperforms model-based classifiers when there are insufficient training data. Since privacy preferences are innately private, we make our recommender privacy-aware by obfuscating people's preferences. Our results show that obfuscation leads to a minimal loss of prediction accuracy (0.76%).


title: Matrix Completion for Weakly-Supervised Multi-Label Image Classification

abstract: In the last few years, image classification has become an incredibly active research topic, with widespread applications. Most methods for visual recognition are fully supervised, as they make use of bounding boxes or pixelwise segmentations to locate objects of interest. However, this type of manual labeling is time consuming, error prone and it has been shown that manual segmentations are not necessarily the optimal spatial enclosure for object classifiers. This paper proposes a weakly-supervised system for multi-label image classification. In this setting, training images are annotated with a set of keywords describing their contents, but the visual concepts are not explicitly segmented in the images. We formulate the weakly-supervised image classification as a low-rank matrix completion problem. Compared to previous work, our proposed framework has three advantages: (1) Unlike existing solutions based on multiple-instance learning methods, our model is convex. We propose two alternative algorithms for matrix completion specifically tailored to visual data, and prove their convergence. (2) Unlike existing discriminative methods, our algorithm is robust to labeling errors, background noise and partial occlusions. (3) Our method can potentially be used for semantic segmentation. Experimental validation on several data sets shows that our method outperforms state-of-the-art classification algorithms, while effectively capturing each class appearance.


title: A Survey on Network Embedding

abstract: Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.


title: Systematic literature reviews in software engineering: Preliminary results from interviews with researchers

abstract: Systematic Literature Reviews (SLRs) have been gaining significant attention from software engineering researchers since 2004. Several researchers have reported their experiences of and lessons learned from applying systematic reviews to different subject matters in software engineering. However, there has been no attempt at independently exploring experiences and perceptions of the practitioners of systematic reviews in order to gain an in-depth understanding of various aspects of systemic reviews as a new research methodology in software engineering. We assert that there is a need of evidence-based body of knowledge about the application of systematic reviews in software engineering. To address this need, we have started an empirical research program that aims to contribute to the growing body of knowledge about systematic reviews in software engineering. This paper reports the design, logistics, and results of the first phase empirical study carried out in this program. The results provide interesting insights into different aspects of systematic reviews based on the analysis of the data gathered from 17 interviewees with varying levels of knowledge of and experiences in systematic reviews. The findings from this study are expected to contribute to the existing knowledge about using systematic reviews and help further improve the state-of-the-practice of this research methodology in software engineering.


title: Complex-YOLO: Real-time 3D Object Detection on Point Clouds

abstract: Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space. Thus, we propose a specific Euler-Region-Proposal Network (E-RPN) to estimate the pose of the object by adding an imaginary and a real fraction to the regression network. This ends up in a closed complex space and avoids singularities, which occur by single angle estimations. The E-RPN supports to generalize well during training. Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency. We achieve state of the art results for cars, pedestrians and cyclists by being more than five times faster than the fastest competitor. Further, our model is capable of estimating all eight KITTI-classes, including Vans, Trucks or sitting pedestrians simultaneously with high accuracy.


title: Ranging and SAR imaging of underground targets using laptop based FMCW RADAR

abstract: This paper discusses an experiment on Synthetic Aperture RADAR imaging using a portable laptop based Frequency Modulated Continuous Wave RADAR. The aim of the paper is to examine Ground Penetrating Radar data with the portable FMCW RADAR after Auto Estimation of Soil Permittivity processing. The permittivity of the soil is estimated by iterative algorithm, where the soil is considered as homogeneous. Objects buried under the grounds were detected using the FMCW RADAR and possible novel applications are discussed.


What Company Do Semantically Ambiguous Words Keep? Insights from Distributional Word Vectors.


Process discovery from event data: Relating models and logs through abstractions


title: Universal data capture technology from semi-structured forms

abstract: This paper describes a universal technology for automated data capture from documents with similar data but different layouts, such as invoices, claim forms, resumes, contracts, loan documents, etc. Prior to data capture, the relevant data are detected on the document image. A formalization of top-down document analysis is suggested and a language for describing document structures is presented. Formalized descriptions in this language can be compiled into executable code. The process of matching such formalized descriptions with actual semi-structured documents in order to find the relevant data is described.


title: Recent advances in intravenous delivery of poorly water-soluble compounds

abstract: In recent years, with the widespread application of high-throughput screening technologies in drug discovery, an increasing number of new chemical entities with extremely poor aqueous solubility have been generated. Their poor solubility represents a major challenge for formulation of these compounds for both oral and parenteral administration. Formulations for intravenous (i.v.) application are of significant importance because they are frequently used in several key therapeutic areas, such as oncology and anesthesia. Furthermore, i.v. formulations of new compounds are often needed to determine basic biopharmaceutical parameters and to obtain proof of concept results in the early phase of product development. This review provides an overview of the recent advances in formulation approaches and drug delivery technologies for poorly water-soluble compounds applicable to i.v. administration. The advantages and disadvantages of different strategies are highlighted and an expert opinion on each technical field is presented.


title: A New Fault Detection Scheme for Networked Control Systems Subject to Uncertain Time-Varying Delay

abstract: Fault detection of networked control systems (NCS) subject to uncertain time-varying delay is studied in this paper. For the convenience of residual generator design, influence caused by network-induced delay is first transformed into time-varying polytopic uncertainty, which greatly facilitates further manipulation. Then design of the optimal residual generator is formulated as a model matching problem, i.e., to design a residual generator best matching the optimal residual generator of NCS in the delay free case. This procedure is assisted by parameter-dependent Lyapunov function matrix based bounded real lemma, which has been proved to be much better than single constant Lyapunov function matrix based results. This approach not only can be used in conditions that the variation part of the delay is less than one sampling period, but also can be applied to scenarios where the variation part of the delay is larger than one sampling period. Simulation results are also given to illustrate effectiveness of the proposed method.