title: Comparison of approaches for developing self-adaptive systems

abstract: The engineering of software systems enables developers to create very powerful, complex and highly customized software systems by utilizing newest technical capabilities. However, these systems often are error-prone, inflexible, non-reusable and expensive to maintain. Self-adaptation attends to these challenges, offering new ways to automate the adjustment of a system's structure and state. For that reason, many software development approaches specifically consider self-adaptability, leading to a high diversity of methodologies with different characteristics and areas of application. This work addresses this issue by presenting a taxonomy for the analysis and comparison of different approaches for developing self-adaptive systems. In addition, different sample approaches are presented, demonstrating how these dimensions can be applied to compare and classify related work.


title: Arabic Natural Language Processing and Machine Learning-Based Systems

abstract: Arabic natural language processing (ANLP) consists of developing techniques and tools that can utilize and analyze the Arabic language in both written and spoken contexts. ANLP makes an important contribution to many existing developed systems. It provides Arabic and non-Arabic speakers with helpful and convenient tools that can be used in different domains. Modern ANLP tools are developed using machine learning (ML) techniques. ML algorithms are widely used in NLP because of their high accuracy rate regardless of the robustness of the data that is used and because of the ease with which they can be implemented. On the other hand, the methodology of ANLP applications based on ML involves several distinct phases. It is, therefore, crucial to recognize and understand these phases in detail as well as the most widely used ML algorithms. This survey discusses this concept in detail, shows the involvement of ML techniques in developing such tools, and identifies well-known techniques used in ANLP. Moreover, this survey discusses the characteristics and complexity of the Arabic language in addition to the importance and needs of ANLP.


title: Stabilization of flicker-like effects in image sequences through local contrast correction

abstract: In this paper, we address the problem of the restoration of image sequences which have been affected by local intensity modifications (local contrast changes). Such artifacts can be encountered in particular in biological or archive film sequences, and are usually due to inconsistent exposures or sparse time sampling. In order to reduce such local artifacts, we introduce a local stabilization operator, called $\LS$, which acts as a time filtering on image patches and relies on a similarity measure which is robust to contrast changes. Thereby, this operator is able to take motion into account without resting on a sophisticated motion estimation procedure. The efficiency of the stabilization is shown on various sequences. The experimental results compares favorably with state of the art approaches.


title: Efficient Destination Prediction Based on Route Choices with Transition Matrix Optimization

abstract: Destination prediction is an essential task in a variety of mobile applications. In this paper, we optimize the matrix operation and adapt a semi-lazy framework to improve the prediction accuracy and efficiency of a state-of-the-art approach. To this end, we employ efficient dynamic-programming by devising several data constructs including Efficient Transition Probability and Transition Probabilities with Detours that are capable of pinpointing the minimum amount of computation. We prove that our method achieves one order of cut in both time and space complexity. The experimental results on real-world and synthetic datasets have shown that our solution consistently outperforms its state-of-the-art counterparts in terms of both efficiency (approximately over 100 times faster) and accuracy (above 30% increase).


title: Apron: A Library of Numerical Abstract Domains for Static Analysis

abstract: This article describes Apron , a freely available library dedicated to the static analysis of the numerical variables of programs by abstract interpretation. Its goal is threefold: provide analysis implementers with ready-to-use numerical abstractions under a unified API, encourage the research in numerical abstract domains by providing a platform for integration and comparison, and provide teaching and demonstration tools to disseminate knowledge on abstract interpretation.


title: An Automatic Collocation Extraction from Arabic Corpus

abstract: Problem statement: The identification of collocations is very important part in natural ::: language processing applications that require some degree of semantic interpretation such as, machine ::: translation, information retrieval and text summarization. Because of the complexities of Arabic, the ::: collocations undergo some variations such as, morphological, graphical, syntactic variation that ::: constitutes the difficulties of identifying the collocation. Approach: We used the hybrid method for ::: extracting the collocations from Arabic corpus that is based on linguistic information and association ::: measures. Results: This method extracted the bi-gram candidates of Arabic collocation from corpus ::: and evaluated the association measures by using the n-best evaluation method. We reported the ::: precision values for each association measure in each n-best list. Conclusion: The experimental results ::: showed that the log-likelihood ratio is the best association measure that achieved highest precision.


title: EEG-Based Attention Tracking During Distracted Driving

abstract: Distracted driving might lead to many catastrophic consequences. Developing a countermeasure to track drivers' focus of attention (FOA) and engagement of operators in dual (multi)–tasking conditions is thus imperative. Ten healthy volunteers participated in a dual-task experiment that comprised two tasks: a lane-keeping driving task and a mathematical problem-solving task (e.g., $24+15=37?$ ) during which their electroencephalogram (EEG) and behaviors were concurrently recorded. Independent component analysis (ICA) was employed as a spatial filter to separate the contributions of independent sources from the recorded EEG data. The power spectra of six components (i.e., frontal, central, parietal, occipital, left motor, and right motor) extracted from single-task conditions were fed into support vector machine (SVM) based on the radial basis function (RBF) kernel to build an FOA assessment system. The system achieved $84.6\pm 5.8\%$ and $86.2\pm 5.4\%$ classification accuracies in detecting the participants' FOAs on the math versus driving tasks, respectively. This FOA assessment system was then applied to evaluate participants' FOAs during dual-task conditions. The detected FOAs revealed that participants' cognitive attention and strategies dynamically changed between tasks to optimize the overall performance, as attention was limited and competed. The empirical results of this study demonstrate the feasibility of a practical system to continuously estimating cognitive attention through EEG spectra.


title: Spiral Mecanum Wheel achieving omnidirectional locomotion in step-climbing

abstract: The vehicle using omnidirectional wheels has ability to move in all directions without changing the body direction unlike a normal four wheel drive vehicle. However most of omnidirectional vehicle are designed for using only on flat ground. In this paper, we propose a new type of omnidirectional wheel, “Spiral Mecanum Wheel”, which enables vehicle climb the step. This new wheel consists of spiral beams and many small rollers, and these small rollers are arranged along the spiral beams. When the vehicle with this spiral Mecanum Wheels moves in normal direction, the edge of the spiral moves to cover the step from above. We have performed the experiments using Spiral Mecanum Wheel and showing the wheel works very well. As a result, in the experiment using single Spiral Mecanum Wheel, this Spiral Mecanum Wheel climbed the step about 83% of the wheel diameter in normal direction motion. The vehicle with Spiral Mecnaum Wheel climbed the step about 37% of the wheel diameter in tangential direction motion and 59% in normal direction.


title: Notes on Deep Learning for NLP

abstract: My notes on Deep Learning for NLP.


title: Degradation of plastic carrier bags in the marine environment.

abstract: There is considerable concern about the hazards that plastic debris presents to wildlife. Use of polymers that degrade more quickly than conventional plastics presents a possible solution to this problem. Here we investigate breakdown of two oxo-biodegradable plastics, compostable plastic and standard polyethylene in the marine environment. Tensile strength of all materials decreased during exposure, but at different rates. Compostable plastic disappeared from our test rig between 16 and 24 weeks whereas approximately 98% of the other plastics remained after 40 weeks. Some plastics require UV light to degrade. Transmittance of UV through oxo-biodegradable and standard polyethylene decreased as a consequence of fouling such that these materials received ∼ 90% less UV light after 40 weeks. Our data indicate that compostable plastics may degrade relatively quickly compared to oxo-biodegradable and conventional plastics. While degradable polymers offer waste management solutions, there are limitations to their effectiveness in reducing hazards associated with plastic debris.


title: Touch gesture authentication framework for touch screen mobile devices

abstract: With the increased popularity of touchscreen mobile phones, touch gesture behavior is becoming more and more important. Increasing demand for safer access in touch screen mobile phones, ancient strategies like pins, tokens, or passwords fail to stay up with the challenges conferred. Most of the current touch gesture authentication schemes facing problem with accuracy based on score classifiers of EER, FAR and FRR. In addition, collecting touch duration feature only may not be able to achieve the desired authentication accuracy and robustness. However, gathering more touch features such as finger pressure, finger size and acceleration would help to get a better authentication accuracy and verification performance. These efforts have allowed us to provide a comprehensive summary of recent work on touch behavior authentication and a frame work to implement the system. Finally, we conclude the future work in this topic which is implement and test the framework of touch gesture gesture-based behavioral biometrics and obtain more accuracy and robustness authentication system.


title: A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers

abstract: Conventional radio-frequency (RF) power amplifiers operating with wideband signals, such as wideband code-division multiple access (WCDMA) in the Universal Mobile Telecommunications System (UMTS) must be backed off considerably from their peak power level in order to control out-of-band spurious emissions, also known as "spectral regrowth." Adapting these amplifiers to wideband operation therefore entails larger size and higher cost than would otherwise be required for the same power output. An alternative solution, which is gaining widespread popularity, is to employ digital baseband predistortion ahead of the amplifier to compensate for the nonlinearity effects, hence allowing it to run closer to its maximum output power while maintaining low spectral regrowth. Recent improvements to the technique have included memory effects in the predistortion model, which are essential as the bandwidth increases. In this paper, we relate the general Volterra representation to the classical Wiener, Hammerstein, Wiener-Hammerstein, and parallel Wiener structures, and go on to describe some state-of-the-art predistortion models based on memory polynomials. We then propose a new generalized memory polynomial that achieves the best performance to date, as demonstrated herein with experimental results obtained from a testbed using an actual 30-W, 2-GHz power amplifier


title: SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)

abstract: The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drugdrug interactions that appear in biomedical literature. We propose two subtasks for the DDIExtraction 2013 Shared Task challenge: 1) the recognition and classification of drug names and 2) the extraction and classification of their interactions. Both subtasks have been very successful in participation and results. There were 14 teams who submitted a total of 38 runs. The best result reported for the first subtask was F1 of 71.5% and 65.1% for the second one.


title: Voice as sound: using non-verbal voice input for interactive control

abstract: We describe the use of non-verbal features in voice for direct control of interactive applications. Traditional speech recognition interfaces are based on an indirect, conversational model. First the user gives a direction and then the system performs certain operation. Our goal is to achieve more direct, immediate interaction like using a button or joystick by using lower-level features of voice such as pitch and volume. We are developing several prototype interaction techniques based on this idea, such as "control by continuous voice", "rate-based parameter control by pitch," and "discrete parameter control by tonguing." We have implemented several prototype systems, and they suggest that voice-as-sound techniques can enhance traditional voice recognition approach.


title: Electrostatic cloaking of surface structure for dynamic wetting

abstract: Dynamic wetting problems are fundamental to understanding the interaction between liquids and solids. Even in a superficially simple experimental situation, such as a droplet spreading over a dry surface, the result may depend not only on the liquid properties but also strongly on the substrate-surface properties; even for macroscopically smooth surfaces, the microscopic geometrical roughness can be important. In addition, because surfaces may often be naturally charged or electric fields are used to manipulate fluids, electric effects are crucial components that influence wetting phenomena. We investigate the interplay between electric forces and surface structures in dynamic wetting. Although surface microstructures can significantly hinder spreading, we find that electrostatics can "cloak" the microstructures, that is, deactivate the hindering. We identify the physics in terms of reduction in contact-line friction, which makes the dynamic wetting inertial force dominant and insensitive to the substrate properties.


title: Topological analysis of data

abstract: Propelled by a fast evolving landscape of techniques and datasets, data science is growing rapidly. Against this background, topological data analysis (TDA) has carved itself a niche for the analysis of datasets that present complex interactions and rich structures. Its distinctive feature, topology, allows TDA to detect, quantify and compare the mesoscopic structures of data, while also providing a language able to encode interactions beyond networks. Here we briefly present the TDA paradigm and some applications, in order to highlight its relevance to the data science community.


title: Progressive Geometric Algorithms

abstract: Progressive algorithms are algorithms that, on the way to computing a complete solution to the problem at hand, output intermediate solutions that approximate the complete solution increasingly well. We present a framework for analyzing such algorithms, and develop efficient progressive algorithms for two geometric problems: computing the convex hull of a planar point set, and finding popular places in a set of trajectories.


title: Spanish Twitter Messages Polarized through the Lens of an English System

abstract: on de polaridad Abstract: In this paper we describe the adaptation of a supervised classication system that was originally developed to detect sentiment on Twitter texts written in English. The Columbia University team adapted this system to participate in Task 1 of the 4th edition of the experimental evaluation workshop for sentiment analysis focused on the Spanish language (TASS 2015). The task consists of determining the global polarity of a group of messages written in Spanish using the social media platform Twitter.


title: Business process management – lessons from European business

abstract: This paper reports on the findings of a survey and case study research into the understanding and application of business process management (BPM) in European companies. The process perspective is increasingly being seen as a mechanism for achieving competitive advantage through performance improvement and in response to market pressures, customer expectations for better and more reliable service and increasing competition. We reveal the level of importance European companies attach to BPM, what it means to them and what they have done in practice. The paper draws on a postal survey conducted with quality directors and business process managers in organisations which are members of the European Foundation for Quality Management (EFQM) and case studies in a number of organisations regarded as being leaders in the adoption of BPM. The study has helped to highlight some interesting approaches and reveal features which are important for BPM to be successful.


title: Design and Evaluation of a Real-Time URL Spam Filtering Service

abstract: On the heels of the widespread adoption of web services such as social networks and URL shorteners, scams, phishing, and malware have become regular threats. Despite extensive research, email-based spam filtering techniques generally fall short for protecting other web services. To better address this need, we present Monarch, a real-time system that crawls URLs as they are submitted to web services and determines whether the URLs direct to spam. We evaluate the viability of Monarch and the fundamental challenges that arise due to the diversity of web service spam. We show that Monarch can provide accurate, real-time protection, but that the underlying characteristics of spam do not generalize across web services. In particular, we find that spam targeting email qualitatively differs in significant ways from spam campaigns targeting Twitter. We explore the distinctions between email and Twitter spam, including the abuse of public web hosting and redirector services. Finally, we demonstrate Monarch's scalability, showing our system could protect a service such as Twitter -- which needs to process 15 million URLs/day -- for a bit under $800/day.


Block-Based static timing analysis with uncertainty


title: Gender classification from hand shape

abstract: Many social interactions and services today depend on gender. In this paper, we investigate the problem of gender classification from hand shape. Our work has been motivated by studies in anthropometry and psychology suggesting that it is possible to distinguish between male and female hands by considering certain geometric features. Our system segments the hand silhouette into six different parts corresponding to the palm and fingers. To represent the geometry of each part, we use region and boundary features based on Zernike moments and Fourier descriptors. For classification, we compute the distance of a given part from two different eigenspaces, one corresponding to the male class and the other corresponding to female class. We have experimented using each part of the hand separately as well as fusing information from different parts of the hand. Using a small database containing 20 males and 20 females, we report classification results close to 98% using score-level fusion and LDA.


title: Unsupervised speaker adaptation for DNN-based TTS synthesis

abstract: Multi-speaker TTS trained with a general DNN has outperformed individually modelled baseline [1]. Multi-speaker DNN takes advantages of larger amount of training data from multiple speakers to find robust transformations in the hidden layers and covers more speaker variability in the output regression layer. In this paper, we propose a new approach to unsupervised speaker adaptation with multi-speaker DNN. It takes advantage of shared hidden transformation to search for the labels of unlabelled acoustic frames and the found labels are used for speaker adaption. Experimental results show that the new approach of unsupervised adaptation can achieve comparable performance with supervised adaptation both objectively and subjectively. We further extend it to cross-lingual adaptation. It can remove non-native accent and improve the naturalness while keep the same speaker's characteristics.


title: Comparison of data mining classification algorithms for breast cancer prediction

abstract: Data mining is an area of computer science with a huge prospective, which is the process of discovering or extracting information from large database or datasets. There are many different areas under Data Mining and one of them is Classification or the supervised learning. Classification also can be implemented through a number of different approaches or algorithms. We have conducted the comparison between three algorithms with help of WEKA (The Waikato Environment for Knowledge Analysis), which is an open source software. It contains different type's data mining algorithms. This paper explains discussion of Decision tree, Bayesian Network and K-Nearest Neighbor algorithms. Here, for comparing the result, we have used as parameters the correctly classified instances, incorrectly classified instances, time taken, kappa statistic, relative absolute error, and root relative squared error.


title: CRUDAW: a novel fuzzy technique for clustering records following user defined attribute weights

abstract: We present a novel fuzzy clustering technique called CRUDAW that allows a data miner to assign weights on the attributes of a data set based on their importance (to the data miner) for clustering. The technique uses a novel approach to select initial seeds deterministically (not randomly) using the density of the records of a data set. CRUDAW also selects the initial fuzzy membership degrees deterministically. Moreover, it uses a novel approach for measuring distance considering the user defined weights of the attributes. While measuring the distance between the values of a categorical attribute the technique takes the similarity of the values into consideration instead of considering the distance to be either 0 or 1. Complete algorithm for CRUDAW is presented in the paper. We experimentally compare our technique with a few existing techniques -- namely SABC, GFCM, and KL-FCM-GM based on various evaluation criteria called Silhouette coefficient, F-measure, purity and entropy. We also use t-test, confidence interval test and time complexity in evaluating the performance of our technique. Four data sets available from UCI machine learning repository are used in the experiments. Our experimental results indicate that CRUDAW performs significantly better than the existing techniques in producing high quality clusters.


title: Proposal for an IoT architecture in industrial processes

abstract: Nowadays, with the advancement of technologies involving IoT (Internet of Things), the industry has demonstrated interest in adapting to such a scenario. The industry 4.0 is a good example of this IoT trend is inserting in industrial environments. This article presents a new proposal for IoT architecture with possible industrial applications based on architectures with recognized and consolidated IoT technologies, which direct and guide development possibilities for new technologies focused on industrial processes. A study on the industry 4.0 and the technologies used in the industry that have some kind of adherence to IoT are presented. Finally, the proposal from IoT architecture in an industrial setting of sugar and alcohol production is demonstrated.


title: A Baseline for Multi-Label Image Classification Using Ensemble Deep CNN

abstract: Recent studies on multi-label image classification have been focusing on designing more complex architectures of deep neural networks such as the use of attention mechanism and region proposal networks. Although performance gains have been reported in literature, the backbone deep models of the proposed approaches and the evaluation metrics employed in different works vary, making it difficult to compare with each other fairly. Moreover, due to the lack of properly investigated baselines, the advantage introduced by the proposed techniques in literature are vague. To address these issues, we make a thorough investigation of the mainstream deep convolutional neural network architectures for multi-label image classification and present a strong baseline. With only data augmentation and model ensemble, we achieve better performance than those previously reported on three benchmark datasets. We hope the work presented in this paper will provide insights to the future studies on multi-label image classification.


title: An overview of deep learning in medical imaging focusing on MRI

abstract: Abstract What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.


title: A hybrid AC/DC micro-grid architecture, operation and control

abstract: This paper presents a hybrid AC/DC micro grid concept to directly integrate DC/AC renewable sources and loads to DC/AC links respectively. The hybrid grid eliminates multiple DC-AC-DC&AC-DC-AC conversions in an individual AC&DC grid. The hybrid grid increases system efficiency, eliminates the embedded AC/DC and DC/DC converters in various home, office and industry facilities which can reduce size and cost of those facilities. The basic architecture of the hybrid grid is introduced in this paper. Different operation modes of the hybrid grid are discussed. The various control algorithms are investigated and proposed to harness the maximum power from various renewable sources, to store energy surplus during low peak loads, to eliminate unbalance problem in AC link, to maintain voltage stability and smooth power transfer between AC and DC links under various generation and load conditions. A prototype of the hybrid grid under construction is presented. Some simulation and test results are presented.


title: The Oxygen Isotope Composition of Ice Wedges of Ayon Island and Paleotemperature Reconstructions of the Late Pleistocene and Holocene of the Northern Chukotka

abstract: Syngenetic ice wedges have been investigated on Ayon Island. Their isotope composition and the geochemical characteristics of both ice wedges and enclosing sediments have been obtained; four ice-wedge stages have been distinguished. Paleo temperature reconstructions for Ayon Island and adjacent territories of northern Chukotka have been obtained based of these results. Almost identical trends in the distribution of ice-wedge isotope characteristics in the Arctic islands and in the lower reaches of the Kolyma River were observed, as well as differences in the magnitude of isotope oscillations during the transition from the Late Pleistocene to the Holocene compared to ice wedges of the Lower Kolyma region.


title: Perceptual and affective mechanisms in facial expression recognition: An integrative review

abstract: Facial expressions of emotion involve a physical component of morphological changes in a face and an affective component conveying information about the expresser’s internal feelings. It remains unresolved how much recognition and discrimination of expressions rely on the perception of morphological patterns or the processing of affective content. This review of research on the role of visual and emotional factors in expression recognition reached three major conclusions. First, behavioral, neurophysiological, and computational measures indicate that basic expressions are reliably recognized and discriminated from one another, albeit the effect may be inflated by the use of prototypical expression stimuli and forced-choice responses. Second, affective content along the dimensions of valence and arousal is extracted early from facial expressions, although this coarse affective representation contributes minimally to categorical recognition of specific expressions. Third, the physical configuration and visu...


title: THORACOLUMBAR BURST FRACTURES: CORRELATION BETWEEN KYPHOSIS AND FUNCTION POST NON-OPERATIVE TREATMENT

abstract: Objective: To assess the correlation between kyphosis and post-traumatic symptoms in patients undergoing conservative treatment for thoracolumbar burst fractures. Methods: A retrospective study was carried out with 36 patients meeting the inclusion criteria for this kind of fracture classified as Denis and Magerl's subtype A3 and treated with anti-gravitational casting or TLSO. The mean age of patients was 50.83 years, ranging from 13 to 83 years, being 20 male and 16 female subjects. The treatment outcome was evaluated based on the SF-36 questionnaire, on Denis scores for pain and work and Frankel clinical and neurological scale. The quantification of pain was based on the visual analogue scale for pain. The measurement of the residual kyphosis was obtained by the Cobb method at admission and at the end of the follow-up. Results: A weak positive correlation (r = 0.563; p > 0.001) was found between residual kyphosis and pain score (EVA). No correlation was found between final kyphosis and SF-36 and Denis scores (p > 0.05). Conclusion: There is no evident correlation between residual kyphosis, functional outcome and patients' symptoms.


title: High power in-line pseudoelliptic evanescent mode filter using series lumped capacitors

abstract: A compact in-line pseudoelliptic evanescent mode filter for high power and wide spectrum applications is presented in this paper. The filter employs series lumped capacitors between adjacent resonators. Such capacitors are properly combined with the series distributed inductance of the evanescent mode sections so as to provide independently controlled transmission zeros. The proposed approach does not require any inductive wire connections between resonators and preserves the wide spurious free stopband characteristic typical of conventional in-line all-pole evanescent mode filters. The experimental results, including temperature and power tests, of a sixth order 11% fractional bandwidth prototype designed at 1.8 GHz with two symmetrically placed transmission zeros are reported.


title: Gang involvement: psychological and behavioral characteristics of gang members, peripheral youth, and nongang Youth

abstract: Research has noted the existence of a loose and dynamic gang structure. However, the psychological processes that underpin gang membership have only begun to be addressed. This study examined gang members, peripheral youth, and nongang youth across measures of criminal activity, the importance they attach to status, their levels of moral disengagement, their perceptions of out-group threat, and their attitudes toward authority. Of the 798 high school students who participated in this study, 59 were identified as gang members, 75 as peripheral youth, and 664 as nongang youth. Gang members and peripheral youth were more delinquent than nongang youth overall; however, gang members committed more minor offenses than nongang youth and peripheral youth committed more violent offenses than nongang youth. Gang members were more anti-authority than nongang youth, and both gang and peripheral youth valued social status more than nongang youth. Gang members were also more likely to blame their victims for their actions and use euphemisms to sanitize their behavior than nongang youth, whereas peripheral youth were more likely than nongang youth to displace responsibility onto their superiors. These findings are discussed as they highlight the importance of examining individual differences in the cognitive processes that relate to gang involvement. Aggr. Behav. 35:1-14, 2010. (c) 2010 Wiley-Liss, Inc. Language: en


title: Smart Generation and Transmission With Coherent, Real-Time Data

abstract: In recent years, much of the discussion involving Bsmart grids( has implicitly involved only the distribution side, notably advanced metering. However, today's electric systems have many challenges that also involve the rest of the system. An enabling technology for improving the power system, which has emerged in recent years, is the ability to measure coherent, real-time data. In this paper, we describe major challenges facing electrical generation and transmission today that avail- ability of these measurements can help address. We overview applications using coherent, real-time measurements that are in use today or proposed by researchers. Specifically, we de- scribe, normalize, and then quantitatively compare key factors for these power applications that influence how the delivery system should be planned, implemented, and managed. These factors include whether a person or computer is in the loop and (for both inputs and outputs) latency, rate, criticality, quantity, and geographic scope. From this, we abstract the baseline communications requirements of a data delivery system supporting these applications and suggest implementation guidelines to achieve them. Finally, we overview the state of the art in the supporting computer science areas of overlay networking and distributed computing (including middleware) and analyze gaps in commercial middleware products, utility standards, and issues that limit low-level network protocols from meeting these requirements when used in isolation.


title: Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems

abstract: This paper proposes a framework named multi-objective ant colony optimization based on decomposition (MoACO/D) to solve bi-objective traveling salesman problems (bTSPs). In the framework, a bTSP is first decomposed into a number of scalar optimization subproblems using Tchebycheff approach. To suit for decomposition, an ant colony is divided into many subcolonies in an overlapped manner, each of which is for one subproblem. Then each subcolony independently optimizes its corresponding subproblem using single-objective ant colony optimization algorithm and all subcolonies simultaneously work. During the iteration, each subproblem maintains an aggregated pheromone trail and an aggregated heuristic matrix. Each subcolony uses the information to solve its corresponding subproblem. After an iteration, a pheromone trail share procedure is evoked to realize the information share of those subproblems solved by common ants. Three MoACO algorithms designed by, respectively, combining MoACO/D with AS, MMAS and ACS are presented. Extensive experiments conducted on ten bTSPs with various complexities manifest that MoACO/D is both efficient and effective for solving bTSPs and the ACS version of MoACO/D outperforms three well-known MoACO algorithms on large bTSPs according to several performance measures and median attainment surfaces.


title: Activity Theory As a Lens for Characterizing the Participatory Unit

abstract: Since the cognitive revolution of the sixties, representation has served as the central concept of cognitive theory and representational theories of mind have provided the establishment view in cognitive science (Fodor, 1980; Gardner, 1985; Vera & Simon, 1993). Central to this line of thinking is the belief that knowledge exists solely in the head, and instruction involves finding the most efficient means for facilitating the “acquisition” of this knowledge (Gagne, Briggs, & Wager, 1993). Over the last two decades, however, numerous educational psychologists and instructional designers have begun abandoning cognitive theories that emphasize individual thinkers and their isolated minds. Instead, these researchers have adopted theories that emphasize the social and contextualized nature of cognition and meaning (Brown, Collins, & Duguid, 1989; Greeno, 1989, 1997; Hollan, Hutchins, & Kirsch, 2000; Lave & Wenger, 1991; Resnick, 1987; Salomon, 1993). Central to these reconceptualizations is an emphasis on contextualized activity and ongoing participation as the core units of analysis (Barab & Kirshner, 2001; Barab & Plucker, 2002; Brown & Duguid, 1991; Cook & Yanow, 1993;


title: Evaluation of Location Obfuscation techniques for privacy in location based information systems

abstract: In recent years, smart-phones and GPS-enabled devices have been a critical factor in the popularization and increasing demand of location based information systems. However, at the same time, the concern of privacy has also increased on users who would like to protect their exact location from attackers or from the service provider. One technique to protect the location is called Location Obfuscation, which consists in non reversible ways to slightly alter the location such that it does not reflect the real location of the user, but still contains enough information to provide a satisfactory service. In this work, the N-Rand, N-Mix and the N-Dispersion techniques are introduced and compared with other two existing techniques, Rand and Distortion, in terms of distance-based metrics. The results show that the N-Rand and N-Dispersion techniques produce a larger minimum distance to the original location, and the greatest average distance to the original path.


title: Finding Fraud in Health Insurance Data with Two-Layer Outlier Detection Approach

abstract: Conventional techniques for detecting outliers address the problem of finding isolated observations that significantly differ from other observations that are stored in a database. For example, in the context of health insurance, one might be interested in finding unusual claims concerning prescribed medicines. Each claim record may contain information on the prescribed drug (its code), volume (e.g., the number of pills and their weight), dosing and the price. Finding outliers in such data can be used for identifying fraud. However, when searching for fraud, it is more important to analyse data not on the level of single records, but on the level of single patients, pharmacies or GP's. ::: ::: In this paper we present a novel approach for finding outliers in such hierarchical data. Our method uses standard techniques for measuring outlierness of single records and then aggregates these measurements to detect outliers in entities that are higher in the hierarchy. We applied this method to a set of about 40 million records from a health insurance company to identify suspicious pharmacies.


title: Some basic cryptographic requirements for chaos-based cryptosystems

abstract: In recent years, a large amount of work on chaos-based cryptosystems have been published. However, many of the proposed schemes fail to explain or do not possess a number of features that are fundamentally important to all kind of cryptosystems. As a result, many proposed systems are difficult to implement in practice with a reasonable degree of security. Likewise, they are seldom accompanied by a thorough security analysis. Consequently, it is difficult for other researchers and end users to evaluate their security and performance. This work is intended to provide a common framework of basic guidelines that, if followed, could benefit every new cryptosystem. The suggested guidelines address three main issues: implementation, key management and security analysis, aiming at assisting designers of new cryptosystems to present their work in a more systematic and rigorous way to fulfill some basic cryptographic requirements. Meanwhile, several recommendations are made regarding some practical aspects of analog chaos-based secure communications, such as channel noise, limited bandwith and attenuation.


Semi-Supervised AUC Optimization without Guessing Labels of Unlabeled Data


Generative Models for Cold-Start Recommendations


X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion


title: Skin detection in video under changing illumination conditions

abstract: Techniques for colour-based tracking of faces or hands often assume a static skin colour model. However, skin color perceived by a camera can change when lighting changes. In common real environments multiple light sources impinge on the skin. Therefore, for robust skin pixel detection, a dynamic skin colour model that can cope with the changes must be employed. We show that skin detection in video can be enhanced by exploiting the knowledge of the range of possible skin colours for the camera used. In normalized colour coordinates this range has a distinct shape we call the skin locus. We developed an adaptive histogram backprojection technique where the skin colour model is updated by pixels in the search region which fall in the skin locus. We demonstrate increased detection capability with webcam videos of faces taken successively under daylight, incandescent lamp, fluorescent light and a combination of these light sources.


title: Unsupervised colorization of black-and-white cartoons

abstract: We present a novel color-by-example technique which combines image segmentation, patch-based sampling and probabilistic reasoning. This method is able to automate colorization when new color information is applied on the already designed black-and-white cartoon. Our technique is especially suitable for cartoons digitized from classical celluloid films, which were originally produced by a paper or cel based method. In this case, the background is usually a static image and only the dynamic foreground needs to be colored frame-by-frame. We also assume that objects in the foreground layer consist of several well visible outlines which will emphasize the shape of homogeneous regions.


title: When paxos meets erasure code: reduce network and storage cost in state machine replication

abstract: Paxos-based state machine replication is a key technique to build highly reliable and available distributed services, such as lock servers, databases and other data storage systems. Paxos can tolerate any minority number of node crashes in an asynchronous network environment. Traditionally, Paxos is used to perform a full copy replication across all participants. However, full copy is expensive both in term of network and storage cost, especially in wide area with commodity hard drives. In this paper, we discussed the non-triviality and feasibility of combining erasure code into Paxos protocol, and presented an improved protocol named RS-Paxos (Reed Solomon Paxos). To the best of our knowledge, we are the first to propose such a combination. Compared to Paxos, RS-Paxos requires a limitation on the number of possible failures. If the number of tolerated failures decreases by 1, RS-Paxos can save over 50% of network transmission and disk I/O. To demonstrate the benefits of our protocol, we designed and built a key-value store based on RS-Paxos, and evaluated it on EC2 with various settings. Experiment results show that RS-Paxos achieves at most 2.5x improvement on write throughput and as much as 30% reduction on latency, in common configurations.


title: Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering

abstract: In this paper, we present the mQA model, which is able to answer questions about the content of an image. The answer can be a sentence, a phrase or a single word. Our model contains four components: a Long Short-Term Memory (LSTM) to extract the question representation, a Convolutional Neural Network (CNN) to extract the visual representation, an LSTM for storing the linguistic context in an answer, and a fusing component to combine the information from the first three components and generate the answer. We construct a Freestyle Multilingual Image Question Answering (FM-IQA) dataset to train and evaluate our mQA model. It contains over 150,000 images and 310,000 freestyle Chinese question-answer pairs and their English translations. The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test. Specifically, we mix the answers provided by humans and our model. The human judges need to distinguish our model from the human. They will also provide a score (i.e. 0, 1, 2, the larger the better) indicating the quality of the answer. We propose strategies to monitor the quality of this evaluation process. The experiments show that in 64.7% of cases, the human judges cannot distinguish our model from humans. The average score is 1.454 (1.918 for human). The details of this work, including the FM-IQA dataset, can be found on the project page: \url{http://idl.baidu.com/FM-IQA.html}.


Ontology-based Information Extraction from Technical Documents


title: Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

abstract: Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks. Specialized neural models have been developed for extracting answers from either text alone or Knowledge Bases (KBs) alone. In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text corpus. Building on recent advances in graph representation learning we propose a novel model, GRAFT-Net, for extracting answers from a question-specific subgraph containing text and KB entities and relations. We construct a suite of benchmark tasks for this problem, varying the difficulty of questions, the amount of training data, and KB completeness. We show that GRAFT-Net is competitive with the state-of-the-art when tested using either KBs or text alone, and vastly outperforms existing methods in the combined setting. Source code is available at this https URL .


Effect Between Quality of Nursing Service on Inpatient’s Loyalty and Satisfaction in dr. Soepraoen Army Hospital Malang


Jimple: Simplifying Java Bytecode for Analyses and Transformations


title: Shorter communication Cognitive defusion and self-relevant negative thoughts: examining the impact of a ninety year old technique

abstract: Cognitive defusion techniques are designed to reduce the functions of thoughts by altering the context in which they occur, rather than the attempting to alter the form, frequency, or situational sensitivity of the thoughts themselves. Applied technologies designed to produce cognitive defusion seem to lead to reductions in the believability of negative thoughts, but defusion techniques are generally only parts of complex packages and the role of defusion techniques per se is note yet known. The present study examined the impact of a cognitive defusion technique first described by Titchener nearly 90 years ago: rapidly repeating a single word. In series of eight single-case alternating treatment designs, this defusion technique was compared to a distraction task, and to a thought control task on reductions in the discomfort and believability of self-relevant negative thoughts. The cognitive defusion technique reduced both discomfort and believability more so than the comparison approaches. Control studies showed that the effect was probably not due to demand characteristics. # 2003 Elsevier Ltd. All rights reserved.


title: A multilevel inverter system for an induction motor with open-end windings

abstract: In this paper, a multilevel inverter system for an open-end winding induction motor drive is described. Multilevel inversion is achieved by feeding an open-end winding induction motor with two two-level inverters in cascade (equivalent to a three-level inverter) from one end and a single two-level inverter from the other end of the motor. The combined inverter system with open-end winding induction motor produces voltage space-vector locations identical to a six-level inverter. A total of 512 space-vector combinations are available in the proposed scheme, distributed over 91 space-vector locations. The proposed inverter drive scheme is capable of producing a multilevel pulsewidth-modulation (PWM) waveform for the phase voltage ranging from a two-level waveform to a six-level waveform depending on the modulation range. A space-vector PWM scheme for the proposed drive is implemented using a 1.5-kW induction motor with open-end winding structure.


title: A Quadratic-Complexity Observability-Constrained Unscented Kalman Filter for SLAM

abstract: This paper addresses two key limitations of the unscented Kalman filter (UKF) when applied to the simultaneous localization and mapping (SLAM) problem: the cubic computational complexity in the number of states and the inconsistency of the state estimates. To address the first issue, we introduce a new sampling strategy for the UKF, which has constant computational complexity. As a result, the overall computational complexity of UKF-based SLAM becomes of the same order as that of the extended Kalman filter (EKF)-based SLAM, i.e., quadratic in the size of the state vector. Furthermore, we investigate the inconsistency issue by analyzing the observability properties of the linear-regression-based model employed by the UKF. Based on this analysis, we propose a new algorithm, termed observability-constrained (OC)-UKF, which ensures the unobservable subspace of the UKF's linear-regression-based system model is of the same dimension as that of the nonlinear SLAM system. This results in substantial improvement in the accuracy and consistency of the state estimates. The superior performance of the OC-UKF over other state-of-the-art SLAM algorithms is validated by both Monte-Carlo simulations and real-world experiments.


title: A Simulation Framework for Evaluating Interference Mitigation Techniques in Heterogeneous Cellular Environments

abstract: Femtocells present an attractive solution for the improvement of a mobile network's services providing better data rates and coverage. Since their deployment results to a heterogeneous network where two layers must utilize the available spectrum, issues of interference arise. A method to address this challenge, is investigating the locations of the newly installed FBS, and enforcing a power controlled transmission of all FBSs that achieves optimal and fair overall performance. Another option that becomes available in inter-cell interference cancellation (ICIC) macrocell environments, is utilizing the available spectrum to complete or partly avoid co-channel operation. In this work, we provide a simulation framework that allows the creation of custom, high configurable, user defined topologies of femtocells with power control and frequency allocation capabilities. It allows the investigation of the margin of improvement in interference when these methods are applied and may work as a decision tool for planning and evaluating heterogeneous networks. To showcase the framework's capabilities, we evaluate and study the behaviour of custom deployed femtocells/macrocells networks and examine the cross-tier interference issues. Facilitated by the framework, we enforce and evaluate each interference mitigation technique for different femtocells' deployment densities. Finally, we compare the results of each method in terms of total throughput, spectral efficiency and cell-edge users' performance.


title: A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data

abstract: Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm (FAST) is proposed and experimentally evaluated in this paper. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent, the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study. Extensive experiments are carried out to compare FAST and several representative feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, with respect to four types of well-known classifiers, namely, the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature selection. The results, on 35 publicly available real-world high-dimensional image, microarray, and text data, demonstrate that the FAST not only produces smaller subsets of features but also improves the performances of the four types of classifiers.


title: A wideband transition from substrate integrated waveguide to differential microstrip lines in multilayer substrates

abstract: In this paper, a novel transition from substrate integrated waveguide (SIW) to differential microstrip lines is presented and discussed. The transition can be used in multilayer environments such as multilayered thin film or low temperature co-fired ceramics (LTCC).


title: FoldMe: interacting with double-sided foldable displays

abstract: In this paper, we present a novel device concept that features double-sided displays which can be folded using predefined hinges. The device concept enables users to dynamically alter both size and shape of the display and also to access the backside using fold gestures. We explore the design of such devices by investigating different types and forms of folding. Furthermore, we propose a set of interaction principles and techniques. Following a user-centered design process, we evaluate our device concept in two sessions with low-fidelity and high-fidelity prototypes.


title: Semantic depth of field

abstract: We present a new technique called Semantic Depth of Field (SDOF) as an alternative approach to focus-and-context displays of information. We utilize a well-known method from photography and cinematography (depth-of-field effect) for information visualization, which is to blur different parts of the depicted scene in dependence of their relevance. Independent of their spatial locations, objects of interest are depicted sharply in SDOF, whereas the context of the visualization is blurred. In this paper, we present a flexible model of SDOF which can be easily adopted to various types of applications. We discuss pros and cons of the new technique, give examples of application, and describe a fast prototype implementation of SDOF.


title: An Inherent Zero-Voltage and Zero-Current-Switching Full-Bridge Converter with No Additional Auxiliary Circuits

abstract: An inherent zero-voltage and zero-current-switching phase-shifted full-bridge converter with reverse-blocking insulated-gate bipolar transistor (IGBT) or non-punch-through IGBT is proposed in this paper. This converter not only ensures that the switches in the lagging leg works at zero-current switching, but also minimizes circulating conduction loss without any additional auxiliary circuits. A 1.2 kW hardware prototype is designed, fabricated, and tested to verify the proposed topology. The control loop design procedures with small-signal models are also presented. A simple, low-cost, and robust democratic current-sharing circuit is also introduced and verified in this study. The proposed converter is a suitable alternative for compact, cost-effective applications with high-voltage input.


title: Depth Dropout: Efficient Training of Residual Convolutional Neural Networks

abstract: Training state-of-the-art deep neural networks is computationally expensive and time consuming. In this paper we present a method that can reduce training time while at the same time maintain nearly the same accuracy as traditional training approaches. This allows for faster experimentation and better use of computational resource. Our method extends the well-known dropout technique by randomly removing entire network layers instead of individual neurons during training and hence reducing the number of expensive convolution operations needed per training iteration. We conduct experiments on object recognition using the CIFAR10 and ImageNet datasets to demonstrate the effectiveness of our approach. Our results show that we can train residual convolutional neural networks (ResNets) 17.5% faster with only 0.4% decrease in error rate or 34.1% faster with 1.3% increase in error rate compared to a baseline model. We also perform analysis on the trade-off between testing accuracy and training speedup as a function of the drop-out ratio.


title: Novel LTCC Distributed-Element Wideband Bandpass Filter Based on the Dual-Mode Stepped-Impedance Resonator

abstract: In this paper, a novel compact 3-D dual-mode stepped-impedance resonator (SIR) with centrally loaded stub is constructed for designing a wideband low-temperature cofired ceramic (LTCC) bandpass filter (BPF). By fully taking advantage of the LTCC technology, the dual-mode SIR, as a cell unit of the proposed filter, is reasonably folded in 3-D environment, and its properties are investigated using the transmission line theory. Accordingly, benefiting from the 3-D circuit layout, the proposed BPF designed by combining the SIR and dual-mode techniques can effectively overcome a vital and common drawback of large circuit size of the LTCC distributed-element filters. For demonstration, a fourth-order wideband BPF centered at 5 GHz is designed, fabricated, and measured. The circuit size of the designed BPF has a compact size of 3.875 × 4.8 × 1. 4m m 3 . The impact of the package on the coupling and routing scheme and frequency response/transmission zero has been analyzed and demonstrated. The simulated and measured results are presented and compared, showing a good agreement.


title: Towards a Privacy-Enhanced Social Networking Site

abstract: Social Networking Sites (SNS), such as Facebook and LinkedIn, have become the established place for keeping contact with old friends and meeting new acquaintances. As a result, a user leaves a big trail of personal information about him and his friends on the SNS, sometimes even without being aware of it. This information can lead to privacy drifts such as damaging his reputation and credibility, security risks (for instance identity theft) and profiling risks. In this paper, we first highlight some privacy issues raised by the growing development of SNS and identify clearly three privacy risks. While it may seem a priori that privacy and SNS are two antagonist concepts, we also identified some privacy criteria that SNS could fulfill in order to be more respectful of the privacy of their users. Finally, we introduce the concept of a Privacy-enhanced Social Networking Site (PSNS) and we describe Privacy Watch, our first implementation of a PSNS.


title: Design Aspects of High-Speed High-Power-Density Laminated-Rotor Induction Machines

abstract: This paper deals with the considerations associated with the design of high-speed high-power-density laminated-rotor induction machines (IMs). The considerations discussed are described by the design of an actual 10-kW machine, which runs at speeds of up to 75 kr/min with a rated power density of 28 MW/m3 for an electrically assisted turbocharger. Using a developed multidomain design environment which puts equal weight on the electromagnetic, mechanical, and thermal aspects, the rotor split ratio, electric and magnetic loadings, lamination material, rotor-bar material, and rotor-bar shape are identified as important and sensitive parameters in the design of high-speed IMs. Finally, general guidelines for designing high-speed high-power-density IMs are presented.


Designing Response Scales in an Applied Setting


title: Reinforcement learning agents providing advice in complex video games

abstract: This article introduces a teacher–student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have differe...


title: Exploiting Optimization for Local Graph Clustering

abstract: Local graph clustering methods aim to identify well-connected clusters around a given "seed set" of reference nodes. The main focus of prior theoretical work has been on worst-case running time properties or on implicit statistical regularization; and the focus of prior empirical work has been to identify structure in large social and information networks. Here, we adopt an optimization perspective on local graph clustering methods. In particular, we clarify the relationship between the local spectral algorithm of (Andersen, Chung and Lang, FOCS '06) and a variant of a well-studied optimization objective. This insight permits us to develop a local spectral graph clustering algorithm that has improved theoretical convergence properties. We also demonstrate the numerical performance of this optimization-based algorithm and some heuristic variants of it.


title: National IQs calculated and validated for 108 nations

abstract: We estimate the validity of the national IQs presented by Lynn and Vanhanen (2002, 2006) by examining whether they are consistent with the educational attainment of school students in math, science and reading comprehension in 108 countries and provinces. The educational attainment scores in a number of studies are integrated to give EAs (educational attainments) for 86 countries and provinces that have measured IQs. The correlation of EA with measured IQ (N=86 countries) is .917, and with measured+estimated IQ (N=108 countries) is .907. Corrected for attenuation, r=1.0. The quality of the data is evaluated, and the discrepancy between IQ and EA was greater for countries with low data quality, especially low quality of the EA data. There are no major anomalies in the relationship between IQ and EA in individual world regions. To some extent IQ and EA predict each other even within world regions. These


A stochastic limit approach to the fractional quantum Hall effect


Being an African: Some Queer Remarks from the Margins


title: Group-based recipe recommendations: analysis of data aggregation strategies

abstract: Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.


title: Gephi: An Open Source Software for Exploring and Manipulating Networks

abstract: Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, filtering, navigating, manipulating and clustering. Finally, by presenting dynamic features of Gephi, we highlight key aspects of dynamic network visualization.


title: Exact low-rank matrix completion via convex optimization

abstract: Suppose that one observes an incomplete subset of entries selected uniformly at random from a low-rank matrix. When is it possible to complete the matrix and recover the entries that have not been seen? We show that in very general settings, one can perfectly recover all of the missing entries from a sufficiently large random subset by solving a convex programming problem. This program finds the matrix with the minimum nuclear norm agreeing with the observed entries. The techniques used in this analysis draw upon parallels in the field of compressed sensing, demonstrating that objects other than signals and images can be perfectly reconstructed from very limited information.


title: A Petri net based deadlock prevention policy for flexible manufacturing systems

abstract: In this paper we illustrate a compositional method for modeling the concurrent execution of working processes in flexible manufacturing systems (FMS) through a special class of Petri nets that we call S/sup 3/PR. In essence, this class is built from state machines sharing a set of places modeling the availability of system resources. The analysis of S/sup 3/PR leads us to characterize deadlock situations in terms of a zero marking for some structural objects called siphons. In order to prevent the system from deadlocks, we propose a policy for resource allocation based on the addition of new places to the net imposing restrictions that prevent the presence of unmarked siphons (direct cause of deadlocks). Finally we present the application of this technique to a realistic FMS case. >


title: Fusion of laserscannner and video based lanemarking detection for robust lateral vehicle control and lane change maneuvers

abstract: The knowledge about lanes and the exact position on the road is fundamental for many advanced driver assistance systems. In this paper, a novel iterative histogram based approach with occupancy grids for the detection of multiple lanes is proposed. In highway scenarios, our approach is highly suitable to determine the correct number of all existing lanes on the road. Additionally, the output of the laserscannner based lane detection is fused with a production-available vision based system. It is shown that both sensor systems perfectly complement each other to increase the robustness of a lane tracking system. The achieved accuracy of the fusion system, the laserscannner and video based system is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control applications.


Improving Video Segmentation by Fusing Depth Cues and the ViBe Algorithm


title: Learning to Represent Knowledge Graphs with Gaussian Embedding

abstract: The representation of a knowledge graph (KG) in a latent space recently has attracted more and more attention. To this end, some proposed models (e.g., TransE) embed entities and relations of a KG into a "point" vector space by optimizing a global loss function which ensures the scores of positive triplets are higher than negative ones. We notice that these models always regard all entities and relations in a same manner and ignore their (un)certainties. In fact, different entities and relations may contain different certainties, which makes identical certainty insufficient for modeling. Therefore, this paper switches to density-based embedding and propose KG2E for explicitly modeling the certainty of entities and relations, which learn the representations of KGs in the space of multi-dimensional Gaussian distributions. Each entity/relation is represented by a Gaussian distribution, where the mean denotes its position and the covariance (currently with diagonal covariance) can properly represent its certainty. In addition, compared with the symmetric measures used in point-based methods, we employ the KL-divergence for scoring triplets, which is a natural asymmetry function for effectively modeling multiple types of relations. We have conducted extensive experiments on link prediction and triplet classification with multiple benchmark datasets (WordNet and Freebase). Our experimental results demonstrate that our method can effectively model the (un)certainties of entities and relations in a KG, and it significantly outperforms state-of-the-art methods (including TransH and TransR).


title: Joint space-time algebraically-interleaved turbo-like Coded Incoherent MIMO systems with optimal and suboptimal MAP probability decoders

abstract: The outstanding coding gains and also the practical complexities of the turbo-coded communication systems, heavily depend on the iterative soft-decision decoding algorithm used for the soft-input/-output component decoders. The goal of this paper is to address the investigation on the energy gaps of the enhanced channel coding entity of [1], when original and also approximated logarithmic-based iterative MAP algorithms are employed for the iterative near maximum-likelihood (ML) decoding. As far as the large decoding delays of the turbo and turbo-like code structures constitute a major disadvantage for these types of channel-codes, the previously introduced high-performance channel-codes of [1] are developed for the original and also approximated iterative BCJR-MAP turbo decoders to enable the higher level of selection flexibility in error-rate versus computational complexity tradeoff. We examine the required energy per bit E b and the noise power spectral density N 0 ratio to attain the BER ≈ 10−4 – 10−5 for the differentially encoded phase-shift keying (D-PSK) MIMO Rayleigh fading channels.


title: FastVentricle: Cardiac Segmentation with ENet

abstract: Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that postprocessing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advance the state-of-the-art in automated segmentation, which makes FCNs a natural choice for ventricular segmentation. However, FCNs are limited by their computational cost, which increases the monetary cost and degrades the user experience of production systems. To combat this shortcoming, we have developed the FastVentricle architecture, an FCN architecture for ventricular segmentation based on the recently developed ENet architecture. FastVentricle is 4\(\times \) faster and runs with 6\(\times \) less memory than the previous state-of-the-art ventricular segmentation architecture while still maintaining excellent clinical accuracy.


title: Design and Development of a Medical Parallel Robot for Cardiopulmonary Resuscitation

abstract: The concept of a medical parallel robot applicable to chest compression in the process of cardiopulmonary resuscitation (CPR) is proposed in this paper. According to the requirement of CPR action, a three-prismatic-universal-universal (3-PUU) translational parallel manipulator (TPM) is designed and developed for such applications, and a detailed analysis has been performed for the 3-PUU TPM involving the issues of kinematics, dynamics, and control. In view of the physical constraints imposed by mechanical joints, both the robot-reachable workspace and the maximum inscribed cylinder-usable workspace are determined. Moreover, the singularity analysis is carried out via the screw theory, and the robot architecture is optimized to obtain a large well-conditioning usable workspace. Based on the principle of virtual work with a simplifying hypothesis adopted, the dynamic model is established, and dynamic control utilizing computed torque method is implemented. At last, the experimental results made for the prototype illustrate the performance of the control algorithm well. This research will lay a good foundation for the development of a medical robot to assist in CPR operation.


A Nonisolated Transformerless High Voltage Gain Buck Boost dc-dc Converter


title: GSA: A Gravitational Search Algorithm

abstract: In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.


title: An intelligent chaotic embedding approach to enhance stego-image quality

abstract: Steganography's role in secret communication extends from concealing information to communication. As one of several possible approaches to embed confidential information in digital images, the Least Significant Bit (LSB) technique has been widely used; however, maintaining the imperceptibility of the stego-image is still a serious concern. In this paper, an Adaptive Random (AR) k-bit embedding approach has been attempted to enhance the quality of stego-images. The original cover is divided into nonoverlapping blocks of equal size. The encrypted confidential data are embedded in each block through four different random walks. The best random walk, which provides the minimum degradation for a particular block, is identified, and is fixed for that block. The decision on the fixed random walk for each block is recorded and kept as the secret key. The AR method has also been combined with the Inverted Pattern approach, referred to as the Adaptive Random Inverted Pattern (ARIP) approach, to further enhance the quality of the stego-image. The estimated Peak Signal to Noise Ratio (PSNR) value for the ARIP method yields 1dB enhancement.


title: The Neural Architecture of Music-Evoked Autobiographical Memories

abstract: The medial prefrontal cortex (MPFC) is regarded as a region of the brain that supports self-referential processes, including the integration of sensory information with self-knowledge and the retrieval of autobiographical information. I used functional magnetic resonance imaging and a novel procedure for eliciting autobiographical memories with excerpts of popular music dating to one's extended childhood to test the hypothesis that music and autobiographical memories are integrated in the MPFC. Dorsal regions of the MPFC (Brodmann area 8/9) were shown to respond parametrically to the degree of autobiographical salience experienced over the course of individual 30 s excerpts. Moreover, the dorsal MPFC also responded on a second, faster timescale corresponding to the signature movements of the musical excerpts through tonal space. These results suggest that the dorsal MPFC associates music and memories when we experience emotionally salient episodic memories that are triggered by familiar songs from our personal past. MPFC acted in concert with lateral prefrontal and posterior cortices both in terms of tonality tracking and overall responsiveness to familiar and autobiographically salient songs. These findings extend the results of previous autobiographical memory research by demonstrating the spontaneous activation of an autobiographical memory network in a naturalistic task with low retrieval demands.


title: Image-Based Modeling of the Human Eye

abstract: Rendering realistic organic materials is a challenging issue. The human eye is an important part of nonverbal communication which, consequently, requires specific modeling and rendering techniques to enhance the realism of virtual characters. We propose an image-based method for estimating both iris morphology and scattering features in order to generate convincing images of virtual eyes. In this regard, we develop a technique to unrefract iris photographs. We model the morphology of the human iris as an irregular multilayered tissue. We then approximate the scattering features of the captured iris. Finally, we propose a real-time rendering technique based on the subsurface texture mapping representation and introduce a precomputed refraction function as well as a caustic function, which accounts for the light interactions at the corneal interface.


title: Real-time illumination invariant lane detection for lane departure warning system

abstract: Invariant property of lane color under various illuminations is utilized for lane detection.Computational complexity is reduced using vanishing point detection and adaptive ROI.Datasets for evaluation include various environments from several devices.Simulation demo demonstrate fast and powerful performance for real-time applications. Lane detection is an important element in improving driving safety. In this paper, we propose a real-time and illumination invariant lane detection method for lane departure warning system. The proposed method works well in various illumination conditions such as in bad weather conditions and at night time. It includes three major components: First, we detect a vanishing point based on a voting map and define an adaptive region of interest (ROI) to reduce computational complexity. Second, we utilize the distinct property of lane colors to achieve illumination invariant lane marker candidate detection. Finally, we find the main lane using a clustering method from the lane marker candidates. In case of lane departure situation, our system sends driver alarm signal. Experimental results show satisfactory performance with an average detection rate of 93% under various illumination conditions. Moreover, the overall process takes only 33ms per frame.


title: Pattern Based Sequence Classification

abstract: Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting patterns found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of a pattern in a given class of sequences by combining the cohesion and the support of the pattern. We use the discovered patterns to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on an improved version of the existing method of classification based on association rules, while the second ranks the rules by first measuring their value specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability. Additionally, we test a number of pattern feature based models that use different kinds of patterns as features to represent each sequence as a feature vector. We then apply a variety of machine learning algorithms for sequence classification, experimentally demonstrating that the patterns we discover represent the sequences well, and prove effective for the classification task.


title: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps

abstract: We propose a deep convolutional object detector for automated driving applications that also estimates classification, pose and shape uncertainty of each detected object. The input consists of a multi-layer grid map which is well-suited for sensor fusion, free-space estimation and machine learning. Based on the estimated pose and shape uncertainty we approximate object hulls with bounded collision probability which we find helpful for subsequent trajectory planning tasks. We train our models based on the KITTI object detection data set. In a quantitative and qualitative evaluation some models show a similar performance and superior robustness compared to previously developed object detectors. However, our evaluation also points to undesired data set properties which should be addressed when training data-driven models or creating new data sets.


title: Multimodal Human Activity Recognition From Wearable Inertial Sensors Using Machine Learning

abstract: This research addresses the problem of human activity recognition using wearable sensors. Due to its diverse applications, such as health monitoring, it has been attracting a considerable research attention. In order to benefit from machine learning, as an efficient tool for pattern discovery, in enhancing the recognition accuracy, the literature has witnessed the construction of several datasets with varying number of sensors and activities, among other variations. One of the state-of-the-art (SOTA) datasets and the most comprehensive to date is the publicly-available Human Gait Database (HuGaDB).We adopt diverse machine learning techniques for human activity recognition based on a comprehensive dataset HuGaDB, for which we show that particular classifiers, sensor positions and placements yield higher recognition accuracies. In particular, we study six classifiers with fourteen features for twelve different activities measured using two wearable senors. Random Forest (RF) is shown to consistently outperform other classifiers. We also present further investigations for combining multimodal data and for varying the parameters of the RF classifier in order to further improve the accuracy. A recognition accuracy of 98.6% was attained. This research is reproducible, and attains SOTA results on a novel and comprehensive dataset.


title: Beneficial psychological effects of a probiotic formulation (Lactobacillus helveticus R0052 and Bifidobacterium longum R0175) in healthy human volunteers

abstract: In a recent clinical study, we demonstrated in the general population that Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 (PF) taken in combination for 30 days decreased the global scores of hospital anxiety and depression scale (HADs), and the global severity index of the Hopkins symptoms checklist (HSCL-90), due to the decrease of the sub-scores of somatization, depression and anger-hostility spheres. Therefore, oral intake of PF showed beneficial effects on anxiety and depression related behaviors in human volunteers. From there, it is interesting to focus on the role of this probiotic formulation in the subjects with the lowest urinary free cortisol levels at baseline.This addendum presents a secondary analyse of the effects of PF in a sub-population of 25 subjects with urinary free cortisol (UFC) levels less than 50 ng/ml at baseline, on psychological distress based on the percentage of change of the perceived stress scale (PSs), the HADs and the HSCL-90 scores between baseline and f...


title: Bucket Renormalization for Approximate Inference

abstract: Probabilistic graphical models are a key tool in machine learning applications. Computing the partition function, i.e., normalizing constant, is a fundamental task of statistical inference but it is generally computationally intractable, leading to extensive study of approximation methods. Iterative variational methods are a popular and successful family of approaches. However, even state of the art variational methods can return poor results or fail to converge on difficult instances. In this paper, we instead consider computing the partition function via sequential summation over variables. We develop robust approximate algorithms by combining ideas from mini-bucket elimination with tensor network and renormalization group methods from statistical physics. The resulting "convergence-free" methods show good empirical performance on both synthetic and real-world benchmark models, even for difficult instances.


title: IMPROVED EXPLAINABILITY OF CAPSULE NETWORKS: RELEVANCE PATH BY AGREEMENT

abstract: Recent advancements in signal processing and machine learning domains have resulted in an extensive surge of interest in deep learning models due to their unprecedented performance and high accuracy for different and challenging problems of significant engineering importance. However, when such deep learning architectures are utilized for making critical decisions such as the ones that involve human lives (e.g., in medical applications), it is of paramount importance to understand, trust, and in one word"explain"the rational behind deep models' decisions. Currently, deep learning models are typically considered as black-box systems, which do not provide any clue on their internal processing actions. Although some recent efforts have been initiated to explain behavior and decisions of deep networks, explainable artificial intelligence (XAI) domain is still in its infancy. In this regard, we consider capsule networks (referred to as CapsNets), which are novel deep structures; recently proposed as an alternative counterpart to convolutional neural networks (CNNs), and posed to change the future of machine intelligence. In this paper, we investigate and analyze structures and behaviors of the CapsNets and illustrate potential explainability properties of such networks. Furthermore, we show possibility of transforming deep learning architectures in to transparent networks via incorporation of capsules in different layers instead of convolution layers of the CNNs.


title: Automatically Producing Plot Unit Representations for Narrative Text

abstract: In the 1980s, plot units were proposed as a conceptual knowledge structure for representing and summarizing narrative stories. Our research explores whether current NLP technology can be used to automatically produce plot unit representations for narrative text. We create a system called AESOP that exploits a variety of existing resources to identify affect states and applies "projection rules" to map the affect states onto the characters in a story. We also use corpus-based techniques to generate a new type of affect knowledge base: verbs that impart positive or negative states onto their patients (e.g., being eaten is an undesirable state, but being fed is a desirable state). We harvest these "patient polarity verbs" from a Web corpus using two techniques: co-occurrence with Evil/Kind Agent patterns, and bootstrapping over conjunctions of verbs. We evaluate the plot unit representations produced by our system on a small collection of Aesop's fables.


title: Codesigned Wideband High-Efficiency Filtering SIW Slot Antenna with High Selectivity and Flat Gain Response

abstract: A substrate integrated waveguide (SIW) slot antenna with wideband bandpass filtering performance is proposed in this journal. It consists of a SIW cavity, a transverse slot, three metal posts, and a SMA connector. Three metal posts split the SIW cavity into two TE110-mode resonators. A long nonresonant transverse slot is utilized to realize a half TE110-mode resonator and generate radiation simultaneously so as to reduce the size and release the use of extra radiator. Three in-band resonance poles and a radiation null at both out-of-bands are obtained. A prototype is fabricated and measured. Measured results demonstrate the proposed antenna is with a center frequency of 4.26 GHz, a fractional bandwidth of 9.1%, a high efficiency of 93%, and flat gain response and good skirt selectivity of 270 dB/GHz and 330 dB/GHz for the lower and upper out-of-band, respectively.


title: Information Hiding Techniques: A Tutorial Review

abstract: Abstract The purpose of this tutorial is to present an overview of various information hiding techniques. A brief history of steganography is provided along with techniques that were used to hide information. Text, image and audio based information hiding techniques are discussed. This paper also provides a basic introduction to digital watermarking. 1. History of Information Hiding The idea of communicating secretly is as old as communication itself. In this section, we briefly discuss the historical development of information hiding techniques such as steganography/ watermarking. Early steganography was messy. Before phones, before mail, before horses, messages were sent on foot. If you wanted to hide a message, you had two choices: have the messenger memorize it, or hide it on the messenger. While information hiding techniques have received a tremendous attention recently, its application goes back to Greek times. According to Greek historian Herodotus, the famous Greek tyrant Histiaeus, while in prison, used unusual method to send message to his son-in-law. He shaved the head of a slave to tattoo a message on his scalp. Histiaeus then waited until the hair grew back on slave’s head prior to sending him off to his son-in-law. The second story also came from Herodotus, which claims that a soldier named Demeratus needed to send a message to Sparta that Xerxes intended to invade Greece. Back then, the writing medium was written on wax-covered tablet. Demeratus removed the wax from the tablet, wrote the secret message on the underlying wood, recovered the tablet with wax to make it appear as a blank tablet and finally sent the document without being detected. Invisible inks have always been a popular method of steganography. Ancient Romans used to write between lines using invisible inks based on readily available substances such as fruit juices, urine and milk. When heated, the invisible inks would darken, and become legible. Ovid in his “Art of Love” suggests using milk to write invisibly. Later chemically affected sympathetic inks were developed. Invisible inks were used as recently as World War II. Modern invisible inks fluoresce under ultraviolet light and are used as anti-counterfeit devices. For example, "VOID" is printed on checks and other official documents in an ink that appears under the strong ultraviolet light used for photocopies. The monk Johannes Trithemius, considered one of the founders of modern cryptography, had ingenuity in spades. His three volume work Steganographia, written around 1500, describes an extensive system for concealing secret messages within innocuous texts. On its surface, the book seems to be a magical text, and the initial reaction in the 16th century was so strong that Steganographia was only circulated privately until publication in 1606. But less than five years ago, Jim Reeds of AT&T Labs deciphered mysterious codes in the third volume, showing that Trithemius' work is more a treatise on cryptology than demonology. Reeds' fascinating account of the code breaking process is quite readable. One of Trithemius' schemes was to conceal messages in long invocations of the names of angels, with the secret message appearing as a pattern of letters within the words. For example, as every other letter in every other word: padiel a porsy mesarpon o meuas peludyn m alpreaxo which reveals "prymus apex." Another clever invention in Steganographia was the "Ave Maria" cipher. The book contains a series of tables, each of which has a list of words, one per letter. To code a message, the message letters are replaced by the corresponding words. If the tables are used in order, one table per letter, then the coded message will appear to be an innocent prayer. The earliest actual book on steganography was a four hundred page work written by Gaspari Schott in 1665 and called Steganographica . Although most of the ideas came from Trithemius, it was a start. Further development in the field occurred in 1883, with the publication of Auguste Kerchoffs’ Cryptographie militaire . Although this work was mostly about cryptography, it describes some principles that are worth keeping in mind when designing a new steganographic system.


title: Selection of eLECTRIC mOTOR dRIVES for electric vehicles

abstract: In this study, six kinds of the drivetrain systems of electric motor drives for EVs are discussed. Furthermore, the requirements of EVs on electric motor drives are presented. The comparative investigation on the efficiency, weight, cost, cooling, maximum speed, and fault-tolerance, safety, and reliability is carried out for switched reluctance motor, induction motor, permanent magnet blushless DC motor, and brushed DC motor drives, in order to find most appropriate electric motor drives for electric vehicle applications. The study shows that switched reluctance motor drives are the prior choice for electric vehicles.


title: Designing location-based mobile games with a purpose: collecting geospatial data with CityExplorer

abstract: The games with a purpose paradigm proposed by Luis von Ahn [9] is a new approach for game design where useful but boring tasks, like labeling a random image found in the web, are packed within a game to make them entertaining. But there are not only large numbers of internet users that can be used as voluntary data producers but legions of mobile device owners, too. In this paper we describe the design of a location-based mobile game with a purpose: CityExplorer. The purpose of this game is to produce geospatial data that is useful for non-gaming applications like a location-based service. From the analysis of four use case studies of CityExplorer we report that such a purposeful game is entertaining and can produce rich geospatial data collections.


title: The influence of match location, quality of opposition, and match status on possession strategies in professional association football

abstract: Abstract The aim of this study was to examine the effects of match location, quality of opposition, and match status on possession strategies in a professional Spanish football team. Twenty-seven matches from the 2005–2006 domestic league season were notated post-event using a computerized match analysis system. Matches were divided into episodes according to evolving match status. Linear regression analysis showed that possession of the ball was greater when losing than when winning (P < 0.01) or drawing (P < 0.05), and playing against strong opposition was associated with a decrease in time spent in possession (P < 0.01). In addition, weighted mean percentage time spent in different zones of the pitch (defensive third, middle third, attacking third) was influenced by match status (P < 0.01) and match location (P < 0.05). A combination of these variables and their interactions can be used to develop a model to predict future possession in football. The findings emphasize the need for match analysts and c...


title: Leveraging Knowledge Bases for Contextual Entity Exploration

abstract: Users today are constantly switching back and forth from applications where they consume or create content (such as e-books and productivity suites like Microsoft Office and Google Docs) to search engines where they satisfy their information needs. Unfortunately, though, this leads to a suboptimal user experience as the search engine lacks any knowledge about the content that the user is authoring or consuming in the application. As a result, productivity suites are starting to incorporate features that let the user "explore while they work". Existing work in the literature that can be applied to this problem takes a standard bag-of-words information retrieval approach, which consists of automatically creating a query that includes not only the target phrase or entity chosen by the user but also relevant terms from the context. While these approaches have been successful, they are inherently limited to returning results (documents) that have a syntactic match with the keywords in the query. We argue that the limitations of these approaches can be overcome by leveraging semantic signals from a knowledge graph built from knowledge bases such as Wikipedia. We present a system called Lewis for retrieving contextually relevant entity results leveraging a knowledge graph, and perform a large scale crowdsourcing experiment in the context of an e-reader scenario, which shows that Lewis can outperform the state-of-the-art contextual entity recommendation systems by more than 20% in terms of the MAP score.