title: Sex Differences in Emotional Evaluation of Film Clips: Interaction with Five High Arousal Emotional Categories

abstract: The present study aimed to investigate gender differences in the emotional evaluation of 18 film clips divided into six categories: Erotic, Scenery, Neutral, Sadness, Compassion, and Fear. 41 female and 40 male students rated all clips for valence-pleasantness, arousal, level of elicited distress, anxiety, jittery feelings, excitation, and embarrassment. Analysis of positive films revealed higher levels of arousal, pleasantness, and excitation to the Scenery clips in both genders, but lower pleasantness and greater embarrassment in women compared to men to Erotic clips. Concerning unpleasant stimuli, unlike men, women reported more unpleasantness to the Compassion, Sadness, and Fear compared to the Neutral clips and rated them also as more arousing than did men. They further differentiated the films by perceiving greater arousal to Fear than to Compassion clips. Women rated the Sadness and Fear clips with greater Distress and Jittery feelings than men did. Correlation analysis between arousal and the other emotional scales revealed that, although men looked less aroused than women to all unpleasant clips, they also showed a larger variance in their emotional responses as indicated by the high number of correlations and their relatively greater extent, an outcome pointing to a masked larger sensitivity of part of male sample to emotional clips. We propose a new perspective in which gender difference in emotional responses can be better evidenced by means of film clips selected and clustered in more homogeneous categories, controlled for arousal levels, as well as evaluated through a number of emotion focused adjectives.


title: Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots

abstract: It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using ‘social bots’ deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.


title: Pattern classification and clustering: A review of partially supervised learning approaches

abstract: The paper categorizes and reviews the state-of-the-art approaches to the partially supervised learning (PSL) task. Special emphasis is put on the fields of pattern recognition and clustering involving partially (or, weakly) labeled data sets. The major instances of PSL techniques are categorized into the following taxonomy: (i) active learning for training set design, where the learning algorithm has control over the training data; (ii) learning from fuzzy labels, whenever multiple and discordant human experts are involved in the (complex) data labeling process; (iii) semi-supervised learning (SSL) in pattern classification (further sorted out into: self-training, SSL with generative models, semi-supervised support vector machines; SSL with graphs); (iv) SSL in data clustering, using additional constraints to incorporate expert knowledge into the clustering process; (v) PSL in ensembles and learning by disagreement; (vi) PSL in artificial neural networks. In addition to providing the reader with the general background and categorization of the area, the paper aims at pointing out the main issues which are still open, motivating the on-going investigations in PSL research.


title: Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions

abstract: KEY MESSAGE ::: Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1% on average and the variability in prediction accuracy decreased by 10.8%. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.


Improving Neural Machine Translation by Incorporating Hierarchical Subword Features


title: Fusing Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks

abstract: Recent skeleton-based action recognition approaches achieve great improvement by using recurrent neural network (RNN) models. Currently, these approaches build an end-to-end network from coordinates of joints to class categories and improve accuracy by extending RNN to spatial domains. First, while such well-designed models and optimization strategies explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, according to the evolution of previous work, we select a set of simple geometric features, and then seperately feed each type of features to a three-layer LSTM framework. Second, we propose a multistream LSTM architecture with a new smoothed score fusion technique to learn classification from different geometric feature streams. Furthermore, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and the fusion results achieve the state-of-the-art performance on four datasets. We also show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.


title: Early Predictions of Movie Success: the Who, What, and When of Profitability

abstract: AbstractWe focus on predicting the profitability of a movie to support movie-investment decisions at early stages of film production. By leveraging data from various sources, and using social network analysis and text mining techniques, the proposed system extracts several types of features, including “who” is in the cast, “what” a movie is about, “when” a movie will be released, as well as “hybrid” features. Experiment results showed that the system outperforms benchmark methods by a large margin. Novel features we proposed made weighty contributions to the prediction. In addition to designing a decision support system with practical utility, we also analyzed key factors of movie profitability. Furthermore, we demonstrated the prescriptive value of our system by illustrating how it can be used to recommend a set of profit-maximizing cast members. This research highlights the power of predictive and prescriptive data analytics in information systems to aid business decisions.


title: Low-loss and energy efficient modulation in silicon photonic waveguides by adiabatic elimination scheme

abstract: High-speed Silicon Photonics calls for solutions providing a small footprint, high density, and minimum crosstalk, as exemplified by the recent development of integrated optical modulators. Yet, the performances of such modulators are hindered by intrinsic material losses, which results in low energy efficiency. Using the concept of Adiabatic Elimination, here, we introduce a scheme allowing for the low-loss modulation in densely packed waveguides. Our system is composed of two waveguides, whose coupling is mediated by an intermediate third waveguide. The signal is carried by the two outer modes, while the active control of their coupling is achieved via the intermediate dark mode. The modulation is performed by the manipulation of the central-waveguide mode index, leaving the signal-carrying waveguides unaffected by the loss. We discuss how Adiabatic Elimination provides a solution for mitigating signal losses and designing relatively compact, broadband, and energy-efficient integrated optical modulators.High-speed Silicon Photonics calls for solutions providing a small footprint, high density, and minimum crosstalk, as exemplified by the recent development of integrated optical modulators. Yet, the performances of such modulators are hindered by intrinsic material losses, which results in low energy efficiency. Using the concept of Adiabatic Elimination, here, we introduce a scheme allowing for the low-loss modulation in densely packed waveguides. Our system is composed of two waveguides, whose coupling is mediated by an intermediate third waveguide. The signal is carried by the two outer modes, while the active control of their coupling is achieved via the intermediate dark mode. The modulation is performed by the manipulation of the central-waveguide mode index, leaving the signal-carrying waveguides unaffected by the loss. We discuss how Adiabatic Elimination provides a solution for mitigating signal losses and designing relatively compact, broadband, and energy-efficient integrated optical modulators.


INFOGRAPHICS IN EDUCATION: REVIEW ON INFOGRAPHICS DESIGN


title: How Much Faster is Fast Enough?: User Perception of Latency & Latency Improvements in Direct and Indirect Touch

abstract: This paper reports on two experiments designed to further our understanding of users' perception of latency in touch- based systems. The first experiment extends previous efforts to measure latency perception by reporting on a unified study in which direct and indirect form-factors are compared for both tapping and dragging tasks. Our results show significant effects from both form-factor and task, and inform system designers as to what input latencies they should aim to achieve in a variety of system types. A follow-up experiment investigates peoples' ability to perceive small improvements to latency in direct and indirect form-factors for tapping and dragging tasks. Our results provide guidance to system designers of the relative value of making improvements in latency that reduce but do not fully eliminate lag from their systems.


title: Semi-supervised learning of hierarchical representations of molecules using neural message passing

abstract: With the rapid increase of compound databases available in medicinal and material science, there is a growing need for learning representations of molecules in a semi-supervised manner. In this paper, we propose an unsupervised hierarchical feature extraction algorithm for molecules (or more generally, graph-structured objects with fixed number of types of nodes and edges), which is applicable to both unsupervised and semi-supervised tasks. Our method extends recently proposed Paragraph Vector algorithm and incorporates neural message passing to obtain hierarchical representations of subgraphs. We applied our method to an unsupervised task and demonstrated that it outperforms existing proposed methods in several benchmark datasets. We also experimentally showed that semi-supervised tasks enhanced predictive performance compared with supervised ones with labeled molecules only.


title: SSL/TLS attacks: Analysis and evaluation

abstract: The Secure Socket Layer /Transport Layer Security (SSL/TLS) provides data confidentiality, integrity and authenticity between two communicating applications. There are many attacks appeared in different versions of SSL/TLS based on vulnerabilities related to the protocol structure and its implementation. This paper presents an analysis and evaluation of some theoretical and practical attacks on SSL/TLS, and divides them to two categories: attacks on Handshake protocol and attack on Record protocol. We evaluate each category based on specific criteria that selected to the current state of the art, such as: Weakness, which identifies the vulnerability of the system, Effect, which identifies the attacker harm the system, and Limitation, to identify the current countermeasures weaknesses.


title: A web enabled secured system for attendance monitoring and real time location tracking using Biometric and Radio Frequency Identification (RFID) technology

abstract: Automated Information Systems has played a major role in the growth, advancement, and modernization of our daily work processes. The main purpose of this paper is to develop a safe and secure web based attendance monitoring system using Biometrics and Radio Frequency Identification (RFID) Technology based on multi-tier architecture, for both computers and smartphones. The system can maintain the attendance records of both students and teachers/staff members of an institution. The system can also detect the current location of the students, faculties, and other staff members anywhere within the domain of institution campus. With the help of android application one can receive live feeds of various campus activities, keep updated with the current topics in his/her enrolled courses as well as track his/her friends on a real time basis. An automated SMS service is facilitated in the system, which sends an SMS automatically to the parents in order to notify that their ward has successfully reached the college. Parents as well as student will be notified via e-mail, if the student is lagging behind in attendance. There is a functionality of automatic attendance performance graph in the system, which gives an idea of the student's consistency in attendance throughout the semester.


title: Big Data: The V's of the Game Changer Paradigm

abstract: The Big Data is the most prominent paradigm now-a-days. The Big Data starts rule slowly from 2003, and expected to rule and dominate the IT industries at least up to 2030. Furthermore, the Big Data conquer the technological war and easily capture the entire market since 2009. The Big Data is blasting everywhere around the World in every domain. The Big Data, a massive amount of data, able to generate billions of revenue. The secret behind of these billions of revenue is ever growing volume. This paper presents the redefinition of volume of Big Data. The volume is redefined by engaging three other V's, namely, voluminosity, vacuum, and vitality. Furthermore, this paper augments two new V's to the Big Data paradigm, namely, vendee and vase. This paper explores all V's of Big Data. There are lots of controversy and confusion regarding V's of Big Data. This paper uncovers the confusions of the V family of the Big Data.


title: An image encryption method based on logistic chaotic mapping and DNA coding

abstract: This paper proposes a new image encryption algorithm, which is based on Logistic chaotic mapping and DNA coding. A variable parameter logistic mapping coupled chaotic system is proposed to generate pseudo-random sequences with good pseudo-random properties. Before encrypting plain-text images, the distinct features of some special images are eliminated by superposing them with a pseudo-random image. Combining with DNA encoding operation, an encrypting algorithm of cross-operation of diffusion and permutation is designed. The simulation results show that the proposed algorithm has large key space, high sensitivity to key and plain-text, and good robustness to resist exhaustive attack, statistic attack, differential attack, etc. It shows that the proposed method has good encryption effect and security.


title: Research on data stream clustering algorithms

abstract: Data stream is a potentially massive, continuous, rapid sequence of data information. It has aroused great concern and research upsurge in the field of data mining. Clustering is an effective tool of data mining, so data stream clustering will undoubtedly become the focus of the study in data stream mining. In view of the characteristic of the high dimension, dynamic, real-time, many effective data stream clustering algorithms have been proposed. In addition, data stream information are not deterministic and always exist outliers and contain noises, so developing effective data stream clustering algorithm is crucial. This paper reviews the development and trend of data stream clustering and analyzes typical data stream clustering algorithms proposed in recent years, such as Birch algorithm, Local Search algorithm, Stream algorithm and CluStream algorithm. We also summarize the latest research achievements in this field and introduce some new strategies to deal with outliers and noise data. At last, we put forward the focal points and difficulties of future research for data stream clustering.


title: Self-disclosure and liking: A meta-analytic review.

abstract: Self-disclosure plays a central role in the development and maintenance of relationships. One way that researchers have explored these processes is by studying the links between self-disclosure and liking. Using meta-analytic procedures, the present work sought to clarify and review this literature by evaluating the evidence for 3 distinct disclosure-liking effects. Significant disclosure-liking relations were found for each effect: (a) People who engage in intimate disclosures tend to be liked more than people who disclose at lower levels, (b) people disclose more to those whom they initially like, and (c) people like others as a result of having disclosed to them. In addition, the relation between disclosure and liking was moderated by a number of variables, including study paradigm, type of disclosure, and gender of the discloser. Taken together, these results suggest that various disclosure-liking effects can be integrated and viewed as operating together within a dynamic interpersonal system. Implications for theory development are discussed, and avenues for future research are suggested. Wanting Mary to like him more, John told her that he sometimes felt insecure with women who were so capable but that he really wanted to work on those feelings. Believing that John was really opening up to her, Mary realized she liked John, and her responses to him made him believe that he was winning her over. Mary told John she wasn't as self-confident as he perceived her to be, she too had doubting moments... Self-disclosures, such as those conveyed by John and Mary above, involve the act of revealing personal information about oneself to another. Such actions have been viewed as central to the development of close relationships (Altman & Taylor, 1973) and to the maintenance of psychological well-being (Jourard, 1964). Since Jourard's (1964, 1971) pioneering work, self-disclosure has been the focus of theoretical discussion and research inquiry in a number of disciplines, including psychology, communication, and sociology. Although the self-disclosure literature is extensive and complex, most of the empirical work within psychology has addressed a small number of self-disclosure phenomena. This article will focus on one of these: the relation between self-disclosure and liking (for more general reviews, see Derlega, Metts, Petronio, & Margulis, 1993; Chelune, 1979;Cozby, 1973;Goodstein&Reinecker, 1974). Interest in the relationship between self-disclosure and liking


title: Child and adolescent mental disorders: the magnitude of the problem across the globe

abstract: Objective: Describe objectively the global gaps in policy, data gathering capacity, and resources to develop and implement services to support child mental health. Methods: Report on the World health Organization (WHO) child and adolescent mental health resources Atlas project. The Atlas project utilized key informants and was supplemented by studies that focused on policy. This report also draws on current epidemiological studies to provide a context for understanding the magnitude of the clinical problem. Results: Current global epidemiological data consistently reports that up to 20% of children and adolescents suffer from a disabling mental illness; that suicide is the third leading cause of death among adolescents; and that up to 50% of all adult mental disorders have their onset in adolescence. While epidemiological data appears relatively uniform globally, the same is not true for policy and resources for care. The gaps in resources for child mental health can be categorized as follows: economic, manpower, training, services and policy. Key findings from the Atlas project include: lack of program development in low income countries; lack of any policy in low income countries and absent specific comprehensive policy in both low and high income countries; lack of data gathering capacity including that for country-level epidemiology and services outcomes; failure to provide social services in low income countries; lack of a continuum of care; and universal barriers to access. Further, the Atlas findings underscored the need for a critical analysis of the 'burden of disease' as it relates to the context of child and adolescent mental disorders, and the importance of defining the degree of 'impairment' of specific disorders in different cultures. Conclusions: The recent finding of substantial gaps in resources for child mental health underscores the need for enhanced data gathering, refinement of the economic argument for care, and need for innovative training approaches. Language: en


title: Terrain data compression using wavelet-tiled pyramids for online 3D terrain visualization

abstract: Last years have witnessed the widespread use of online terrain visualization applications. However, the significant improvements achieved in sensing technologies have allowed an increasing size of the terrain databases. These increasing sizes represent a serious drawback when terrain data must be transmitted and rendered at interactive rates. In this paper, we propose a novel wavelet-tiled pyramid for compressing terrain data that replaces the traditional multiresolution pyramid usually used in wavelet compression schemes. The new wavelet-tiled pyramid modifies the wavelet analysis and synthesis processes, allowing an efficient transmission and reconstruction of terrain data in those applications based on multiresolution tiled pyramids. A comparative performance evaluation with the currently existing techniques shows that the proposed scheme obtains a better compression ratio of the terrain data, reducing the storage space and transmission bandwidth required, and achieving a better visual quality of the virtual terrain reconstructed after data decompression.


title: Security Issues in Wireless Sensor Networks: A Survey

abstract: Due to a wide range of applications, wireless sensor networks (WSNs) have recently attracted a lot of interest to the researchers. Limited computational capacity and power usage are two major challenges to ensure security in WSNs. Recently, more secure communication or data aggregation techniques have discovered. So, familiarity with the current research in WSN security will benefit researchers greatly. In this paper, security related issues and challenges in WSNs are investigated. We identify the security threats and review proposed security mechanisms for WSNs. Moreover, we provide a brief discussion on the future research direction in WSN security.


title: Author manuscript, published in "N/P" Improvements on learning Tetris with the Cross-Entropy method 23 NOTES IMPROVEMENTS ON LEARNING TETRIS WITH CROSS-ENTROPY

abstract: For playing the game of Tetris well, training a controller by the cross-entropy method seems to be a viable way (Szita and Lȍrincz, 2006; Thiery and Scherrer, 2009). We consider this method to tune an evaluation-based one-piece controller as suggested by Szita and Lȍrincz and we introduce some improvements. In this context, we discuss the influence of the noise, and we perform experiments with several sets of features such as those introduced by Bertsekas and Tsitsiklis (1996), by Dellacherie (Fahey, 2003), and some original features. This approach leads to a controller that outperforms the previous known results. On the original game of Tetris, we show that with probability 0.95 it achieves at least 910, 000 ± 5% lines per game on average. On a simplified version of Tetris considered by most research works, it achieves 35, 000, 000 ± 20% lines per game on average. We used this approach when we took part with the program BCTS in the 2008 Tetris domain Reinforcement Learning Competition and won the competition.


title: Investigating the Learning-Theory Foundations of Game-Based Learning: A Meta-Analysis.

abstract: Past studies on the issue of learning-theory foundations in game-based learning stressed the importance of establishing learning-theory foundation and provided an exploratory examination of established learning theories. However, we found research seldom addressed the development of the use or failure to use learning-theory foundations and categorized these learning theories into relative types and synthesized their development. We investigate this issue from the perspective of learning theories invoked to underpin educational computer game design and use based on the four types of learning theories: behaviourism, cognitivism, humanism and constructivism. Because the investigation needs to examine and analyse the results from a large number of independent previous studies, this study applied the meta-analysis method to present a more comprehensive description and discussion of the influence and implications of the findings. This study shows the distribution of development trends for the use of learning theory as a theoretical foundation, as opposed to those that fail to use learning theory in game-based learning, along with the distribution of types and principles of learning theories that used a learning-theory foundation. These new findings can supplement the results of previous studies with regard to the issue of learning-theory foundations in game-based learning. The contributions of this study for the issue of learning-theory foundations in game-based learning are discussed.


title: Algorithm for M-FSK intrapulse radar signal analysis

abstract: Modern radar uses more and more complex waveforms. Some waveforms are developed intentionally to make their intercept almost impossible. The main distinctive features of radar signal are hidden in its intrapulse structure. The intrapulse modulation analysis of a detected signal is a major task of an ELINT/ESM system. As a result of measurement, for each pulse specific description, so called pulse descriptor word (PDW) or finger printing, containing primary parameters is created. To determine a PDW in case of M-ary Frequency Shift Keying intrapulse modulation (M-FSK intrapulse modulation) in the recent hardware implementation of electronic intelligence system receivers is hardly achieved. The algorithm for unknown radar signals with M-FSK intrapulse modulation by new approach in signal analysis is described. The proposed algorithm is performed on the base of Subspectral Decomposition Method (SDM). The SDM principle and results of modeling and simulation of M-FSK intrapulse modulation signal recognition and decomposition are presented.


title: STriDER: Self-Excited Tripedal Dynamic Experimental Robot

abstract: STriDER (Self-Excited Tripedal Dynamic Experimental Robot) is a novel three-legged walking machine that exploits the concept of actuated passive dynamic locomotion to dynamically walk with high energy efficiency and minimal control. Unlike other passive dynamic walking machines, this unique tripedal locomotion robot is inherently stable with its tripod stance, can change directions, and is relatively easy to implement, making it practical to be used for real life applications. STriDER begins its step with a stable stance like a camera tripod. As the center of gravity of the robot shifts forward pass the "pivot line" defined by the two feet of the stance legs, the robot begins to fall in the direction perpendicular to the pivot line. The middle leg naturally swings between the two stance legs using the concept of actuated passive dynamic locomotion. The swing leg then catches the fall and the robot resets to its original tripod posture in preparation for its next step. STriDER can easily change its direction of walking, simply by changing the sequence of choice of the swing leg and the stance legs. In this video, we present the concept of this novel walking machine and the mechanical design of the first prototype. The results from the dynamic simulation and a simple experiment for a single step are presented for comparison


title: Big Data and the SP Theory of Intelligence

abstract: Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This project a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.


title: Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns

abstract: The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns.


title: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation

abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations. However, extreme sparsity of user-POI matrices creates a severe challenge. To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit weighted matrix factorization for this task since it usually serves collaborative filtering with implicit feedback better. Besides, researchers have recently discovered a spatial clustering phenomenon in human mobility behavior on the LBSNs, i.e., individual visiting locations tend to cluster together, and also demonstrated its effectiveness in POI recommendation, thus we incorporate it into the factorization model. Particularly, we augment users' and POIs' latent factors in the factorization model with activity area vectors of users and influence area vectors of POIs, respectively. Based on such an augmented model, we not only capture the spatial clustering phenomenon in terms of two-dimensional kernel density estimation, but we also explain why the introduction of such a phenomenon into matrix factorization helps to deal with the challenge from matrix sparsity. We then evaluate the proposed algorithm on a large-scale LBSN dataset. The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon into matrix factorization improves recommendation performance.


title: Emerging drug discovery approaches for selective targeting of “precursor” metastatic breast cancer cells: highlights and perspectives

abstract: Breast cancer is a prevalent disease and a major cause of morbidity and cancer-related deaths among women worldwide. A significant number of patients at the time of primary diagnosis present metastatic disease, at least to locoregional lymph nodes, which results in somewhat unpredictable prognosis that often prompts adjuvant systemic therapies of various kinds. The time course of distant recurrence is also unpredictable with some patients sustaining a recurrence within months after diagnosis, even during adjuvant treatments, while others can experience recurrence years or decades after initial diagnosis. To date, clinically approved therapeutics yielded marginal benefits for patients with systemic metastatic breast disease, since despite high clinical responses to various therapies, the patients virtually always become resistant and tumor relapses. Molecular profiling studies established that breast cancer is highly heterogeneous and encompasses diverse histological and molecular subtypes with distinct biological and clinical implications in particular in relation to the incidence of progression to metastasis. The latter has been recognized to result from late genetic events during the multistep progression proposed by the dominant theory of carcinogenesis. However, there is evidence that the dissemination of primary cancer can also be initiated at a very early stage of cancer development, originating from rare cell variants, possibly cancer stem-like cells (CSC), with invasive potential. These precursor metastatic cancer cells with stem-like properties are defined by their ability to self-renew and to regenerate cell variants, which have high plasticity and intrinsic invasive properties required for dissemination and tropism toward specific organs. Equally relevant to the CSC hypothesis for metastasis formation is the epithelial-mesenchymal transition (EMT) process, which is critical for the acquisition of cancer cell invasive behavior and for selection/gain of CSC properties. These exciting concepts have led to the formulation of various approaches for targeting precursor metastatic cells, and these have taken on greater priority in therapeutic drug discovery research by both academia and pharmaceuticals. In this review, we focus on current efforts in medicinal chemistry to develop small molecules able to target precursor metastatic cells via interference with the CSC/EMT differentiation program, self-renewal, and survival. It is not meant to be comprehensive and the reader is referred to selected reviews that provide coverage of related basic aspects. Rather, emphasis is given to promising molecules with CSC/EMT signaling at the preclinical stage and in clinical trials that are paving the way to new generations of anti-metastasis drugs.


title: Exploratory Analysis of Co-Change Graphs for Code Refactoring

abstract: Version Control Systems (VCS) have always played an essential role for developing reliable software. Recently, many new ways of utilizing the information hidden in VCS have been discovered. Clustering layouts of software systems using VCS is one of them. It reveals groups of related artifacts of the software system, which can be visualized for easier exploration. In this paper we use an Expectation Maximization (EM) based probabilistic clustering algorithm and visualize the clustered modules using a compound node layout algorithm. Our experiments with repositories of two medium size software tools give promising results indicating improvements over many previous approaches.


title: Soil microbial degradation of neonicotinoid insecticides imidacloprid, acetamiprid, thiacloprid and imidaclothiz and its effect on the persistence of bioefficacy against horsebean aphid Aphis craccivora Koch after soil application.

abstract: BACKGROUND ::: The neonicotinoids imidacloprid, imidaclothiz, acetamiprid and thiacloprid consist of similar structural substituents but differ considerably with respect to soil use. Therefore, the effects of soil microbial activity on the degradation and bioefficacy persistence of the four neonicotinoids were evaluated. ::: ::: ::: RESULTS ::: In unsterilised soils, 94.0% of acetamiprid and 98.8% of thiacloprid were degraded within 15 days, while only 22.5% of imidacloprid and 25.1% of imidaclothiz were degraded over a longer period of 25 days. In contrast, in sterilised soils, the degradation rates of acetamiprid and thiacloprid were respectively only 21.4% and 27.6%, whereas the degradation rates of imidaclothiz and imidacloprid were respectively 9.0% and almost 0% within 25 days. The degradation products of imidacloprid and imidaclothiz were identified as olefin, nitroso or guanidine metabolites, the degradation product of thiacloprid was identified as an amide metabolite and no degradation product of acetamiprid was detected. A bioefficacy assay revealed that the bioefficacy and persistence of imidacloprid, imidaclothiz, acetamiprid and thiacloprid against horsebean aphid A. craccivora were related to their degradation rate and the bioefficacy of their degradation products in soil. ::: ::: ::: CONCLUSION ::: Soil microbial activity played a key role in the bioefficacy persistence of neonicotinoid insecticides and therefore significantly affected their technical profile after soil application.


Approximate Linear Programming for Logistic Markov Decision Processes


title: Granular material interactive manipulation: Touching sand with haptic feedback

abstract: We present a novel approach for a virtual granular material interactive manipulation with force feedback. A user can interactively change a height-field model of sand by dragging objects inside. The virtual sand behaves like real sand moving to the sides and falling back, filling holes and irregularities on the surface. The dragging object position is controlled by a haptic device that provides a position in 3D space that correspondingly changes the sand model. The sand model provides force feedback to the haptic device resulting in two principal forces; the repulse that has vertical direction and the viscous drag. The resulting force is delivered back to the haptic device. The user can sense the sand’s response as the dragging object moves through the virtual sand. This results in the haptic-visual feedback providing a higher degree of plausibility than visual feedback alone.


Relay Optimization Method


title: Breathing Detection: Towards a Miniaturized, Wearable, Battery-Operated Monitoring System

abstract: This paper analyzes the main challenges associated with noninvasive, continuous, wearable, and long-term breathing monitoring. The characteristics of an acoustic breathing signal from a miniature sensor are studied in the presence of sources of noise and interference artifacts that affect the signal. Based on these results, an algorithm has been devised to detect breathing. It is possible to implement the algorithm on a single integrated circuit, making it suitable for a miniature sensor device. The algorithm is tested in the presence of noise sources on five subjects and shows an average success rate of 91.3% (combined true positives and true negatives).


title: Well-being and problematic Facebook use

abstract: We examined the possible relation with well-being and problematic Facebook use.Life satisfaction negatively associated with problematic Facebook use.Subjective vitality negatively associated with problematic Facebook use.Flourishing negatively associated with problematic Facebook use.Subjective happiness negatively associated with problematic Facebook use. Social networking sites are getting more widespread online communication forms all over the world. Especially adolescents and emerging adults use these sites to make connection, and share something. Although, Facebook is one of the most popular sites, research is still in nascent phase on the excessive use of Facebook. Hence, it is fundamental to determine potential predictors of overuse of Facebook. The present study investigated the relationship between well-being and problematic Facebook use. Participants were 311 university students 179(58%) female, 132(42%) male, Mage=20.86years, SD=1.61] who completed the questionnaire packet including the Bergen Facebook Addiction Scale (Andreassen, Torsheim, Brunborg, & Pallesen, 2012), the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985), the Subjective Vitality Scale (Ryan & Frederick, 1997), the Flourishing Scale (Diener et al., 2010), and the Subjective Happiness Scale (Lyubomirsky & Lepper, 1999). A stepwise regression analyses was used with four independent variables (life satisfaction, subjective vitality, flourishing, and subjective happiness) to explain variance in problematic Facebook use. The results showed that life satisfaction, subjective vitality, flourishing, and subjective happiness were significant negative predictors of problematic Facebook use. The significance and limitations of the results are discussed.


title: Issues and security measures of mobile banking apps

abstract: Mobile apps is a used to designate the act or process by which application software is developed for handheld devices, such as personal digital assistants or mobile phones. These applications can be pre-installed on phones during manufacturing platforms, or delivered as web applications using server-side or client-side processing (e.g. JavaScript) to provide an "application-like" experience within a Web browser. However there are some cases where the mobile internet banking apps occurred some problems that might cause loss of money. Therefore, in this paper we will examine issues on the architecture, and some security issues of mobile internet banking apps. And then we will explore some security measuras to deal with the associated security challenges.


title: Microsoft SQL Server 2008 Management and Administration

abstract: If you need to deploy, manage, or secure Microsoft SQL Server 2008, this is the complete, fast-paced, task-based reference youve been searching for. Authored by a world-class expert on SQL Server in the enterprise, this book goes far beyond the basics, taking on the complex tasks that DBAs need to make the most of Microsofts flagship database platform. SQL Server MVP, Ross Mistry presents proven techniques for SQL Server 2008 installation, upgrades, backup/restore, data transfer, indexing, high availability, security, and much more. He draws on extensive testing in high-profile production environments to offer step-by-step solutions and powerful tips you wont find anywhere else. Every chapter begins with a section identifying SQL Server 2008s most significant new improvements, and concludes with a convenient summary of best practices. Each chapter also outlines the benefits of leveraging Windows Server 2008. Understand how to: Master DBA tips, tricks, and best practices proven in actual enterprise environments Install, upgrade or transition to SQL Server 2008. Harden and Secure an implementation. Encrypt SQL Server from an end-to-end perspective. Implement high availabilityand leverage SQL Server 2008s major improvements to failover clustering and database mirroring Save time with SQL Server 2008s new policy-based management tools Performance tune and troubleshoot a SQL Server 2008 environment. Optimize application performance and manage workloads with the powerful new Resource Governor Implement Performance Studio, maintenance plans, Transparent Data Encryption and much moreBonus Content: The book is based on Windows Server 2008 Step by step instructions of how to implement a failover cluster on Windows Server 2008 SQL Server PoweShell Administration Tasks Consolidate and virtualize SQL Server with Hyper-V Step by step instructions on how to install Hyper-V Proactively Monitor SQL Server with Operations Manager Install Windows Server 2008 certificates to encrypt SQL Server dataContributing Writers include: Hilary Cotter SQL Server MVP John Welch SQL Server MVP Marco Shaw PowerShell MVP Maciej Pilecki SQL Server MVP


title: Social Structure of Facebook Networks

abstract: We study the social structure of Facebook"friendship"networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes - gender, class year, major, high school, and residence - at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on the user characteristics. We thereby compare the relative importances of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.


Investigation of the Bioequivalence of Rosuvastatin 20 mg Tablets after a Single Oral Administration in Mediterranean Arabs Using a Validated LC-MS/MS Method


Deformable devices with integrated functional nanomaterials for wearable electronics


title: Multi-Modal Learning: Study on A Large-Scale Micro-Video Data Collection

abstract: Micro-video sharing social services, as a new phenomenon in social media, enable users to share micro-videos and thus gain increasing enthusiasm among people. One distinct characteristic of micro-videos is the multi-modality, as these videos always have visual signals, audio tracks, textual descriptions as well as social clues. Such multi-modality data makes it possible to obtain a comprehensive understanding of videos and hence provides new opportunities for researchers. However, limited efforts thus far have been dedicated to this new emerging user-generated contents (UGCs) due to the lack of large-scale benchmark dataset. Towards this end, in this paper, we construct a large-scale micro-video dataset, which can support many research domains, such as popularity prediction and venue estimation. Based upon this dataset, we conduct an initial study in popularity prediction of micro-videos. Finally, we identify our future work.


title: The Bible as a Parallel Corpus: Annotating the ‘Book of 2000 Tongues’

abstract: We report on a project to annotate biblical texts in order to create an aligned multilingual Bible corpus for linguistic research, particularly computational linguistics, including automatically creating and evaluating translation lexicons and semantically tagged texts. The output of this project will enable researchers to take advantage of parallel translations across a wider number of languages than previously available, providing, with relatively little effort, a corpus that contains careful translations and reliable alignment at the near-sentence level. We discuss the nature of the text, our annotation process, preliminary and planned uses for the corpus, and relevant aspects of the Corpus Encoding Standard (CES) with respect to this corpus. We also present a quantitative comparison with dictionary and corpus resources for modern-day English, confirming the relevance of this corpus for research on present day language.


The Indian Ocean: Perspectives on a Strategic Arena.


The Market Effect of Healthcare Security: Do Patients Care about Data Breaches?


title: On the Importance of Eliminating Errors in Cryptographic Computations

abstract: We present a model for attacking various cryptographic schemes by taking advantage of random hardware faults. The model consists of a black-box containing some cryptographic secret. The box interacts with the outside world by following a cryptographic protocol. The model supposes that from time to time the box is affected by a random hardware fault causing it to output incorrect values. For example, the hardware fault flips an internal register bit at some point during the computation. We show that for many digital signature and identification schemes these incorrect outputs completely expose the secrets stored in the box. We present the following results: (1) The secret signing key used in an implementation of RSA based on the Chinese Remainder Theorem (CRT) is completely exposed from a single erroneous RSA signature, (2) for non-CRT implementations of RSA the secret key is exposed given a large number (e.g. 1000) of erroneous signatures, (3) the secret key used in Fiat--Shamir identification is exposed after a small number (e.g. 10) of faulty executions of the protocol, and (4) the secret key used in Schnorr's identification protocol is exposed after a much larger number (e.g. 10,000) of faulty executions. Our estimates for the number of necessary faults are based on standard security parameters such as a 1024-bit modulus, and a 2-40 identification error probability. Our results demonstrate the importance of preventing errors in cryptographic computations. We conclude the paper with various methods for preventing these attacks.


title: Personality and information behavior in web search

abstract: In this paper, we describe a quantitative study of personality aspects and their relationship with web search information behavior. We start with introducing personality and give an overview of information behavior research concerning personality aspects. In our study of 30 participants, their personality traits were operationalized by using a version of the Big 5, the B5T, a psychometric questionnaire that maps personality on different dimensions. The participants performed search tasks in a web context and data concerning their information behavior was collected via search logs as well as questionnaires. We show that there are selective correlations of slight and intermediate strength between the variables of information behavior and the personality dimensions. Finally, we discuss possible explanations and implications as well as new impulses for information behavior and retrieval research.


title: Generating English summaries of time series data using the Gricean maxims

abstract: We are developing technology for generating English textual summaries of time-series data, in three domains: weather forecasts, gas-turbine sensor readings, and hospital intensive care data. Our weather-forecast generator is currently operational and being used daily by a meteorological company. We generate summaries in three steps: (a) selecting the most important trends and patterns to communicate; (b) mapping these patterns onto words and phrases; and (c) generating actual texts based on these words and phrases. In this paper we focus on the first step, (a), selecting the information to communicate, and describe how we perform this using modified versions of standard data analysis algorithms such as segmentation. The modifications arose out of empirical work with users and domain experts, and in fact can all be regarded as applications of the Gricean maxims of Quality, Quantity, Relevance, and Manner, which describe how a cooperative speaker should behave in order to help a hearer correctly interpret a text. The Gricean maxims are perhaps a key element of adapting data analysis algorithms for effective communication of information to human users, and should be considered by other researchers interested in communicating data to human users.


title: Three Factors Influencing Minima in SGD

abstract: We investigate the dynamical and convergent properties of stochastic gradient descent (SGD) applied to Deep Neural Networks (DNNs). Characterizing the relation between learning rate, batch size and the properties of the final minima, such as width or generalization, remains an open question. In order to tackle this problem we investigate the previously proposed approximation of SGD by a stochastic differential equation (SDE). We theoretically argue that three factors - learning rate, batch size and gradient covariance - influence the minima found by SGD. In particular we find that the ratio of learning rate to batch size is a key determinant of SGD dynamics and of the width of the final minima, and that higher values of the ratio lead to wider minima and often better generalization. We confirm these findings experimentally. Further, we include experiments which show that learning rate schedules can be replaced with batch size schedules and that the ratio of learning rate to batch size is an important factor influencing the memorization process.


title: Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem

abstract: This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a sharp finite-time regret bound of order O(K log T) on a very general class of dueling bandit problems that matches a lower bound proven in (Yue et al., 2012). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art.


title: A genetic programming approach to designing convolutional neural network architectures

abstract: The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.


title: HOSLIM: Higher-Order Sparse LInear Method for Top-N Recommender Systems

abstract: Current top-N recommendation methods compute the recommendations by taking into account only relations between pairs of items, thus leading to potential unused information when higher-order relations between the items exist. Past attempts to incorporate the higher-order information were done in the context of neighborhood-based methods. However, in many datasets, they did not lead to significant improvements in the recommendation quality. We developed a top-N recommendation method that revisits the issue of higher-order relations, in the context of the model-based Sparse LInear Method (SLIM). The approach followed (Higher-Order Sparse LInear Method, or HOSLIM) learns two sparse aggregation coefficient matrices S and S′ that capture the item-item and itemset-item similarities, respectively. Matrix S′ allows HOSLIM to capture higher-order relations, whose complexity is determined by the length of the itemset. Following the spirit of SLIM, matrices S and S′ are estimated using an elastic net formulation, which promotes model sparsity. We conducted extensive experiments which show that higher-order interactions exist in real datasets and when incorporated in the HOSLIM framework, the recommendations made are improved. The experimental results show that the greater the presence of higher-order relations, the more substantial the improvement in recommendation quality is, over the best existing methods. In addition, our experiments show that the performance of HOSLIM remains good when we select S′ such that its number of nonzeros is comparable to S, which reduces the time required to compute the recommendations.


title: Knowledge Based Neural Network for Text Classification

abstract: Automatic text classification has gained huge popularity with the advancement of information technology. Bayesian method has been found highly appropriate for text classification but it suffers from a number of problems. When there is large number of categories, lack of uniformity in training data becomes a big problem. Some nodes may get less training documents, while other may get a very large number. Therefore, some nodes are biased over others. Besides, presence of noise data or outliers also creates problems. Moreover, when documents are very small, just like a line item describing a product, the problem becomes more difficult. In this paper we describe a method that combines naive Bayesian text classification technique and neural networks to handle these problems. We start with a naive Bayesian classifier, which has the linear separating surfaces. We modify the separating surfaces using neural network to find better separating surfaces and hence better classification accuracy over validation data.


title: Enhancing perceptual and attentional skills requires common demands between the action video games and transfer tasks

abstract: Despite increasing evidence that shows action video game play improves perceptual and cognitive skills, the mechanisms of transfer are not well understood. In line with previous work, we suggest that transfer is dependent upon common demands between the game and transfer task. In the current study, participants played one of four action games with varying speed, visual, and attentional demands for twenty hours. We examined whether training enhanced performance for attentional blink, selective attention, attending to multiple items, visual search and auditory detection. Non-gamers who played the game (Modern Combat) with the highest demands showed transfer to tasks of attentional blink and attending to multiple items. The game (MGS Touch) with fewer attentional demands also decreased attentional blink, but to a lesser degree. Other games failed to show transfer, despite having many action game characteristics but at a reduced intensity. The results support the common demands hypothesis


abstract: The chapter 5 'Assessment of some indicators within an impact' looks in detail at indicators for seven impacts on the environment: direct toxicity of air pollutants, natural habitat fragmentation, ...


title: Live sports event detection based on broadcast video and web-casting text

abstract: Event detection is essential for sports video summarization, indexing and retrieval and extensive research efforts have been devoted to this area. However, the previous approaches are heavily relying on video content itself and require the whole video content for event detection. Due to the semantic gap between low-level features and high-level events, it is difficult to come up with a generic framework to achieve a high accuracy of event detection. In addition, the dynamic structures from different sports domains further complicate the analysis and impede the implementation of live event detection systems. In this paper, we present a novel approach for event detection from the live sports game using web-casting text and broadcast video. Web-casting text is a text broadcast source for sports game and can be live captured from the web. Incorporating web-casting text into sports video analysis significantly improves the event detection accuracy. Compared with previous approaches, the proposed approach is able to: (1) detect live event only based on the partial content captured from the web and TV; (2) extract detailed event semantics and detect exact event boundary, which are very difficult or impossible to be handled by previous approaches; and (3) create personalized summary related to certain event, player or team according to user's preference. We present the framework of our approach and details of text analysis, video analysis and text/video alignment. We conducted experiments on both live games and recorded games. The results are encouraging and comparable to the manually detected events. We also give scenarios to illustrate how to apply the proposed solution to professional and consumer services.


title: Ten Mistakes To Avoid When Injecting Botulinum Toxin.

abstract: Injection of botulinum toxin is currently the most common cosmetic procedure in the United States, and in recent years it has become-together with dermal fillers-the mainstay of therapy for the prevention and treatment of facial aging. However, in some cases the treatment may lead to a somewhat unnatural appearance, usually caused by loss of facial expression or other telltale signs. In the present article, we review the 10 mistakes that should be avoided when injecting botulinum toxin. We also reflect on how treatment with botulinum toxin influences us through our facial expressions, both in terms of how we feel and what others perceive.


Lessons Learned from AlphaGo


title: Clustering algorithm for intuitionistic fuzzy sets

abstract: The intuitionistic fuzzy set (IFS) theory, originated by Atanassov [K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20 (1986) 87-96], has been used in a wide range of applications, such as logic programming, medical diagnosis, pattern recognition, and decision making, etc. However, so far there has been little investigation of the clustering techniques of IFSs. In this paper, we define the concepts of association matrix and equivalent association matrix, and introduce some methods for calculating the association coefficients of IFSs. Then, we propose a clustering algorithm for IFSs. The algorithm uses the association coefficients of IFSs to construct an association matrix, and utilizes a procedure to transform it into an equivalent association matrix. The @l-cutting matrix of the equivalent association matrix is used to cluster the given IFSs. Moreover, we extend the algorithm to cluster interval-valued intuitionistic fuzzy sets (IVIFSs), and finally, demonstrate the effectiveness of our clustering algorithm by experimental results.


title: Analysis and Design Techniques Applied to Hybrid Vehicle Drive Machines—Assessment of Alternative IPM and Induction Motor Topologies

abstract: In this paper, different analysis and design techniques are used to analyze the drive motor in the 2004 Prius hybrid vehicle and to examine alternative spoke-type magnet rotor (buried magnets with magnetization which is orthogonal to the radial direction) and induction motor arrangements. These machines are characterized by high transient torque requirement, compactness, and forced cooling. While rare-earth magnet machines are commonly used in these applications, there is an increasing interest in motors without magnets, hence the investigation of an induction motor. This paper illustrates that the machines operate under highly saturated conditions at high torque and that care should be taken when selecting the correct analysis technique. This is illustrated by divergent results when using I-Psi loops and dq techniques to calculate the torque.


title: Functional annotation of genes using hierarchical text categorization

abstract: This paper addresses the task of functional annotation of genes from biomedical literature. We view this task as a hierarchical text categorization problem with Gene Ontology as a class hierarchy. We present a novel global hierarchical learning approach that takes into account the semantics of a class hierarchy. This algorithm with AdaBoost as the underlying learning procedure significantly outperforms the corresponding “flat” approach, i.e. the approach that does not consider any hierarchical information. In addition, we propose a novel hierarchical evaluation measure that gives credit to partially correct classification and discriminates errors by both distance and depth in a class hierarchy.


title: Bowel vaginoplasty in children and young women: an institutional experience with 55 patients

abstract: INTRODUCTION AND HYPOTHESIS ::: Absence of a vagina owing to congenital Mullerian defects or other acquired causes requires reconstruction of the female genital passage. We present our experience using various bowel segments. ::: ::: ::: METHODS ::: Bowel vaginoplasty was performed in 55 patients from January 2004 through May 2014 for cervicovaginal atresia (20), Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome (20), distal vaginal atresia (8), cloaca (2), cervical atresia (1), complex urogenital sinus (1), transverse vaginal septum (1), rhabdomyosarcoma of the vagina (1), and traumatic stricture of the vagina (1). The bowel segments used were sigmoid (50), ileum (2), anorectovestibular fistula ( 2), and loop rectovaginoplasty (1). ::: ::: ::: RESULTS ::: Thirty-nine patients who had the proximal vagina or uterus anastomosed to the bowel segment reported regular menstrual flows. Three patients are sexually active with satisfactory coital function. None of our patients developed pyometra. Five patients had neovaginal mucosal prolapse. Two patients had severe stenosis requiring excision of the neovagina. Seven patients had mild stenosis requiring dilatations in 6 patients and V-Y meatoplasty for 1 patient. One patient had a descending colon anastomotic leak requiring a diversion ileostomy. ::: ::: ::: CONCLUSIONS ::: Genital reconstruction with bowel vaginoplasty is a highly skilled operation that provides a durable and lubricated replacement of the vagina with good outcomes. Utero-coloneovaginoplasty is a safe procedure preserving the menstrual flow in patients with a functional uterine fundus.


title: Progress in surgical strategies for treatment of simple congenital syndactyly: A systematic review

abstract: Plenty of surgical strategies for treatment of simple syndactyly have been reported in the past decades. We are most interested in what progresses have been made over the recent 10 years. Our aim was to make a summary of the modified techniques that have been described in the last decade. We reviewed English articles from PubMed and Chinese articles from CKNI and CQVIP published between January 2007 and December 2016. We limited the inclusion criteria to simple syndactyly and excluded complex, complicated, and syndrome-associated syndactylies. We identified 1650 articles and selected 94 for inclusion. Almost half of the included papers proposed modified flaps for web reconstruction. In most cases, a zigzag incision and Buck-Gramcko flaps were still the first choice to separate the fused digits and create the nail folds. Several new donor sites were recommended for skin grafts and several substitute biomaterials were introduced to replace skin grafts. External fixator devices were applied in a wider range for desyndactylization. However, it was not considered to be superior to other graft-less strategies. Postoperative complications were discussed in most papers, but an effective strategy to eradicate these is lacking. With the development of biomaterials and surgeon’s experiences, more and more modified techniques have been conceived. However, no specific technique can be announced to be superior to others. Furthermore, more sensitive outcome measures should be established, and relationship between operative procedures and postoperative complications should be further defined. Level of evidence: not ratable.


title: Factorization tricks for LSTM networks

abstract: We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is"matrix factorization by design"of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and states into the independent groups. Both approaches allow us to train large LSTM networks significantly faster to the state-of the art perplexity. On the One Billion Word Benchmark we improve single model perplexity down to 23.36.


title: A general map matching algorithm for transport telematics applications

abstract: This paper describes a map-matching algorithm designed to support the navigational functions of a real-time vehicle performance and emissions monitoring system currently under development, and other transport telematics applications. The algorithm is used together with the outputs of an extended Kalman filter formulation for the integration of GPS and dead reckoning data, and a spatial digital database of the road network, to provide continuous, accurate and reliable vehicle location on a given road segment. This is irrespective of the constraints of the operational environment, thus alleviating outage and accuracy problems associated with the use of stand-alone location sensors. The map-matching algorithm has been tested using real field data and has been found to be superior to existing algorithms, particularly in how it performs at road intersections.


title: Autonomous discovery of the goal space to learn a parameterized skill

abstract: A parameterized skill is a mapping from multiple goals/task parameters to the policy parameters to accomplish them. Existing works in the literature show how a parameterized skill can be learned given a task space that defines all the possible achievable goals. In this work, we focus on tasks defined in terms of final states (goals), and we face on the challenge where the agent aims to autonomously acquire a parameterized skill to manipulate an initially unknown environment. In this case, the task space is not known a priori and the agent has to autonomously discover it. The agent may posit as a task space its whole sensory space (i.e. the space of all possible sensor readings) as the achievable goals will certainly be a subset of this space. However, the space of achievable goals may be a very tiny subspace in relation to the whole sensory space, thus directly using the sensor space as task space exposes the agent to the curse of dimensionality and makes existing autonomous skill acquisition algorithms inefficient. In this work we present an algorithm that actively discovers the manifold of the achievable goals within the sensor space. We validate the algorithm by employing it in multiple different simulated scenarios where the agent actions achieve different types of goals: moving a redundant arm, pushing an object, and changing the color of an object.


title: ALL OUR SONS: THE DEVELOPMENTAL NEUROBIOLOGY AND NEUROENDOCRINOLOGY OF BOYS AT RISK

abstract: Why are boys at risk? To address this question, I use the perspective of regulation theory to offer a model of the deeper psychoneurobiological mechanisms that underlie the vulnerability of the developing male. The central thesis of this work dictates that significant gender differences are seen between male and female social and emotional functions in the earliest stages of development, and that these result from not only differences in sex hormones and social experiences but also in rates of male and female brain maturation, specifically in the early developing right brain. I present interdisciplinary research which indicates that the stress-regulating circuits of the male brain mature more slowly than those of the female in the prenatal, perinatal, and postnatal critical periods, and that this differential structural maturation is reflected in normal gender differences in right-brain attachment functions. Due to this maturational delay, developing males also are more vulnerable over a longer period of time to stressors in the social environment (attachment trauma) and toxins in the physical environment (endocrine disruptors) that negatively impact right-brain development. In terms of differences in gender-related psychopathology, I describe the early developmental neuroendocrinological and neurobiological mechanisms that are involved in the increased vulnerability of males to autism, early onset schizophrenia, attention deficit hyperactivity disorder, and conduct disorders as well as the epigenetic mechanisms that can account for the recent widespread increase of these disorders in U.S. culture. I also offer a clinical formulation of early assessments of boys at risk, discuss the impact of early childcare on male psychopathogenesis, and end with a neurobiological model of optimal adult male socioemotional functions.


title: Planar building facade segmentation and mapping using appearance and geometric constraints

abstract: Segmentation and mapping of planar building facades (PBFs) can increase a robot’s ability of scene understanding and localization in urban environments which are often quasi-rectilinear and GPS-challenged. PBFs are basic components of the quasi-rectilinear environment. We propose a passive vision-based PBF segmentation and mapping algorithm by combining both appearance and geometric constraints. We propose a rectilinear index which allows us to segment out planar regions using appearance data. Then we combine geometric constraints such as reprojection errors, orientation constraints, and coplanarity constraints in an optimization process to improve the mapping of PBFs. We have implemented the algorithm and tested it in comparison with state-of-the-art. The results show that our method can reduce the angular error of scene structure by an average of 82.82%.


DYNAMIC CHARACTERISTICS OF BUILDINGS ESTIMATED FROM STRONG MOTION RECORDS


title: Neural Correlates of Contrast and Humor: Processing Common Features of Verbal Irony

abstract: Irony is a kind of figurative language used by a speaker to say something that contrasts with the context and, to some extent, lends humor to a situation. However, little is known about the brain regions that specifically support the processing of these two common features of irony. The present study had two main aims: (i) investigate the neural basis of irony processing, by delivering short ironic spoken sentences (and their literal counterparts) to participants undergoing fMRI; and (ii) assess the neural effect of two irony parameters, obtained from normative studies: degree of contrast and humor appreciation. Results revealed activation of the bilateral inferior frontal gyrus (IFG), posterior part of the left superior temporal gyrus, medial frontal cortex, and left caudate during irony processing, suggesting the involvement of both semantic and theory-of-mind networks. Parametric models showed that contrast was specifically associated with the activation of bilateral frontal and subcortical areas, and that these regions were also sensitive to humor, as shown by a conjunction analysis. Activation of the bilateral IFG is consistent with the literature on humor processing, and reflects incongruity detection/resolution processes. Moreover, the activation of subcortical structures can be related to the reward processing of social events.


title: Good Features to Correlate for Visual Tracking

abstract: During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods has emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the winner of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.


title: Status epilepticus: A new rodent model

abstract: Kindling, the phenomenon of producing a persistently seizure-prone area of brain by administering repetitive low intensity electrical stimulation, has recently undergone intensive investigation. The most commonly used interstimulus interval in these experiments has been 24 h. We have found, using an interstimulus interval of one minute, that it is possible to produce long-term self-sustained seizures (SSS) in mice via hippocampal electrode placements. These results suggest an animal model for the clinically-defined syndrome of status epilepticus. ::: ::: Effective stimulus parameters for the production of SSS were a 1.0 sec, 400 μA (constant current) train of 60 c/sec 1 msec biphasic square wave pulses. The intertrain interval was 60 sec. With stimulating electrodes located in hippocampal subfields CA2, CA3, CA4 or in the dentate gyrus a slowly developing form of SSS (Non-Lethal SSS) was seen. This form of seizure was characterized by a slow (100–130 stimulation) increase in the intensity and duration of afterdischarge. Behaviorally the major manifestations of Non-Lethal SSS were automatisms or stereotyped behaviors (repetitive grooming, chewing, etc.) and behavioral arrest. These animals displayed performance deficits to an inhibitory avoidance task 24–48 h after the cessation of SSS. ::: ::: Electrode placements in subfield CA1 of hippocampus generally resulted in a more quickly (40–50 stimulations) developing form of SSS (Lethal SSS). Behaviorally these mice exhibited both preconvulsive and convulsive motor activities. All CA1-stimulated animals died in convulsion within 1 h of the onset of SSS. ::: ::: Non-Lethal SSS is discussed as a model for psychomotor status epilepticus. Lethal SSS is discussed as a model for convulsive status epilepticus.


title: Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields

abstract: In the context of autonomous driving, where humans may need to take over in the event where the computer may issue a takeover request, a key step towards driving safety is the monitoring of the hands to ensure the driver is ready for such a request. This work, focuses on the first step of this process, which is to locate the hands. Such a system must work in real-time and under varying harsh lighting conditions. This paper introduces a fast ConvNet approach, based on the work of original work of OpenPose by Cao, et. al. for full body joint estimation. The network is modified with fewer parameters and retrained using our own day-time naturalistic autonomous driving dataset to estimate joint and affinity heatmaps for driver \& passenger's wrist and elbows, for a total of 8 joint classes and part affinity fields between each wrist-elbow pair. The approach runs real-time on real-world data at 40 fps on multiple drivers and passengers. The system is extensively evaluated both quantitatively and qualitatively, showing at least 95% detection performance on joint localization and arm-angle estimation.


title: A De-Identification Pipeline for Ultrasound Medical Images in DICOM Format

abstract: Clinical data sharing between healthcare institutions, and between practitioners is often hindered by privacy protection requirements. This problem is critical in collaborative scenarios where data sharing is fundamental for establishing a workflow among parties. The anonymization of patient information burned in DICOM images requires elaborate processes somewhat more complex than simple de-identification of textual information. Usually, before sharing, there is a need for manual removal of specific areas containing sensitive information in the images. In this paper, we present a pipeline for ultrasound medical image de-identification, provided as a free anonymization REST service for medical image applications, and a Software-as-a-Service to streamline automatic de-identification of medical images, which is freely available for end-users. The proposed approach applies image processing functions and machine-learning models to bring about an automatic system to anonymize medical images. To perform character recognition, we evaluated several machine-learning models, being Convolutional Neural Networks (CNN) selected as the best approach. For accessing the system quality, 500 processed images were manually inspected showing an anonymization rate of 89.2%. The tool can be accessed at https://bioinformatics.ua.pt/dicom/anonymizer and it is available with the most recent version of Google Chrome, Mozilla Firefox and Safari. A Docker image containing the proposed service is also publicly available for the community.


Distinct representations of phonemes, syllables, and supra-syllabic sequences in the speech production network


title: Mortality in international professional football (soccer): a descriptive study.

abstract: The objective of this study was to describe the characteristics of mortalities from 2007 to 2013 in active (during career) and recently retired (post career) professional footballers. An observational prospective study was conducted. From 2007, the World Footballers' Union (FIFPro) and its related national footballers' unions (more than 70 countries distributed across all continents) collected descriptive data (football-related, cause, etc.) on mortality of active (during career) and recently retired (postcareer before reaching 45 years of age) professional footballers by means of several official sources. A total of 214 deaths were recorded among active and recently retired professional footballers, leading to an overall mortality rate of 0.47 per 1000 footballers per year. Of the 214 deaths, 183 were recorded among active players and 31 among recently retired players. Among the active players, 17% of the fatalities were related to football participation. Disease was the leading cause of death among professional footballers (55%), of which up to 33% accounted for suspected cardiac pathology. Accidents accounted for 25% of the overall deaths, and suicide for 11%. From 2007 to 2013, 214 deaths were recorded among active (during career) and recently retired (post career) professional footballers. Leading cause of death was disease (55%), one third of which were accounted for by suspected cardiac pathology, while accidents accounted for 25% of all deaths, and suicide for 11%. Attention to the predictive validity and application of heart-related precompetition medical assessment should be given, and mental health support should be developed and implemented both during and after a professional football career to prevent potential suicidal behaviors.


title: A lightweight method for virtual machine introspection

abstract: A method for the introspection of virtual machines is proposed. The main distinctive feature of this method is that it makes it possible to obtain information about the system operation using the minimum knowledge about its internal organization. The proposed approach uses rarely changing parts of the application binary interface, such as identifiers and parameters of system calls, calling conventions, and the formats of executable files. The lightweight property of the introspection method is due to the minimization of the knowledge about the system and by its high performance. The introspection infrastructure is based on the QEMU emulator, version 2.8. Presently, monitoring the file operations, processes, and API function calls are implemented. The available introspection tools (RTKDSM, Panda, and DECAF) get data for the analysis using kernel structures. All the data obtained (addresses of structures, etc.) is written to special profiles. Since the addresses and offsets strongly depend not only on the version of the operating system but also on the parameters of its assembly, these tools have to store a large number of profiles. We propose to use parts of the application binary interface because they are rarely modified and it is often possible to use one profile for a family of OSs. The main idea underlying the proposed method is to intercept the system and library function calls and read parameters and returned values. The processor provides special instructions for calling system and user defined functions. The capabilities of QEMU are extended by an instrumentation mechanism to enable one to track each executed instruction and find the instructions of interest among them. When a system call occurs, the control is passed to the system call detector that checks the number of the call and decides to which module the job should be forwarded. In the case of an API function call, the situation is similar, but the API function detector checks the function address. An introspection tool consisting of a set of modules is developed. These modules are dynamic libraries that are plugged in QEMU. The modules can interact by exchanging data.


title: Towards proactive event-driven computing

abstract: Event driven architecture is a paradigm shift from traditional computing architectures which employ synchronous, request-response interactions. In this paper we introduce a conceptual architecture for what can be considered the next phase of that evolution: proactive event-driven computing. Proactivity refers to the ability to mitigate or eliminate undesired future events, or to identify and take advantage of future opportunities, by applying prediction and automated decision making technologies. We investigate an extension of the event processing conceptual model and architecture to support proactive event-driven applications, and propose the main building blocks of a novel architecture. We first describe several extensions to the existing event processing functionality that is required to support proactivity; next, we extend the event processing agent model to include two more type of agents: predictive agents that may derive future uncertain events based on prediction models, and proactive agents that compute the best proactive action that should be taken. Those building blocks are demonstrated through a comprehensive scenario that deals with proactive decision making, ensuring timely delivery of critical material for a production plant.


title: Proactive Threat Detection for Connected Cars Using Recursive Bayesian Estimation

abstract: Upcoming disruptive technologies around autonomous driving of connected cars have not yet been matched with appropriate security by design principles and lack approaches to incorporate proactive preventative measures in the wake of increased cyber-threats against such systems. In this paper, we introduce proactive anomaly detection to a use-case of hijacked connected cars to improve cyber-resilience. First, we manifest the opportunity of behavioral profiling for connected cars from recent literature covering related underpinning technologies. Then, we design and utilize a new data set file for connected cars influenced by the automatic dependent surveillance–broadcast surveillance technology used in the aerospace industry to facilitate data collection and sharing. Finally, we simulate the analysis of travel routes in real time to predict anomalies using predictive modeling. Simulations show the applicability of a Bayesian estimation technique, namely, Kalman filter. With the analysis of future state predictions based on the previous behavior, cyber-threats can be addressed with a vastly increased time window for a reaction when encountering anomalies. We discuss that detecting real-time deviations for malicious intent with the predictive profiling and behavioral algorithms can be superior in effectiveness than the retrospective comparison of known-good/known-bad behavior. When quicker action can be taken while connected cars encounter cyberattacks, more effective engagement or interception of command and control will be achieved.


title: A fuzzy inhomogenous multiattribute group decision making approach to solve outsourcing provider selection problems

abstract: Considering various situations and characteristics of supply chain management, we regard the outsourcing provider selection as a type of fuzzy inhomogenous multiattribute group decision making (MAGDM) problems with fuzzy alternatives' comparisons and incomplete weight information. Hereby we focus on developing a new fuzzy linear programming method for solving such MAGDM problems. In this method, the decision makers' preferences are given through pair-wise alternatives' comparisons with fuzzy truth degrees represented as trapezoidal fuzzy numbers (TrFNs). Intuitionistic fuzzy sets, TrFNs, intervals and real numbers are used to express the inhomogenous decision information. Under the condition that the fuzzy positive ideal solution (PIS) and fuzzy negative ideal solution (NIS) are known, the fuzzy consistency and inconsistency indices are defined on the basis of the relative closeness degrees and expressed with TrFNs. The attribute weights are estimated through constructing a new fuzzy linear programming model, which is solved by the developed method of fuzzy linear programming with TrFNs. Through solving the constructed linear goal programming model, we obtain the collective comprehensive relative closeness degrees of alternatives to the fuzzy PIS, which are used to rank the alternatives. The effectiveness of the proposed method is verified with an example of IT outsourcing provider selection.


title: Home-Assistant Robot for an Aging Society

abstract: Many countries around the world face three major issues associated with their aging societies: a declining population, an increasing proportion of seniors, and an increasing number of single-person households. To explore assistive technologies that can help solve the problems faced by aging societies, we have tested several information and robot technologies. This paper introduces research on a home-assistant robot, which improves the ease and productivity of home activities. For people who work hard outside the home, the assistant robot performs chores in their home environment while they are away. A case study of a life-sized robot with a humanlike functional body performing daily chores is presented. An integrated software system incorporating modeling, recognition, and manipulation skills, as well as a motion generation approach based on the software system, is explained. Moreover, because housekeepers perform chores one after another in their daily environment, we also aim to develop a system for continuously performing a series of tasks by including failure detection and recovery.


title: Lithium-Ion Battery Systems

abstract: The production of lithium-ion (Li-ion) batteries has been continually increasing since their first introduction into the market in 1991 because of their excellent performance, which is related to their high specific energy, energy density, specific power, efficiency, and long life. Li-ion batteries were first used for consumer electronics products such as mobile phones, camcorders, and laptop computers, followed by automotive applications that emerged during the last decade and are still expanding, and finally industrial applications including energy storage. There are four promising cell chemistries considered for energy storage applications: 1) LiMn $_{2}{\hbox {O}}_{4}$ /graphite cell chemistry uses low-cost materials that are naturally abundant; 2) LiNi $_{1-{X-Y}2}$ Co $_{X}$ Al $_{Y}$ O $_{2}$ /graphite cell chemistry has high specific energy and long life; 3) LiFePO $_{4}$ /graphite (or carbon) cell chemistry has good safety characteristics; and 4) Li $_{4}{\hbox {Ti}}_{5}{\hbox {O}}_{12}$ is used as the negative electrode material in Li-ion batteries with long life and good safety features. However, each of the cell chemistries has some disadvantages, and the development of these technologies is still in progress. Therefore, it is too early to predict which cell chemistry will be the main candidate for energy storage applications, and we have to remain vigilant with respect to trends in technological progress and also consider changes in economic and social conditions before this can be determined.


title: Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography

abstract: Autonomous robots are complex systems that require the interaction between numerous heterogeneous ::: components (software and hardware). Because of the increase in complexity of robotic ::: applications and the diverse range of hardware, robotic middleware is designed to manage the ::: complexity and heterogeneity of the hardware and applications, promote the integration of new ::: technologies, simplify software design, hide the complexity of low-level communication and the ::: sensor heterogeneity of the sensors, improve software quality, reuse robotic software infrastructure ::: across multiple research efforts, and to reduce production costs. This paper presents a literature ::: survey and attribute-based bibliography of the current state of the art in robotic middleware design. ::: The main aim of the survey is to assist robotic middleware researchers in evaluating the ::: strengths and weaknesses of current approaches and their appropriateness for their applications. ::: Furthermore, we provide a comprehensive set of appropriate bibliographic references that are classified ::: based on middleware attributes.


title: CodeTracker: A Lightweight Approach to Track and Protect Authorization Codes in SMS Messages

abstract: Short message service (SMS) authorization codes play an important role in the application ecosystem, as a number of transactions (e.g., personal identification and online banking) require users to provide a code for authorization purposes. However, authorization codes in SMS messages can be stolen and forwarded by attackers, which introduces serious security concerns. In this paper, we propose CodeTracker , a lightweight approach to track and protect SMS authorization codes. Specifically, we leverage the taint tracking technique to mark the authorization code with taint tags at the origin of the incoming SMS messages (taint sources), and then, we propagate the tags in the system. To this end, we modify the related array structure, array operations, string operations, inter-process communication mechanism, and file operations for secondary storage of SMS authorization codes to ensure that the taint tags cannot be removed. When the authorization code is sent out via either SMS messages or network connections (taint sinks), we extract the taint tag of the data and enforce pre-defined security policies to prevent the code from being leaked. We have developed a prototype of CodeTracker on Android’s ART virtual machine and used 1, 218 SMS-stealing Android malware samples to evaluate the system. The evaluation results show that CodeTracker can effectively track and protect SMS authorization codes with a small performance overhead (<2% on average).