title: RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis

abstract: Summary: High-throughput omics datasets often contain technical replicates, included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses. We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics. Availability: freely available at http://www.repexplore.tk Contact: enrico.glaab@uni.lu


title: A comprehensive evaluation of scholarly paper recommendation using potential citation papers

abstract: To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication profiles of the researchers themselves. While using such a publication citation network has been shown to enhance performance, the network is often sparse, making recommendation difficult. To alleviate this sparsity, in our former work, we identified "potential citation papers" through the use of collaborative filtering. Also, as different logical sections of a paper have different significance, as a secondary contribution, we investigated which sections of papers can be leveraged to represent papers effectively. While this initial approach works well for researchers vested in a single discipline, it generates poor predictions for scientists who work on several different topics in the discipline (hereafter, "intra-disciplinary"). We thus extend our previous work in this paper by proposing an adaptive neighbor selection method to overcome this problem in our imputation-based collaborative filtering framework. On a publicly-available scholarly paper recommendation dataset, we show that recommendation accuracy significantly outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR, when using our adaptive neighbor selection method. While recommendation performance is enhanced for all researchers, improvements are more marked for intra-disciplinary researchers, showing that our method does address the targeted audience.


title: Selective Hydrogenation of m-Dinitrobenzene to m-Nitroaniline over Ru-SnOx/Al2O3 Catalyst

abstract: Abstract: Series catalysts of Ru-SnO x /Al 2 O 3 with varying SnO x loading of 0–3 wt% were prepared, and their catalytic activity and selectivity have been discussed and compared for the selective hydrogenation of m -dinitrobenzene ( m -DNB) to m -nitroaniline ( m -NAN). The Ru-SnO x /Al 2 O 3 catalysts were characterized by X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and hydrogen temperature-programmed reduction (H 2 -TPR) and desorption (H 2 -TPD). Under the modification of SnO x , the reaction activity increased obviously, and the best selectivity to m- NAN reached above 97% at the complete conversion of m -DNB. With the increasing of the SnO x loading, the amount of active hydrogen adsorption on the surface of the catalyst increased according to the H 2 -TPD analysis, and the electron transferred from Ru to SnO x species, as determined by XPS, inducing an electron-deficient Ru, which is a benefit for the absorption of the nitro group. Therefore, the reaction rate and product selectivity were greatly enhanced. Moreover, the Ru-SnO


title: A Wearable Nutrition Monitoring System

abstract: Maintaining appropriate levels of food intake anddeveloping regularity in eating habits is crucial to weight lossand the preservation of a healthy lifestyle. Moreover, maintainingawareness of one's own eating habits is an important steptowards portion control and ultimately, weight loss. Though manysolutions have been proposed in the area of physical activitymonitoring, few works attempt to monitor an individual's foodintake by means of a noninvasive, wearable platform. In thispaper, we introduce a novel nutrition-intake monitoring systembased around a wearable, mobile, wireless-enabled necklacefeaturing an embedded piezoelectric sensor. We also propose aframework capable of estimating volume of meals, identifyinglong-term trends in eating habits, and providing classificationbetween solid foods and liquids with an F-Measure of 85% and86% respectively. The data is presented to the user in the formof a mobile application.


title: Morphological characteristics of polypyrrole film fabricated by electrochemical polymerization

abstract: The morphological characteristics such as surface area, thickness, and roughness of the Polypyrrole (PPy) on metal electrodes of electrochemical sensors affect sensitivity and selectivity of electrochemical sensors because the surface morphology of PPy affects reduction and oxidation of the electrodes. The PPy morphology was investigated by changing several polymerization parameters such as polymerization time, electrolytes, and metal electrodes. Indium tin oxide (ITO) or Au was used as a seed metal electrode on which PPy was electropolymerized. The electropolymerization was performed in an electrolyte, either sodium dodecyl sulfate (NaDS) or sodium dodecyl benzene sulfonate (NaDBS). A homogeneous PPy morphology was obtained on ITO electrode when NaDBS solution was used as an electrolyte, but when NaDS solution was used for polymerizing on ITO electrode, the electropolymerized PPy showed uniform surface morphology and film thickness up to 1 min polymerization time while further polymerization resulted in non-uniform networks like a spider web. This increases the detection surface area with identical metal electrodes of electrochemical sensors. When Au was used, neither NaDBS nor NaDS solution resulted in any morphology change of the PPy film regardless of the polymerization time. The effect of the PPy morphology on the electrochemical sensors will also be presented.


title: Kafka, Samza and the Unix Philosophy of Distributed Data

abstract: Apache Kafka is a scalable message broker, and Apache Samza is a stream processing framework built upon Kafka. They are widely used as infrastructure for implementing personalized online services and real-time predictive analytics. Besides providing high throughput and low latency, Kafka and Samza are designed with operational robustness and long-term maintenance of applications in mind. In this paper we explain the reasoning behind the design of Kafka and Samza, which allow complex applications to be built by composing a small number of simple primitives – replicated logs and stream operators. We draw parallels between the design of Kafka and Samza, batch processing pipelines, database architecture, and the design philosophy of Unix.


title: An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes

abstract: Classification problems involving multiple classes can be addressed in different ways. One of the most popular techniques consists in dividing the original data set into two-class subsets, learning a different binary model for each new subset. These techniques are known as binarization strategies. In this work, we are interested in ensemble methods by binarization techniques; in particular, we focus on the well-known one-vs-one and one-vs-all decomposition strategies, paying special attention to the final step of the ensembles, the combination of the outputs of the binary classifiers. Our aim is to develop an empirical analysis of different aggregations to combine these outputs. To do so, we develop a double study: first, we use different base classifiers in order to observe the suitability and potential of each combination within each classifier. Then, we compare the performance of these ensemble techniques with the classifiers' themselves. Hence, we also analyse the improvement with respect to the classifiers that handle multiple classes inherently. We carry out the experimental study with several well-known algorithms of the literature such as Support Vector Machines, Decision Trees, Instance Based Learning or Rule Based Systems. We will show, supported by several statistical analyses, the goodness of the binarization techniques with respect to the base classifiers and finally we will point out the most robust techniques within this framework.


title: The use of triangulation in qualitative research.

abstract: Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). Triangulation also has been viewed as a qualitative research strategy to test validity through the convergence of information from different sources. Denzin (1978) and Patton (1999) identified four types of triangulation: (a) method triangulation, (b) investigator triangulation, (c) theory triangulation, and (d) data source triangulation. The current article will present the four types of triangulation followed by a discussion of the use of focus groups (FGs) and in-depth individual (IDI) interviews as an example of data source triangulation in qualitative inquiry.


title: The South Slavic Pronominal Clitics

abstract: Abstract. The paper highlights several prominent aspects of pronominal clitics in Balkan Slavic. The author posits a common South-Slavic underlying structure, in which the pronominal clitics are derived in head-positions of KP phrases, from where they move to the second-position clitic-cluster. In Macedonian and Bulgarian, the KPs are subsequently replaced by DPs and the pronominal clitics come to be generated as heads of AgrO and AgrIO. In clitic-doubling structures, they check the features of the XP*s with which they associate, the checking being done by raising the XPs to the specifiers of the phrases projected by the clitics. While in most European languages the pronominal clitics occur in either second or pre-verbal position, the Macedonian pronominal clitics occur in either, the type of clitichood following from the morphological features of the head of the clause. In imperative clauses, the pronominal clitics of all the South Slavic languages are, however, enclitic. The fact that the behavior of the Bulgarian and Serbo-Croatian pronominal clitics does not change when the mood of the clause is changed can be explained through the assumption that Bulgarian and Serbo-Croatian have weak Mood operators--whatever rules apply for the derivation of indicative clauses with clitics also apply for the derivation of imperative clauses with clitics. The behavior of the Macedonian and Bulgarian pronominal clitics in negative imperative clauses can also be explained through the strength of an operator--the Neg operator. The position of the Serbo-Croatian and Slovenian negation operator relative to the verb and the pronominal clitics can, however, be accounted for if one assumes that the phonologically deficient negation operator and the verb form a negative verb in the lexicon. ********** 1. From Second-Position to Pre-Verbal Clitics In South Slavic, two major types of clausal pronominal clitics (dative and accusative) have been identified: pre-verbal and second position ones. Both types of pronominal clitics occur in clusters, which, depending on the language and type of clause, may also include auxiliary clitics, modal clitics, interrogative clitics and negation clitics. The major difference between the (clusters of) clitics of the two types--the one to which they owe their names--is their distinct placement in the clause: while pre-verbal clitics are placed to the immediate left of V, whether tensed or non-tensed, second-position clitics are placed to the right of an initial clausal element. (1) For most analysts (e.g., Dimitrova-Vulchanova 1995; Franks 1998, 2000; Franks and Progovac 1994; Progovac 1996, 1998, 1999, 2000; Rivero 1997; Roberts 1994; Tomic 1996; Wilder and Cavar 1994a, 1994b) the "initial" element is a CP-initial word or phrase, if a CP is projected, or an IP-initial word or phrase, if no CP is projected. Radanovic-Kocic (1996) and Boskovic (2000, 2001), however, argue that it is an initial word or phrase after an intonation boundary. (2) The syntactic hosting of the clitics does not always go hand in hand with their phonological hosting. In particular, Bulgarian clitics are syntactically pre-verbal, but phonologically they are predominately enclitic. (3) As a result of this discrepancy between syntactic and phonological orientation, the Bulgarian clitics share a restriction with the second-position clitics--they cannot appear first in the clause. (4) While Boskovic (2001) maintains that the pronominal clitics of all the South Slavic languages originate in agreement phrases, I argue that only the pronominal clitics of the clitic-doubling South Slavic languages are derived there. (5) In Serbo-Croatian (6) and Slovenian, the dative and accusative pronominal clitics are derived as K (case) heads of KPs in argument positions and, on their way to wherever the second-position clitic cluster is formed, they move to AgrIO and AgrO, respectively, to check their agreement features. …


title: Detection of DDoS Backscatter Based on Traffic Features of Darknet TCP Packets

abstract: In this work, we propose a method to discriminate backscatter caused by DDoS attacks from normal traffic. Since DDoS attacks are imminent threats which could give serious economic damages to private companies and public organizations, it is quite important to detect DDoS backscatter as early as possible. To do this, 11 features of port/IP information are defined for network packets which are sent within a short time, and these features of packet traffic are classified by Suppurt Vector Machine (SVM). In the experiments, we use TCP packets for the evaluation because they include control flags (e.g. SYN-ACK, RST-ACK, RST, ACK) which can give label information (i.e. Backscatter or non-backscatter). We confirm that the proposed method can discriminate DDoS backscatter correctly from unknown dark net TCP packets with more than 90% accuracy.


A Hybrid Approach to Detect Credit Card Fraud


title: Natural language question answering: the view from here

abstract: As users struggle to navigate the wealth of on-line information now available, the need for automated question answering systems becomes more urgent. We need systems that allow a user to ask a question in everyday language and receive an answer quickly and succinctly, with sufficient context to validate the answer. Current search engines can return ranked lists of documents, but they do not deliver answers to the user.Question answering systems address this problem. Recent successes have been reported in a series of question-answering evaluations that started in 1999 as part of the Text Retrieval Conference (TREC). The best systems are now able to answer more than two thirds of factual questions in this evaluation.


title: A least-squares approach to anomaly detection in static and sequential data

abstract: We describe a probabilistic, nonparametric method for anomaly detection, based on a squared-loss objective function which has a simple analytical solution. The method emerges from extending recent work in nonparametric least-squares classification to include a ''none-of-the-above'' class which models anomalies in terms of non-anamalous training data. The method shares the flexibility of other kernel-based anomaly detection methods, yet is typically much faster to train and test. It can also be used to distinguish between multiple inlier classes and anomalies. The probabilistic nature of the output makes it straightforward to apply even when test data has structural dependencies; we show how a hidden Markov model framework can be incorporated in order to identify anomalous subsequences in a test sequence. Empirical results on datasets from several domains show the method to have comparable discriminative performance to popular alternatives, but with a clear speed advantage.


title: Friends With Benefits: On the Positive Consequences of Pet Ownership

abstract: Social support is critical for psychological and physical well-being, reflecting the centrality of belongingness in our lives. Human interactions often provide people with considerable social support, but can pets also fulfill one’s social needs? Although there is correlational evidence that pets may help individuals facing significant life stressors, little is known about the well-being benefits of pets for everyday people. Study 1 found in a community sample that pet owners fared better on several well-being (e.g., greater self-esteem, more exercise) and individual-difference (e.g., greater conscientiousness, less fearful attachment) measures. Study 2 assessed a different community sample and found that owners enjoyed better well-being when their pets fulfilled social needs better, and the support that pets provided complemented rather than competed with human sources. Finally, Study 3 brought pet owners into the laboratory and experimentally demonstrated the ability of pets to stave off negativity caused by social rejection. In summary, pets can serve as important sources of social support, providing many positive psychological and physical benefits for their owners.


A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia


title: Agile and Kanban in Coordination

abstract: Iterative development and Kanban are not mutually exclusive competing methodologies, in essence they are complementary processes that when used together can facilitate higher performance in large development teams. This paper is a walkthrough of the Network Gaming team's implementation of Iterative Agile alongside Lean Kanban here at WMS Gaming. The intent is to cover the processes created and the improvements achieved with this coordinated Agile / Lean system.


title: Fast rate analysis of some stochastic optimization algorithms

abstract: In this paper, we revisit three fundamental and popular stochastic optimization algorithms (namely, Online Proximal Gradient, Regularized Dual Averaging method and ADMM with online proximal gradient) and analyze their convergence speed under conditions weaker than those in literature. In particular, previous works showed that these algorithms converge at a rate of O(ln T/T) when the loss function is strongly convex, and O(1/√T) in the weakly convex case. In contrast, we relax the strong convexity assumption of the loss function, and show that the algorithms converge at a rate O(ln T/T) if the expectation of the loss function is locally strongly convex. This is a much weaker assumption and is satisfied by many practical formulations including Lasso and Logistic Regression. Our analysis thus extends the applicability of these three methods, as well as provides a general recipe for improving analysis of convergence rate for stochastic and online optimization algorithms.


title: Enhancement and bandwidth compression of noisy speech

abstract: Over the past several years there has been considerable attention focused on the problem of enhancement and bandwidth compression of speech degraded by additive background noise. This interest is motivated by several factors including a broad set of important applications, the apparent lack of robustness in current speech-compression systems and the development of several potentially promising and practical solutions. One objective of this paper is to provide an overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise. A second objective is to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.


title: Examining the Effect of Academic Procrastination on Achievement Using LMS Data in e-Learning

abstract: Introduction Procrastination refers to the delay of initiation or timely completion of a task (Lay, 1986). Procrastination is a prevalent phenomenon especially among college students due to the relatively flexible learning environment compared to K-12 circumstances. Some studies (e.g., Ellis & Knaus, 1977; Schouwenburg, 2004) reported that approximately 70% of college students considered themselves procrastinators, and academic procrastination has been reported across the world (e.g., Ferrari, Diaz-Morales, O'Callaghan, Diaz, & Argumedo, 2007; Klassen et al., 2010; Seo, 2011). In addition, as e-learning is becoming popular, the likelihood of procrastination is also growing because of the self-directed nature of the e-learning environment. For example, an annual report (Allen & Seaman, 2013) that surveyed over 2,800 colleges and universities in the U.S. reported that a total of 6.7 million students had taken at least one online course, but many students expressed difficulties in planning and regulating their studies. Studies have shown that procrastinators delay or participate less in learning activities due to a failure in self-regulation (Dewitte & Lens, 2000; Steel, 2007), and procrastination leads to poor achievement (Akinsola, Tella, & Tella, 2007). Because procrastinators had a relatively short period of time for completing tasks or preparing for exams, they hurried to finish their work or crammed the study material. Furthermore, high procrastinators have reported that they feel a compulsion to dropout frequently throughout a course and experience difficulty in maintaining motivation (Michinov, Brunot, Le Bohec, Juhel, & Delaval, 2011). Therefore, procrastination has been considered a proxy for disengaged learning, and it negatively impacts achievement (Asarta & Schmidt, 2013; Melton, 1970). Delays in studying weekly learning materials, participating in activities, and completing tasks are typical procrastination in e-learning, and the consequence of procrastination in e-learning is considered more damaging on achievement than procrastination in traditional classrooms (Tuckman, 2005) and often results in withdrawals (Doherty, 2006). Therefore, special attention should be paid to procrastination in e-learning. However, there is not enough empirical research on academic procrastination in the e-learning environment. Furthermore, most procrastination studies have used self-reported questionnaires to measure the behavioral tendencies of procrastination either in academic tasks or in daily tasks. Although these instruments (e.g., Lay, 1986; Tuckman, 1991) have been validated and used in many studies, they are intrusive to students and limited in capturing actual procrastination behaviors displayed during a course. This issue can be resolved in an e-learning course because all student behaviors are recorded in a learning management system (LMS). The generated log data convey information regarding who did it, what was taken, and when it was taken (Hershkovitz & Nachmias, 2009). More than 80% of surveyed institutions in the U.S. (Abdous, He, & Yen, 2012) and Korea (Korea Education and Research Information Service, 2012) have reported using LMS as a medium for e-learning course delivery, and the majority of traditional universities have also used LMSs to support their regular classes. This trend indicates that it is not difficult to trace and identify students' learning patterns throughout a course in a field. Therefore, it would be valuable to clarify the effect of academic procrastination on achievement using LMS data. Several studies have explored the significant indicators from log data to e-learning achievement. Participation indices, including the number of pages to read, time spent reading materials, and the number of original postings, were commonly used in the research. However, recent studies (Asarta & Schmidt, 2013; Jo & Kim, 2013) have claimed that consistent learning behaviors, such as avoiding cramming or regularity of learning pace, are more critical than the frequency of access or amount of time spent viewing. …


title: A novel series expansion for the multivariate normal probability integrals based on Fourier series

abstract: Abstract. In this article, we derive a series expansion of the multivariate normal probability integrals based on Fourier series. The basic idea is to transform the limits of each integral from hi to ∞ to be from −∞ to ∞ by multiplying the integrand by a periodic square wave that approximates the domain of the integral. This square wave is expressed by its Fourier series expansion. Then a Cholesky decomposition of the covariance matrix is applied to transform the integrand to a simple one that can be easily evaluated. The resultant formula has a simple pattern that is expressed as multiple series expansion of trigonometric and exponential functions.


Siamese Networks : A Thing or Two to Know


title: T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data

abstract: Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods which employ dimensionality reduction to two or three dimensions are often inefficient and/or ineffective for these datasets. This paper introduces t-SNE-CUDA, a GPU-accelerated implementation of t-distributed Symmetric Neighbor Embedding (t-SNE) for visualizing datasets and models. t-SNE-CUDA significantly outperforms current implementations with 50-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for the first time, visualization of the neural network activations on the entire ImageNet dataset - a feat that was previously computationally intractable. We also demonstrate visualization performance in the NLP domain by visualizing the GloVe embedding vectors. From these visualizations, we can draw interesting conclusions about using the L2 metric in these embedding spaces. t-SNE-CUDA is publicly available athttps://github.com/CannyLab/tsne-cuda


title: A Fast and Series-Stacked IGBT Switch With Balanced Voltage Sharing for Pulsed Power Applications

abstract: The series configuration of fast semiconductor switches seems to be the key component in the high-voltage and fast rising time pulse generation. In this approach, two important issues must be considered. The first is to provide a safe operating condition for the switches in transient intervals. The second is to design a gate drive system with the capability of driving a large number of discrete devices simultaneously. The aim of this paper is to obviate these two requirements. First, different factors affecting the unbalanced voltage sharing between the series switches are discussed. In this investigation, the switch-to-ground parasitic capacitance effect has been recognized as the major effect on the unbalanced voltage sharing in the transient interval. Two schemes for abating this effect are proposed. To solve the unbalanced voltage distribution, the structure with a snubber circuit in the clamp mode operation is suggested. This scheme can be used for any number of switches without destructively affecting their behavior. In addition, the output pulse with a fast rising time could be obtained by the proposed gate drive system. In order to evaluate the operation of the proposed structure, a stacked switch with the voltage capability of 36 kV is tested experimentally. The characteristics of the obtained pulse are the fast rising time (69.5 ns) with the dV/dt of 460 kv/ $\mu \text{s}$ and the wide range of the pulsewidth adjusting to 0.5–15 $\mu \text{s}$ . In addition, the voltage variance of the switches level in the series structure is about 10%.


Unsupervised Deep Learning for Optical Flow Estimation.


title: BrightStat.com: Free statistics online

abstract: Powerful software for statistical analysis is expensive. Here I present BrightStat, a statistical software running on the Internet which is free of charge. BrightStat's goals, its main capabilities and functionalities are outlined. Three different sample runs, a Friedman test, a chi-square test, and a step-wise multiple regression are presented. The results obtained by BrightStat are compared with results computed by SPSS, one of the global leader in providing statistical software, and VassarStats, a collection of scripts for data analysis running on the Internet. Elementary statistics is an inherent part of academic education and BrightStat is an alternative to commercial products.


title: Fog Computing: Principles, Architectures, and Applications

abstract: Abstract The Internet of Everything (IoE) solutions gradually bring every object online, and processing data in a centralized cloud does not scale to requirements of such an environment. This is because there are applications such as health monitoring and emergency response that require low latency, so delay caused by transferring data to the cloud and then back to the application can seriously impact the performance. To this end, Fog computing has emerged, where cloud computing is extended to the edge of the network to decrease the latency and network congestion. Fog computing is a paradigm for managing a highly distributed and possibly virtualized environment that provides compute and network services between sensors and cloud data centers. This chapter provides a background and motivations regarding the emergence of Fog computing, and defines its key characteristics. In addition, a reference architecture for Fog computing is presented, and recent related development and applications are discussed.


title: Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings

abstract: Learning automatically the structure of object categories remains an important open problem in computer vision. In this paper, we propose a novel unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure. Our approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep neural network that detects landmarks consistently with such visual effects. Furthermore, we show that the learned landmarks establish meaningful correspondences between different object instances in a category without having to impose this requirement explicitly. We assess the method qualitatively on a variety of object types, natural and man-made. We also show that our unsupervised landmarks are highly predictive of manually-annotated landmarks in face benchmark datasets, and can be used to regress these with a high degree of accuracy.


title: PhoneyC: A Virtual Client Honeypot

abstract: The number of client-side attacks has grown significantly in the past few years, shifting focus away from defendable positions to a broad, poorly defended space filled with vulnerable clients. Just as honeypots enabled deep research into server-side attacks, honeyclients can permit the deep study of client-side attacks. A complement to honeypots, a honeyclient is a tool designed to mimic the behavior of a user-driven network client application, such as a web browser, and be exploited by an attacker's content. These systems are instrumented to discover what happened and how. This paper presents PhoneyC, a honeyclient tool that can provide visibility into new and complex client-side attacks. PhoneyC is a virtual honeyclient, meaning it is not a real application but rather an emulated client. By using dynamic analysis, PhoneyC is able to remove the obfuscation from many malicious pages. Furthermore, PhoneyC emulates specific vulnerabilities to pinpoint the attack vector. PhoneyC is a modular framework that enables the study of malicious HTTP pages and understands modern vulnerabilities and attacker techniques.


title: Abstract A normative study of family media habits

abstract: The present study documents family media habits in six areas: electronic and print Media Use, parental Monitoring of children's media, parental Consistency regarding rules for children's media use, parents' reports of observable Media Effects on their children, parents' Knowledge about media and media effects, and how much children participate in Alternative Activities to electronic media. A random national sample of 527 parents of 2- to 17-year-olds were surveyed. Normative descriptive statistics in each of the six categories are provided. In general, families' scores in five of the six categories are positively correlated, indicating that families that practice positive media habits in one area tend to practice positive habits in other areas. Many family media habits predict children's school performance. Furthermore, having TVs in children's bedrooms is correlated with lower scores in all six areas, as well as with lower school performance. This study provides a normative baseline for future research.


title: TransformHealth Care ? Potential Health Benefits , Savings , And Costs The adoption of interoperable EMR systems could produce efficiency and safety savings of $ 142 – $ 371 billion

abstract: To broadly examine the potential health and financial benefits of health information technology (HIT), this paper compares health care with the use of IT in other industries. It estimates potential savings and costs of widespread adoption of electronic medical record (EMR) systems, models important health and safety benefits, and concludes that effective EMR implementation and networking could eventually save more than $81 billion annually--by improving health care efficiency and safety--and that HIT-enabled prevention and management of chronic disease could eventually double those savings while increasing health and other social benefits. However, this is unlikely to be realized without related changes to the health care system.


title: Transformational Leadership and Moral Reasoning

abstract: Terms such as moral and ethical leadership are used widely in theory, yet little systematic research has related a sociomoral dimension to leadership in organizations. This study investigated whether managers' moral reasoning (n=132) was associated with the transformational and transactional leadership behaviors they exhibited as perceived by their subordinates (n=407). Managers completed the Defining Issues Test (J. R. Rest, 1990), whereas their subordinates completed the Multifactor Leadership Questionnaire (B. M. Bass & B. J. Avolio, 1995). Analysis of covariance indicated that managers scoring in the highest group of the moral-reasoning distribution exhibited more transformational leadership behaviors than leaders scoring in the lowest group. As expected, there was no relationship between moral-reasoning group and transactional leadership behaviors. Implications for leadership development are discussed.


title: Time series shapelets: a novel technique that allows accurate, interpretable and fast classification

abstract: Classification of time series has been attracting great interest over the past decade. While dozens of techniques have been introduced, recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems, especially for large-scale datasets. While this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. First, the nearest neighbor algorithm requires storing and searching the entire dataset, resulting in a high time and space complexity that limits its applicability, especially on resource-limited sensors. Second, beyond mere classification accuracy, we often wish to gain some insight into the data and to make the classification result more explainable, which global characteristics of the nearest neighbor cannot provide. In this work we introduce a new time series primitive, time series shapelets, which addresses these limitations. Informally, shapelets are time series subsequences which are in some sense maximally representative of a class. We can use the distance to the shapelet, rather than the distance to the nearest neighbor to classify objects. As we shall show with extensive empirical evaluations in diverse domains, classification algorithms based on the time series shapelet primitives can be interpretable, more accurate, and significantly faster than state-of-the-art classifiers.


POLICE DISCRETION WITH YOUNG OFFENDERS


Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic


title: Effect of Classroom Modification on Attention and Engagement of Students With Autism or Dyspraxia

abstract: Students with autism display sensory sensitivities to environmental stimuli that affect their attending and engagement in classroom learning activities. The purpose of the study was to determine whether attending of 4 male students, ages 13–20, increased after the installation of sound-absorbing walls and halogen lighting. The multiple single-subject, mixed-method design, AB(B+C), included a 2-wk baseline and two intervention phases: 2 wk after sound-absorbing wall installation using the Owens Corning Basement Finishing System™ (Owens Corning, Toledo, OH) and 2 wk after halogen light installation. We calculated nonattending frequencies from videotaped class sessions and used visual analysis to measure within-phase and between-phase characteristics. Results included increased frequency and stability of attending and engagement and improved classroom performance, comfort, and mood. Journaling provided students’ perspective on the modifications and reflected overall increased sensory comfort and themes of improved classroom environment, positive emotional response (mood), and improved classroom performance


title: EEG mu component responses to viewing emotional faces

abstract: Abstract Simulation theories for the perceptual processing of emotional faces assert that observers recruit the neural circuitry involved in creating their own emotional facial expressions in order to recognize the emotions and infer the feelings of others. The EEG mu rhythm is a sensorimotor oscillation hypothesized to index simulation of some actions during perceptual processing of these actions. The purpose of this research was to extend the study of mu rhythm simulation responses during perceptual tasks to the domain of emotional face perception. Subjects viewed happy and disgusted face photos with empathy and non-empathy task instructions while EEG responses were measured. EEG components were isolated and analyzed using a blind source separation (BSS) method. Mu components were found to respond to the perception of happy and disgusted faces during both empathy and non-empathy tasks with an event-related desynchronization (ERD), activation that is consistent with face simulation. Significant differences were found between responses to happy and to disgusted faces across the right hemisphere mu components beginning about 500 ms after stimulus presentation. These findings support a simulation account of perceptual face processing based on a sensorimotor mirroring mechanism, and are the first report of distinct EEG mu responses to observation of positively and negatively valenced emotional faces.


title: A Deeper Look into Dependency-Based Word Embeddings

abstract: We investigate the effect of various dependency-based word embeddings on distinguishing between functional and domain similarity, word similarity rankings, and two downstream tasks in English. Variations include word embeddings trained using context windows from Stanford and Universal dependencies at several levels of enhancement (ranging from unlabeled, to Enhanced++ dependencies). Results are compared to basic linear contexts and evaluated on several datasets. We found that embeddings trained with Universal and Stanford dependency contexts excel at different tasks, and that enhanced dependencies often improve performance.


title: Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline

abstract: “Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.


title: Understanding Android Security

abstract: Google's Android platform is a widely anticipated open source operating system for mobile phones. This article describes Android's security model and attempts to unmask the complexity of secure application development. The authors conclude by identifying lessons and opportunities for future enhancements.


title: Net-Trim: A Layer-wise Convex Pruning of Deep Neural Networks

abstract: Model reduction is a highly desirable process for deep neural networks. While large networks are theoretically capable of learning arbitrarily complex models, overfitting and model redundancy negatively affects the prediction accuracy and model variance. Net-Trim is a layer-wise convex framework to prune (sparsify) deep neural networks. The method is applicable to neural networks operating with the rectified linear unit (ReLU) as the nonlinear activation. The basic idea is to retrain the network layer by layer keeping the layer inputs and outputs close to the originally trained model, while seeking a sparse transform matrix. We present both the parallel and cascade versions of the algorithm. While the former enjoys computational distributability, the latter is capable of achieving simpler models. In both cases, we mathematically show a consistency between the retrained model and the initial trained network. We also derive the general sufficient conditions for the recovery of a sparse transform matrix. In the case of standard Gaussian training samples of dimension $N$ being fed to a layer, and $s$ being the maximum number of nonzero terms across all columns of the transform matrix, we show that $\mathcal{O}(s\log N)$ samples are enough to accurately learn the layer model.


title: Job and life attitudes of male executives.

abstract: Despite executives' important positions in organizations , their attitudes have not received much research attention. To remedy this deficiency, the authors tested a hypothesized model of executive attitudes involving job satisfaction, life satisfaction, job stress, and work-family conflict. Using data from a large, representative sample of male executives (because of the small number of female executives in the study, the analyses were confined to men), the authors obtained LISREL results indicating support for the overall model and the specific relationships within the model. These results—the first to simultaneously consider job satisfaction, life satisfaction, job stress, and work-family conflict—constitute the most comprehensive evidence to date on executive attitudes. The meaning and contributions of the findings are discussed. Executives occupy positions of high pay, power, and prestige. Their decisions have significant consequences for large numbers of employees as well as for shareholders, communities, and other stakeholders. This is true for both their personal decisions, such as whether to stay or leave an organization, and their decisions about policies and strategies affecting others. These decisions are undoubtedly driven, in part, by the attitudes of executives toward their work. Executive work attitudes, and their antecedents and consequences, thus represent an important area of study. Better understanding of these attitudes may provide insight into behaviors such as executive job search or performance. Moreover, executives may make decisions about their organizations' policies on the basis of beliefs that others are like them. If executives' work attitudes differ from those of others, such decisions may be based on a faulty premise and thus be flawed. In fact, researchers calling for increased diversity in executive ranks have used this argument (Cox, 1991). Unfortunately, little research currently exists on executive attitudes. Previous research on executives has focused on topics such as executive pay (e.g., Kerr & Bettis, 1987), personal characteristics of executives (e.g., Miller, Kets de Vries, & Toulouse, 1982), and the influence of executives on organizationa l effectiveness (e.g., A. K. Gupta & Govindarajan, 1984). Some


title: A study of smoothing methods for language models applied to information retrieval

abstract: Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and to then rank documents by the likelihood of the query according to the estimated language model. A central issue in language model estimation is smoothing, the problem of adjusting the maximum likelihood estimator to compensate for data sparseness. In this article, we study the problem of language model smoothing and its influence on retrieval performance. We examine the sensitivity of retrieval performance to the smoothing parameters and compare several popular smoothing methods on different test collections. Experimental results show that not only is the retrieval performance generally sensitive to the smoothing parameters, but also the sensitivity pattern is affected by the query type, with performance being more sensitive to smoothing for verbose queries than for keyword queries. Verbose queries also generally require more aggressive smoothing to achieve optimal performance. This suggests that smoothing plays two different role---to make the estimated document language model more accurate and to "explain" the noninformative words in the query. In order to decouple these two distinct roles of smoothing, we propose a two-stage smoothing strategy, which yields better sensitivity patterns and facilitates the setting of smoothing parameters automatically. We further propose methods for estimating the smoothing parameters automatically. Evaluation on five different databases and four types of queries indicates that the two-stage smoothing method with the proposed parameter estimation methods consistently gives retrieval performance that is close to---or better than---the best results achieved using a single smoothing method and exhaustive parameter search on the test data.


title: Push the limit of WiFi based localization for smartphones

abstract: Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., $6\sim8m$) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m$, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.


title: Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records

abstract: In this work we propose a new method for the rhythm classification of short single-lead ECG records, using a set of high-level and clinically meaningful features provided by the abductive interpretation of the records. These features include morphological and rhythm-related features that are used to build two classifiers: one that evaluates the record globally, using aggregated values for each feature; and another one that evaluates the record as a sequence, using a Recurrent Neural Network fed with the individual features for each detected heartbeat. The two classifiers are finally combined using the stacking technique, providing an answer by means of four target classes: Normal sinus rhythm (N), Atrial fibrillation (A), Other anomaly (O) and Noisy (∼). The approach has been validated against the 2017 Physionet/CinC Challenge dataset, obtaining a final score of 0.83 and ranking first in the competition.


title: Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks

abstract: Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms.


title: Routing questions for collaborative answering in community question answering

abstract: Community Question Answering (CQA) service enables its users to exchange knowledge in the form of questions and answers. By allowing the users to contribute knowledge, CQA not only satisfies the question askers but also provides valuable references to other users with similar queries. Due to a large volume of questions, not all questions get fully answered. As a result, it can be useful to route a question to a potential answerer. In this paper, we present a question routing scheme which takes into account the answering, commenting and voting propensities of the users. Unlike prior work which focuses on routing a question to the most desirable expert, we focus on routing it to a group of users - who would be willing to collaborate and provide useful answers to that question. Through empirical evidence, we show that more answers and comments are desirable for improving the lasting value of a question-answer thread. As a result, our focus is on routing a question to a team of compatible users. We propose a recommendation model that takes into account the compatibility, topical expertise and availability of the users. Our experiments over a large real-world dataset shows the effectiveness of our approach over several baseline models.


title: Deep Sentence Embedding Using the Long Short Term Memory Network: Analysis and Application to Information Retrieval

abstract: This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detect the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding method, the Paragraph Vector, is performed. The results show that the proposed method in this paper significantly outperforms Paragraph Vector method for web document retrieval task.


title: Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology

abstract: Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis. Nevertheless, this kind of visual inspection is subjective, error-prone and time-consuming. In order to overcome the issues, numerous methods of automatic malaria diagnosis have been proposed so far. In particular, many researchers have used mathematical morphology as a powerful tool for computer aided malaria detection and classification. Mathematical morphology is not only a theory for the analysis of spatial structures, but also a very powerful technique widely used for image processing purposes and employed successfully in biomedical image analysis, especially in preprocessing and segmentation tasks. Microscopic image analysis and particularly malaria detection and classification can greatly benefit from the use of morphological operators. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection and identification in stained blood smears images.


title: Beyond Bitcoin: The Rise of Blockchain World

abstract: The brave new world of blockchain potentially transforms the financial structures we have come to know and feel ambivalent about. What does a decentralized, secure system mean for our society?


title: Efficient secure similarity computation on encrypted trajectory data

abstract: Outsourcing database to clouds is a scalable and cost-effective way for large scale data storage, management, and query processing. Trajectory data contain rich spatio-temporal relationships and reveal many forms of individual sensitive information (e.g., home address, health condition), which necessitate them to be encrypted before being outsourced for privacy concerns. However, efficient query processing over encrypted trajectory data is a very challenging task. Though some achievements have been reported very recently for simple queries (e.g., SQL queries, kNN queries) on encrypted data, there is rather limited progress on secure evaluation of trajectory queries because they are more complex and need special treatment. In this paper, we focus on secure trajectory similarity computation that is the cornerstone of secure trajectory query processing. More specifically, we propose an efficient solution to securely compute the similarity between two encrypted trajectories, which reveals nothing about the trajectories, but the final result. We theoretically prove that our solution is secure against the semi-honest adversaries model as all the intermediate information in our protocols can be simulated in polynomial time. Finally we empirically study the efficiency of the proposed method, which demonstrates the feasibility of our solution.


title: A Comparison of Methods for Clustering 16S rRNA Sequences into OTUs

abstract: Recent studies of 16S rRNA sequences through next-generation sequencing have revolutionized our understanding of the microbial community composition and structure. One common approach in using these data to explore the genetic diversity in a microbial community is to cluster the 16S rRNA sequences into Operational Taxonomic Units (OTUs) based on sequence similarities. The inferred OTUs can then be used to estimate species, diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into OTUs, relatively little guidance is available on their relative performance and the choice of key parameters for each method. In this study, we conducted a comprehensive evaluation of ten existing OTU inference methods. We found that the appropriate dissimilarity value for defining distinct OTUs is not only related with a specific method but also related with the sample complexity. For data sets with low complexity, all the algorithms need a higher dissimilarity threshold to define OTUs. Some methods, such as, CROP and SLP, are more robust to the specific choice of the threshold than other methods, especially for shorter reads. For high-complexity data sets, hierarchical cluster methods need a more strict dissimilarity threshold to define OTUs because the commonly used dissimilarity threshold of 3% often leads to an under-estimation of the number of OTUs. In general, hierarchical clustering methods perform better at lower dissimilarity thresholds. Our results show that sequence abundance plays an important role in OTU inference. We conclude that care is needed to choose both a threshold for dissimilarity and abundance for OTU inference.


title: Veiling glare and perceived black in high dynamic range displays

abstract: A perceptual experiment was conducted to measure the visibility of black-level differences in the proximity of a bright glare source. In a controlled viewing environment, visual difference thresholds were adaptively measured using dark, shadow-detail images shown on a high dynamic range liquid crystal display while an external LED lamp was used to induce intra-ocular glare over a small range of eccentricities. This high-contrast situation is relevant to HDR displays which may have bright regions in displayed images as well as to viewingenvironments which include lamps or other light sources. The resulting difference thresholds are modeled with a combination of the CIE total glare equation, the DICOM contrast visibility model, and a newestimate of adaptation luminance.


title: Twinning in prosimians

abstract: Abstract Twinning pedigrees for Lemur catta and Galago crassicaudatus argentatus colonies at the Oregon Regional Primate Research Center have been constructed. Unlike-sexed twin pairs were more numerous than like-sexed twin pairs in both species. Evidence is presented which supports the hypothesis that twinning in prosimians, as in man, is genetically controlled.


title: Radiological and clinical features of sarcomatoid hepatocellular carcinoma in 11 cases.

abstract: OBJECTIVE ::: To evaluate the imaging and clinical findings of pathologically proven sarcomatoid hepatocellular carcinoma (HCC) in 11 cases. ::: ::: ::: METHODS ::: We retrospectively reviewed the imaging findings and clinical features of 11 patients with pathologically proven sarcomatoid HCC at 2 university-based tertiary institutions from July 1997 to June 2006. ::: ::: ::: RESULTS ::: The most common gross morphologies of sarcomatoid HCC were massive expanding pattern (n = 6 [54%]) or multinodular confluent (n = 5 [46%]) with partial encapsulation (n = 9 [82%]). The most common enhancement pattern was peripheral enhancement with central necrosis (n = 8 [72%]) without a dominant dynamic pattern of enhancement. Six (54%) of 11 patients were positive for hepatitis B surface antigen or hepatitis C virus antibody. Only 1 patient (9%) had more than 500 ng/mL of preoperative serum alpha-fetoprotein. ::: ::: ::: CONCLUSIONS ::: In a patient with low-level alpha-fetoprotein and no history of viral hepatitis, sarcomatoid HCC usually presents as a large mass with peripheral enhancement, central necrosis, variable enhancement of the solid portion with or without tumor capsule, and intrahepatic metastasis.


When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages


title: Automatic Panoramic Image Stitching using Invariant Features

abstract: This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.


Clustering in large graphs and matrices


3 D Binary Signatures


title: Validity of the Executive Function Theory of Attention-Deficit/Hyperactivity Disorder: A Meta-Analytic Review

abstract: One of the most prominent neuropsychologic theories of attention-deficit/hyperactivity disorder (ADHD) suggests that its symptoms arise from a primary deficit in executive functions (EF), defined as neurocognitive processes that maintain an appropriate problem-solving set to attain a later goal. To examine the validity of the EF theory, we conducted a meta-analysis of 83 studies that administered EF measures to groups with ADHD (total N = 3734) and without ADHD ( N = 2969). Groups with ADHD exhibited significant impairment on all EF tasks. Effect sizes for all measures fell in the medium range (.46–.69), but the strongest and most consistent effects were obtained on measures of response inhibition, vigilance, working memory, and planning. Weaknesses in EF were significant in both clinic-referred and community samples and were not explained by group differences in intelligence, academic achievement, or symptoms of other disorders. ADHD is associated with significant weaknesses in several key EF domains. However, moderate effect sizes and lack of universality of EF deficits among individuals with ADHD suggest that EF weaknesses are neither necessary nor sufficient to cause all cases of ADHD. Difficulties with EF appear to be one important component of the complex neuropsychology of ADHD.


title: 2D cutting stock problem: a new parallel algorithm and bounds

abstract: This work introduces a set of important improvements in the resolution of the Two Dimensional Cutting Stock Problem. It presents a new heuristic enhancing existing ones, an original upper bound that lowers the upper bounds in the literature, and a parallel algorithm for distributed memory machines that achieves linear speedup. Many components of the algorithm are generic and can be ported to parallel branch and bound and A* skeletons. Among the new components there is a comprehensive mpi-compatible synchronization service which facilitates the writing of time-based balancing schemes.


title: Cello: a disk scheduling framework for next generation operating systems

abstract: In this paper, we present the Cello disk scheduling framework for meeting the diverse service requirements of applications. Cello employs a two-level disk scheduling architecture, consisting of a class-independent scheduler and a set of class-specific schedulers. The two levels of the framework allocate disk bandwidth at two time-scales: the class-independent scheduler governs the coarse-grain allocation of bandwidth to application classes, while the class-specific schedulers control the fine-grain interleaving of requests. The two levels of the architecture separate application-independent mechanisms from application-specific scheduling policies, and thereby facilitate the co-existence of multiple class-specific schedulers. We demonstrate that Cello is suitable for next generation operating systems since: (i) it aligns the service provided with the application requirements, (ii) it protects application classes from one another, (iii) it is work-conserving and can adapt to changes in work-load, (iv) it minimizes the seek time and rotational latency overhead incurred during access, and (v) it is computationally efficient.


title: PWM rectifier with LCL-filter using different current control structures

abstract: The control of a PWM rectifier with LCL-filter using a minimum number of sensors is analyzed. In addition to the DC-link voltage either the converter or line current is measured. Two different ways of current control are shown, analyzed and compared by simulations as well as experimental investigations. Main focus is spent on active damping of the LCL filter resonance and on robustness against line inductance variations.


Cluster Analysis and Mathematical Programming


title: Tilejunction: Mitigating Signal Noise for Fingerprint-Based Indoor Localization

abstract: In indoor localization based on Wi-Fi fingerprinting, a target sends its received signal strength indicator (RSSI) of access points (APs) to a server to estimate its position. Traditionally, the server estimates the target position by matching the RSSI with the fingerprints stored in the database. Due to signal noise in fingerprint collection and target measurement, this often results in a geographically disperse set of reference points (RPs), leading to unsatisfactory estimation accuracy. To mitigate the noise problem, we propose a novel, efficient, and highly accurate localization scheme termed Tilejunction . Based on only the first two moments of the measured signal, Tilejunction maps the target RSSI of each AP to a convex hull termed signal “tile” where the target is likely within. Using a novel comparison metric for random signals, we formulate a linear programming (LP) problem to localize the target at the junction of the tiles. To further improve its computational efficiency, Tilejunction employs an information-theoretic measure to keep only those APs whose signals show sufficient differentiation in the site. It also partitions the site into multiple clusters to substantially reduce the search space in the LP optimization. We have implemented Tilejunction. Our extensive simulation and experimental measurements show that it outperforms other recent state-of-the-art approaches (e.g. RADAR, KL-divergence, etc.) with significantly lower localization error (often by more than $30$ percent).


title: Selection-based Text Entry in Virtual Reality

abstract: In recent years, Virtual Reality (VR) and 3D User Interfaces (3DUI) have seen a drastic increase in popularity, especially in terms of consumer-ready hardware and software. While the technology for input as well as output devices is market ready, only a few solutions for text input exist, and empirical knowledge about performance and user preferences is lacking. In this paper, we study text entry in VR by selecting characters on a virtual keyboard. We discuss the design space for assessing selection-based text entry in VR. Then, we implement six methods that span different parts of the design space and evaluate their performance and user preferences. Our results show that pointing using tracked hand-held controllers outperforms all other methods. Other methods such as head pointing can be viable alternatives depending on available resources. We summarize our findings by formulating guidelines for choosing optimal virtual keyboard text entry methods in VR.


title: Progress in the Use of Autologous Regenerative Platelet-based Therapies in Implant Dentistry.

abstract: The field of medicine is rapidly moving towards the development of personalized treatments and non-invasive tools to achieve a more predictable and optimal tissue regeneration. In this sense, the goal of periodontal healing is to arrest disease progression and functionally regenerate all the tissues that comprise the periodontium. The latter implies a well-orchestrated interaction among oral cells, growth factors and extracellular matrix. Although several procedures are performed in an attempt to regenerate lost periodontal tissue, outcomes are not always predictable. Growth factors represent a class of biologically active polypeptides that have a critical role in the healing process. Their use provides a new paradigm to understand the regenerative medicine. The use of platelet- rich plasma (PRP) products as a local source and delivery system of autologous growth factors has emerged recently. Among them, PRGF stands for its remarkable stimulatory effect on oral tissue regeneration, making it a very safe and successful tool with a great value in Dentistry.


title: The narrator: NLG for digital storytelling

abstract: We present the Narrator, an NLG component used for the generation of narratives in a digital story-telling system. We describe how the Narrator works and show some examples of generated stories.


title: Resource Allocation in Cloud Computing Using the Uncertainty Principle of Game Theory

abstract: Virtualization of resources on the cloud offers a scalable means of consuming services beyond the capabilities of small systems. In a cloud that offers infrastructure such as processor, memory, hard disk, etc., a coalition of virtual machines formed by grouping two or more may be needed. Economical management of cloud resources needs allocation strategies with minimum wastage, while configuring services ahead of actual requests. We propose a resource allocation mechanism for machines on the cloud, based on the principles of coalition formation and the uncertainty principle of game theory. We compare the results of applying this mechanism with existing resource allocation methods that have been deployed on the cloud. We also show that this method of resource allocation by coalition-formation of the machines on the cloud leads not only to better resource utilization but also higher request satisfaction.


title: Journal of Communication ISSN 0021-9916 ORIGINAL ARTICLE Social Media and the Decision to Participate in Political Protest: Observations From Tahrir

abstract: Based on a survey of participants in Egypt’s Tahrir Square protests, we demonstrate that social media in general, and Facebook in particular, provided new sources of information the regime could not easily control and were crucial in shaping how citizens made individual decisions about participating in protests, the logistics of protest, and the likelihood of success. We demonstrate that people learned about the protests primarily through interpersonal communication using Facebook, phone contact, or face-to-face conversation. Controlling for other factors, social media use greatly increased the odds that a respondent attended protests on the first day. Half of those surveyed produced and disseminated visuals from the demonstrations, mainly through Facebook.


title: Origins of Human Communication

abstract: Winner, 2009 Eleanor Maccoby Book Award in Developmental Psychology, presented by the American Psychological Association. and Honorable Mention, Literature, Language & Linguistics category, 2008 PROSE Awards presented by the Professional/Scholarly Publishing Division of the Association of American Publishers. Human communication is grounded in fundamentally cooperative, even shared, intentions. In this original and provocative account of the evolutionary origins of human communication, Michael Tomasello connects the fundamentally cooperative structure of human communication (initially discovered by Paul Grice) to the especially cooperative structure of human (as opposed to other primate) social interaction. Tomasello argues that human cooperative communication rests on a psychological infrastructure of shared intentionality (joint attention, common ground), evolved originally for collaboration and culture more generally. The basic motives of the infrastructure are helping and sharing: humans communicate to request help, inform others of things helpfully, and share attitudes as a way of bonding within the cultural group. These cooperative motives each created different functional pressures for conventionalizing grammatical constructions. Requesting help in the immediate you-and-me and here-and-now, for example, required very little grammar, but informing and sharing required increasingly complex grammatical devices. Drawing on empirical research into gestural and vocal communication by great apes and human infants (much of it conducted by his own research team), Tomasello argues further that humans' cooperative communication emerged first in the natural gestures of pointing and pantomiming. Conventional communication, first gestural and then vocal, evolved only after humans already possessed these natural gestures and their shared intentionality infrastructure along with skills of cultural learning for creating and passing along jointly understood communicative conventions. Challenging the Chomskian view that linguistic knowledge is innate, Tomasello proposes instead that the most fundamental aspects of uniquely human communication are biological adaptations for cooperative social interaction in general and that the purely linguistic dimensions of human communication are cultural conventions and constructions created by and passed along within particular cultural groups. Jean Nicod Lectures A Bradford Book


A Fast Algorithm for Image Deblurring with Total Variation Regularization”, CAAM


title: Method to find community structures based on information centrality

abstract: Community structures are an important feature of many social, biological and technological networks. Here we study a variation on the method for detecting such communities proposed by Girvan and Newman and based on the idea of using centrality measures to define the community boundaries ( M. Girvan and M. E. J. Newman, Community structure in social and biological networks Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002)). We develop an algorithm of hierarchical clustering that consists in finding and removing iteratively the edge with the highest information centrality. We test the algorithm on computer generated and real-world networks whose community structure is already known or has been studied by means of other methods. We show that our algorithm, although it runs to completion in a time O(n 4 ), is very effective especially when the communities are very mixed and hardly detectable by the other methods.


title: Impact of robot failures and feedback on real-time trust

abstract: Prior work in human trust of autonomous robots suggests the timing of reliability drops impact trust and control allocation strategies. However, trust is traditionally measured post-run, thereby masking the real-time changes in trust, reducing sensitivity to factors like inertia, and subjecting the measure to biases like the primacy-recency effect. Likewise, little is known on how feedback of robot confidence interacts in real-time with trust and control allocation strategies. An experiment to examine these issues showed trust loss due to early reliability drops is masked in traditional post-run measures, trust demonstrates inertia, and feedback alters allocation strategies independent of trust. The implications of specific findings on development of trust models and robot design are also discussed.


title: Understanding and Managing Human Threats to the Coastal Marine Environment

abstract: Coastal marine habitats at the interface of land and sea are subject to threats from human activities in both realms. Researchers have attempted to quantify how these various threats impact different coastal ecosystems, and more recently have focused on understanding the cumulative impact from multiple threats. Here, the top threats to coastal marine ecosystems and recent efforts to understand their relative importance, ecosystem-level impacts, cumulative effects, and how they can best be managed and mitigated, are briefly reviewed. Results of threat analysis and rankings will differ depending on the conservation target (e.g., vulnerable species, pristine ecosystems, mitigatable threats), scale of interest (local, regional, or global), whether externalities are considered, and the types of management tools available (e.g., marine-protected areas versus ecosystem-based management). Considering the cumulative effect of multiple threats has only just begun and depends on spatial analysis to predict overlapping threats and a better understanding of multiple-stressor effects and interactions. Emerging conservation practices that hold substantial promise for protecting coastal marine systems include multisector approaches, such as ecosystem-based management (EBM), that account for ecosystem service valuation; comprehensive spatial management, such as ocean zoning; and regulatory mechanisms that encourage or require cross-sector goal setting and evaluation. In all cases, these efforts require a combination of public and private initiatives for success. The state of our ecological understanding, public awareness, and policy initiatives make the time ripe for advancing coastal marine management and improving our stewardship of coastal and marine ecosystems.


title: Integrating Learning and Reasoning with Deep Logic Models

abstract: Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overtakes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning.


title: Image autocoregistration and InSAR interferogram estimation using joint subspace projection

abstract: In this paper, we propose a new method to estimate synthetic aperture radar interferometry (InSAR) interferometric phase in the presence of large coregistration errors. The method takes advantage of the coherence information of neighboring pixel pairs to automatically coregister the SAR images and employs the projection of the joint signal subspace onto the corresponding joint noise subspace to estimate the terrain interferometric phase. The method can automatically coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the terrain interferometric phase (interferogram) as the coregistration error reaches one pixel. The effectiveness of the method is also verified with the real data from the Spaceborne Imaging Radar-C/X Band SAR and the European Remote Sensing 1 and 2 satellites.


title: OWL 2: The Next Step for OWL

abstract: Since achieving W3C recommendation status in 2004, the Web Ontology Language (OWL) has been successfully applied to many problems in computer science. Practical experience with OWL has been quite positive in general; however, it has also revealed room for improvement in several areas. We systematically analyze the identified shortcomings of OWL, such as expressivity issues, problems with its syntaxes, and deficiencies in the definition of OWL species. Furthermore, we present an overview of OWL 2-an extension to and revision of OWL that is currently being developed within the W3C OWL Working Group. Many aspects of OWL have been thoroughly reengineered in OWL 2, thus producing a robust platform for future development of the language.


title: Wafer defect pattern recognition by multi-class support vector machines by using a novel defect cluster index

abstract: Wafer yield is an important index of efficiency in integrated circuit (IC) production. The number and cluster intensity of wafer defects are two key determinants of wafer yield. As wafer sizes increase, the defect cluster phenomenon becomes more apparent. Cluster indices currently used to describe this phenomenon have major limitations. Causes of process variation can sometimes be identified by analyzing wafer defect patterns. However, human recognition of defect patterns can be time-consuming and inaccurate. This study presents a novel recognition system using multi-class support vector machines with a new defect cluster index to efficiently and accurately recognize wafer defect patterns. A simulated case demonstrates the effectiveness of the proposed model.


title: PSIM based dynamic simulator for analysis of SPHEV operation

abstract: This paper describes a PSIM simulator for the analytical approach of the HEV (Hybrid Electric Vehicle) power train operation. For this research, a series-parallel type HEV (SPHEV) are chosen to apply the Forward-Facing approach simulation method. As the traction power of the series-parallel type HEV can be supplied from both of the ICE (Internal Combustion Engine) and the traction motor, the planetary gear is used for the mechanical connection among ICE, generator, and motor. For the ICE, a gasoline fuel type engine is selected and modeled with DC motor. IPMSM (Interior Permanent Magnet Synchronous Motor) and 3.7V 40Ah Lithium Polymer battery are chosen as a traction motor and ESS (Energy Storage System). DC/DC converter is used for the electrical connection between battery and DC-Link. After all components of the SPHEV power train have been designed and modeled separately, all components of SPHEV are assembled and controlled by the primary controller which is programed according to the desired driving strategy of HEV. Finally, some remarkable PSIM simulation results are discussed.


Aware Person Re-identification across Multiple Resolutions


{PEN}: Design and Evaluation of Partial-Erase for 3D NAND-Based High Density SSDs


title: Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

abstract: Data originating from some specific fields, for in-stance tourist arrivals, may exhibit a high degree of fluctuations as well as non-linear characteristics due to time varying behaviors. This paper proposes a new hybrid method to perform prediction for such data. The proposed hybrid model of wavelet transform and long-short-term memory (LSTM) recurrent neural network (RNN) is able to capture non-linear attributes in tourist arrival time series. Firstly, data is decomposed into constitutive series through wavelet transform. The decomposition is expressed as a function of a combination of wavelet coefficients, which have different levels of resolution. Then, LSTM neural network is used to train and simulate the value at each level to find the bias vectors and weighting coefficients for the prediction value. A sliding windows model is employed to capture the time series nature of the data. An evaluation is conducted to compare the proposed model with other RNN algorithms, i.e., Elman RNN and Jordan RNN, as well as the combination of wavelet transform with each of them. The result shows that the proposed model has better performance in terms of training time than the original LSTM RNN, while the accuracy is better than the hybrid of wavelet-Elman and the hybrid of wavelet-Jordan.


title: Intrusion Detection in Computer Networks based on Machine Learning Algorithms

abstract: Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements; they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, machine learning-based intrusion detection systems have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. This work aims to compare efficiency of machine learning methods in intrusion detection system, including artificial neural networks and support vector machine, with the hope of providing reference for establishing intrusion detection system in future. Compared with other related works in machine learning-based intrusion detectors, we propose to calculate the mean value via sampling different ratios of normal data for each measurement, which lead us to reach a better accuracy rate for observation data in real world. We compare the accuracy, detection rate, false alarm rate for 4 attack types. The extensive experimental results on the KDD-cup intrusion detection benchmark dataset demonstrate that the proposed approach produces higher performance than KDD Winner, especially for U2R and U2L type attacks.


title: A cross-layer protocol of wireless sensor networks for tailings reservoir on-line monitoring

abstract: The application of wireless sensor networks is a important direction of tailings reservoir on-line monitoring system's development. In this paper, a high reliability and energy-efficient cross-layer protocol (HREE) is proposed to solve tailings reservoir on-line monitoring WSN's problems of interferences and energy-limited. HREE consists of reliable-efficient uneven clustering routing protocol (REUC) and single interface based multi-channel MAC protocol (SIMC). REUC chooses the optimal cluster heads by neighbor node density to avoid the peripherization of cluster-head, and divides the network into clusters with uneven sizes to balance the energy consumption. A combination of residual energy and link quality is used to decide the data path of HREE. Under uneven clustering frame, SIMC provides different channels for each neighbor clusters and dynamically switches the intra-cluster channels. The simulation results show that the implementation of HREE can effectively improve WSN's reliability and prolong WSN's lifetime. HREE is more suitable for tailings reservior on-line monitoring system than traditional single layer protocols.


title: Contextual modulation of value signals in reward and punishment learning

abstract: Compared with reward seeking, punishment avoidance learning is less clearly understood at both the computational and neurobiological levels. Here we demonstrate, using computational modelling and fMRI in humans, that learning option values in a relative—context-dependent—scale offers a simple computational solution for avoidance learning. The context (or state) value sets the reference point to which an outcome should be compared before updating the option value. Consequently, in contexts with an overall negative expected value, successful punishment avoidance acquires a positive value, thus reinforcing the response. As revealed by post-learning assessment of options values, contextual influences are enhanced when subjects are informed about the result of the forgone alternative (counterfactual information). This is mirrored at the neural level by a shift in negative outcome encoding from the anterior insula to the ventral striatum, suggesting that value contextualization also limits the need to mobilize an opponent punishment learning system.


title: Obstacle detection in stereo bird's eye view images

abstract: Navigation is an essential function for a mobile robot to make it navigate in its environment. The navigation based on bird's eye view images is a newly proposed indoor visual navigation framework, which can help to simplify the navigation problems, such as self-localization, map-building, path planning and other competences. In the paper, an obstacle detection method is proposed, which is applied in the stereo bird's view image based indoor visual navigation. Firstly, the mathematic principle of obstacle detection is derived by using four camera assumptions and imaging geometric relationship. Then the algorithm implementation was explained. Finally, practical experiments were implemented and the results proved that the proposed method can work well.


title: CHARACTERISING URBAN SPRAWL FROM REMOTE SENSING DATA AND USING LANDSCAPE METRICS

abstract: Urban sprawl refers to the outgrowth of urban areas caused by uncontrolled, uncoordinated and unplanned growth. This outgrowth seen along the periphery of cities, along highways, and along roads connecting a city, lacks basic amenities like sanitation, treated water supply, primary health centre, etc. as planners were unable to visualise such growth during planning, policy and decision-making. Sprawl generally infers to some type of uncoordinated development with impacts such as loss of agricultural land, open space and ecologically sensitive habitats in and around urban areas due to lack of integrated and holistic approaches in regional planning. In developing countries, where urbanisation rates are high, urban sprawl is a significant contributor of the land use change. However, characterising sprawl has become a contentious issue with numerous arguments both for and against the phenomenon. Meanwhile, effective metrics to characterise sprawl in India are required to characterise this. We have attempted to capture urban sprawl over the landscape and hence adopt landscape metrics for characterising sprawl. We further ascribe that prevalence of sprawl leads to erosion of landscape elements. The satellite remote sensing data for Bangalore, India, of Landsat TM and Landsat ETM+ for 1992 and 2000, were used and through a multi-stage classification process, post-classification change detection was performed. Bangalore city is popularly known as ‘the garden city’ of India is experiencing rapid urban growth and consequent to the software boom, the city is now referred as ‘IT Capital’ of India. The growth Bangalore is experiencing has significant implications on land use. The landscape metrics adopted were computed and the compared for these two time-periods to characterise sprawl. It was evident from these metrics the amount of dispersion and dispersed growth taking place in radial direction across Bangalore.The study demonstrates the utility of using landscape metrics for characterising urban sprawl.


title: Facts, Events, and Inflection: When Language and Memory Dissociate

abstract: We report on two brain-damaged patients who show contrasting patterns of deficits in memory and language functioning. One patient (AW) suffers from a lexical retrieval deficit and failed to produce many irregularly inflected words such as spun, forgotten, and mice, but demonstrated intact production of regularly inflected words such as walked and rats. She also had preserved declarative memory for facts and events. The other patient (VP) presented with a severe declarative memory deficit but showed no signs of impairment in producing either regular or irregular inflections. These patterns of deficits reveal that the retrieval of irregular inflections proceeds relatively autonomously with respect to declarative memory. We interpret these deficits with reference to three current theories of lexical structure: (a) Pinker's "words and rules" account, which assumes distinct mechanisms for processing regular and irregular inflections and proposes that lexical and semantic processing are subserved by distinct but interacting cognitive systems; (b) Ullman's "declarative/procedural" model, which assumes that mechanisms for the retrieval of irregular inflections are part of declarative memory; (c) Joanisse and Seidenberg's connectionist model, in which semantic information is critical for the retrieval of irregular inflections.


title: Fuzzy pre-compensated fuzzy self-tuning fuzzy PID controller of 3 DOF planar robot manipulators

abstract: Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. Proportional-integral-derivative (PID)-type fuzzy controller is a well-known conventional motion control strategy for manipulators which ensures global asymptotic stability. To enhance the PID-type fuzzy controller performance for the control of rigid planar robot manipulators, in this paper, a fuzzy pre-compensation of a fuzzy self tuning fuzzy PID controller is proposed. The proposed control scheme consists of a fuzzy logic-based pre-compensator followed by a fuzzy self tuning fuzzy PID controller. In the fuzzy self tuning fuzzy PID controller, a supervisory hierarchical fuzzy controller (SHFC) is used for tuning the input scaling factors of the fuzzy PID controller according to the actual tracking position error and the actual tracking velocity error. Numerical simulations using the dynamic model of a three DOF planar rigid robot manipulator with uncertainties show the effectiveness of the approach in set point tracking problems. Our results show that the proposed controller has superior performance compared to a conventional fuzzy PID controller.


Jointly Parse and Fragment Ungrammatical Sentences


title: A Contactless Slipring System Based on Axially Traveling Magnetic Field

abstract: It is a preferable method to transfer power wirelessly using contactless slipring systems for rotary applications. The current single or multiple-unit single-phase systems often have limited power transfer capability, so they may not be able to meet the load requirements. This paper presents a contactless slipring system based on axially traveling magnetic field that can achieve a high output power level. A new index termed mutual inductance per pole is introduced to simplify the analysis of the mutually coupled poly-phase system to a single-phase basis. Both simulation and practical results have shown that the proposed system can transfer 2.7 times more power than a multiple-unit (six individual units) single-phase system with the same amount of ferrite and copper materials at higher power transfer efficiency. It has been found that the new system can achieve about 255.6 W of maximum power at 97% efficiency, compared to 68.4 W at 90% of a multiple-unit (six individual units) single-phase system.


title: A roadmap for parametric CAD efficiency in the automotive industry

abstract: 3D CAD systems are used in product design for simultaneous engineering and to improve productivity. CAD tools can substantially enhance design performance. Although 3D CAD is a widely used and highly effective tool in mechanical design, mastery of CAD skills is complex and time-consuming. The concepts of parametric-associative models and systems are powerful tools whose efficiency is proportional to the complexity of their implementation. The availability of a framework for actions that can be taken to improve CAD efficiency can therefore be highly beneficial. Today, a clear and structured approach does not exist in this way for CAD methodology deployment. The novelty of this work is therefore to propose a general strategy for utilizing the advantages of parametric CAD in the automotive industry in the form of a roadmap. The main stages of the roadmap are illustrated by means of industrial use cases. The first results of his research are discussed and suggestions for future work are given.


title: Does the Online Card Payment Landscape Unwittingly Facilitate Fraud?

abstract: An extensive study of the current practice of online payment using credit and debit cards reveals the intrinsic security challenges caused by differences in how payment sites operate.