title: Sexual selection for moral virtues

abstract: Moral evolution theories have emphasized kinship, reciprocity, group selection, and equilibrium selection. Yet, moral virtues are also sexually attractive. Darwin suggested that sexual attractiveness may explain many aspects of human morality. This paper updates his argument by integrating recent research on mate choice, person perception, individual differences, costly signaling, and virtue ethics. Many human virtues may have evolved in both sexes through mutual mate choice to advertise good genetic quality, parenting abilities, and/or partner traits. Such virtues may include kindness, fidelity, magnanimity, and heroism, as well as quasi-moral traits like conscientiousness, agreeableness, mental health, and intelligence. This theory leads to many testable predictions about the phenotypic features, genetic bases, and social-cognitive responses to human moral virtues.


title: Flowstrates: An Approach for Visual Exploration of Temporal Origin-Destination Data

abstract: Many origin-destination datasets have become available in the recent years, e.g. flows of people, animals, money, material, or network traffic between pairs of locations, but appropriate techniques for their exploration still have to be developed. Especially, supporting the analysis of datasets with a temporal dimension remains a significant challenge. Many techniques for the exploration of spatio-temporal data have been developed, but they prove to be only of limited use when applied to temporal origin-destination datasets. We present Flowstrates, a new interactive visualization approach in which the origins and the destinations of the flows are displayed in two separate maps, and the changes over time of the flow magnitudes are represented in a separate heatmap view in the middle. This allows the users to perform spatial visual queries, focusing on different regions of interest for the origins and destinations, and to analyze the changes over time provided with the means of flow ordering, filtering and aggregation in the heatmap. In this paper, we discuss the challenges associated with the visualization of temporal origin-destination data, introduce our solution, and present several usage scenarios showing how the tool we have developed supports them.


Knowledge Graph Entity Representation and Retrieval


Service-Oriented Architecture: Concepts and Implementation


title: Faster RER-CNN: application to the detection of vehicles in aerial images

abstract: Detecting small vehicles in aerial images is a difficult job that can be challenging even for humans. Rotating objects, low resolution, small inter-class variability and very large images comprising complicated backgrounds render the work of photo-interpreters tedious and wearisome. Unfortunately even the best classical detection pipelines like Faster R-CNN cannot be used off-the-shelf with good results because they were built to process object centric images from day-to-day life with multi-scale vertical objects. In this work we build on the Faster R-CNN approach to turn it into a detection framework that deals appropriately with the rotation equivariance inherent to any aerial image task. This new pipeline (Faster Rotation Equivariant Regions CNN) gives, without any bells and whistles, state-of-the-art results on one of the most challenging aerial imagery datasets: VeDAI and give good results w.r.t. the baseline Faster R-CNN on two others: Munich and GoogleEarth .


title: Autonomous Driving System Design for Formula Student Driverless Racecar

abstract: This paper summarizes the work of building the autonomous system including detection system and path tracking controller for a formula student autonomous racecar. A LIDAR-vision cooperating method of detecting traffic cone which is used as track mark is proposed. Detection algorithm of the racecar also implements a precise and high rate localization method which combines the GPS-INS data and LIDAR odometry. Besides, a track map including the location and color information of the cones is built simultaneously. Finally, the system and vehicle performance on a closed loop track is tested. This paper also briefly introduces the Formula Student Autonomous Competition (FSAC) in 2017.


title: Toward Active Cannulas: Miniature Snake-Like Surgical Robots

abstract: We have developed a new class of continuously flexible snake-like robots, called active cannulas, that consist of several telescoping pre-curved superelastic tubes. The devices derive bending actuation not from tendon wires or other external mechanisms, but from elastic energy stored in the backbone itself. This allows active cannulas to have a small diameter and a high degree of dexterity, which should enable them to navigate through complex anatomy to sites inaccessible by current surgical robotic devices. Active cannulas may also enhance patient safety because their inherent compliance mitigates potential trauma from inadvertent tool-tissue collision. A consequence of our design is that dexterity improves with miniaturization. A kinematic description of active cannula shape requires a model of the elastic interaction of telescoping pre-curved flexible tubes, and we derive a two-"link" beam mechanics-based model. Experiments using curved nitinol tubes and wires validate the model.


title: Natural Language Processing in Biomedicine: A Unified System Architecture Overview

abstract: In modern electronic medical records (EMR) much of the clinically important data - signs and symptoms, symptom severity, disease status, etc. - are not provided in structured data fields, but rather are encoded in clinician generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we will review briefly. Additionally, challenges facing current research efforts in biomedical NLP include the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge.


title: On platooning control using IEEE 802.11p in conjunction with visible light communications

abstract: The control of a platoon using IEEE 802.11p is an active research challenge in the field of vehicular networking and cooperative automated vehicles. IEEE 802.11p is a promising technology for direct vehicle to vehicle communication, but there are concerns about its usage for the control of platoons as it suffers packet losses due to congestion in highly dense scenarios. On the other hand, Visible Light Communication (VLC) recently gained attention as a short range technology for vehicular applications. VLC could be used to support or backup IEEE 802.11p, increasing reliability and scalability, and hence the safety of platooning systems. In this paper, we perform a large-scale simulation campaign using VLC integrated with IEEE 802.11p for platooning. We particularly demonstrate the benefits, but also the limitations, of such heterogeneous networking.


title: Knowledge sourcing methods

abstract: Many knowledge management (KM) initiatives in organizations seek to improve how employees draw on each others' expertise, experience, advice, and opinions, which we call knowledge sourcing behavior. Employees can source knowledge recorded in document form, through dyadic conversations, or in-group settings. We proposed and tested a theory to support the idea that employees' use of different classes of knowledge sourcing methods produced different kinds of performance outcomes. Our findings suggested that (1) different classes of knowledge sourcing methods are not as interchangeable as the KM literature might suggest, (2) technology-based methods are neither inherently superior nor inferior to traditional methods and (3) that group knowledge sourcing supports a wider range of performance outcomes than other methods. Together, these results highlight the importance of aligning KM efforts with their intended outcome. Before launching a project designed to enhance knowledge sourcing, managers should decide which performance outcome they wish to affect and select a KM tool that is aligned with the desired effect.


SPARQLAS - Implementing SPARQL Queries with OWL Syntax


title: Security and privacy challenges in industrial internet of things

abstract: Today, embedded, mobile, and cyberphysical systems are ubiquitous and used in many applications, from industrial control systems, modern vehicles, to critical infrastructure. Current trends and initiatives, such as "Industrie 4.0" and Internet of Things (IoT), promise innovative business models and novel user experiences through strong connectivity and effective use of next generation of embedded devices. These systems generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. Cyberattacks on IoT systems are very critical since they may cause physical damage and even threaten human lives. The complexity of these systems and the potential impact of cyberattacks bring upon new threats. This paper gives an introduction to Industrial IoT systems, the related security and privacy challenges, and an outlook on possible solutions towards a holistic security framework for Industrial IoT systems.


title: A 40-Gb/s PAM-4 Transmitter Based on a Ring-Resonator Optical DAC in 45-nm SOI CMOS

abstract: The next generations of large-scale data-centers and supercomputers demand optical interconnects to migrate to 400G and beyond. Microring modulators in silicon-photonics VLSI chips are promising devices to meet this demand due to their energy efficiency and compatibility with dense wavelength division multiplexed chip-to-chip optical I/O. Higher order pulse amplitude modulation (PAM) schemes can be exploited to mitigate their fundamental energy–bandwidth tradeoff at the system level for high data rates. In this paper, we propose an optical digital-to-analog converter based on a segmented microring resonator, capable of operating at 20 GS/s with improved linearity over conventional optical multi-level generators that can be used in a variety of applications such as optical arbitrary waveform generators and PAM transmitters. Using this technique, we demonstrate a PAM-4 transmitter that directly converts the digital data into optical levels in a commercially available 45-nm SOI CMOS process. We achieved 40-Gb/s PAM-4 transmission at 42-fJ/b modulator and driver energies, and 685-fJ/b total transmitter energy efficiency with an area bandwidth density of 0.67 Tb/s/mm2. The transmitter incorporates a thermal tuning feedback loop to address the thermal and process variations of microrings’ resonance wavelength. This scheme is suitable for system-on-chip applications with a large number of I/O links, such as switches and general-purpose and specialized processors in large-scale computing and storage systems.


title: Saving the world wide web from vulnerable JavaScript

abstract: JavaScript is the most popular client-side scripting language for Web applications. Exploitable JavaScript code exposes end users to integrity and confidentiality violations. Client-side vulnerabilities can cost an enterprise money and reputation, and cause serious damage to innocent users of the Web application. In spite of all this, recent research in the area of information-flow security has focused more on other languages that are more suitable for server-side programming, such as Java. Static analysis of JavaScript code is very challenging due to the dynamic nature of the language. This paper presents Actarus, a novel, product-quality static taint analysis for JavaScript that scales to large programs and soundly models all the JavaScript constructs with the exception of reflective calls. This paper discusses the experimental results obtained by running Actarus on a collection of 9,726 Web pages obtained by crawling the 50 most visited Web sites worldwide as well as 19 other popular Web sites. The results expose 526 vulnerabilities in 11 sites. Those vulnerabilities, if exploited, can allow malicious JavaScript code execution.


title: Security and privacy threats in IoT architectures

abstract: In this paper, we describe developments towards the Internet of Things (IoT) and discuss architecture visions for the IoT. Our emphasis is to analyze the known and new threats for the security, privacy and trust (SPT) at different levels of architecture. Our strong view is that the IoT will be an important part of the global huge ICT infrastructure ("future Internet") humanity will be strongly relying on in the future with relatively few data centers connected to trillions of sensors and other "things" over gateways, various access networks and a global network connecting them. While the infrastructure is globally connected, it is divided into millions of management domains, such as homes, smart cities, power grids, access points and networks, data centers, etc. It will evolve both bottom-up and top-down. An important question is what consequences a bottom-up and top-down construction of the IoT infrastructure has for the security, privacy and trust and what kind of regulation is appropriate. We review the currently emerging privacy regulation in EU.


title: Aerosol optical characterization by nephelometer and lidar: The Baltimore Supersite experiment during the Canadian forest fire smoke intrusion

abstract: [1] High spatial and temporal resolution elastic backscatter lidar data from Baltimore are analyzed with a near-end approach to estimate vertical profiles of the aerosol extinction coefficient. The near-end approach makes use of the (1) aerosol scattering coefficient measured at the surface with a nephelometer (0.530 μm), (2) surface level particle size distribution, and (3) refractive index calculated using Mie theory to estimate the aerosol extinction coefficient boundary condition for the lidar equation. There was a broad range of atmospheric turbidity due to a strong haze event, which occurred because of smoke transport from Canadian forest fires, and led to a wide range of observed atmospheric properties. The index of refraction for aerosols estimated during the entire study period is 1.5–0.47 i, which is typical for soot. The measured surface level aerosol scattering coefficient ranged from σp = 0.002 to σp = 0.541 km−1, and the computed aerosol extinction coefficient spanned values κp = 0.01 to κp = 1.05 km−1. The derived mass concentration and the mass scattering ranges were 3.96–194 μg m−3 and 0.05–3.260 m2g−1, respectively. The aerosol optical properties were dominated by light absorption by soot.


Theory Psychology Charles Lambdin not significance tests are − − Significance tests as sorcery : Science is empirical


F‐bar aided edge‐based smoothed finite element method using tetrahedral elements for finite deformation analysisof nearly incompressible solids


title: Open Source: Concepts, Benefits, and Challenges

abstract: With the emergence of free and open source software (F/OSS) projects (e.g. Linux) as serious contenders to well-established proprietary software, advocates of F/OSS are quick to generalize the superiority of this approach to software development. On the other hand, some wellestablished software development firms view F/OSS as a threat and vociferously refute the claims of F/OSS advocates. This article represents a tutorial on F/OSS that tries objectively to identify and present open source software’s concepts, benefits, and challenges. From our point of view, F/OSS is more than just software. We conceptualize it as an IPO system that consists of the license as the boundary of the system, the community that provides the input, the development process, and the software as the output. After describing the evolution and definition of F/OSS, we identify three approaches to benefiting from F/OSS that center on (1) the software, (2) the community, and (3) the license respectively. Each approach is fit for a specific situation and provides a unique set of benefits and challenges. We further illustrate our points by refuting common misconceptions associated with F/OSS based upon our conceptual framework.


title: Robust Scene Text Recognition with Automatic Rectification

abstract: Recognizing text in natural images is a challenging task with many unsolved problems. Different from those in documents, words in natural images often possess irregular shapes, which are caused by perspective distortion, curved character placement, etc. We propose RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text. RARE is a specially-designed deep neural network, which consists of a Spatial Transformer Network (STN) and a Sequence Recognition Network (SRN). In testing, an image is firstly rectified via a predicted Thin-Plate-Spline (TPS) transformation, into a more "readable" image for the following SRN, which recognizes text through a sequence recognition approach. We show that the model is able to recognize several types of irregular text, including perspective text and curved text. RARE is end-to-end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems. State-of-the-art or highly-competitive performance achieved on several benchmarks well demonstrates the effectiveness of the proposed model.


title: The effects of online shopping factors on customers

abstract: the aim of this study is to look at the quick changes in the environment of the internet, carefully that make a competitive business landscape for online repurchases. It creates challenges and opportunities for businesses and also to examine the elements that can affect the intentions of online consumers to repay the service and product. The purpose of the present study is supplying a literature that can help of online stakeholder. On top of that, the results reinforce many existing literature in the context of the online shopping factors effect on customer repurchase. It prepares for in advance functional information for controlling businesses particularly in online shopping factors effect on customer repurchase. The finding of this study is able to help to businessman in developing their internet and online shopping as well.


The Reliability of ISO / IEC PDTR 15504 Assessments


title: Incremental Learning for Robust Visual Tracking

abstract: Visual tracking, in essence, deals with non-stationary image streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail in the presence of significant variation of the object's appearance or surrounding illumination. One reason for such failures is that many algorithms employ fixed appearance models of the target. Such models are trained using only appearance data available before tracking begins, which in practice limits the range of appearances that are modeled, and ignores the large volume of information (such as shape changes or specific lighting conditions) that becomes available during tracking. In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target. The model update, based on incremental algorithms for principal component analysis, includes two important features: a method for correctly updating the sample mean, and a forgetting factor to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large changes in pose, scale, and illumination.


title: Run-time detection of hardware Trojans: The processor protection unit

abstract: Typical SOC designs use processors and therefore, trust in such processor cores is essential. The 2011 Embedded Systems Challenge (ESC 2011) [1] showed a wide range of possibilities to attack a processor through hardware Trojans. We propose an approach to detect suspicious behavior of a processor and thus assess if the processor is trustworthy or not. A countermeasure, called Processor Protection Unit (PPU) is presented focusing on its design to be particularly resilient against hardware Trojan insertion.


title: Virtual smartphone over IP

abstract: The number of smartphone users and mobile application offerings are growing rapidly. A smartphone is often expected to offer PC-like functionality. In this paper, we present Virtual Smartphone over IP system that allows users to create virtual smartphone images in the mobile cloud and to customize each image to meet different needs. Users can easily and freely tap into the power of the data center by installing the desired mobile applications remotely in one of these images. Because the mobile applications are controlled remotely, they are not constrained by the limit of processing power, memory and battery life of a physical smartphone.


title: The commonsense algorithm as a basis for computer models of human memory, inference, belief and contextual language comprehension

abstract: The notion of a commonsense algorithm is presented as a basic data structure for modeling human cognition. This data structure unifies many current ideas about human memory and information processing. The structure is defined by specifying a set of proposed cognitive primitive links which, when used to build up large structures of actions, states, statechanges and tendencies, provide an adequate formalism for expressing human plans and activities, as well as general mechanisms and computer algorithms. The commonsense algorithm is a type of framework (as Minsky has defined the term) for representing algorithmic processes, hopefully the way humans do.


title: Training linear SVMs in linear time

abstract: Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for high-dimensional sparse data commonly encountered in applications like text classification, word-sense disambiguation, and drug design. These applications involve a large number of examples n as well as a large number of features N, while each example has only s << N non-zero features. This paper presents a Cutting Plane Algorithm for training linear SVMs that provably has training time 0(s,n) for classification problems and o(sn log (n))for ordinal regression problems. The algorithm is based on an alternative, but equivalent formulation of the SVM optimization problem. Empirically, the Cutting-Plane Algorithm is several orders of magnitude faster than decomposition methods like svm light for large datasets.


title: A Picture of Instagram is Worth More Than a Thousand Words: Workload Characterization and Application

abstract: Participatory sensing systems (PSSs) have the potential to become fundamental tools to support the study, in large scale, of urban social behavior and city dynamics. To that end, this work characterizes the photo sharing system Instagram, considered one of the currently most popular PSS on the Internet. Based on a dataset of approximately 2.3 million shared photos, we characterize user's behavior in the system showing that there are several advantages and opportunities for large scale sensing, such as a global coverage at low cost, but also challenges, such as a very unequal photo sharing frequency, both spatially and temporally. We also observe that the temporal photo sharing pattern is a good indicator about cultural behaviors, and also says a lot about certain classes of places. Moreover, we present an application to identify regions of interest in a city based on data obtained from Instagram, which illustrates the promising potential of PSSs for the study of city dynamics.


title: Circuit Modeling of Injection Probes for Bulk Current Injection

abstract: In this paper, two procedures are developed for lumped-parameter circuit modeling of injection probes for bulk current injection (BCI). Both procedures are based on frequency-domain scattering-parameter measurements, and refer to a clamped wiring composed of a single-ended interconnection. One procedure exploits a black-box approach, requires a calibration fixture, and is suited for practical implementation. The other is based on circuit interpretation of coupling and propagation effects, and is aimed at a theoretical analysis of injection. The former procedure requires an accurate deembedding of fixture-related effects, and the latter requires a detailed knowledge of the geometry of the probe interior parts. The two procedures lead to probe circuit models topologically equivalent, with lumped-Pi structure, performing well in the frequency band of interest for BCI. In the derivation, it is shown that the probe input impedance is the central quantity for the characterization of the frequency-dependent properties of the ferrite core, and for the modeling of inductive coupling (dominant effect). The probe circuit models developed in this paper go over the frequency limitations of previous models, and allow for accurate description of the frequency-dependent voltage transfer ratio and series impedance of the probe.


title: Automatically Ordering Events and Times in Text

abstract: The book offers a detailed guide to temporal ordering, exploring open problems in the field and providing solutions and extensive analysis. It addresses the challenge of automatically ordering events and times in text. Aided by TimeML, it also describes and presents concepts relating to time in easy-to-compute terms. Working out the order that events and times happen has proven difficult for computers, since the language used to discuss time can be vague and complex. Mapping out these concepts for a computational system, which does not have its own inherent idea of time, is, unsurprisingly, tough. Solving this problem enables powerful systems that can plan, reason about events, and construct stories of their own accord, as well as understand the complex narratives that humans express and comprehend so naturally. This book presents a theory and data-driven analysis of temporal ordering, leading to the identification of exactly what is difficult about the task. It then proposes and evaluates machine-learning solutions for the major difficulties. It is a valuable resource for those working in machine learning for natural language processing as well as anyone studying time in language, or involved in annotating the structure of time in documents.


title: DOE-AND-SCA: A Novel SCA Based on DNN With Optimal Eigenvectors and Automatic Cluster Number Determination

abstract: Spectral clustering algorithm (SCA) is one of the widely used clustering algorithms (CAs), which is proved to be efficient in many applications including unsupervised image identification and gene prediction. However, most SCAs are confronted with several problems: 1) It is difficult for SCAs to handle multi-scale data sets; 2) It is difficult to set cluster number in advance for various applications; 3) It is also difficult to choose the most appropriate eigenvectors to reflect the data distribution; and 4) Moreover, SCAs are sensitive to the parameters. To handle these problems, we propose a novel SCA based on dynamic nearest-neighbors (DNN) with optimal eigenvector and automatic cluster number determination, namely DOE-AND-SCA. There into, first, we design a novel similarity function based on DNN for multi-scale data, making the similarity metric more accurate; Second, the cluster number is automatically determined, and the cluster centers are also automatically determined by normal fitting, based on the density and minimum distance distribution of the data points; Third, the optimal eigenvectors are selected on the basis of global and local features of the data set for more accurate data distribution reflection; Fourth, two main parameters, including the optimal density difference threshold and the number of intervals, are self-adaptive. The efficiency of DOE-AND-SCA is testified on abundant of simulation data sets, by comparing with other outstanding algorithms. And finally, DOE-AND-SCA is also applied to image recognition problems.


title: On the privacy of anonymized networks

abstract: The proliferation of online social networks, and the concomitant accumulation of user data, give rise to hotly debated issues of privacy, security, and control. One specific challenge is the sharing or public release of anonymized data without accidentally leaking personally identifiable information (PII). Unfortunately, it is often difficult to ascertain that sophisticated statistical techniques, potentially employing additional external data sources, are unable to break anonymity. In this paper, we consider an instance of this problem, where the object of interest is the structure of a social network, i.e., a graph describing users and their links. Recent work demonstrates that anonymizing node identities may not be sufficient to keep the network private: the availability of node and link data from another domain, which is correlated with the anonymized network, has been used to re-identify the anonymized nodes. This paper is about conditions under which such a de-anonymization process is possible. We attempt to shed light on the following question: can we assume that a sufficiently sparse network is inherently anonymous, in the sense that even with unlimited computational power, de-anonymization is impossible? Our approach is to introduce a random graph model for a version of the de-anonymization problem, which is parameterized by the expected node degree and a similarity parameter that controls the correlation between two graphs over the same vertex set. We find simple conditions on these parameters delineating the boundary of privacy, and show that the mean node degree need only grow slightly faster than log n with network size n for nodes to be identifiable. Our results have policy implications for sharing of anonymized network information.


title: A Survey of Payload-Based Traffic Classification Approaches

abstract: Internet traffic classification has been the subject of intensive study since the birth of the Internet itself. Indeed, the evolution of approaches for traffic classification can be associated with the evolution of the Internet itself and with the adoption of new services and the emergence of novel applications and communication paradigms. Throughout the years many approaches have been proposed for addressing technical issues imposed by such novel services. Deep-Packet Inspection (DPI) has been a very important research topic within the traffic classification field and its concept consists of the analysis of the contents of the captured packets in order to accurately and timely discriminate the traffic generated by different Internet protocols. DPI was devised as a means to address several issues associated with port-based and statistical-based classification approaches in order to achieve an accurate and timely traffic classification. Many research works proposed different DPI schemes while many open-source modules have also become available for deployment. Surveys become then valuable tools for performing an overall analysis, study and comparison between the several proposed methods. In this paper we present a survey in which a complete and thorough analysis of the most important open-source DPI modules is performed. Such analysis comprises an evaluation of the classification accuracy, through a common set of traffic traces with ground truth, and of the computational requirements. In this manner, this survey presents a technical assessment of DPI modules and the analysis of the obtained evaluation results enable the proposal of general guidelines for the design and implementation of more adequate DPI modules.


title: A wafer-scale neuromorphic hardware system for large-scale neural modeling

abstract: Modeling neural tissue is an important tool to investigate biological neural networks. Until recently, most of this modeling has been done using numerical methods. In the European research project "FACETS" this computational approach is complemented by different kinds of neuromorphic systems. A special emphasis lies in the usability of these systems for neuroscience. To accomplish this goal an integrated software/hardware framework has been developed which is centered around a unified neural system description language, called PyNN, that allows the scientist to describe a model and execute it in a transparent fashion on either a neuromorphic hardware system or a numerical simulator. A very large analog neuromorphic hardware system developed within FACETS is able to use complex neural models as well as realistic network topologies, i.e. it can realize more than 10000 synapses per neuron, to allow the direct execution of models which previously could have been simulated numerically only.


title: Communication in open source software development mailing lists

abstract: Open source software (OSS) development teams use electronic means, such as emails, instant messaging, or forums, to conduct open and public discussions. Researchers investigated mailing lists considering them as a hub for project communication. Prior work focused on specific aspects of emails, for example the handling of patches, traceability concerns, or social networks. This led to insights pertaining to the investigated aspects, but not to a comprehensive view of what developers communicate about. Our objective is to increase the understanding of development mailing lists communication. We quantitatively and qualitatively analyzed a sample of 506 email threads from the development mailing list of a major OSS project, Lucene. Our investigation reveals that implementation details are discussed only in about 35% of the threads, and that a range of other topics is discussed. Moreover, core developers participate in less than 75% of the threads. We observed that the development mailing list is not the main player in OSS project communication, as it also includes other channels such as the issue repository.


title: Domain Independent Approach for Aspect Oriented Sentiment Analysis for Product Reviews

abstract: The Sentiment analysis from text documents is emerging field for the research in Natural Language Processing (NLP) and text mining. Feature specific opinion matters more than the overall opinion. Given a collection of review texts, the goal is to detect the individual product aspects comments by reviewers and to decide whether the comments are rather positive or negative. In this research paper unsupervised approach for domain independent feature specific sentiment analysis has been proposed. SentiWordNet lexical resource has been used to determine the polarity of identified features. Research work has shown the promising results over the previously used approaches using SentiWordNet. Newly introduced SentiWordNet 3.0 has been proved to be an important lexical resource.


title: Avoidance of information technology threats: a theoretical perspective

abstract: This paper describes the development of the technology threat avoidance theory (TTAT), which explains individual IT users' behavior of avoiding the threat of malicious information technologies. We articulate that avoidance and adoption are two qualitatively different phenomena and contend that technology acceptance theories provide a valuable, but incomplete, understanding of users' IT threat avoidance behavior. Drawing from cybernetic theory and coping theory, TTAT delineates the avoidance behavior as a dynamic positive feedback loop in which users go through two cognitive processes, threat appraisal and coping appraisal, to decide how to cope with IT threats. In the threat appraisal, users will perceive an IT threat if they believe that they are susceptible to malicious IT and that the negative consequences are severe. The threat perception leads to coping appraisal, in which users assess the degree to which the IT threat can be avoided by taking safeguarding measures based on perceived effectiveness and costs of the safeguarding measure and self-efficacy of taking the safeguarding measure. TTAT posits that users are motivated to avoid malicious IT when they perceive a threat and believe that the threat is avoidable by taking safeguarding measures; if users believe that the threat cannot be fully avoided by taking safeguarding measures, they would engage in emotion-focused coping. Integrating process theory and variance theory, TTAT enhances our understanding of human behavior under IT threats and makes an important contribution to IT security research and practice.


Trajectory data mining: A review of methods and applications


title: Mr. Bennet, his coachman, and the Archbishop walk into a bar but only one of them gets recognized: On The Difficulty of Detecting Characters in Literary Texts

abstract: Characters are fundamental to literary analysis. Current approaches are heavily reliant on NER to identify characters, causing many to be overlooked. We propose a novel technique for character detection, achieving significant improvements over state of the art on multiple datasets.


Protecting Location Privacy in Spatial Crowdsourcing using Encrypted Data.


title: Dynamic modeling and computed torque control of a 3-DOF spherical parallel manipulator

abstract: This paper investigates the dynamic modeling of a 3-degree-of-freedom spherical parallel manipulator, called the Agile Eye, first build at Laval University. The approach used in this paper is based on detaching the manipulator into several subsystems and applying a consecutive synergy between kinematic analysis, Lagrangian and Newtonian approaches. In this regard, the manipulator under study is detached to four subsystems. After writing down the kinematic equations of all the three subsystems, the Lagrangian and Newtonian approaches are blended and finally the dynamic model of the 3-DOF Agile Eye is obtained. Finally, the problem leads to a system of 12 equations and 18 unknowns, which has been simplified to have a fully constraint system of equations. The results are put into contrast by the one obtained with a analyser software, MD-Adams. Then a co-simulation between MATLAB and MD-Adams has been accomplished in order to control the Agile Eye with computed torque control method. The latter method has led to the end-effector (EE) to follow the desired trajectory perfectly.


Invariants of Lie color algebras acting on graded algebras


title: Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers

abstract: A feature-based emotion recognition model is proposed for EEG-based BCI.The approach combines statistical-based feature selection methods and SVM emotion classifiers.The model is based on Valence/Arousal dimensions for emotion classification.Our combined approach outperformed other recognition methods. Current emotion recognition computational techniques have been successful on associating the emotional changes with the EEG signals, and so they can be identified and classified from EEG signals if appropriate stimuli are applied. However, automatic recognition is usually restricted to a small number of emotions classes mainly due to signal's features and noise, EEG constraints and subject-dependent issues. In order to address these issues, in this paper a novel feature-based emotion recognition model is proposed for EEG-based Brain-Computer Interfaces. Unlike other approaches, our method explores a wider set of emotion types and incorporates additional features which are relevant for signal pre-processing and recognition classification tasks, based on a dimensional model of emotions: Valence and Arousal. It aims to improve the accuracy of the emotion classification task by combining mutual information based feature selection methods and kernel classifiers. Experiments using our approach for emotion classification which combines efficient feature selection methods and efficient kernel-based classifiers on standard EEG datasets show the promise of the approach when compared with state-of-the-art computational methods.


title: Causal Effects in Mediation Modeling: An Introduction With Applications to Latent Variables

abstract: Causal inference in mediation analysis offers counterfactually based causal definitions of direct and indirect effects, drawing on research by Robins, Greenland, Pearl, VanderWeele, Vansteelandt, Imai, and others. This type of mediation effect estimation is little known and seldom used among analysts using structural equation modeling (SEM). The aim of this article is to describe the new analysis opportunities in a way that is accessible to SEM analysts and show examples of how to perform the analyses. An application is presented with an extension to a latent mediator measured with multiple indicators.


title: MS) “Mapping the Moral Domain

abstract: The moral domain is broader than the empathy and justice concerns assessed by existing measures of moral competence, and it is not just a subset of the values assessed by value inventories. To fill the need for reliable and theoretically grounded measurement of the full range of moral concerns, we developed the Moral Foundations Questionnaire on the basis of a theoretical model of 5 universally available (but variably developed) sets of moral intuitions: Harm/Care, Fairness/Reciprocity, Ingroup/Loyalty, Authority/Respect, and Purity/Sanctity. We present evidence for the internal and external validity of the scale and the model, and in doing so we present new findings about morality: (a) Comparative model fitting of confirmatory factor analyses provides empirical justification for a 5-factor structure of moral concerns; (b) convergent/discriminant validity evidence suggests that moral concerns predict personality features and social group attitudes not previously considered morally relevant; and (c) we establish pragmatic validity of the measure in providing new knowledge and research opportunities concerning demographic and cultural differences in moral intuitions. These analyses provide evidence for the usefulness of Moral Foundations Theory in simultaneously increasing the scope and sharpening the resolution of psychological views of morality.


title: Using natural language processing (NLP) for designing socially intelligent robots

abstract: The design of natural interaction with social robots is highly complex process, given the huge design space of robots in terms of appearance and behavior and the challenges arising when using face detection and speech recognition in the wild. More natural and highly autonomous interaction is necessary to foster trust and engagement and hence establishing a long-term social relationship between users and robots. In this abstract, an adaptive and interactive dialogue system is designed to exchange a chat with a user using personal information stored in his/her user profile. NLP is used to extract user's basic information, hobbies and interests for building a rich user profile. The information from the user profile is used to customize and adapt subsequent dialogues in a way to build trust and initiate comfort between users themselves and the robot. The user profile is continuously updated whenever new information is extracted in subsequent dialogues. Face detection (FD) is used to identify the user. An artificial neural network (ANN) based FD system is used to increase the system's predictability. Failure to recognize a user's face leads to creating a new user profile for the new unidentified user. NLP failure leads to storing the whole sentence and manual fixing thereafter.


title: Classifying Web corpora into domain and genre using automatic feature identification

abstract: Texts in representative corpora are typically classified into their domain and genre. However, it is not clear if existing domain and genre typologies can be applied at all to unlabeled data collected from the Web, for instance, to results of crawling. This study attempts to establish the most suitable categories for describing domains and genres of arbitrary web texts and to estimate the accuracy of their automatic classification using machine learning methods, such as Support Vector Machine (SVM) and clustering (repeated bisections and graph clustering). We also discuss methods for inducing the most discriminative features to perform this classification. The method has been designed to work with few or no linguistic resources and has been validated on a variety of languages: English, German, Chinese and Russian.


A Formal Approach to Software Architecture


title: Design aspects of the Trans-Rotary Magnetic Gear

abstract: Several design aspects of a recently introduced magnetic device are studied in this paper. The device, named Trans-Rotary Magnetic Gear (TROMAG), resembles a mechanical lead-screw. It can convert linear motion to rotation, and vice-versa, through magnetic fields, while offering a gearing action. For instance, high force, low speed translation can be converted to high speed, low torque rotation. Once integrated with a conventional winding, the resultant system may be used as a compact machine for high force linear motion applications. The paper investigates the design of the TROMAG by means of two-dimensional (2D) and three-dimensional (3D) Finite Element Analysis (FEA). Piece-wise helix effects, 2D approximation, demagnetization possibility, magnet dimensions impact, scaling, and flux density distribution are studied.


title: The behavioralist as tax collector: Using natural field experiments to enhance tax compliance

abstract: Tax collection problems date back to the earliest recorded history of mankind. This paper begins with a simple theoretical construct of paying (rather than declaring) taxes, which we argue has been an overlooked aspect of tax compliance. This construct is then tested in two large natural field experiments. Using administrative data from more than 200,000 individuals in the UK, we show that including social norms and public goods messages in standard tax payment reminder letters considerably enhances tax compliance. The field experiments increased taxes collected by the Government in the sample period and were cost-free to implement, demonstrating the potential importance of such interventions in increasing tax compliance.


title: Sinc-Cauchy hybrid wavelet kernel for Support Vector Machines

abstract: Support vector machine (SVM) is a machine-learning algorithm, which learns to perform the classification task through a supervised learning procedure, based on pre-classified data examples. Support vector classification using a Sinc-Cauchy hybrid wavelet kernel is presented in this paper. A hybrid wavelet kernel construction for support vector machine is introduced. The construction involves a multi-dimensional sinc wavelet function together with Cauchy kernel. We show that the hybrid kernel is an admissible kernel. Hybrid kernels provide better classification of the signal points in the mapped feature space. The Sinc-Cauchy hybrid kernel thus constructed is used for the classification of cardiac single photon emission computed tomography (SPECT) images and cardiac arrhythmia signals. The experimental results show that promising generalization performance can be achieved with the hybrid kernel, compared to conventional kernels.


Hey Cortana ! Exploring the use cases of a Desktop based Digital


title: Guiding Neural Machine Translation with Retrieved Translation Pieces

abstract: One of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen translation examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect $n$-grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call "translation pieces". We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved translation pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected translation pieces. We show our method improves NMT translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the translation time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation.


title: Optimizing the performance of vehicle-to-grid (V2G) enabled battery electric vehicles through a smart charge scheduling model

abstract: A smart charge scheduling model is presented for potential (1) vehicle-to-grid (V2G) enabled battery electric vehicle (BEV) owners who are willing to participate in the grid ancillary services, and (2) grid operators. Unlike most V2G implementations, which are considered from the perspective of power grid systems, this model includes a communication network architecture for connecting system components that supports both BEV owners and grid operators to efficiently monitor and manage the charging and ancillary service activities. This model maximizes the net profit to each BEV participant while simultaneously satisfying energy demands for his/her trips. The performance of BEVs using the scheduling model is validated by estimating optimal annual financial benefits under different scenarios. An analysis of popular BEV models revealed that one of the existing BEVs considered in the study can generate an annual regulation profit of $454, $394 and $318 when the average daily driving distance is 20 miles, 40 miles and 60 miles, respectively. All popular BEV models can completely compensate the energy cost and generate a positive net profit, through the application of the scheduling model presented in this paper, with an annual driving distance of approximately 15,000 miles. Simulation analysis indicated that the extra load distribution from the optimized BEV charging operations were well balanced compared to the unmanaged BEV operations.


title: CPW-Fed Wideband Printed Dipole Antenna for Digital TV Applications

abstract: A co-planar waveguide (CPW)-fed wideband printed dipole antenna is developed to fulfill digital TV applications. This antenna is designed with multiple loops to effectively extend the frequency range of operations. Additionally, the parameters associated with the proposed antenna structure are well investigated and adequately determined. Experimental results demonstrate that the proposed CPW-fed dipole antenna can operate at a frequency band from 430 MHz to 1180 MHz in which the return loss is better than 10 dB. The measured radiation patterns look like a dipole-type pattern. Particularly, the proposed antenna has a gain ranging from 1.78 to 2.83 dBi. Therefore, the antenna proposed herein is very suitable to various digital TV applications for high-performance receiving.


title: Efficient Resource Allocation in Device-to-Device Communication Using Cognitive Radio Technology

abstract: Device-to-device (D2D) communication is developed as a new paradigm to enhance network performance according to LTE and WiMAX advanced standards. The D2D communication may have dedicated spectrum (overlay) or shared spectrum (underlay). However, the allocated dedicated spectrum may not be effectively used in the overlay mode, while interference between the D2D users and cellular users cause impairments in the underlay mode. Can the resource allocation of a D2D system be optimized using the cognitive approach where the D2D users opportunistically access the underutilized radio spectrum? That is the focus of this paper. In this paper, the transmission rate of the D2D users is optimized while simultaneously satisfying five sets of constraints related to power, interference, and data rate, modeling D2D users as cognitive secondary users. Furthermore, a two-stage approach is considered to allocate the radio resources efficiently. A new adaptive subcarrier allocation scheme is designed first, and then, a novel power allocation scheme is developed utilizing geometric water-filling approach that provides optimal solution with low computation complexity for this nonlinear problem. Numerical results show that the proposed approach achieved significant performance enhancement than the existing schemes.


title: HellRank: a Hellinger-based centrality measure for bipartite social networks

abstract: Measuring centrality in a social network, especially in bipartite mode, poses several challenges such as requirement of full knowledge of the network topology and lack of properly detection of top-k behavioral representative users. In this paper, to overcome the aforementioned challenging issues, we propose an accurate centrality measure, called HellRank, to identify central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of the Hellinger distance on a bipartite network and find an upper and lower bounds for this distance. The computation of HellRank centrality measure can be distributed by letting each node uses only local information on its immediate neighbors and therefore do not need a central entity to have full knowledge of the network topological structure. We experimentally evaluate performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.


An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing


title: A visual analytics loop for supporting model development

abstract: Threats in cybersecurity come in a variety of forms, and combating such threats involves handling a huge amount of data from different sources. It is absolutely necessary to use algorithmic models to defend against these threats. However, all models are sensitive to deviation from the original contexts in which the models were developed. Hence, it is not really an overstatement to say that ‘all models are wrong’. In this paper, we propose a visual analytics loop for supporting the continuous development of models during their deployment. We describe the roles of three types of operators (monitors, analysts and modelers), present the visualization techniques used at different stages of model development, and demonstrate the utility of this approach in conjunction with a prototype software system for corporate insider threat detection. In many ways, our environment facilitates an agile approach to the development and deployment of models in cybersecurity.


title: A Novel Cost-Based Model for Data Repairing

abstract: Integrity constraint based data repairing is an iterative process consisting of two parts: detect and group errors that violate given integrity constraints (ICs); and modify values inside each group such that the modified database satisfies those ICs. However, most existing automatic solutions treat the process of detecting and grouping errors straightforwardly (e.g., violations of functional dependencies using string equality), while putting more attention on heuristics of modifying values within each group. In this paper, we propose a revised semantics of violations and data consistency w.r.t. a set of ICs. The revised semantics relies on string similarities, in contrast to traditional methods that use syntactic error detection using string equality. Along with the revised semantics, we also propose a new cost model to quantify the cost of data repair by considering distances between strings. We show that the revised semantics provides a significant change for better detecting and grouping errors, which in turn improves both precision and recall of the following data repairing step. We prove that finding minimum-cost repairs in the new model is NP-hard, even for a single FD. We devise efficient algorithms to find approximate repairs. In addition, we develop indices and optimization techniques to improve the efficiency. Experiments show that our approach significantly outperforms existing automatic repair algorithms in both precision and recall.


Peer Review #1 of "Protein expression profile changes of lung tissue in patients with pulmonary hypertension (v0.1)"


title: A capacitance-compensation technique for improved linearity in CMOS class-AB power amplifiers

abstract: A nonlinear capacitance-compensation technique is developed to help improve the linearity of CMOS class-AB power amplifiers. The method involves placing a PMOS device alongside the NMOS device that works as the amplifying unit, such that the overall capacitance seen at the amplifier input is a constant, thus improving linearity. The technique is developed with the help of computer simulations and Volterra analysis. A prototype two-stage amplifier employing the scheme is fabricated using a 0.5-/spl mu/m CMOS process, and the measurements show that an improvement of approximately 8 dB in both two-tone intermodulation distortion (IM3) and adjacent-channel leakage power (ACP1) is obtained for a wide range of output power. The linearized amplifier exhibits an ACP1 of -35 dBc at the designed output power of 24 dBm, with a power-added efficiency of 29% and a gain of 23.9 dB, demonstrating the potential utility of the design approach for 3GPP WCDMA applications.


title: Local Clustering in Provenance Graphs (Extended Version)

abstract: Systems that capture and store data provenance, the record of how an object has arrived at its current state, accumulate historical metadata over time, forming a large graph. Local clustering in these graphs, in which we start with a seed vertex and grow a cluster around it, is of paramount importance because it supports critical provenance applications such as identifying semantically meaningful tasks in an object’s history and selecting appropriate truncation points for returning an object’s ancestry or lineage. Generic graph clustering algorithms are not effective at producing semantically meaningful clusters in provenance graphs. We identify three key properties of provenance graphs and exploit them to justify two new centrality metrics we developed, specifically for use in performing local clustering on provenance graphs.


Morphological Galaxy Classification Using Machine Learning


Approximating polyhedra with spheres for time-critical collision detection


title: The social turn in K-12 programming: moving from computational thinking to computational participation

abstract: In this conceptual paper, we argue that recent developments in K-12 programming education are suggestive of what can be called a "social turn", a shift in the field in which learning to code has shifted from being a predominantly individualistic and tool-oriented approach to now one that is decidedly sociologically and culturally grounded in the creation and sharing of digital media. We discuss in detail three dimensions of this social turn (1) from writing code to creating applications, (2) from composing "from scratch" to remixing the work of others, and (3) from designing tools to facilitating communities. These three shifts illustrate how the development of artifacts, tools, and communities of programming lead us to move from computational thinking to computational participation. We draw from examples of past and current research, both inside and outside of school, and situate these developments in the context of current discussions around computational thinking, which has become a driving force in revitalizing programming in K-12 curricula and altogether broadening participation in computing.


title: Bacterial and protozoal causes of pregnancy loss in the bitch and queen

abstract: Several bacterial species have been implicated in canine and feline pregnancy loss. Brucella canis is one of the more important bacterial infectious agents that cause pregnancy loss in the bitch. Brucella has been documented in the queen but in general infectious abortion from bacteria and protozoal agents is uncommon in the species. Protozoal causes of pregnancy loss in the bitch and queen are less common than in other species. Etiology, clinical signs, diagnosis and treatment of bacterial and protozoal causes of pregnancy loss in the bitch and queen are reviewed. Veterinary practitioners should be aware that many of these organisms have zoonotic potential.


title: Empirical Analysis of Data Breach Litigation

abstract: In recent years, many lawsuits have been filed by individuals seeking legal redress for harms caused by the loss or theft of their personal information. However, very little is known about the drivers, mechanics, and outcomes of those lawsuits, making it difficult to assess the effectiveness of litigation at balancing organizations' usage of personal data with individual privacy rights. Using a unique and manually collected database, we analyze court dockets for more than 230 federal data breach lawsuits from 2000 to 2010. We investigate two questions: Which data breaches are being litigated? and Which data breach lawsuits are settling? Our results suggest that the odds of a firm being sued are 3.5 times greater when individuals suffer financial harm, but 6 times lower when the firm provides free credit monitoring. Moreover, defendants settle 30 percent more often when plaintiffs allege financial loss, or when faced with a certified class action suit. By providing the first comprehensive empirical analysis of data breach litigation, our findings offer insight into the debate over privacy litigation versus privacy regulation.


title: Applications of Rhetorical Structure Theory

abstract: Rhetorical Structure Theory (RST) is a theory of text organization that has led to areas of application beyond discourse analysis and text generation, its original goals. In this article, we review the most important applications in several areas: discourse analysis, theoretical linguistics, psycholinguistics, and computational linguistics. We also provide a list of resources useful for work within the RST framework. The present article is a complement to our review of the theoretical aspects of the theory (Taboada and Mann, 2006).


title: Building Security Perimeters to Protect Network Systems Against Cyber Threats [Future Directions]

abstract: Due to the wide variety of devices used in computer network systems, cybersecurity plays a major role in securing and improving the performance of the network or system. Although cybersecurity has received a large amount of global interest in recent years, it remains an open research space. Current security solutions in network-based cyberspace provide an open door to attackers by communicating first before authentication, thereby leaving a black hole for an attacker to enter the system before authentication. This article provides an overview of cyberthreats, traditional security solutions, and the advanced security model to overcome current security drawbacks.


Cognitive Radio for Flexible Mobile Multimedia Communications


title: Contextualize, Show and Tell: A Neural Visual Storyteller

abstract: We present a neural model for generating short stories from image sequences, which extends the image description model by Vinyals et al. (Vinyals et al., 2015). This extension relies on an encoder LSTM to compute a context vector of each story from the image sequence. This context vector is used as the first state of multiple independent decoder LSTMs, each of which generates the portion of the story corresponding to each image in the sequence by taking the image embedding as the first input. Our model showed competitive results with the METEOR metric and human ratings in the internal track of the Visual Storytelling Challenge 2018.


title: Relational Deep Reinforcement Learning

abstract: We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy. Our results show that in a novel navigation and planning task called Box-World, our agent finds interpretable solutions that improve upon baselines in terms of sample complexity, ability to generalize to more complex scenes than experienced during training, and overall performance. In the StarCraft II Learning Environment, our agent achieves state-of-the-art performance on six mini-games -- surpassing human grandmaster performance on four. By considering architectural inductive biases, our work opens new directions for overcoming important, but stubborn, challenges in deep RL.


Application of Logistic Regression in Resolving Influential Risk Factors Subject to Corrosion Under Insulation


title: Data-driven fluid simulations using regression forests

abstract: Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, realtime fluid simulations have been possible only under very restricted conditions. In this paper we propose a novel machine learning based approach, that formulates physics-based fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos. We used a regression forest to approximate the behaviour of particles observed in the large training set of simulations obtained using a traditional solver. Our GPU implementation led to a speed-up of one to three orders of magnitude compared to the state-of-the-art position-based fluid solver and runs in real-time for systems with up to 2 million particles.


title: Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering

abstract: This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.


title: Dynamic visibility graph for path planning

abstract: In this paper, we propose a fast dynamic visibility graph (DVG) for constructing a reduced roadmap among convex polygonal obstacles. DVG is extracted from the global environment with the simple geometric method and rules. Moreover, the data preprocessing is based on the concept of V-circle. Through V-circle, the process is speeded up greatly. Finally, DVG is extended to deal with multi-target problems that traditionally require a lot of time for reconstructing configuration space (C-space).


title: Integrating Geometrical Context for Semantic Labeling of Indoor Scenes using RGBD Images

abstract: Inexpensive structured light sensors can capture rich information from indoor scenes, and scene labeling problems provide a compelling opportunity to make use of this information. In this paper we present a novel conditional random field (CRF) model to effectively utilize depth information for semantic labeling of indoor scenes. At the core of the model, we propose a novel and efficient plane detection algorithm which is robust to erroneous depth maps. Our CRF formulation defines local, pairwise and higher order interactions between image pixels. At the local level, we propose a novel scheme to combine energies derived from appearance, depth and geometry-based cues. The proposed local energy also encodes the location of each object class by considering the approximate geometry of a scene. For the pairwise interactions, we learn a boundary measure which defines the spatial discontinuity of object classes across an image. To model higher-order interactions, the proposed energy treats smooth surfaces as cliques and encourages all the pixels on a surface to take the same label. We show that the proposed higher-order energies can be decomposed into pairwise sub-modular energies and efficient inference can be made using the graph-cuts algorithm. We follow a systematic approach which uses structured learning to fine-tune the model parameters. We rigorously test our approach on SUN3D and both versions of the NYU-Depth database. Experimental results show that our work achieves superior performance to state-of-the-art scene labeling techniques.


title: Strategic Communication: An Integral Component of Counterinsurgency Operations

abstract: The tactical successes achieved by the United States and its allies in the war on terrorism will mean little if the war for the hearts and minds of citizens in the Muslim world is lost. In a global counter-insurgency campaign, the military and other U.S. government agencies not only have to battle elusive foes, but also have to work closely with their counterparts in host nations, conduct sophisticated strategic communication efforts, support infrastructure development, and engage constructively with the local populace, non-governmental organizations (NGOs), and the media. Counter-insurgency is a separate and distinct form of warfare; it is a competition between ideologies and distinct socio-political movements. Successful counter-insurgency operations focus on diffusing violent socio-political movements, which is best done by drawing on a full spectrum of communications methods and thoughtful actions that encompasses programs across many agencies and non-governmental entities. Efforts to win hearts and minds will be much more effective if these efforts are coordinated, or at least if they do not work at cross-purposes. Strategic communication is a critical component of such a strategy, and will be the focus of this paper. The United States government faces a formidable challenge when it comes to strategic communication. Not everyone recognizes or fully appreciates the subtleties and complexities of strategy in today’s environment. The United States defense establishment is comfortable with fighting a conventional war, and is uncomfortable with the


title: Understanding Search-Engine Optimization

abstract: Because users rarely click on links beyond the first search results page, boosting search-engine ranking has become essential to business success. With a deeper knowledge of search-engine optimization best practices, organizations can avoid unethical practices and effectively monitor strategies approved by popular search engines.


title: Low power divider using vedic mathematics

abstract: Divider is an inevitable and basic hardware module employed in advanced and high speed digital signal processing (DSP) units of high precision. It is widely used in radar technology, communication, industrial control systems and linear predictive coding (LPC) algorithms in speech processing. This paper proposes a fast, low power and cost effective architecture of a divider using the ancient Indian Vedic division algorithm. The merits of the proposed architecture are proved by comparing the gate count, power consumption and delay against the conventional divider architectures. The validation of the proposed architecture has resulted in 52.93 percent reduction in power dissipation against comparison with conventional divider using repeated subtraction. The designs were implemented using industry standard Cadence® software using 45nm technology library. The design has been validated on FPGA Spartan-3E kit. The validation results show appreciable reduction in circuit latency and in Look-Up-Table (LUT) utilization using proposed Vedic divider than the conventional divider.


title: FuSSO: Functional Shrinkage and Selection Operator

abstract: We present the FuSSO, a functional analogue to the LASSO, that eciently nds a sparse set of functional input covariates to regress a real-valued response against. The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear form to the mapping of functional covariates to the response. We provide a statistical backing for use of the FuSSO via proof of asymptotic sparsistency under various conditions. Furthermore, we observe good results on both synthetic and real-world data.


title: A Hybrid Both Filter and Wrapper Feature Selection Method for Microarray Classification

abstract: Gene expression data is widely used in disease analysis and cancer diagnosis. However, since gene expression data could contain thousands of genes simultaneously, successful microarray classification is rather difficult. Feature selection is an important pre-treatment for any classification process. Selecting a useful gene subset as a classifier not only decreases the computational time and cost, but also increases classification accuracy. In this study, we applied the information gain method as a filter approach, and an improved binary particle swarm optimization as a wrapper approach to implement feature selection; selected gene subsets were used to evaluate the performance of classification. Experimental results show that by employing the proposed method fewer gene subsets needed to be selected and better classification accuracy could be obtained.


title: Self-Healing Hydrogels.

abstract: Over the past few years, there has been a great deal of interest in the development of hydrogel materials with tunable structural, mechanical, and rheological properties, which exhibit rapid and autonomous self-healing and self-recovery for utilization in a broad range of applications, from soft robotics to tissue engineering. However, self-healing hydrogels generally either possess mechanically robust or rapid self-healing properties but not both. Hence, the development of a mechanically robust hydrogel material with autonomous self-healing on the time scale of seconds is yet to be fully realized. Here, the current advances in the development of autonomous self-healing hydrogels are reviewed. Specifically, methods to test self-healing efficiencies and recoveries, mechanisms of autonomous self-healing, and mechanically robust hydrogels are presented. The trends indicate that hydrogels that self-heal better also achieve self-healing faster, as compared to gels that only partially self-heal. Recommendations to guide future development of self-healing hydrogels are offered and the potential relevance of self-healing hydrogels to the exciting research areas of 3D/4D printing, soft robotics, and assisted health technologies is highlighted.


title: Cloud computing usance among SMEs: Comparative analysis of China and Nigeria

abstract: Cloud computing is an emerging technology in recent years and has been very beneficial to Small and Medium-sized Enterprises (SMEs). However, not many SMEs have adopted this technology to explore the opportunity therein. This paper discusses the potential benefits, level of adoption and usance of cloud computing among SMEs in China and Nigeria. The paper went further to identify the margin that exists between the level of adoption of the two countries and also point out some of the challenges peculiar to each country that prevent the full adoption of cloud computing among SMEs and finally make suggestions on how the Nigerian SMEs can improve their enterprise using the cloud computing. In addition, the paper provides evidence-based intervention for cloud service providers, the private sector and the government to improve the adoption of cloud computing among SMEs to ultimately drive the enterprises for the potential economic benefit.


title: Studying information technology in organizations: Research approaches and assumptions

abstract: We examined 155 information systems research articles published from 1983 to 1988 and found that although this research is not rooted in a single over-arching theoretical perspective, it does exhibit a single set of philosophical assumptions regarding the nature of the phenomena studied by information systems researchers, and what constitutes valid knowledge about those phenomena. We believe that a single research perspective for studying information systems phenomena is unnecessarily restrictive, and argue that there exist other philosophical assumptions that can inform studies of the relationships between information technology, people, and organizations. In this paper, we present two additional research philosophies for consideration-the interpretive and the critical-and for each we provide empirical examples to illustrate how they are used. We conclude by suggesting that much can be gained if a plurality of research perspectives is effectively employed to investigate information systems phenomena.


title: Estimation of Wasserstein distances in the Spiked Transport Model

abstract: We propose a new statistical model, the spiked transport model, which formalizes the assumption that two probability distributions differ only on a low-dimensional subspace. We study the minimax rate of estimation for the Wasserstein distance under this model and show that this low-dimensional structure can be exploited to avoid the curse of dimensionality. As a byproduct of our minimax analysis, we establish a lower bound showing that, in the absence of such structure, the plug-in estimator is nearly rate-optimal for estimating the Wasserstein distance in high dimension. We also give evidence for a statistical-computational gap and conjecture that any computationally efficient estimator is bound to suffer from the curse of dimensionality.


title: Uncut free pocketing tool-paths generation using pair-wise offset algorithm

abstract: Abstract For die-cavity pocketing, contour-parallel machining is the most popular machining strategy. Two issues in generating contour-parallel tool paths for pocketing are: (1) a robust two-dimensional (2D)-curve offsetting algorithm; and (2) detecting and removing uncut regions. The 2D-curve offsetting solution has been widely studied, because it has so many potential applications. However, though the importance of the uncut problem in pocketing has long been recognized there have been few reported investigations on detecting and removing uncut regions. This paper presents a procedure for generating pocketing tool paths leaving no uncut regions. For the 2D-curve offsetting algorithm, we employ the PWID offset algorithm suggested by Choi and Park (Computer Aided Design, 31(12) (1999) 735) and expand the algorithm for offsetting areas having islands. Based on the expanded PWID offset algorithm , our solution to the uncut problem removes the uncut regions. Empirical tests show the usefulness of the proposed procedure for improving the productivity of pocket machining.


Robust DNN-Based VAD Augmented with Phone Entropy Based Rejection of Background Speech.


Estimation of speaker's height and vocal tract length from speech signal.


title: Role of attributes selection in classification of Chronic Kidney Disease patients

abstract: In the present days the Chronic Kidney Disease (CKD) is a common problem to the public. CKD is generally considered as kidney damage and is usually measured with the GFR (Glomerular Filtration Rate). Several researchers from health care and academicians are working on the CKD problem to have an efficient model to predict and classify the CKD patient in the initial stage of CKD, so that the necessary treatment can be provided to prevent or cure CKD. In this work classification models have been built with different classification algorithms, Wrappersubset attribute evaluator and bestfirst search method to predict and classify the CKD and non CKD patients. These models have applied on recently collected CKD dataset downloaded from the UCI repository. The models have shown better performance in classifying CKD and non CKD cases. Results of different models are compared. From the comparison it has been observed that classifiers performed better on reduced dataset than the original dataset.


title: Recognition of Affect, Judgment, and Appreciation in Text

abstract: The main task we address in our research is classification of text using fine-grained attitude labels. The developed @AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the fine-grained level (14 labels) was 62%, on the middle level (7 labels) - 71%, and on the top level (3 labels) - 88%.


title: Resolving occlusion in augmented reality

abstract: Current state-of-the-art augmented reality systems simply overlay computer-generated visuals on the real-world imagery, for example via video or optical see-through displays. However, overlays are not effective when displaying data in three dimensions, since occlusion between the real and computer-generated objects is not addressed. We present a video see-through augmented reality system capable of resolving occlusion between real and computer-generated objects. The heart of our system is a new algorithm that assigns depth values to each pixel in a pair of stereo video images in near-real-time. The algorithm belongs to the class of stereo matching algorithms and thus works in fully dynamic environments. We describe our system in general and the stereo matching algorithm in particular.


title: YesWorkflow: A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts

abstract: Scientific workflow management systems offer features for composing complex computational pipelines from modular building blocks, executing the resulting automated workflows, and recording the provenance of data products resulting from workflow runs. Despite the advantages such features provide, many automated workflows continue to be implemented and executed outside of scientific workflow systems due to the convenience and familiarity of scripting languages (such as Perl, Python, R, and MATLAB), and to the high productivity many scientists experience when using these languages. YesWorkflow is a set of software tools that aim to provide such users of scripting languages with many of the benefits of scientific workflow systems. YesWorkflow requires neither the use of a workflow engine nor the overhead of adapting code to run effectively in such a system. Instead, YesWorkflow enables scientists to annotate existing scripts with special comments that reveal the computational modules and dataflows otherwise implicit in these scripts. YesWorkflow tools extract and analyze these comments, represent the scripts in terms of entities based on the typical scientific workflow model, and provide graphical renderings of this workflow-like view of the scripts. Future version of YesWorkflow will also allow the prospective provenance of the data products of these scripts to be queried in ways similar to those available to users of scientific workflow systems.


title: Stress and hippocampal plasticity.

abstract: The hippocampus is a target of stress hormones, and it is an especially plastic and vulnerable region of the brain. It also responds to gonadal, thyroid, and adrenal hormones, which modulate changes in synapse formation and dendritic structure and regulate dentate gyrus volume during development and in adult life. Two forms of structural plasticity are affected by stress: Repeated stress causes atrophy of dendrites in the CA3 region, and both acute and chronic stress suppresses neurogenesis of dentate gyrus granule neurons. Besides glucocorticoids, excitatory amino acids and N-methyl-D-aspartate (NMDA) receptors are involved in these two forms of plasticity as well as in neuronal death that is caused in pyramidal neurons by seizures and by ischemia. The two forms of hippocampal structural plasticity are relevant to the human hippocampus, which undergoes a selective atrophy in a number of disorders, accompanied by deficits in declarative episodic, spatial, and contextual memory performance. It is important, from a therapeutic standpoint, to distinguish between a permanent loss of cells and a reversible atrophy.


title: Language Acquisition and Brain Development

abstract: Language acquisition is one of the most fundamental human traits, and it is obviously the brain that undergoes the developmental changes. During the years of language acquisition, the brain not only stores linguistic information but also adapts to the grammatical regularities of language. Recent advances in functional neuroimaging have substantially contributed to systems-level analyses of brain development. In this Viewpoint, I review the current understanding of how the “final state” of language acquisition is represented in the mature brain and summarize new findings on cortical plasticity for second language acquisition, focusing particularly on the function of the grammar center.


title: Social Phishing

abstract: Sometimes a "friendly" email message tempts recipients to reveal more online than they otherwise would, playing right into the sender's hand.


Creating Brave Spaces within and through Student- Faculty Pedagogical Partnerships


title: Sentiment analysis of students' comment using lexicon based approach

abstract: In education system, students' feedback is important to measure the quality of teaching. Students' feedback can be analyzed using lexicon based approach to identify the students' positive or negative attitude. In most of the existing teaching evaluation system, the intensifier words and blind negation words are not considered. The level of opinion result isn't displayed: whether positive or negative opinion. To address this problem, we propose to analyze the students' text feedback automatically using lexicon based approach to predict the level of teaching performance. A database of English sentiment words is created as a lexical source to get the polarity of words. By analyzing the sentiment information including intensifier words extracting from students' feedback, we are able to determine opinion result of teachers, describing the level of positive or negative opinions. This system shows the opinion result of teachers that is represented as to whether strongly positive, moderately positive, weakly positive, strongly negative, moderately negative, weakly negative or neutral.


title: Toward a More Pragmatic Approach to Morality: A Critical Evaluation of Kohlberg's Model

abstract: In this article, the authors evaluate L. Kohlberg’s (1984) cognitive–developmental approach to morality, find it wanting, and introduce a more pragmatic approach. They review research designed to evaluate Kohlberg’s model, describe how they revised the model to accommodate discrepant findings, and explain why they concluded that it is poorly equipped to account for the ways in which people make moral decisions in their everyday lives. The authors outline in 11 propositions a framework for a new approach that is more attentive to the purposes that people use morality to achieve. People make moral judgments and engage in moral behaviors to induce themselves and others to uphold systems of cooperative exchange that help them achieve their goals and advance their interests.


title: Maximal Labelled-Clique and Click-Biclique Problems for Networked Community Detection

abstract: Discovering "closely related entities" in any network, a.k.a. communities, is a key goal for various network analytic applications. In particular, cliques and bicliques are two community structures that have influenced several tools and techniques in Big Data and social networking. A clique is a complete subgraph of an undirected graph and similarly, a biclique is a complete bipartite subgraph. A maximal clique (or biclique) is a clique that is not subset of any other clique (or biclique). Algorithms to list all maximal cliques in general graphs or bicliques in bipartite graphs have been previously studied. In this paper, we enhance these solutions explaining how a novel structure, that we call clique-biclique can be used to unravel richer communities with respect to different problems in a wide variety of networks. We then give two algorithms for efficiently listing maximal labelled-cliques and maximal clique-bicliques. The first algorithm is an extension of the maximal clique enumeration and the second cleverly combines enumeration of maximal cliques and maximal bicliques. We conduct an experimental analysis over different synthetic and real datasets to evaluate performance of the algorithms, and we found that even richer communities compared to cliques and bicliques can be efficiently found in most networks of interest.


title: Feasible Computationally Efficient Path Planning for UAV Collision Avoidance

abstract: This paper presents a robust computationally efficient real-time collision avoidance algorithm for Unmanned Aerial Vehicle (UAV), namely Memory-based Wall Following-Artificial Potential Field (MWF-APF) method. The new algorithm switches between Wall-Following Method (WFM) and Artificial Potential Field method (APF) with improved situation awareness capability. Historical trajectory is taken into account to avoid repetitive wrong decision. Furthermore, it can be effectively applied to platform with low computing capability. As an example, a quad-rotor equipped with limited number of Time-of-Flight (TOF) rangefinders is adopted to validate the effectiveness and efficiency of this algorithm. Both software simulation and physical flight test have been conducted to demonstrate the capability of the MWF-APF method in complex scenarios.


title: Vision-Based Lane Keeping - A Survey

abstract: Ahstract- Several intelligent transportation system applications, such as automatic steering systems, lane departure warning systems, and driver performance monitoring systems, depend on obtaining accurate estimates of the vehicle's trajectory with respect to the lane the vehicle is driving in. However, lane marking detection and tracking using video or laser sensors tend to be extremely noisy, requiring robust methods for lane marking detection and tracking. Therefore, it has been an active field of research for decades. In this paper, we survey methods for detecting lane marking as well as tracking lane boundaries in real time focusing on vision-based system. We present a generic problem and elaborate the broad ranged proposed method in vision-based system. Finally, we identify these gaps and recommend research direction that may bridge them.


title: Biological Activity of a Phloroglucinol Glucoside Derivative from Conyza aegyptiaca

abstract: The phloroglucinol glucoside derivative [2,4-dihydroxy-6-(beta-D-glucopyranosyloxy)phenyl]-butan-1-one (1), roseoside (2), and kaempferol-3-O-beta-D-glucopyranoside (3) were isolated from the aerial parts of Conyza aegyptiaca (L.). To the best of our knowledge, this is the first isolation of compounds 1-3 from C. aegyptiaca. Their structures were determined by spectroscopic techniques including, IR, HR-EIMS, and extensive 500 MHz 1D- and 2D-NMR analyses (1H, 13C NMR, DEPT, 1H-1H COSY, HMQC and HMBC experiments). The antioxidant activity of 1, using the DPPH assay, was investigated; in addition, 1 was investigated against different types of cell lines, including Hep-G2, HCT-116, and RAW 264.7 for its cytotoxic effects. Also, this is the first report on the activity of 1.


title: Camera Calibration from Images of Spheres

abstract: This paper introduces a novel approach for solving the problem of camera calibration from spheres. By exploiting the relationship between the dual images of spheres and the dual image of the absolute conic (IAC), it is shown that the common pole and polar with regard to the conic images of two spheres are also the pole and polar with regard to the IAC. This provides two constraints for estimating the IAC and, hence, allows a camera to be calibrated from an image of at least three spheres. Experimental results show the feasibility of the proposed approach.


title: Pushing Networks to the Limit PERSPECTIVE Scale-Free Networks: A Decade and Beyond

abstract: For decades, we tacitly assumed that the components of such complex systems as the cell, the society, or the Internet are randomly wired together. In the past decade, an avalanche of research has shown that many real networks, independent of their age, function, and scope, converge to similar architectures, a universality that allowed researchers from different disciplines to embrace network theory as a common paradigm. The decade-old discovery of scale-free networks was one of those events that had helped catalyze the emergence of network science, a new research field with its distinct set of challenges and accomplishments.


title: An Improvement Stereo Vision Images Processing for Object Distance Measurement

abstract: Human has the ability to roughly estimate the distance of objects because of the stereo vision of human’s eyes. In this paper we proposed an improved stereo vision system to accurately measure the distance of objects in real world. Object distance is very useful for obstacle avoidance and navigation of autonomous vehicles. Recent researches have used stereo cameras for different applications such as 3D image construction, distance measurement, and occlusion detection. The proposed measurement procedure is a three-phase process: object detection, segmentation, and distance calculation. In distance calculation, we proposed a new algorithm to reduce the error. The result shows our measurement system is capable of providing objects distance with less than 5% of measurement error.


title: Introduction: attachment theory and psychotherapy.

abstract: In this introduction to the JCLP: In Session 69(11) issue on attachment theory and psychotherapy, the key points of attachment theory (Bowlby, 1969, 1973, 1981) and its relevance to psychotherapy are briefly described. The aim of this issue is to provide case illustrations of how an attachment theory perspective and principles can expand our understanding of psychotherapy practice.


title: Fake News Detection Enhancement with Data Imputation

abstract: Raw datasets collected for fake news detection usually contain some noise such as missing values. In order to improve the performance of machine learning based fake news detection, a novel data preprocessing method is proposed in this paper to process the missing values. Specifically, we have successfully handled the missing values problem by using data imputation for both categorical and numerical features. For categorical features, we imputed missing values with the most frequent value in the columns. For numerical features, the mean value of the column is used to impute numerical missing values. In addition, TF-IDF vectorization is applied in feature extraction to filter out irrelevant features. Experimental results show that Multi-Layer Perceptron (MLP) classifier with the proposed data preprocessing method outperforms baselines and improves the prediction accuracy by more than 15%.


title: Towards a Generic and Flexible Citation Classifier Based on a Faceted Classification Scheme

abstract: Citations are a valuable resource for characterizing scientific publications that has already been used in applications such as summarization and information retrieval. These applications could be even better served by expanding citation information. We aim to achieve this by extracting and classifying citation information from the text, so that subsequent applications may make use of it. We make three contributions to the advancement of fine-grained citation classification. First, our work uses a standard classification scheme for citations that was developed independently of automatic classification and therefore is not bound to any particular citation application. Second, to address the lack of available annotated corpora and reproducible results for citation classification, we are making available a manually-annotated corpus as a benchmark for further citation classification research. Third, we introduce new features designed for citation classification and compare them experimentally with previously proposed citation features, showing that these new features improve classification accuracy.


title: Fundamental concepts of CS1: procedural vs. object oriented paradigm - a case study

abstract: For some time, there has been an ongoing debate among Computer Science (CS) educators about the advantages and disadvantages of the shift from the procedural to the Object-Oriented (OO) paradigm. In our institution, we decided to implement this shift in the year 2005, when we changed our introductory course and started, right from the beginning, to teach the Java language, instead of the procedural facet of C++. While we still offered the two different paradigms in parallel, we had two similar groups each taking one of the paradigms. These two groups served as the research population of the study we describe in this paper. We examined whether the shift from the procedural to the OO paradigm had an effect on students' perception of fundamental concepts taught in the introductory course.