title: Configurable toolset for static verification of operating systems kernel modules

abstract: An operating system (OS) kernel is a critical software regarding to reliability and efficiency. Quality of modern OS kernels is already high enough. However, this is not the case for kernel modules, like, for example, device drivers that, due to various reasons, have a significantly lower level of quality. One of the most critical and widespread bugs in kernel modules are violations of rules for correct usage of a kernel API. One can find all such violations in modules or can prove their correctness using static verification tools that need contract specifications describing obligations of a kernel and modules relative to each other. This paper considers present methods and toolsets for static verification of kernel modules for different OSs. A new method for static verification of Linux kernel modules is proposed. This method allows one to configure the verification process at all its stages. It is shown how it can be adapted for checking kernel components of other OSs. An architecture of a configurable toolset for static verification of Linux kernel modules that implements the proposed method is described, and results of its practical application are presented. Directions for further development of the proposed method are discussed in conclusion.


title: Scalable Influence Maximization with General Marketing Strategies

abstract: In this paper, we study scalable algorithms for influence maximization with general marketing strategies (IM-GMS), in which a marketing strategy mix is modeled as a vector $\mathbf{x}=(x_1, \ldots, x_d)$ and could activate a node $v$ in the social network with probability $h_v(\mathbf{x})$. The IM-GMS problem is to find the best strategy mix $\mathbf{x}^*$ that maximize the influence spread due to influence propagation from the activated seeds, subject to the budget constraint that $\sum_{j\in [d]} x_j \le k$. We adapt the scalable reverse influence sampling (RIS) approach and design a scalable algorithm that provides a $(1-1/e -\varepsilon)$ approximate solution (for any $\varepsilon>0$), with running time near-linear in the network size. We further extend IM-GMS to allow partitioned budget constraint, and show that our scalable algorithm provides a $(1/2-\varepsilon)$ solution in this case. Through extensive experiments, we demonstrate that our algorithm is several orders faster than the Monte Carlo simulation based hill-climbing algorithm, and also outperforms other baseline algorithms proposed in the literature.


title: Factors that Influence Customers’ Buying Intention on Shopping Online

abstract: On-line commerce through Internet is gaining attention from students today. The aim of this research is to study the factors influencing student’s buying intention through internet shopping in an institution of higher learning in Malaysia. Several factors such as usefulness, ease of use, compatibility, privacy, security, normative-beliefs and attitude that influence student’s buying intention were analyzed. Respondents who were selected are studying in a public institution of higher learning in Penang, Malaysia. Based on theory of reasoned action (TRA), the technology acceptance model (TAM) concluded that there are two salient beliefs which are ease of use and usefulness. This theory has been applied on the study to adopt technology user different and has been emerged as a model in investigation to increase predictive power. Such theory was used in this study to explain students’ buying intention on-line. Besides the ease of use and usefulness, others factors such as: compatibility, privacy, security, normative beliefs and self-efficacy are utilized at this TAM. The results support seven hypotheses from nine. Compatibility, usefulness, ease of use and security has been found to be important predictors toward attitude in on-line shopping.


title: Teaching Children to Become Fluent and Automatic Readers.

abstract: Over the past several years. there has been a renewed focus on what it means to be a fluent reader. as well as on ways in which teachers can aid the transition from deliberate. monotonous reading to tluid and expressive reading (e.g., Kuhn & Stahl, 2003: National Reacling Panel, 2000; Rasinski & Hoffman, 2003). However, although there is general agreement that tluency is an essential component of skilled reading. there continue to be both theoretical and practical questions regarding the ways in which instruction can best be implemented to facilitate fluent reading. This article reports on a large-scale study of two instructional interventions that have been successful in assisting the reading development of second graders from schools with moderate to high levels of poverty. ::: ::: Two major, recent reviews of fluent reading (Kuhn & Stahl, 2003; National Reading Panel, 2000) indicate that fluency-oriented approaches to literacy instruction are effective at increasing students’ accurate and automatic word recognition, assisting with their comprehension, and promoting their use of prosodic features, such as stress. Pitch. and suitable phrasing. These approaches include repeated readings (Dahl. 1979; Samuels, 1979), as well as a range of methods that integrate repetition as part of their practice, such as reading while listening (Chomsky, 1978), cross-aged reading (Labbo & Teale, 1990), and paired repeated reading (Koskinen & Blum, 1984). One key aspect of these approaches is that they combine extensive opportunities to read connected text with the provision of scaffolding. That is. they provide learners with support through either feedback or modeling that emphasizes appropriate decoding, phrasing. and expression. ::: ::: However, when comparing approaches that implement repetition with those based on the scaffolded reading of a more extensive range of texts, Kuhn and Stahl (2003) found little difference in learner achievement. Given this. it is unclear whether the gains in fluency result from the repetition per se or from the scaffolded reading of significant amounts of connected text. To gain a better understanding of this issue. we contrasted two interventions. one based on the scaffolded repetition of a single text and a second based on the supported reading of multiple texts, to determine their effectiveness within the literacy curriculum.


title: Waalbot: An Agile Small-Scale Wall Climbing Robot Utilizing Pressure Sensitive Adhesives

abstract: This paper proposes a small-scale agile wall climbing robot able to navigate on smooth surfaces of any orientation, including vertical and inverted surfaces, which uses adhesive elastomer materials for attachment. Using two actuated legs with rotary motion and two passive revolute joints at each foot the robot can climb and steer in any orientation. Due to its compact design, a high degree of miniaturization is possible. It has onboard power, sensing, computing, and wireless communication which allow for semi-autonomous operation. Various aspects of a functioning prototype design and performance are discussed in detail, including leg and feet design and gait control. The current prototype can climb 90deg slopes at a speed of 6 cm/s and steer to any angle. This robot is intended for inspection and surveillance applications and, ultimately, space missions


title: Revisiting Urban War Nibbling: Mobile Passive Discovery of Classic Bluetooth Devices Using Ubertooth One

abstract: The ubiquitous nature of Bluetooth technology presents opportunities for intelligence gathering based on historical and real-time device presence data. This information can be of value to law enforcement agencies, intelligence organizations, and industry. Despite the introduction of the Bluetooth Low Energy standard that incorporates anonymity preservation mechanisms, the presence of devices that support Classic Bluetooth that uses unique and persistent device identifiers is expected to remain significant for a number of years. The common approach to finding discoverable Classic Bluetooth devices relies on a standard inquiry process that is not truly passive. Furthermore, this approach fails to detect devices that remain undiscoverable. Ubertooth One, a low-cost open source Bluetooth development platform, can assist with overcoming this limitation in a truly passive manner, making it an attractive digital forensic instrument. Using vehicle-based sensors and parallel multi-method device discovery, we conduct a practical evaluation of Ubertooth One for passive discovery and contrast its discovery rate to the standard method. Based on 83 comparative field experiments, we show that Ubertooth One can produce forensically sound observations while able to discover up to ten times as many devices. We also show that this method can identify repeat device presence, as we observe 2370 instances of repeat observations on different days in single and multiple location scenarios. We conclude that this passive technique can complement the standard method and has the potential be used as a viable alternative.


title: AgOnt: Ontology for Agriculture Internet of Things

abstract: Recent advances in networking and sensor technologies allow various physical world objects connected to form the Internet of Things (IOT). As more sensor networks are being deployed in agriculture today, there is a vision of integrating different agriculture IT system into the agriculture IOT. The key challenge of such integration is how to deal with semantic heterogeneity of multiple information resources. The paper proposes an ontology-based approach to describe and extract the semantics of agriculture objects and provides a mechanism for sharing and reusing agriculture knowledge to solve the semantic interoperation problem. AgOnt, ontology for the agriculture IOT, is built from agriculture terminologies and the lifecycles including seeds, grains, transportation, storage and consumption. According to this unified meta-model, heterogeneous agriculture data sources can be integrated and accessed seamlessly.


title: JSAV: the JavaScript algorithm visualization library

abstract: Learning abstract concepts in data structures and algorithms (DSA) courses is often difficult for students. To improve understanding of DSA topics, numerous algorithm visualization (AV) systems and stand-alone AVs have been developed, supporting a wide range of algorithms and different engagement strategies. Prior studies show that active engagement of students is necessary to make AVs educationally effective. In this paper, we introduce JSAV, a new JavaScript framework for creating engaging algorithm visualizations with active learning features. JSAV is meant to be used with HTML5-based online learning materials. We describe the special features of JSAV that support active learning, and discuss its potential for use in online education projects.


title: An The Foreign-Language Effect : Thinking in a Foreign Tongue Reduces Decision Biases

abstract: Would you make the same decisions in a foreign language as you would in your native tongue? It may be intuitive that people would make the same choices regardless of the language they are using, or that the difficulty of using a foreign language would make decisions less systematic. We discovered, however, that the opposite is true: Using a foreign language reduces decision-making biases. Four experiments show that the framing effect disappears when choices are presented in a foreign tongue. Whereas people were risk averse for gains and risk seeking for losses when choices were presented in their native tongue, they were not influenced by this framing manipulation in a foreign language. Two additional experiments show that using a foreign language reduces loss aversion, increasing the acceptance of both hypothetical and real bets with positive expected value. We propose that these effects arise because a foreign language provides greater cognitive and emotional distance than a native tongue does.


title: PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

abstract: In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space res earch community has inspired work in observation scheduling, planetary rover ex ploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating fo rce behind the progress that has been made in planning since 1998. The third com petition (held in 2002) set the planning community the challenge of handling tim e and numeric resources. This necessitated the development of a modelling langua ge capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power --- exceeding the capabilities of current planning technology --- and presents a number of important challenges to the research community.


title: Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition

abstract: Statisticians have made great progress in creating methods that ::: reduce our reliance on parametric assumptions. However, this explosion in ::: research has resulted in a breadth of inferential strategies that both create ::: opportunities for more reliable inference as well as complicate the choices ::: that an applied researcher has to make and defend. Relatedly, researchers advocating ::: for new methods typically compare their method to at best 2 or 3 ::: other causal inference strategies and test using simulations that may or may ::: not be designed to equally tease out flaws in all the competing methods. The ::: causal inference data analysis challenge, “Is Your SATT Where It’s At?”, ::: launched as part of the 2016 Atlantic Causal Inference Conference, sought to ::: make progress with respect to both of these issues. The researchers creating ::: the data testing grounds were distinct from the researchers submitting methods ::: whose efficacy would be evaluated. Results from 30 competitors across ::: the two versions of the competition (black-box algorithms and do-it-yourself ::: analyses) are presented along with post-hoc analyses that reveal information ::: about the characteristics of causal inference strategies and settings that affect ::: performance. The most consistent conclusion was that methods that flexibly ::: model the response surface perform better overall than methods that fail to ::: do so. Finally new methods are proposed that combine features of several of ::: the top-performing submitted methods.


title: Real-time visual tracking of complex structures

abstract: Presents a framework for three-dimensional model-based tracking. Graphical rendering technology is combined with constrained active contour tracking to create a robust wire-frame tracking system. It operates in real time at video frame rate (25 Hz) on standard hardware. It is based on an internal CAD model of the object to be tracked which is rendered using a binary space partition tree to perform hidden line removal. A Lie group formalism is used to cast the motion computation problem into simple geometric terms so that tracking becomes a simple optimization problem solved by means of iterative reweighted least squares. A visual servoing system constructed using this framework is presented together with results showing the accuracy of the tracker. The paper then describes how this tracking system has been extended to provide a general framework for tracking in complex configurations. The adjoint representation of the group is used to transform measurements into common coordinate frames. The constraints are then imposed by means of Lagrange multipliers. Results from a number of experiments performed using this framework are presented and discussed.


title: Automatic 3D industrial point cloud modeling and recognition

abstract: 3D modeling of point clouds is an important but time-consuming process, inspiring extensive research in automatic methods. Prior efforts focus on primitive geometry, street structures or indoor objects, but industrial data has rarely been pursued. Our work presents a method for automatic modeling and recognition of 3D industrial site point clouds, dividing the task into 3 separate sub-problems: pipe modeling, plane classification, and object recognition. The results are integrated to obtain the complete model, revealing some issues during the integration, solved by utilizing information gained from each individual process. Experiments show that the presented method automatically models large and complex industrial scenes with a quality that outperforms leading commercial modeling software and is comparable to professional hand-made models.


title: Building Information Science ontology (OIS) with Methontology and Protégé

abstract: Ontology is the backbone of the semantic web and can overcome semantic barriers. Domain ontology provides a common understanding of the knowledge of a particular domain. Information Science, meanwhile, is an interdisciplinary science that is yet to be defined. It is necessary to develop Ontology of Information Science (OIS) to represent the unified domain knowledge. ::: This paper presents a representation of specific domain knowledge by providing a definition, scope, and boundaries of Information Science (IS). The methodology followed is Methontology, which is based on the IEEE standard for the development of a software life-cycle process. The achieved results are Information Science OIS ontology has fourteen facets: actors, method, practice, studies, mediator, kinds, domains, resources, legislation, philosophy and theories, societal, tools, time and space. It provides broader base of classes to offer opportunity to enrich OIS ontology. Also, it could be a basis for multiple ontologies to be built. OIS is structured at class level and subclass but does not provide the individual level. The model was evaluated by domain experts based on specific criteria and using reasoner FaCT++ to check the ontology usefulness, and how it could be transferred into application ontology for Information Science education. The paper then discusses the OIS ontology, particularly its structure and development.


P2P ARCHITECTURE FOR UBIQUITOUS SUPPLY CHAIN


title: A comprehensive view of Hadoop research—A systematic literature review

abstract: Context: In recent years, the valuable knowledge that can be retrieved from petabyte scale datasets – known as Big Data – led to the development of solutions to process information based on parallel and distributed computing. Lately, Apache Hadoop has attracted strong attention due to its applicability to Big Data processing. Problem: The support of Hadoop by the research community has provided the development of new features to the framework. Recently, the number of publications in journals and conferences about Hadoop has increased consistently, which makes it difficult for researchers to comprehend the full body of research and areas that require further investigation. Solution: We conducted a systematic literature review to assess research contributions to Apache Hadoop. Our objective was to identify gaps, providing motivation for new research, and outline collaborations to Apache Hadoop and its ecosystem, classifying and quantifying the main topics addressed in the literature. Results: Our analysis led to some relevant conclusions: many interesting solutions developed in the studies were never incorporated into the framework; most publications lack sufficient formal documentation of the experiments conducted by authors, hindering their reproducibility; finally, the systematic review presented in this paper demonstrates that Hadoop has evolved into a solid platform to process large datasets, but we were able to spot promising areas and suggest topics for future research within the framework.


title: Automatic system for facial expression recognition based histogram of oriented gradient and normalized cross correlation

abstract: In this paper we built an automatic system for performance reviews of a new approach of facial expression recognition FER which is essentially based on Histogram of Oriented Gradient and Normalized Cross Correlation. The performance was evaluated by varying two parameters: face resolution and colour space of images. Results show that good recognition can be attained using low resolution, in particular 64 by 64 pixels in RGB images.


Multimodal Implicit Feedback for Recommender Systems


title: Critical factors in the performance of novelty search

abstract: Novelty search is a recently proposed method for evolutionary computation designed to avoid the problem of deception, in which the fitness function guides the search process away from global optima. Novelty search replaces fitness-based selection with novelty-based selection, where novelty is measured by comparing an individual's behavior to that of the current population and an archive of past novel individuals. Though there is substantial evidence that novelty search can overcome the problem of deception, the critical factors in its performance remain poorly understood. This paper helps to bridge this gap by analyzing how the behavior function, which maps each genotype to a behavior, affects performance. We propose the notion of descendant fitness probability (DFP), which describes how likely a genotype's descendants are to have a certain fitness, and formulate two hypotheses about when changes to the behavior function will improve novelty search's performance, based on the effect of those changes on behavior and DFP. Experiments in both artificial and deceptive maze domains provide substantial empirical support for these hypotheses.


title: On mouse dynamics as a behavioral biometric for authentication

abstract: The idea of using one's behavior with a pointing device, such as a mouse or a touchpad, as a behavioral biometric for authentication purposes has gained increasing attention over the past decade. A number of interesting approaches based on the idea have emerged in the literature and promising experimental results have been reported; however, we argue that limitations in the past experimental evaluations of these approaches raise questions about their true effectiveness in a practical setting. In this paper, we review existing authentication approaches based on mouse dynamics and shed light on some important limitations regarding how the effectiveness of these approaches has been evaluated in the past. We present the results of several experiments that we conducted to illustrate our observations and suggest guidelines for evaluating future authentication approaches based on mouse dynamics. We also discuss a number of avenues for additional research that we believe are necessary to advance the state of the art in this area.


title: Differential Effects of Prenatal and Postnatal Nutritional Environment on β-Cell Mass Development and Turnover in Male and Female Rats

abstract: Fetal nutrient and growth restriction is associated with development of type 2 diabetes. Although the exact mechanisms responsible for this association remain debated, intrauterine and/or postnatal maldevelopment of β-cell mass has been proposed as a potential mechanism. To address this hypothesis, β-cell mass development and turnover was assessed in rats exposed to either intrauterine and/or postnatal caloric/growth restriction. In total, four groups of male and female Sprague Dawley rats (n = 69) were developed and studied: 1) control rats, i.e. control mothers rearing control pups; 2) intrauterine calorically and growth-restricted rats, i.e. 50% prenatal calorically restricted pups cross-fostered to control mothers; 3) postnatal calorically and growth-restricted rats, i.e. 50% calorically restricted mothers rearing pups born to control mothers; and 4) prenatal and postnatal calorically and growth restricted rats, i.e. 50% calorically restricted mothers rearing intrauterine 50% calorically restricted pu...


title: Agent-organized networks for dynamic team formation

abstract: Many multi-agent systems consist of a complex network of autonomous yet interdependent agents. Examples of such networked multi-agent systems include supply chains and sensor networks. In these systems, agents have a select set of other agents with whom they interact based on environmental knowledge, cognitive capabilities, resource limitations, and communications constraints. Previous findings have demonstrated that the structure of the artificial social network governing the agent interactions is strongly correlated with organizational performance. As multi-agent systems are typically embedded in dynamic environments, we wish to develop distributed, on-line network adaptation mechanisms for discovering effective network structures. Therefore, within the context of dynamic team formation, we propose several strategies for agent-organized networks (AONs) and evaluate their effectiveness for increasing organizational performance.


title: Emotionally Focused Couples Therapy: Status and Challenges

abstract: This article presents the basis for, and the research on, emotionally focused couples therapy (EFT), now recognized as one of the most researched and most effective approaches to changing distressed marital relationships. Drawing on attachment theory and the research on interactional patterns in distressed relationships, we describe the theoretical context of EFT. We then outline the nature of the clinical interventions used in EFT and the steps hypothesized to be crucial to couple change. The central role of accessing and working with emotional issues in the relationship context is highlighted. Following this presentation, we review both the outcome and process research on EFT and present meta-analytic data from randomized clinical trials to substantiate the clinical impact of EFT on couple adjustment. Finally, the empirical and clinical challenges facing EFT are summarized.


Deep Learning Architecture for Univariate Time Series Forecasting


title: Skill Acquisition and the LISP Tutor

abstract: An analysis of student learning with the LISP tutor indicates that while LISP is complex, learning it is simple. The key to factoring out the complexity of LISP is to monitor the learning of the 500 productions in the LISP tutor which describe the programming skill. The learning of these productions follows the power-law learning curve typical of skill acquisition. There is transfer from other programming experience to the extent that this programming experience involves the same productions. Subjects appear to differ only on the general dimensions of how well they acquire the productions and how well they retain the productions. Instructional manipulations such as remediation, content of feedback, and timing of feedback are effective to the extent they give students more practice programming, and explain to students why correct solutions work.


title: Dual Discriminator Generative Adversarial Nets

abstract: We propose in this paper a novel approach to tackle the problem of mode collapse encountered in generative adversarial network (GAN). Our idea is intuitive but proven to be very effective, especially in addressing some key limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and reverse KL divergences into a unified objective function, thus it exploits the complementary statistical properties from these divergences to effectively diversify the estimated density in capturing multi-modes. We term our method dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has two discriminators; and together with a generator, it also has the analogy of a minimax game, wherein a discriminator rewards high scores for samples from data distribution whilst another discriminator, conversely, favoring data from the generator, and the generator produces data to fool both two discriminators. We develop theoretical analysis to show that, given the maximal discriminators, optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL divergences between data distribution and the distribution induced from the data generated by the generator, hence effectively avoiding the mode collapsing problem. We conduct extensive experiments on synthetic and real-world large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made our best effort to compare our D2GAN with the latest state-of-the-art GAN's variants in comprehensive qualitative and quantitative evaluations. The experimental results demonstrate the competitive and superior performance of our approach in generating good quality and diverse samples over baselines, and the capability of our method to scale up to ImageNet database.


title: Effects of three tapering techniques on the performance, forces and psychometric measures of competitive swimmers

abstract: The 100-m and 400-m swim time, tethered swimming forces, mood states and self-ratings of well-being of 27 competitive swimmers were measured before and after 4 weeks of intense training and after 1 week and 2 weeks of tapering for major competition. The swimmers were divided into three groups. Each group completed one of three taper regimes similar to those currently performed by swimmers in preparation for competition: (a) reduced training frequency according to each athlete's daily ratings of well-being, (b) reduced training volume, and (c) reduced training volume and intensity. Significant improvements in the Profile of Mood States measures of tension, depression and anger (P < 0.05) were observed after 1 week of tapering, with significant improvements in total mood disturbance and fatigue (P < 0.05) and peak tethered swimming force (P < 0.01) after 2 weeks. Non-significant improvements in 100-m and 400-m swim time (P > 0.05) were observed and no significant differences were revealed among the three tapering techniques. These data highlighted the importance of providing sufficient recovery before competition, since 1 week of reduced training was not long enough to maximise the benefits of tapering. However, none of the three types of tapering currently used by competitive swimmers could be shown to be more beneficial than the others.


title: Real Time Video Scene Detection and Classification

abstract: The VISION (video indexing for searching over networks) digital video library system has been developed in our laboratory as a testbed for evaluating automatic and comprehensive mechanisms for video archive creation and content-based search, filtering and retrieval of video over local and wide area networks. In order to provide access to video footage within seconds of broadcast, we have developed a new pipelined digital video processing architecture which is capable of digitizing, processing, indexing and compressing video in real time on an inexpensive general purpose computer. These videos were automatically partitioned into short scenes using video, audio and closed-caption information. The resulting scenes are indexed based on their captions and stored in a multimedia database. A client-server-based graphical user interface was developed to enable users to remotely search this archive and view selected video segments over networks of different bandwidths. Additionally, VISION classifies the incoming videos with respect to a taxonomy of categories and will selectively send users videos which match their individual profiles.


An intelligent real time design methodology for component selection: an approach to managing uncertainty


title: Nonintrusive Load Monitoring and Diagnostics in Power Systems

abstract: This paper describes a transient event classification scheme, system identification techniques, and implementation for use in nonintrusive load monitoring. Together, these techniques form a system that can determine the operating schedule and find parameters of physical models of loads that are connected to an AC or DC power distribution system. The monitoring system requires only off-the-shelf hardware and recognizes individual transients by disaggregating the signal from a minimal number of sensors that are installed at a central location in the distribution system. Implementation details and field tests for AC and DC systems are presented.


title: Why marketplace experimentation is harder than it seems: the role of test-control interference

abstract: Classical statistical inference of experimental data assumes that the treatment affects the test group but not the control group. This assumption will typically be violated when experimenting in marketplaces because of general equilibrium effects: changing test demand affects the supply available to the control group. We illustrate this with an email marketing campaign performed by eBay. Ignoring test-control interference leads to estimates of the campaign's effectiveness which are too large by a factor of around two. We present the simple economics of this bias in a supply and demand framework, showing that the bias is larger in magnitude where there is more inelastic supply, and is positive if demand is elastic.


title: Assessing Implicit Computational Thinking in Zoombinis Gameplay: Pizza Pass, Fleens & Bubblewonder Abyss

abstract: Players can build implicit understanding of challenging scientific concepts when playing digital science learning games [1]. In this study, we examine implicit computational thinking (CT) skills among upper elementary and middle school students during Zoombinis gameplay. We report on the development of a human labeling system for gameplay evidence of four CT skills: problem decomposition, pattern recognition, algorithmic thinking, and abstraction. We define labels that identify use of these skills in three Zoombinis puzzles, based on analysis of video data from both CT novices (upper elementary and middle school students) and CT experts (computer scientists and expert Zoombinis players). Future work will involve the construction of detectors for implicit CT skills based on these human labels, in order to analyze gamelog data at scale and give feedback to teachers.


title: Radar Site Studies with Automated Propagation Effect Calculations Using QSiteAnalysis

abstract: Planning radar sites is very important for several civilian and military applications. Depending on the security or defence issue different requirements exist regarding the radar coverage and the radar sites. QSiteAnalysis offers several functions to automate, improve and speed up this highly complex task. Wave propagation effects such as diffraction, refraction, multipath and atmospheric attenuation are considered for the radar coverage calculation. Furthermore, an automatic optimisation of the overall coverage is implemented by optimising the radar sites. To display the calculation result, the calculated coverage is visualised in 2D and 3D. Therefore, QSiteAnalysis offers several functions to improve and automate radar site studies.


title: Data mining based crime investigation systems: Taxonomy and relevance

abstract: The number of crime incidents that is reported per day in India is increasing dramatically. The criminals today use various advanced technologies and commit crimes in really tactful ways. This makes crime investigation a more complicated process. Thus the police officers have to perform a lot of manual tasks to get a thread for investigation. This paper deals with the study of data mining based systems for analyzing crime information and thus automates the crime investigation procedure of the police officers. The majority of these frameworks utilize a blend of data mining methods such as clustering and classification for the effective investigation of the criminal acts.


title: A Unified Approach to Deterministic Encryption: New Constructions and a Connection to Computational Entropy

abstract: This paper addresses deterministic public-key encryption schemes (DE), which are designed to provide meaningful security when only source of randomness in the encryption process comes from the message itself. We propose a general construction of DE that unifies prior work and gives novel schemes. Specifically, its instantiations include: The first construction from any trapdoor function that has sufficiently many hardcore bits.The first construction that provides "bounded" multi-message security (assuming lossy trapdoor functions). The security proofs for these schemes are enabled by three tools that are of broader interest: A weaker and more precise sufficient condition for semantic security on a high-entropy message distribution. Namely, we show that to establish semantic security on a distribution M of messages, it suffices to establish indistinguishability for all conditional distribution M|E, where E is an event of probability at least 1/4. (Prior work required indistinguishability on all distributions of a given entropy.)A result about computational entropy of conditional distributions. Namely, we show that conditioning on an event E of probability p reduces the quality of computational entropy by a factor of p and its quantity by log21/p.A generalization of leftover hash lemma to correlated distributions. We also extend our result about computational entropy to the average case, which is useful in reasoning about leakage-resilient cryptography: leaking ? bits of information reduces the quality of computational entropy by a factor of 2? and its quantity by ?.


title: Over-current relay model implementation for real time simulation & Hardware-in-the-Loop (HIL) validation

abstract: Digital microprocessor based relays are currently being utilized for safe, reliable and efficient operation of power systems. The overcurrent protection relay is the most exten sively used component to safeguard power systems from the detrimental effects of faults. Wrong settings in overcurrent relay parameters can lead to false tripping or even bypassing fault conditions which can lead to a Therefore it is important to validate the settings of power protection equipment and to confirm its performance when subject to different fault conditions. This paper presents the modeling of an overcurrent relay in SimPowerSystems (MATLAB/Simulink ). The overcurrent relay has the features of instantaneous, time definite and inverse definite minimum time (IDMT) characteristics. A power system is modeled in SimPowerSystems and this overcurrent relay model is incorporated in the test case. The overall model is then simulated in real-time using Opal-RT's eMEGAsim real-time simulator to analyze the relay's performance when subjected to faults and with different characteristic settings in the relay model. Finally Hardware-in-the- Loop validation of the model is done by using the overcurrent protection feature in Schweitzer Engineering Laboratories Relay SEL-487E. The event reports generated by the SEL relays during Hardware-in the-Loop testing are compared with the results obtained from the standalone testing and software model to validate the model. algorithms for adaptive protection, designing system integrity protection schemes (SIPS), remedial action schemes (RAS) and other applications (2). In this paper, a detailed model for overcurrent relays is provided. To assess the performance of this model a test system is designed in SPS and is simulated in RT using Opal RT's eMEGAsim real-time simulator. Once the SIL model is certified, it is further validated by HIL simulation using the overcurrent protection function of Schweitzer Engineering Laboratories (SEL) relay SEL-487E. The results obtained by the SIT.., and the HIL are compared. SPS does not have a dedicated library for protection functions. Therefore it is necessary to validate the model with HIL so that the validated software model can be used for accurate representation of power system protection components. Similar approach for protection relay model validation has been demonstrated in (3) ( 4) using different modeling platform and RTS. The remainder of the paper is arranged as follows. Section II offers a literature review on overcurrent relays. Section III presents the details of the overcurrent relay modelled in SPS. Section IV focuses on the design of test system in SPS and incorporation of proposed relay model in the test case. SIL simulation and results are discussed in Section V, while standalone testing and HIL validation is explained in Section VI. Finally in Section VII , conclusions are drawn and future work is outlined.


title: An innovative embedded interposer carrier for high density interconnection

abstract: It is well known that 3DIC integration is the next generation semiconductor technology with the advantages of small form factor, high performance and low power consumption. However the device TSV process and design rules are not mature. Assembly the chips on top of the Si interposer is the current most desirable method to achieve the requirement of good performance. In this study, a new packaging concept, the Embedded Interposer Carrier (EIC) technology was developed. It aims to solve some of the problems facing current interposer assemble issues. It eliminates the joining process of silicon interposer to the laminate carrier substrate. The concept of EIC is to embed one or multiple interposer chips into the build-up dielectric layers in the laminated substrate. The process development of EIC structure is investigated in this paper. EIC technology not only can shrink an electronic package and system size but also provide a better electronic performance for high-bandwidth applications. EIC technology can be one of the potential solutions for 3D System-in-Package.


STUDIES IN LOGIC , GRAMMAR AND RHETORIC 5 ( 18 ) 2002


title: Annotation for free: video tagging by mining user search behavior

abstract: The problem of tagging is mostly considered from the perspectives of machine learning and data-driven philosophy. A fundamental issue that underlies the success of these approaches is the visual similarity, ranging from the nearest neighbor search to manifold learning, to identify similar instances of an example for tag completion. The need to searching for millions of visual examples in high-dimensional feature space, however, makes the task computationally expensive. Moreover, the results can suffer from robustness problem, when the underlying data, such as online videos, are rich of semantics and the similarity is difficult to be learnt from low-level features. This paper studies the exploration of user searching behavior through click-through data, which is largely available and freely accessible by search engines, for learning video relationship and applying the relationship for economic way of annotating online videos. We demonstrated that, by a simple approach using co-click statistics, promising results were obtained in contrast to feature-based similarity measurement. Furthermore, considering the long tail effect that few videos dominate most clicks, a new method based on~polynomial~semantic indexing is proposed to learn a latent space~for alleviating the sparsity problem of click-through data. The proposed approaches are then applied for three major tasks in tagging: tag assignment, ranking, and enrichment. On~a bipartite graph constructed from click-through data with~over 15 million queries and 20 million video URL clicks,~we showed that annotation can be performed for free with competitive performance and minimum computing resource, representing a new and promising paradigm for video tagging in addition to machine learning and data-driven methodologies.


title: Active Learning Explored in Open Elective Course: Internet of Things (IoT)

abstract: The first challenge in offering this course is identifying the content of the course, at what level the course needs to be introduced, since both hardware and software components are involved in IoT. Second challenge is involving students in learning process as it is not only theory based and different knowledge level students are part of this course. To address these challenges we followed activity based approach for teaching and learning this course. Various real-world problems are identified, and assigned to different teams consisting of four students. Outcome based evaluation is followed to assess the group activity. The author's observations indicate that the activity based learning addresses graduate attributes of UG program, such as Design/Development of solutions, Modern tool usage, Engineer and society and Life-long learning. The average attainment of the mentioned graduate attributes is 79.18% for the course in continuous internal assessment.


title: Calibrated Data Simplification for Energy-Efficient Location Sensing in Internet of Things

abstract: The Internet of Things (IoT) has gradually changed the way of people’s lives due to its ability of connecting everything together, and meanwhile the accurate location sensing plays a crucial role in achieving this goal. Up to now, as one of the most representative outdoor localization systems, the global positioning system has been widely used, but its performance may be dramatically declined in indoor environment due to the serious multipath effect and signal attenuation caused by the complicated indoor structure. At the same time, the location fingerprint-based localization approach has become a popular one in indoor environment, and meanwhile the corresponding calibrated signal simplification in location database construction has been primarily considered due to its significant practical meaning in avoiding the blind signal sampling. In this paper, we propose to use an information-theoretic lens to construct the energy-efficient location fingerprint database for the localization in IoT. Interestingly, by analyzing the information loss in signal sampling, we analogize the database construction process into the information propagation process in a lossy channel, and then formulate the relations of sample capacity and localization error from an information-theoretic view. After that, by selecting an appropriate time interval to sample the independent and nonredundant signal, the minimum number of sampled signal under the given expected localization accuracy is determined. Finally, the extensive experimental results show that compared with the state-of-the-art approaches, the proposed one can effectively simplify the calibrated data for the energy-efficient location database construction in different wireless localization networks.


title: A 56-Gb/s Wireline Transceiver in 20nm CMOS

abstract: A feasibility of a 56-Gb/s CMOS transceiver is discussed. Simulations showed that the CMOS- inverter-based transmitter can generate 56-Gb/s signals with an inductive peaking. A receiver front- end suited for baud-rate clock recovery is demonstrated in 20-nm CMOS. Sharing the comparators for the data decision and phase detection minimizes the number of comparators in the front-end and reduces the power consumption. The front-end has a continuous-time linear equalizer followed by a 1-tap speculative decision-feedback equalizer. The front- end operates at 56Gb/s with a bit error rate of less than 10-12 with a 0.4UI margin in the bathtub curve. It occupies 0.27mm2 and consumes 177mW of power from a 0.9-V supply.


title: Quantifying training intensity distribution in elite endurance athletes: is there evidence for an "optimal" distribution?

abstract: This study was designed to quantify the daily distribution of training intensity in a group of well-trained junior cross-country skiers and compare the results of three different methods of training intensity quantification. Eleven male athletes performed treadmill tests to exhaustion to determine heart rate and VO2 corresponding to ventilatory thresholds (VT1, VT2), maximal oxygen consumption (VO2max), and maximal heart rate. VT1 and VT2 were used to delineate three intensity zones. During the same time period, all training sessions (N=384, 37 strength training, 347 endurance) performed over 32 consecutive days were quantified using continuous heart rate registration and session Rating of Perceived Exertion (RPE). In addition, a subset of 60 consecutive training sessions was quantified using blood lactate measurements. Intensity distribution across endurance training sessions (n=318) was similar when based on heart rate analysis (75+/-3%, zone 1; 8+/-3%, zone 2; 17+/-4%, zone 3) or session RPE (76+/-4%, zone 1; 6+/-5%, zone 2; 18+/-7%, zone 3). Similarly, from measurements of 60 consecutive sessions, 71% were performed with <or=2.0 mM blood lactate, 7% between 2 and 4 mM, and 22% with over 4 mM (mean=9.5+/-2.8 mM). In this group of nationally competitive junior skiers, training was organized after a polarized pattern, with most sessions performed clearly below (about 75%) or with substantial periods above (15-20%) the lactate accommodation zone, which is bounded by VT1 and VT2. The pattern quantified here is similar to that reported in observational studies of elite endurance athletes across several sports. It appears that elite endurance athletes train surprisingly little at the lactate threshold intensity.


title: Predicting Cognitive Control From Preschool to Late Adolescence and Young

abstract: ABSTRACT— In this longitudinal study, the proportion oftime preschoolers directed their attention away from re-warding stimuli during a delay-of-gratiﬁcation task waspositivelyassociatedwithefﬁciency(greaterspeedwithoutreduced accuracy) at responding to targets in a go/no-gotaskmorethan10yearslater.Theoverallﬁndingssuggestthat preschoolers’ ability to effectively direct their atten-tionawayfromtemptingaspectsoftherewardsinadelay-of-gratiﬁcationtaskmaybeadevelopmentalprecursorfortheabilitytoperform inhibitorytaskssuchasthego/no-gotaskyearslater.Becauseperformanceonthego/no-gotaskhas previously been characterized as involving activationoffronto-striatal regions, thepresentﬁndingsalsosuggestthat performance in the delay-of-gratiﬁcation task mayserve as an early marker of individual differences in thefunctional integrity of this circuitry.Cognitive control is the foundation of the ability to guide andcontrolbehaviorandoptimizeoutcomes(BraverCMiller & Cohen, 2001). When individuals perform tasks in theservice of a desired goal, cognitive control enables them tosuppressattentionandresponsestoirrelevantinformation,evenwhenthatinformationishighlysalient(Allport,1987).Althoughindividualdifferencesincognitivecontrolseemtobeevidentasearly as 18 months of age (Rothbart, Derryberry, & Posner,1994),thedegreetowhichthesedifferencesinearlylifepredictcognitivecontrollaterinlifehasnotbeenassessed.Thepresentlongitudinal investigation examined this ability with the delay-of-gratiﬁcation task in preschoolers and the go/no-go paradigmwhen they became adolescents and young adults. Both tasksrequire the individual to effectively control attentional andbehavioral responses to salient information, a hallmark of cog-nitive control and cognitive development (Casey, Durston, F Durston, Thomas, Yang, et al., 2002). Moreover,


title: EEG delta oscillations as a correlate of basic homeostatic and motivational processes

abstract: Abstract Functional significance of delta oscillations is not fully understood. One way to approach this question would be from an evolutionary perspective. Delta oscillations dominate the EEG of waking reptiles. In humans, they are prominent only in early developmental stages and during slow-wave sleep. Increase of delta power has been documented in a wide array of developmental disorders and pathological conditions. Considerable evidence on the association between delta waves and autonomic and metabolic processes hints that they may be involved in integration of cerebral activity with homeostatic processes. Much evidence suggests the involvement of delta oscillations in motivation. They increase during hunger, sexual arousal, and in substance users. They also increase during panic attacks and sustained pain. In cognitive domain, they are implicated in attention, salience detection, and subliminal perception. This evidence shows that delta oscillations are associated with evolutionary old basic processes, which in waking adults are overshadowed by more advanced processes associated with higher frequency oscillations. The former processes rise in activity, however, when the latter are dysfunctional.


title: Mechanisms of memory stabilization: are consolidation and reconsolidation similar or distinct processes?

abstract: Consolidation of new memories depends on a crucial phase of protein synthesis. It is widely held that, once consolidated, memories are stable and resilient to disruption. However, established memories become labile when recalled and require another phase of protein synthesis to be maintained. Therefore, it has been proposed that when a memory is reactivated it must undergo additional consolidation (reconsolidation) to persist. To determine whether reconsolidation recapitulates consolidation, in the past few years several groups have investigated whether the same molecules and pathways mediate the formation of a memory and its maintenance after reactivation. At first glance, the results appear conflicting: although both processes appear to engage the same molecules and mechanisms, brain areas involved in consolidation after initial training are not required for reconsolidation. In addition, the formation of a memory and its maintenance after reactivation seem to have distinctive temporal molecular requirements. This review concludes with a working model that could explain the apparent controversy of memory vulnerability after reactivation.


title: Learning to Remember Translation History with a Continuous Cache

abstract: Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory network, which stores recent hidden representations as translation history. The probability distribution over generated words is updated online depending on the translation history retrieved from the memory, endowing NMT models with the capability to dynamically adapt over time. Experiments on multiple domains with different topics and styles show the effectiveness of the proposed approach with negligible impact on the computational cost.


title: Knowledge exchange using Web 2.0 technologies in NGOs

abstract: Purpose – Members' knowledge is the most important resource in NGOs and it is important to stimulate its exchange. Knowledge that is needed to act in complex situations is hard to exchange, but Web 2.0 technologies provide a toolbox to develop a knowledge management strategy. The paper aims to specify optimal conditions and necessary measures that NGOs should take in order to successfully use Web 2.0 technologies for knowledge exchange.Design/methodology/approach – NGOs and Web 2.0 technologies have specific characteristics in common. It is suggested that these characteristics require certain prerequisites in order to make the use of Web 2.0 technologies successful. Using an iterative and participative design method, a case study is presented that investigated prior boundary conditions and implemented a Web 2.0‐based platform as knowledge management strategy in a large NGO.Findings – NGOs and Web 2.0 are based on strong voluntariness, they are democratic, contribution and personality are strongly tied to ...


title: MIMO radar theory and experimental results

abstract: The continuing progress of Moore's law has enabled the development of radar systems that simultaneously transmit and receive multiple coded waveforms from multiple phase centers and to process them in ways that have been unavailable in the past. The signals available for processing from these multiple-input multiple-output (MIMO) radar systems appear as spatial samples corresponding to the convolution of the transmit and receive aperture phase centers. The samples provide the ability to excite and measure the channel that consists of the transmit/receive propagation paths, the target and incidental scattering or clutter. These signals may be processed and combined to form an adaptive coherent transmit beam, or to search an extended area with high resolution in a single dwell. Adaptively combining the received data provides the effect of adaptively controlling the transmit beamshape and the spatial extent provides improved track-while-scan accuracy. This paper describes the theory behind the improved surveillance radar performance and illustrates this with measurements from experimental MIMO radars.


title: Software Project Risk in the Public Sector

abstract: Controlling risk in software projects is a major contributor to successful outcomes, in both the private and public sectors. Few studies investigate risk management in the public sector. This paper reports a study of risk practices in government agencies in an Australian State. It finds that risk management practices lag behind project management practices and that there is scope for improvement in both. Ten major risk factors are identified. Also, some conventional conceptions of project and risk management are challenged. The paper concludes by discussing implications of the findings for research and practice in the public sector.


title: Application discoverability and user satisfaction in mobile application stores: An environmental psychology perspective

abstract: In order to appeal to consumers, mobile application stores face the challenge of finding ways to achieve the seemingly conflicting goals of providing a large quantity of applications and simultaneously making the specific applications that users desire easy to discover. Drawing on environmental psychology, this paper reports a study investigating how quantity-related facilitators and environment-related facilitators are related to application discoverability, which in turn impacts user satisfaction with the application store. We find that quantity-related facilitators (perceived quantity-sufficiency, perceived quantity-overload, and information specificity of search) and environment-related facilitators (application store coherence and user-generated reviews) all influence application discoverability, which affects user satisfaction. These facilitators play a role in managing the conflicting goals in mobile application stores.


title: Service quality, customer satisfaction, and customer value: A holistic perspective

abstract: Abstract The author proposes and tests an integrative model of service quality, customer value, and customer satisfaction. Using a sample from the luxury segment of the hotel industry, this study provides preliminary results supporting a holistic approach to hospitality customers’ postpurchase decision-making process. The model appears to possess practical validity as well as explanatory ability. Implications are discussed and suggestions are developed for both marketers and researchers.


title: A 233-MHz 80%-87% efficient four-phase DC-DC converter utilizing air-core inductors on package

abstract: We demonstrate an integrated buck dc-dc converter for multi-V CC microprocessors. At nominal conditions, the converter produces a 0.9-V output from a 1.2-V input. The circuit was implemented in a 90-nm CMOS technology. By operating at high switching frequency of 100 to 317 MHz with four-phase topology and fast hysteretic control, we reduced inductor and capacitor sizes by three orders of magnitude compared to previously published dc-dc converters. This eliminated the need for the inductor magnetic core and enabled integration of the output decoupling capacitor on-chip. The converter achieves 80%-87% efficiency and 10% peak-to-peak output noise for a 0.3-A output current and 2.5-nF decoupling capacitance. A forward body bias of 500 mV applied to PMOS transistors in the bridge improves efficiency by 0.5%-1%.


title: A review - different scheduling algorithms in cloud computing environment

abstract: A very efficient computing environment is provided by cloud computing where the customers or several tenants are in need of multiple resources to be provided as a service over the internet. The utilization of resources is to be scheduled efficiently so that it helps in reducing the time for task completion. This is task scheduling which is most essential and important part in cloud computing environment. In task scheduling allocation of certain tasks to particular resources at a particular time instance is done. There are different techniques that are proposed to solve the problems of task scheduling. This paper discusses about the study of various resource scheduling algorithms in a cloud computing environment.


title: Examining perceptions of academic stress and its sources among university students: The Perception of Academic Stress Scale

abstract: The development of a scale to measure perceived sources of academic stress among university students. Based on empirical evidence and recent literature review, we developed an 18-item scale to measure perceptions of academic stress and its sources. Experts (n = 12) participated in the content validation process of the instrument before it was administered to (n = 100) students. The developed instrument has internal consistency reliability of 0.7 (Cronbach’s alpha), there was evidence for content validity, and factor analysis resulted in four correlated and theoretically meaningful factors. We developed and tested a scale to measure academic stress and its sources. This scale takes 5 minutes to complete.


title: Ant-Based Clustering and Topographic Mapping

abstract: Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real data collections. Our results demonstrate the ability of ant-based clustering and sorting to automatically identify the number of clusters inherent in a data collection, and to produce high quality solutions; indeed, we show that it is particularly robust for clusters of differing sizes and for overlapping clusters. The results obtained for topographic mapping are, however, disappointing. We provide evidence that the solutions generated by the ant algorithm are barely topology-preserving, and we explain in detail why results have—in spite of this—been misinterpreted (much more positively) in previous research.


title: ANALYSIS OF THE ORIENTATION OF TEACHERS’ PROJECTS: A CASE STUDY OF WUHAN CITY, CHINA

abstract: It is common for teachers to do research, and the orientation of their research determines whether they can enhance teachers’ development. This research uses the content-analyzing software Rost Content Mining and the database Qingbo Data, to analyze the projects that teachers in city W apply for. The results of this research were as follows: teachers’ researches focus on concrete strategies, the topics that they selected were from hot points, and the methods that they used were experience summarizing, Teachers’ researches are nearly the same between areas, and are different between educational stages. In order to improve the orientation system, the following suggestions were recommended as follows: (i) providing enough guidance for teachers to orientate their research, so as to improve teachers’ ability of self-decision in their research; (ii) giving teachers more training to improve their theory, so as to change their focus on experience; (iii) promoting teachers’ attention to students development rather than technology; (iv) enhancing teachers’ consciousness of developing local resource and making their research based on their local resource. Article visualizations:


title: Computation of configuration-space obstacles using the fast Fourier transform

abstract: A method is proposed for computing the configuration-space map of obstacles. The map is used in motion-planning algorithms. The method derives from the observation that, when the robot is a rigid object that can only translate, the configuration space is a convolution of the workspace and the robot. It makes use of the fast Fourier transform (FFT) algorithm to compute this convolution. The method is particularly promising for workspaces with many and/or complicated obstacles, or when the shape of the robot is not simple. It is an inherently parallel method, and it can significantly benefit from existing experience and hardware on the FFT.<<ETX>>


title: Cognitive and system factors contributing to diagnostic errors in radiology.

abstract: OBJECTIVE ::: In this article, we describe some of the cognitive and system-based sources of detection and interpretation errors in diagnostic radiology and discuss potential approaches to help reduce misdiagnoses. ::: ::: ::: CONCLUSION ::: Every radiologist worries about missing a diagnosis or giving a false-positive reading. The retrospective error rate among radiologic examinations is approximately 30%, with real-time errors in daily radiology practice averaging 3-5%. Nearly 75% of all medical malpractice claims against radiologists are related to diagnostic errors. As medical reimbursement trends downward, radiologists attempt to compensate by undertaking additional responsibilities to increase productivity. The increased workload, rising quality expectations, cognitive biases, and poor system factors all contribute to diagnostic errors in radiology. Diagnostic errors are underrecognized and underappreciated in radiology practice. This is due to the inability to obtain reliable national estimates of the impact, the difficulty in evaluating effectiveness of potential interventions, and the poor response to systemwide solutions. Most of our clinical work is executed through type 1 processes to minimize cost, anxiety, and delay; however, type 1 processes are also vulnerable to errors. Instead of trying to completely eliminate cognitive shortcuts that serve us well most of the time, becoming aware of common biases and using metacognitive strategies to mitigate the effects have the potential to create sustainable improvement in diagnostic errors.


title: Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore

abstract: The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of personal transportation in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.


title: Approximate String Matching with Gaps

abstract: In this paper we consider several new versions of approximate string matching with gaps. The main characteristic of these new versions is the existence of gaps in the matching of a given pattern in a text. Algorithms are devised for each version and their time and space complexities are stated. These specific versions of approximate string matching have various applications in computerized music analysis.


title: Efficient WFST-Based One-Pass Decoding With On-The-Fly Hypothesis Rescoring in Extremely Large Vocabulary Continuous Speech Recognition

abstract: This paper proposes a novel one-pass search algorithm with on-the-fly composition of weighted finite-state transducers (WFSTs) for large-vocabulary continuous-speech recognition. In the standard search method with on-the-fly composition, two or more WFSTs are composed during decoding, and a Viterbi search is performed based on the composed search space. With this new method, a Viterbi search is performed based on the first of the two WFSTs. The second WFST is only used to rescore the hypotheses generated during the search. Since this rescoring is very efficient, the total amount of computation required by the new method is almost the same as when using only the first WFST. In a 65k-word vocabulary spontaneous lecture speech transcription task, our proposed method significantly outperformed the standard search method. Furthermore, our method was faster than decoding with a single fully composed and optimized WFST, where our method used only 38% of the memory required for decoding with the single WFST. Finally, we have achieved high-accuracy one-pass real-time speech recognition with an extremely large vocabulary of 1.8 million words


title: Exploring feedback strategies to improve public speaking: an interactive virtual audience framework

abstract: Good public speaking skills convey strong and effective communication, which is critical in many professions and used in everyday life. The ability to speak publicly requires a lot of training and practice. Recent technological developments enable new approaches for public speaking training that allow users to practice in a safe and engaging environment. We explore feedback strategies for public speaking training that are based on an interactive virtual audience paradigm. We investigate three study conditions: (1) a non-interactive virtual audience (control condition), (2) direct visual feedback, and (3) nonverbal feedback from an interactive virtual audience. We perform a threefold evaluation based on self-assessment questionnaires, expert assessments, and two objectively annotated measures of eye-contact and avoidance of pause fillers. Our experiments show that the interactive virtual audience brings together the best of both worlds: increased engagement and challenge as well as improved public speaking skills as judged by experts.


title: Pencil code: block code for a text world

abstract: Pencil Code is a block-based coding tool that helps beginners work with text-based web programming languages. It has been used to allow help first-time programmers of all ages create programs in JavaScript and CoffeeScript. Pencil Code allows students to toggle between text code and blocks freely. This approach allows students to transition smoothly from blocks to text as they become familiar with syntax. It also allows educators to create block vocabularies for specific lessons without working with an entirely new programming language.


title: Tracking the student's performance in Web-based education using Scrum methodology

abstract: The Internet, communication and mobile technologies are emerging as predominant paradigm in the learning of students. Various technologies have created numerous opportunities for the learners to explore in the learning landscape. Even though traditional teaching and learning are still popular in India, the scope for Web based education is positive. It is commonly believed that the Web based education is poised to penetrate deep into the educational system in India in near future. While global web-based education system faces different subjects, ideas and different kind of talented student's, the biggest challenge is communication and tracking the performance of student's. To deal with this challenge, we can use scrum. Scrum is the essence and one of the powerful agile methodologies. This paper presents about scrum and scrum backlog.


title: Information transfer rate in a five-classes brain-computer interface

abstract: The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain-computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the EEG patterns is based on band power estimates and hidden Markov models. We propose a method that combines the EEG patterns based on separability into subsets of two, three, four, and five mental tasks. The information transfer rates of the BCI systems comprised of these subsets are reported. The achieved information transfer rates vary from 0.42 to 0.81 bits per trial and reveal that the upper limit of different mental tasks for a BCI system is three. In each subject, different combinations of three tasks resulted in the best performance.


title: Extracting Named Entities Using Named Entity Recognizer and Generating Topics Using Latent Dirichlet Allocation Algorithm for Arabic News Articles

abstract: US National Science Foundation grants DUE-1141209 and IIS-1319578. ::: Grant # 4-029-1-007 from the Qatar National Research Fund (a member of Qatar Foundation).


title: A survey of MAC based QoS implementations for WiMAX networks

abstract: We present a comprehensive survey of proposed Quality of Service (QoS) mechanisms in the Media Access Control (MAC) sublayer of WiMAX based wireless networks. QoS support in WiMAX is a fundamental design requirement, and is considerably more difficult than in wired networks, mainly because of the variable and unpredictable characteristics of wireless links. We discuss various QoS architectures, signaling mechanisms and admission control techniques proposed in the WiMAX research literature, summarizing the operation of each, and providing comparative evaluations that include advantages and limitations.


title: Chinese resume information extraction based on semi-structured text

abstract: A Chinese resume information extraction system (CRIES) based on semi-structured text is designed and implemented to obtain formatted information by extracting text content of every field from resumes in different formats and update information automatically based on the web. Firstly, ideas to classify resumes, some constraints obtained by analyzing resume features and overall extraction strategy is introduced. Then two extraction algorithms for parsing resumes in different text formats are given. Consequently, the system was implemented by java programming. Finally, use the system to resolve the resume samples, and the statistical analysis and system optimization analysis are carried out according to the accuracy rate and recall rate of the extracted results.


title: Understanding the impact change of a highly cited article: a content-based citation analysis

abstract: Researchers tend to cite highly cited articles, but how these highly cited articles influence the citing articles has been underexplored. This paper investigates how one highly cited essay, Hirsch’s “h-index” article (H-article) published in 2005, has been cited by other articles. Content-based citation analysis is applied to trace the dynamics of the article’s impact changes from 2006 to 2014. The findings confirm that citation context captures the changing impact of the H-article over time in several ways. In the first two years, average citation mention of H-article increased, yet continued to decline with fluctuation until 2014. In contrast with citation mention, average citation count stayed the same. The distribution of citation location over time also indicates three phases of the H-article “Discussion,” “Reputation,” and “Adoption” we propose in this study. Based on their locations in the citing articles and their roles in different periods, topics of citation context shifted gradually when an increasing number of other articles were co-mentioned with the H-article in the same sentences. These outcomes show that the impact of the H-article manifests in various ways within the content of these citing articles that continued to shift in nine years, data that is not captured by traditional means of citation analysis that do not weigh citation impacts over time.


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title: Annotation and Classification of an Email Importance Corpus

abstract: This paper presents an email importance corpus annotated through Amazon Mechanical Turk (AMT). Annotators annotate the email content type and email importance for three levels of hierarchy (senior manager, middle manager and employee). Each email is annotated by 5 turkers. Agreement study shows that the agreed AMT annotations are close to the expert annotations. The annotated dataset demonstrates difference in proportions of content type between different levels. An email importance prediction system is trained on the dataset and identifies the unimportant emails at minimum 0.55 precision with only text-based features.


title: Lerot: an online learning to rank framework

abstract: Online learning to rank methods for IR allow retrieval systems to optimize their own performance directly from interactions with users via click feedback. In the software package Lerot, presented in this paper, we have bundled all ingredients needed for experimenting with online learning to rank for IR. Lerot includes several online learning algorithms, interleaving methods and a full suite of ways to evaluate these methods. In the absence of real users, the evaluation method bundled in the software package is based on simulations of users interacting with the search engine. The software presented here has been used to verify findings of over six papers at major information retrieval venues over the last few years.


title: Fake News: A Definition

abstract: Despite being a new term, ‘fake news’ has evolved rapidly. This paper argues that it should be reserved for cases of deliberate presentation of (typically) false or misleading claims as news, where these are misleading by design. The phrase ‘by design’ here refers to systemic features of the design of the sources and channels by which fake news propagates and, thereby, manipulates the audience’s cognitive processes. This prospective definition is then tested: first, by contrasting fake news with other forms of public disinformation; second, by considering whether it helps pinpoint conditions for the (recent) proliferation of fake news.


title: Comparison of Brushless DC Motor Drives with 180/120-Degree Inverter Systems

abstract: This paper compares the brushless dc motors with 180/120-degree inverters and the average-value modelling of these systems. In 180-degree inverters, each phase is always connected to the dc source and the average voltages are readily known. In 120-degree inverters, each phase is open-circuited for a fraction of revolution resulting in discontinuous phase current and different commutation-conduction patterns, which makes it challenging to obtain an accurate average-value model. This paper investigates the averaging approach that neglects the commutation time, and shows that this approach may degrade model accuracy when the stator resistance is relatively small. The paper also suggests a new approach to overcome the limitations of existing models.


title: Adversarial Cross-Modal Retrieval

abstract: Cross-modal retrieval aims to enable flexible retrieval experience across different modalities (e.g., texts vs. images). The core of cross-modal retrieval research is to learn a common subspace where the items of different modalities can be directly compared to each other. In this paper, we present a novel Adversarial Cross-Modal Retrieval (ACMR) method, which seeks an effective common subspace based on adversarial learning. Adversarial learning is implemented as an interplay between two processes. The first process, a feature projector, tries to generate a modality-invariant representation in the common subspace and to confuse the other process, modality classifier, which tries to discriminate between different modalities based on the generated representation. We further impose triplet constraints on the feature projector in order to minimize the gap among the representations of all items from different modalities with same semantic labels, while maximizing the distances among semantically different images and texts. Through the joint exploitation of the above, the underlying cross-modal semantic structure of multimedia data is better preserved when this data is projected into the common subspace. Comprehensive experimental results on four widely used benchmark datasets show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods.


title: A Comparative Study Of Indian And Western Music Forms

abstract: Music in India has very ancient roots. Indian classical music is considered to be one of the oldest musical traditions in the world but compared to Western music very little work has been done in the areas of genre recognition, classification, automatic tagging, comparative studies etc. In this work, we investigate the structural differences between Indian and Western music forms and compare the two forms of music in terms of harmony, rhythm, microtones, timbre and other spectral features. To capture the temporal and static structure of the spectrogram, we form a set of global and local frame-wise features for 5genres of each music form. We then apply Adaboost classification and GMM based Hidden Markov Models for four types of feature sets and observe that Indian Music performs better as compared to Western Music. We have achieved a best accuracy of 98.0% and 77.5% for Indian and Western musical genres respectively. Our comparative analysis indicates that features that work well with one form of music may not necessarily perform well with the other form. The results obtained on Indian Music Genres are better than the previous state-of-the-art.


Question-answering in an industrial setting


title: Quantifying over play: Constraining undesirable solutions in puzzle design

abstract: Motivated by our ongoing efforts in the development of Refraction 2, a puzzle game targeting mathematics education, we realized that the quality of a puzzle is critically sensitive to the presence of alternative solutions with undesirable properties. Where, in our game, we seek a way to automatically synthesize puzzles that can only be solved if the player demonstrates specific concepts, concern for the possibility of undesirable play touches other interactive design domains. To frame this problem (and our solution to it) in a general context, we formalize the problem of generating solvable puzzles that admit no undesirable solutions as an NPcomplete search problem. By making two design-oriented extensions to answer set programming (a technology that has been recently applied to constrained game content generation problems) we offer a general way to declaratively pose and automatically solve the high-complexity problems coming from this formulation. Applying this technique to Refraction, we demonstrate a qualitative leap in the kind of puzzles we can reliably generate. This work opens up new possibilities for quality-focused content generators that guarantee properties over their entire combinatorial space of play.


title: EXEMPLAR-BASED UNIT SELECTION FOR VOICE CONVERSION UTILIZING TEMPORAL INFORMATION

abstract: Although temporal information of speech has been shown to play an important role in perception, most of the voice conversion approaches assume the speech frames are independent of each other, thereby ignoring the temporal information. In this study, we improve conventional unit selection approach by using exemplars which span multiple frames as base units, and also take temporal information constraint into voice conversion by using overlapping frames to generate speech parameters. This approach thus provides more stable concatenation cost and avoids discontinuity problem in conventional unit selection approach. The proposed method also keeps away from the over-smoothing problem in the mainstream joint density Gaussian mixture model (JD-GMM) based conversion method by directly using target speaker’s training data for synthesizing the converted speech. Both objective and subjective evaluations indicate that our proposed method outperforms JD-GMM and conventional unit selection methods. Index Terms: Voice conversion, unit selection, multi-frame exemplar, temporal information


title: Anatomy of a Failed Knowledge Management Initiative: Lessons from PharmaCorp's Experiences

abstract: On a sunny morning in July 1999, Samuel Parsons, Head of Knowledge Management at PharmaCorp, convened his regular Monday team meeting. He looked stressed. After dealing with a couple of administrative issues he said: ‘Last Friday evening I was informed that Wilco Smith, Head of Pharma Global Order Handling Services, no longer wants knowledge management. His only question now is how to off-board the knowledge management staff.’ Thus came to an end a three-year initiative that at the outset was considered to be ‘the knowledge management showcase for the firm’. This paper is for managers who have an interest in operationalizing knowledge and want to avoid the traps others have fallen into. It examines the case of PharmaCorp, a global organization and one of the largest in its industry. The case provides managers with five key lessons. First, manage knowledge interdependencies across communities of practice; second, contextualize knowledge within natural groups of activities; third, avoid an over-emphasis on explicit knowledge; fourth, let knowledge management recipients determine tacit and explicit knowledge; and fifth, manage the input from external consultants. Copyright © 2002 John Wiley & Sons, Ltd.


title: Robust i-vector based adaptation of DNN acoustic model for speech recognition.

abstract: In the past, conventional i-vectors based on a Universal Background Model (UBM) have been successfully used as input features to adapt a Deep Neural Network (DNN) Acoustic Model (AM) for Automatic Speech Recognition (ASR). In contrast, this paper introduces Hidden Markov Model (HMM) based ivectors that use HMM state alignment information from an ASR system for estimating i-vectors. Further, we propose passing these HMM based i-vectors though an explicit non-linear hidden layer of a DNN before combining them with standard acoustic features, such as log filter bank energies (LFBEs). To improve robustness to mismatched adaptation data, we also propose estimating i-vectors in a causal fashion for training the DNN, restricting the connectivity among hidden nodes in the DNN and applying a max-pool non-linearity at selected hidden nodes. In our experiments, these techniques yield about 5-7% relative word error rate (WER) improvement over the baseline speaker independent system in matched condition, and a substantial WER reduction for mismatched adaptation data.


title: Design of a wideband Balun for Archimedean spiral antenna in energy harvesting

abstract: This paper presents the design of a wideband Balun for Archimedean spiral antenna. The Balun is designed with a microstrip Line (MSL) to parallel stripline (PSL) transition which is used as a balanced feed for Archimedean spiral antenna. This transition Balun has the wideband performance of 194% over the band of 0.5GHz to 18.5 GHz. The back-to-back MSL to PSL has a return loss of better than 15dB over the band of operation. The performance of the Balun was validated with the spiral antenna. The measured results were compared with the simulated results and it shows that the Balun operates well over wideband frequency range. The Balun is designed and fabricated using the commonly available FR4 substrate with permittivity of 4.2. The designs have been simulated and optimized using 3D Electromagnetic Solver CST Microwave Studio.


title: Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms

abstract: A measure of search difficulty, fitness distance correlation (FDC), is introduced and its power as a predictor of genetic algorithm (GA) performance is investigated. The sign and magnitude of this correlation can be used to predict the performance of a GA on many problems where the global maxima are already known. FDC can be used to correctly classify easy deceptive problems and easy and difficult non-deceptive problems as difficult, it can be used to indicate when Gray coding will prove better than binary coding, it produces the expected answers when applied to problems over a wide range of apparent difficulty, and it is also consistent with the surprises encountered when GAs were used on the Tanese and royal road functions. The FDC measure is a consequence of an investigation into the connection between GAs and heuristic search.


title: A fast APRIORI implementation

abstract: The efficiency of frequent itemset mining algorithms is determined mainly by three factors: the way candidates are generated, the data structure that is used and the implementation details. Most papers focus on the first factor, some describe the underlying data structures, but implementation details are almost always neglected. In this paper we show that the effect of implementation can be more important than the selection of the algorithm. Ideas that seem to be quite promising, may turn out to be ineffective if we descend to the implementation level. We theoretically and experimentally analyze APRIORI which is the most established algorithm for frequent itemset mining. Several implementations of the algorithm have been put forward in the last decade. Although they are implementations of the very same algorithm, they display large differences in running time and memory need. In this paper we describe an implementation of APRIORI that outperforms all implementations known to us. We analyze, theoretically and experimentally, the principal data structure of our solution. This data structure is the main factor in the efficiency of our implementation. Moreover, we present a simple modification of APRIORI that appears to be faster than the original algorithm.


title: Taggram: Exploring Geo-data on Maps through a Tag Cloud-Based Visualization

abstract: Searching and exploring on digital maps are normally performed through simple text boxes and zoom-and-pan interfaces. In this paper, however, we present a novel technique, namely Taggram, which combines texts onto maps to support geo-tagged data exploration. It is designed to show geo-tagged data in form of size-varied and colorized tags, benefiting by the concepts of Tag Clouds, and to support exploring those data interactively through a fisheye menu adaptation. The technique was demonstrated for geo-tagged data exploration; however, as other thematic geo-data can be partially represented in abstract form of texts, Taggram can be a method for the presentation and exploration of such geo-data in other application scenarios.


title: Advances in Offender Profiling: A Systematic Review of the Profiling Literature Published Over the Past Three Decades

abstract: Despite the immense popularity of offender profiling as both a topic of fascination for the general public as well as an academic field of study, concerns have been raised about the development of this area of scientific inquiry. The present study provides a preliminary step towards moving the field forward as it reviews the type and quality of studies dealing with offender profiling over the past 31 years. Based on a content analysis of 132 published articles, the review indicates that researchers investigating this phenomenon rarely publish multiple articles, and they are generally reported across many different journals, thereby making knowledge synthesis and knowledge transfer problematic. In addition, the majority of papers published in the area are discussion pieces (e.g., discussing what profiling is, how profiles are constructed, and when profiling is useful), despite the fact that the processes underlying offender profiling are still not well understood. Finally, although peer-reviewed articles exploring this topic have steadily increased, the statistical sophistication of these studies is sorely lacking, with most including no statistics or formal analyses of data. Suggestions for future research and recommendations to streamline efforts in this field are provided based on the results of this review.


title: Face recognition using subspaces techniques

abstract: With many applications in various domains, Face Recognition technology has received a great deal of attention over the decades in the field of image analysis and computer vision. It has been studied by scientists from different areas of psychophysical sciences and those from different areas of computer science. Psychologists and neuro-scientists mainly deal with the human perception part of the topic where as engineers studying on machine recognition of human faces deal with the computational aspects of Face Recognition. Face Recognition is an important and natural human ability of a human being. However developing a computer algorithm to do the same thing is one of the toughest tasks in computer vision. Research over the past several years enables similar recognitions automatically. Various face recognition techniques are represented through various classifications such as, Image-based face recognition and Video-based recognition, Appearance-based and Model-based, 2D and 3D face recognition methods. This paper gives a review of different face recognition techniques available as of today. The focus is on subspace techniques, investigating the use of image pre-processing applied as a preliminary step in order to reduce error rates. The Principle Component Analysis, Linear Discriminant Analysis and their modified methods of face recognition are implemented under subspace techniques, computing False Acceptance Rates (FAR)and False Rejection Rates (FRR) on a standard test set of images that pose typical difficulties for recognition. By applying a range of image processing techniques it is demonstrated that the performance is highly dependent on the type of pre-processing steps used and that Equal Error Rates (EER) of the Eigenface and Fisherface methods can be reduced using the method proposed in this paper.


title: Consumer Demand for Cynical and Negative News Frames

abstract: Commentators regularly lament the proliferation of both negative and/or strategic (“horse race”) coverage in political news content. The most frequent account for this trend focuses on news norms and/or the priorities of news journalists. Here, we build on recent work arguing for the importance of demand-side, rather than supply-side, explanations of news content. In short, news may be negative and/or strategy-focused because that is the kind of news that people are interested in. We use a lab study to capture participants’ news-selection biases, alongside a survey capturing their stated news preferences. Politically interested participants are more likely to select negative stories. Interest is associated with a greater preference for strategic frames as well. And results suggest that behavioral results do not conform to attitudinal ones. That is, regardless of what participants say, they exhibit a preference for negative news content.


title: Challenges in acute pain management.

abstract: Abstract The management of acute pain remains challenging, with many patients suffering inadequate pain control following surgery. Certain populations are at unique risk for unrelieved pain. Evidence-based approaches taking into account patients' specific needs and problems will likely substantially improve their perioperative experience. These patients must be identified in the preoperative process, and an anesthetic/analgesic plan discussed and formulated. A targeted multimodal approach to pain management should be considered the best clinical practice. The most challenging patients may benefit most from the surveillance of an acute pain service that is able to monitor and coordinate care into the postoperative period.


title: A 72% PAE, 95-W, single-chip GaN FET S-band inverse class-F power amplifier with a harmonic resonant circuit

abstract: This paper describes a high-efficiency, high-output-power GaN power amplifier for S-band radar applications. The amplifier uses an inverse class-F configuration for high efficiency. The matching circuit includes a 2nd harmonic resonant circuit to compensate for GaN FET parasitics. The developed GaN single-chip power amplifier delivers output power of 95 W with power added efficiency (PAE) of 72% and high linear gain of 19.8 dB at 2.6 GHz. To the best of our knowledge, this is the highest efficiency for S-band power amplifiers ever reported with nearly 100-W output power.


Side-Channel Attacks on Everyday Applications : Distinguishing Inputs with F LUSH + R ELOAD


title: Cross-lingual Dataless Classification for Languages with Small Wikipedia Presence

abstract: This paper presents an approach to classify documents in any language into an English topical label space, without any text categorization training data. The approach, Cross-Lingual Dataless Document Classification (CLDDC) relies on mapping the English labels or short category description into a Wikipedia-based semantic representation, and on the use of the target language Wikipedia. Consequently, performance could suffer when Wikipedia in the target language is small. In this paper, we focus on languages with small Wikipedias, (Small-Wikipedia languages, SWLs). We use a word-level dictionary to convert documents in a SWL to a large-Wikipedia language (LWLs), and then perform CLDDC based on the LWL's Wikipedia. This approach can be applied to thousands of languages, which can be contrasted with machine translation, which is a supervision heavy approach and can be done for about 100 languages. We also develop a ranking algorithm that makes use of language similarity metrics to automatically select a good LWL, and show that this significantly improves classification of SWLs' documents, performing comparably to the best bridge possible.


Incongruent Headlines: Yet Another Way to Mislead Your Readers