title: Comparing Prediction Market Structures, With an Application to Market Making

abstract: Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Various market making algorithms have been proposed in the literature and deployed in practice, but there has been little effort to evaluate their benefits and disadvantages in a systematic manner. We introduce a novel experimental design for comparing market structures in live trading that ensures fair comparison between two different microstructures with the same trading population. Participants trade on outcomes related to a two-dimensional random walk that they observe on their computer screens. They can simultaneously trade in two markets, corresponding to the independent horizontal and vertical random walks. We use this experimental design to compare the popular inventory-based logarithmic market scoring rule (LMSR) market maker and a new information based Bayesian market maker (BMM). Our experiments reveal that BMM can offer significant benefits in terms of price stability and expected loss when controlling for liquidity; the caveat is that, unlike LMSR, BMM does not guarantee bounded loss. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.


title: Image Copy-Move Forgery Detection Based on SURF

abstract: As the advent and growing popularity of image editing software, digital images can be manipulated easily without leaving obvious visual clues. If the tampered images are abused, it may lead to potential social, legal or private consequences. To this end, it’s very necessary and also challenging to find effective methods to detect digital image forgeries. In this paper, a fast method to detect image copy-move forgery is proposed based on the SURF (Speed up Robust Features) descriptors, which are invariant to rotation, scaling etc. Results of experiments indicate that the proposed method is valid in detecting the image region duplication and quite robust to additive noise and blurring.


Serious Games for the Police: Opportunities and Challenges


title: Filterbank learning using Convolutional Restricted Boltzmann Machine for speech recognition

abstract: Convolutional Restricted Boltzmann Machine (ConvRBM) as a model for speech signal is presented in this paper. We have developed ConvRBM with sampling from noisy rectified linear units (NReLUs). ConvRBM is trained in an unsupervised way to model speech signal of arbitrary lengths. Weights of the model can represent an auditory-like filterbank. Our proposed learned filterbank is also nonlinear with respect to center frequencies of subband filters similar to standard filterbanks (such as Mel, Bark, ERB, etc.). We have used our proposed model as a front-end to learn features and applied to speech recognition task. Performance of ConvRBM features is improved compared to MFCC with relative improvement of 5% on TIMIT test set and 7% on WSJ0 database for both Nov'92 test sets using GMM-HMM systems. With DNN-HMM systems, we achieved relative improvement of 3% on TIMIT test set over MFCC and Mel filterbank (FBANK). On WSJ0 Nov'92 test sets, we achieved relative improvement of 4–14% using ConvRBM features over MFCC features and 3.6–5.6% using ConvRBM filterbank over FBANK features.


title: Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

abstract: Augmenting X-ray imaging with 3D roadmap to improve guidance is a common strategy. Such approaches benefit from automated analysis of the X-ray images, such as the automatic detection and tracking of instruments. In this paper, we propose a real-time method to segment the catheter and guidewire in 2D X-ray fluoroscopic sequences. The method is based on deep convolutional neural networks. The network takes as input the current image and the three previous ones, and segments the catheter and guidewire in the current image. Subsequently, a centerline model of the catheter is constructed from the segmented image. A small set of annotated data combined with data augmentation is used to train the network. We trained the method on images from 182 X-ray sequences from 23 different interventions. On a testing set with images of 55 X-ray sequences from 5 other interventions, a median centerline distance error of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The segmentation of the instruments in 2D X-ray sequences is performed in a real-time fully-automatic manner.


On the Relationship between P-log and LPMLN


title: Emotional and psychophysiological responses to tempo, mode, and percussiveness

abstract: People often listen to music to influence their emotional state. However, the specific musical characteristics which cause this process are not yet fully understood. We have investigated the influence of the musical characteristics of tempo, mode and percussiveness on our emotions. In a quest towards ecologically valid results, 32 participants listened to 16 pop and 16 rock songs while conducting an office task. They rated experienced arousal, valence, and tension, while skin conductance and cardiovascular responses were recorded. An increase in tempo was found to lead to an increase in reported arousal and tension and a decrease in heart rate variability. More arousal was reported during minor than major mode songs. Level and frequency of skin conductance responses increased with an increase in percussiveness. Physiological responses revealed patterns that might not have been revealed by self-report. Interaction effects further suggest that musical characteristics interplay in modulating emotions. So, tempo, mode, and percussiveness indeed modulate our emotions and, consequently, can be used to direct emotions. Music presentation revealed subtly different results in a laboratory setting, where music was altered with breaks, from those in a more ecologically valid setting where continuous music was presented. All in all, this enhances our understanding of the influence of music on emotions and creates opportunities seamlessly to tap into listeners’ emotional state through their physiological responses.


title: The intended and actual adoption of online purchasing: a brief review of recent literature. Institute of Transport Studies

abstract: The rapid growth of e-commerce is imposing profound impacts on modern society. On the supply side, the emergence of e-commerce is greatly changing the operation behavior of some retailers and is increasing product internationalization due to its geographically unlimited nature. On the demand side, the pervasiveness of e-commerce affects how, where, and when consumers shop, and indirectly influences the way in which we live our lives. However, the development of e-commerce is still in an early stage, and why consumers choose (or do not choose) online purchasing is far from being completely understood. To better evaluate and anticipate those profound impacts of e-commerce, therefore, it is important to further refine our understanding of consumers’ e-shopping behavior. A number of studies have investigated e-shopping behavior, and reviewing them is valuable for further improving our understanding. This report aims to summarize previous e-shopping research in a systematic way. In this review, we are interested primarily in the potential benefits and costs that the internet offers for the business-to-consumer segment of e-commerce in the transaction (purchase) channel. An overview of the 65 empirical studies analyzed in this report is provided in the Appendix. Most previous studies fall into one or more of several theoretical frameworks, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory, and others. Among them, social psychological theories (the theory of reasoned action, the theory of planned behavior, the technology acceptance model) were widely applied. As shown in the applications of different theories, e-shopping behavior is not a simple decision process, and thus an integration of various theories is necessary to deal with its complexities. We suggest synthesizing these theories through the development of a comprehensive list of benefits and costs, using each of the key constructs of the pertinent theories as a guide to identifying the nature of those benefits and costs. The dependent variables mainly include e-shopping intention and actual e-shopping behavior (a few studies used attitudes toward e-shopping). E-shopping intention was measured by various dimensions. Among them, the directly-stated intention to purchase online is the most frequently used measure. Although some studies used a unidimensional measure, most adopted a latent construct to assess consumers’ e-shopping intentions. Actual e-shopping behavior mainly includes three dimensions: adoption, spending, and frequency. Most studies examined one or more of these three dimensions directly, while a few studies constructed a latent variable to measure actual e-shopping behavior. When both behavioral intention and actual behavior are included in model development, attention should be paid to the time precedence between intention and behavior. With respect to sampling, a choice-based sampling approach is probably preferable given that online shopping activity accounts for a minor proportion of all consumers, and a far smaller proportion of total retail sales. In previous studies, most chose internet/computer/email users or students as their subjects. Generally, a student sample is a natural choice for some particular products such as books. However, parameter estimates developed from a student sample lack generalizability to a larger population because of its homogeneity. By contrast, a more general sample of internet/computer/email users is more applicable for e-shopping behavior research. The characteristics of products strongly influence the degree to which they are suitable for selling online. Mixing product categories in e-shopping behavior research tends to yield vague or inconsistent results. It is therefore necessary to explicitly consider product characteristics when exploring consumers’ e-shopping behavior. However, relatively little effort has been invested into product classification in the context of e-shopping. Although Nelson’s dichotomized system (search and experience goods) and Peterson et al.’s three-dimensional system (cost, tangibility, and differentiability) provide useful guides for product type classification, each has some shortcomings. Therefore, more research should focus on the construction of product classification systems. Different methodologies have been applied in previous research. Generally, descriptive analysis is used to describe consumers’ e-shopping behavior; correlational analysis goes beyond descriptive analysis and attempts to analyze how two variables are related; and multivariate analysis is mainly used to explain consumers’ behavior using many variables considered together. Therefore, although descriptive and correlational analyses are important steps in helping to construct multivariate analyses, multivariate studies provide more information than these other two types of analyses. Multivariate analysis is ideal to study e-shopping behavior in depth. Among multivariate analysis techniques, multiple regression, structural equations modeling, and discrete choice models were most frequently used. Previous studies have identified various determinants of consumers’ e-shopping behavior. These determinants mainly cover three essential elements: characteristics of e-shopping as a shopping channel, consumer characteristics, and vendor and product characteristics. Among these characteristics, the former two have been examined extensively in previous research, confirming their importance in understanding e-shopping behavior. Specifically, the dimensions of channel characteristics of e-shopping include e-shopping service quality, relative advantages, perceived risk of and confidence in e-shopping, and trust. Consumer characteristics include their shopping orientations, personality, social and psychological characteristics, computer/internet experience, in-home shopping experience, and socio-demographics.


title: Reply: Premature Reports of Demise

abstract: This invention relates to banding machines of the kind capable of automatically applying a wrapping band around a flat article or articles, e.g., stacks of banknotes, and may be integrated with machines for counting and delivering numbers of articles, e.g., banknotes, to a banding station. The banding machines comprises a platform, the working surface of which receives an article to be banded, means for feeding a band from a magazine and positioning the band adjacent to the platform, yieldably mounted support means adapted to move adjacent said platform, reciprocable constant-stroke gripper means adapted to transfer the article from the working surface to the support means with the band therebetween, and thereafter to move to the limit of its stroke thereby to grip both the article and the band, and means for bringing together and uniting the two ends of the band. The components of the machine preferably move in a rotary manner through band-loading, article-loading, band-uniting, and banded-article-delivery stations.


Attentive Path Combination for Knowledge Graph Completion


title: AppCaulk: Data Leak Prevention by Injecting Targeted Taint Tracking into Android Apps

abstract: As Android is entering the business domain, leaks of business-critical and personal information through apps become major threats. Due to the context-insensitive nature of the Android permission model, information flow policies cannot be enforced by on-board mechanisms. We therefore propose AppCaulk, an approach to harden any existing Android app by injecting a targeted dynamic taint analysis, which tracks and blocks unwanted information flows at runtime. Critical data flows are first discovered using a static taint analysis and the relevant data propagation paths are instrumented by a taint tracking code at register level. At runtime the dynamic taint analysis woven into the app detects and blocks data leaks as they are about to occur. In contrast to existing taint analysis approaches like Taint droid, AppCaulk does not require modification of the Android middleware and can thus be applied to any stock Android installation. In this paper, we explain the design of AppCaulk, describe the evaluation of its prototype, and compare its effectiveness with Taint droid.


title: Search, align, and repair: data-driven feedback generation for introductory programming exercises

abstract: This paper introduces the "Search, Align, and Repair" data-driven program repair framework to automate feedback generation for introductory programming exercises. Distinct from existing techniques, our goal is to develop an efficient, fully automated, and problem-agnostic technique for large or MOOC-scale introductory programming courses. We leverage the large amount of available student submissions in such settings and develop new algorithms for identifying similar programs, aligning correct and incorrect programs, and repairing incorrect programs by finding minimal fixes. We have implemented our technique in the SARFGEN system and evaluated it on thousands of real student attempts from the Microsoft-DEV204.1X edX course and the Microsoft CodeHunt platform. Our results show that SARFGEN can, within two seconds on average, generate concise, useful feedback for 89.7% of the incorrect student submissions. It has been integrated with the Microsoft-DEV204.1X edX class and deployed for production use.


title: Latent syntactic structure-based sentiment analysis

abstract: People share their opinions about things like products, movies and services using social media channels. The analysis of these textual contents for sentiments is a gold mine for marketing experts, thus automatic sentiment analysis is a popular area of applied artificial intelligence. We propose a latent syntactic structure-based approach for sentiment analysis which requires only sentence-level polarity labels for training. Our experiments on three domains (movie, IT products, restaurant) show that a sentiment analyzer that exploits syntactic parses and has access only to sentence-level polarity annotation for in-domain sentences can outperform state-of-the-art models that were trained on out-domain parse trees with sentiment annotation for each node of the trees. In practice, millions of sentence-level polarity annotations are usually available for a particular domain thus our approach is applicable for training a sentiment analyzer for a new domain while it can exploit the syntactic structure of sentences as well.


title: Some Vulnerabilities Are Different Than Others - Studying Vulnerabilities and Attack Surfaces in the Wild.

abstract: The security of deployed and actively used systems is a moving target, influenced by factors not captured in the existing security metrics. For example, the count and severity of vulnerabilities in source code, as well as the corresponding attack surface, are commonly used as measures of a software product’s security. But these measures do not provide a full picture. For instance, some vulnerabilities are never exploited in the wild, partly due to security technologies that make exploiting them difficult. As for attack surface, its effectiveness has not been validated empirically in the deployment environment. We introduce several security metrics derived from field data that help to complete the picture. They include the count of vulnerabilities exploited and the size of the attack surface actually exercised in real-world attacks. By evaluating these metrics on nearly 300 million reports of intrusion-protection telemetry, collected on more than six million hosts, we conduct an empirical study of security in the deployment environment. We find that none of the products in our study have more than 35% of their disclosed vulnerabilities exploited in the wild. Furthermore, the exploitation ratio and the exercised attack surface tend to decrease with newer product releases. We also find that hosts that quickly upgrade to newer product versions tend to have reduced exercised attack-surfaces. The metrics proposed enable a more complete assessment of the security posture of enterprise infrastructure. Additionally, they open up new research directions for improving security by focusing on the vulnerabilities and attacks that have the highest impact in practice.


title: A path to precision in the ICU

abstract: Precision medicine is increasingly touted as a groundbreaking new paradigm in biomedicine. In the ICU, the complexity and ambiguity of critical illness syndromes have been identified as fundamental justifications for the adoption of a precision approach to research and practice. Inherently protean diseases states such as sepsis and acute respiratory distress syndrome have manifestations that are physiologically and anatomically diffuse, and that fluctuate over short periods of time. This leads to considerable heterogeneity among patients, and conditions in which a “one size fits all” approach to therapy can lead to widely divergent results. Current ICU therapy can thus be seen as imprecise, with the potential to realize substantial gains from the adoption of precision medicine approaches. A number of challenges still face the development and adoption of precision critical care, a transition that may occur incrementally rather than wholesale. This article describes a few concrete approaches to addressing these challenges. First, novel clinical trial designs, including registry randomized controlled trials and platform trials, suggest ways in which conventional trials can be adapted to better accommodate the physiologic heterogeneity of critical illness. Second, beyond the “omics” technologies already synonymous with precision medicine, the data-rich environment of the ICU can generate complex physiologic signatures that could fuel precision-minded research and practice. Third, the role of computing infrastructure and modern informatics methods will be central to the pursuit of precision medicine in the ICU, necessitating close collaboration with data scientists. As work toward precision critical care continues, small proof-of-concept studies may prove useful in highlighting the potential of this approach.


title: Student Evaluation of Teaching Effectiveness: An assessment of student perception and motivation

abstract: Over the past century, student ratings have steadily continued to take precedence in faculty evaluation systems in North America and Australia, are increasingly reported in Asia and Europe and are attracting considerable attention in the Far East. Since student ratings are the most, if not the only, influential measure of teaching effectiveness, active participation by and meaningful input from students can be critical in the success of such teaching evaluation systems. Nevertheless, very few studies have looked into students' perception of the teaching evaluation system and their motivation to participate. This study employs expectancy theory to evaluate some key factors that motivate students to participate in the teaching evaluation process. The results show that students generally consider an improvement in teaching to be the most attractive outcome of a teaching evaluation system. The second most attractive outcome was using teaching evaluations to improve course content and format. Using teaching evaluations for a professor's tenure, promotion and salary rise decisions and making the results of evaluations available for students' decisions on course and instructor selection were less important from the students' standpoint. Students' motivation to participate in teaching evaluations is also impacted significantly by their expectation that they will be able to provide meaningful feedback. Since quality student input is an essential antecedent of meaningful student evaluations of teaching effectiveness, the results of this study should be considered thoughtfully as the evaluation system is designed, implemented and operated.


title: Using Psychology Theories in Archival Financial Accounting Research

abstract: Psychologists have studied human behavior for over a century and, as a result, have developed a robust set of theories regarding how people behave. Most financial accounting issues deal with matters of human behavior, such as the judgments and decisions of managers, investors, analysts, and auditors. Consequently, psychology offers a rich pool of theories from which financial accounting researchers can draw to motivate hypotheses and interpret results. Despite this, archival accounting researchers traditionally have relied almost solely on theories based in financial economics. We argue that two major obstacles to the use of psychology theories by archival researchers has been a lack of awareness about the theories that are available and when their use would be most productive. Our paper attempts to bridge this gap. Specifically, we describe a number of psychology theories that are applicable to financial accounting issues, lay out the circumstances where they may be especially useful to archival researchers, and provide a number of specific examples of how psychology theories provide new insights about financial accounting issues.


title: Validation and verification of simulation models

abstract: In this paper we discuss validation and verification of simulation models. Four different approaches to deciding model validity are described; two different paradigms that relate validation and verification to the model development process are presented; various validation techniques are defined; conceptual model validity, model verification, operational validity, and data validity are discussed; a way to document results is given; a recommended procedure for model validation is presented; and accreditation is briefly discussed.


title: Reducing elliptic curve logarithms to logarithms in a finite field

abstract: Abstruct- Elliptic cuwe cryptosystems have the potential to provide relatively small block size, high-security public key schemes that can be efficiently implemented. As with other known public key schemes, such as RSA and discrete exponentiation in a finite field, some care must be exercised when selecting the parameters involved, in this case the elliptic curve and the underlying field. Specific classes of cuwes that give little or no advantage over previously known schemes are discussed. The main result of the paper is to demonstrate the reduction of the elliptic curve logarithm problem to the logarithm problem in the multiplicative group of an extension of the underlying finite field. For the class of supersingular elliptic curves, the reduction takes probabilistic polynomial time, thus providing a probabilistic subexponential time algorithm for the former problem. Index Tem- Discrete logarithms, elliptic curves, public key CryPtOSraPhY.


title: Two-axis Scheimpflug focusing for particle image velocimetry

abstract: The objective of this work is to demonstrate the capabilities of a two-axis, three-dimensional Particle Image Velocimetry (PIV) system for use as a largescale wind tunnel research tool. A two-axis PIV system provides the capability for camera placement virtually anywhere within a three-dimensional space relative to the laser sheet plane of measurement. The Scheimpflug focusing method is applied in two orthogonal axes to achieve off-axis focusing. The two-axis, three-dimensional PIV system uses off-axis stereoscopic viewing to determine two perspective measurements of a three-dimensional velocity field within the laser sheet. A unique calibration procedure is used to determine the optical parameters that describe the position of the camera systems and which are necessary to perform the three-dimensional reconstruction of the displacements. This paper presents this unique calibration for a two-axis, three-dimensional PIV system. This two-axis, three-dimensional PIV system was demonstrated first by measuring three-dimensional displacements of a target. Maximum instantaneous errors are estimated as 2% or less for all velocity components based upon the measured target displacements, and these error estimates were verified by a propagation of errors calculation. The error estimates are for instantaneous displacements and include all error sources. The two-axis, three-dimensional PIV system was also used to make measurements of the freestream velocity within a wind tunnel. The out-of-plane freestream component of velocity measured by the PIV system differed from the expected value by less than 1.1 %.


title: Interactive Planarization and Optimization of 3D Meshes

abstract: Constraining 3D meshes to restricted classes is necessary in architectural and industrial design, but it can be very challenging to manipulate meshes while staying within these classes. Specifically, polyhedral meshes—those having planar faces—are very important, but also notoriously difficult to generate and manipulate efficiently. We describe an interactive method for computing, optimizing and editing polyhedral meshes. Efficiency is achieved thanks to a numerical procedure combining an alternating least-squares approach with the penalty method. This approach is generalized to manipulate other subsets of polyhedral meshes, as defined by a variety of other constraints.


title: Carbohydrate to protein ratio in food and cognitive performance in the morning

abstract: Abstract The effect of different carbohydrate to protein ratios in food on cognitive functions and the relation between postprandial metabolic and cognitive changes were studied in 15 healthy male students. Subjects were tested in three sessions, separated by 1 week, for short-term changes in mood states, objective cognitive functions, blood parameters, and indirect calorimetry using a repeated-measures, counterbalanced cross-over design. Measurements were made after an overnight fast before and hourly during 3.5 h after test meal ingestion. The isoenergetic (1670 kJ) test meals consisted of three carbohydrate to protein ratios, i.e. a carbohydrate-rich (CHO[4:1]), balanced (BAL[1:1]), and protein-rich (PRO[1:4]) meal, respectively. Overall accuracy in short-term memory was best after the PRO[1:4] meal concomitant to the least variation in glucose metabolism and glucagon to insulin ratio (GIR). Related to changes in glucose metabolism and/or in the ratios of large neutral amino acids (LNAA), respectively, attention and decision times were transiently improved within the first hour after the CHO[4:1] meal, whereas after the first hour the BAL[1:1] and PRO[1:4] meal resulted in improved performance. Overall reaction times of a central task were fastest after the BAL[1:1] meal concomitant to the highest overall tyrosine (Tyr) to LNAA ratio. Our findings suggest that the carbohydrate to protein ratio in food specifically influences higher cognitive functions in the morning. Except for a transient positive effect of rising blood glucose after a carbohydrate-rich meal, a protein-rich or balanced meal seems to result in better overall cognitive performance presumably because of less variation in glucose metabolism and/or higher modulation in LNAA ratios indicated by the overall GIR.


title: Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses

abstract: Dynamic mode decomposition (DMD) is an Arnoldi-like method based on the Koopman operator. It analyzes empirical data, typically generated by nonlinear dynamics, and computes eigenvalues and eigenmodes of an approximate linear model. Without explicit knowledge of the dynamical operator, it extracts frequencies, growth rates, and spatial structures for each mode. We show that expansion in DMD modes is unique under certain conditions. When constructing mode-based reduced-order models of partial differential equations, subtracting a mean from the data set is typically necessary to satisfy boundary conditions. Subtracting the mean of the data exactly reduces DMD to the temporal discrete Fourier transform (DFT); this is restrictive and generally undesirable. On the other hand, subtracting an equilibrium point generally preserves the DMD spectrum and modes. Next, we introduce an “optimized” DMD that computes an arbitrary number of dynamical modes from a data set. Compared to DMD, optimized DMD is superior at calculating physically relevant frequencies, and is less numerically sensitive. We test these decomposition methods on data from a two-dimensional cylinder fluid flow at a Reynolds number of 60. Time-varying modes computed from the DMD variants yield low projection errors.


title: Performance Evaluation and Comparative Analysis of SubCarrier Modulation Wake-up Radio Systems for Energy-Efficient Wireless Sensor Networks

abstract: Energy-efficient communication is one of the main concerns of wireless sensor networks nowadays. A commonly employed approach for achieving energy efficiency has been the use of duty-cycled operation of the radio, where the node’s transceiver is turned off and on regularly, listening to the radio channel for possible incoming communication during its on-state. Nonetheless, such a paradigm performs poorly for scenarios of low or bursty traffic because of unnecessary activations of the radio transceiver. As an alternative technology, Wake-up Radio (WuR) systems present a promising energy-efficient network operation, where target devices are only activated in an on-demand fashion by means of a special radio signal and a WuR receiver. In this paper, we analyze a novel wake-up radio approach that integrates both data communication and wake-up functionalities into one platform, providing a reconfigurable radio operation. Through physical experiments, we characterize the delay, current consumption and overall operational range performance of this approach under different transmit power levels. We also present an actual single-hop WuR application scenario, as well as demonstrate the first true multi-hop capabilities of a WuR platform and simulate its performance in a multi-hop scenario. Finally, by thorough qualitative comparisons to the most relevant WuR proposals in the literature, we state that the proposed WuR system stands out as a strong candidate for any application requiring energy-efficient wireless sensor node communications.


Fish remains in Ordovician rocks in Bighorn mountains, Wyoming, with a résumé of Ordovician geology of the northwest


title: Open Source Big Data Analytics Frameworks Written in Scala

abstract: Frameworks for big data arguably began with Google's use of MapReduce. Since then, a huge amount of progress has been made in the development of big data frameworks, many of which have been released as open source. Further to increase portability and ease of set-up, many are coded in a Java Virtual Machine (JVM) based language, e.g., Java or Scala. In addition, processing of big data involves the flow of data, and of course, the processing of data as it flows. This computational paradigm is a natural for functional programming. Furthermore, the map, reduce and combiner have analogs in functional programming. There has been a trend in the last few years toward developing open source big data frameworks written in Scala to support big data analytics. Scala is a modern JVM language that supports both object-oriented and functional programming paradigms.


title: Workplace Violence: A Survey of Emergency Physicians in the State of Michigan

abstract: Study objective We seek to determine the amount and type of work-related violence experienced by Michigan attending emergency physicians. Methods A mail survey of self-reported work-related violence exposure during the preceding 12 months was sent to randomly selected emergency physician members of the Michigan College of Emergency Physicians. Work-related violence was defined as verbal, physical, confrontation outside of the emergency department (ED), or stalking. Results Of 250 surveys sent, 177 (70.8%) were returned. Six were blank (3 were from retired emergency physicians), leaving 171 (68.4%) for analysis. Verbal threats were the most common form of work-related violence, with 74.9% (95% confidence interval [CI] 68.4% to 81.4%) of emergency physicians indicating at least 1 verbal threat in the previous 12 months. Of the emergency physicians responding, 28.1% (95% CI 21.3% to 34.8%) indicated that they were victims of a physical assault, 11.7% (95% CI 6.9% to 16.5%) indicated that they were confronted outside of the ED, and 3.5% (95% CI 0.8% to 6.3%) experienced a stalking event. Emergency physicians who were verbally threatened tended to be less experienced (11.1 versus 15.1 years in practice; mean difference −4.0 years [95% CI −6.4 to −1.6 years]), as were those who were physically assaulted (9.5 versus 13.1 years; mean difference −3.6 years [95% CI −5.9 to −1.3 years]). Urban hospital location, emergency medicine board certification, or on-site emergency medicine residency program were not significantly associated with any type of work-related violence. Female emergency physicians were more likely to have experienced physical violence (95% CI 1.4 to 5.8) but not other types of violence. Most (81.9%; 95% CI 76.1% to 87.6%) emergency physicians were occasionally fearful of workplace violence, whereas 9.4% (95% CI 5.0% to 13.7%) were frequently fearful. Forty-two percent of emergency physicians sought various forms of protection as a result of the direct or perceived violence, including obtaining a gun (18%), knife (20%), concealed weapon license (13%), mace (7%), club (4%), or a security escort (31%). Conclusion Work-related violence exposure is not uncommon in EDs. Many emergency physicians are concerned about the violence and are taking measures, including personal protection, in response to the fear.


title: Unifying Textual and Visual: A Theoretical Account of the Visual Perception of Programming Languages

abstract: Firm principles which can be relied on to analyze and discuss textual and graphical code representations are still missing. We propose a framework relying on ScanVis, an extension of the Semiology of Graphics that models the perception and scanning of abstract graphics, to model and to provide plausible explanations of phenomena pertaining to the visual perception of representations of code. This framework unifies many aspects of the visual layout and appearance of programming languages and reveals similarities and substantial differences in the visual operations required by those notations. We also show how the framework may help compare and generate representations of programming languages with respect to visual perception. This work suggests that the gap between textual and graphical languages is narrow, and that all kind of programming languages should rely on the capability of the human visual system.


title: Automatically characterizing large scale program behavior

abstract: Understanding program behavior is at the foundation of computer architecture and program optimization. Many programs have wildly different behavior on even the very largest of scales (over the complete execution of the program). This realization has ramifications for many architectural and compiler techniques, from thread scheduling, to feedback directed optimizations, to the way programs are simulated. However, in order to take advantage of time-varying behavior, we must first develop the analytical tools necessary to automatically and efficiently analyze program behavior over large sections of execution.Our goal is to develop automatic techniques that are capable of finding and exploiting the Large Scale Behavior of programs (behavior seen over billions of instructions). The first step towards this goal is the development of a hardware independent metric that can concisely summarize the behavior of an arbitrary section of execution in a program. To this end we examine the use of Basic Block Vectors. We quantify the effectiveness of Basic Block Vectors in capturing program behavior across several different architectural metrics, explore the large scale behavior of several programs, and develop a set of algorithms based on clustering capable of analyzing this behavior. We then demonstrate an application of this technology to automatically determine where to simulate for a program to help guide computer architecture research.


title: Interaction between BDNF and Serotonin: Role in Mood Disorders

abstract: Brain-derived neurotrophic factor (BDNF) and serotonin (5-hydroxytryptamine, 5-HT) are two seemingly distinct signaling systems that play regulatory roles in many neuronal functions including survival, neurogenesis, and synaptic plasticity. A common feature of the two systems is their ability to regulate the development and plasticity of neural circuits involved in mood disorders such as depression and anxiety. BDNF promotes the survival and differentiation of 5-HT neurons. Conversely, administration of antidepressant selective serotonin reuptake inhibitors (SSRIs) enhances BDNF gene expression. There is also evidence for synergism between the two systems in affective behaviors and genetic epitasis between BDNF and the serotonin transporter genes.


title: Fear of movement/(re)injury in chronic low back pain and its relation to behavioral performance

abstract: Summary Two studies are presented that investigated 'fear of movement/(re)injury' in chronic musculoskeletal pain and its relation to behavioral performance. The 1st study examines the relation among fear of movement/(re)injury (as measured with the Dutch version of the Tampa Scale for Kinesiophobia (TSK-DV)) (Kori et al. 1990), biographical variables (age, pain duration, gender, use of supportive equipment, compensation status), pain-related variables (pain intensity, pain cognitions, pain coping) and affective distress (fear and depression) in a group of 103 chronic low back pain (CLBP) patients. In the 2nd study, motoric, psychophysiologic and self-report measures of fear are taken from 33 CLBP patients who are exposed to a single and relatively simple movement. Generally, findings demonstrated that the fear of movement/(re)injury is related to gender and compensation status, and more closely to measures of catastrophizing and depression, but in a much lesser degree to pain coping and pain intensity. Furthermore, subjects who report a high degree of fear of movement/(re)injury show more fear and escape/avoidance when exposed to a simple movement. The discussion focuses on the clinical relevance of the construct of fear of movement/(re)injury and research questions that remain to be answered.


Initializing neural networks for hierarchical multi-label text classification


title: Globally optimal line clustering and vanishing point estimation in Manhattan world

abstract: The projections of world parallel lines in an image intersect at a single point called the vanishing point (VP). VPs are a key ingredient for various vision tasks including rotation estimation and 3D reconstruction. Urban environments generally exhibit some dominant orthogonal VPs. Given a set of lines extracted from a calibrated image, this paper aims to (1) determine the line clustering, i.e. find which line belongs to which VP, and (2) estimate the associated orthogonal VPs. None of the existing methods is fully satisfactory because of the inherent difficulties of the problem, such as the local minima and the chicken-and-egg aspect. In this paper, we present a new algorithm that solves the problem in a mathematically guaranteed globally optimal manner and can inherently enforce the VP orthogonality. Specifically, we formulate the task as a consensus set maximization problem over the rotation search space, and further solve it efficiently by a branch-and-bound procedure based on the Interval Analysis theory. Our algorithm has been validated successfully on sets of challenging real images as well as synthetic data sets.


title: © IDOSI Publications, 2009 The Utilization and Integrating of ICT in Chemistry Teaching in Iranian High Schools

abstract: The purpose of this paper is to find some features to develop the utilization of ICT (Information and Communication Technologies) in practical classes and derive some findings useful to experts, making ICT applicable in classes. A questionnaire was developed to investigate experts' views toward integration of ICT in practice classroom. Twenty high school science experts were randomly selected to fill the questionnaires. It was found that in general they highly agreed to change chemistry curriculum using ICT for accessing the internet and other teaching-related facilities. The surveyed experts had very positive views toward ICT and their comments were effective for changing the learning environment.


Pigmentation of the hard palate


title: AoA and ToA Accuracy for Antenna Arrays in Dense Multipath Channels

abstract: The accuracy that can be achieved in time of arrival (ToA) estimation strongly depends on the utilized signal bandwidth. In an indoor environment multipath propagation usually causes a degradation of the achievable accuracy due to the overlapping signals. A similar effect can be observed for the angle of arrival (AoA) estimation using arrays. This paper derives a closed-form equation for the Cramer-Rae lower bound (CRLB) of the achievable AoA and the ToA error variances, considering the presence of dense multipath. The Fisher information expressions for both parameters allows an evaluation of the influence of channel parameters and system parameters such as the array geometry. Our results demonstrate that the AoA estimation accuracy is strongly related to the signal bandwidth, due to the multipath influence. The theoretical results are evaluated with experimental data.


title: Defeating fake-quality MP3

abstract: MP3 is the most popular audio format nowadays in our daily life,e.g., music on the Internet are often of this format. However, low bit rate MP3s are sometimes transcoded to high bit rate, resulting in fake-quality MP3, to seek commercial benefit. This paper presents a method to defeat this kind of fake-quality MP3. Our experimental study and theoretical analysis have demonstrated that there are fewer MDCT coefficients of small values in fake-quality MP3 than in normal MP3. We use the numbers of small-value MDCT coefficients as features to discriminate fake-quality MP3 from normal MP3. Experimental results show the effectiveness of the proposed method. To our best knowledge, this work is the first one to find out fake-quality MP3.


Predicting Movie Success Using Neural Network


title: Classification Uncertainty of Deep Neural Networks Based on Gradient Information

abstract: We study the quantification of uncertainty of Convolutional Neural Networks (CNNs) based on gradient metrics. Unlike the classical softmax entropy, such metrics gather information from all layers of the CNN. We show for the EMNIST digits data set that for several such metrics we achieve the same meta classification accuracy -- i.e. the task of classifying predictions as correct or incorrect without knowing the actual label -- as for entropy thresholding. We apply meta classification to unknown concepts (out-of-distribution samples) -- EMNIST/Omniglot letters, CIFAR10 and noise -- and demonstrate that meta classification rates for unknown concepts can be increased when using entropy together with several gradient based metrics as input quantities for a meta classifier. Meta classifiers only trained on the uncertainty metrics of known concepts, i.e. EMNIST digits, usually do not perform equally well for all unknown concepts. If we however allow the meta classifier to be trained on uncertainty metrics for some out-of-distribution samples, meta classification for concepts remote from EMNIST digits (then termed known unknowns) can be improved considerably.


title: Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development

abstract: Twitter has received much attention recently. An important characteristic of Twitter is its real-time nature. We investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center of the event location. We regard each Twitter user as a sensor and apply particle filtering, which are widely used for location estimation. The particle filter works better than other comparable methods for estimating the locations of target events. As an application, we develop an earthquake reporting system for use in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (93 percent of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and notification is delivered much faster than JMA broadcast announcements.


title: Circuit design for broad band EMI reduction in LCD driver IC

abstract: In this paper, the circuit for electromagnetic interface (EMI) reduction in liquid crystal display (LCD) is proposed. The segmented driver circuit and cascode level shifter are employed in the column driver IC for the EMI reduction. The proposed column driver IC reduces the switching peak current in LCD panel by adopting the segmented operation in data latch. It provides an oscillating transient current with significantly reduced peak currents. The column driver IC and clock controller are integrated in 0.35 μm CMOS technology with 1-poly and 4-metal process. The simulation test shows that the proposed column driver circuit for LCD driver IC significantly reduces EMI noise level by more than 15 dB with 20 segmented operation in data latch at 40 MHz frequency.


title: Data Mining Application in Higher Learning Institutions

abstract: One of the biggest challenges that higher learning institutions face today is to improve the quality of managerial decisions. The managerial decision making process becomes more complex as the complexity of educational entities increase. Educational institute seeks more efficient technology to better manage and support decision making procedures or assist them to set new strategies and plan for a better management of the current processes. One way to effectively address the challenges for improving the quality is to provide new knowledge related to the educational processes and entities to the managerial system. This knowledge can be extracted from historical and operational data that reside in the educational organization's databases using the techniques of data mining technology. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large data sets. This paper presents the capabilities of data mining in the context of higher educational system by i) proposing an analytical guideline for higher education institutions to enhance their current decision processes, and ii) applying data mining techniques to discover new explicit knowledge which could be useful for the decision making processes.


Recurrent neural network and LSTM models for lexical utterance classification.


title: f-GANs in an Information Geometric Nutshell

abstract: Nowozin \textit{et al} showed last year how to extend the GAN \textit{principle} to all $f$-divergences. The approach is elegant but falls short of a full description of the supervised game, and says little about the key player, the generator: for example, what does the generator actually converge to if solving the GAN game means convergence in some space of parameters? How does that provide hints on the generator's design and compare to the flourishing but almost exclusively experimental literature on the subject? ::: In this paper, we unveil a broad class of distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --- and show tight connections with the three other key GAN parameters: loss, game and architecture. In particular, we show that current deep architectures are able to factorize a very large number of such densities using an especially compact design, hence displaying the power of deep architectures and their concinnity in the $f$-GAN game. This result holds given a sufficient condition on \textit{activation functions} --- which turns out to be satisfied by popular choices. The key to our results is a variational generalization of an old theorem that relates the KL divergence between regular exponential families and divergences between their natural parameters. We complete this picture with additional results and experimental insights on how these results may be used to ground further improvements of GAN architectures, via (i) a principled design of the activation functions in the generator and (ii) an explicit integration of proper composite losses' link function in the discriminator.


title: Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

abstract: To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.


title: Biological and nanotechnological applications using interactions between ionic liquids and nucleic acids

abstract: Nucleic acids have emerged as powerful biological and nanotechnological tools. In biological and nanotechnological experiments, methods of extracting and purifying nucleic acids from various types of cells and their storage are critical for obtaining reproducible experimental results. In nanotechnological experiments, methods for regulating the conformational polymorphism of nucleic acids and increasing sequence selectivity for base pairing of nucleic acids are important for developing nucleic acid-based nanomaterials. However, dearth of media that foster favourable behaviour of nucleic acids has been a bottleneck for promoting the biology and nanotechnology using the nucleic acids. Ionic liquids (ILs) are solvents that may be potentially used for controlling the properties of the nucleic acids. Here, we review researches regarding the behaviour of nucleic acids in ILs. The efficiency of extraction and purification of nucleic acids from biological samples is increased by IL addition. Moreover, nucleic acids in ILs show long-term stability, which maintains their structures and enhances nuclease resistance. Nucleic acids in ILs can be used directly in polymerase chain reaction and gene expression analysis with high efficiency. Moreover, the stabilities of the nucleic acids for duplex, triplex, and quadruplex (G-quadruplex and i-motif) structures change drastically with IL cation-nucleic acid interactions. Highly sensitive DNA sensors have been developed based on the unique changes in the stability of nucleic acids in ILs. The behaviours of nucleic acids in ILs detailed here should be useful in the design of nucleic acids to use as biological and nanotechnological tools.


Do Monetary Incentives Crowd Out Intrinsic Motivation ? A Field Test in the Workplace . ∗


title: Integration of Secondary Mathematics and Science Methods Courses: A Model.

abstract: The integration of mathematics and science teaching and learning is gaining interest in many elementary and secondary classrooms. National organizations such as SSMA, NCTM, and AAAS are giving strong support for integrating of mathematics and science in the curriculum. If mathematics and science are to be successfully integrated, it would seem reasonable that mathematics and science courses for teachers also be integrated. This article describes (a) reasons for integrating methods courses, (b) the evolution of an integrated methods course in a small university, (c) topics covered in the course together with descriptions of student assignments and activities, (d) procedures used in student evaluation, and (e) student opinions of the integrated course.


title: Write-Behind Logging

abstract: The design of the logging and recovery components of database management systems (DBMSs) has always been influenced by the difference in the performance characteristics of volatile (DRAM) and non-volatile storage devices (HDD/SSDs). The key assumption has been that non-volatile storage is much slower than DRAM and only supports block-oriented read/writes. But the arrival of new non-volatile memory (NVM) storage that is almost as fast as DRAM with fine-grained read/writes invalidates these previous design choices. ::: ::: This paper explores the changes that are required in a DBMS to leverage the unique properties of NVM in systems that still include volatile DRAM. We make the case for a new logging and recovery protocol, called write-behind logging, that enables a DBMS to recover nearly instantaneously from system failures. The key idea is that the DBMS logs what parts of the database have changed rather than how it was changed. Using this method, the DBMS flushes the changes to the database before recording them in the log. Our evaluation shows that this protocol improves a DBMS's transactional throughput by 1.3×, reduces the recovery time by more than two orders of magnitude, and shrinks the storage footprint of the DBMS on NVM by 1.5×. We also demonstrate that our logging protocol is compatible with standard replication schemes.


title: Airshark: detecting non-WiFi RF devices using commodity WiFi hardware

abstract: In this paper, we propose Airshark -- a system that detects multiple non-WiFi RF devices in real-time and using only commodity WiFi hardware. To motivate the need for systems like Airshark, we start with measurement study that characterizes the usage and prevalence of non-WiFi devices across many locations. We then present the design and implementation of Airshark. Airshark extracts unique features using the functionality provided by a WiFi card to detect multiple non-WiFi devices including fixed frequency devices (e.g., ZigBee, analog cordless phone), frequency hoppers (e.g., Bluetooth, game controllers like Xbox), and broadband interferers (e.g., microwave ovens). Airshark has an average detection accuracy of 91-96%, even in the presence of multiple simultaneously active RF devices operating at a wide range of signal strengths (-80 to -30 dBm), while maintaining a low false positive rate. Through a deployment in two production WLANs, we show that Airshark can be a useful tool to the WLAN administrators in understanding non-WiFi interference.


title: Safe semi-autonomous control with enhanced driver modeling

abstract: During semi-autonomous driving, threat assessment is used to determine when controller intervention that overwrites or corrects the driver's input is required. Since today's semi-autonomous systems perform threat assessment by predicting the vehicle's future state while treating the driver's input as a disturbance, controller intervention is limited to just emergency maneuvers. In order to improve vehicle safety and reduce the aggressiveness of maneuvers, threat assessment must occur over longer prediction horizons where driver's behavior cannot be neglected. We propose a framework that divides the problem of semi-autonomous control into two components. The first component reliably predicts the vehicle's potential behavior by using empirical observations of the driver's pose. The second component determines when the semi-autonomous controller should intervene. To quantitatively measure the performance of the proposed approach, we define metrics to evaluate the infor-mativeness of the prediction and the utility of the intervention procedure. A multi-subject driving experiment illustrates the usefulness, with respect to these metrics, of incorporating the driver's pose while designing a semi-autonomous system.


A New Method for Interference Reduction in the Smoothed Pseudo Wigner-Ville Distribution


title: DCTM: Discrete-Continuous Transformation Matching for Semantic Flow

abstract: Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images. While variations due to scale and rotation have been examined, there lack practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space. To address this problem, we present a discrete-continuous transformation matching (DCTM) framework where dense affine transformation fields are inferred through a discrete label optimization in which the labels are iteratively updated via continuous regularization. In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor. Experimental results show that this model outperforms the state-of-the-art methods for dense semantic correspondence on various benchmarks.


title: Fitness functions for searching the Mandelbrot set

abstract: The Mandelbrot set is a famous fractal. It serves as the source of a large number of complex mathematical images. Evolutionary computation can be used to search the Mandelbrot set for interesting views. This study compares the results of using several different fitness functions for this search. Some of the fitness functions give substantial control over the appearance of the resulting views while others simply locate parts of the Mandelbrot set in which there are complicated structures. All of the fitness functions are based on finding desirable patterns in the number of iterations of the basic Mandelbrot formula to diverge on a set of points arranged in a regular grid near the boundary of the set. It is shown that using different fitness functions causes an evolutionary algorithm to locate difference types of views into the Mandelbrot set.


COBALTOUS OXIDE IN AN OXYGEN POTENTIAL GRADIENT: MORPHOLOGICAL STABILITY OF THE PHASE BOUNDARIES


Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches


title: System identification, estimation and control for a cost effective open-source quadcopter

abstract: This paper describes system identification, estimation and control of translational motion and heading angle for a cost effective open-source quadcopter — the MikroKopter. The dynamics of its built-in sensors, roll and pitch attitude controller, and system latencies are determined and used to design a computationally inexpensive multi-rate velocity estimator that fuses data from the built-in inertial sensors and a low-rate onboard laser range finder. Control is performed using a nested loop structure that is also computationally inexpensive and incorporates different sensors. Experimental results for the estimator and closed-loop positioning are presented and compared with ground truth from a motion capture system.


title: Toward Assessing Data Quality of Ontology Matching on the Web

abstract: Nowadays the semantic Web is a leading Web technology, which enables semantic interoperability between structurally and semantically heterogeneous web sources and web users. Ontologies are a key of semantic interoperability and the main vehicle of the development of the semantic Web. One of the most challenging and important tasks of ontology engineering is integration of ontologies because with the purpose to built a common ontology for all Web sources and consumers in a domain. The present paper describes an approach of assessing data quality of ontology matching that allows evaluating correctness of mapping concepts and relationships from one ontological fragment to another. The purpose of correct mapping is to find identical and synonymous concepts and relationships in ontologies facilitating their integration to a common ontology, which serves as a base for attaining interoperability.


title: Control of multifunctional prosthetic hands by processing the electromyographic signal

abstract: The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contray, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none ofthese methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.


title: Web Browser Fingerprinting Using Only Cascading Style Sheets

abstract: Many commercial Websites employ Web browser fingerprinting to track visitors using Hypertext Transfer Protocol (HTTP) headers, JavaScript, and other methods. Although a user can disable JavaScript or utilize a prevention tool to avoid being tracked, countermeasures against Web browser fingerprinting using Cascading Style Sheets (CSS) have not been established. Therefore, in this paper, we propose a method of fingerprinting that employs only CSS and discuss the effectiveness of our method.


title: Robust Binarization for Video Text Recognition

abstract: This paper presents an automatic binarization method for color text areas in images or videos, which is robust to complex background, low resolution or video coding artefacts. Based on a specific architecture of convolutional neural networks, the proposed system automatically learns how to perform binarization, from a training set of synthesized text images and their corresponding desired binary images, without making any assumptions or using tunable parameters. The proposed method is compared to state-of-the-art binarization techniques, with respect to Gaussian noise and contrast variations, demonstrating the robustness and the efficiency of our method. Text recognition experiments on a database of images extracted from video frames and web pages, with two classical OCRs applied on the obtained binary images show a strong enhancement of the recognition rate by more than 40%.


title: Time-Frequency Distributions Based on Compact Support Kernels: Properties and Performance Evaluation

abstract: This paper presents two new time-frequency distributions (TFDs) based on kernels with compact support (KCS) namely the separable (CB) (SCB) and the polynomial CB (PCB) TFDs. The implementation of this family of TFDs follows the method developed for the Cheriet-Belouchrani (CB) TFD. The mathematical properties of these three TFDs are analyzed and their performance is compared to the best classical quadratic TFDs using several tests on multicomponent signals with linear and nonlinear frequency modulation (FM) components including the noise effects. Instead of relying solely on visual inspection of the time-frequency domain plots, comparisons include the time slices' plots and the evaluation of the Boashash-Sucic's normalized instantaneous resolution performance measure that permits to provide the optimized TFD using a specific methodology. In all presented examples, the KCS-TFDs show a significant interference rejection, with the component energy concentration around their respective instantaneous frequency laws yielding high resolution measure values.


title: Methodological aspects of EEG and body dynamics measurements during motion

abstract: EEG involves recording, analysis, and interpretation of voltages recorded on the human scalp originating from brain grey matter. EEG is one of the favorite methods to study and understand processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements, that are performed in response to the environment. However, there are methodological difficulties when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions of how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determination of real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks.


Net neutrality: A progress report


title: Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network

abstract: In restructured markets where transactions process is competitive, forecasting of electricity price is inevitably an important available tool for market participants. Due to the sensitivity of forecasting issues in market's performance, and high prediction error resulted from the behavior of price series, nowadays probabilistic forecasting highly attracted participants’ attention. In this paper, a probabilistic approach for the hourly electricity price forecasting is presented. In the proposed method, the uncertainty of predictor model is considered as the uncertainty factor. The bootstrapping technique is used to implement the uncertainty and since the method is needed to be fast and of low computational cost in the daily forecasting, a generalized learning method is applied, which has high accuracy and speed. This newly presented learning method is based on generalized extreme learning machine approach to be used for improved wavelet neural networks. Also in order to reach more accommodation, the predictor model with the changes of price time series, the wavelet preprocessing is used. Effective performance of the proposed model is validated by testing on data of Ontario and Australian electricity markets.


title: Visualization Techniques of Trajectory Data: Challenges and Limitations

abstract: As a consequence of the prevalence of mobile computing and location based services, huge amounts of movement data are nowadays being collected. While the research interest on the analysis of trajectory data has also significantly increased, there are still several open challenges in areas related to geographic information systems. Despite the existence of several techniques for the visualization of movement data, it is still unclear how usable and useful these techniques are, how can they be improved, and in which tasks they should be used. In this paper, we highlight the current limitations in the visual exploration of trajectory data, and present the ongoing research aiming to address those issues. For that, we propose the development of taxonomies addressing visualization tasks, techniques, and data, based on empirical data, through systematic comparative usability studies, and present an overview of the current results.


title: Theoretical Comparison between the Gini Index and Information Gain Criteria

abstract: Knowledge Discovery in Databases (KDD) is an active and important research area with the promise for a high payoff in many business and scientific applications. One of the main tasks in KDD is classification. A particular efficient method for classification is decision tree induction. The selection of the attribute used at each node of the tree to split the data (split criterion) is crucial in order to correctly classify objects. Different split criteria were proposed in the literature (Information Gain, Gini Index, etc.). It is not obvious which of them will produce the best decision tree for a given data set. A large amount of empirical tests were conducted in order to answer this question. No conclusive results were found. In this paper we introduce a formal methodology, which allows us to compare multiple split criteria. This permits us to present fundamental insights into the decision process. Furthermore, we are able to present a formal description of how to select between split criteria for a given data set. As an illustration we apply the methodology to two widely used split criteria: Gini Index and Information Gain.


title: Application of Evidential Reasoning rules to identification of asthma control steps in children

abstract: The UK is one of these countries in Europe, which have the highest death rate from asthma, and the rules to identify asthma control steps are vague in the current official guideline of asthma management. In this research, diagnosis rules on asthma control steps are developed to supplement the current guideline and to assist patients to monitor and manage their asthma on daily basis. The main challenge of developing the rules is missing values. Although the data examined have prodigious volumes of records for patients, no one have all and different patients have different information recorded. The large proportion of missing values lead to comparatively limited powers of some techniques like Decision Tree Analysis, Logistic Regression, ANN, Bayes' Rule and SVM. This research explores the Evidential Reasoning (ER) rule to develop prognostic rules for asthma control steps. ER is prior-free probabilistic inference and has not been applied to disease diagnosing and monitoring. The results are represented as probability distributions on asthma control steps given any combination of evidence, even if some combinations are not recorded in the current database. In practice, it could help clinicians to identify asthma control steps of patients, prescribe corresponding treatments, and monitor the effectiveness of the treatment and the progress of patients in asthma control management.


title: Animal-Vehicle Collision Mitigation System for Automated Vehicles

abstract: Detecting large animals on roadways using automated systems such as robots or vehicles is a vital task. This can be achieved using conventional tools such as ultrasonic sensors, or with innovative technology based on smart cameras. In this paper, we investigate a vision-based solution. We begin the paper by performing a comparative study between three detectors: 1) Haar-AdaBoost; 2) histogram of oriented gradient (HOG)-AdaBoost; and 3) local binary pattern (LBP)-AdaBoost, which were initially developed to detect humans and their faces. These detectors are implemented, evaluated, and compared to each other in terms of accuracy and processing time. Based on our evaluation and comparison results, we design a two-stage architecture which outperforms the aforementioned detectors. The proposed architecture detects candidate regions of interest using LBP-AdaBoost in the first stage, which offers robustness to false positives in real-time conditions. The second stage is based on support vector machine classifiers that were trained using HOG features. The training data are generated from our novel dataset called large animal dataset, which contains common and thermographic images of large road-animals. We emphasize that no such public dataset currently exists.


title: A language extension for expressing constraints on data access

abstract: Controlled sharing of information is needed and desirable for many applications and is supported in operating systems by access control mechanisms. This paper shows how to extend programming languages to provide controlled sharing. The extension permits expression of access constraints on shared data. Access constraints can apply both to simple objects, and to objects that are components of larger objects, such as bank account records in a bank's data base. The constraints are stated declaratively, and can be enforced by static checking similar to type checking. The approach can be used to extend any strongly-typed language, but is particularly suitable for extending languages that support the notion of abstract data types.


title: Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets

abstract: Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST by adding noise to the MNIST dataset, and three labeled datasets formed by adding noise to the offline Bangla numeral database. Then we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST, n-MNIST and noisy Bangla datasets, our framework shows promising results and outperforms traditional Deep Belief Networks.


title: Reliability and validity of the Functional Assessment of Cancer Therapy-Colorectal (FACT-C) quality of life instrument

abstract: Background: This paper describes the development of the Functional Assessment of Cancer Therapy-Colorectal (FACT-C) – a questionnaire assessing quality of life concerns pertinent to colorectal cancer patients. This self-report instrument combines specific concerns related to colorectal cancer with concerns that are common to all cancer patients as assessed with the FACT-General (FACT-G). Method: Three separate and distinct validation samples were used with different ethnic and stage of disease compositions. Results: Results suggest that the FACT-C is a reliable and valid measure in both its English and Spanish language versions. Specifically, the FACT-C evidenced good internal consistency reliability and concurrent validity, as well as an ability to distinguish between groups based on functional status and extent of disease. The FACT-C was also found to be sensitive to changes in functional status. Colorectal cancer-specific items form the Colorectal Cancer Subscale (CCS) which was found to have adequate convergent and divergent validity. Internal consistency was adequate in most samples. In addition, the CCS was able to distinguish among groups that differed in functional status and was sensitive to change in functional status. Conclusion: It is recommended that the entire FACT-C (rather than simply the CCS) be used when studying patients with colorectal cancer in order to provide a comprehensive assessment of quality of life. Recommendations are offered concerning the appropriate use of these measures in clinical research and directions for future research with colorectal cancer patients.


title: Early onset cannabis use and psychosocial adjustment in young adults.

abstract: The relationships between early onset (prior to 16 years) cannabis use and later psychosocial adjustment was examined in a birth cohort of New Zealand children studied to age 18 years. Early onset users had significantly higher rates of later substance use, juvenile offending, mental health problems, unemployment and school dropout. The linkages between early onset cannabis use and later outcomes were largely explained by two routes that linked cannabis use to later adjustment. First, those electing to use cannabis were a high risk population characterized by social disadvantage, childhood adversity, early onset behavioural difficulties and adverse peer affiliations. Secondly, early onset cannabis use was associated with subsequent affiliations with delinquent and substance using peers, moving away from home and dropping out of education with these factors in turn, being associated with increased psychosocial risk. The implications of these results are examined.


title: Information Technology Competencies, Organizational Agility, and Firm Performance: Enabling and Facilitating Roles

abstract: The hypercompetitive aspects of modern business environments have drawn organizational attention toward agility as a strategic capability. Information technologies are expected to be an important competency in the development of organizational agility. This research proposes two distinct roles to understand how information technology competencies shape organizational agility and firm performance. In their enabling role, IT competencies are expected to directly enhance entrepreneurial and adaptive organizational agility. In their facilitating role, IT competencies should enhance firm performance by helping the implementation of requisite entrepreneurial and adaptive actions. Furthermore, we argue that the effects of the dual roles of IT competencies are moderated by multiple contingencies arising from environmental dynamism and other sources. We test our model and hypotheses through a latent class regression analysis on data from a sample of 109 business-to-business electronic marketplaces. The results provide support for the enabling and facilitating roles of IT competencies. Moreover, we find that these dual effects vary according to environmental dynamism. The results suggest that managers should account for multiple contingencies observed and unobserved while assessing the effects of IT competencies on organizational agility and firm performance.


title: The NLM Medical Text Indexer System for Indexing Biomedical Literature

abstract: In the face of a growing workload and dwindling resources, the US National Library of Medicine (NLM) created the Indexing Initiative project in the mid-1990s. This cross-library team’s mission is to explore indexing methodologies that can help ensure that MEDLINE and other NLM document collections maintain their quality and currency and thereby contribute to NLM’s mission of maintaining quality access to the biomedical literature. The NLM Medical Text Indexer (MTI) is the main product of this project and has been providing indexing recommendations based on the Medical Subject Headings (MeSH) vocabulary since 2002. In 2011, NLM expanded MTI’s role by designating it as the first-line indexer (MTIFL) for a few journals; today the MTIFL workflow includes about 100 journals and continues to increase. Due to a close collaboration with the Index Section at NLM, MTI continues to grow and expand its ability to provide assistance to the indexers. This paper provides an overview of MTI’s functionality, performance, and its evolution over the years.


title: Detection of hacking behaviors and communication patterns on social media

abstract: Hackers make extensive use of online communities, sharing knowledge, tools, as well as performing coordination and recruitment activities. In order to detect such behaviors, this paper proposes a set of indicators which analyze online communication patterns, including technical discussions, expression of positive and negative sentiments and threats, recruitment activities, and user profiling. The indicators are processing streaming social media and search for online behaviors and communication patterns characteristic of hackers with different motivations and skills. Our initial evaluation of indicators using twitter data shows that there is a significant variation in indicator values across different types of hackers. For example, hackers with higher level skills tend to use technical topics in their conversation more often than hackers with lower skills, whereas hackers motivated by profit and ideology tend to express recruitment language more often than attackers motivated by revenge and prestige. These results support our hypothesis that detection of hacking behaviors on social media needs to take into account the differences in intentions, motivations, and skills of different types of hackers.


title: A Survey on Human Performance Capture and Animation

abstract: With the rapid development of computing technology, three-dimensional (3D) human body models and their dynamic motions are widely used in the digital entertainment industry. Human performance mainly involves human body shapes and motions. Key research problems in human performance animation include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical effects. In this survey, according to the main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion simulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animation.


A Secure, Efficient, and Transparent Network Architecture for Bitcoin


title: Handling sparsity via the horseshoe

abstract: This paper presents a general, fully Bayesian framework for sparse supervised-learning problems based on the horseshoe prior. The horseshoe prior is a member of the family of multivariate scale mixtures of normals, and is therefore closely related to widely used approaches for sparse Bayesian learning, including, among others, Laplacian priors (e.g. the LASSO) and Student-t priors (e.g. the relevance vector machine). The advantages of the horseshoe are its robustness at handling unknown sparsity and large outlying signals. These properties are justied theoretically via a representation theorem and accompanied by comprehensive empirical experiments that compare its performance to benchmark alternatives.


title: Fast Keypoint Recognition in Ten Lines of Code

abstract: While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well to handle large number of classes. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.


Review of Amateur-Built 2 . 4-GHz Wireless-LAN Directional Wire Antennas


title: An adaptive image Euclidean distance

abstract: The image Euclidean distance (IMED) considers the spatial relationship between the pixels of different images and can easily be embedded in existing image recognition algorithms that are based on Euclidean distance. IMED uses the prior knowledge that pixels located near one another have little variance in gray scale values, and defines a metric matrix according to the spatial distance between pixels. In this paper, we propose an adaptive image Euclidean distance (AIMED), which considers not only the prior spatial knowledge, but also the prior gray level knowledge from images. The most important advantage of the proposed AIMED over IMED is that AIMED makes the metric matrix adaptive to the content of the concerned images. Two ways of using gray level information are proposed. One is based on gray level distances, and the other is based on cosine dissimilarity of gray levels. Experiments on two facial databases and a handwritten digital database show that AIMED achieves the highest classification accuracy when it is embedded in nearest neighbor classifiers, principal component analysis, and support vector machines.


title: Adaptive-latency DRAM: Optimizing DRAM timing for the common-case

abstract: In current systems, memory accesses to a DRAM chip must obey a set of minimum latency restrictions specified in the DRAM standard. Such timing parameters exist to guarantee reliable operation. When deciding the timing parameters, DRAM manufacturers incorporate a very large margin as a provision against two worst-case scenarios. First, due to process variation, some outlier chips are much slower than others and cannot be operated as fast. Second, chips become slower at higher temperatures, and all chips need to operate reliably at the highest supported (i.e., worst-case) DRAM temperature (85° C). In this paper, we show that typical DRAM chips operating at typical temperatures (e.g., 55° C) are capable of providing a much smaller access latency, but are nevertheless forced to operate at the largest latency of the worst-case. Our goal in this paper is to exploit the extra margin that is built into the DRAM timing parameters to improve performance. Using an FPGA-based testing platform, we first characterize the extra margin for 115 DRAM modules from three major manufacturers. Our results demonstrate that it is possible to reduce four of the most critical timing parameters by a minimum/maximum of 17.3%/54.8% at 55°C without sacrificing correctness. Based on this characterization, we propose Adaptive-Latency DRAM (AL-DRAM), a mechanism that adoptively reduces the timing parameters for DRAM modules based on the current operating condition. AL-DRAM does not require any changes to the DRAM chip or its interface. We evaluate AL-DRAM on a real system that allows us to reconfigure the timing parameters at runtime. We show that AL-DRAM improves the performance of memory-intensive workloads by an average of 14% without introducing any errors. We discuss and show why AL-DRAM does not compromise reliability. We conclude that dynamically optimizing the DRAM timing parameters can reliably improve system performance.


title: Automatic extraction of synonyms in a dictionary

abstract: We propose a method for automatic synonym extraction in a dictionary. Our method is based on an algorithm that computes similarity measures between vertices in graphs. This algorithm can be thought of as a generalization of Kleinberg’s web search algorithm to structure graphs that are more general than the hub-authority graph used by Kleinberg. We use the 1913 Webster’s Dictionary and apply our method on four synonym queries. The results obtained are analyzed and compared to those obtained with two other methods.


title: Effective connectivity within the frontoparietal control network differentiates cognitive control and working memory

abstract: Abstract Cognitive control and working memory rely upon a common fronto-parietal network that includes the inferior frontal junction (IFJ), dorsolateral prefrontal cortex (dlPFC), pre-supplementary motor area/dorsal anterior cingulate cortex (pSMA/dACC), and intraparietal sulcus (IPS). This network is able to flexibly adapt its function in response to changing behavioral goals, mediating a wide range of cognitive demands. Here we apply dynamic causal modeling to functional magnetic resonance imaging data to characterize task-related alterations in the strength of network interactions across distinct cognitive processes. Evidence in favor of task-related connectivity dynamics was accrued across a very large space of possible network structures. Cognitive control and working memory demands were manipulated using a factorial combination of the multi-source interference task and a verbal 2-back working memory task, respectively. Both were found to alter the sensitivity of the IFJ to perceptual information, and to increase IFJ-to-pSMA/dACC connectivity. In contrast, increased connectivity from the pSMA/dACC to the IPS, as well as from the dlPFC to the IFJ, was uniquely driven by cognitive control demands; a task-induced negative influence of the dlPFC on the pSMA/dACC was specific to working memory demands. These results reflect a system of both shared and unique context-dependent dynamics within the fronto-parietal network. Mechanisms supporting cognitive engagement, response selection, and action evaluation may be shared across cognitive domains, while dynamic updating of task and context representations within this network are potentially specific to changing demands on cognitive control.


title: MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP

abstract: We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes Mikolov et al. [2013b]'s word2vec features, Le and Mikolov [2014]'s paragraph vector (batch and online) and Luong et al. [2015]'s model for bilingual distributed representations. MultiVec also includes different distance measures between words and sequences of words. The toolkit is written in C++ and is aimed at being fast (in the same order of magnitude as word2vec), easy to use, and easy to extend. It has been evaluated on several NLP tasks: the analogical reasoning task, sentiment analysis, and crosslingual document classification.


title: Design and Calibration of a New 6 DOF Haptic Device

abstract: For many applications such as tele-operational robots and interactions with virtual environments, it is better to have performance with force feedback than without. Haptic devices are force reflecting interfaces. They can also track human hand positions simultaneously. A new 6 DOF (degree-of-freedom) haptic device was designed and calibrated in this study. It mainly contains a double parallel linkage, a rhombus linkage, a rotating mechanical structure and a grasping interface. Benefited from the unique design, it is a hybrid structure device with a large workspace and high output capability. Therefore, it is capable of multi-finger interactions. Moreover, with an adjustable base, operators can change different postures without interrupting haptic tasks. To investigate the performance regarding position tracking accuracy and static output forces, we conducted experiments on a three-dimensional electric sliding platform and a digital force gauge, respectively. Displacement errors and force errors are calculated and analyzed. To identify the capability and potential of the device, four application examples were programmed.


title: Mercator: A scalable, extensible Web crawler

abstract: This paper describes Mercator, a scalable, extensible Web crawler written entirely in Java. Scalable Web crawlers are an important component of many Web services, but their design is not well-documented in the literature. We enumerate the major components of any scalable Web crawler, comment on alternatives and tradeoffs in their design, and describe the particular components used in Mercator. We also describe Mercator’s support for extensibility and customizability. Finally, we comment on Mercator’s performance, which we have found to be comparable to that of other crawlers for which performance numbers have been published.


title: Nanofiber Web Textile Dry Electrodes for Long-Term Biopotential Recording

abstract: Electrode properties are key to the quality of measured biopotential signals. Ubiquitous health care systems require long-term monitoring of biopotential signals from normal volunteers and patients in home or hospital environments. In these settings it is appropriate to use dry textile electrode networks for monitoring purposes, rather than the gel or saline-sponge skin interfaces used with Ag/AgCl electrodes. In this study, we report performance test results of two different electrospun conductive nanofiber webs, and three metal plated fabrics. We evaluated contact impedance, step response, noise and signal fidelity performance indices for all five dry electrodes, and compared them to those of conventional Ag/AgCl electrodes. Overall, we found nanofiber web electrodes matched Ag/AgCl electrode performance more closely than metal plated fabric electrodes, with the contact resistance and capacitance of Ag plated PVDF nanofiber web electrodes being most similar to Ag/AgCl over the 10 Hz to 500 kHz frequency range. We also observed that step responses of all three metal-plated fabrics were poorer than those for nanofiber web electrodes and Ag/AgCl. Further, noise standard deviation and noise power spectral densities were generally lower in nanofiber web electrodes than metal plated fabrics; and waveform fidelity of ECG-like traces recorded from nanofiber web electrodes was higher than for metal plated fabrics. We recommend textile nanofiber web electrodes in applications where flexibility, comfort and durability are required in addition to good electrical characteristics.


title: Technique for Determining Bridge Displacement Response Using MEMS Accelerometers

abstract: In bridge maintenance, particularly with regard to fatigue damage in steel bridges, it is important to determine the displacement response of the entire bridge under a live load as well as that of each member. Knowing the displacement response enables the identification of dynamic deformations that can cause stresses and ultimately lead to damage and thus also allows the undertaking of appropriate countermeasures. In theory, the displacement response can be calculated from the double integration of the measured acceleration. However, data measured by an accelerometer include measurement errors caused by the limitations of the analog-to-digital conversion process and sensor noise. These errors distort the double integration results. Furthermore, as bridges in service are constantly vibrating because of passing vehicles, estimating the boundary conditions for the numerical integration is difficult. To address these problems, this paper proposes a method for determining the displacement of a bridge in service from its acceleration based on its free vibration. To verify the effectiveness of the proposed method, field measurements were conducted using nine different accelerometers. Based on the results of these measurements, the proposed method was found to be highly accurate in comparison with the reference displacement obtained using a contact displacement gauge.


title: A Hierarchical Approach for Fast and Robust Ellipse Extraction

abstract: This paper presents a hierarchical approach for fast and robust ellipse extraction from images. At the lowest level, the image is described as a set of edge pixels, from which line segments are extracted. Then, line segments that are potential candidates of elliptic arcs are linked to form arc segments according to connectivity and curvature relations. After that, arc segments that belong to the same ellipse are grouped together. Finally, a robust statistical method, namely RANSAC, is applied to fit ellipses. This method does not need a high dimensional parameter space like Hough transform based algorithms, and so it reduces the computation and memory requirements. Experiments on both synthetic and real images demonstrate that the proposed method has excellent performance in handling occlusion and overlapping ellipses.