title: A Comparison of Oral Motor and Production Training for Children with Speech Sound Disorders

abstract: Despite the many debates about the usefulness of nonspeech oral motor exercises (NSOMEs) in the treatment of speech disorders, few controlled experiments have evaluated their efficacy in the remediation of phonological/articulatory disorders (PADs). More importantly, the relative effect of NSOMEs compared with traditional production treatment (PT) has not been established. The current study employed an alternating treatment design to evaluate changes in production of sounds targeted by NSOMEs and PT in nine children with PAD. Each subject received treatment on two linguistically distinct sounds in which one sound was treated with NSOMEs and the second sound was targeted with PT. The difference in treatment efficacy, measured as the percentage change in target production for NSOMEs versus PT, was compared using a paired T test. Because NSOMEs typically are used to ready a child for subsequent PT, comparison of PT treatment accuracy was made between NSOME-first and PT-first sessions. Results demonstrated a statistically significant effect of treatment type with greater production gains with PT compared with NSOMEs. Further, no facilitative effect of NSOMEs on PT was noted; however, the choice of distinct treatment targets may have contributed to this null effect. Although additional investigation is warranted, the current investigation does not support the efficacy of NSOMEs in the treatment of PAD.


title: Effectiveness of the transpalatal arch in controlling orthodontic anchorage in maxillary premolar extraction cases: A systematic review and meta-analysis.

abstract: ABSTRACT Objective: To evaluate the effectiveness of the transpalatal arch (TPA) as an anchorage device in preventing maxillary molar mesialization during retraction of the anterior teeth after premolar extraction. Materials and Methods: This systematic review intended to include patients indicated for upper premolar bilateral extraction and subsequent retraction of anterior teeth, considering the use of TPA as an anchorage tool in one of the treatment groups. The search was systematically performed, up to April 2015, in the following electronic databases: Medline, Embase, and all evidence-based medicine reviews via OVID, Cochrane Library, Scopus, PubMed, and Web of Science. Risk of bias assessment was performed using Cochrane's Risk of Bias Tool for randomized clinical trials (RCTs) and Methodological Index for Nonrandomized Studies (MINORS) for non-RCTs. Results: Fourteen articles were finally included. Nine RCTs and five non-RCTs presented moderate to high risk of bias. Only one study investigated the ...


title: Taking the terror out of bioterrorism: planning for a bioterrorist event from a local perspective.

abstract: There is a growing concern in the public health community over the potential for domestic biological and chemical acts of terrorism. These types of events do not respect city limits, county lines, or other geopolitical borders and pose a unique challenge for local health departments that have a critical role in detecting, preparing for, and responding to such events. Because direct support for most public health service, including bioterrorism preparedness, occurs primarily at the local level, this is the logical starting point for all planning activities. Language: en


title: Efficient GPU Screen-Space Ray Tracing

abstract: We present an efficient GPU solution for screen-space 3D ray tracing against a depth buffer by adapting the perspective-correct DDA line rasterization algorithm. Compared to linear ray marching, this ensures sampling at a contiguous set of pixels and no oversampling. This paper provides for the first time full implementation details of a method that has been proven in production of recent major game titles. After explaining the optimizations, we then extend the method to support multiple depth layers for robustness. We include GLSL code and examples of pixel-shader ray tracing for several applications.


title: Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks

abstract: The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting. Most natural language processing applications expect segmented and well-formatted texts as input, which is not available in ASR output. This paper proposes a novel technique of jointly modeling multiple correlated tasks such as punctuation and capitalization using bidirectional recurrent neural networks, which leads to improved performance for each of these tasks. This method could be extended for joint modeling of any other correlated sequence labeling tasks.


title: Clustering memes in social media streams

abstract: The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here, we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to “forget” old memes and replace them over time with the new ones. The evaluation of our framework is carried out using a dataset of Twitter trending topics. Over a 1-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.


title: Gyroscope-based video stabilisation with auto-calibration

abstract: We propose a technique for joint calibration of a wide-angle rolling shutter camera (e.g. a GoPro) and an externally mounted gyroscope. The calibrated parameters are time scaling and offset, relative pose between gyroscope and camera, and gyroscope bias. The parameters are found using non-linear least squares minimisation using the symmetric transfer error as cost function.


title: Lust, Love, and Life: A Qualitative Study of Swedish Adolescents' Perceptions and Experiences with Pornography

abstract: There is a widespread concern in Western society about the visibility of pornography in public places and on the Internet. What are the consequences for young men and women, and how do they think about gender, sexuality, and pornography? Data was collected, through 22 individual interviews and seven focus groups, from 51 participants (36 women and 37 men aged 14–20 years) in Sweden. The results indicated a process of both normalization and ambivalence. Pornography was used as a form of social intercourse, a source of information, and a stimulus for sexual arousal. Pornography consumption was more common among the young men than among the women. For both the young men and women, the pornographic script functioned as a frame of reference in relation to bodily ideals and sexual performances. Most of the participants had acquired the necessary skills of how to deal with the exposure to pornography in a sensible and reflective manner.


title: ICN-Fog: An Information-Centric Fog-to-Fog Architecture for Data Communications

abstract: Fog computing is an emerging architecture for bringing processing, storage, and control from the Cloud closer to the Things/Users. Fog has mostly been studied in the vertical continuum between the Things/Users and the Cloud to provide resources traditionally existing in the remote Cloud to the applications. This paper introduces ICN-Fog, a novel horizontal Fog-to-Fog layer enabled by Information-Centric Networking. ICN-Fog enriches applications with horizontal data transfer in the Fog layer, distributed processing among Fog nodes, and built-in mobility support thanks to the smart connectionless name-based Fog-to-Fog data communications. We explain the rationale behind our design and demonstrate the advantages of the proposed Fog architecture through two representative case studies.


title: Differential item functioning of the CAGE, TWEAK, BMAST and AUDIT by gender and ethnicity

abstract: The importance of having available an efficient, sound instrument for the detection of alcohol dependence and problem drinking in a medical setting is underscored by the generally poor performance of clinical judgment and blood alcohol level (BAL) in detecting these conditions (Soderstrom, Dischinger, Smith et al., 1992; Whitney, 1983; Yates, Hadfield & Peters, 1987). Although a number of screening devices have been developed for use in medical settings, such as the CAGE (Ewing, 1984) and the MAST (Selzer, 1971), these instruments were primarily developed using samples of male alcoholics. Other, newer screening devices, such as the AUDIT (Saunders, Aasland, Babor et al., 1993) and the TWEAK (Russell et al., 1994), have been developed to identify problem drinking in primary care settings, but these instruments have seen limited development in these settings in general, as well as among women and ethnic minorities. Information relating to the psychometric properties of these criterion-referenced instruments has been of mixed quality, and studies have generally been limited to an examination of the sensitivity, specificity, and predictive value, or summary measures such as the receiver operating characteristic curve (ROC) (Hsiao, Bartko & Potter, 1989), which plots sensitivity as a function of 1 - specificity. Studies have also evaluated instrument performance at standard cut points (Cherpitel, 1995a; Fleming & Barry, 1989), and some studies have used ROC analysis to determine optimal cut points (Barry & Fleming, 1993; Ross, Gavin & Skinner, 1990). Studies examining the relative performance of these instruments against each other have been sparse, however, and have generally been limited to comparisons between two of the standard screening instruments (Barry & Fleming, 1993; Magruder-Habib, Stevens & Alling, 1993; Ross, Gavin & Skinner, 1990) or between one or two of these instruments and laboratory tests (Bernadt, Taylor, Mumford et al., 1982; Girela, Villanueva, Hernandez-Cueto & Luna, 1994; Skinner, Holt, Schuller et al., 1984). An exception has been the work of Chan and Russell (Chan, Pristach, Welte & Russell, 1993; Russell et al., 1994) examining the comparative performance of a screening instrument developed for detecting pregnancyrisk drinking in prenatal and general populations. Previous analyses (e.g., Cherpitel,1995a) have not focused on the potential for item bias by gender and ethnicity for these alcohol screening instruments, although this testing process has been recommended as good psychometric practice (Nunnally & Bernstein, 1994). This type of work is important, because if individual items on an alcohol screening instrument function differently for two groups, then the overall sensitivity for that instrument will differ as a function of group membership. The present study was designed to test the individual items on the CAGE, TWEAK, brief MAST, and AUDIT for differential item functioning by gender and ethnic identification among a sample of emergency room patients at a large Southern medical center. Differential item functioning, or DIF, is a psychometric term currently used in place of the term item bias (Camilli and Shepard, 1994). These data have been previously used by Cherpitel (1995a and 1995b; Cherpitel & Clark, 1995) to examine the psychometric properties of the scales (e.g., sensitivity and specificity), but not item functioning. Method For a more detailed description of the participants, procedures and instruments, please see Cherpitel (1995a). A probability sample of every third patient 18 years old and older was drawn over a six-month period from the University of Mississippi Medical Center emergency room. Interviews were obtained from 89% (N = 1,330) of those interviews selected (N = 1,498). Analyses were conducted on those participants who were current drinkers and reported consuming three or more drinks at one time at least once in their lives (N = 492). …


title: What to believe : Bayesian methods for data analysis

abstract: Although Bayesian models of mind have attracted great interest from cognitive scientists, Bayesian methods for data analysis have not. This article reviews several advantages of Bayesian data analysis over traditional null-hypothesis significance testing. Bayesian methods provide tremendous flexibility for data analytic models and yield rich information about parameters that can be used cumulatively across progressive experiments. Because Bayesian statistical methods can be applied to any data, regardless of the type of cognitive model (Bayesian or otherwise) that motivated the data collection, Bayesian methods for data analysis will continue to be appropriate even if Bayesian models of mind lose their appeal.


title: Towards automatic analysis of gestures and body expressions in depression

abstract: Depression is a common mental disorder and one of the main causes of disease burden worldwide. Several studies in depression address the relation between non-verbal cues and different levels of depression. Manual coding of non-verbal cues is the common practice for running such studies, which is time consuming and non-objective. Recent research has looked into automatic detection of cues associated with depression. However, most of the work has focussed on facial cues such as facial expressions, gaze and head pose. Few studies have looked into multimodal features for analysis of depression, mainly focusing on facial movements, head movements and vocal prosody. Body gestures are an under-studied modality in that field. We propose to investigate assessment of depression using automatic detection of nonverbal signals of body gestures. Moreover, we propose the use of multimodal fusion of features to incorporate body as well as face and head for better inference of depression level. Automatic analysis of such body cues can serve as a tool for experimental psychologists. Also, it can assist physicians in diagnosing by providing quantitative measures after or during face to face sessions or telemedicine sessions or even in systems like a virtual coach.


title: CoInCar-Sim: An Open-Source Simulation Framework for Cooperatively Interacting Automobiles

abstract: While motion planning techniques for automated vehicles in a reactive and anticipatory manner have already been widely presented, cooperative motion planning has only been addressed recently. For the latter, interaction between traffic participants is crucial. Consequently, simulations where other traffic participants follow simple behavioral rules can no longer beused for development and evaluation. To close this gap, we present a multi vehicle simulation framework. Conventional simulation agents, using a simple, rule-based behavior, are replaced by multiple instances of sophisticated behavior generation algorithms. Thus, development, test and simulative evaluation of cooperative planning approaches is facilitated. The framework is implemented using the Robot Operating System (ROS) and its code will be released open source.


title: Highly isolated power supply design for gate drivers of the solid state transformer

abstract: This paper describes a high voltage isolation over 30kV gate driver power supply for solid-state-transformers (SSTs). In order to implement this, several dc-dc converter topologies with a highly isolated high frequency (HF) transformer are examined. The simulation study about the electrostatic field of the HF transformer is performed. Moreover, some galvanic insulation materials are analyzed to enhance the isolation between the primary and the secondary sides of the HF transformer. Finally, the fabricated dc-dc converter and HF transformer are tested and the performance is verified through the experiments.


title: Near or Far, Wide Range Zero-Shot Cross-Lingual Dependency Parsing

abstract: Cross-lingual transfer is the major means toleverage knowledge from high-resource lan-guages to help low-resource languages. In this paper, we investigate cross-lingual trans-fer across a broad spectrum of language dis-tances. We posit that Recurrent Neural Net-works (RNNs)-based encoders, since explic-itly incorporating surface word order, are brit-tle for transferring across distant languages,while self-attentive models are more flexibleon modeling word order information; thusthey would be more robust in the cross-lingualtransfer setting. We test our hypothesis bytraining dependency parsers on only Englishcorpus and evaluating them on 31 other lan-guages. With detailed analysis, we find inter-esting patterns showing that RNNs-based ar-chitectures can transfer well for languages thatare close to English, while self-attentive mod-els are have better cross-lingual transferabilityacross a wide range of languages.


title: Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing

abstract: Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, includin...


title: Efficient self-shadowed radiosity normal mapping

abstract: In Valve's Source graphics engine, bump mapping is combined with precomputed radiosity lighting to provide realistic surface illumination. When bump map data is derived from geometric descriptions of surface detail (such as height maps), only the lighting effects caused by the surface orientation are preserved. The significant lighting cues due to lighting occlusion by surface details are lost. While it is common to use another texture channel to hold an "ambient occlusion" field, this only provides a darkening effect which is independent of the direction from which the surface is being lit and requires an auxiliary channel of data. In this chapter, we present a modification to the Radiosity Normal Mapping system that we have described in this course in the past. This modification provides a directional occlusion function to the bump maps, which requires no additional texture memory and is faster than our previous non-shadowing solution.


title: DataSlicer: Task-Based Data Selection for Visual Data Exploration

abstract: In visual exploration and analysis of data, determining how to select and transform the data for visualization is a challenge for data-unfamiliar or inexperienced users. Our main hypothesis is that for many data sets and common analysis tasks, there are relatively few "data slices" that result in effective visualizations. By focusing human users on appropriate and suitably transformed parts of the underlying data sets, these data slices can help the users carry their task to correct completion. ::: To verify this hypothesis, we develop a framework that permits us to capture exemplary data slices for a user task, and to explore and parse visual-exploration sequences into a format that makes them distinct and easy to compare. We develop a recommendation system, DataSlicer, that matches a "currently viewed" data slice with the most promising "next effective" data slices for the given exploration task. We report the results of controlled experiments with an implementation of the DataSlicer system, using four common analytical task types. The experiments demonstrate statistically significant improvements in accuracy and exploration speed versus users without access to our system.


title: HARPY: Hypernyms and Alignment of Relational Paraphrases

abstract: Collections of relational paraphrases have been automatically constructed from large text corpora, as a WordNet counterpart for the realm of binary predicates and their surface forms. However, these resources fall short in their coverage of hypernymy links (subsumptions) among the synsets of phrases. This paper closes this gap by computing a high-quality alignment between the relational phrases of the Patty taxonomy, one of the largest collections of this kind, and the verb senses of WordNet. To this end, we devise judicious features and develop a graph-based alignment algorithm by adapting and extending the SimRank random-walk method. The resulting taxonomy of relational phrases and verb senses, coined HARPY, contains 20,812 synsets organized into a Directed Acyclic Graph (DAG) with 616,792 hypernymy links. Our empirical assessment, indicates that the alignment links between Patty and WordNet have high accuracy, with Mean Reciprocal Rank (MRR) score 0.7 and Normalized Discounted Cumulative Gain (NDCG) score 0.73. As an additional extrinsic value, HARPY provides fine-grained lexical types for the arguments of verb senses in WordNet.


Autonomous off-road navigation with end-to-end learning for the LAGR program


title: Automated classification of software issue reports using machine learning techniques: an empirical study

abstract: Software developers, testers and customers routinely submit issue reports to software issue trackers to record the problems they face in using a software. The issues are then directed to appropriate experts for analysis and fixing. However, submitters often misclassify an improvement request as a bug and vice versa. This costs valuable developer time. Hence automated classification of the submitted reports would be of great practical utility. In this paper, we analyze how machine learning techniques may be used to perform this task. We apply different classification algorithms, namely naive Bayes, linear discriminant analysis, k-nearest neighbors, support vector machine (SVM) with various kernels, decision tree and random forest separately to classify the reports from three open-source projects. We evaluate their performance in terms of F-measure, average accuracy and weighted average F-measure. Our experiments show that random forests perform best, while SVM with certain kernels also achieve high performance.


title: Hot-Stuff the Linear, Optimal-Resilience, One-Message BFT Devil.

abstract: We describe a protocol called `Hot-Stuff the Linear, Optimal-Resilience, One-Message BFT Devil' (in short, Hot-Stuff) for $n = 3f+1$ replicas, of which $2f+1$ are honest, to agree on a replicated, ever-changing state. The protocol is always safe against a threshold $f$ of Byzantine failures, even when the system is asynchronous. Progress is guaranteed under periods of synchrony. The per-round communication cost in Hot-Stuff is linear, hence $O(n^2)$ overall cost to a decision during periods of synchrony, an improvement of $O(n^2)$ over previous asynchronous BFT protocols. Hot-Stuff uses one type of message exchange, and is succinctly described in under twenty lines of pseudo-code.


title: Mixed-Initiative Feature Engineering Using Knowledge Graphs

abstract: This paper proposes a mixed-initiative feature engineering approach using explicit knowledge captured in a knowledge graph complemented by a novel interactive visualization method. Using the explicitly captured relations and dependencies between concepts and their properties, feature engineering is enabled in a semi-automatic way. Furthermore, the results (and decisions) obtained throughout the process can be utilized for refining the features and the knowledge graph. Analytical requirements can then be conveniently captured for feature engineering -- enabling integrated semantics-driven data analysis and machine learning.


title: Public Debt and Growth 1

abstract: This paper explores the impact of high public debt on long-run economic growth. The analysis, based on a panel of advanced and emerging economies over almost four decades, takes into account a broad range of determinants of growth as well as various estimation issues including reverse causality and endogeneity. In addition, threshold effects, nonlinearities, and differences between advanced and emerging market economies are examined. The empirical results suggest an inverse relationship between initial debt and subsequent growth, controlling for other determinants of growth: on average, a 10 percentage point increase in the initial debt-to-GDP ratio is associated with a slowdown in annual real per capita GDP growth of around 0.2 percentage points per year, with the impact being somewhat smaller in advanced economies. There is some evidence of nonlinearity with higher levels of initial debt having a proportionately larger negative effect on subsequent growth. Analysis of the components of growth suggests that the adverse effect largely reflects a slowdown in labor productivity growth mainly due to reduced investment and slower growth of capital stock.


title: Choice Architecture and Smartphone Privacy: There's a Price for That

abstract: Under certain circumstances, consumers are willing to pay a premium for privacy. We explore how choice architecture affects smartphone users’ stated willingness to install applications that request varying permissions. We performed two experiments to gauge smartphone users’ stated willingness to pay premiums to limit their personal information exposure when installing applications. When participants were comparison shopping between multiple applications that performed similar functionality, a quarter of our sample indicated a willingness to pay a $1.50 premium for the application that requested the fewest permissions—though only when viewing the requested permissions of each application side-by-side. In a second experiment, we more closely simulated the user experience by asking them to valuate a single application that featured multiple sets of permissions based on five between-subjects conditions. In this scenario, the requested permissions had a much smaller impact. Our results suggest that many smartphone users are concerned with their privacy and are willing to pay premiums for applications that are less likely to request access to personal information, but that the current choice architectures do not support this. We propose improvements for smartphone application markets that could result in decreased satisficing and increased rational behavior.


title: In Situ Design for Mental Illness: Considering the Pathology of Bipolar Disorder in mHealth Design

abstract: In this paper, we argue that atypical cognitive, perceptual and behavioral characteristics associated with serious mental illnesses should be taken into consideration when designing health technologies. While applications have been developed to assist in the treatment of these illnesses, the specific psychological characteristics of these disorders have rarely been considered extensively in the design process. Here, we explore how an understanding of the low-level characteristics of bipolar disorder, combined with a clinically-validated treatment and patients' lived experience, can inform mHealth design. We present a novel method -- in situ design -- to support ecologically valid design, and demonstrate its use through the co-development with 9 individuals with bipolar disorder of MoodRhythm, a mobile application designed to track and stabilize daily routines. We provide evidence that mHealth design elements tailored to the characteristics and needs of individuals with bipolar disorder can result in engaging interactions.


title: Pulse forming network for Marx generator with boosting operation

abstract: This paper presents Solid State Marx Generator in which capacitors are replaced by Pulse Forming Network (PFN) through which we can get controlled pulse. The magnitude of output square impulse wave can be increased to high value with the low supply voltage without using converter or transformer. To achieve high voltage the PFN inductors and capacitors are connected in boost converter mode temporarily. It increases the capacitor voltage up to its desired value. The proposed topology of single stage Marx generator using six sectionPFN with boosting operation is simulated in MATLAB and satisfactory results are obtained.


title: Perspectives and limits for cement kilns as a destination for RDF

abstract: Abstract RDF, the high calorific value fraction of MSW obtained by conventional separation systems, can be employed in technological plants (mainly cement kilns) in order to obtain a useful energy recovery. It is interesting and important to evaluate this possibility within the general framework of waste-to-energy solutions. The solution must be assessed on the basis of different aspects, namely: technological features and clinker characteristics; local atmospheric pollution; the effects of RDF used in cement kilns on the generation of greenhouse gases; the economics of conventional solid fuels substitution and planning perspectives, from the point of view of the destination of RDF and optimal cement kiln policy. The different experiences of this issue throughout Europe are reviewed, and some applications within Italy are also been considered. The main findings of the study are that the use of RDF in cement kilns instead of coal or coke offers environmental benefits in terms of greenhouse gases, while the formation of conventional gaseous pollutants is not a critical aspect. Indeed, the generation of nitrogen oxides can probably be lower because of lower flame temperatures or lower air excess. The presence of chlorinated micro-pollutants is not influenced by the presence of RDF in fuel, whereas depending on the quality of the RDF, some problems could arise compared to the substituted fuel as far as heavy metals are concerned, chiefly the more volatile ones.


title: MPI Streams for HPC Applications

abstract: Data streams are a sequence of data flowing between source and destination processes. Streaming is widely used for signal, image and video processing for its efficiency in pipelining and effectiveness in reducing demand for memory. The goal of this work is to extend the use of data streams to support both conventional scientific applications and emerging data analytic applications running on HPC platforms. We introduce an extension called MPIStream to the de-facto programming standard on HPC, MPI. MPIStream supports data streams either within a single application or among multiple applications. We present three use cases using MPI streams in HPC applications together with their parallel performance. We show the convenience of using MPI streams to support the needs from both traditional HPC and emerging data analytics applications running on supercomputers.


title: Depressive symptomatology among university students in Denizli, Turkey: prevalence and sociodemographic correlates.

abstract: AIM ::: To determine overall and subgroup prevalence of depressive symptomatology among university students in Denizli, Turkey during the 1999-2000 academic year, and to investigate whether sociodemographic factors were associated with depressive symptoms in university students. ::: ::: ::: METHODS ::: A stratified probability sample of 504 Turkish university students (296 male, 208 female) was used in a cross-sectional study. Data were obtained by self-administered questionnaire, including questions on sociodemographic characteristics and problem areas. The revised Beck Depression Inventory (BDI) was used to determine depressive symptoms of the participants. BDI scores 17 or higher were categorized as depressive for logistic regression analysis. Student t-test and linear regression were used for continuous data analysis. ::: ::: ::: RESULTS ::: Out of all participants, 26.2% had a BDI score 17 or higher. The prevalence of depressive symptoms increased to 32.1% among older students, 34.7% among students with low socioeconomic status, 31.2% among seniors, and 62.9% among students with poor school performance. The odds ratio of depressive symptoms was 1.84 (95% confidence interval [CI], 1.03-3.28) in students with low socioeconomic status and 7.34 (95% CI, 3.36-16.1) in students with poor school performance in the multivariate logistic model. The participants identified several problem areas: lack of social activities and shortage of facilities on the campus (69.0%), poor quality of the educational system (54.8%), economic problems (49.3%), disappointment with the university (43.2%), and friendship problems (25.9%). ::: ::: ::: CONCLUSIONS ::: Considering the high frequency of depressive symptoms among Turkish university students, a student counseling service offering mental health assistance is necessary. This service should especially find the way to reach out to poor students and students with poor school performance.


title: Parking Spot Estimation and Mapping Method for Mobile Robots

abstract: Self-driving vehicles rely on detailed semantic maps of the environment for operating. In this letter, we propose a method to autonomously generate such a semantic map enriched with knowledge of parking spot locations. Our method detects and uses parked vehicles in the surroundings to estimate parking lot topology and infer vacant parking spots via a graph-based approach. We show that our method works for parking lot structures in different environments, such as structured parking lots, unstructured/unmarked parking lots, and typical suburban environments. Using the proposed graph-based approach to infer the parking lot structure, we can extend the estimated parking spots by 57%, averaged over six different areas with ten trials each. We also show that the accuracy of our algorithm increases when combining multiple trials over multiple days. With ten trials combined, we managed to estimate the whole parking lot structure and detected all parking spots in four out of the six evaluated areas.


title: PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms

abstract: Analog/mixed-signal machine learning (ML) accelerators exploit the unique computing capability of analog/mixed-signal circuits and inherent error tolerance of ML algorithms to obtain higher energy efficiencies than digital ML accelerators. Unfortunately, these analog/mixed-signal ML accelerators lack programmability, and even instruction set interfaces, to support diverse ML algorithms or to enable essential software control over the energy-vs-accuracy tradeoffs. We propose PROMISE, the first end-to-end design of a PROgrammable MIxed-Signal accElerator from Instruction Set Architecture (ISA) to high-level language compiler for acceleration of diverse ML algorithms. We first identify prevalent operations in widely-used ML algorithms and key constraints in supporting these operations for a programmable mixed-signal accelerator. Second, based on that analysis, we propose an ISA with a PROMISE architecture built with silicon-validated components for mixed-signal operations. Third, we develop a compiler that can take a ML algorithm described in a high-level programming language (Julia) and generate PROMISE code, with an IR design that is both language-neutral and abstracts away unnecessary hardware details. Fourth, we show how the compiler can map an application-level error tolerance specification for neural network applications down to low-level hardware parameters (swing voltages for each application Task) to minimize energy consumption. Our experiments show that PROMISE can accelerate diverse ML algorithms with energy efficiency competitive even with fixed-function digital ASICs for specific ML algorithms, and the compiler optimization achieves significant additional energy savings even for only 1% extra errors.


title: Conceptualizing and testing formative constructs: tutorial and annotated example

abstract: Although abundant advice is available for how to develop and validate multi-item scales based on reflective constructs, scant attention has been directed to how to construct and validate formative constructs. Such advice is important because (1) theory suggests many constructs are formative and (2) recent advances in software render testing models with formative constructs more tractable. In this tutorial, our goal is to enhance understanding of formative constructs at the conceptual, statistical and methodological levels. Specifically, we (1) provide general principles for specifying whether a construct should be conceptually modeled as reflective or formative, (2) discuss the statistical logic behind formative constructs, and (3) illustrate how to model and evaluate formative constructs. In particular, we provide a tutorial in which we test and validate professional reward structure, a formative construct, in two popular structural equation modeling programs: EQS and PLS. We conclude with a summary of guidelines for how to conduct and evaluate research using formative constructs.


title: User Modeling in Adaptive Interfaces

abstract: In this paper we examine the notion of adaptive user interfaces, interactive systems that invoke machine learning to improve their interaction with humans. We review some previous work in this emerging area, ranging from software that filters information to systems that support more complex tasks like scheduling. After this, we describe three ongoing research efforts that extend this framework in new directions. Finally, we review previous work that has addressed similar issues and consider some challenges that are presented by the design of adaptive user interfaces.


title: Self-reconfigurable robotics architecture utilising fuzzy and deliberative reasoning

abstract: This paper proposes a generic approach towards combining fuzzy logic and ontology-based deliberative reasoning to enable self-reconfigurability within a distributed system architecture. An Ontology-based rational agent uses outputs from a fuzzy inference system (reconfiguration layer) which passively monitors the performance of the lower-level sub-systems (application layer) in order to perform system reconfiguration. A reconfiguration is required to guarantee optimal performance within a complex robotics architecture when anomalous system and environmental changes take place. More importantly, this process of reconfiguration offers greater fault tolerance and reliability in novel scenarios as compared to isolated engineered systems. The current research work will apply the proposed framework to a visual navigation system for autonomous planetary rover missions. This demonstrates the method's success through an increase in system performance following a reconfiguration routine carried out within the application layer between two different types of visual navigation methods. Experimental analysis is carried out using real-world data, concluding that the proposed reconfigurable architecture gives superior performance against standard engineered techniques.


title: Testing the DeLone and McLean Model of IS success in the user developed application domain.

abstract: DeLone and McLean’s (1992) model of information systems success has received much attention amongst researchers. This study provides the first empirical test of the entire DeLone and McLean model in the user developed application domain. Overall, the model was not supported by the data. Of the nine hypothesised relationships tested four were found to be significant and the remainder not significant. The model provided strong support for the relationships between perceived system quality and user satisfaction, perceived information quality and user satisfaction, user satisfaction and intended use, and user satisfaction and perceived individual impact.


title: COMPONENTS OF PERFECTIONISM AND PROCRASTINATION IN COLLEGE STUDENTS

abstract: In the present research we examined the relationships between individual differences in perfectionism and procrastinatory behavior in college students. A sample of 131 students (56 males, 75 females) completed measures of self-oriented, other-oriented, and socially prescribed perfectionism, as well as measures of academic procrastination and general procrastination. Participants also completed ratings of factors related to procrastination (i.e., fear of failure, task aversiveness). Correlational analyses revealed it was the socially prescribed perfectionism dimension that was most closely correlated with both generalized procrastination and academic procrastination, especially among males. There were few significant correlations involving self-oriented and other-oriented perfectionism. However, the fear of failure component of procrastination was associated broadly with all the perfectionism dimensions. Overall, the results suggest that procrastination stems, in part, from the anticipation of social disapproval from individuals with perfectionistic standards for others.


title: Denial-of-Service attacks on 6LoWPAN-RPL networks: Threats and an intrusion detection system proposition

abstract: RPL (Routing Protocol for Low-power and lossy networks) is a specic routing protocol designed to optimize


title: A 2-approximation algorithm for the undirected feedback vertex set problem

abstract: A feedback vertex set of a graph is a subset of vertices that contains at least one vertex from every cycle in the graph. The problem considered is that of finding a minimum feedback vertex set given a weighted and undirected graph. We present a simple and efficient approximation algorithm with performance ratio of at most 2, improving previous best bounds for either weighted or unweighted cases of the problem. Any further improvement on this bound, matching the best constant factor known for the vertex cover problem, is deemed challenging. ::: The approximation principle, underlying the algorithm, is based on a generalized form of the classical local ratio theorem, originally developed for approximation of the vertex cover problem, and a more flexible style of its application.


title: The Relation of Moral Emotion Attributions to Prosocial and Antisocial Behavior: A Meta-Analysis.

abstract: This meta-analytic review of 42 studies covering 8,009 participants (ages 4–20) examines the relation of moral emotion attributions to prosocial and antisocial behavior. A significant association is found between moral emotion attributions and prosocial and antisocial behaviors (d = .26, 95% CI [.15, .38]; d = .39, 95% CI [.29, .49]). Effect sizes differ considerably across studies and this heterogeneity is attributed to moderator variables. Specifically, effect sizes for predicted antisocial behavior are larger for self-attributed moral emotions than for emotions attributed to hypothetical story characters. Effect sizes for prosocial and antisocial behaviors are associated with several other study characteristics. Results are discussed with respect to the potential significance of moral emotion attributions for the social behavior of children and adolescents.


title: FINITE ELEMENT ANALYSIS TO DETERMINE THE EFFECT OF MONOLIMB FLEXIBILITY ON STRUCTURAL STRENGTH AND INTERACTION BETWEEN RESIDUAL LIMB AND PROSTHETIC SOCKET

abstract: Monolimb refers to a kind of transtibial prostheses having the socket and shank molded into one piece of thermoplastic material. One of its characteristics is that the shank is made of a material that can deform during walking, which can simulate ankle joint motion to some extent. Changes in shank geometry can alter the stress distribution within the monolimb and at the residual limb-socket interface and, respectively, affect the deformability and structural integrity of the prosthesis and comfort perceived by amputees. This paper describes the development of a finite-element model for the study of the structural behavior of monolimbs with different shank designs and the interaction between the limb and socket during walking. The von Mises stress distributions in monolimbs with different shank designs at different walking phases are reported. With the use of distortion energy theory, possible failure was predicted. The effect of the stiffness of the monolimb shanks on the stress distribution at the limb-socket interface was studied. The results show a trend-the peak stress applied to the limb was lowered as the shank stiffness decreased. This information is useful for future monolimb optimization.


title: Domain Space Transfer Extreme Learning Machine for Domain Adaptation

abstract: Extreme learning machine (ELM) has been applied in a wide range of classification and regression problems due to its high accuracy and efficiency. However, ELM can only deal with cases where training and testing data are from identical distribution, while in real world situations, this assumption is often violated. As a result, ELM performs poorly in domain adaptation problems, in which the training data (source domain) and testing data (target domain) are differently distributed but somehow related. In this paper, an ELM-based space learning algorithm, domain space transfer ELM (DST-ELM), is developed to deal with unsupervised domain adaptation problems. To be specific, through DST-ELM, the source and target data are reconstructed in a domain invariant space with target data labels unavailable. Two goals are achieved simultaneously. One is that, the target data are input into an ELM-based feature space learning network, and the output is supposed to approximate the input such that the target domain structural knowledge and the intrinsic discriminative information can be preserved as much as possible. The other one is that, the source data are projected into the same space as the target data and the distribution distance between the two domains is minimized in the space. This unsupervised feature transformation network is followed by an adaptive ELM classifier which is trained from the transferred labeled source samples, and is used for target data label prediction. Moreover, the ELMs in the proposed method, including both the space learning ELM and the classifier, require just a small number of hidden nodes, thus maintaining low computation complexity. Extensive experiments on real-world image and text datasets are conducted and verify that our approach outperforms several existing domain adaptation methods in terms of accuracy while maintaining high efficiency.


title: Data Profiling with Metanome

abstract: Data profiling is the discipline of discovering metadata about given datasets. The metadata itself serve a variety of use cases, such as data integration, data cleansing, or query optimization. Due to the importance of data profiling in practice, many tools have emerged that support data scientists and IT professionals in this task. These tools provide good support for profiling statistics that are easy to compute, but they are usually lacking automatic and efficient discovery of complex statistics, such as inclusion dependencies, unique column combinations, or functional dependencies. ::: ::: We present Metanome, an extensible profiling platform that incorporates many state-of-the-art profiling algorithms. While Metanome is able to calculate simple profiling statistics in relational data, its focus lies on the automatic discovery of complex metadata. Metanome's goal is to provide novel profiling algorithms from research, perform comparative evaluations, and to support developers in building and testing new algorithms. In addition, Metanome is able to rank profiling results according to various metrics and to visualize the, at times, large metadata sets.


title: Effect of unshielded and shielded Rogowski coil sensor performance for partial discharge measurement

abstract: This paper presents the analysis of constructed shielded Rogowski coil (RC) sensor for partial discharge (PD) measurement. The RC sensor with rectangular shape has been designed in order to compare the performance unshielded and shielded type in term of sensitivity. Solidwork 3D CAD software has been used to design the core of RC sensor with their casing. Marker Board Replicator 2X 3D printer is used to print the design. An experiment has been conducted to evaluate the difference performance of unshielded and shielded RC sensor with amplitude range PD signal between 1 V to 5 V respectively and the frequency is fixed to 1 MHz. The difference terminating resistance has been installed to the RC sensor too. From the experiment, the percentage average losses of unshielded and shielded RC sensor from 1 V to 5 V at 14.48 % is achieved. The low level of PD signal is challenging to be detected by the RC sensor. The terminating resistance of the coil is a part of parameter which can improve the performance of RC sensor.


title: China Brain Project: Basic Neuroscience, Brain Diseases, and Brain-Inspired Computing

abstract: The China Brain Project covers both basic research on neural mechanisms underlying cognition and translational research for the diagnosis and intervention of brain diseases as well as for brain-inspired intelligence technology. We discuss some emerging themes, with emphasis on unique aspects.


title: Real-Time Implementation of Vision-Based Lane Detection and Tracking

abstract: This paper presents a real-time implementation on lane detection and tracking system in order to localize lane boundaries and estimate a linear-parabolic lane model. It is realized using TMS320DM642 DSP board. Video frame is first captured with CCD camera and stored in video port buffer. Next, input image is split into sky and road region with horizon localization. Lane analysis is applied on the road region to remove road pixels. Only lane markings are the interests for the lane detection process. Once lane boundaries are located, the possible edge pixels are scanned to continuously obtain the lane model. Linear-parabolic model is used to construct the geometry of the lane. The model parameters are updated with Kalman filtering. Video sequences are tested to verify the performance of the system and it has good performance.


title: The Critical Importance of Retrieval—and Spacing—for Learning

abstract: We examined the impact of repeated testing and repeated studying on long-term learning. In Experiment 1, we replicated Karpicke and Roediger’s (2008) influential results showing that once information can be recalled, repeated testing on that information enhances learning, whereas restudying that information does not. We then examined whether the apparent ineffectiveness of restudying might be attributable to the spacing differences between items that were inherent in the between-subjects design employed by Karpicke and Roediger. When we controlled for these spacing differences by manipulating the various learning conditions within subjects in Experiment 2, we found that both repeated testing and restudying improved learning, and that learners’ awareness of the relative mnemonic benefits of these strategies was enhanced. These findings contribute to understanding how two important factors in learning—test-induced retrieval processes and spacing—can interact, and they illustrate that such interactions can p...


title: The sustainability of Papua New Guinea agricultural systems: the conceptual background

abstract: Abstract Papua New Guinea offers an opportunity to study the Intensification and sustainability of village agriculture under the pressures of demographic change and social and economic development. This paper describes research investigating the process of agricultural intensification. Theories of intensification suggest the process is associated with increasing population, or with increased production for social purposes, or both. Intensification brings about changes that must be overcome by innovation. Innovation may demand temporary or permanent increases in labour, and changes in the social relations of production, but intensification without innovation is likely to lead to land degradation. A tension exists between the environment, social organization, production and the adoption of innovative techniques, which may determine whether intensification is sustainable. The research is presently identifying, mapping and systematically describing agricultural systems. Preliminary results suggest intensification is positively associated with altitude, sweet potato (lpomoea batatas) cultivation, and with a number of innovative agronomic techniques.


title: Sliding Window Top-K Monitoring over Distributed Data Streams

abstract: AbstractMost of the traditional top-k algorithms are based on a single-server setting. They may be highly inefficient and/or cause huge communication overhead when applied to a distributed system environment. Therefore, the problem of top-k monitoring in distributed environments has been intensively investigated recently. This paper studies how to monitor the top-k data objects with the largest aggregate numeric values from distributed data streams within a fixed-size monitoring window W, while minimizing communication cost across the network. We propose a novel algorithm, which adaptively reallocates numeric values of data objects among distributed nodes by assigning revision factors when local constraints are violated and keeps the local top-k result at distributed nodes in line with the global top-k result. We also develop a framework that combines a distributed data stream monitoring architecture with a sliding window model. Based on this framework, extensive experiments are conducted on top of Apache Storm to verify the efficiency and scalability of the proposed algorithm.


title: Quality, productivity and economic benefits of software reuse: a review of industrial studies

abstract: Systematic software reuse is proposed to increase productivity and software quality and lead to economic benefits. Reports of successful software reuse programs in industry have been published. However, there has been little effort to organize the evidence systematically and appraise it. This review aims to assess the effects of software reuse in industrial contexts. Journals and major conferences between 1994 and 2005 were searched to find observational studies and experiments conducted in industry, returning eleven papers of observational type. Systematic software reuse is significantly related to lower problem (defect, fault or error) density in five studies and to decreased effort spent on correcting problems in three studies. The review found evidence for significant gains in apparent productivity in three studies. Other significant benefits of software reuse were reported in single studies or the results were inconsistent. Evidence from industry is sparse and combining results was done by vote-counting. Researchers should pay more attention to using comparable metrics, performing longitudinal studies, and explaining the results and impact on industry. For industry, evaluating reuse of COTS or OSS components, integrating reuse activities in software processes, better data collection and evaluating return on investment are major challenges.


A Shape-Shifting Wall Display that Supports Individual and Group Activities


title: Towards Neural Network-based Reasoning

abstract: We propose Neural Reasoner, a framework for neural network-based reasoning over natural language sentences. Given a question, Neural Reasoner can infer over multiple supporting facts and find an answer to the question in specific forms. Neural Reasoner has 1) a specific interaction-pooling mechanism, allowing it to examine multiple facts, and 2) a deep architecture, allowing it to model the complicated logical relations in reasoning tasks. Assuming no particular structure exists in the question and facts, Neural Reasoner is able to accommodate different types of reasoning and different forms of language expressions. Despite the model complexity, Neural Reasoner can still be trained effectively in an end-to-end manner. Our empirical studies show that Neural Reasoner can outperform existing neural reasoning systems with remarkable margins on two difficult artificial tasks (Positional Reasoning and Path Finding) proposed in [8]. For example, it improves the accuracy on Path Finding(10K) from 33.4% [6] to over 98%.


title: Anchor Box Optimization for Object Detection

abstract: In this paper, we propose a general approach to optimize anchor boxes for object detection. Nowadays, anchor boxes are widely adopted in state-of-the-art detection frameworks. However, these frameworks usually pre-define anchor box shapes in heuristic ways and fix the sizes during training. To improve the accuracy and reduce the effort of designing anchor boxes, we propose to dynamically learn the anchor shapes, which allows the anchors to automatically adapt to the data distribution and the network learning capability. The learning approach can be easily implemented with stochastic gradient descent and can be plugged into any anchor box-based detection framework. The extra training cost is almost negligible and it has no impact on the inference time or memory cost. Exhaustive experiments demonstrate that the proposed anchor optimization method consistently achieves significant improvement ($\ge 1\%$ mAP absolute gain) over the baseline methods on several benchmark datasets including Pascal VOC 07+12, MS COCO and Brainwash. Meanwhile, the robustness is also verified towards different anchor initialization methods and the number of anchor shapes, which greatly simplifies the problem of anchor box design.


title: Erythromelalgia: a cutaneous manifestation of neuropathy?*

abstract: The low prevalence of erythromelalgia, classified as an orphan disease, poses diagnostic and therapeutic difficulties. The aim of this review is to be an update of the specialized bibliography. Erythromelalgia is an infrequent episodic acrosyndrome affecting mainly both lower limbs symmetrically with the classic triad of erythema, warmth and burning pain. Primary erythromelalgia is an autosomal dominant inherited disorder, while secondary is associated with myeloproliferative diseases, among others. In its etiopathogenesis, there are neural and vascular abnormalities that can be combined. The diagnosis is based on exhaustive clinical history and physical examination. Complications are due to changes in the skin barrier function, ischemia and compromise of cutaneous nerves. Because of the complexity of its pathogenesis, erythromelalgia should always be included in the differential diagnosis of conditions that cause chronic pain and/or peripheral edema. The prevention of crisis is based on a strict control of triggers and promotion of preventive measures. Since there is no specific and effective treatment, control should focus on the underlying disease. However, there are numerous topical and systemic therapies that patients can benefit from.


title: Cognitive, physical, sensory, and functional affordances in interaction design

abstract: In reaction to Norman's (1999) essay on misuse of the term affordance in human-computer interaction literature, this article is a concept paper affirming the importance of this powerful concept, reinforcing Norman's distinctions of terminology, and expanding on the usefulness of the concepts in terms of their application to interaction design and evaluation. We define and use four complementary types of affordance in the context of interaction design and evaluation: cognitive affordance, physical affordance, sensory affordance, and functional affordance. The terms cognitive affordance (Norman's perceived affordance) and physical affordance (Norman's real affordance) refer to parallel and equally important usability concepts for interaction design, to which sensory affordance plays a supporting role. We argue that the concept of physical affordance carries a mandatory component of utility or purposeful action (functional affordance). Finally, we provide guidelines to help designers think about how these fo...


title: IEDC: An Integrated Approach for Overlapping and Non-overlapping Community Detection

abstract: Abstract Community detection is a task of fundamental importance in social network analysis that can be used in a variety of knowledge-based domains. While there exist many works on community detection based on connectivity structures, they suffer from either considering the overlapping or non-overlapping communities. In this work, we propose a novel approach for general community detection through an integrated framework to extract the overlapping and non-overlapping community structures without assuming prior structural connectivity on networks. Our general framework is based on a primary node based criterion which consists of the internal association degree along with the external association degree. The evaluation of the proposed method is investigated through the extensive simulation experiments and several benchmark real network datasets. The experimental results show that the proposed method outperforms the earlier state-of-the-art algorithms based on the well-known evaluation criteria.


title: Thorough static analysis of device drivers

abstract: Bugs in kernel-level device drivers cause 85% of the system crashes in the Windows XP operating system [44]. One of the sources of these errors is the complexity of the Windows driver API itself: programmers must master a complex set of rules about how to use the driver API in order to create drivers that are good clients of the kernel. We have built a static analysis engine that finds API usage errors in C programs. The Static Driver Verifier tool (SDV) uses this engine to find kernel API usage errors in a driver. SDV includes models of the OS and the environment of the device driver, and over sixty API usage rules. SDV is intended to be used by driver developers "out of the box." Thus, it has stringent requirements: (1) complete automation with no input from the user; (2) a low rate of false errors. We discuss the techniques used in SDV to meet these requirements, and empirical results from running SDV on over one hundred Windows device drivers.


title: Antimicrobial Stewardship in Immunocompromised Hosts

abstract: The global spread of antimicrobial resistance has limited the availability of antimicrobial agents to treat infections that affect immunocompromised hosts. Efforts to optimize the selection, dosing, and duration of antimicrobial therapy to improve patient outcomes and minimize selective pressure on antimicrobial resistance are relevant to immunocompromised patients. Collaborative efforts between health care providers with expertise in the diagnosis and treatment of patients with various degrees of immunosuppression are pivotal for the success of antimicrobial stewardship programs in immunocompromised patients.


title: An IC for linearizing RF power amplifiers using envelope elimination and restoration

abstract: Efficient power amplifiers (PAs) are desirable because power amplifiers typically dominate the power consumed in portable radio devices. Cellular systems such as the advanced mobile phone system (AMPS) employ modulation schemes that generate constant amplitude RF outputs to use efficient but nonlinear PAs. Modern digital communication standards such as the North American dual-mode cellular (NADC) system require nonconstant amplitude RF outputs to maximize frequency spectral usage, and therefore require linear PAs. The traditional approach to linear RF power amplification is to back-off the output power of a PA until its distortion is reduced to an acceptable level. Power back-off can lead to significant reduction in output power and efficiency. The envelope elimination and restoration (EER) system is an alternative to power back-off, to simultaneously achieve efficiency and linearity in RF PAs.


title: Microbes Drive Evolution of Animals and Plants: the Hologenome Concept

abstract: The hologenome concept of evolution postulates that the holobiont (host plus symbionts) with its hologenome (host genome plus microbiome) is a level of selection in evolution. Multicellular organisms can no longer be considered individuals by the classical definitions of the term. Every natural animal and plant is a holobiont consisting of the host and diverse symbiotic microbes and viruses. Microbial symbionts can be transmitted from parent to offspring by a variety of methods, including via cytoplasmic inheritance, coprophagy, direct contact during and after birth, and the environment. A large number of studies have demonstrated that these symbionts contribute to the anatomy, physiology, development, innate and adaptive immunity, and behavior and finally also to genetic variation and to the origin and evolution of species. Acquisition of microbes and microbial genes is a powerful mechanism for driving the evolution of complexity. Evolution proceeds both via cooperation and competition, working in parallel.


title: Learning Tetris Using the Noisy Cross-Entropy Method

abstract: The cross-entropy method is an efficient and general optimization algorithm. However, its applicability in reinforcement learning (RL) seems to be limited because it often converges to suboptimal policies. We apply noise for preventing early convergence of the cross-entropy method, using Tetris, a computer game, for demonstration. The resulting policy outperforms previous RL algorithms by almost two orders of magnitude.


title: Corporate Social and Financial Performance : A Meta-analysis

abstract: Most theorizing on the relationship between corporate social/environmental performance (CSP) and corporate financial performance (CFP) assumes that the current evidence is too fractured or too variable to draw any generalizable conclusions. With this integrative, quantitative study, we intend to show that the mainstream claim that we have little generalizable knowledge about CSP and CFP is built on shaky grounds. Providing a methodologically more rigorous review than previous efforts, we conduct a meta-analysis of 52 studies (which represent the population of prior quantitative inquiry) yielding a total sample size of 33,878 observations. The meta-analytic findings suggest that corporate virtue in the form of social responsibility and, to a lesser extent, environmental responsibility is likely to pay off, although the operationalizations of CSP and CFP also moderate the positive association. For example, CSP appears to be more highly correlated with accounting-based measures of CFP than with market-based ...


title: Submitted 6/08; Published xx/xx Distributed Algorithms for Topic Models

abstract: We describe distributed algorithms for two widely-used topic models, namely the Latent Dirichlet Allocation (LDA) model, and the Hierarchical Dirichet Process (HDP) model. In our distributed algorithms the data is partitioned across separate processors and inference is done in a parallel, distributed fashion. We propose two distributed algorithms for LDA. The first algorithm is a straightforward mapping of LDA to a distributed processor setting. In this algorithm processors concurrently perform Gibbs sampling over local data followed by a global update of topic counts. The algorithm is simple to implement and can be viewed as an approximation to Gibbs-sampled LDA. The second version is a model that uses a hierarchical Bayesian extension of LDA to directly account for distributed data. This model has a theoretical guarantee of convergence but is more complex to implement than the first algorithm. Our distributed algorithm for HDP takes the straightforward mapping approach, and merges newly-created topics either by matching or by topic-id. Using five real-world text corpora we show that distributed learning works well in practice. For both LDA and HDP, we show that the converged test-data log probability for distributed learning is indistinguishable from that obtained with single-processor learning. Our extensive experimental results include learning topic models for two multi-million document collections using a 1024-processor parallel computer.


title: Synthetic off-line signature image generation

abstract: This paper proposes a novel methodology to generate static/off-line signatures of new identities. The signature of the new synthetic identity is obtained particularizing the random variables of a statistical distribution of global signature properties. The results mimic real signature shapes and writing style properties, which are estimated from static signature databases. New instances, as well as forgeries, from the synthetic identities are obtained introducing a natural variability from the synthetic individual properties. As additional novelty, an ink deposition model based on a ballpoint is developed for realistic static signature image generation. The range of the static signature generator has been established matching the performance obtained with the synthetic databases and those obtained with two public databases.


title: Outcomes of variation in technique and variation in accuracy of measurement in penile length measurement

abstract: Accurate data regarding the size of the erect penis is of great importance to several disciplines working with male patients, but little data exists on the best technique to measure penile length. While some previous small studies have suggested good correlation between stretched penile length, others have shown significant variability. Penile girth has been less well studied, and little data exist on the possible errors induced by differing observers and different techniques. Much of the published data report penile length measured from the penopubic skin junction-to-glans tip (STT) rather than pubic bone-to-tip (BTT). We wished to assess the accuracy of different techniques of penile measurements with multiple observers. Men who achieved full erection using dynamic penile Doppler ultrasound for the diagnosis of sexual dysfunction or a desire for objective penile measurement were included in the study. Exclusion criteria were penile scarring, curvature, or congenital abnormality. In each case, the penis was measured by one of the seven andrology specialists in a private air-conditioned (21 °C) environment. Each patient had three parameters measured: circumference (girth) of the penile shaft, length from suprapubic skin-to-distal glans (STT), and pubis-to-distal glans (BTT). The three measurements were recorded in the stretched flaccid state, and the same three measurements were then repeated in the fully erect state, following induction of full erection with intracavernosal injection. We analyzed the accuracy of each flaccid measurement using the erect measurements as a reference, for the overall patient population and for each observer. In total, 201 adult men (mean age 49.4 years) were included in this study. Assessing the penis in the stretched and flaccid state gave a mean underestimate of the erect measurement of ~20% (STT length 23.39%, BTT length 19.86%, and circumference 21.38%). In this large, multicenter, multi-observer study of penis size, flaccid measurements were only moderately accurate in predicting erect size. They were also significantly observer dependent. Measuring penile length from pubic bone to tip of glans is more accurate and reliable, the discrepancy being most notable in overweight patients.


title: Computational Approaches to Motor Control

abstract: This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.


title: Continuous and Cuffless Blood Pressure Monitoring Based on ECG and SpO2 Signals ByUsing Microsoft Visual C Sharp

abstract: Background: One of the main problems especially in operating room and monitoring devices is measurement of Blood Pressure (BP) by sphygmomanometer cuff. Objective: In this study we designed a new method to measure BP changes continuously for detecting information between cuff inflation times by using vital signals in monitoring devices. This will be achieved by extraction of the time difference between each cardiac cycle and a relative pulse wave. Methods: Finger pulse and ECG signals in lead I were recorded by a monitoring device. The output of monitoring device is inserted in a computer by serial network communication. A software interface is used to display and process the signals in the computer. Time difference between each cardiac cycle and pulse signal was calculated throughout R wave detection in ECG and peak of pulse signal by the software. The relation between time difference in two waves and BP was determined then the coefficients of equation were obtained in different physical situations. The results of estimating BP were compared with the results of sphygmomanometer method and the error rate was calculated. Results: In this study, 25 subjects participated among them 15 were male and 10 were female. The results show that BP is linearly related to time difference. Average of coefficient correlation is 0.9±0.03 for systolic and 0.82±0.04 for diastolic blood pressure. The highest error percentage was calculated 8% for male and 11% for female group. Significant difference was observed between the different physical situation and arm movement changes. The relationship between time difference and age was estimated in a linear relationship with a correlation coefficient of 0.76. Conclusion: By determining linear relation values with high accuracy, BP can be measured with insignificant error. Therefore it can be suggested as a new method to measure the blood pressure continuously.


title: DeeperLab: Single-Shot Image Parser

abstract: We present a single-shot, bottom-up approach for whole image parsing. Whole image parsing, also known as Panoptic Segmentation, generalizes the tasks of semantic segmentation for 'stuff' classes and instance segmentation for 'thing' classes, assigning both semantic and instance labels to every pixel in an image. Recent approaches to whole image parsing typically employ separate standalone modules for the constituent semantic and instance segmentation tasks and require multiple passes of inference. Instead, the proposed DeeperLab image parser performs whole image parsing with a significantly simpler, fully convolutional approach that jointly addresses the semantic and instance segmentation tasks in a single-shot manner, resulting in a streamlined system that better lends itself to fast processing. For quantitative evaluation, we use both the instance-based Panoptic Quality (PQ) metric and the proposed region-based Parsing Covering (PC) metric, which better captures the image parsing quality on 'stuff' classes and larger object instances. We report experimental results on the challenging Mapillary Vistas dataset, in which our single model achieves 31.95% (val) / 31.6% PQ (test) and 55.26% PC (val) with 3 frames per second (fps) on GPU or near real-time speed (22.6 fps on GPU) with reduced accuracy.


title: Learning to Select Knowledge for Response Generation in Dialog Systems

abstract: End-to-end neural models for intelligent dialogue systems suffer from the problem of generating uninformative responses. Various methods were proposed to generate more informative responses by leveraging external knowledge. However, few previous work has focused on selecting appropriate knowledge in the learning process. The inappropriate selection of knowledge could prohibit the model from learning to make full use of the knowledge. Motivated by this, we propose an end-to-end neural model which employs a novel knowledge selection mechanism where both prior and posterior distributions over knowledge are used to facilitate knowledge selection. Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process. Meanwhile, a prior distribution, which is inferred from utterances only, is used to approximate the posterior distribution so that appropriate knowledge can be selected even without responses during the inference process. Compared with the previous work, our model can better incorporate appropriate knowledge in response generation. Experiments on both automatic and human evaluation verify the superiority of our model over previous baselines.


title: Ethical and privacy principles for learning analytics

abstract: The massive adoption of technology in learning processes comes with an equally large capacity to track learners. Learning analytics aims at using the collected information to understand and improve the quality of a learning experience. The privacy and ethical issues that emerge in this context are tightly interconnected with other aspects such as trust, accountability and transparency. In this paper, a set of principles is identified to narrow the scope of the discussion and point to pragmatic approaches to help design and research learning experiences where important ethical and privacy issues are considered. [ABSTRACT FROM AUTHOR]


title: Cat Swarm Optimization for Clustering

abstract: Cat Swarm Optimization (CSO) is one of the new heuristic optimization algorithm which based on swarm intelligence. Previous research shows that this algorithm has better performance compared to the other heuristic optimization algorithms: Particle Swarm Optimization (PSO) and weighted-PSO in the cases of function minimization. In this research a new CSO algorithm for clustering problem is proposed. The new CSO clustering algorithm was tested on four different datasets. The modification is made on the CSO formula to obtain better results. Then, the accuracy level of poposed algorith was compared to those of K-means and PSO clustering. The modification of CSO formula can improve the performance of CSO Clustering. The comparison indicates that CSO clustering can be considered as a sufficiently accurate clustering method


title: Multi-modal feature fusion for action recognition in RGB-D sequences

abstract: Microsoft Kinect's output is a multi-modal signal which gives RGB videos, depth sequences and skeleton information simultaneously. Various action recognition techniques focused on different single modalities of the signals and built their classifiers over the features extracted from one of these channels. For better recognition performance, it's desirable to fuse these multi-modal information into an integrated set of discriminative features. Most of current fusion methods merged heterogeneous features in a holistic manner and ignored the complementary properties of these modalities in finer levels. In this paper, we proposed a new hierarchical bag-of-words feature fusion technique based on multi-view structured spar-sity learning to fuse atomic features from RGB and skeletons for the task of action recognition.


title: Neuropsychological, Cognitive, and Theoretical Considerations for Evaluation of Bilingual Individuals

abstract: As the number of bilinguals in the USA grows rapidly, it is increasingly important for neuropsychologists to be equipped and trained to address the unique challenges inherent in conducting ethical and competent neuropsychological evaluations with this population. Research on bilingualism has focused on two key cognitive mechanisms that introduce differences between bilinguals and monolinguals: (a) reduced frequency of language-specific use (weaker links), and (b) competition for selection within the language system in bilinguals (interference). Both mechanisms are needed to explain how bilingualism affects neuropsychological test performance, including the robust bilingual disadvantages found on verbal tasks, and more subtle bilingual advantages on some measures of cognitive control. These empirical results and theoretical claims can be used to derive a theoretically informed method for assessing cognitive status in bilinguals. We present specific considerations for measuring degree of bilingualism for both clients and examiners to aid in determinations of approaches to testing bilinguals, with practical guidelines for incorporating models of bilingualism and recent experimental data into neuropsychological evaluations. This integrated approach promises to provide improved clinical services for bilingual clients, and will also contribute to a program of research that will ultimately reveal the mechanisms underlying language processing and executive functioning in bilinguals and monolinguals alike.


title: Trunk Muscle Activity During Lumbar Stabilization Exercises on Both a Stable and Unstable Surface

abstract: STUDY DESIGN ::: Controlled laboratory study. ::: ::: ::: OBJECTIVES ::: To clarify whether differences in surface stability influence trunk muscle activity. ::: ::: ::: BACKGROUND ::: Lumbar stabilization exercises on unstable surfaces are performed widely. One perceived advantage in performing stabilization exercises on unstable surfaces is the potential for increased muscular demand. However, there is little evidence in the literature to help establish whether this assumption is correct. ::: ::: ::: METHODS ::: Nine healthy male subjects performed lumbar stabilization exercises. Pairs of intramuscular fine-wire or surface electrodes were used to record the electromyographic signal amplitude of the rectus abdominis, the external obliques, the transversus abdominis, the erector spinae, and lumbar multifidus. Five exercises were performed on the floor and on an unstable surface: elbow-toe, hand-knee, curl-up, side bridge, and back bridge. The EMG data were normalized as the percentage of the maximum voluntary contraction, and data between doing each exercise on the stable versus unstable surface were compared using a Wilcoxon signed-rank test. ::: ::: ::: RESULTS ::: With the elbow-toe exercise, the activity level for all muscles was enhanced when performed on the unstable surface. When performing the hand-knee and side bridge exercises, activity level of the more global muscles was enhanced when performed on an unstable surface. Performing the curl-up exercise on an unstable surface, increased the activity of the external obliques but reduced transversus abdominis activation. ::: ::: ::: CONCLUSION ::: This study indicates that lumbar stabilization exercises on an unstable surface enhanced the activities of trunk muscles, except for the back bridge exercise.


title: A novel hybrid classification model of artificial neural networks and multiple linear regression models

abstract: Highlights? Proposing a novel hybrid model for classification problems. ? Using linear methods for improving the performance of the traditional artificial neural networks (ANNs). ? Combining the multiple linear regression (MLR) and multilayer perceptron (MLP) as hybrid classification model (MLRP). ? Overcoming the linear modeling limitation of the traditional MLPs. ? Obtaining more general and more accurate classification model. The classification problem of assigning several observations into different disjoint groups plays an important role in business decision making and many other areas. Developing more accurate and widely applicable classification models has significant implications in these areas. It is the reason that despite of the numerous classification models available, the research for improving the effectiveness of these models has never stopped. Combining several models or using hybrid models has become a common practice in order to overcome the deficiencies of single models and can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. In this paper, a novel hybridization of artificial neural networks (ANNs) is proposed using multiple linear regression models in order to yield more general and more accurate model than traditional artificial neural networks for solving classification problems. Empirical results indicate that the proposed hybrid model exhibits effectively improved classification accuracy in comparison with traditional artificial neural networks and also some other classification models such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), K-nearest neighbor (KNN), and support vector machines (SVMs) using benchmark and real-world application data sets. These data sets vary in the number of classes (two versus multiple) and the source of the data (synthetic versus real-world). Therefore, it can be applied as an appropriate alternate approach for solving classification problems, specifically when higher forecasting accuracy is needed.


title: A hybrid machine translation architecture guided by syntax

abstract: This article presents a hybrid architecture which combines rule-based machine translation (RBMT) with phrase-based statistical machine translation (SMT). The hybrid translation system is guided by the rule-based engine. Before the transfer step, a varied set of partial candidate translations is calculated with the SMT system and used to enrich the tree-based representation with more translation alternatives. The final translation is constructed by choosing the most probable combination among the available fragments using monotone statistical decoding following the order provided by the rule-based system. We apply the hybrid model to a pair of distantly related languages, Spanish and Basque, and perform extensive experimentation on two different corpora. According to our empirical evaluation, the hybrid approach outperforms the best individual system across a varied set of automatic translation evaluation metrics. Following some output analysis to better understand the behaviour of the hybrid system, we explore the possibility of adding alternative parse trees and extra features to the hybrid decoder. Finally, we present a twofold manual evaluation of the translation systems studied in this paper, consisting of (i) a pairwise output comparison and (ii) a individual task-oriented evaluation using HTER. Interestingly, the manual evaluation shows some contradictory results with respect to the automatic evaluation; humans tend to prefer the translations from the RBMT system over the statistical and hybrid translations.


title: Exploiting Noise Correlation for Channel Decoding with Convolutional Neural Networks

abstract: Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation-convolutional neural network (BP-CNN) architecture to exploit noise correlation for channel decoding under correlated noise. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and hence result in better decoding performance. To train a well-behaved CNN model, we define a new loss function which involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the BER of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework.


title: Knowledge Processes and Ontologies

abstract: In this article, we present an approach for ontology-based knowledge management (KM) that includes a tool suite and a methodology for developing ontology-based KM systems. It builds on the distinction between knowledge processes and knowledge metaprocesses, and is illustrated by CHAR (Corporate History AnalyzeR), a KM system for corporate history analysis.


title: The developmental origins of a disposition toward empathy: Genetic and environmental contributions.

abstract: The authors investigated the development of a disposition toward empathy and its genetic and environmental origins. Young twins' (N = 409 pairs) cognitive (hypothesis testing) and affective (empathic concern) empathy and prosocial behavior in response to simulated pain by mothers and examiners were observed at multiple time points. Children's mean level of empathy and prosociality increased from 14 to 36 months. Positive concurrent and longitudinal correlations indicated that empathy was a relatively stable disposition, generalizing across ages, across its affective and cognitive components, and across mother and examiner. Multivariate genetic analyses showed that genetic effects increased, and that shared environmental effects decreased, with age. Genetic effects contributed to both change and continuity in children's empathy, whereas shared environmental effects contributed to stability and nonshared environmental effects contributed to change. Empathy was associated with prosocial behavior, and this relationship was mainly due to environmental effects.


title: Social Hash: an assignment framework for optimizing distributed systems operations on social networks

abstract: How objects are assigned to components in a distributed system can have a significant impact on performance and resource usage. Social Hash is a framework for producing, serving, and maintaining assignments of objects to components so as to optimize the operations of large social networks, such as Facebook's Social Graph. The framework uses a two-level scheme to decouple compute-intensive optimization from relatively low-overhead dynamic adaptation. The optimization at the first level occurs on a slow timescale, and in our applications is based on graph partitioning in order to leverage the structure of the social network. The dynamic adaptation at the second level takes place frequently to adapt to changes in access patterns and infrastructure, with the goal of balancing component loads. ::: ::: We demonstrate the effectiveness of Social Hash with two real applications. The first assigns HTTP requests to individual compute clusters with the goal of minimizing the (memory-based) cache miss rate; Social Hash decreased the cache miss rate of production workloads by 25%. The second application assigns data records to storage subsystems with the goal of minimizing the number of storage subsystems that need to be accessed on multiget fetch requests; Social Hash cut the average response time in half on production workloads for one of the storage systems at Facebook.


title: Visual Link Retrieval in a Database of Paintings

abstract: This paper examines how far state-of-the-art machine vision algorithms can be used to retrieve common visual patterns shared by series of paintings. The research of such visual patterns, central to Art History Research, is challenging because of the diversity of similarity criteria that could relevantly demonstrate genealogical links. We design a methodology and a tool to annotate efficiently clusters of similar paintings and test various algorithms in a retrieval task. We show that pre-trained convolutional neural network can perform better for this task than other machine vision methods aimed at photograph analysis. We also show that retrieval performance can be significantly improved by fine-tuning a network specifically for this task.


Speech Emotion Classification Using Tree-Structured Sparse Logistic Regression


title: Mobile Apps opinion analysis using emoticon

abstract: Use of mobile has increased day by day. Nowadays, the mobile users prefer to use smartphones to access all types of mobile Application. These smartphones are converted from phone to smartphone by using inbuilt or downloaded mobile Applications. Generally, users download mobile Applications depending on how many users already have downloaded that Application?, What are the ratings and reviews?, What are the comments?, etc. Ranking fraud in the mobile App market refers to false or wrong activities which have a reason of pushing up the Apps on the popularity list. Certainly, it becomes more frequent for App developers to use fraud means, such as increase their App's sales or posting fake App ratings, to commit ranking fraud. There are very less understanding and analysis on how to prevent this ranking misrepresentation. Moreover, we demonstrate an optimization-based aggregation method for ranking extortion and ranking misrepresentation recognition framework for versatile Apps. It is divided into three parts: 1) ranking based evidence, 2) rating based evidence and 3) review based evidence, by demonstrating Apps' ranking, rating and survey practices through measurable theories tests. They also works on an optimization based aggregation method to coordinate every of the confirmations for misrepresentation location. We have used here opinion analysis for finding how much a review is positive or negative. This review score is used to enhance the rating score of the user and the emoticons in the reviews or comments. It has been given special weight to enhance the review score. User has provided the rating, review & comments. We are using all of them to increases the accuracy of the final rating score.


title: Cognitive apprenticeship in clinical practice: can it stimulate learning in the opinion of students?

abstract: Learning in clinical practice can be characterised as situated learning because students learn by performing tasks and solving problems in an environment that reflects the multiple ways in which their knowledge will be put to use in their future professional practice. Collins et al. introduced cognitive apprenticeship as an instructional model for situated learning comprising six teaching methods to support learning: modelling, coaching, scaffolding, articulation, reflection and exploration. Another factor that is looked upon as conducive to learning in clinical practice is a positive learning climate. We explored students’ experiences regarding the learning climate and whether the cognitive apprenticeship model fits students’ experiences during clinical training. In focus group interviews, three groups of 6th-year medical students (N = 21) discussed vignettes representing the six teaching methods and the learning climate to explore the perceived occurrence of the teaching methods, related problems and possibilities for improvement. The students had experienced all six teaching methods during their clerkships. Modelling, coaching, and articulation were predominant, while scaffolding, reflection, and exploration were mainly experienced during longer clerkships and with one clinical teacher. The main problem was variability in usage of the methods, which was attributed to teachers’ lack of time and formal training. The students proposed several ways to improve the application of the teaching methods. The results suggest that the cognitive apprenticeship model is a useful model for teaching strategies in undergraduate clinical training and a valuable basis for evaluation, feedback, self-assessment and faculty development of clinical teachers.


title: Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement

abstract: `AI & Law' research has been around since the 1970s, even though with shifting emphasis. This is an overview of the contributions of digital technologies, both artificial intelligence and non-AI smart tools, to both the legal professions and the police. For example, we briefly consider text mining and case-automated summarization, tools supporting argumentation, tools concerning sentencing based on the technique of case-based reasoning, the role of abductive reasoning, research into applying AI to legal evidence, tools for fighting crime and tools for identification.


title: Dual Band-Notched Small Monopole Antenna With Novel Coupled Inverted U-Ring Strip and Novel Fork-Shaped Slit for UWB Applications

abstract: This letter proposes a novel, small, printed monopole antenna for ultrawideband (UWB) applications with dual band-notch function. By cutting an inverted fork-shaped slit in the ground plane, additional resonance is excited, and hence much wider impedance bandwidth can be produced. To generate dual band-notch characteristics, we use a coupled inverted U-ring strip in the radiating patch. The measured results reveal that the presented dual band-notch monopole antenna offers a wide bandwidth with two notched bands, covering all the 5.2/5.8-GHz WLAN, 3.5/5.5-GHz WiMAX, and 4-GHz C-bands. The proposed antenna has a small size of 12 $\,\times\,$ 18 mm $^{2}$ or about $0.15 \lambda \times 0.25 \lambda$ at 4.2 GHz (first resonance frequency), which has a size reduction of 28% with respect to the previous similar antenna. Simulated and measured results are presented to validate the usefulness of the proposed antenna structure UWB applications.