nils-herrmann/clean_scirepeval huggingface dataset:
Self-aware Object Tracking in Multi-Camera Networks

This chapter discusses another example of self-aware and self-expressive systems: a multi-camera network for object tracking. It provides a detailed description of how the concepts of self-awareness and self-expression can be implemented in a real network of smart cameras. In contrast to traditional cameras, smart cameras are able to perform image analysis on-board and collaborate with other cameras in order to analyse the dynamic behaviour of objects in partly unknown environments. Self-aware and self-expressive smart cameras are even able to reason about their current state and to adapt their algorithms in response to changes in their environment and the network. Self-awareness and self-expression allow them to manage the trade-off among performance, flexibility, resources and reliability during runtime. Due to the uncertainties and dynamics in the network a fixed configuration of the cameras is infeasible. We adopt the concepts of self-awareness and self-expression for autonomous monitoring of the state and progress of each camera in the network and adapt its behaviour to changing conditions. In this chapter we focus on describing the building blocks for self-aware camera networks and demonstrate the key characteristics in a multi-camera object tracking application both in simulation and in a real camera network. The proposed application implements the goal sharing Lukas Esterle Alpen-Adria-Universität Klagenfurt, Austria, e-mail: lukas.esterle@aau.at Jennifer Simonjan Alpen-Adria-Universität Klagenfurt, Austria, e-mail: jennifer.simonjan@aau.at Georg Nebehay Austrian Institute of Technology, Austria, e-mail: gnebehay@gmail.com Roman Pflugfelder Austrian Institute of Technology, Austria,  roman.pflugfelder@ait.ac.at Gustavo Fernández Domı́nguez Austrian Institute of Technology, Austria,  Bernhard Rinner Alpen-Adria-Universität Klagenfurt, Austria, e-mail: bernhard.rinner@aau.at
Children ’ s Motivations for Video Game Play in the Context of Normal Development

Electronic games are now an everyday part of childhood and adolescence. The debate has moved from whether children should play video games to how to maximize potential benefits and to identify and minimize potential harms. To do this, we must understand what motivates children to play electronic games and what needs the games meet. Drawing on a survey of 1,254 middle school children, focus groups with boys and their parents, and findings from other quantitative and qualitative research, the author describes a variety of motivations for video game play (including games with violent content) and how these may vary based on factors such as mood, environment, personality, and developmental stage. The findings are put into the context of normal development, and suggestions are given for parents, educators, and researchers.
Marginalized Denoising Dictionary Learning With Locality Constraint

Learning good representation for images is always a hot topic in machine learning and pattern recognition fields. Among the numerous algorithms, dictionary learning is a well-known strategy for effective feature extraction. Recently, more discriminative sub-dictionaries have been built by Fisher discriminative dictionary learning with specific class labels. Different types of constraints, such as sparsity, low rankness, and locality, are also exploited to make use of global and local information. On the other hand, as the basic building block of deep structure, the auto-encoder has demonstrated its promising performance in extracting new feature representation. To this end, we develop a unified feature learning framework by incorporating the marginalized denoising auto-encoder into a locality-constrained dictionary learning scheme, named marginalized denoising dictionary learning. Overall, we deploy low-rank constraint on each sub-dictionary and locality constraint instead of sparsity on coefficients, in order to learn a more concise and pure feature spaces meanwhile inheriting the discrimination from sub-dictionary learning. Finally, we evaluate our algorithm on several face and object data sets. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.
Haptic jamming: A deformable geometry, variable stiffness tactile display using pneumatics and particle jamming

Many controllable tactile displays present the user with either variable mechanical properties or adjustable surface geometries, but controlling both simultaneously is challenging due to electromechanical complexity and the size/weight constraints of haptic applications. This paper discusses the design, manufacturing, control, and preliminary evaluation of a novel haptic display that achieves both variable stiffness and deformable geometry via air pressure and a technique called particle jamming. The surface of the device consists of a flat, deformable layer of hollow silicone cells filled with coffee grounds. It selectively solidifies in different regions when the air is vacuumed out of individual cells, jamming the coffee particles together. The silicone layer is clamped over a chamber with regulated air pressure. Different sequences of air pressure and vacuum level adjustment allow regions of the surface to display a small rigid lump, a large soft plane, and various other combinations of lump size and stiffness. Experimental data from individual cells show that surface stiffness increases with vacuum level and the elliptical shape of the cells become increasingly spherical with increased chamber pressure.
Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media

Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
The effects of caffeine on heart rate variability in newborns with apnea of prematurity

Apnea of prematurity is a common complication in premature newborns and caffeine is a widespread medication used to treat this complication. Caffeine may have adverse effects on the cardiovascular and central nervous system, yet its effects on the autonomic nervous system modulation of heart rate have not been studied in premature newborns, which was the objective of our study.We prospectively studied 21 premature newborns who were treated with caffeine. We analyzed heart rate variability by power spectral density and by dynamic nonlinear analyses methods.There were no changes in heart rate, blood pressure or the autonomic nervous system tone following administration of caffeine, nor were the nonlinear dynamical properties of the system altered by caffeine.Caffeine does not have detrimental effects on heart rate variablility, heart rate or blood pressure in conventional doses given to premature newborns.
Challenges of data integration and interoperability in big data

The enormous volumes of data created and maintained by industries, research institutions are on the verge of outgrowing its infrastructure. The advancements in the organization's work flow include data storage, data management, data maintenance, data integration, and data interoperability. Among these levels, data integration and data interoperability can be the two major focus areas for the organizations which tend to implement advancements in their workflow. Overall, data integration and data interoperability influence the organization's performance. The data integration and data interoperability are complex challenges for the organizations deploying big data architectures due to the heterogeneous nature of data used by them. Therefore, it requires a comprehensive approach to negotiate the challenges in integration and interoperability. This paper focuses on the challenges of data integration and data interoperability in big data.
Topology Preserving Domain Adaptation for Addressing Subject Based Variability in SEMG Signal

A subject independent computational framework is one which does not require to be calibrated by the specific subject data to be ready to be used on the subject. The greatest challenge in developing such a framework is the variation in parameters across subjects which is termed as subject based variability. Spectral and amplitude variations in surface myoelectric signals (SEMG) are analyzed to determine the fatigue state of a muscle. But variations in the spectrum and magnitude of myoelectric signals across subjects cause variations in both marginal and conditional probability distributions in the features extracted across subjects, making it difficult to model the signal for any automated signal classification. However we observe that the manifold of the multidimensional SEMG data have an inherent similarity as the physiological state moves from no fatigue to fatigue state. In this paper we exploit this specific feature of the SEMG data and propose a domain adaptation technique that is based on intrinsic manifold of the data preserved in a low dimensional space, thus reducing the marginal probability differences between the subjects, followed by an instance selection methodology, based on similar conditional probabilities in the mapped domain. The proposed method provides significant improvement in subject independent accuracies compared to cases without any domain adaptation methods and also compared to other state-of-the-art domain adaptation methodologies.
Cable-suspended planar parallel robots with redundant cables: controllers with positive cable tensions

Cable-suspended robots are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the end-effector but not push it. From a scientific point of view, this feature makes feedback control of cable-suspended robots lot more challenging than their counterpart parallel-actuated robots. In the case with redundant cables, feedback control laws can be designed to make all tensions positive while attaining desired control performance. This paper describes these approaches and their effectiveness is demostrated through simulations of a three degree-offreedom cable suspended robots with four, five, and six cables.
Speech Recognition in Noisy Environment-an Implementation on MATLAB

Speech is one of the ways to express ourselves naturally. So, speech can be used as a means to communicate with machines. In this work, using MATLAB as a platform isolated word recognizer is achieved. Speech signals get distorted by many kinds of noises. Hence, it is necessary to reduce the noise contained in the speech signal. This is called speech enhancement. Speech enhancement aims at improving the intelligibility of the speech. Noise has been removed using Spectral Subtraction with Over Subtraction technique. The feature extraction is carried out using MFCC and feature matching is achieved using HMM.
Social support: a conceptual analysis.

Using the methodology of Walker and Avant, the purpose of this paper was to identify the most frequently used theoretical and operational definitions of social support. A positive relationship between social support and health is generally accepted in the literature. However, the set of dimensions used to define social support is inconsistent. In addition, few measurement tools have established reliability and validity. Findings from this conceptual analysis suggested four of the most frequently used defining attributes of social support: emotional, instrumental, informational, and appraisal. Social network, social embeddedness, and social climate were identified as antecedents of social support. Social support consequences were subsumed under the general rubric of positive health states. Examples were personal competence, health maintenance behaviours, effective coping behaviours, perceived control, sense of stability, recognition of self-worth, positive affect, psychological well-being, and decreased anxiety and depression. Recommendations for future research were made.
Crazyswarm: A large nano-quadcopter swarm

We define a system architecture for a large swarm of miniature quadcopters flying in dense formation indoors. The large number of small vehicles motivates novel design choices for state estimation and communication. For state estimation, we develop a method to reliably track many small rigid bodies with identical motion-capture marker arrangements. Our communication infrastructure uses compressed one-way data flow and supports a large number of vehicles per radio. We achieve reliable flight with accurate tracking (< 2 cm mean position error) by implementing the majority of computation onboard, including sensor fusion, control, and some trajectory planning. We provide various examples and empirically determine latency and tracking performance for swarms with up to 49 vehicles.
Developing An Arabic Treebank: Methods, Guidelines, Procedures, And Tools

In this paper we address the following questions from our experience of the last two and a half years in developing a large-scale corpus of Arabic text annotated for morphological information, part-of-speech, English gloss, and syntactic structure: (a) How did we ‘leapfrog’ through the stumbling blocks of both methodology and training in setting up the Penn Arabic Treebank (ATB) annotation? (b) How did we reconcile the Penn Treebank annotation principles and practices with the Modern Standard Arabic (MSA) traditional and more recent grammatical concepts? (c) What are the current issues and nagging problems? (d) What has been achieved and what are our future expectations?
k -Anonymous Data Mining: A Survey

Data mining technology has attracted significant interest as a means of identifying patterns and trends from large collections of data. It is however evident that the collection and analysis of data that include personal information may violate the privacy of the individuals to whom information refers. Privacy protection in data mining is then becoming a crucial issue that has captured the attention of many researchers. In this chapter, we first describe the concept of k-anonymity and illustrate different approaches for its enforcement. We then discuss how the privacy requirements characterized by k-anonymity can be violated in data mining and introduce possible approaches to ensure the satisfaction of k-anonymity in data mining.
Interference reduction in ECG signal acquisition: Ground electrode removal (case study)

An ECG signal is susceptible to a significant interference due to the surrounding sources of noise. The identification of these sources is necessary to acquire an ECG signal since its level is low (few millivolts) and is also immersed in a noisy environment. We propose the elimination of the ground electrode to reduce common-mode interference. We provide a qualitative model for these sources of interference and prove the effectiveness of ground electrode removal in interference reduction through an experimental design.
Looking good: factors affecting the likelihood of having cosmetic surgery

The present study examined various factors associated with the likelihood of having cosmetic surgery in a community sample of Austrian participants. One-hundred and sixty-eight women and 151 men completed a questionnaire measuring how likely they were to consider common cosmetic procedures. The results showed that women were more likely than men to consider most cosmetic procedures. Path analysis revealed that personal experience of having had cosmetic surgery was a significant predictor of future likelihood, while media exposure (viewing advertisements or television programs, or reading articles about cosmetic surgery) mediated the influence of vicarious experience and sex. These results are discussed in relation to previous work examining the factors associated with the likelihood of having cosmetic surgery.
Deep Context: End-to-end Contextual Speech Recognition

In automatic speech recognition (ASR) what a user says depends on the particular context she is in. Typically, this context is represented as a set of word n-grams. In this work, we present a novel, all-neural, end-to-end (E2E) ASR system that utilizes such context. Our approach, which we refer to as Contextual Listen, Attend and Spell (CLAS) jointly-optimizes the ASR components along with embeddings of the context n-grams. During inference, the CLAS system can be presented with context phrases which might contain-of-vocabulary (OOV) terms not seen during training. We compare our proposed system to a more traditional contextualization approach, which performs shallow-fusion between independently trained LAS and contextual n-gram models during beam search. Across a number of tasks, we find that the proposed CLAS system outperforms the baseline method by as much as 68% relative WER, indicating the advantage of joint optimization over individually trained components.
Symmetric-Aperture Antenna for Broadband Circular Polarization

A novel symmetric-aperture antenna is proposed for broadband circularly polarized (CP) radiation using a coplanar waveguide (CPW) feed. The proposed antenna consists of a CPW-feed and a wide symmetric aperture along the diagonal axis. A symmetric-aperture consists of two diamond-shaped sections. The symmetric-aperture and CPW-feeding structure are fabricated on the same plane of a dielectric substrate. Wideband CP radiation is achieved by changing the arms of symmetric-aperture and stripline length. The measured bandwidths of 3-dB axial ratio (AR) and 10-dB return loss are around 68% (2.4-4.85 GHz) and 107% (1.6-5.25 GHz), respectively. The measured boresight gain is more than 3.0 dBic over the CP bandwidth. Overall antenna size is around 0.7λo × 0.7λo × 0.019λo at 3.5 GHz.
The Repertory Grid Technique: A Method for the Study of Cognition in Information Systems

Recent studies have confirmed the importance of understanding the cognition of users and information systems (IS) professionals. These works agree that organizational cognition is far too critical to be ignored as it can impact on IS outcomes. While cognition has been considered in a variety of IS contexts, no specific methodology has dominated. A theory and method suitable to the study of cognition defined as personal constructs that individuals use to understand IT in organizations is Kelly's (1955) Personal Construct Theory and its cognitive mapping tool known as the repertory grid (RepGrid). This article expounds on the potential of this technique to IS researchers by considering the variety of ways the RepGrid may be employed. The flexibility of the RepGrid is illustrated by examining published studies in IS. The diagnostic qualities of the RepGrid and its mapping outcomes can be used for practical intervention at the individual and organizational levels.
On the use of X-vectors for Robust Speaker Recognition

Text-independent speaker verification (SV) is currently in the process of embracing DNN modeling in every stage of SV system. Slowly, the DNN-based approaches such as end-to-end modelling and systems based on DNN embeddings start to be competitive even in challenging and diverse channel conditions of recent NIST SREs. Domain adaptation and the need for a large amount of training data are still a challenge for current discriminative systems and (unlike with generative models), we see significant gains from data augmentation, simulation and other techniques designed to overcome lack of training data. We present an analysis of a SV system based on DNN embeddings (x-vectors) and focus on robustness across diverse data domains such as standard telephone and microphone conversations, both in clean, noisy and reverberant environments. We also evaluate the system on challenging far-field data created by re-transmitting a subset of NIST SRE 2008 and 2010 microphone interviews. We compare our results with the stateof-the-art i-vector system. In general, we were able to achieve better performance with the DNN-based systems, but most importantly, we have confirmed the robustness of such systems across multiple data domains.
Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel

Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.
Algorithms for Unevenly Spaced Time Series : Moving Averages and Other Rolling Operators

This paper describes algorithms for efficiently calculating certain rolling time series operators for unevenly spaced data. In particular, we show how to calculate simple moving averages (SMAs), exponential moving averages (EMAs), and related operators in linear time with respect to the number of observations in a time series. A web appendix provides an implementation of these algorithms in the programming language C and a package (forthcoming) for the statistical software R.