Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory tracking control of full coordinates and full constrained forces for nonholonomic systems

The aim of this paper is to present a simple and general control framework and convergent conditions applicable to nonholonomic systems, by using which one can control full generalized coordinates, including independent and dependent ones, and full constrained forces of the nonholonomic system to track their desired trajectory stably if the planned trajectory satisfies the proposed convergent conditions. So the trajectory planning problem of nonholonomic systems is mentioned, and to deal with such problem a novel method named “task planing method” is proposed. And then by using the proposed control framework the control problem of nonholonomic systems can be transformed into solving problems of two ordinary differential equations (ODEs). At final, there is an example about trajectory tracking control of nonholonomic system with one nonholonomic constraint, with the effectiveness of proposed control framework and convergent conditions verified.
Measuring generalization quality

The concept of measure functions for generalization performance is suggested. This concept provides an alternative way of designing and selecting generalization algorithms. In addition, it makes a clear distinction between modeling and solving a computational problem. The modeling is captured in a measure function that, for each possible combination of a training set and a generalization, assigns a value describing how good the generalization is. The computational problem is to find a generalization maximizing the measure function. With this concept in use, some recently debated facts about quality of generalization will become clarified. In addition to their theoretical relevance, we argue that measure functions are of great value for practical applications. For instance, (i) they force us to make explicit the relevant features of the generalization problem at hand, (ii) they provide a deeper understanding of existing generalization algorithms, and (iii) they help us in the construction of problem-specific algorithms. We illustrate the second point by an experiment that indicates that the difference between generalizations computed by different algorithms is often smaller than the difference between the generalizations computed by different versions of the same algorithm. The third point is supported by a novel algorithm based on incremental search for a generalization that optimizes a given measure function.
How Social Security and Medicare affect retirement behavior in a world of incomplete markets

This paper provides an empirical analysis of how the U.S. Social Security and Medicare system affects the labor supply of older males in the presence of incomplete markets. The authors estimate a dynamic programming model of the joint labor supply and Social Security acceptance decision. The model is able to account for a wide variety of phenomena observed in the data, including the pronounced peaks in the distribution of retirement ages at sixty-two and sixty-five. Overall, the authors' model suggests that several puzzling aspects of retirement behavior can be viewed as artifacts of particular details of the Social Security rules.
International Journal of Special Education an Analysis of Pre-school Teachers' and Student Teachers' Attitudes to Inclusion and Their Self-efficacy

The aim of this study was to investigate what the self-efficacy and attitudes of preschool teachers and student teachers towards inclusive education were and to elucidate the relationship between self-efficacy and the attitudes on inclusion. Therefore, the present study investigated the self-efficacy perceptions and attitudes of student teachers towards inclusive education, who received the special education course in the Department of Preschool Education at Faculty of Vocational Education in Selcuk University and preschool education teachers who work in nursery classes and nursery schools affiliated to Konya Local Education Authority. Two scales were used in the study. These were: (1) the Opinions Relative to Inclusion Scale and (2) the Teacher Self-efficacy Perception Scale. Results indicated that: (a) attitudes of preschool education teachers and the student teachers were undecided; (b) the two groups considered themselves efficient for being teachers in terms of the three dimensions of the self efficacy scale; (c) the attitudes' scores of the student teachers towards inclusive education were higher than the teachers' scores; (d) the scores of the teachers' self efficacy were higher than the student teachers' scores;(e) the attitudes of the teachers towards inclusive education were effected by their self efficacy perceptions in terms of teaching dimension; (f) the student teachers' perceptions on self efficacy were not effected by their attitudes towards inclusive education; (g) the student teachers should receive more courses on education of children with special educational needs during their university education; and (h) teachers should receive more support services than they have for how to educate children with SEN from the support units in accordance with the child's needs, type and severity of the child's handicap conditions. Further research is needed due to the fact that there may be a difference between the student teachers'scores on their attitudes towards special education according to how many credits they have received the courses on special education and practice in schools what they have learned from the courses regarding special education. inclusive education concerns special educational applications based on the principle that individuals requiring special education continue their education together with their peers without handicaps in institutions of preschool, primary, secondary and non-formal education and where support services are offered (Milli Eğitim Bakanlığı [MEB], 2006). In inclusive education, children with Special Educational Needs [SEN] are included within a programme for children demonstrating a normal progress from preschool education onwards and efforts are made to get them to …
The Relationship between Organizational Conduct and National Culture

This paper puts forwards different existing theories on classifying cultures of different countries. The first theory it introduces is of Hofstede’s cultural aspects, then the Hall’s Method of classifying culture into low and high context, and lastly the model of cultural differences by Trompenaars. Following that is the discussion on the interaction between organizational behaviour and the national culture. The focus would be the influence of culture on employee motivation, communication, organizational changes and conflict resolution.
Formant estimation of whispered speech based on spectral segmentation

Whispered speech, without vocal cord vibration and always in low SNR, is more difficult both in its analysis and recognition. Thus its formant estimation becomes prominent in each field. The proposed algorithm is based on spectral segmentation. The complete spectrum is segmented into K segments, each of which contains a single formant. Here, improved dynamic programming and selective LP (linear predictive) methods are used. The former offers segment boundaries, and the latter leads to the parameters of formant frequency and its bandwidth as well. For whispered speech, the gain of vocal tract transfer function is also important. The tests are carried on Chinese whispered vowels, and the proposed algorithm is proved to be efficient. In low SNR, the segment based LP method is obviously superior to the conventional LPC and LSP
A Proposed Service Broker Policy for Data Center Selection in Cloud Environment with Implementation

Cloud Services may be offered as public or private or combined. There is the demand of timely, repeatable, and controllable methodologies for evaluation of algorithms, applications, and policies before actual development of cloud products. Using simulation, we can study the behavior of them in cloud environment. Cloud-Analyst, the Cloud-Sim based tool, is useful to model and analyze cloud computing environment and applications. The simulator uses different algorithms (Service Broker Algorithms, Load balancing algorithms etc) and generates report as per configuration. With the help of simulation report, we can modify the cloud environment as per requirement. The service proximity based routing policy used in the simulator selects data center from the earliest region to route the user requests. When there is the situation to select one data center from those with same region, the policy selects data-center randomly without considering cost-effectiveness or other parameters. We have proposed the extended service proximity based routing policy. Using proposed policy, we can have cost effective routing of user requests to the data center than in the original one. And thus, some of the research issues related to cost can be explored.
Color-Image Quality Assessment: From Prediction to Optimization

While image-difference metrics show good prediction performance on visual data, they often yield artifact-contaminated results if used as objective functions for optimizing complex image-processing tasks. We investigate in this regard the recently proposed color-image-difference (CID) metric particularly developed for predicting gamut-mapping distortions. We present an algorithm for optimizing gamut mapping employing the CID metric as the objective function. Resulting images contain various visual artifacts, which are addressed by multiple modifications yielding the improved color-image-difference (iCID) metric. The iCID-based optimizations are free from artifacts and retain contrast, structure, and color of the original image to a great extent. Furthermore, the prediction performance on visual data is improved by the modifications.
Hot topic analysis and content mining in social media

Sina Weibo has become an increasingly critical social media in China for sharing latest news, marketing new products, and discussing controversial issues. The rising importance of Sina Weibo on the society makes it very important to understand “what”, “when”, “who” on hot topics that are being continuously tweeted and searched by millions of active users. In this paper, we develop a systematic approach to characterize temporal distribution of hot topics searched by Sina Weibo users over a four-month time-span and to uncover correlated hot topics that are not only tweeted by the same users, but also appear in the similar set of tweet messages. We analyze real-time Sina Weibo tweet data streams and study volume correlations and temporal gaps between user searches and tweeting activities on hot topics. In addition, we examine the correlations between hot topic searches on social media and on search engines to understand hot topics and user behaviors across different platforms. Given the challenges of analyzing massive amount of tweet data, we explore Hadoop MapReduce framework to effectively process millions of tweets from the collected data-sets, and quantify the performance benefits of MapReduce on analyzing tweet streams. To the best of our knowledge, this paper is the first effort to characterize temporal search patterns of hot topics on Sina Weibo and to study their correlations with tweeting data streams as well as search engine statistics.
Serum Metabolomics Reveals Higher Levels of Polyunsaturated Fatty Acids in Lepromatous Leprosy: Potential Markers for Susceptibility and Pathogenesis

Background Leprosy is a disease of the skin and peripheral nervous system caused by the obligate intracellular bacterium Mycobacterium leprae. The clinical presentations of leprosy are spectral, with the severity of disease determined by the balance between the cellular and humoral immune response of the host. The exact mechanisms that facilitate disease susceptibility, onset and progression to certain clinical phenotypes are presently unclear. Various studies have examined lipid metabolism in leprosy, but there has been limited work using whole metabolite profiles to distinguish the clinical forms of leprosy. Methodology and Principal Findings In this study we adopted a metabolomics approach using high mass accuracy ultrahigh pressure liquid chromatography mass spectrometry (UPLC-MS) to investigate the circulatory biomarkers in newly diagnosed untreated leprosy patients. Sera from patients having bacterial indices (BI) below 1 or above 4 were selected, subjected to UPLC-MS, and then analyzed for biomarkers which distinguish the polar presentations of leprosy. We found significant increases in the abundance of certain polyunsaturated fatty acids (PUFAs) and phospholipids in the high-BI patients, when contrasted with the levels in the low-BI patients. In particular, the median values of arachidonic acid (2-fold increase), eicosapentaenoic acid (2.6-fold increase) and docosahexaenoic acid (1.6-fold increase) were found to be greater in the high-BI patients. Significance Eicosapentaenoic acid and docosahexaenoic acid are known to exert anti-inflammatory properties, while arachidonic acid has been reported to have both pro- and anti-inflammatory activities. The observed increase in the levels of several lipids in high-BI patients may provide novel clues regarding the biological pathways involved in the immunomodulation of leprosy. Furthermore, these results may lead to the discovery of biomarkers that can be used to investigate susceptibility to infection, facilitate early diagnosis and monitor the progression of disease.
pregnancy A prominent lack of IgG1-Fc fucosylation of platelet alloantibodies in

doi:10.1182/blood-2013-09-527978Prepublished online November 15, 2013;2014 123: 471-480€€€€der Schoot, Manfred Wuhrer and Gestur VidarssonBjorn Skogen, Mette Kjaer Killie, Terje E. Michaelsen, Masja de Haas, Theo Rispens, C. Ellen vanEinarsdottir, Leendert Porcelijn, Dave Jackson, Belinda Kumpel, Andre M. Deelder, Dennis Blank, Rick Kapur, Iwan Kustiawan, Anne Vestrheim, Carolien A. M. Koeleman, Remco Visser, Helga K. €
The Effect Of Team Cohesion On Peer Justice: A Team-Level Analysis

This study was conducted to examine the relationship between team cohesion and peer justice (procedural, interpersonal, informational) and to test whether female team leadership and team task type had moderating effect on the relationship between team cohesion and peer justice. The design of research was based upon survey data obtained from questionnaires distributed to 415 team member in 66 service and production teams in Turkey. The individual responses of team cohesion and peer justice were aggregated to the team level since the theoretical level of the study was team. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test hypotheses in the model. As predicted, the findings indicated that team cohesion was positively associated with peer procedural justice, peer interpersonal justice and peer informational justice but neither female team leadership nor team task type had moderating effect on the relationship between team cohesion and all dimensions of peer justice. © 2019 Published by Future Academy www.FutureAcademy. org.uk
The Coastal Flooding by Extreme Events (CoFEE) Project

Abstract The CoFEE project will investigate the present and future flood risk due to extreme storms at coastal sites in the eastern Irish Sea. It will consider both the rate of coastal flooding and the extent of inundation. It will access an extensive historical coastal dataset and will exploit the extensive contemporary field data collected by the POL Coastal Observatory. Coastal flooding models will be evaluated and parameterisations will be optimised to obtain the best agreement between the observations and the model predictions. The project will also examine changes to the speed and extent of coastal flooding attributable to changes in the mean sea level and extreme events in accord with global climate change predictions. It will identify those coast types most sensitive to inundation as a result of extreme events and how the risk will change in the future. In addition, simple morphological models will be used to assess the consequences of natural coastal evolution with respect to coastal flooding. The paper is intended to inform and initiate dialogue between researches engaged in similar projects and end users concerned with managing and adaptation to changes in coastal storm frequency and severity.
Combining DagMaps and Sugiyama Layout for the Navigation of Hierarchical Data

This paper presents a novel technique for exploring and navigating large hierarchies, not restricted to trees but to directed acyclic graphs (DAGs). The technique combines two different visualizations emphasizing different points of view the user can adopt when analyzing the content of the hierarchy. Usual hierarchical (node-link) layout reflect the relative position of nodes in the hierarchy while a variation of a treemap emphasizes node attributes. The classical treemap algorithm has been adapted in order to deal with DAGs. Linking the two views enables the user to visually and interactively explore elements of the hierarchy with respect to selected attributes, while being able to locate the node in the DAG.
Temperature effects on trench-gate punch-through IGBTs

The switching characteristics (turn-on and turn-off) and forward conduction drop of trench-gate punch-through insulated gate bipolar transistors (IGBTs) are examined over a temperature range of from -50/spl deg/C to 125/spl deg/C. An analytical description of the forward conduction voltage drop is presented based on temperature dependencies of the appropriate physical parameters and mechanisms. A physics-based PSpice model, incorporating much of the device behavior, is also described. Results from the model are compared to experimental waveforms.
Revealing Density-Based Clustering Structure from the Core-Connected Tree of a Network

Clustering is an important technique for mining the intrinsic community structures in networks. The density-based network clustering method is able to not only detect communities of arbitrary size and shape, but also identify hubs and outliers. However, it requires manual parameter specification to define clusters, and is sensitive to the parameter of density threshold which is difficult to determine. Furthermore, many real-world networks exhibit a hierarchical structure with communities embedded within other communities. Therefore, the clustering result of a global parameter setting cannot always describe the intrinsic clustering structure accurately. In this paper, we introduce a novel density-based network clustering method, called graph-skeleton-based clustering (gSkeletonClu). By projecting an undirected network to its core-connected maximal spanning tree, the clustering problem can be converted to detect core connectivity components on the tree. The density-based clustering of a specific parameter setting and the hierarchical clustering structure both can be efficiently extracted from the tree. Moreover, it provides a convenient way to automatically select the parameter and to achieve the meaningful cluster tree in a network. Extensive experiments on both real-world and synthetic networks demonstrate the superior performance of gSkeletonClu for effective and efficient density-based clustering.
XY-pic User’s Guide

XY-pic is a package for typesetting graphs and diagrams using Knuth’s TEX typesetting system. XY-pic works with most of the many formats available; e.g., plain TEX, LTEX, and AMS-TEX. Several styles of input for various diagram types are supported; they all share a mnemonic notation based on the logical composition of visual components. This guide concentrates on how to typeset “matrix-like” diagrams, such as commutative diagrams, in the following style:
Modeling zonal electricity prices by anisotropic diffusion embeddings

many fields including economics, collection of time series such as stocks or energy prices are governed by a similar non-linear dynamical process. These time series are often measured hourly, thus, each day can be viewed as a high-dimensional data point. In this paper, we apply a spectral method, which based on anisotropic diffusion kernels to the model high dimensional electricity price data. We demonstrate the proposed method on price data that was collected from several zones. We show that even though the observed output spaces differ by local spatial influences and noise, the common global parameters that drive the underlying process are extracted.
Robust detection of heart beats using dynamic thresholds and moving windows

Background: This contribution relates to the PhysioNet/CinC Challenge 2014 on Robust Detection of Heart Beats in Multimodal Data. The aim is to locate heart beats in continuous long-term data. Methods: The beat detection system is build up of several parts. Preprocessing consists of high pass filtering followed by standardization. Extrema of a moving window were used to capture the heart beat impulse. A windowed approach led to dynamic thresholds. Valid parts of the channels were determined and the locations of beats were extracted. The beat locations of various channels were compared during the multichannel fusion procedure and dynamic delay correction. Doubtful locations were checked using RR distances. Results: The algorithm was tested on the training data set for this challenge (one hundred 10-minute recordings) and on several freely available PhysioNet databases which were annotated by physicians. The algorithm had the best score applied to the hidden Phase 1 dataset of the 2014 PhysioNetlCinC challenge. Conclusion: The developed algorithm presents a promising approach to detect heart beats in multivariate records.
On the Feasibility of Using OWL 2 DL Reasoners for Ontology Matching Problems

In this paper we discuss the feasibility of using OWL 2 DL reasoners to diagnose the integration of large-scale ontologies via mappings. To this end, we have extended our ontology matching system LOGMAP with complete OWL 2 DL reasoning and diagnosis capabilities. We have evaluated the new system, which we call LOGMAP-FULL, with the largest matching problems of the Ontology Alignment Evaluation Initiative, and we have compared its performance with LOGMAP, which currently relies on a highly-scalable (but incomplete) reasoner.
Proposal for implementation of a ranking model for Olympic Taekwondo competitions using PageRank

ABSTRACT In this study, a PageRank model is proposed to overcome the existing problems in estimating the Olympic Taekwondo rankings. For this purpose, PageRank value was calculated by using the results of 144 matches in the Men’s −58 kg class at the World Gran Prix Taekwondo competition. As a result, we identified the possibility of applying the PageRank model to calculate the Olympic Taekwondo ranking. In addition, there was a difference between the rankings calculated by the PageRank method and the Accumulative Point Index System method which is currently used, and the PageRank method was found to be more validity.
Information extraction as a filtering task

Information extraction is usually approached as an annotation task: Input texts run through several analysis steps of an extraction process in which different semantic concepts are annotated and matched against the slots of templates. We argue that such an approach lacks an efficient control of the input of the analysis steps. In this paper, we hence propose and evaluate a model and a formal approach that consistently put the filtering view in the focus: Before spending annotation effort, filter those portions of the input texts that may contain relevant information for filling a template and discard the others. We model all dependencies between the semantic concepts sought for with a truth maintenance system, which then efficiently infers the portions of text to be annotated in each analysis step. The filtering view enables an information extraction system (1) to annotate only relevant portions of input texts and (2) to easily trade its run-time efficiency for its recall. We provide our approach as an open-source extension of Apache UIMA and we show the potential of our approach in a number of experiments.