Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Team-Member Exchange on Turnover Intention: A Cross-Cultural Perspective on a Selected Aspect of Employee Engagement

Abstract Research indicates that engaged employees demonstrate higher job satisfaction and lower turnover intention. However, employee engagement itself is influenced by and consists of a number of different factors. This study focuses on one specific aspect of employee engagement: the social interaction of team-members (i.e., team-member exchange, TMX) as a source of employee engagement. Drawing on social exchange theory, social information processing theory, and engagement literature, we investigate the role of societal culture on TMX, and we argue that the negative relationship of TMX and turnover intention is stronger in cultures high in collectivism and high in power distance. We combined the individual-level data of 6,664 employees from 14 countries with country-level data regarding the cultural values collectivism and power distance. Multilevel analysis supported the hypothesis that culture moderates the relationship between TMX and turnover intention such that the effect was stronger in cultures high in collectivism. However, power distance did not moderate the relationship between TMX and turnover intention. This study contributes to the research field by adding a cross-cultural perspective and testing country-level moderation effects. The practical implications of our findings are discussed.
DNA Sequence Explains Seemingly Disordered Methylation Levels in Partially Methylated Domains of Mammalian Genomes

For the most part metazoan genomes are highly methylated and harbor only small regions with low or absent methylation. In contrast, partially methylated domains (PMDs), recently discovered in a variety of cell lines and tissues, do not fit this paradigm as they show partial methylation for large portions (20%–40%) of the genome. While in PMDs methylation levels are reduced on average, we found that at single CpG resolution, they show extensive variability along the genome outside of CpG islands and DNase I hypersensitive sites (DHS). Methylation levels range from 0% to 100% in a roughly uniform fashion with only little similarity between neighboring CpGs. A comparison of various PMD-containing methylomes showed that these seemingly disordered states of methylation are strongly conserved across cell types for virtually every PMD. Comparative sequence analysis suggests that DNA sequence is a major determinant of these methylation states. This is further substantiated by a purely sequence based model which can predict 31% (R2) of the variation in methylation. The model revealed CpG density as the main driving feature promoting methylation, opposite to what has been shown for CpG islands, followed by various dinucleotides immediately flanking the CpG and a minor contribution from sequence preferences reflecting nucleosome positioning. Taken together we provide a reinterpretation for the nucleotide-specific methylation levels observed in PMDs, demonstrate their conservation across tissues and suggest that they are mainly determined by specific DNA sequence features.
How effective developers investigate source code: an exploratory study

Prior to performing a software change task, developers must discover and understand the subset of the system relevant to the task. Since the behavior exhibited by individual developers when investigating a software system is influenced by intuition, experience, and skill, there is often significant variability in developer effectiveness. To understand the factors that contribute to effective program investigation behavior, we conducted a study of five developers performing a change task on a medium-size open source system. We isolated the factors related to effective program investigation behavior by performing a detailed qualitative analysis of the program investigation behavior of successful and unsuccessful developers. We report on these factors as a set of detailed observations, such as evidence of the phenomenon of inattention blindness by developers skimming source code. In general, our results support the intuitive notion that a methodical and structured approach to program investigation is the most effective.
ARAPrototyper: Enabling Rapid Prototyping and Evaluation for Accelerator-Rich Architectures

Compared to conventional general-purpose processors, accelerator-rich architectures (ARAs) can provide orders-of-magnitude performance and energy gains and are emerging as one of the most promising solutions in the age of dark silicon. However, many design issues related to the complex interaction between general-purpose cores, accelerators, customized on-chip interconnects, and memory systems remain unclear and difficult to evaluate. In this paper we design and implement the ARAPrototyper to enable rapid design space explorations for ARAs in real silicons and reduce the tedious prototyping efforts far down to manageable efforts. First, ARAPrototyper provides a reusable baseline prototype with a highly customizable memory system, including interconnect between accelerators and buffers, interconnect between buffers and last-level cache (LLC) or DRAM, coherency choice at LLC or DRAM, and address translation support. Second, ARAPrototyper provides a clean interface to quickly integrate users' own accelerators written in high-level synthesis (HLS) code. The whole design flow is highly automated to generate a prototype of ARA on an FPGA system-on-chip (SoC). Third, to quickly develop applications that run seamlessly on the ARA prototype, ARAPrototyper provides a system software stack, abstracts the accelerators as software libraries, and provides APIs for software developers. Our experimental results demonstrate that ARAPrototyper enables a wide range of design space explorations for ARAs at manageable prototyping efforts, which has 4,000X to 10,000X faster evaluation time than full-system simulations. We believe that ARAPrototyper can be an attractive alternative for ARA design and evaluation.
Croatian web text summarizer (CroWebSum)

The paper describes automatic summarization of newspaper texts in Croatian language. The goal of the CroWebSum is to generate high-quality extracts that are both coherent and keep relevant information from the original text. The preliminary evaluation shows that extracts in the size of 10 % of the original text have good coherence, while the extract in the size of 5 % of the original text still conveys the most relevant information. Also, while cutting down news to SMS size (maximum 160 characters), CroWebSum performed quite well. The research brought us to conclusion that we should develop a technique that uses context vectors to calculate the semantic similarity between the terms in the document as well as pronoun resolution algorithm in order to improve the text summarization for Croatian language.
Delay-oriented data traffic migration in maritime mobile communication environments

Maritime ship-to-shore communication has to satisfy different user requirements while suffering dynamic communication circumstances. Satellite networks are the primary means to communicate between ship and shore. The idea of compensating the deficiency of satellite communication with other terrestrial networks is not new. However, focus was often on how to keep users always best connected, without considering respective application needs or the potential disconnections. In this paper, we propose a delay-oriented satellite data traffic migration solution, exploiting the route repetitiveness and predictability of ships, leveraging the delay tolerance of many communication applications, with the support of delay tolerant networking architecture. We use model-checking techniques to support our claim that a delay-oriented data traffic migration approach can provide users with not only more reliable delivery but also preferred services based on some reasonable assumptions. Issues and suggestions related to implementing the DTN-based traffic migration are discussed additionally.
Effects of Dietary Selenium, Sulphur and Copper Levels on Selenium Concentration in the Serum and Liver of Lamb

Thirty-two lambs were distributed in eight treatments under 2×2×2 factorial experiment to compare the effects of two levels of selenium (0.2 to 5 mg/kg dry matter [DM]), sulphur (0.25% and 0.37%) and copper (8 and 25 mg/kg DM) levels on selenium concentration in liver and serum of lambs. A liver biopsy was done on all animals and blood samples were collected from the jugular vein prior to the beginning of the treatments. The blood was sampled every thirty days and the liver was sampled after 90 days, at the slaughter. Increasing differences were noticed during the data collection period for the serum selenium concentration, and it was found to be 0.667 mg/L in animals fed with 5 mg Se/kg DM and normal sulphur and copper concentrations in their diet. However, a three-way interaction and a reduction of selenium concentration to 0.483 mg/L was verified when increasing copper and sulphur concentration levels to 25 ppm and 0.37% respectively. The liver selenium concentration was also high for diets containing higher selenium concentrations, but the antagonist effect with the increased copper and sulphur levels remained, due to interactions between these minerals. Therefore, for regions where selenium is scarce, increasing its concentration in animal diets can be an interesting option. For regions with higher levels of selenium, the antagonistic effect of interaction between these three minerals should be used by increasing copper and sulphur dietary concentrations, thus preventing possible selenium poisoning.
Averaging refined sample centers for faster out-of-core clustering

A novel sample based clustering technique has been developed in this paper. Since traditional k-means algorithm is very time consuming for large disk resident data, sample based, out-of-core clustering techniques have gained high popularity recently. We have used the concept of elimination of measurement errors by averaging over a number of samples. Here, samples or original data set are chosen randomly, they are clustered individually, in association with a refinement technique, to produce a number of sets of refined cluster centers. Average of these refined centers are expected to form a near approximation of the true centers of the original data set. A comparison of the proposed method with some existing ones proves the efficiency and usefulness of the former.
SIMSEG/ACM A Simulation Environment for Artificial Consumer Markets

The ACM-Artificial Consumer Market is part of the integrated simulation endeavor named the "Ar-tificial Economy". Complementing and extending the concepts developed in the SIMSEG simulation environment of Working Paper No. 60 this report proceeds in two steps. (1) it outlines the basic con-structs and consumer behavior phenomena implemented in the ACM in a nontechnical manner. (2) it elaborates the formal structure and relationships in full detail. The ACM was never headed for mimicking any real consumer market. However, it is ambitious enough to capture a number of behavioral mechanisms that are deemed crucial for exposing the Artificial Firms' analytical and strategic agents to a challenging artificial marketplace. (author's abstract)
The Effects of Discourse Connectives Prediction on Implicit Discourse Relation Recognition

Implicit discourse relation recognition is difficult due to the absence of explicit discourse connectives between arbitrary spans of text. In this paper, we use language models to predict the discourse connectives between the arguments pair. We present two methods to apply the predicted connectives to implicit discourse relation recognition. One is to use the sense frequency of the specific connectives in a supervised framework. The other is to directly use the presence of the predicted connectives in an unsupervised way. Results on PDTB2 show that using language model to predict the connectives can achieve comparable F-scores to the previous state-of-art method. Our method is quite promising in that not only it has a very small number of features but also once a language model based on other resources is trained it can be more adaptive to other languages and domains.
Sensitivity evaluation of SNoW and SVM based face detection methods

This paper studies the performance sensitivity of the variations in different factors on SNoW and SVM based face detection techniques. These factors include combinations of training set, pixel transitions in sliding window, pre-processing methods (rotation, scaling, light correction, masking filter), and bootstrapping in negative training patterns. It was found that performance of the two detection methods is significantly sensitive to these factors
A note on the number of solutions of the coplanar P4P problem

We present a novel constrained system for the coplanar P4P problem. By converting perspective transformation to affine transformation and using invariance to 3D affine transformation, we explore the relationship between the image of the absolute conic (IAC) and the world coordinate of camera optical center from four coplanar correspondences and show how the coplanar P4P problem is cast into the problems of solving the common intersection points of a system of five spheres. In particular, we claim that, if the four control points are coplanar, the upper bound of the coplanar P4P problem under distance-based definition or orthogonal-transformation-based definition is 2 and also attainable. From the point of view of efficient numerical solution, we also propose a solving technique based on singular value decomposition (SVD) in order to estimating the camera pose under the effects of image noise. Finally, The advantages of our method are demonstrated by testing on synthetic data.
Carrying the Role-Playing Metaphor to Interactive Learning Environments

We motivate the role-playing metaphor for intelligent educational interfaces in two ways: first, as an enjoyable interaction style between users and animated agents, and secondly taken more literally we argue that the concept of social role should be considered in the design of agents’ mental models. In particular, we introduce social control programs that qualify the agent’s expression of its affective state by the social context. We also describe a web-based language learning system that uses animated agents as conversational partners in role-playing environments.
Improved Object Tracking with Particle Filter and Mean Shift

In this paper, we present a new object tracking algorithm based on particle filtering technique and the mean shift algorithm. The particle filtering technique is a powerful technique for tracking objects in image sequences with complex background. It has been proved to be a robust method of tracking in non-linear and non-Gaussian case. But two common problems of the particle filter technique are the degeneracy phenomenon and the huge computational cost. To solve these problems, our new tracking algorithm uses the mean shift algorithm inside the particle filter. With the help of the mean shift algorithm, we can sample more particles of higher weights, and discard those particles whose contribution to the tracking is almost zero. The experiment results show that the new algorithm reduces the degeneracy problem and the computational cost of the particle filter.
Propositionalizing the EM algorithm by BDDs

We propose an Expectation-Maximization (EM) algorithm which works on binary decision diagrams (BDDs). The proposed algorithm, BDD-EM algorithm, opens a way to apply BDDs to statistical learning. The BDD-EM algorithm makes it possible to learn probabilities in statistical models described by Boolean formulas, and the time complexity is proportional to the size of BDDs representing them. We apply the BDD-EM algorithm to prediction of intermittent errors in logic circuits and demonstrate that it can identify error gates in a 3bit adder circuit.
A Complete Analysis of the l_1, p Group-Lasso

The Group-Lasso is a well-known tool for joint regularization in machine learning methods. While the l_{1,2} and the l_{1,\infty} version have been studied in detail and efficient algorithms exist, there are still open questions regarding other l_{1,p} variants. We characterize conditions for solutions of the l_{1,p} Group-Lasso for all p-norms with 1 > 2.
Least-square mean effect : Application to the Analysis of SLR Time Series

In this paper, we evidence an artifact due to the least square estimation method and, in particular, to the current modeling used to derive station position time series from spacegeodetic measurements. Indeed, to compute such series, we in fact estimate constant (typically over one week) updates of station positions with respect to a priori models (ITRF2000, solid Earth tides, polar tide and oceanic loading effects). Thus, these estimations must underline the physical models which were not taken into account in the a priori modeling (atmospheric and hydrologic loading effects and even unknown signals, in our case).
Characterization of Halorubrum sfaxense sp. nov., a New Halophilic Archaeon Isolated from the Solar Saltern of Sfax in Tunisia

An extremely halophilic archaeon, strain ETD6, was isolated from a marine solar saltern in Sfax, Tunisia. Analysis of the 16S rRNA gene sequence showed that the isolate was phylogenetically related to species of the genus Halorubrum among the family Halobacteriaceae, with a close relationship to Hrr. xinjiangense (99.77% of identity). However, value for DNA-DNA hybridization between strain ETD6 and Hrr.xinjiangense were about 24.5%. The G+C content of the genomic DNA was 65.1 mol% (T(m)). Strain ETD6 grew in 15–35% (w/v) NaCl. The temperature and pH ranges for growth were 20–55°C and 6–9, respectively. Optimal growth occurred at 25% NaCl, 37°C, and pH 7.4. The results of the DNA hybridization against Hrr. xinjiangense and physiological and biochemical tests allowed genotypic and phenotypic differentiation of strain ETD6 from other Hrr. species. Therefore, strain ETD6 represents a novel species of the genus Halorubrum, for which the name Hrr. sfaxense sp. nov. is proposed. The Genbank EMBL-EBI accession number is GU724599.
Multi-Stage Improved Route Planning Approach: theoretical foundations

A new approach to the static route planning problem, based on a multi-staging concept and a \emph{scope} notion, is presented. The main goal (besides implied efficiency of planning) of our approach is to address---with a solid theoretical foundation---the following two practically motivated aspects: a \emph{route comfort} and a very \emph{limited storage} space of a small navigation device, which both do not seem to be among the chief objectives of many other studies. We show how our novel idea can tackle both these seemingly unrelated aspects at once, and may also contribute to other established route planning approaches with which ours can be naturally combined. We provide a theoretical proof that our approach efficiently computes exact optimal routes within this concept, as well as we demonstrate with experimental results on publicly available road networks of the US the good practical performance of the solution.
Voted Approach for Part of Speech Tagging in Bengali

Part of Speech (POS) tagging is the task of labeling each word in a sentence with its appropriate syntactic category called part of speech. POS tagging is a very important preprocessing task for language processing activities. In this paper, we report about our work on POS tagging for Bengali by combining different POS tagging systems using three weighted voting techniques. The individual POS taggers are based on Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM) frameworks. The POS taggers use a tag set of 27 POS tags, defined for the Indian languages. The individual system makes use of the different contextual information of the words along with the variety of word3level features that are helpful in predicting the various POS classes. The POS tagger has been trained and tested with 57,341 and 35K tokens, respectively. It has been experimentally verified that the lexicon, named entity recognizer and different word suffixes are effective in handling the unknown word problems and improve the accuracy of the POS tagger significantly. Experimental results show the effectiveness of the proposed voted POS tagger with an accuracy of 92.35%, which is an improvement of 5.29% over the least performing ME based system and 2.23% over the best performing SVM based system.
Effect of different sports activities on body composition in pubescent girls

Introduction: The purpose of the study was to determine the effects of different sports on body fat and lean body mass in pubescent girls. Material and Methods: The sample of 40 girls (12 ± 1.2 years of age) were divided into 4 groups: 10 volleyball players, 10 basketball players, 10 swimmers, and 10 girls formed the control group. Body height was measured using portable stadiometer. We used InBody 230 to measure body mass, body mass index, fat mass, lean mass, visceral fat, and sum of skeletal muscles. The measured variables were evaluated using the Body-vision program and Statistica 12.0 cz. For normal distribution we used Shapiro-Wilk normality test. The differences between the groups were determined by Kruskal-Wallis test, p<0.05. Results: We found statistically significant differences in fat mass between the groups of swimmers and the control in the body fat (10.62%; p= 0.033) and fat mass in upper limbs (1.42 kg; p= 0.027). Statistically significant difference was found between the group of swimmers and volleyball players in the lower limbs fat mass (2.43 kg; p= 0.045). Furthermore, statistically significant difference in lean mass was found between the volleyball players and the control in the weight of the lower limbs muscle mass (3.59 kg; p= 0.047). Conclusions: Our findings show that sports such as volleyball, basketball, and swimming provide appropriate development of lean mass in pubescent girls. We may conclude that pubertal girls practicing sports have lower body fat mass and higher lean body mass compared to youth who do not practice sports.
CRRT: Congestion-Aware and Rate-Controlled Reliable Transport in Wireless Sensor Networks

For successful data collection in wireless sensor networks, it is important to ensure that the required delivery ratio is maintained while keeping a fair rate for every sensor. Furthermore, emerging high-rate applications might require complete reliability and the transfer of large volume of data, where persistent congestion might occur. These requirements demand a complete but efficient solution for data transport in sensor networks which reliably transports data from many sources to one or more sinks, avoids congestion and maintains fairness. In this paper, we propose congestion-aware and rate-controlled reliable transport (CRRT), an efficient and low-overhead data transport mechanism for sensor networks. CRRT uses efficient MAC retransmission to increase one-hop reliability and end-to-end retransmission for loss recovery. It also controls the total rate of the sources centrally, avoids the congestion in the bottleneck based on congestion notifications from intermediate nodes and centrally assigns the rate to the sources based on rate assignment policy of the applications. Performance of CRRT is evaluated in NS-2 and simulation results demonstrate the effectiveness of CRRT.