Miracle-dz/UniFAR-FTUs huggingface dataset:
Electoral Systems Used around the World

We give an overview of the diverse electoral systems used in local, national, or super-national elections around the world. We discuss existing methods for selecting single and multiple winners and give real-world examples for some more elaborate systems. Eventually, we elaborate on some of the better known strengths and weaknesses of various methods from both the theoretical and practical points of view.
Verbal Prefixes and Syntactic - Semantic Classification of Verbs in CROVALLEX

In this paper we aim to describe the latest work on the syntactic-semantic classification of verbs in Croatian language. We tried to extract the specific semantics and function of verb prefixes in Croatian and capture these specific features in the Croatian verb valency lexicon CROVALLEX. The syntactic and semantic connection between base verbs and derived prefixed verbs is revealed and modelled as rules which contribute to the enrichment of the CROVALLEX lexicon.
An Explanation of Decision-Making under Uncertainty: A Qualitative Research Approach

This paper presents a research study of how decision-making (D-M) under uncertainty and complexity is experienced from the accounts of a discrete set of decision-makers, using Grounded Theory Methodology. D-M in uncertain and complex situations is a balance between facts and the decision-maker's previous experiences. What drives a decision-maker are the emotional forces, which guide a D-M process or the decision maker's learning story, constructing a social web from where he tries to obtain information to fill the lack of it and support the emotional feeling for what is the best.
Towards a generic framework for trustworthy spatial crowdsourcing

Many studies foresee significant future growth in the number of mobile smart phone users, the phone's hardware and software features, and the broadband bandwidth. Therefore, a transformative area of research is to fully utilize this new platform for various tasks, among which the most promising is spatial crowdsourcing. Spatial crowdsourcing (SC) engages individuals, groups, and communities in the act of collecting, analyzing, and disseminating urban, social, and other spatiotemporal information. This new paradigm of data collection has shown to be useful when traditional means fail (e.g., due to disaster), are censored or do not scale in time and space. Two major impediments to the success of spatial crowdsourcing in real-world applications are scalability and trust issues. Without scale considerations, it is impossible to develop a generic multi-campaign spatial crowdsourcing system (SC-system) that can efficiently and in real-time match many requesters' tasks to numerous workers. Without trust, the SC-system cannot evaluate the credibility of the contributed data, rendering it ineffective for replacing the traditional data collection means. In this paper, we survey and study both issues of scale and trust in spatial crowdsourcing.
Preoperative Pain, Symptoms, and Psychological Factors related to Higher Acute Pain Trajectories during Hospitalization for Total Knee Arthroplasty

Objectives Unrelieved postoperative pain after total knee arthroplasty (TKA) is a significant problem. This longitudinal study investigated how preoperative pain intensity, as well as a comprehensive list of preoperative and perioperative factors, influenced the severity of acute average and worst pain after TKA. Methods Prior to surgery, 203 patients completed a demographic questionnaire, Lee Fatigue Scale, Fatigue Severity Scale, Hospital Anxiety and Depression Scale, and Brief Illness Perception Questionnaire. Brief Pain Inventory was completed prior to surgery as well as through postoperative days (POD) 0 to 4. Clinical data were extracted from medical records. Results Several factors were associated with higher levels of preoperative and postoperative pain. Lower preoperative average and worst pain intensity scores were associated with increases in average and worst postoperative pain from POD1 to POD4. A higher number of comorbidities, higher C-reactive protein values, and higher pain interference with function were associated with higher preoperative levels of average pain. Older age, higher fatigue levels, and higher scores on identity and emotional responses to osteoarthritis (OA) were associated with higher preoperative levels of worst pain. Lower perceived consequences of OA were associated with higher pain from POD1 to POD4. Males and patients with lower preoperative scores for average pain had higher worst pain following surgery. Discussion Patients at higher risk for more severe postoperative pain can be identified through an assessment of pain and other risk factors identified in this study. Future research needs to test the efficacy of interventions that modify patients’ perceptions of living with OA and pain intensity before surgery on short and long term postoperative outcomes.
Task shape classification and workload characterization of google cluster trace

Understanding workload characteristics is crucial for optimizing and improving the performance of large scale data produced by different industries. In this paper, we analyse a large scale production workload trace (version 2) [1] which is recently made publicly available by Google. We discuss statistical summary of the data. Further we perform k-means clustering to identify common groups of job. Cluster analysis provides insight into the data by dividing the objects into groups (clusters) of objects, such that objects in a cluster are more similar to each other than to the objects in other clusters. This work presents a simple technique for constructing workload characteristics and also provides production insights into understanding workload performance in cluster machine.
Quantum Dots and Their Multimodal Applications: A Review

Semiconducting quantum dots, whose particle sizes are in the nanometer range, have very unusual properties. The quantum dots have band gaps that depend in a complicated fashion upon a number of factors, described in the article. Processing-structure-properties-performance relationships are reviewed for compound semiconducting quantum dots. Various methods for synthesizing these quantum dots are discussed, as well as their resulting properties. Quantum states and confinement of their excitons may shift their optical absorption and emission energies. Such effects are important for tuning their luminescence stimulated by photons (photoluminescence) or electric field (electroluminescence). In this article, decoupling of quantum effects on excitation and emission are described, along with the use of quantum dots as sensitizers in phosphors. In addition, we reviewed the multimodal applications of quantum dots, including in electroluminescence device, solar cell and biological imaging.
Central European Journal of Biology

The effect of rapid and depot testosterone and estradiol on spatial performance in water maze of receptor types. So, particular brain regions can be targets for the actions of different classes of steroids. For example, estrogen receptors are thinly distributed in the neocortex of the rat, but are prevalent in preoptic and hypothalamic areas and the anterior pituitary. Also some neurons express receptors for more than one steroid. Authors reported testosterone modified spatial abilities resulting from animal research [3] and clinical studies of individuals exposed prenatally to unusual hormone environments. Congenital adrenal hyperplasia results in women with enhanced spatial performance [4], as does a lifelong deficiency in gonadotropin-releasing hormone (GnRH) in men [5].
Decision tree decomposition-based complex feature selection for text chunking

Incorporating a method of feature selection into a classification model often provides a number of advantages. In this paper we propose a new feature selection method based on the discriminative perspective of improving the classification accuracy. The feature selection method is developed for a classification model for text chunking. For effective feature selection, we utilize a decision tree as an intermediate feature space inducer. To select a more compact feature set with less computational load, we organized a partially ordered feature space according to the IGR distribution of features. Experimental results show that: (1) the computational complexity on high-dimensional feature space can be reduced by selecting features based on the decision tree decomposition; (2) the text chunking system using the proposed feature selection can significantly improve the performance compared with a decision tree classifier.
Additive Functions in Boolean Models of Gene Regulatory Network Modules

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.
Confronting substellar theoretical models with stellar ages

Abstract By definition, brown dwarfs never reach the main-sequence, cooling and dimming over their entire lifetime, thus making substellar models challenging to test because of the strong dependence on age. Currently, most brown dwarfs with independently determined ages are companions to nearby stars, so stellar ages are at the heart of the effort to test substellar models. However, these models are only fully constrained if both the mass and age are known. We have used the Keck adaptive optics system to monitor the orbit of HD 130948BC, a brown dwarf binary that is a companion to the young solar analog HD 130948A. The total dynamical mass of 0.109 ± 0.003 M⊙ is the most precise mass measurement (3%) for any brown dwarf binary to date and shows that both components are substellar for any plausible mass ratio. The ensemble of available age indicators from the primary star suggests an age comparable to the Hyades, with the most precise age being 0.79+0.22−0.15 Gyr based on gyrochronology. Therefore, HD 130948BC is unique among field L and T dwarfs as it possesses a well-determined mass, luminosity, and age. Our results indicate that substellar evolutionary models may underpredict the luminosity of brown dwarfs by as much as a factor of ≈2–3×. The implications of such a systematic error in evolutionary models would be far-reaching, for example, affecting determinations of the initial mass function and predictions of the radii of extrasolar gas-giant planets. This result is largely based on the reliability of stellar age estimates, and the case study of HD 130948A highlights the difficulties in determining the age of an arbitrary field star, even with the most up-to-date chromospheric activity and gyrochronology relations. In order to better assess the potential systematic errors present in substellar models, more refined age estimates for HD 130948A and other stars with binary brown dwarf companions (e.g., ϵ Ind Bab) are critically needed.
Modeling and control of a planar continuum robot

This paper presents a method to model planar continuum robots with backbone, and introduces a method to control, based on the Jacobian matrix. The model includes forces and moments applied to the robot, and is exact in contrast to constant curvature models. Then the position control strategy is presented to put the robot tip in a desired position. Results are illustrated using a three-section continuum robot.
Precision and reliability of 5 platelet function tests in healthy volunteers and donors on daily antiplatelet agent therapy.

BACKGROUND Anticoagulation protocols used during mechanical circulatory support call for titration of antiplatelet agents. We compared the precision and reliability of 5 platelet function tests in healthy volunteers and donors on daily antiplatelet therapy to distinguish their efficacy for titrating antiplatelet therapy. METHODS We assessed arachidonic acid-induced platelet function by light transmission aggregometry (LTA), Multiplate impedance aggregometry, VerifyNow, and platelet mapping by thromboelastography (TEG PM). We assessed ADP-induced platelet function by the same methods and flow cytometry. Forty healthy volunteers and 10-13 volunteers on daily aspirin and/or clopidogrel therapy were evaluated. We compared tests for intraassay precision, interassay precision (samples from 2 separate blood draws), and reliability coefficient. RESULTS For arachidonic acid-induced platelet aggregation in healthy volunteers, intra- and interassay CVs were ≤ 10% for all methods. Intra- and interassay precision among donors on daily aspirin was ≤ 30% for all methods except LTA (38% interassay CV) and TEG PM (95% intraassay and 104% interassay CV). For ADP-induced platelet function, intra- and interassay precision was ≤ 10% and ≤ 30% for all methods. Only Multiplate demonstrated moderate or greater (R > 0.40) reliability coefficients for arachidonic acid-induced platelet function among all subjects. All methods of ADP-induced platelet function, except TEG PM, demonstrated substantial or greater (R > 0.60) reliability among all subjects. CONCLUSIONS TEG PM is least suited to monitor effects of antiplatelet agents. Multiplate impedance aggregometry was the only method to demonstrate an acceptable reliability coefficient among healthy volunteers and donors on both aspirin and clopidogrel therapy.
Extraction of brain regions affected by Alzheimer disease via fusion of brain multispectral MR images

This paper proposes a new method for extraction of regions affected by Alzheimer's disease from multispectral medical images. In this method, first two modals of magnetic resonance images are fused to achieve an image with high information content. Statistical features of fused image are extracted and then are grouped into three clusters with the help of an unsupervised algorithm to perform initial segmentation. Labeling members of clusters and rearranging image yields final image. Results of quantitative analysis prove combination of fusion and segmentation to result in an image with higher values of quantitative metrics and better visual outcome.
Helping Our Own: The HOO 2011 Pilot Shared Task

The aim of the Helping Our Own (HOO) Shared Task is to promote the development of automated tools and techniques that can assist authors in the writing task, with a specific focus on writing within the natural language processing community. This paper reports on the results of a pilot run of the shared task, in which six teams participated. We describe the nature of the task and the data used, report on the results achieved, and discuss some of the things we learned that will guide future versions of the task.
Gambling with Rose-Tinted Glasses on: Use of Emotion-Regulation Strategies Correlates with Dysfunctional Cognitions in Gambling Disorder Patients

Background and aims Existing research shows that gambling disorder patients (GDPs) process gambling outcomes abnormally when compared against healthy controls (HCs). These anomalies present the form of exaggerated or distorted beliefs regarding the expected utility of outcomes and one’s ability to predict or control gains and losses, as well as retrospective reinterpretations of what caused them. This study explores the possibility that the emotional regulation strategies GDPs use to cope with aversive events are linked to these cognitions. Methods 41 GDPs and 45 HCs, matched in sociodemographic variables, were assessed in gambling severity, emotion-regulation strategies (cognitive emotion-regulation questionnaire, CERQ), and gambling-related cognitions (gambling-related cognitions scale, GRCS). Results GDPs showed higher scores in all gambling-related cognition dimensions. Regarding emotion regulation, GDPs were observed to use self-blame and catastrophizing, but also positive refocusing, more often than controls. Additionally, in GDPs, putatively adaptive CERQ strategies shared a significant portion of variance with South Oaks gambling screen severity and GRCS beliefs. Shared variability was mostly attributable to the roles of refocusing on planning and putting into perspective at positively predicting severity and the interpretative bias (GDPs propensity to reframe losses in a more benign way), respectively. Discussion and conclusions Results show links between emotion-regulation strategies and problematic gambling-related behaviors and cognitions. The pattern of those links supports the idea that GDPs use emotion-regulation strategies, customarily regarded as adaptive, to cope with negative emotions, so that the motivational and cognitive processing of gambling outcomes becomes less effective in shaping gambling-related behavior.
Implementation of new services to support ubiquitous computing for campus life

Many new services have emerged to realize ubiquitous computing environments, owing to the increasing supply of mobile devices and more widespread Internet and wireless network facilities. We introduce the U-Campus (ubiquitous computing for campus) service. This study has been conducted through interdisciplinary research by four participating departments. The U-campus project is part of the UTOPIA project (Ubiquitous computing TOwn Project: Intelligent context Awareness), which covers town- and city-wide environments. Currently, a test bed for U-campus has been constructed to support various services suck as U-profile, U-messaging and U-campus tour guide services. Students, professors, school staffs, and campus visitors can easily access the various available campus services by using image-based sensors and mobile devices such as CDMA cellular phones or WiFi smart phones. The major purpose of this research is to provide a context-aware U-campus environment to users by utilizing sensors and mobile devices currently available in the public sector and the marketplace.
The study of prediction of same attack group by comparing similarity of domain

Due to the rapid development of the IT industry, the threat of cyber-attacks has been increasing gradually and more intelligent. These days, the same attack groups carry out cyber-attack continuously by using their resources that are necessary to the attack. To respond more effectively to cyber terrors occurred by same the attack groups with the same purpose, we need to detect the same attack groups by comparing similarity of attack-resources. In this paper, we propose the prediction of the same group by comparing similarity of distributed domain (that is one of attack resources).
A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge

For argumentation mining, there are several sub-tasks such as argumentation component type classification, relation classification. Existing research tends to solve such sub-tasks separately, but ignore the close relation between them. In this paper, we present a joint framework incorporating logical relation between sub-tasks to improve the performance of argumentation structure generation. We design an objective function to combine the predictions from individual models for each sub-task and solve the problem with some constraints constructed from background knowledge. We evaluate our proposed model on two public corpora and the experiment results show that our model can outperform the baseline that uses a separate model significantly for each sub-task. Our model also shows advantages on component-related sub-tasks compared to a state-of-the-art joint model based on the evidence graph.
The Effects of Plyometric Training on Sprint Performance: A Meta-Analysis

Sáez de Villarreal, E, Requena, B, and Cronin, JB. The effects of plyometric training on sprint performance. A meta-analysis. J Strength Cond Res 26(2): 575–584, 2012—The purpose of this meta-analysis was to attempt to gain a clear picture of the magnitude of sprint performance improvements expected after chronic plyometric training (PT) and to identify specific factors that influence the treatment effects. Studies employing a PT intervention and containing data necessary to calculate effect size (ES) were included in the analysis. A total of 26 studies with a total of 56 ES met the inclusion criterion. Analysis of ES demonstrated that the strategies that seem to maximize the probability of obtaining significantly (p < 0.05) greater improvement in sprint performance included training volume for <10 weeks; a minimum of 15 sessions; and high-intensity programs with >80 combined jumps per session. To optimize sprint enhancement, the combination of different types of plyometrics and the use of training programs that incorporate greater horizontal acceleration (i.e., sprint-specific plyometric exercises, jumps with horizontal displacement) would be recommended, rather than using only one form of jump training (p < 0.05). No extra benefits were found to be gained from doing plyometrics with added weight. The loading parameters identified in this analysis should be considered by the professional sprinters and specialized trainers with regard to the most appropriate dose-response trends PT to optimized sprint performance gains.
A model-based approach for text clustering with outlier detection

Text clustering is a challenging problem due to the high-dimensional and large-volume characteristics of text datasets. In this paper, we propose a collapsed Gibbs Sampling algorithm for the Dirichlet Process Multinomial Mixture model for text clustering (abbr. to GSDPMM) which does not need to specify the number of clusters in advance and can cope with the high-dimensional problem of text clustering. Our extensive experimental study shows that GSDPMM can achieve significantly better performance than three other clustering methods and can achieve high consistency on both long and short text datasets. We found that GSDPMM has low time and space complexity and can scale well with huge text datasets. We also propose some novel and effective methods to detect the outliers in the dataset and obtain the representative words of each cluster.