Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach for Energy Efficient Execution of Hybrid Parallel Programs

Hybrid programming model is becoming increasingly popular for HPC applications as it has the dual-advantage of exploiting inter-node distributed-memory scalability and intra-node shared-memory performance in a cluster system. One of the key challenges for energy efficient execution of hybrid programs is to determine time and energy efficient hardware configurations among a large system configuration space. Given a hybrid program with an execution time deadline and an energy budget, we propose a measurement-based analytical modelling approach to determine these system configurations. In contrast to current approaches, we model both inter and intra-node resource overlaps, memory contention among cores within a node and network contention across multiple nodes. The model invalidated against direct measurement using five representative HPC applications on Intel Xeon and ARM clusters having diverse time-energy performance. We show that a Pareto frontier consisting of optimal configurations exist for a hybrid program running on homogeneous clusters. To further optimize the Pareto frontier, we introduce a new metric, useful computation ratio (UCR) to quantify the degree of resource contentions and communication overheads in an execution. We discuss how UCR and Pareto-optimal configurations can be used in conjunction by system's designers to gain further insights into system resource imbalances, and how application developers can further fine-tune their hybrid programs.
Learning to Control First Order Linear Systems with Discrete Time Reinforcement Learning

Reinforcement learning (RL) is a powerful method for learning policies in environments with delayed feedback. It is typically used to learn a control policy on systems with an unknown model. Ideally, it would be desirable to apply RL to learning controllers for first-order linear systems (FOLS), which are used to model many processes in Cyber Physical Systems. However, a challenge in using RL techniques in FOLS is dealing with the mismatch between the continuous-time modeling in the linear-systems framework and the discretetime perspective of classical RL. In this paper, we show that the optimal continuous-time value function can be approximated as a linear combination over a set of quadratic basis functions, the coefficients of which can be learned in a model-free way by methods such as Q-learning. In addition, we show that the performance of the learned controller converges to the performance of the optimal continuous-time controller as the step-size approaches zero.
NONLINEAR ROBUST OPTIMIZATION WITH UNCERTAIN EQUALITY CONSTRAINTS

The problem of nonlinear optimization for process design under uncertainty is addressed in this paper. A novel robust optimization framework is proposed to address general nonlinear problems under uncertainty. To address the limitation of single point linearization with respect to uncertain parameters for a large uncertainty region, an iterative algorithm is developed. The new method applies local linearization around multiple realizations of the uncertainty, and iteratively solves a robust optimization problem. The proposed method can handle uncertainty in both inequality constraints and equality constraints.
Investigation into the Constructs of the FLCAS

This study proposed various models with different constructs of the Foreign Language Classroom Anxiety Scale (FLCAS) developed by Horwitz et al. (1986) and tested the competitive models with a view to finding the constructs that best account for the FLCAS. The FLCAS was administered to 918 EFL university students in Korea, and the data were analyzed by performing confirmatory factor analysis (CFA). The results of data analyses indicated that even though the fit indices for all the hypothesized models in general did not meet the cutoff points of acceptabi lity, the model containing four constructs fit the data better than the models containing one, two, or three constructs of the FLCAS. Implications of these findings followed by futu re research areas were provided to deepen the insight into foreign language anxiety in the classroom in general and the FLCAS in particular.
On the scalability of BGP: the roles of topology growth and update rate-limiting

The scalability of BGP routing is a major concern for the Internet community. Scalability is an issue in two different aspects: increasing routing table size, and increasing rate of BGP updates. In this paper, we focus on the latter. Our objective is to characterize the churn increase experienced by ASes in different levels of the Internet hierarchy as the network grows. We look at several "what-if" growth scenarios that are either plausible directions in the evolution of the Internet or educational corner cases, and investigate their scalability implications. In addition, we examine the effect of the BGP update rate-limiting timer (MRAI), considering both major variations with which it has been deployed. Our findings explain the dramatically different impact of multi-homing and peering on BGP scalability, identify which topological growth scenarios will lead to faster churn increase, and emphasize the importance of not rate-limiting explicit withdrawals (despite what RFC-4271 recently required).
Negotiations of gender discourse: experiences of co-education in a Swedish sports initiative for children

Abstract The goal of this article is to explore how children perceive gender in a Swedish government-financed sports initiative and in leisure-time sports activities. We draw on group interviews with children and participant observations from two field studies of elementary school children participating in co-ed soccer and physical activity projects. A focal point of the analysis is how children negotiate their views of boys and girls participating in sports utilizing linguistic and material resources. The interviews included reproduction of traditional views of boys/men as being superior at sports but also demonstrated negotiations and objections against this perception. In addition, we present examples from participant observations regarding potential challenges to gender discourse.
Silicon Nanowire Photocathodes for Photoelectrochemical Hydrogen Production

The performance of silicon for water oxidation and hydrogen production can be improved by exploiting the antireflective properties of nanostructured silicon substrates. In this work, silicon nanowires were fabricated by metal-assisted electroless etching of silicon. An enhanced photocurrent density of −17 mA/cm2 was observed for the silicon nanowires coated with an iron sulphur carbonyl catalyst when compared to bare silicon nanowires (−5 mA/cm2). A substantial amount of 315 µmol/h hydrogen gas was produced at low bias potentials for the silicon nanowires coated with an iron sulphur carbonyl catalyst.
Highly Efficient Architecture for Scalable Focused Crawling Using Incremental Parallel Web Crawler

With the growing industrial impact over the recent years in computer science, data mining has established itself as one of the most important disciplines. In the fast growing Web and in an appropriate amount of time, locating the resources that are precise and relevant is a huge challenge for the all-purpose single process crawlers, which makes the enhanced and the convincing algorithm in demand. Gradually Large scale search engines frequently update their index and in a timely behavior which are not capable to present such information. In this study a scalable focused crawling is proposed with an incremental parallel Web crawler, the Web pages can be crawled concurrently that are relevant to multiple pre-defined topics. Furthermore, to solve the issue of URL distribution, a compound decision model based on multi-objective decision making method is introduced, which will consider multiple factors synthetically such as load balance and relevance, the update frequency issue can be solved by the local repository decision. The result shows that our proposed system will efficiently produce high quality, relevance and freshness with significantly low memory requirement.
A Novel Calibration Framework for the Physical Layer Abstraction of (Turbo) Codeword IC Receivers

Modern wireless communication systems rely on limited feedback for Fast Link Adaptation (FLA). FLA in presence of advanced receivers needs new physical layer abstractions (PHY abstractions) that are able to capture their performance in order to derive the limited feedback metrics (e.g., Channel Quality Indicators). Recently, a PHY abstraction was proposed for the class of iterative linear minimum mean square error based soft interference cancellation and decoding also referred to as turbo Linear Codeword Interference Cancellation (turbo L-CWIC) receivers in 3GPP. This PHY abstraction needs to be calibrated and a first one-dimensional (1D) calibration method was proposed. In this paper, we revisit the calibration method by proposing a more rigorous framework to minimize the predicted throughput error that dramatically influences the FLA mechanisms. We also suggest a novel multi- dimensional calibration approach. Monte Carlo simulations show that it outperforms the 1D at the expense of the knowledge of the number of independent fading blocks experienced by a codeword.
Properties of a specific interleukin 1 (IL 1) receptor on human Epstein Barr virus-transformed B lymphocytes: identity of the receptor for IL 1-alpha and IL 1-beta.

The properties of specific human interleukin 1 (IL 1) receptors on human Epstein Barr virus-transformed B lymphocytes (EBV-B) were studied. Purified human IL 1-beta from a myelomonocytic cell line (THP-1) was labeled with 125I by the Bolton-Hunter method without detectable loss of biological activity. Among four EBV-B cell lines tested, a pre-B cell type (VDS-O) specifically bound the highest amount of 125I-IL 1-beta. Maximal binding was reached within 20 min at 4 degrees C. Scatchard plot analysis of the binding of 125I-IL 1-beta to VDS-O cells yielded a Kd (dissociation constant) of 2.4 to 5.9 X 10(-10) M with 110 to 220 binding (receptor) sites/cell. The binding of 125I-IL 1-beta to VDS-O cells was also inhibited by F(ab)'2 fragments of anti-human IL 1 and recombinant human IL 1-alpha, as well as by unlabeled human IL 1-beta but not by recombinant lymphotoxin, recombinant tumor necrosis factor, or phorbol myristic acid, suggesting that IL 1-alpha and IL 1-beta bind specifically to the same receptor. The m.w. of IL 1 receptor on human EBV-B cells was estimated to be 60,000 by both the chemical cross-linking method and high pressure liquid chromatography (HPLC) gel filtration analysis of receptor extracted from membrane enriched fraction by a non-ionic detergent (CHAPS). The isoelectric point of solubilized human IL 1 receptor was 7.3 on HPLC chromatofocusing. The evidence of existence of IL 1 receptor on human EBV-B cells additionally supports the hypothesis that IL 1 may be an autocrine signal for these cells.
Multilingual Sentiment Analysis Using Emoticons and Keywords

Nowadays the World Wide Web has evolved into a leading communication channel and information exchange medium. Especially after the introduction of the so-called web 2.0 and the explosion that followed regarding user generated content, the amount of data available over the internet has attracted the interest of both the scientific and business community. Their efforts focus on identifying the inner structures of data and the knowledge that can be derived by analyzing them. Web 2.0 is the subject of study and research in a number of areas. One of these areas is sentiment analysis, where the main goal is to study and draw conclusions about subjectivity, polarity and the feeling that is expressed in user generated content, which mainly consist of free text documents. The goal of this paper is to apply sentiment analysis on multilingual data, focusing on documents written in Greek. We developed an integrated framework that accepts user generated documents and then identifies the polarity of the text (neutral, negative or positive) and the sentiment expressed through it (joy, love, anger or sadness). We followed a semi-supervised approach which led to the development of two techniques for the automatic collection of training data without any human intervention. Our approach involves the detection and use of self-defining features that are available within the data. We take into account two emotionally rich features: a) emoticons and b) lists of emotionally intense keywords. These features are evaluated on data coming from a popular forum, using various classifiers and feature vectors. Our experimental results point to various conclusions about the effectiveness, advantages and limitations of applying such methods on Greek data. Using keywords we achieved 90% mean accuracy on identifying the subjectivity level and 93% on correctly identifying the polarity level, whereas using emoticons the mean accuracy for each of these levels was 74% and 77% respectively.
A New Content-based Image Retrieval Using Color Correlogram and Inner Product Metric

The color correlogram is a simple statistical descriptor of a color image that has been widely used for content-based image retrieval (CBIR) systems. To measure similarity between two images using the correlogram, the traditional approaches use the relative distance. In this paper, to improve performance of the CBIR systems, the inner product metric is used to measure similarity of images instead of the relative distance. Results of experiments proved that the CBIR using the inner product metric has better performance than the one using the relative distance.
An Empirical Study on Influencing Factors of Trade Credit Risk Management Performance in China

Firstly, the author has analyzed some relative papers of Jianmin Jia, Allen N. Berger, Xiao-jun Shi and Robert Culla, etc, and found out mistakes existing in these papers. On the basis, the author has conducted an empirical study on influencing factors of Trade Credit Risk management performance by using the method of Factor Analysis and Pearson Correlation Analysis. Research shows: key influencing factors are "setting credit risk management Functions", "assessing credit of client" and "drawing up credit policy", enterprises should take efforts to improve management performance from above three aspects.
Effectiveness Of Feature Detection Operators On The Performance Of Iris Biometric Recognition System

Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.
The Performance of SQL-on-Hadoop Systems - An Experimental Study

Hadoop is now the de facto standard for storing and processing big data, not only for unstructured data but also for some structured data. As a result, providing SQL analysis functionality to the big data resided in HDFS becomes more and more important. Hive is a pioneer system that supports SQL-like analysis to the data in HDFS. However, the performance of the early-version of Hive is not satisfactory. This leads to the quick emergence of dozens of SQL-on-Hadoop systems that try to support interactive SQL query processing to the data stored in HDFS. This paper firstly gives a brief technical review on recent efforts of SQL-on-Hadoop systems. Then we test and compare the performance of three representative SQL-on-Hadoop systems, based on the TPC-H benchmark. According to the results, we show that such systems can benefit more from applications of many parallel query processing techniques that have been widely studied in the traditional massively parallel processing databases.
Predicting accidental falls in people with multiple sclerosis — a longitudinal study

Objective: To investigate accidental falls and near fall incidents in people with multiple sclerosis with respect to clinical variables and the predictive values of four tests. Design: A longitudinal, multi-centred cohort study with prospectively collected falls. Procedures: Self-reported incidents during the three months following a standardized test procedure. Subjects: Seventy-six people with multiple sclerosis and an Expanded Disability Status Scale score between 3.5 and 6.0. Main outcome measures: Berg Balance Scale, Timed Up and Go cognitive, Four Square Step Test (FSST) and 12-item Multiple Sclerosis Walking Scale. Results: Forty-eight people (63%) registered 270 falls. Most falls occurred indoors during activities of daily life. We found a correlation of rs=0.57 between near falls and falls, and of rs = 0.82 between registered and retrospectively recalled falls. Fallers and non-fallers differed significantly regarding Expanded Disability Status Score (odds ratio (OR) 1.99, 95% confidence interval (CI) 1.22; 3.40), spasticity (OR 1.14, CI 1.02; 1.31), proprioception (OR 2.50, CI 1.36; 5.12) and use of walking aids (OR 2.27, CI 1.23; 4.37). Reported use of walking aids both indoors and outdoors increased the odds of falling fivefold while disturbed proprioception increased the odds 2.5—15.6 times depending on severity. The odds of falling were doubled for each degree of increased Expanded Disability Status Score and more than doubled for each degree of increased spasticity. The Berg Balance Scale, use of walking aids and Timed Up and Go cognitive best identified fallers (73—94%) and proprioception, Expanded Disability Status Score, 12-item Multiple Sclerosis Walking Scale and Four Square Step Test best identified non-fallers (75—93%). Conclusions: In clinical practice, looking at the use of walking aids, investigating proprioception and spasticity, rating Expanded Disability Status Score and using Berg Balance Scale or Timed Up and Go cognitive all contribute when identifying fallers.
Analyzing Misinformation: Perceptions of Congressional Candidates' Ideologies*

Many studies use the seven-point issue and ideological scales in the American National Election Study (NES) series to examine not only the willingness or ability of citizens to place themselves and political candidates on the scales, but also as measures of the citizen's true positions on the scales and of the citizen's true perception of the candidates' positions on those scales. These are then used as important independent variables in electoral and other decision models. Thus, it is essential for us to understand the meaning of these measures. What do they really measure? How accurate are they as measures? This analysis will focus on respondents' perceptions of candidate positions. Many of the questions here can be directly related to the debate about the content of measures of the respondent's own position on issues. Many studies have found little stability in repeated measures of the respondent's own expressed position on issues and ideology. Two basic interpretations have been offered. Converse (1970) initially argued that many respondents without attitudes responded randomly and that there were two groups of respondents, one with stable attitudes and the other simply expressing a random response reflecting their nonattitudes. Achen (1975) and others argued instead that the fault was not that of the respondents, but that vague questions encouraged a variability in response, a measurement error that would be differentiated from the respondent's true underlying stable attitude. A primary piece of evidence in the debate has been that levels of political sophis
Metabolic Effects of Exercise Training Among Fitness-Nonresponsive Patients With Type 2 Diabetes: The HART-D Study

OBJECTIVE To evaluate the impact of exercise training (ET) on metabolic parameters among participants with type 2 diabetes mellitus (T2DM) who do not improve their cardiorespiratory fitness (CRF) with training. RESEARCH DESIGN AND METHODS We studied participants with T2DM participating in the Health Benefits of Aerobic and Resistance Training in Individuals With Type 2 Diabetes (HART-D) trial who were randomized to a control group or one of three supervised ET groups for 9 months. Fitness response to ET was defined as a change in measured peak absolute oxygen uptake (ΔVO2peak, in liters per minute) from baseline to follow-up. ET participants were classified based on ΔVO2peak into fitness responders (ΔVO2peak ≥5%) and nonresponders (ΔVO2peak <5%), and changes in metabolic profiles were compared across control, fitness responder, and fitness nonresponder groups. RESULTS A total of 202 participants (mean age 57.1 ± 7.9 years, 63% women) were included. Among the exercise groups (n = 161), there was substantial heterogeneity in ΔVO2peak; 57% had some improvement in CRF (ΔVO2peak >0), with only 36.6% having a ≥5% increase in VO2peak. Both fitness responders and nonresponders (respectively) had significant improvements in hemoglobin A1c and measures of adiposity (ΔHbA1c: −0.26% [95% CI −0.5 to −0.01] and −0.26% [−0.45 to −0.08]; Δwaist circumference: −2.6 cm [−3.7 to −1.5] and −1.8 cm [−2.6 to −1.0]; Δbody fat: −1.07% [−1.5 to −0.62] and −0.75% [−1.09 to −0.41]). No significant differences were observed in the degree of change of these metabolic parameters between fitness responders and nonresponders. Control group participants had no significant changes in any of these metabolic parameters. CONCLUSIONS ET is associated with significant improvements in metabolic parameters irrespective of improvement in cardiorespiratory fitness.
Knowledge sharing and knowledge management system avoidance: The role of knowledge type and the social network in bypassing an organizational knowledge management system

Knowledge sharing is a difficult task for most organizations, and there are many reasons for this. In this article, we propose that the nature of the knowledge shared and an individual's social network influence employees to find more value in person‐to‐person knowledge sharing, which could lead them to bypass the codified knowledge provided by a knowledge management system (KMS). We surveyed employees of a workman's compensation board in Canada and used social network analysis and hierarchical linear modeling to analyze the data. The results show that knowledge complexity and knowledge teachability increased the likelihood of finding value in person‐to‐person knowledge transfer, but knowledge observability did not. Contrary to expectations, whether the knowledge was available in the KMS had no impact on the value of person‐to‐person knowledge transfer. In terms of the social network, individuals with larger networks tended to perceive more value in the person‐to‐person transfer of knowledge than those with smaller networks.