Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the energy efficiency of data-intensive applications running on clusters

Abstract As an alternative to traditional computing architecture, cloud computing now is rapidly growing. However, it is based on models like cluster computing in general. Now supercomputers are getting more and more powerful, helping scientists have more indepth understanding of the world. At the same time, clusters of commodity servers have been mainstream in the IT industry, powering not only large Internet services but also a growing number of data-intensive scientific applications, such as MPI based deep learning applications. In order to reduce the energy cost, more and more efforts are made to improve the energy consumption of HPC systems. Because I/O accesses account for a large portion of the execution time for data intensive applications, it is critical to design energy-aware parallel I/O functions for addressing challenges related to HPC energy efficiency. As the de facto standard for designing parallel applications in cluster environment, the Message Passing Interface has been widely used in high performance computing, therefore, getting the energy consumption information of MPI applications is critical for improving the energy efficiency of HPC systems. In this work we first present our energy measurement tool, a software framework that eases the energy collection in cluster environment. And then we present an approach which can optimise the parallel I/O operation’s energy efficiency. The energy scheduling algorithm is evaluated in a cluster. We try to optimize the energy efficiency of BTIO. Before doing the optimization, we first modeled the execution time and energy consumption. In BTIO, a three-dimensional array is partitioned in a block-tridiagonal pattern and assigned across a square number of processes. Each process is responsible for many (the square root of the number of participating processors) subsets of the entire data set. All parameters of the elemental operations define a multidimensional configuration space. We first modeled the elemental operations. The configuration space of an elemental operation is the cross product of the individual values of the parameters. We sampled points which are evenly distributed in the configuration space, and tested the execution time and consumed energy with MEMT. We divided all the sampled points into training and test set. The majority (75%) of the points were used to iteratively train the execution time and energy models, and the left points were used for testing the models. The models of a collective I/O operation is the combination of these elemental models. The two figures show the modeled versus measured execution time and energy consumption of 250 BTIO operations. We can see that the models behave well.
Concentration and Competition in Nepalese Banking

This paper examines the evolution of market concentration and tests the market competition of Nepalese banking industry for an unbalanced panel of 15-25 banks for the period of 20012009. The market concentration is measured by Hirschman-Herfindahl indices and concentration ratios, and market competition is tested under Panzar-Rosse approach. The concentration measures indicate decreasing trend and low level of market concentration in Nepalese banking industry over the sample period. The test of market competition/contestability by using Panzar-Rosse approach rejects both the hypotheses for monopoly and perfect competition indicating monopolistic market behaviors among the Nepalese banks. In addition, the market for interest-based income is found to be more competitive than that of the market for fee-based income. The results further indicate that the size of bank has positive, and equity capitalization has negative impact on revenue generation. The results are robust across different specifications and across different estimation techniques.
Factors Influencing Food Choice among Ethnic Minority Adolescents

The Asian diet has been implicated in a number of diseases such as coronary heart disease (CHD), non‐insulin dependent diabetes myelitis (NIDDM), rickets and anaemia. It has also been established that chronic diseases such as CHD and cancer, which have a long lag phase before symptoms appear, are also significant to young people, as their dietary patterns over the years can influence the onset of these diseases. Argues that research on the diet of the young cannot exclude the psychological and cultural factors involved in the motives behind food choice decision‐making processes, for any health education programme to be effective.
Evaluating and Comparing Size, Complexity and Coupling Metrics as Web Applications Vulnerabilities Predictors

— Most security and privacy issues in software are related to exploiting code vulnerabilities. Many studies have tried to find the correlation between the software characteristics (complexity, coupling, etc.) quantified by corresponding code metrics and its vulnerabilities and to propose automatic prediction models that help developers locate vulnerable components to minimize maintenance costs. The results obtained by these studies cannot be applied directly to web applications because a web application differs in many ways from a non-web application: development, use, etc. and a lot of evaluation of these conclusions has to be made. The purpose of this study is to evaluate and compare the vulnerabilities prediction power of three types of code metrics in web applications. There are a few similar studies that targeted non-web application and to the best of our knowledge, there are no similar studies that targeted web applications. The results obtained show that unlike non-web applications where complexity metrics have better vulnerability prediction power, in web applications the metrics that give better prediction are the coupling metrics with high recall (> 75%) and fewer costs in terms of inspection (<25%).
Enhancing Teaching and Learning in Open and Distance Learning:: A Mechanism to Facilitate Student Success

This paper considers the place of students learning in an open and distance learning (ODL) institution. All students need access to a variety of support services. We need to think about the variety of support services that can facilitate student success in ODL. There are many challenges facing distance learning students in the changing context of flexible teaching and learning. Distance learning students may also have different learning styles preferences and coping strategies. Questionnaire surveys and focus group interviews were integrated and conducted with students who are studying at a distance in different distance learning institutions. The focus was on the causes of student dropout in ODL institutions preventive measures of this escalating challenge and the possible ways of student retention. The findings from the research raised a possible solution to effective student support in different ways and at different levels. Distance learning institutions claim that they provide a comprehensive student support system, but students disputed this. Instead, students complain of “individual isolation and loneliness”. The lack of coherent student support systems causes a low throughput rate because of a high dropout rate. A convenient sampling of 100 research interview participants and 426 respondents who returned questionnaires were selected from the four provinces of South Africa. The study therefore recommends that students should be engaged as early as possible by using techniques and strategies needed to facilitate effective teaching and learning.
Exploring Deep Spectrum Representations via Attention-Based Recurrent and Convolutional Neural Networks for Speech Emotion Recognition

The automatic detection of an emotional state from human speech, which plays a crucial role in the area of human–machine interaction, has consistently been shown to be a difficult task for machine learning algorithms. Previous work on emotion recognition has mostly focused on the extraction of carefully hand-crafted and highly engineered features. Results from these works have demonstrated the importance of discriminative spatio-temporal features to model the continual evolutions of different emotions. Recently, spectrogram representations of emotional speech have achieved competitive performance for automatic speech emotion recognition (SER). How machine learning algorithms learn the effective compositional spatio-temporal dynamics for SER has been a fundamental problem of deep representations, herein denoted as deep spectrum representations. In this paper, we develop a model to alleviate this limitation by leveraging a parallel combination of attention-based bidirectional long short-term memory recurrent neural networks with attention-based fully convolutional networks (FCN). The extensive experiments were undertaken on the interactive emotional dyadic motion capture (IEMOCAP) and FAU aibo emotion corpus (FAU-AEC) to highlight the effectiveness of our approach. The experimental results indicate that deep spectrum representations extracted from the proposed model are well-suited to the task of SER, achieving a WA of 68.1% and a UA of 67.0% on IEMOCAP, and 45.4% for UA on FAU-AEC dataset. Key results indicate that the extracted deep representations combined with a linear support vector classifier are comparable in performance with eGeMAPS and COMPARE, two standard acoustic feature representations.
Identifying regime shifts in Indian stock market: A Markov switching approach

Seeking for the existence of bull and bear regimes in the Indian stock market, a two state Markov switching autoregressive model (MS (2)-AR (2)) is used to identify bull and bear market regimes. The model predicts that Indian stock market will remain under bull regime with very high probability compared to bear regime. The results also identify the bear phases during all major global economic crises including recent US sub-prime (2008) and European debt crisis (2010). The paper concludes that the Indian stock market is more sensitive to external shocks implying that there is ample scope of policy interventions.
Communication, collaboration and identity: factor analysis of academics’ perceptions of online networking

Since the advent of online social networking sites, much has been written about their potential for transforming academia, as communication and collaboration underpin many scholarly activities. However, the extent to which these benefits are being realised in practice is unclear. As the uptake of tools by academics continues to grow, there is a question as to whether differences exist in their use and if any patterns or underlying factors are at play. This article presents the results of an online survey addressing this gap. A disciplinary divide was evident in terms of preferred academic social networking platforms, while perceptions about how academics use online networking for different purposes are linked to job position. Exploratory factor analysis identified four components representing different strategies used by academics in their approaches to online networking, including maintaining a personal learning network, promoting the professional self, seeking and promoting publications, and advancing careers. Published : 10 April 2018 Citation : Research in Learning Technology 2018, 26 : 2013 - http://dx.doi. org/10.25304/rlt.v26. 2013
Mediation effect of knowledge management on the relationship between transformational leadership and job performance of librarians in university libraries in Nigeria

This paper proposes a mediation model of transformational leadership for improving the job performance of librarians in university libraries in Nigeria. A quantitative approach underpinned by positivist philosophy was considered appropriate for this type of research and adopted to accomplish the main goal of this study. A questionnaire was used to collect data from a total of 215 librarians who participated in the study. Factor analysis and multiple regressions were used to analyse data. The results of the study reveal that knowledge management has full and partial influence on the relationship between transformational leadership and job performance of librarians.
Super-Resolution Integrated Building Semantic Segmentation for Multi-Source Remote Sensing Imagery

Multi-source remote sensing imagery has become widely accessible owing to the development of data acquisition systems. In this paper, we address the challenging task of the semantic segmentation of buildings via multi-source remote sensing imagery with different spatial resolutions. Unlike previous works that mainly focused on optimizing the segmentation model, which did not enable the severe problems caused by the unaligned resolution between the training and testing data to be fundamentally solved, we propose to integrate SR techniques with the existing framework to enhance the segmentation performance. The feasibility of the proposed method was evaluated by utilizing representative multi-source study materials: high-resolution (HR) aerial and low-resolution (LR) panchromatic satellite imagery as the training and testing data, respectively. Instead of directly conducting building segmentation from the LR imagery by using the model trained using the HR imagery, the deep learning-based super-resolution (SR) model was first adopted to super-resolved LR imagery into SR space, which could mitigate the influence of the difference in resolution between the training and testing data. The experimental results obtained from the test area in Tokyo, Japan, demonstrate that the proposed SR-integrated method significantly outperforms that without SR, improving the Jaccard index and kappa by approximately 19.01% and 19.10%, respectively. The results confirmed that the proposed method is a viable tool for building semantic segmentation, especially when the resolution is unaligned.
Battery-Less Location Tracking for Internet of Things: Simultaneous Wireless Power Transfer and Positioning

We propose a battery-less location tracking system that enables 3-D positioning of an Internet of Things (IoT) device powered by the radio frequency (RF) wireless power transfer (WPT). In the proposed system, a power beacon is equipped with a phased antenna array that has the dual purposes of high-efficiency WPT and phase-based accurate positioning. In order to enhance the efficiency of the RF WPT, we propose a beam focusing algorithm that dynamically controls the respective phases of antenna elements to place the focal point of the electro-magnetic (EM) wave onto the target IoT device. We also propose a phase-based positioning algorithm that requires only one multiantenna anchor point for determining the distance as well as the direction from the anchor point. We analyze the Cramer–Rao lower bound (CRLB) of the phase-based positioning with a single multiantenna anchor point, and show that the distance from the anchor point can be estimated as long as the IoT device lies within the radiative near-field region. We propose a joint location tracking and WPT algorithm that performs 3-D positioning and beam-focused WPT in a unified way. We have built a real-life testbed with a large-scale antenna array with 64 antenna elements for testing the proposed algorithm. The experimental results show the effectiveness of the proposed algorithm in a real environment.
Implemented and perceived high-performance work system and its effect on branch performance

PurposeThe purpose of this paper is to examine the influence of high-performance work systems (HPWS) on unit performance relative to the mediating roles of collective human capital (CHC) at the unit level and perceived HPWS at the employee level.Design/methodology/approachData were collected from 181 branch managers and 504 employees. The proposed path model was tested using the statistical package M-plus (v. 7) using a 2-1-2 multilevel approach for mediation analysis.FindingsGenerally, branch managers actively implement HPWS, and employees perceive a fairly high level of HPWS. Further, the path model indicated that CHC at the unit level and perceived HPWS at the employee level partially mediate the relationship between implemented HPWS and unit performance.Originality/valueThis study is the first to explore multilevel mediating mechanisms in the context of the largest four state-owned banks in China. A 2-1-2 multilevel analysis procedure was used to separate measurement error into relevant employee- and branch-level components to create more precise assessments of multivariate associations. Such analyses have not yet been conducted in research on HPWS prior to this study of the Chinese banking sector, but they are essential for teasing out the micro- and macro-level effects of HPWS.
Comparative analysis of the support phase during first two steps after leaving the starting blocks

Background: Biomechanical analysis of accelerated running dynamics provide valuable information about movement execution. The key phase of the movement is the ground contact phase, in which force impulses applied on the human body are generated. Objective: The main goal of the study was to analyze differences in the support phase during first and second ground contact off the blocks. Methods: 10 male athletes (22.9 ± 4.6 years) took part in laboratory experiment. Force-plate Kistler 9281 EA (Winterthur, Switzerland) was used to determine the contact forces in both steps independently. Matlab Software (The MathWorks, Inc., Natick, USA) was used for calculations of the force impulse, produced velocities and average acceleration during the support phase. Matlab also provided the tool for statistical processing of the results (paired T-test and correlation analysis). Results: Significant differences (α = 0.01) were identified between the first and second step's support phase in contact duration, produced horizontal velocity and average horizontal acceleration. Produced horizontal velocity achieved value 1.117 ± 0.081 ms-1 during first and 0.835 ± 0.074 ms-1 during second ground contact after blocks. Average acceleration showed negative correlation with the height of the athlete during the first ground contact off the blocks (r = -0.42). If braking force was present during first 20-40 ms of ground contact, it led to longer duration of support phase and was coupled with a smaller value of average acceleration. Conclusions: Braking phase during first steps after the blocks should be considered always as an imperfection of movement execution. Taller athletes seem to be disadvantaged during first ground contact after leaving the blocks. The first and second step's ground phases are significantly different.
A Keyword-Searchable ABE Scheme From Lattice in Cloud Storage Environment

Currently, the security situation of data security and user privacy protection is increasingly serious in cloud environment. Ciphertext data storage can prevent the risk of user’s privacy disclosure. But how to search keyword on ciphertext data without revealing keyword information becomes a new challenge. Searchable encryption (SE) is put forward for this reason, which can be used to realize ciphertext-search directly. In terms of multi-user data sharing, public-key encryption with keywords search (PEKS) is more widely used than symmetric searchable encryption. PEKS has been widely studied and developed by researchers in recent years. However, the existing PEKS scheme often lacks flexible access policy. Therefore, combining the advantage of policy control on attribute-based encryption (ABE), and as that the bilinear pairing related assumption is fragile in post-quantum era, lattice-based cryptography is considered as one of secure encryption technology against quantum attack. With this background, in this paper, we give a keyword-searchable ABE scheme based on the hardness of lattice problems, our scheme supports flexible attribute control policy by integrating ABE and PEKS, and the security of new scheme is proved under the learning with errors (LWE) assumption. As lattice-based cryptographic technology is currently thought to be resistant to quantum attacks, so the new scheme has stronger security in a quantum era.
A Review Structure Based Ensemble Model for Deceptive Review Spam

Consumers’ purchase behavior increasingly relies on online reviews. Accordingly, there are more and more deceptive reviews which are harmful to customers. Existing methods to detect spam reviews mainly take the problem as a general text classification task, but they ignore the important features of spam reviews. In this paper, we propose a novel model, which splits a review into three parts: first sentence, middle context, and last sentence, based on the discovery that the first and last sentence express stronger emotion than the middle context. Then, the model uses four independent bidirectional long-short term memory (LSTM) models to encode the beginning, middle, end of a review and the whole review into four document representations. After that, the four representations are integrated into one document representation by a self-attention mechanism layer and an attention mechanism layer. Based on three domain datasets, the results of in-domain and mix-domain experiments show that our proposed method performs better than the compared methods.
Effects of instructor's facial expressions on students' learning with video lectures

This study investigated whether video lectures instructed by a heightened level of expressiveness instructor were better than those instructed by a conventional level of expressiveness instructor or audio‐only in promoting students' online learning, and what kind of effects do instructor's facial expressions have on students' learning. Three different types of video lectures (one with a heightened level of expressiveness instructor, one with a conventional level of expressiveness instructor and one with the instructor' audio only) were used to study the effects of instructor's facial expressions. Sixty‐nine participants from Chinese Normal University were selected to learn with these video lectures. Results of this study indicated that video lectures with a heightened level of expressiveness instructor were better than those with a conventional level of expressiveness instructor and audio‐only ones with regard to improving students' arousal level and learning satisfaction. Instructor's facial expressions had no significant influence on participants' short‐term recall (p = .90), while instructor's facial expressions did influence students' medium‐term recall (p = .03). Instructor's facial expressions played a key role in students' online learning. [ABSTRACT FROM AUTHOR]