Reviewerly/exHarmony huggingface dataset:
Research on Introducing Sports Education Mode into Campus Football Teaching

Sports education mode and campus football teaching have some similarities in teaching characteristics, which are aimed at stimulating students' interest in football and improving students' football technical level and actual combat ability in the process of sports participation. Compared with the foreign physical education mode, the current domestic traditional physical education teaching methods still have shortcomings, which need to constantly learn from the teaching experience of developed countries, so as to cultivate more campus football talents.
Review of Cyber Security on Oil and Gas Industry in United Arab Emirates: Analysis on the Effectiveness of the National Institute of Standards and Technology’s (NIST) Cybersecurity Framework

In view of increasing cyber attacks, the adoption of an effective security framework is essential for any organization involved in critical sectors, such as the oil and gas sector, to ensure the highest level of security and compliance. In this regard, as one of the major oil producers and exporters, the United Arab Emirates (UAE) needs to employ an effective security framework to protect the critical infrastructures of its oil and gas sector. As such, this study iscarried out to examine the potential harms of cyber attacks by focusing on the elements of the cybersecurity framework of the National Institute of Standards and Technology. The research methodology used in this research is based on a systematic review of the current literature on cyber security and frameworks. The review revealsthat NIST’s cybersecurity framework has certain features that apparently make it more effective than others. Essentially, this framework has three main components, namely the framework core, implementation tiers, and profile, which are defined based on an organization’s business. Specifically, the framework core has five functions, namely identify, protect, detect, respond, and recover. The findings of this research can help guide policy makers in the UAE’s oil and gas industry to make informed decisions on how to best mitigate cyber threats against oil companies. In particular, the findings can assist policy makers to determine whether the NIST’s cybersecurity framework can play an essential role in strengthening cyber security in the UAE's oil and gas sector. Moreover, the findings can help practitioners to recommend appropriate practical measures to strengthen the preparedness and responses of oil and gas companies in the UAE to cyber attacks.
Simplified QMOOD Model Proposal Based on Correlation Analysis in Different Client Applications

One of the commonly used models to measure the design quality of object-oriented software is the QMOOD model. The model works on a simple set of metrics; however, it may not be easy to obtain these metrics for different client applications even with the same/similar design. The main reason for this is that metric collection tools work differently in programming languages and development environments used to develop software for different clients. Moreover, metric aggregation tools used in some development environments may not be suitable for working with another development environment technology. In such cases, a quality metric that can be collected from the source code of a client application cannot be obtained for another client application. For this reason, it will be useful to simplify the model for some clients in order to match the quality assessment made using the QMOOD model with the real world. In this context, in this study, 9 versions of the Kubernetes project JAVA client have been examined. For these versions, all quality metrics needed in the QMOOD model were collected using different metric collection tools and the change of design quality attributes was obtained. In the evaluation, it was determined that the change in design quality characteristics coincided with the real world. From this point of view, the binary cross correlation of design properties was calculated. As a consequence, a QMOOD model proposal with 27% simplification that can be used in quality measurement for different client applications of the Kubernetes project is included.
Emerging Research Directions Exploring Grief in Organizations

Grief is an increasingly salient component of organizational life. Yet, we know relatively little about: (1) how employees experience and manage grief in their workplaces, (2) the impact of grief on employees and organizations, and (3) how colleagues, leaders, and organizations can best aid grieving employees as they move through grief towards recovery. Our symposium reports qualitative and quantitative research that extends the understanding of grief in the management literature, provides research-based recommendations for organizations in managing grieving employees, and subsequently strongly contributes to the Academy of Management’s conference theme “Bringing the Manager Back in Management.” The symposium includes four papers showcasing the influence of grief on employees’ work lives. Taken together, our papers and discussion, led by Dr. Gianpiero Petriglieri, endeavors to provide insight into the consequences of grief and mourning for employees and organizations, build an emerging community of scholars focused on issues related to grief and mourning, and generate a strong program of future academic research. Misery Loves Company? An Examination of Sharing Grief at Work Presenter: Seoin Yoon; Texas A&M U., College Station Presenter: Nitya Chawla; Texas A&M U. Presenter: Ji Koung Kim; Texas A&M U. Presenter: Yu Tse Heng; U. of Washington, Seattle Discovering a New Abnormal: The Role of Interpersonal Support at Work Presenter: Jessi Hinz; Case Western Reserve U. Work as Refuge: Job Crafting as Coping with Difficult Life Events Presenter: Pletneva Lidiia; ESSEC Business School “Careful the Wish You Make”: When Entrepreneurial Success Ushers in Grief Presenter: Hilary M. Hendricks; U. of Michigan
Effectiveness of School Operational Assistance Program Implementation at SD Inpres Dekai, Yahukimo

The objective of this study was to find out the effectiveness of the implementation and the inhibiting factors for the implementation of the School Operational Assistance (BOS) program at SD Inpres Dekai, Yahukimo Regency. The research used a mixed-method, namely using both qualitative and quantitative data. The qualitative data were obtained through in-depth interviews with informants who were members of BOS management at SD Inpres Dekai, including the person in charge and team members, teachers who were not BOS management members, education observers, community elements, and school committees. Meanwhile, the quantitative data were obtained from the Ministry of Education and Culture. The results of the research showed that the implementation of the BOS program at SD Inpres Dekai was generally considered ineffective based on input, process, and output factors. On the one hand, the results of this research showed that the objectives of the BOS program at SD Inpres Dekai had been achieved. On the other hand, however, there were still many things to be improved in terms of the process, such as involving student parents as the BOS management team members, conducting socialization, and synchronizing student data at Dapodikdasmen. The inhibiting factors for the implementation of the BOS program at SD Inpres Dekai are also shown ini the results of this study.
Students of Indonesian-Language Politeness through Moral Education at Acehnese Universities

The aim of this study is to examine the conditions of students at Acehnese colleges, institues, and universities and the consequences of Indonesian-language political students who get moral education at the institutions. The method of research is descriptive with a qualitative approach. Islamic educational students from State Islamic University of Ar-Raniry, State Islamic Institute of Langsa, State Islamic Institute of Lhokseumawe, and State College for Islamic Studies of Gajah Putih Takengon serve as the resource persons. Observation, semi-structured interviews, and documentation were used to collect data in this study. Data analysis was accomplished by data reduction, data visualization, and conclusion drawings. The findings of this study shed light on the state of students' politeness at Majority Islamic institution, namely the category of courteous. Additionally, it incorporates admirable principles into its collages. That is, the students who are courteous are pleasant, which is consistent with Luqman's indication of moral education in paragraphs 18-19. The ramifications of this study are significant for student attitudes about language in lectures and in other activities involving the academic community at institutions. Each individual's community life will also be impacted by this civility.
Awareness and Use of Resources Description and Access (RDA) among Librarians in Nigeria Libraries

Many initiatives have been launched by countries who would like to keep themselves up-to-date by using and implementing resources description and access (RDA) in their library catalogues. This study examined the awareness and use of (RDA) among librarians in Nigeria libraries. Three objectives were guided the study. The target population consist of 354 certified librarians of Nigeria from various libraries and institutions across Nigeria that was inducted by the Librarians’ Registration Council of Nigeria (LRCN) on the 8th November, 2019.A social survey method of research was adopted for the study and questionnaire as a research instrument was used. The outcome of this study reveals that majority 171(70%) of the respondents were aware of resources description and access. Even though there awareness does not influence there usage, since majority 196(81%) had not been used (RDA). The findings, also, indicated the areas of librarian’s preparation for RDA, such as alternative power supply, alternative fund, availability of network, training on RDA, access to computer, effective bandwidths, computer skill, technology know how, stable technology among others. Appropriate recommended were proffered.
An efficient quality-driven of face occlusion detection and recognition

This paper affords a identify and discover human faces in a photograph irrespective of their position, scale, in-aircraft rotation, orientation, pose, illumination, etc. To efficaciously become aware of the individual through surprising the history and different noises withinside the photograph. It has to be easy and powerful for the customers and become aware of faces which might be covered with a scarf, mask, shades, etc, This is one of the vital components that influences the overall performance of face reputation. Many algorithms and technology are proposed to clear up the occluded face reputation wherein we use a haar cascade set of rules that is the simplest one. Initially, this set of rules wishes loads of nice pix(pix of faces) and terrible pix(pix without faces) to teach the classifier then we should extract features from it. Facial detection is prompted through the readability of the photograph, colored or black and white pix. It can simplest help frontal detection of pix and the education does take loads of time with a purpose to separate a terrible face from a nice face.
Tractable Computation of Expected Kernels

Computing the expectation of kernel functions is a ubiquitous task in machine learning, with applications from classical support vector machines to exploiting kernel embeddings of distributions in probabilistic modeling, statistical inference, causal discovery, and deep learning. In all these scenarios, we tend to resort to Monte Carlo estimates as expectations of kernels are intractable in general. In this work, we characterize the conditions under which we can compute expected kernels exactly and efficiently, by leveraging recent advances in probabilistic circuit representations. We first construct a circuit representation for kernels and propose an approach to such tractable computation. We then demonstrate possible advancements for kernel embedding frameworks by exploiting tractable expected kernels to derive new algorithms for two challenging scenarios: 1) reasoning under missing data with kernel support vector regressors; 2) devising a collapsed black-box importance sampling scheme. Finally, we empirically evaluate both algorithms and show that they outperform standard baselines on a variety of datasets.
Blockchain-Based DBRMS in Smart Grid

Demand Response Management and the tracking is a fundamental aspects of a successful smart grid system. Often-times customers request for energy on smart grid system to meet their emergency energy need. However, delays in load request by mission critical centers have the potential to cause loss of lives. Also, possible blackout due to a faulty transmission lines can negatively impact on productivity therefore, we proposed a blockchain-based demand response service that improve operations of current smart grid networks.
재해로 인한 고장 설비의 복구 우선순위에 관한 연구

This paper considers on what basis should the restoration priority of failed components be determined. The criteria qualitatively mentioned in the existing procedures are modified to be quantitative, and the case study simulates whether the two criteria (recoverd capacity of system or power flow of component) work effectively in all possible disasters. In order to compare and analyze which restoration priority is better, the resilience from the moment of an disastrous event to the moment all failed compoents are repaired is compared with each other. Among the methods for quantification the resilience of the power system, in order to express the resilience curve as an metric, the area between the resilience curve and the maximum capacity in the normal system is calculated. In disastrous events where many components fail at the same time, the restoration of high-voltage main feeder first may be a better option. To evaluate the strategy to determine the restoration priority, a technique using Bus Injection to Branch Current(BIBC) matrix to identify the unserved customers of a radial distribution system is proposed.
Estimating Geo‐Referenced Cloud‐Base Height With Whole‐Sky Imagers

Abstract Accurate and frequent cloud‐base height calculation is beneficial to solar farming and agricultural activities and also indicative for future meteorology conditions. This study used paired whole‐sky imagers to estimate cloud‐base height with high quality. The study obtained quality assured cloud base feature points with a robust Shi‐Tomasi‐Scale Invariant Feature Transform algorithm before employing double‐eye locating approach, which is fast and easy to implement operationally. The estimated cloud‐base heights have high spatial coverages. Evaluated against ceilometer observations, estimated cloud‐base heights have a high accuracy level, with R 2 = 0.92 and RMSE = 424 m. The method is generally unbiased with NME = 9.2%. The high‐quality cloud‐base features in this study are quantitatively geo‐referenced, which would benefit a lot to meteorological studies such as fusion with satellite observations, evaluation for model simulations and upper‐level wind specifications.
Effect of Centrality Measures for Keyword Extraction from Turkish Documents

Keywords are salient words that best describe the content and the topic of a text document. As text documents are not usually annotated by their authors, automatic keyword extraction has become a challenging research topic. In this study, we investigate the performance of graph-centrality measures as initial node weights in graph-based keyword extraction on Tukish bank documents. To this aim, we focus on degree, eigenvector, betweenness, and closeness centralities and investigate their performance on keyword extraction on a 553 bank document dataset in Turkish. The experimental results show eigenvector centrality achieved the best results.
국내 연구용원자로 전출력 내부사건 1단계 확률론적안전성평가

This report documents the results of an at-power internal events Level 1 Probabilistic Safety Assessment (PSA) for a Korea research reactor (KRR). The aim of the study is to determine the accident sequences, construct an internal level 1 PSA model, and estimate the core damage frequency (CDF). The accident quantification is performed using the AIMS-PSA software version 1.2c along with a fault tree reliability evaluation expert (FTREX) quantification engine. The KRR PSA model is quantified using a cut-off value of 1.0E-15/yr to eliminate the non-effective minimal cut sets (MCSs). The final result indicates a point estimate of 4.55E-06/yr for the overall CDF attributable to internal initiating events in the core damage state for the KRR. Loss of Electric Power (LOEP) is the predominant contributor to the total CDF via a single initiating event (3.68E-6/yr), providing 80.9% of the CDF. The second largest contributor is the beam tube loss of coolant accident (LOCA), which accounts for 9.9% (4.49E-07/yr) of the CDF.
APLIKASI LAYANAN JAHIT MENGGUNAKAN FRAMEWORK REACT NATIVE

Human lifes inseparable from primary needs, namely clothing, food and boards. Clothing is a basic human need for clothing used to protect the human body. Current development making clothes can not only protect the human body but also develop into fashion with various models which of course require tailoring services to sew clothes according to customer needs. The process of the customer getting the desired tailor service when this is still using word of mouth or ordering services atau sew on one tailor who has subscribed. Batam city has many services  home tailors and customers who still use these methods and less efficient for a tailor and a prospect. This study aims to build a sewing service application which supports the provision of home sewing services and customers. Development  this application uses the waterfall model and testing The application uses the black box testing method. Programming language that used is PHP and Javascript using the react native framework as framework and MySQL as database. This application is expected to make it easier for a customer and tailor to be able to connect with each other using the application.
Performance evaluation of Heuristic and Metaheuristic Algorithms for Independent and Static Task Scheduling in Cloud Computing

Cloud computing is the general name of the services that enable the use of information technology resources or services to users over the internet on demand. Independent and static task scheduling is an important problem in cloud computing and deals with the optimal mapping of tasks to resources when task lengths are predetermined and can work independently from each other. In this study, the performances of FCFS, SJF, Min-Min, Max- Min heuristics, and ABC, PSO metaheuristics were measured on this problem. It has been observed that Min-Min, Max-Min and ABC algorithms are more successful than others according to the maximum completion time criterion. Considering the ease of implementation and fast running time, it has been observed that Min-Min and Max-Min heuristics are sufficient in solving this problem, and metaheuristic approaches do not contribute much.
The Influence Of Hrm Practices On Job Satisfaction - The Mediating Role Of Organizational Commitment: Case Of Federal Authority For Identity And Citizenship (ICA) UAE

The impact of HRM practices on employee satisfaction has been a leading area of research in the developed world for many years, while organizational commitment has not been studies in government sector in Unite Arab Emirates (UAE).The aim of this study is to examine the mediation role of organizational commitment on the association between HRM practices and job satisfactionin Federal Authority for Identity and Citizenship (ICA) in UAE using quantitative methods. The result of this study showed a high degree of model fit between HRM practices, organizational commitment, and job satisfaction as well as organizational commitment partially mediates the relationship between HRM practice and job satisfaction. This result lead to a final conclusion that HRM practices, organizational commitment, and job satisfaction are fundamental variables to the success of public and private organizations. Various organizations can benefit from the outcome of this study by knowing the employees level of satisfaction. For the employees indirectly helps them to convey their expectations regard this project highlights the scope for future improvement in above requirements provided to the employees.
Reward-Weighted Regression Converges to a Global Optimum

Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework. In this family, learning at each iteration consists of sampling a batch of trajectories using the current policy and fitting a new policy to maximize a return-weighted log-likelihood of actions. Although RWR is known to yield monotonic improvement of the policy under certain circumstances, whether and under which conditions RWR converges to the optimal policy have remained open questions. In this paper, we provide for the first time a proof that RWR converges to a global optimum when no function approximation is used, in a general compact setting. Furthermore, for the simpler case with finite state and action spaces we prove R-linear convergence of the state-value function to the optimum.
Using E-Modules in Teaching Technology & Livelihood Education For The Revitalized Alternative Delivery Mode Program of Holy Spirit National High School

This study aims to reveal the effects of e-modules strategy in teaching technology and livelihood education as an alternative mode of program in Holy Spirit National High School. The sample of the study consists of (56) male and female grade 7 students randomly chosen from two sections, students are divided into two groups: Control and Experimental. Researchers have developed an e-materials relevant to carry out module’s strategy for teaching technology and livelihood concepts in the text book for grade seven students, though the development of achievement test to measure the direct and the postponed achievement by the study's sample. The consistency and reliability of the test have been obtained. The study's results revealed that students in the experimental group are exceptional over students in the control group in both types of achievement. At the same time results showed the lack of difference between male and female students in direct and long-term achievement test. Based upon the study's results, researcher recommends the necessity to use more modern means to teach students the TLE domain, to hold courses and workshops for teachers training on implementing modules strategy. Furthermore, to make available textbook and references which facilities this style of teaching. Finally, is to conduct similar studies over different domains in like electricity, electronics, food and beverage, cookery.
Cleaning Landmark and Geolocation Dataset using Deep Learning Methods

In this paper, a deep learning based approach to dataset cleaning has been explained by utilizing Google Landmark Challenge dataset v2, which is a noisy dataset that consists of images photographed by people. Study also includes improvements on the concept of image classification and recognition for large noisy datasets mainly revolving around the approach to dataset cleaning which are also listed. Results achieved using this approach has been detailed with both quantitative methods like graphs regarding the reduction of classes and number of eliminated noisy images and qualitative methods like visual analysis of said images. In conclusion, it is observed that using confidence score outputs of a deep learning network, it is possible to remove noisy samples from a dataset. This paper also includes the specific threshold values for the achieved results on this dataset using explained model architecture for better reproducibility.