Reviewerly/exHarmony huggingface dataset:
WEB DESIGN TRAINING APPROACH IN HIGH SCHOOL STAGE OF SECONDARY PROFILED EDUCATION

The paper examines a learning method in teaching website building technologies to students in a learning environment where a role-playing game from the practice of a software company is simulated. The data collection and evaluation of the results was carried out through the qualitative methods of participant observation, interviewing, content analysis and survey card. The article analyzes the state of the researched problem by reviewing the normative base of web design training at school, the scientific and educational literature on the methods of learning through role play and teamwork. A staging of the implementation of an experiment for web design training is presented, describing the general framework for organizing the learning process, the stages of conducting the research, as well as the plot and scenario of conducting. The final results of the research were analyzed on the basis of a discussion with students and data from survey cards. The training, according to the experiment, is in the form of a simulation game and takes place in six academic hours of 40 minutes in three consecutive weeks, passing through four stages. The work of two teams of students with representative roles was studied: manager, web designer, graphic designer, stylist and web programmer. The final product of the role-playing game is the development of a website, and its final result for the learning process is knowledge and skills acquired during the formal training by the teacher and the informal training between the students during their team work.
Community and Society

Education is embedded in the concept of community and society. It is part of the glue that brings and keeps people together in groups. Throughout human history communities and societies have evolved through learning. The specialisation of function, and the subsequential need to trade, required knowledge, skills, information and social structures. These needed to be passed on from generation to generation, with progressive adaption and refinement. The goals, values and methods of the education of each generation were moulded by the needs of society at any particular point in history.
How Can AI Promote the High-Quality Training of “Belt and Road” International Chinese-Language Talents

This study aims to give full play to the advantages of artificial intelligence in the field of “Belt and Road” international Chinese talent training, overcome the problems of poor talent training, explore the path of high-quality talent training, so as to promote the accurate matching of talent demand side and supply side, realize the effective allocation of education and teaching resources, build a diversified mode of talent training, and systematically provide high-quality talents for “Belt and Road” construction. This study has certain reference value for the reform of talent training system and policy formulation in colleges and universities.
A Divisive Approach for All Pairs Shortest Path on Large-Scale Graphs with Limited Resources

Solving the all pairs shortest path (APSP) problem is crucial for graph theory and various practical applications, but remains challenging for large graphs with an enormous number of nodes and edges when the computing resources is limited. In this work, based on an effective graph partition method, we further propose a divisive approach to obtain the APSP for large graphs. A large graph is firstly divided into smaller subgraphs that may not be of an equal size, and then getting the APSP of each subgraph separately. By concatenating the APSPs of the subgraphs, the APSP of the whole graph can be obtained. Experimental results confirm the effectiveness and correctness of our approach for obtaining APSP on large graphs with limited computing resources.
Visualization and Immersion: Fascination 3D

Three-dimensional (3D) imaging has always been somewhat fascinating in its own right because it corresponds to natural spatial vision. With the increasing emergence of personal computers (PCSs), 3D applications first found their way into games and soon into “serious” applications, especially in training and education. Starting with a brief flashback, we'll look at some of the possibilities of 3D visualization per se and the uses of virtual reality in a 3D learning environment, as well as the current challenges associated with it.
Development of a Short Term Solar Power Forecaster Using Artificial Neural Network and Particle Swarm Optimization Techniques (ANN-PSO)

Globally, the use of renewable energy has increased significantly since the late twentieth century. Nigeria is also leading the exponential growth of renewable energy use. This article will predict the solar energy collected in 11 power distribution company areas (DISCO) in Nigeria: Abuja, Benin, Eko, Enugu, Ibadan, Ikeja, Jos, Kaduna, Kano, Port-Harcourt, and Yola. Artificial Neural networks and Particle Swarm Optimization (ANN-PSO) techniques are used to forecast solar irradiance. This research compares the results using cognitive acceleration coefficients. From this study, the regression coefficient (R) values of 0.9968 and 0.99533 were obtained from Yola and Ikeja Distribution Company, respectively. Also, mean absolute percentage error (MAPE) values of 3.07% in Yola and 5.67% in Jos were obtained. The normalized root means square error (nRMSE) values of 0.9813, 2.4522, and 0.9470 were obtained from Yola, Ikeja, and Benin DISCOs, respectively, and mean squared error (MSE) values of 2.29% in Abuja, 1.80% in Ibadan, 1.83% in Ikeja, and 0.0915% in Jos. The simulation was also performed for July 2021, which was not part of the dataset used in this study. The result of the forecaster revealed high levels of forecasting accuracy.
Automated Generation of Multi-tenant Database Systems Based upon System Design Diagrams

Energetic technological advancements in the past decade, have led to an exponential increase in the amount of data being stored. Consequently, the need for effective Database Management Systems (DBMSs) which embody security considerations from the initial stages of database design is imperative in modern information systems development. This prerequisite is further accentuated by the recent transitioning from single tenant information systems to multi-tenant based on public cloud infrastructures. Within this context, this paper proposes a Computer-Aided Software Engineering (CASE) tool capable of generating secure database related structures and processes. A comprehensive overview of a specific information system design is provided to the CASE tool by means of entity-relationship diagram, entity life history and dataflow diagram. Subsequently, crucial security controls targeted towards the protection of database objects are incorporated by means of discretionary and role-based access controls. Following the encoding of the system’s design data is adorned with the required security elements, the CASE tool performs an in-depth analysis that aids in the detection of potential security pitfalls. Provided that the database design enhanced with security elements is satisfactory, i.e., all checks conducted are successful, the CASE tool generates Structured Query Language (SQL) language constructs for well-known multi-tenant scenarios.
Diagnosis Expert System on Breast Cancer Using Fuzzy Logic and Clustering Technique

The disease of breast cancer is now widespread throughout the world. Expert systems are useful tools that have been effective in the diagnosis of various diseases. Therefore, this paper develops an expert system for diagnosis and detection of breast cancer using fuzzy logic enabled with clustering classifier. The fuzzy logic provides a bridge in improving the classification and detection accuracy of breast cancer disease. To balance out the dataset and avoid the costly overhead of collecting and labeling a large set of training tuples or patterns, clustering was utilized for data segmentation. The classifier can then make use of data of each group in terms of how similar they are. These tools have been developed to improve and alter the current expert systems, increasing their usefulness for the reasons for which they were designed. The fuzzy rule-based method includes expert knowledge to address the patient's ailment and render a reliable judgment in accordance with established rules. The proposed model can be applied as a clinical decision support tool to help healthcare professionals.
Transparency in Content and Source Moderation

Content moderation is defined as the process of screening and monitoring user-generated content online. To provide a safe environment for both users and brands, platforms must moderate content to ensure that it falls within pre-established guidelines of acceptable behavior specific to the platform and its audience. Many social media companies employ thousands of employees or volunteers to moderate content manually. These moderators discuss the nature of any questionable posts off-site and remove them if they are deemed inappropriate. Certain platforms also employ automated moderation of content through machine learning models. However, many of them often do not give users any or accurate reasons when their posts are taken down. This lack of transparency in moderation can cause users to believe that their posts were evaluated in a biased manner. To increase users’ trust in the unbiased nature of a platform and still allow for extensive and robust content moderation, we propose a novel algorithm in this chapter. An adaptive machine learning model is used as the initial moderation layer, and then users are allowed to moderate posts through a trust-based social network algorithm. Since machine learning models can gradually improve their performance through feedback and feedback is given in a self-policing fashion, the system enforces both accuracy and transparency for content moderation.
Assessment of the Modeling of the Energy insolation over Three Stations in Tunisia

Accurate hourly global horizontal irradiance (GHI) data is needed in areas where ground measurements are limited or lacking. A previous model was used to predict the GHI of different stations in Tunisia. The obtained results are confronted with GHI Data earth stations. The precision of each model has been calculated using (R, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , MPE, and RMSE). Based on the statistical results of the different locations in Tunisia, we find that the numerical model offers the best performance.
Preface

The Survey of Economic and Social Developments in the Arab Region is an annual flagship publication of the Economic and Social Commission for Western Asia (ESCWA). This publication is mandated by paragraph 173 of General Assembly resolution 35/56; paragraphs 2 to 4 of ESCWA resolution 270 (XXIV); and paragraphs 1 and 2 of ESCWA resolution 303 (XXVII). The publication seeks to contribute to efforts by member States to reform economic institutions and develop and implement policies based on principles of good governance as integral to economic planning and policymaking that supports inclusive and sustainable development. The 2020–2021 edition analyses the most recent socioeconomic developments from January 2020 to June 2021.
End-to-end verifiable cumulative voting without tallying authorities

In this paper, we propose the first end-to-end (E2E) verifiable e-voting system for cumulative voting without requiring any tallying authorities. To our knowledge, none of the existing e-voting systems implemented for cumulative voting are end-to-end verifiable; if there is any bug or tampering at the tallying software, the tally would be inadvertently modified without any voter noticing this. Although there are existing voting systems (e.g., mix-net-based) that could be adapted to support cumulative voting with E2E verifiability, they generally require a set of tallying authorities, which can lead to substantial complexity of finding and managing such authorities in practice. We address this issue by adopting novel cryptographic techniques to achieve E2E verifiability for cumulative voting, but without involving any tallying authorities. We formally define a model to prove the security of our system, and present the efficiency analysis to show that our proposed solution is feasible for practical use.
Secure and Accurate Summation of Many Floating-Point Numbers

Motivated by the importance of floating-point computations, we study the problem of securely and accurately summing many floating-point numbers. Prior work has focused on security absent accuracy or accuracy absent security, whereas our approach achieves both of them. Specifically, we show how to implement floating-point superaccumulators using secure multi-party computation techniques, so that a number of participants holding secret shares of floating-point numbers can accurately compute their sum while keeping the individual values private.
Smart Video Surveillance Using YOLO Algorithm and OpenCV

Abstract: Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with highlevel context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which can learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures.
Understanding the Competency Assessment of the 2022 Revised Music Curriculum - Through a comparative analysis of the 2015 revised music curriculum

2015년 국가 교육과정에 역량의 개념이 도입되었고 OECD의 Education 2030 프로젝트가 실시된 이래로 학생 평가에 대한 방향이 전환되었다. 수업 끝에 단순히 완결된 지식과 기능을 평가하는 평가를 지양하고, 수업의 전 과정을 통하여 지식, 기능, 태도를 맥락적으로 활용하도록 함으로써 역량 함양을 지원하는 평가로 변화하였다. 본 연구는 최근 고시된 2022 개정 음악과 교육과정에서의 역량 평가에 대해 이해하는 것을 목적으로 한다. 이를 위해 평가의 시각에서 2015 개정 음악과 교육과정의 문제점이 어떻게 개선되고 보완되었는지 알아보았다. 그리고 역량의 개념을 어떻게 평가에 반영하였는지 분석하였다. 2022 개정 음악과 교육과정을 분석한 결과, 교육과정의 각 부분이 음악 교과 역량을 중심으로 연계되었을 뿐 아니라 구체적이고 실용적인 내용으로 구성되었음을 파악하였다. 우선, 5개의 교과 역량이 교과의 특성을 가짐과 동시에 이해하기 쉽도록 재개념화 및 재구조화되었다. 교육과정의 성격에서는 미래 역량의 필요성을 통해 역량 평가의 당위성을 이해할 수 있다. 음악교육의 목적은 역량 함양 그 자체로 제시되었다. 내용 체계와 성취기준은 수행성, 맥락성, 총체성이라는 역량의 특성이 반영되었다. 마지막으로 평가의 방향과 방법을 통해 역량 평가의 현장 적용 방안이 제시되었다. 본 연구가 새 교육과정에서의 역량 평가에 대한 이해를 높이고 현장 적용에의 안내 자료가 되기를 기대한다.In 2015, the concept of competency was introduced into the national curriculum, and the direction of student assessment was transferred due to the OECD Education 2030 project. It is changing from simply evaluating learned knowledge and skills to evaluating competency to utilize knowledge, skills, and attitudes in context throughout the entire class. The purpose of this study is to understand the competency assessment of the 2022 revised music curriculum. It analyzed how the problems of the 2015 revised music curriculum were solved and supplemented. And it was analyzed how the concept of competency was reflected in the curriculum. As a result of analyzing the 2022 revised music curriculum, it was found that each part of the curriculum was not only linked around the musical competencies, but also consisted of specific and practical contents. First of all, five music subject competencies have the characteristics of the music and have been re-conceptualized and restructured for easy understanding. The legitimacy of competency assessment is understood through the necessity of future competencies presented in the nature of music subjects. The purpose of music education was suggested to cultivate competence. The content system and achievement standards reflected the characteristics of competencies such as performativity, context-dependency, and wholeness. Finally, the application and implementation plan of competency assessment was presented through the direction and method of assessment. It is hoped that this study will enhance understanding of competency assessment in the new curriculum and serve as a guide to field application.
The Effect of Foreign aid and Vocational Training on Poverty Reduction Strategy: An Empirical Evidence from Chad Republic

This study examines the mediation effect of the vocational training on the relationship between foreign aid and poverty reduction strategy in the republic of Chad.The number of the sample involved in this study was 335 as given from Krejcie and Morgan's.Thus, the researcher used both random and nonrandom sampling.First, community leaders and members of independent associations were grouped according to the level that they are serving.Then the sample was selected respectively and used purposive sampling method for the convenience in getting the response from the study respondents.Purposive sampling technique was used as the researcher obtained data from specific group in particular districts.Therefore, a structured questionnaire with a 5-point Likert scale was used as the instrument for data collection.The Partial Least Square Structural Equation Modeling (PLS-SEM) was used to test the hypotheses of this study.The results found that poverty reduction highly influences by foreign aid and vocational training.To this fact, it is highly recommended that the government of Chad to focus on vocational training; for instance, the government can mainly create a training programs on how to successfully execute national development plans in particular (poverty reduction strategy).Also, foreign aid plays an important role in fighting poverty in Chad, thus more effort need to be made on the management of the aid coming to the country.
Comparing ALNS and NSGA-II on Bi-Objective Travelling Thief Problem

Real-life optimization problems consist of various conflicting optimization problems.Due to the interdependency of these sub-optimization problems, their solution is more difficult than single optimization problems.For this reason, most of the real-life optimization problems are considered multi-objective optimization problems by nature, and different methods and approaches are applied.In this paper, a recent multi-objective optimization problem, the biobjective travelling thief problem (BiTTP) was studied.Two state-of-the-art algorithms, ALNS and NSGA-II, were hybridized with certain local search methods and applied on six problem instances of Polyakovskiy's problem benchmark set.As a result, the performances of these two algorithms were evaluated and a new set of Pareto front sets were generated for the related benchmark instances.
Development of Augmented Reality Assisted Textbooks to Develop Patriotism Character for Students of Grade 4th in Elementary School

This research was carried out with the aim of producing appropriate augmented reality-assisted textbooks to improve patriotism character of fourth grade elementary school students, to determine the effectiveness of appropriate augmented reality-assisted textbooks in increasing patriotism character of fourth grade elementary school students.This type of research is Research and Development (R & D) with reference to the development model of Brog & Gall (1983).The subject of preliminary field testing involved six students; the subject of the main field testing involved nine students.Data collection techniques used observation guidelines, interview guidelines, need analysis, assessment validation sheets, student response scales, teacher response scales, and selfassessment scales for the patriotism character.The data analysis used t test and MANOVA test with a significance level of 0.05.The results showed that: the developed textbook assisted with augmented reality has the criteria of "feasible" based on the validation of material experts, media experts, linguists, teacher responses, and student responses; the book is also proven to be effective in increasing patriotism character.The effectiveness of this textbook can be seen from the results of the t test and the MANOVA test which shows a significant increase in understanding the concept and patriotism character with a p value of 0.000 less than the significance of 0.05 (p <0.05).
Stressor Classification of Filipino Political Tweets Using LDA, SVM, XGBoost, Logistic Regression

With the advancement of technology, Filipinos have a means of connecting to social media mainly to share what they are doing or what they feel now.This could lead to people venting out their stress on platforms such as Twitter.One of the topics that cause people a lot of stress is Politics and many social media users share their opinions in a stressful manner on Twitter.This paper will focus on detecting the reason for stress called stressors from the tweet.This will be done by collecting tweets based on their hashtags and NLP technique called topic modeling specifically LDA to form the topic of stress for stress detection.Then Machine learning algorithms of SVM, XGBoost, and Logistic Regression will be used on the tweets and topics created by the Topic modeling to create and train a model that can predict stressors based on the tweets and topics.
Robust framework to prioritize blockchain-based supply chain challenges: the fuzzy best-worst approach for multiple criteria decision-making

Purpose This study aims to identify and prioritize the challenges to adopting blockchain in supply chain management and to make its taxonomic model. Moreover, validate whether these challenging factors exist in the real world and, if they exist, then in what percentage. Design/methodology/approach This research adopted the fuzzy best-worst method (F-BWM), which integrates fuzzy set theory with the best-worst method to identify and prioritize the prominent challenges of the blockchain-based supply chain by developing a weighted multi-criteria model. Findings A total of 20 challenges ( CH ' s ) were identified. Lack of storage capacity/scalability and lack of data privacy challenges were found as key challenges. The findings of this study will provide a robust framework of the challenges that will assist academic researchers and industry practitioners in considering the most significant category concerning their working area. Practical implications Blockchain provides the best solution for tracing and tracking where RFID has not succeeded. It can improve quality management in a supply chain network by improving standards and speeding up operations. For inventory management, blockchain provides transparency of documentation for both parties within no time. Originality/value To the best of the authors' knowledge, no previous research has adopted the fuzzy best-worst method to prioritize the identified challenges of blockchain implementation in the supply chain. Moreover, no study provides a taxonomic model for the challenges of implementing a blockchain-based supply chain.
HEALTH INSURANCE TRACKING SYSTEM USING BLOCKCHAIN

The paper presents a new system that utilizes blockchain technology to automate the transfer of will after a person's death for health insurance claims.By utilizing smart contracts and Chainlink cron, the system is designed to perform the will transfer process at specific intervals without requiring manual intervention.This new system enhances the transparency and security of the insurance claim process and ensures that the funds are transferred to the intended beneficiary accurately and promptly.Overall, the proposed system is intended to simplify and streamline the process of health insurance claim by automating it through blockchain technology.