Reviewerly/exHarmony huggingface dataset:
Quality Evaluation and Informatization Analysis of Physical Education Teaching Reform Based on Artificial Intelligence

With the continuous reform of China’s education and the development of the educational environment, English will no longer be the third subject for Chinese students and physical education will replace it as the third subject. The teaching mode has also gradually changed from the traditional artificial mode to the current smart education. In the context of artificial intelligence, physical education can also apply this technology to daily teaching. After physical education becomes the third subject, it is necessary to reform the existing teaching mode and conduct quality evaluation and informatization analysis. In order to achieve this effect, we use artificial intelligence action scenes to detect students' detailed actions and identify key actions and then use computer vision system to create regression models and Bayesian formulas to give the criteria for judging the subsequent training points of the computer. Then, according to the training data of each student in the training process, the quality analysis and informatization evaluation of the teaching reform of physical education are carried out. Using the action bank algorithm as the basic algorithm of feature extraction, a template research method based on multispectral clustering is proposed to facilitate its dissemination in the computer background database. Then, through experiments to compare the before and after optimization of the algorithm, the data analysis of the resolution, time consumption, and detection error of the action bank model were carried out, and it was found that the performance was improved. Then, by means of mean shift detection method and spatiotemporal action detection method, the resolution, time consumption, and detection error of the action bank model are optimized to achieve the quality evaluation and informatization analysis of physical education teaching reform.
BSPPF: Blockchain-Based Security and Privacy Preventing Framework for Data Middle Platform in the Era of IR 4.0

Security and privacy issues about Big Data have emerged as one of the most pressing concerns in both academic circles and the business world in the era of IR 4.0. These worries are made worse because businesses are beginning to construct their own Big Data-based Data Middle Platform. The research team has summarized the security and privacy protection issues of Big Data-based Data Middle Platform by combining previous research and providing a table that lists some contributions that the blockchain uses for security and privacy protection in Big Data. A general Data Middle Platform architecture is also be given in the Methodology section. Based on this architecture, the team unveiled blockchain-based security and privacy protection framework on the Data Middle Platform in this study. This approach emphasizes mitigating the security and privacy issues posed by sensitive data entering the Data Middle Platform and data published by the Data Middle Platform. Following that, the researchers developed a testing system based on this framework to determine whether or not the framework was both feasible and practicable. The trials revealed that the framework is applicable, even though it requires further development and refinement.
Wavelet-Domain Information-Hiding Technology with High-Quality Audio Signals on MEMS Sensors

Due to the rapid development of sensor technology and the popularity of the Internet, not only has the amount of digital information transmission skyrocketed, but also its acquisition and dissemination has become easier. The study mainly investigates audio security issues with data compression for private data transmission on the Internet or MEMS (micro-electro-mechanical systems) audio sensor digital microphones. Imperceptibility, embedding capacity, and robustness are three main requirements for audio information-hiding techniques. To achieve the three main requirements, this study proposes a high-quality audio information-hiding technology in the wavelet domain. Due to the fact that wavelet domain provides a useful and robust platform for audio information hiding, this study applies multi-coefficients of discrete wavelet transform (DWT) to hide information. By considering a good, imperceptible concealment, we combine signal-to-noise ratio (SNR) with quantization embedding for these coefficients in a mathematical model. Moreover, amplitude-thresholding compression technology is combined in this model. Finally, the matrix-type Lagrange principle plays an essential role in solving the model so as to reduce the carrying capacity of network transmission while protecting personal copyright or private information. Based on the experimental results, we nearly maintained the original quality of the embedded audio by optimization of signal-to-noise ratio (SNR). Moreover, the proposed method has good robustness against common attacks.
THE EFFECT OF WATERPROOF COSMETICS ON THE LEGITIMACY OF WUDHU (Study on Islamic Boarding School Students University of Muhammadiyah Surakarta)

This study aims to determine How Waterproof Cosmetics Affect the Validity of Wudhu. This type of research uses a qualitative type with field studies in the Islamic Boarding School for Students of the Muhammadiyah University of Surakarta. While the research approach used isPhenomenologicalwith the analysis of the data used is using inductive analysis. The results of this study indicate that first, the use of waterproof cosmetics by students is very high, and significantly it affects the use of waterproof cosmetics. Second, the use of waterproof cosmetics gives the invalid status of the legal requirements for ablution due to the presence of substances that block the neutralization of water entering the skin pores which is a condition for valid ablution.
Visual Discrete Format: An Alternative to Likert-Type Formats of Survey Items Sensitive Enough to Measure Small Changes in Stable Constructs Such as Self-Concept in Science

A visual discrete format was developed for use in surveys as an alternative to a Likert-type format to measure changes in a relatively stable construct before and after an intervention. Visitors to a science centre ranging in age from 8 years old upwards were asked to complete surveys that included a seven-item questionnaire scale on self-concept in science using either the Likert-type format (n=446) or the visual discrete format (n=375), before and after their visit. A new set of statements to assess self-concept in science were developed and validated so they could be conducted on either format. Matched responses were used to calculate internal consistency, standard deviation, confidence interval and percentage of missing values; these were all similar for both formats. In the visual discrete format, text labels were replaced by visual labels with a single image of different size for each response option. While a Likert-type format describes a level of agreement/disagreement with a specific item, the visual discrete format relates more to choosing the best reflection of the self in relation to that item. The Likert-type format included a set of emoji in its descriptions to appeal to younger participants. The visual discrete questionnaire scale detected a small increase with medium effect size in self-concept in science after the visit to the science centre while the Likert-type questionnaire scale did not detect any change. This suggests the proposed new format can not only be a viable and useful alternative, but potentially more sensitive under certain conditions.
Empowering Digital Literacy for Underprivileged Youth in Jakarta

The research entitled the digital literacy empowerment model for underprivileged youths uses qualitative methods. Researchers use a descriptive type of research to describe the facts, conditions, motivations of the youth in their digital literacy skills. The aim of the research is to provide the most suitable empowerment model for underprivileged youths in Jakarta area. The study was conducted on 52 youths in the Jakarta area, namely the Marunda Rusunawa, Palmerah and Bidaracina areas. This study tries to measure the digital literacy framework, which includes digital skills, digital safety, digital ethics, and digital culture. The study was carried out during the Covid 19 pandemic thus empowering is delivered under strict Covid 19 protocols. The empowering of Digital literation become very important.
A Combination of Forward Chaining and Certainty Factor Methods for Early Detection of Fever : Dengue Hemorrhagic Fever, Malaria and Typhoid

Abstract. Purpose: Dengue Hemorrhagic and Malaria fevers are the most common arthropod-borne diseases caused by mosquito bites and they also have similar signs and symptoms. Based on the problems, the researcher makes an expert system that aims to help people early detect fever diseases. This system is expected to help and support the infectious disease prevention and control program by the Ministry of Health of the Republic of Indonesia.Methods: This study uses an expert system with a combination of Forward Chaining and Certainty Factor to detect the symptoms of fever. Forward Chaining is a technique that begins with gathering information related to known facts, then combining rules to produce conclusions. The certainty Factor method is used to define a measure of certainty against a fact or rule and to describe the level of expert confidence in dealing with problems. There are 32 symptoms of the disease consisting of dengue fever, malaria and typhoid, it was obtained based on the literature and interviews with internal medicine specialist with 20 case datasets.Result: Based on 20 test data, obtained one data that does not match the test results and the desired target so that the system accuracy obtained is 95%. In addition, the combination of Forward Chaining and Certainty factor has better accuracy when compared to expert systems in previous studies.Novelty: Forward Chaining to find three rules and assigning weights to the Certainty Factor that has been set by the expert makes the combination of the two methods produce better accuracy.
The Use of KWL Strategy on Students' Reading Comprehension

Reading is a dynamic process of catching the words' meaning and getting the text's information. In reading comprehension, students must engage with their reading to comprehend the text. However, reading interest is still a big problem in Indonesia. This research aims to prove the effectiveness of using KWL strategy on students' reading comprehension. The KWL strategy stands for what I Know, what I Want to learn, and what I did Learn. This research applied a pre-experimental method with one group pre-test and post-test design as the research methodology. The tenth-grade students in SMAN 1 Cisaat are taken as the population of this research. Thirty-five students were selected as the samples using a random sampling technique with the lottery method. The tests were given to measure their reading comprehension ability, and the data were analyzed using the t-test formula. From the calculation, the researcher finds the result of the t-test value is 11.28 with the degree of freedom (df) is 34, and the t-table value at the significant level of 0.05 is 2.042. the result shows that the value of the ttest is higher than t-table 11.28 2.042. it means that the alternative hypothesis (ha) is received. Therefore, the researcher concludes that using the KWL strategy affects students' ability in reading comprehension.
Combination of Backpropagation Neural Network and Particle Swarm Optimization for Water Production Prediction in Municipal Waterworks

Abstract.Purpose: As the population grows, the need for clean water also increases. Municipal Waterworks (PDAM) is an institution that regulates and manages the procurement of clean water for the community. So, the amount of water produced and distributed should be adjusted to the demand for water. Predictions on PDAM water production need to be done as planning and better preparation and facilitating and assisting in decision-making.Methods: The study used the Neural Network backpropagation algorithm combined with Particle Swarm Optimization (PSO) to predict the amount of water PDAM should produce. Backpropagation has a good ability to make predictions. But backpropagation has a weakness that causes it to get stuck at a local minimum. This is influenced by the determination of weights that are not optimal. In this study, PSO had a role in optimizing error values on the network to gain optimal weight. Result: This study obtained MSE values in the training and testing process of 0.00179 and 0.00081 from the combination model of backpropagation ANN and PSO. It is smaller than the ANN model without using an optimization algorithm.Novelty: The combination of JST backpropagation and PSO can improve predictions' accuracy and produce optimum weights.
Performance Comparison of Similarity Measure Algorithm as Data Preprocessing Stage: Text Normalization in Bahasa

Purpose: More and more data are stored in text form due to technological developments, making text data processing more difficult. It also causes problems in the text preprocessing algorithm, one of which is when two texts are identical, but are considered distinct by the algorithm. Therefore, it is necessary to normalize the text to get the standard form of words in a particular language. Spelling correction is often used to normalize text, but for Bahasa Indonesia, there has not been much research on the spell correction algorithm. Thus, there needs to be a comparison of the most appropriate spelling correction algorithms for the normalization process to be effective.Methods: In this study, we compared three algorithms, namely Levenshtein Distance, Jaro-Winkler Distance, and Smith-Waterman. These algorithms were evaluated using questionnaire data and tweet data, which both are in Bahasa Indonesia.Result: The fastest normalization time is obtained by the Jaro-Winkler, taking an average of 31.01 seconds for questionnaire data and 59.27 seconds for tweet data. The best accuracy is obtained by the Levenshtein Distance with a value of 44.90% for the questionnaire data and 60.04% for the tweet data. Novelty: The novelty of this research is to compare the similarity measure algorithm in Bahasa Indonesia. Therefore, the most suitable similarity measure algorithm for Bahasa Indonesia will be obtained.
Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization

Abstract. Purpose: This research was conducted to create a classification model in the form of the most optimal decision tree. Optimal in this case is the combination of parameters used that will produce the highest accuracy compared to other parameter combinations. From this best model, it will be used to predict the nutritional status class for the new data.Methods/Study design/approach: The dataset used is from Nutritional Status Monitoring in 2017 in Riau Province, Indonesia. From the dataset, the Knowledge Discovery in Database (KDD) stages were carried out to build several classification models in the form of decision trees. The decision tree that has the highest accuracy will then be selected to predict the class for the new data. Predictions for new data (unclassified data) will be made in a web-based system.Result/Findings: Particle Swarm Optimization is used to find optimal parameters. Before PSO is used, there are 213 parameters in the dataset that can be used to do classification. However, using many such parameters is time-consuming. After PSO is used, the optimal parameters found are the combination of 4 parameters, which can produce the most optimal decision tree. The 4 chosen parameters are gender, age (in months), height, and the way to measure the height (either stand up or lie down). The most optimal decision tree has an accuracy of 94.49%. From the most optimal decision tree, a web-based system was built to predict the class for new data (unclassified data).Novelty/Originality/Value: Particle Swarm Optimization (PSO) is a method that can help to select the most optimal parameters, or in other words produce the highest classification accuracy. The combination of parameters selected has also been confirmed by the nutritionist. The prediction system has been declared feasible to be used by nutritionists through the User Acceptance Test (UAT).
Impact of High Commitment HRM Practices on the Non-Academic Staff Performance: Mediating Role of Affective Commitment and Job Satisfaction

Purpose: The purpose of this study is to scrutinize the relationship between non-academic staff and student support services by developing a conceptual model of the mediation impact of affective commitment and job satisfaction on the relationship between high commitment HRM practices and non-academic staff performance.&#x0D; Design/Methodology/Approach: For this study, primary data has been collected from survey methods of 250 respondents from non-academic staff and students of HEIs in public and private sectors. In order to examine the mediating mechanism of variables, 8 hypotheses were constructed based on the conceptual model and literature study and tested using the SPSS 25 and Smart PLS using the bootstrapping approach.&#x0D; Findings: The study's findings demonstrate that high-commitment HRM practices have a statistically significant influence on non-academic staff perceptions of behavioral outcomes such as affective commitment and satisfaction. Likewise, the extracted results show a statistically positive association between non-academic performance and student support services.&#x0D; Implication/Originality/Value: HEIs carry out HCHRPs, they will be able to replicate the level of organizational commitment and job satisfaction of their administrative staff which lead to student support services.
A fast method for blindly identifying Reed‐Solomon codes based on matrix analysis

For the identification of Reed-Solomon (RS) codes, the existing methods need to test the possible codeword length and the primitive polynomials exhaustively. Exhaustive search leads to high computational complexity. To overcome this limitation and fulfill the requirement in practice, a fast blind identification method is proposed. Different with most methods, our identification method is discussed in Galois Field of 2. First, by using Gaussian elimination, the bit matrix is transformed to the simplest upper triangular form. If the assumed codeword length is right, the matrix is of deficient rank, and the parameters of an RS code can be estimated according to the rank. The null space of the matrix, defined as the equivalent binary parity check matrix, can also be obtained from the simplification result. Then, a candidate set of primitive polynomials is constructed according to the estimated codeword length. By using the matrix null space, the correctness of a candidate primitive polynomial is tested through matrix analysis. Finally, by combining the estimated parameters, the generator polynomial of an RS code is identified. To decrease the bit errors in the matrix, soft-decision data is used to select reliable bits. Experimental results show the effectiveness and robustness of our method.
Psychological Well-Being as Cognitive-Emotional Component of Student Self-Regulated Learning

The manuscript reports the theoretical aspects of studying the concepts of students’ psychological well-being in the process of self-regulated learning. The essence of student psychological well-being through a prism of cognitive and emotional personality factors has been theoretically studied. The role of psychological well-being and its components in the process of self-regulated learning activities of HEI students has been described. Psychological well-being has been viewed as an integral indicator of the level of personality, which affects resistance, and manifests itself in subjectively tangible satisfaction and life satisfaction. Self-regulated learning has been considered as a metacognitive level of initiation, motivation, monitoring, evaluation and control of all kinds and forms of internal and external activities by the subject of learning, which are aimed at reaching the predetermined goals of learning, personal and professional self-development and self-realization. The results of empirical research with the use of K. Ryff’s questionnaire entitled “Scales of Psychological Well-being,” as adapted by S. Karskanova, allowed us to evaluate personal growth, positive relationships with others, goals in life, managing environment, autonomy and self-acceptance which are indicators of individuals’ psychological well-being. Methods of statistical data processing such as descriptive statistics (group median - Me, arithmetic mean - M, standard deviation - σ) and the Kruskal-Wallis test, confirmed the existence of a statistically significant linear age-related decrease in indicators on the “Autonomy” Scale for the senior students. Analysis of the empirical data on the levels of defined psychological well-being has showed a linear correlation between psychological well-being and its levels. It has been concluded that the cognitive-emotional aspects of psychological well-being of modern student youth in learning activities have been dominated by dependent types of self-regulation in learning. The results have indicated that the most appropriate means to improve the psychological well-being of students, as a component of the emotional and behavioral level, is the formation of their active life position, responsibility for their own activities, motivation and autonomy in self-regulated learning activities. Prospective directions for future research have been described.
Investigation of Prospective Teachers’ Use of Mobile Technologies in Teaching Activities

The purpose of this research is to examine the pre-service teachers’ use of mobile technologies in teaching activities. In this study, the survey method, one of the research methods, was used. The 199 prospective teachers studying at the Faculty of Education, a private university in Northern Cyprus, were included in the workgroup of the research. The 5-point Likert-type scales were used to measure the frequencies of using mobile technologies in instructional activities, motivation, and attitudes towards mobile technologies, competencies in mobile technology use, and its social effects. It was found that social factors had the lowest effect on the use of mobile technologies. Additionally, no gender-based difference was found in the mobile use frequency, motivation, attitude, competency, and social impact of prospective teachers. Moreover, the frequency, motivation, attitudes, competency, and social impact levels of students from the Department of Computer and Instructional Technologies are significantly higher than those departments from Guidance and Psychological Counselling, Special Education, Music Teaching, Elementary School Mathematics Teacher Education, Department of Pre-School Teaching, Classroom Teaching Department, Social Sciences Teacher Education, Turkish Language Teaching. It was concluded that having problems for mobile learning activities that can be used in the scope of mobile learning significantly influences the frequency of use, motivation, attitude, competency, and social impact of the students. Along with this, it was found that the following factors, given in order of importance, are influential on the frequency of the use of mobile technologies by prospective teachers in instructional activities: motivation, attitude, competency, and social impact.