Reviewerly/exHarmony huggingface dataset:
Using YouTube Video to Improve Students’ Speaking Skill at Eleventh of SMA Wahidiyah Gumukmas Jember

This research was direction to improve students’ speaking skill though YouTube Video and to know how the implementation of the technique improving students’ speaking skill in English speaking. This research was accomplished at Eleventh of SMA Wahidiyah Gumukmas Jember in which consisted of 4 students as respondent. To solve students’ problem in teaching learning English especially in speaking skill, the researcher applied Classroom Action Research (CAR) designed by Burns which consisted of four phases; planning, acting, observing and reflecting, on each cycle. There were two kinds of data gained in this study; qualitative and quantitative data. The qualitative data can derive from the observation, fieldnote and interview. Meanwhile, the quantitative data can derive from the pre-test and post-test. The result of this study showed that the implementation of YouTube Video to improve students’ speaking skill was successful. It was proven by the data that were collected from this study. The qualitative data showed improvement of the students’ motivation, interest and confident to speaking English after the implementation of YouTube Video as media. The result of the test showed improvements in students’ speaking score. In pre-test, there was no one students 0% who pass the minimum mastery criterion KKM out of 4 students which the mean score was only 55. Then, in post-test for cycle I, the percentage of students who passed the minimum mastery criterion KKM improve to 50% or 2 out of 4 students passed the minimum mastery criterion KKM with 70 as the mean score. Next, in post-test for cycle II, the mean score was 82. The result of the test shows significant improvement where 100% of the students could achieve the score above the minimum criterion which at least 75. Based on the result above, YouTube Video may be concluded successfully improve students’ speaking skill.
Strategies on Learning Speaking used by International Accounting Students

This research aimed to investigate the strategies that are used by the international accounting students at universitas Tanjungpura and to explain the most dominant learning speaking strategies used by international accounting students in learning speaking. The total numbers of participants in this research are 34 students. This research used a descriptive quantitative design, and the data were collected from questionnaires. The findings of this study revealed that international accounting students in the universitas Tanjungpura class used nearly all of the strategies for learning English. The highest average was Metacognitive strategy, with 52,0% total average. The second strategy is Cognitive strategies with the average 44,1%; the third strategy was effective strategy with the average 38,3%, the fourth strategy was the social strategy with the average 38,2%, fifth strategy was compensation with the average 33,8%, and the last strategy was memory strategy with average 11,0%.
The Impact of the COVID-19 Pandemic on Premenstrual Symptoms and Its Relationship with the Quality of Life

Lifestyle changes can negatively affect premenstrual symptoms. The purpose of this study is to determine the effect of changes in the lifestyle of students on premenstrual symptoms and their relationship with their quality of life during the global epidemic process. The research that was planned with a descriptive and correlational pattern was conducted who receive education in the Faculty of Health Sciences in the structure of a public university in İstanbul. The sample of the study consisted of 336 female students. “The Personal Information Form”, “the Scale of Premenstrual Syndrome (PMSS)”, “the Scale of Life Quality Short Form - SF-36” was implemented upon the students that were included in the study. The total PMSS score of the students participating in the study was 147.12±37.41. It was determined that 32.4% of the students had Covid-19 infection. When the total mean scores of PMSS were evaluated according to the lifestyle behaviors of the students; It was determined that PMSS total score differed significantly according to appetite status, exercise, tea and coffee consumption and sleep patterns. According to the results of the study, it was determined that nutritional behaviors, physical activity status, sleep and stress levels changed greatly with the COVID-19 epidemic and it affected PMS. It was determined that this situation affected the quality of life negatively both physically and mentally.
PENERAPAN DATA MINING UNTUK MEMPREDIKSI HARGA BAHAN PANGAN DI INDONESIA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR

Food is one of the important sectors for human life, because the basic human need is food. Over time, food prices in Indonesia are often unstable. This of course has a big impact on the community and farmers. The cause of this food price volatility can be caused by several factors, such as environmental factors, pest and planthopper attacks, and dry land. The application of one of the data mining methods in the process of predicting food prices, especially predicting the price of rice in Indonesia with this prediction, people throughout Indonesia can be more vigilant in the future to prevent hunger. The benefit of predicting food prices for farmers is to prevent large losses caused by the drop in food prices. Meanwhile, the Government's goal is to maintain food security in the territory of the Indonesian Government to prevent hunger. The dataset used comes from the National PIHPS Website. The method used is the K-Nearest Neighbor (KNN) regression algorithm and for testing it uses RMSE and MAE. The results of this study, the K-Nearest Neighbor method with the regression model can predict the price of rice in January 2019 - December 2021 which is recorded every month with the addition of variable data Harvested Area (ha), Production Yield (tons) of the total experiment using range k = (2,10) then the best prediction results are by using k = 2 with MAE and RMSE for training data 52.77 and 96.40 and for testing data 55.55 and 81.64 and K = 2 parameters which have been normalized.
INFLUENCE OF PASSIVE CONTINUANCE COMMITMENT ON TURNOVER INTENTIONS AMONG HEALTH PROFESSIONALS OF NATIONAL REFERRAL HOSPITALS IN KENYA

The aim of this study was to establish the influence of passive continuance commitment on turnover intentions among health professionals of public national referral hospitals in Kenya. The study was supported by theory of equity. Descriptive and correlational research designs were used and positivistic research philosophy followed. A target population of 3,641 health professionals was used to determine a sample of 349 respondents. Multistage sampling and proportionate stratified sampling techniques were used. Questionnaire was used to collect data. The finding showed that passive continuance commitment indicated a strong relationship with turnover intentions of health professionals . This indicated that for any public national referral hospital to maintain turnover intentions passive continuance commitment has to be improved. The study indicated that availability of alternative jobs elsewhere, the need of health workers to support their families and the benefits provided to workers stimulates them to leave or stay longer in national referral hospitals in Kenya. The study recommended that the findings can be used by Ministry of Health to enhance universal health care so as to achieve vision 2030 medical and public health targets. The study can be used by healthcare related parastatals or religious based organizations in the health sector to provide public and medical healthcare in Kenya. Measures to improve continuance commitment may include Motivation of employees to achieve self-driven goals by rewarding excellent performers and improvement of working conditions by replacing old and defaced machines, payment of employees prevailing market salaries in time and providing protective gears. Key Words: Passive Continuance Commitment, Health, Healthcare, Hospitals CITATION: Ndambuki, S. M., Matata, K., Sasaka, P., &amp; Ibua, M. (2023). Influence of passive continuance commitment on turnover intentions among health professionals of national referral hospitals in Kenya. The Strategic Journal of Business &amp; Change Management, 10 (1), 367 – 379.
INFLUENCE OF ACTIVE CONTINUANCE COMMITMENT ON TURNOVER INTENTIONS AMONG HEALTH PROFESSIONALS OF NATIONAL REFERRAL HOSPITALS IN KENYA

This study established the influence of active continuance commitment on turnover intentions among health professionals of public national referral hospitals in Kenya. The study was supported by psychological ownership theory. Descriptive and correlational research designs were used and positivistic research philosophy followed. A target population of 3,641 health professionals was used to determine a sample of 349 respondents. Multistage sampling and proportionate stratified sampling techniques were used. Questionnaire was used to collect data. The findings indicated that active continuance commitment demonstrated strong positive and statistically significant relationship with turnover intentions. A positive and significant effect of active continuance commitment on turnover intentions were established. The study recommended that human resource managers and those of national referral hospitals could use the study to develop human resource policies, guidelines and code of conduct that can be followed by Health professionals. The findings can be used to develop training programs and performance service charters of organizations to deal with active continuance commitment issues. Ministry of Health leadership in the country can use the findings to enhance universal health coverage goals in Kenya. Health related parastatals and financial institutions may use the findings to develop appropriate training and development programs on continuance commitment. Key Words: Health, Turnover, Continuance Commitment CITATION: Ndambuki, S. M., Matata, K., Sasaka, P., &amp; Ibua, M. (2023). Influence of active continuance commitment on turnover intentions among health professionals of national referral hospitals in Kenya. The Strategic Journal of Business &amp; Change Management, 10 (1), 355 – 366.
STUDY AND DETERMINATION OF EYE ASPECT RATIO USING DATA MINING

This research paper studies the landmarks of the eye and analyses the value for Eye Aspect Ratio, which is the ratio of the average of Euclidean distance between pairs of landmarks in vertical position and the Euclidean distance between the horizontal landmarks of the eyes. This ratio has a value lying in a particular range. This ratio is of crucial importance in the blink detection using computer vision. Also this value will be used for other computer vision related tasks like behavior and facial expression detection. Our aim is to find the range of EAR value using which the results of detection will have optimal error in accuracy. An exploratory data analysis on data collected from real world and for various scenarios is conducted. A supervised machine learning model will also be trained and deployed for frame to frame prediction. Their combined result is demonstrated with visualization.
VERBAL AND NON-VERBAL METHODS OF INFLUENCE IN EDUCATIONAL VIDEO CONTENT

The article focuses on the place of video content on the Internet and the distribution of video content in social networks. Currently, as a result of the active development of social networks and the Internet, interest in educational video content is increasing. The format of short informative videos attracts students with accessibility and understandability. The authors prove that the choice of the habitual, easily recognized form of presentation of educational materials facilitates the perception of information and makes it possible to motivate schoolchildren to expand their knowledge on their own. The authors clarify concept of speech impact in relation to a new form of “learning through entertainment”. Frequent methods of speech impact in videos are indicated. The actualization of verbal and non-verbal methods of influencing the audience is emphasized. Attempts are made to identify the reasons, on the one hand, for the successful memorization of scientific information by schoolchildren after watching the teacher’s video, and on the other hand, the teacher’s reluctance to present educational material in a form that is understandable for the “child of digitalization” in the classroom at school. To reach a consensus between the teacher in a real school and the online blogger teacher, who is often the same person, it is important to be able to combine modern methods of explaining new scientific knowledge based on students’ clip thinking with the traditional system of sequential systematic mastering of basic subjects.
Research on the Application of Artificial Intelligence Empowered Education Management

The technological revolution and industrial transformation have driven artificial intelligence to become the core driving force for a new round. This article explores the application scenarios of artificial intelligence in empowering education management and supply, student learning and evaluation, and teacher teaching and development in the field of educational management. This study proposes to promote the construction of new infrastructure for education management, dual empowerment of education and technology, support the reshaping of future education forms through artificial intelligence, strengthen the cultivation of professional talents in artificial intelligence, attach importance to ethical issues in the application of artificial intelligence in education management, stimulate teachers' high-level thinking and initiative, and lead the next generation of educational artificial intelligence innovation, in order to construct the transformation and innovation of artificial intelligence, improve the quality and efficiency of school education management work, and promote high-quality development of education.
A Collaborative Filtering Algorithm Based on Sentiment Analysis in Review Texts

To improve the performance of collaborative filtering algorithm, a collaborative filtering algorithm based on sentiment analysis in review texts (CF_SA) is proposed in this paper. First, LDA (Latent Dirichlet Allocation) is used to form the user topic feature matrix and calculate user review similarity. Secondly, the ALBERT(A Lite Bidirectional Encoder Representation from Transformers) model and BiLSTM(Bi-directional Long Short-Term Memory) neural network are used to mine users' emotional tendencies in item review texts, improve the user rating table, and calculate user rating similarity. Next, the user review similarity and user rating similarity are combined to obtain the final user similarity and predict the user's rating for the item. Finally, experiments were conducted on the Douban Film Review dataset. Compared with classic recommendation algorithms, the results show that the proposed algorithm has good recommendation performance.
Design and Implementation of Child Missing Registration and Mutual Aid Mini Program Based on Java

With the rapid development of the internet, information dissemination has become faster, which makes it even more important to expedite the handling of child missing cases across the country. In this paper, a missing child registration and mutual aid mini program based on Java was designed and developed. The mini program can record various information about missing children and publicize their basic information. More importantly, it can share information with local public security systems. In the age of information technology, people can make better use of this mini program to participate in the search for missing children and improve the success rate of search operations, bringing positive impacts to families and society.
Why Enroll in a Graduate-level Vocational Education Program?: Exploring Views of Graduate Students Using Latent Dirichlet Allocation Topic Modelling

Why Enroll in a Graduate-level Vocational Education Program? : Exploring Views of Graduate Students Using Latent Dirichlet Allocation Topic Modelling. Objectives: The main objective of this study was to identify the most frequent words, the sentiment, and the underlying themes in the students’ responses regarding their enrollment in a graduate-level vocational education program. Methods: Word Cloud generation, VADER Sentiment Analysis and Latent Dirichlet Topic modelling were utilized to analyze the collected data. Findings: The most frequent words in the responses of students were “promotion”, “personal”, “development”, “future”, “possible”, “knowledge”, and “career”. The sentiment analysis showed a positive overall polarity of 0.477 with no negative sentiment. The themes that emerged from the analysis were the desire for increased knowledge in vocational education, the requirement for a graduate degree, and the desire for career promotion. The findings provide insights into the needs and expectations of students and can be used by institutions to design programs and improve the educational experience of students. Keywords: vocational education, sentiment analysis, graduate student, topic modelling. Abstrak: Mengapa Mendaftar di Program Pendidikan Kejuruan Tingkat Pascasarjana? : Menjelajahi Pandangan Mahasiswa Pascasarjana Menggunakan Latent Dirichlet Allocation Topic Modelling. Tujuan: Tujuan utama dari penelitian ini adalah untuk mengidentifikasi kata-kata yang paling sering muncul, sentimen, dan tema yang mendasari tanggapan siswa mengenai pendaftaran mereka di program pendidikan kejuruan tingkat pascasarjana. Metode: Pembuatan Word Cloud, Analisis Sentimen VADER, dan pemodelan Topik Dirichlet Laten digunakan untuk menganalisis data yang dikumpulkan. Temuan: Kata-kata yang paling sering muncul dalam tanggapan siswa adalah “promosi”, “pribadi”, “pengembangan”, “masa depan”, “mungkin”, “pengetahuan”, dan “karir”. Analisis sentimen menunjukkan polaritas keseluruhan positif sebesar 0,477 tanpa sentimen negatif. Tema yang muncul dari analisis tersebut adalah keinginan untuk meningkatkan pengetahuan dalam pendidikan kejuruan, persyaratan untuk mendapatkan gelar sarjana, dan keinginan untuk promosi karir. Temuan ini memberikan wawasan tentang kebutuhan dan harapan siswa dan dapat digunakan oleh institusi untuk merancang program dan meningkatkan pengalaman pendidikan siswa. Kata kunci: pendidikan vokasi, analisis sentimen, mahasiswa pascasarjana, pemodelan topik. DOI: http://dx.doi.org/10.23960/jpp.v13.i2.202337
Learning Representations for Bipartite Graphs Using Multi-task Self-supervised Learning

Representation learning for bipartite graphs is a challenging problem due to its unique structure and characteristics. The primary challenge is the lack of extensive supervised data and the bipartite graph structure, where two distinct types of nodes exist with no direct connections between the nodes of the same kind. Hence, recent algorithms utilize Self Supervised Learning (SSL) to learn effective node embeddings without needing costly labeled data. However, conventional SSL methods learn through a single pretext task, making the trained model specific to the downstream task. This paper proposes a novel approach for learning generalized representations of bipartite graphs using multi-task SSL. The proposed method utilizes multiple self-supervised tasks to learn improved embeddings that capture different aspects of the bipartite graphs, such as graph structure, node features, and local-global information. We utilize deep multi-task learning (MTL) to further assist in learning generalizable self-supervised solution. To mitigate negative transfer when related and unrelated tasks are trained in MTL, we propose a novel DST++ algorithm. The proposed DST++ optimization algorithm improves existing DST by considering task affinities and groupings for better initialization and training. The end-to-end proposed method with complimentary SSL tasks and DST++ multi-task optimization is evaluated on three tasks: node classification, link prediction, and node regression, using four publicly available benchmark datasets. The results demonstrate that our proposed method outperforms state-of-the-art methods for representation learning in bipartite graphs. Specifically, our method achieves up to 12% improvement in accuracy for node classification and up to 9% improvement in AUC for link prediction tasks compared to the baseline methods.
AMERICAN SIGN LANGUAGE (ASL) RECOGNITION USING OPENCV

One of the biggest challenges for thosewho are deaf or dumb is the lack of access to communication. Here, we'll createa OpenCV based system for converting American sign language to text. Computer vision model for the dumb and deaf are a revolutionary system that has the potential to improve the quality of life for those whoare affected by hearing or speech impairments. The computer vision model will detect hand movements and translate them into text in real-time, allowing deaf and dumb individuals to communicate with others more effectively. Traditionalforms of communication such as writing can be difficult and time-consuming, and they also rely on the presence of an interpreter or someone who can read orwrite. Computer vision supply a more efficient and immediate solution to this problem. The CV model are designed to detect hand movements and translate them into text, depending on the user's preferences. This allows deaf and dumb individuals to communicate with others in real-time, without the need for aninterpreter or someone who can read orwrite. The system is configured to display the text or speech on a nearby device, so that others can see and understand the message as well.
EMERGING CONCEPT OF INTERNET OF BEHAVIOUR (IOB)

The world is entering a period of profound transition and fast development. The Internet of Behaviour (IoB) approach is an emerging concept. The "INTERNET OF BEHAVIOR" is an addendum of the "INTERNET OF THINGS". It is capable of generating patterns that impact people's actions. It has the power to energies every accomplishment and hence get clients closer to promoting the product. The Internet of Things has played a pivotal part in today's technological revolution, resulting in the development of smart systems. But, despite hearing the word frequently, we may not completely comprehend what the Internet of Behavior is capable of, as well as how we might participate in it. This article introduces the concept of Internet of Behaviour and discusses how it might assist enhance sales.
DATA SECURITY AND DATA BREACHES

: This research paper focuses on the topic of data security and data breaches specifically in web applications. As more businesses and organizations move their operations online, web applications have become a prime target for cyber attacks. This paper examines common vulnerabilities in web applications such as SQL injection, cross-site scripting, and insecure session management and how they can be exploited to gain unauthorized access to sensitive data. Venmo, a popular peer-to-peer mobile payment app, has faced several data breaches in recent years, raising concerns about the safety of user information. This research paper will also examine the causes and consequences of these data breaches and discusses best practices for secure web application development.