Reviewerly/exHarmony huggingface dataset:
Education Development in Higher Education Institutions: Transition to Qualitative Teaching

In light of the ongoing transformations at the health level, the economic level, and the continuous technological transformations, the priority of moving education in higher education institutions towards creativity and quality versus quantity has become a national demand. This is reflected in the transition from traditional education to qualitative learning. Therefore, the study focuses, through a set of objectives, on analyzing the mechanism of transformation from traditional education to creative education by learning, qualitative learning that ensures the continuity and progress of the state. The researcher used the descriptive approach and comparative analysis of local and international literature to reach a set of results with a theoretical and an applied dimension The study is characterized by a set of modern proposals that enhance the forms of teaching in higher education institutions through lectures and through independent work of students. The addition to what the researcher suggested is the use of the concentration factor, which measures the level of information abundance of the student, and the student's academic load index. Through this study, the researcher hopes that the real integration between the pre-university stage and the university stage is achieved at the present time.
Mediating Effects of Mobile Phone Use on the Relationship between Mobile Addiction and Academic Behavior

Since there is little research on the mediative effect of excessive mobile phone use on mobile addiction and academic behavior among university students, hence, the current study sought to study the relationship between these three variables.Data was collected from 435 university students through an online Google form survey sent through a WhatsApp link.Overall, the result shows that that level of mobile addiction was moderate.More than 40% of the respondents had experienced the impact of excessive use of mobile phone while more than 16% experienced negative academic behaviour.Besides, structural equation model fit and measurement models were used to analyze the data with a two-stage structural equation modelling.The first stage involved the assessment of measurement model or also known as Confirmatory Factor Analysis (CFA) for negative academic behavior, excessive mobile phone use and mobile addiction.The measurement model achieved a good fitness index where, RMSEA = 0.064, NFI = 0.9, CFI = 0.932, TLI = 0.921, and Relative Chi-square (Chisq/df) = 2.855.In the second stage of assessment of the structural equation model, the study confirmed that excessive mobile phone use mediates the relationship between mobile addiction and negative academic behavior among students in higher education in Malaysia.The novelty of this study lies in the exploratory analysis of these three variables with a systematic method to find a conclusive result.In summary the findings imply that higher education institutions need to have better guidelines to regulate mobile phone use among students in higher learning institutes.
Operationalising ‘toxicity’ in the manosphere: Automation, platform governance and community health

Social media platforms have been struggling to moderate at scale. In an effort to better cope with content moderation discussion has turned to the role that automated machine-learning (ML) tools might play. The development of automated systems by social media platforms is a notoriously opaque process and public values that pertain to the common good are at stake within these often-obscured processes. One site in which social values are being negotiated is in the framing of what is considered ‘toxic’ by platforms in the development of automated moderation processes. This study takes into consideration differing notions of toxicity – community, platform and societal by examining three measures of toxicity and community health (the ML tool Perspective API; Reddit’s 2020 Content Policy; and the Sense of Community Index-2) and how they are operationalised in the context of r/MGTOW – an antifeminist group known for its misogyny. Several stages of content analysis were conducted on the top posts and comments in r/MGTOW to examine how these different measures of toxicity operate. This paper provides insight into the logics and technicalities of automated moderation tools, platform governance structures, and frameworks for understanding community metrics to interrogate existing uses of ‘toxicity’ as applied to cultural or social subcommunities online. We make a distinction between two used terms: civility and toxicity. Our analysis points to a tension between current social framings and operationalised notions of ‘toxicity’. We argue that there is a clear distinction between civility and toxicity – incivility is a measure of internal perceptions of harm within a community, whereas toxicity is a measure of the capacity for social harms outside of the bounds of the community. This nuanced understanding will enable more targeted interventions to be developed to destabilise the internal conditions that make groups like r/MGTOW internally ‘healthy’ yet externally toxic.
Cyber Bullying Text Detection Using Machine Learning

Abstract: With the upward thrust of the Internet, using social media has exploded, and it's emerged because the most powerful networking platform of the ordinal century. However, exaggerated social networking oftentimes has negative consequences for society, causative to some unwanted phenomena at the side of online abuse, harassment, cyberbullying, cybercrime, and trolling. Cyberbullying causes severe mental and physical distress in several people, particularly ladies and children, and might even cause suicide damaging social impact of online harassment attracts attention. Several incidences of online harassment, equivalent to sharing personal chats, spreading rumours, and creating sexual remarks, have recently occurred everywhere on the planet. As a result, specialists are paying nearer interest to detect bullying the big texts or messages on social media. By combining natural language processing and machine learning the aim of this observation is to create and construct a powerful method for detecting online abusive and bullying texts. The accuracy stage of six different machine learning techniques is evaluated the usage by of extraordinary features, particularly the count vectorizer
Toxic Comments Classification

Abstract: Conversational toxicity is a problem that might drive people to cease truly expressing themselves and seeking out other people's opinions out of fear of being attacked or harassed. The purpose of this research is to employ natural language processing (NLP) techniques to detect toxicity in writing, which might be used to alert people before transmitting potentially toxic informational messages. Natural language processing (NLP) is a part of machine learning that enables computers to comprehend natural language. Understanding, analysing, manipulating, and maybe producing human data language with the help of a machine are all possibilities. Natural Language Processing (NLP) is a type of artificial intelligence that allows machines to understand and interpret human language instead of simply reading it. Machines can understand written or spoken text and execute tasks such as speech recognition, sentiment analysis, text classification, and automatic text summarization using natural language processing (NLP).
Media Memory: Theoretical Aspect

The article is devoted to the study of the theory of media memory and its role in the construction of collectively shared ideas about the past. The author proceeds in his work from McLuhan’s expansive interpretation of media. The processes of mediatization of communicative and cultural memory are being analyzed. Considering the views of Y. M. Lotman and J. Assman on cultural and communicative memory, the author comes to the conclusion that their theories explaining the functioning of collective memory in the era of oral and written communication, cease to work when describing the mechanisms of memory reproduction in the digital environment. Theories of mediatization of memory regard communicative memory are limited to J. Assman’s understanding of, that is, as a memory of the recent past covered by the time of the existence of contemporary digital media. Based on the works of J. Garde-Hansen, E. Hopkins and others, the author considered the theory of digital memory showing its difference from mediamemory. The processes of mediatization of collective memory in the digital environment lead to the emergence of media memory as a special virtual mechanism for constructing ideas about the past. Media memory possesses the characteristics of communicative, cultural and digital memory simultaneously, being a phenomenon of the digital era, where the change in the means of communication has led to the transformation of the content of memories and ways of their reproduction.
The Dynamic Role of Big Data Analytics in Learning and Development and Its Impact on Risk Analysis in Stock Market

Big data analytics is making a paradigm shift in all industries, including banking, financial institutions and stock broking companies. The financial big data analytics (FBDA) is considered as one of the key areas of enhancing the management, governance, performing critical analysis and support the management, regulators and managers to make profound decisions. It is noted that the big data is transforming the finance sector mainly stock market domain in assisting the risk managers and investors to measure the risk in an effective manner so that they can invest in better assets like stocks, bonds, mutual funds, exchange traded funds (ETF) etc. for enhancing the return on investments. Every day there are millions of financial transactions which occur in the financial community around the world, the financial practitioners and risk management analyst need to upgrade their knowledge through continuous learning and focus in analysing the risk efficiently so as to enhance their investment plan and realise the financial goals. The risk management is considered as the systemic modelling which enables in the estimation of the interrelationship amongst the financial institutions, big data applies the major characteristic which is comprised of 4 Vs—Volume, Velocity, Variety and Veracity this enables in analysing the large volume of data available in stock prices, forecast the volatility and manage the risk in an appropriate manner. This paper enables in presenting a conceptual understanding on the dynamic role of big data analytics in learning and development and its impact of risk analysis in stock market, this paper enables in presenting sophisticated methods for risk control based on big data analytics in creating new finance aspects by analysing the overall feelings based on massive textual information from the social networks, apply various technologies in creating ways of estimating risk and enhance their return on investments (ROI).
A Comparative Study on Semantic Segmentation Algorithms for Autonomous Driving Vehicles

Abstract: Semantic segmentation refers to the process of classifying each pixel in an image for better understanding and analysis of image. This is how machines look at the real world and identifies different objects. These are the times when autonomous vehicle industry is blooming and establishing new heights. There are so many research studies going on around semantic segmentation that are advancing to break boundaries in the world of computer vision. Despite of so much of progress made in the field in the recent years, autonomous vehicles needs more improved and efficient models to ride on the roads. In this research paper we compare the currently proved popular choices of models for semantic segmentation with respect to autonomous vehicles on different parameters to create an in-depth analysis on which all models and their variations improve and affect the quality of real time segmentation of the real world. The popular model architecture choices that were assessed and compared in this research were Fully Convolutional Network (FCN), U- Net and DeepLab. The data used for analysis was taken from Lyft Perception Challenge on Lyft Perception Challenge on Udacity.
Sign Language Prediction Model using Convolution Neural Network.

The barrier between the hearing and the deaf communities in Kenya is a major challenge leading to a major gap in the communication sector where the deaf community is left out leading to inequality. The study used primary and secondary data sources to obtain information about this problem, which included online books, articles, conference materials, research reports, and journals on sign language and hand gesture recognition systems. To tackle the problem, CNN was used. Naturally captured hand gesture images were converted into grayscale and used to train a classification model that is able to identify the English alphabets from A-Z. Then identified letters are used to construct sentences. This will be the first step into breaking the communication barrier and the inequality. A sign language recognition model will assist in bridging the exchange of information between the deaf and hearing people in Kenya. The model was trained and tested on various matrices where we achieved an accuracy score of a 99% value when run on epoch of 10, the log loss metric returning a value of 0 meaning that it predicts the actual hand gesture images. The AUC and ROC curves achieved a 0.99 value which is excellent.
Integration of Education Planning Funds Base on Philanthropy: Drop Out School Mitigation

The increasing number of dropouts during the Covid-19 pandemic needs to find an alternative solution by planning for philanthropy-based education financing. This paper aims to discuss education financing planning and to know the role of philanthropy in rescuing dropout students. The research uses a qualitative method with a literature study approach with content analysis techniques. Educational institutions must plan education financing in the budget work plan by seeking alternative funding assistance for students dropping out of school due to economic problems. Mitigation of dropout students is carried out by identifying students and parents, providing counselling services, collaborating with donor agencies, continuing formal or non-formal schools and finding donors or sponsors. The role of philanthropy in Indonesia in education has provided positive support, equality and sustainability of the quality of human development, including dropout students. The research contribution provides an overview for educational institutions to find and prepare special budgets to overcome school financial difficulties and overcome students who leave due to not being able to carry out their financial obligations during the COVID-19 pandemic. The implication is that education financing planning efforts in mitigating dropouts are integrated into philanthropic institutions.
Development of Local History Digital Book Teaching Materials At SMA 13 Jambi City

This study aims to determine students' needs for digital-based learning media. The research method used in this research is through research and development, with the ADDIE development model. Sources and methods of data collection were collected from document studies such as from libraries, internet, scientific articles, books, journals and documents related to research themes, then field observations. The results of the research, these writings serve as reading values and recommendations for the development of local historical awareness in Indonesia, especially in Jambi City. The exploration of this research tries to reveal the application of digital books as a medium in learning in schools.
Improving the Grade 8 Students' Vocabulary Skill Through Localized Game-Based Activities

This study aimed to improve the Grade 8 students’ vocabulary skill through localized game-based activities. The participants of the study were the 35 Grade 8 students who were officially enrolled at the Mariano Marcos State University – Laboratory High School, Batac Campus, for the School Year 2021-2022. The data on students’ level of vocabulary performance were determined based on the students’ scores in the pretest and posttest, respectively, using a 20-item teacher-made test. Frequency distribution, means and mean difference were used to treat the data. Findings reveal that the Grade 8 have varied levels of vocabulary skill before and after the application of the intervention. Likewise, there is a significant change on the students’ pretest and posttest scores. Moreover, this study found that the use of localized game-based activities such as Pinoy Henyo and Bugtong Bugtong was effective in improving the students’ vocabulary skill.
Measurement disturbance tradeoffs in three-qubit unsupervised quantum classification

We consider measurement disturbance tradeoffs in quantum machine learning protocols which seek to learn about quantum data. We study the simplest example of a binary classification task in the unsupervised regime. Specifically, we investigate how a classification of two qubits, that can each be in one of two unknown states, affects our ability to perform a subsequent classification on three qubits when a third is added. Surprisingly, we find a range of strategies in which a nontrivial first classification does not affect the success rate of the second classification. There is, however, a nontrivial measurement disturbance tradeoff between the success rate of the first and second classifications, and we fully characterize this tradeoff analytically.
Method for creating synthetic data sets for training neural network models for object recognition

Introduction: The lack of training data leads to low accuracy of visual pattern recognition. One way to solve this problem is to use real data in combination with synthetic data. Purpose: To improve the performance of pattern recognition systems in computer vision by mixing real and synthetic data for training, and to reduce the time needed for preparing training data. Results: We have built an intelligent information system on the basis of the proposed method which allows the generation of synthetic images. The system allows to generate large and representative samples of images for pattern recognition neural network training. We have also developed software for the synthetic image generator for neural network training. The generator has a modular architecture, which makes it easy to modify, remove or add individual stages to the synthetic image generation pipeline. One can adjust individual parameters (like lighting or blurring) for generated images. The experiment was aimed to compare the accuracy of pattern recognition for a neural network trained on different training samples. The combination of real and synthetic data in model training showed the best recognition performance. Artificially generated training samples, in which the scale of background objects is approximately equal to the scale of the object of interest, and the number of objects of interest in the frame is higher, turned out to be more efficient than other artificially constructed training samples. Changing focal length of the camera in the synthetic image generation scene had no effect on the recognition performance. Practical relevance: The proposed image generation method allows to create a large set of artificially constructed data for training neural networks in pattern recognition in less time than it would take to create the same set of real data.
MANAJEMEN PENDIDIKAN BERBASIS INFORMASI DI ERA SOCIETY 5.0

In the era of information system development, it will be seen that the orientation of future education is so fast, so that education management is the most important in education and is also most needed, because the education management information system is a supporting factor for the progress of formal and informal educational institutions so that in an effort to implement an education management information system, it cannot be supported. by the employee's own performance manually. because in the era of the industrial revolution 4.0, there have been changes in the education management sports system, more so on the declaration of Society 5.0 as a wide door for the community to solve problems, so that improving the quality and public services related to the management of quality school education information in the service process to the school also needs to This needs to be considered so that it is necessary to apply an education management information system which is one of the important factors in the quality of education and of course also helps all levels of teachers, administrative staff in improving quality in schools, especially the community.
STROKE PREDICTION USING MACHINE LEARNING

A Stroke is a health condition that causes damage by tearing the blood vessels in the brain.It can also occur when there is a halt in the blood flow and other nutrients to the brain.According to the World Health Organization (WHO), stroke is the leading cause of death and disability globally.Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke.Different machine learning (ML) models have been developed to predict the likelihood of a stroke occurring in the brain.This research uses a range of physiological parameters and machine learning algorithms, such as Logistic Regression (LR), Decision Tree (DT) Classification, Random Forest (RF) Classification, and Voting Classifier, to train four different models for reliable prediction.Random Forest was the best performing algorithm for this task with an accuracy of approximately 96 percent.The dataset used in the development of the method was the open-access Stroke Prediction dataset.The accuracy percentage of the models used in this investigation is significantly higher than that of previous studies, indicating that the models used in this investigation are more reliable.Numerous model comparisons have established their robustness, and the scheme can be deduced from the study analysis.
Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms

Video segmentation is crucial in a variety of practical applications especially in computer visions. Most of recent works in video segmentation are focusing on Deep learning based video segmentation, there are rooms for improvement in respect of the evolutionary algorithms. This paper aims to propose the novel method to video segmentation by using the optimization of segmentation parameters based on ensemble-based random forest and gradient boosting decision tree. The experimental results show Pareto front of segmentation parameters (hue, brightness, luminance, and saturation). Our optimization model yields accuracy: 85% +/-8.85 % (micro average: 85.00 %), average class precision: 84.88%, and average class recall: 85%. We also show the video segmentation results based on our optimization method and compare our results with Kinect-based video segmentation.
The Effect of Job Satisfaction as a Mediator in the Relationship between Person-Environment Fit and Job Performance: Evidence from the Food Industries in Libya

This study investigates the effects of job satisfaction as mediator in the relationship between P-E fit and job performance. Random sampling is adopted, 300 questionnaires among employees in the food industries in Libya were distributed, and 230 out of them were returned and valid for analysis. The findings revealed that P-E fit had direct and positive effect on job performance. Moreover, job satisfaction partially mediated the relationship between P-E fit and job satisfaction. Future recommendations are recommended in this study.