Reviewerly/exHarmony huggingface dataset:
Using “Gramter” to Enhance Grammatical Skills among Standard Six Intermediate Level Pupils

Integration of digital technology in education is seen as a sign of growth in the context of the Malaysian education system. Since the use of digital technology has been accepted and practiced by the young learners, social media is no longer a foreign subject to them. Instagram is commonly known as one of the many platforms that would help to enhance social skills, but due to its current features, people have made full use of it and not only restrict it for social use. Instagram has introduced many new features that could captivate users' attention for hours. Due to this, the usage of Instagram is subconsciously affecting and improving their language learning skills, mainly grammar. Instagram first launched filters in 2010 and they are being used for so many purposes. Hence, it is an oppurtunity to utilize the Instagram filter as a learning tool. Based on previous studies, many Malaysian pupils suffer from writing in English and confidently express their ideas due to the fear of making grammar mistakes. Thus, this study aims to utilize an Instagram filter (GRAMTER) to enhance grammatical skills among standard six intermediate Pupils. Qualitative research was conducted and data was collected from 20 pupils in one of the schools in Rawang. Observations were made and interview sessions were conducted to collect data needed. The finding shows an outstanding difference between pre-test and post-test results. Pupils also accepted GRAMTER as a tool to enhance their grammatical skills. It is hoped that these findings could provide insights for teachers in integrating technology in education.
An Investigation on the Use of Tiktok to Enhance Speaking Skills

The introduction of CEFR is aimed to help elevate and reform the English language education system in Malaysia. The new format is quite descriptive and thorough, aiming at mastering all the four skills; reading, writing, speaking and listening which then impacts on the students’ motivation level when learning due to the vast difference in the expectation of language mastery. Speaking skills have been given equal weightage in the current English language education unlike the previous examination system which only prioritized reading and writing skills. TikTok is observed to be a widely used application among teenagers. The need of having to master speaking skills in order to be addressed as a fluent candidate and using TikTok as a tool to encourage pupils to engage in a lesson has led to the emergence of the study aim which investigates the usage of TikTok as a teaching medium in enhancing speaking skills among Malaysian Certificate of Education (SPM) candidates around 16 to 17 years old. The Design and Development Research method was used to collect data and upon analyzing the data based on SPM marking descriptors, it was found around 37 % of pupils gained score 13-18 (excellent) while the the majority (45 %) of the respondents received score 13-18 (good) and the remaining 18% gained score 7-12 (intermediate). Comparison of derived data with a conventional teaching lesson proves the effectiveness of TikTok. The role of TikTok as a language teaching and learning platform would be the most significant implications of the study.
Investigating Indigenous Learners’ English Language Learning Strategies

As language learning strategies on indigenous learners are still scarce in Malaysia, a linguistic predicament is apparent where English language is acquired as a third or foreign language compared to their mainstream peers. This study aimed to investigate the English language learning strategies among Indigenous learners from the Temuan sub-ethnic in a district of a state in Malaysia. A set of Strategy Inventory Language Learning (SILL) questionnaire was utilised to collect quantitative data from 32 indigenous pupils. Descriptive analysis was made by providing mean, percentage, and frequency. Findings show that indigenous learners have a moderate level of usage in language learning strategies. The use of indirect strategies of affective and social strategies was found to be habitual and more engaged than direct strategies. Although the level of usage of all six language learning strategies among indigenous learners in this study indicates average intensity, the dominance of Affective and Social strategies is indisputably apparent. This study reveals that the indigenous learners are more inclined towards using indirect strategies in language learning. This implies that an integration of communicative approaches and “fun learning” are recommended to accommodate better language acquisition for the indigenous learners.
Improving Student’s English Vocabulary Using Duolingo Application

This research was Classroom Action Research (CAR) which aimed to implement the use of the Duolingo Application to improve students' English vocabulary. It was conducted in two cycles by the researcher. The subject in this research was the students of 12th grade social 3 of SMA Negeri 1 Selakau in Academic Year 2021/2022 which consists of 15 students. The researcher found that the problem of this research was the students could not understand the vocabulary of the verb, then they had difficulty in understanding the English textbook and it was a big problem for the twelfth' grade students face the English examination. The data in this research were collected by using an observation checklist and test. The research showed the use of the Duolingo Application successfully could improve students’ vocabulary of present and past verbs. As observed by the researcher, the improvement was indicated by aspects of students' active and passive in answering the vocabulary questions from the teacher. The results found through the process of using the Duolingo Application, students were likely to raise their hands to answer the translation of twenty list questions of vocabulary from the teacher. Then, the students have a better understanding of the vocabulary’s meaning by a small number of students did not raise their hands, raise their hands, and answered correctly in a quick time. Therefore, this research was successful in improving the students’ vocabulary of the verb by using Duolingo Application.
Development of Copyright in Global Perspective: Challenges and Opportunities for Pakistan

This study aims to investigate the development of copyright laws with the lens of a global perspective; thus shedding light on the challenges and opportunities for Pakistan. This study employed quantitative methodology and analyzed the data of the Copyright Ordinance of 1962 with a content analysis approach.Different concepts were developed and a brief discussion was made to examine the efficacy and their implications in the light of protection of the copyright laws, procedures to claim damages, compensation and power of attorney to use the original work of the authors or creators. The study concluded the world has moved forward at the same pace they developed in every walk of life but Pakistan was far behind in implementing the copyright laws in its original spirit. The study recommended Pakistan enhance its trust and widen its credibility by ensuring strict implementation of copyright laws. The study may open new avenues for future researchers and examine various angles of copyright while keeping in view the challenges, issues and opportunities in third-world countries.
An Explainable Model-Agnostic Algorithm for CNN-Based Biometrics Verification

This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting, the classes to be recognized have not been seen during training. In addition, instead of using the softmax output, face descriptors are usually obtained from a layer before the classification layer. The model is adapted to achieve explainability via cosine similarity between feature vectors of perturbated versions of the input image. The method is showcased for face biometrics with two CNN models based on MobileNetv2 and ResNet50.
A Trend Analysis of Test Smells in Python Test Code Over Commit History

Software testing is an essential activity for developing and maintaining high-quality software. Unit testing with test code (test cases) is a fundamental testing activity, and developers can test their production code whenever they create or modify the code. However, such quality assurance relies on the correctness of the test code. If a test code had a flaw, it would mislead the developers about the hidden faults and prevent early detection of the faults. This paper focuses on "test smells," which may cause test code flaws in Python programs, and analyzes their changing trends over commit history (code changes) toward better Python test code management. Through an empirical data analysis of 100 open-source projects, the paper reports the following findings: (1) a few kinds of test smells constitute the majority of smells detected in the studied projects, and (2) most kinds of smells tend to increase over commits, i.e., many test smells are likely to have remained in test code as technical debt.
Analysis of Cybersecurity Measures for Detection, Prevention, and Misbehaviour of Social Systems

The rapid proliferation of digital financial products has given rise to profound challenges in safeguarding consumer interests, notably concerning fraud and scams. These issues, which carry the potential to erode trust in digital services, are especially pronounced in developing nations. Consequently, preventing victimization has emerged as a paramount policy imperative. Notably, recent revelations have illustrated the misuse of Large Language Models (LLMs) for fraudulent activities, impersonation, and the generation of malicious software. Concurrently, other researchers have delved into the broader issue of AI alignment. This underscores the imperative for both developers and practitioners to remain cognizant of the security-related challenges posed by such models. The detection of online sexual predatory behaviors and the combatting of abusive language on social media platforms have assumed paramount importance in contemporary research. This concern is driven by mounting apprehensions surrounding online safety, particularly for vulnerable segments of the population, including children and adolescents. Researchers have been diligently exploring diverse techniques and approaches to devise effective detection systems capable of identifying and mitigating these inherent risks. This paper conducts a comprehensive analysis of prevalent challenges spanning various global sectors and assesses their far-reaching consequences. Additionally, it undertakes an evaluation of the associated hurdles while identifying optimal strategies to address these multifaceted challenges.
Hands Tech: An AR-Based Framework for Real-Time Monitoring and its Application in 4.0 Industry

Sensor data is essential for process production, but direct interaction with equipment can lead to health risks, loss of time, and disruption. This article describes the creation of a framework based on Augmented Reality technology that allows users to view the real world through the camera while also seeing digital visual elements superimposed on the scene. An application using coupled tanks was developed, with visual resources that include the real-time display of the values corresponding to the volume of each tank. There is also a dashboard that presents information relevant to the plant control process, such as sensor and actuator outputs. The application efficiently creates a communication bridge between mobile and industrial plants, allowing operators to access equipment's relevant information quickly and safely without interrupting production.
The Ability to Work with Scientific Documents as an Opportunity for Academic Growth

The article presents the project “The ability to work with scientific documents as an opportunity for academic growth", which took place at the University of Economics – Varna. The series of trainings conducted within the framework of the initiative and the results achieved are described. Readers' attitudes and needs were analyzed based on a survey conducted after the seminars. Guidelines for further development and encouragement of knowledge for working with scientific information are outlined.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI POINT OF SALE BERBASIS WEB PADA CV. YANTI

CV. Yanti is a company that provides daily necessities (groceries). Every sale of this company can record many sales transactions, from the number of these transactions the company does not have an information system that can manage data transaction which makes it difficult for the company to manage each sales transaction. This research is intended to facilitate the company in managing existing sales transactions in the CV. Yanti. The supporting theory used in completing the system design analysis on CV. Yanti, namely Information Systems, Waterfall, Point of Sales, and others. Based on the above problems, researchers analyzed the system, and collected data through literature studies, observations and direct interviews on the CV. Yanti, from these results, researchers can find the main problem and can propose problem solution. The research used is the design of information systems consisting of system architecture design, use case diagrams, activity diagrams, class diagrams, menu structure design, form design and user manuals, and for manufacturing systems using JavaScript and PostgreSQL databases. The results showed that the design of this Point of Sale information system can provide convenience in managing data related to sales transactions on the CV. Yanti and management of sales transaction data becomes easier and more efficient.Keywords: Point of Sales, Analysis and Design of Information System, JavaScript and PostgreSQL
Economic Empowerment through the Production of Fish Pellet, Organic Fertilizers from Maggots, and Catfish Nuggets from Budikdamber in Kronggahan Hamlet, Gamping, Sleman

Sleman Regency produces the biggest waste pile, which is recorded at 701.95 tons/day, the highest among other Regencies/Cities. (www.sipsn.menlhk.go.id). National data shows that not all waste generated can be managed. Unmanaged waste is 12.4% per year. This unmanaged waste has the potential to cause significant environmental and health problems. Efforts to minimize organic waste have been carried out by Kronggahan residents, namely by utilizing waste bioconversion technology with maggot black soldier fly (BSF). Besides being able to reduce the generation of waste produced by BSF maggot, it can be used as an alternative fish feed that has economic value and high nutritional content for fish and livestock. With this technology using BSF, it can reduce organic waste by ± 70%. In addition to the use of organic waste, Padukuhan Kronggahan has also developed several activities such as the manufacture of processed ready-to-eat food products, the development of aquaponics and budikdamber (budidaya ikan dalam ember) where the crops can be an added value for food security in Padukuhan Kronggahan. The purpose of this program is to increase the carrying capacity of the community and stakeholders related to the development of Rumah Maggot to become a vehicle for education and community economic empowerment with an environmental perspective, increase community knowledge regarding community empowerment programs in environment-based waste management, foster knowledge and public awareness in maintaining and improving health to prevent an increase in the prevalence of PTM which will affect the quality of life of the community and the economic impact due to long-term treatment, increase knowledge and skills related to Making Maggot into Maggot Pellets, an alternative to processing fish and vegetables produced from budikdamber and packaging pellet products, cassava fertilizers and processed healthy food for increase the economic value and marketing methods of finished products The method of implementing this program is socialization, coordination and consolidation, preparation of educational media, and training packages that are integrated with other productive economic activities. With these efforts, it is hoped that the program will be able to socialize more broadly, be able to be duplicated or become a pilot for other regions or agencies in terms of community empowerment-based organic waste management and have economic value.
Sustainable electronic document security: a comprehensive framework integrating encryption, digital signature and watermarking algorithms

&#x0D; &#x0D; &#x0D; &#x0D; Protecting electronic documents, especially those containing sensitive data, is a major challenge in an open web. The data security industry has long struggled to manage the security of e-books, data shows that information security issues can cause significant economic losses after three years Although cryptographic methods have been proposed as a solution in of these challenges Focusing on speed and efficiency The shortcomings of traditional encryption methods have been thoroughly examined Although a number of network management techniques assure retention privacy and integrity though, when it comes to encryption. Digital signature algorithms, although effective in detecting unauthorized changes and limiting the scope for copyright protection, do not ensure the confidentiality of shared electronic documents When addressing research gaps addressing this issue, the paper proposes a security framework for electronic documents that combines three important security mechanisms: encryption, digital signatures, and watermark algorithms Dhishu By matching their strengths, constraints are overcome. The combination of encryption and digital signatures is explored as a promising approach to protecting electronic documents, ensuring authenticity and confidentiality Importantly, the need to explore security mechanisms such as digital is highlighted emphasis on handwriting, encryption, and watermarking systems in depth.&#x0D; &#x0D; &#x0D; &#x0D;
A weighted average ensemble learning based on the cuckoo search algorithm for fraud transactions detection

The identification and prevention of unauthorized credit card transactions are the goals of credit card fraud transaction detection. Cardholders and financial institutions can lose significant amounts of money with fraudulent transactions, which is why detection and prevention should be done as soon as possible. This paper suggests a new weighted average ensemble learning approach based on 20 decision trees (DT). The Cuckoo Search algorithm was used for searching for optimal weights. In this case, the ensemble learning model can be described as the weighted sum of predicted model probabilities, where each model’s probability is multiplied by its respective weight. For evaluation, many ensemble learning-based decision trees are used, including a decision tree, Random Forest (RF), Adaboost (AD), Gradient Boosting Tree Classifier (GB), and Extra tress (ET). To assess the effectiveness of the proposed solution, a randomly undersampled dataset of credit card transactions was employed. Several experiments are conducted based on a real dataset, which demonstrates a better prediction of the proposed ensemble learning approach in terms of accuracy (ACC), precision (PRE), recall score (REC), and F-measure (F).
Development of Ai-Based Posture Monitoring System to Assist Yoga Training

In the modern era of remote fitness and wellness, maintaining correct posture during exercises and yoga practices is vital for maximizing the benefits and preventing injuries. However, monitoring one's posture can be challenging, often leading to improper form and reduced effectiveness. To address this issue, this project presents an innovative real-time posture monitoring system using computer vision and pose estimation techniques. The system employs the Mediapipe library to detect and analyze the user's body posture through a camera feed. It begins by capturing a reference posture from an image and calculating its landmark coordinates. These coordinates serve as the baseline for correct posture. As the user performs exercises in front of the camera, their live posture is continuously evaluated against the reference posture using Euclidean distance as a similarity metric. A crucial contribution of this project lies in its ability to provide immediate feedback to the user regarding their posture. The system visualizes landmarks on the user's body, highlighting correct and incorrect positions in distinct colors. If the similarity score between the live posture and the reference posture surpasses a certain threshold, the landmarks appear in green, indicating good posture. Conversely, red landmarks indicate discrepancies, prompting the user to adjust their form. Experimental results showcase the effectiveness of the proposed system in real-time posture assessment. Users can actively observe their posture and make necessary adjustments, fostering improved exercise outcomes and reduced risk of injuries. By offering a convenient and immediate feedback mechanism, this project bridges the gap between remote fitness practices and professional guidance, ultimately promoting safer and more effective workout routines
Improvement of Classification Accuracy in Machine Learning Algorithm by Hyper-Parameter Optimization

The manual optimization of hyperparameters is a straightforward and well-known approach, but it is not scalable, particularly when there are several settings and options. In nearly every area of daily life, machine learning offers more logical guidance than humans can. It has already been noted in the literature that correct Hyper-Parameter optimization has a significant impact on a machine learning algorithm’s performance. Manual search is one method for performing Hyper-Parameter optimization, however it takes a lot of time. Some of the common techniques used for hyperparameter optimization include grid search, random search, and optimization procedure. The main model training and structural hyper-parameters are introduced in the first part, along with their significance and approaches for defining the value range. The research then concentrates on the main optimization techniques and their applicability, examining their effectiveness and accuracy, particularly for the random forest ensemble algorithm. In this study, we present a novel approach for enhancing the Random Forest algorithm’s hyperparameters using the Parkinson’s Disease Data Set. Accuracy, precision, recall and F1 score were taken into account while comparing the performances of each of these strategies.
Multi-Stroke Handwriting Character Recognition and Enhancing Proficiency with CNN: A Touch-Based Writing Approach

Handwriting is an essential skill, and difficulties in this area can significantly impact learning and communication. In recent years, Convolutional Neural Networks (CNNs) have evolved as a powerful tool for image recognition and analysis, making them well-suited for handwriting recognition and improvement tasks. The goal of this study is to create and build a touch-based therapy application to improve hand dexterity for handwriting preparation using the CNN model. The system leveraged a large dataset of handwriting samples and employed CNN to learn and recognize patterns in handwritten characters. The CNN model was trained using supervised learning techniques, optimizing its ability to accurately classify and analyze different handwriting styles. To assess the effectiveness of the proposed system, a sequence of tests was conducted. The trained CNN model showed promising accuracy in recognizing and analyzing multi-user free-style multi-stroke handwriting characters and providing valuable feedback to the users. Participants who utilized the system reported improvements in letter formation, alignment, and overall handwriting legibility.