Reviewerly/exHarmony huggingface dataset:
Upaya meningkatkan hasil belajar IPS melalui strategi peer lessons pada Siswa Kelas IV di SDN 2 Kalangsari

Based on initial observations showing low student learning outcomes, classroom action research was conducted on class IV students at SDN 2 Kalangsari. The average score on student test results in social studies learning is still below the minimum completeness criteria (KKM) set by the school, namely 75. In the 2022–2023 school year, the aim of this research is to improve social studies learning. learning outcomes of class IV students at SD Negeri 2 Kalangsari, Tasikmalaya City by implementing the Peer Lessons learning strategy. Classroom action research is this type of research. The research subjects were 12 male and 6 female students in class IV of SD Negeri 2 Kalangsari, Tasikmalaya City in the even semester of the 2022–2023 academic year. This research was carried out in 2 cycles, with 2 meetings in each cycle. Tests and observations are the methods used in data collection. As many as 75% of students who took part in the learning process met the Minimum Completeness Criteria (KKM), namely 75. This increase in social studies learning outcomes is a sign of research success. Research findings show that implementing peer lessons learning strategies improves social studies learning outcomes. After the first cycle of action, 7 students (38.89%) did not complete, while 11 students (61.11%) achieved completion. After cycle II activities, there were 3 students (16.67%) and 15 students (83.33%) who had not completed their studies. Based on the research findings, it can be said that the researcher has a clear understanding of the classroom environment. Therefore, researchers and class teachers work together to develop action plans that can improve social studies learning outcomes for class IV students.
ANALISIS KANDUNGAN LOGAM PADA LIPSTIK YANG BEREDAR DI KOTA BATUSANGKAR

Research has been conducted to analyze the content of heavy metals (Pb, Cd, Mn) in lipstick circulating in Batusangkar Market. Samples are lipsticks that are registered and not registered with BPOM with various brands, colors and prices sold in the batusangkar market and totaling 10 samples. The sample was destroyed using a mixture of nitric acid and hydrogen peroxide. Furthermore, the content of Pb, Cd and Mn was measured using Atomic Absorption Spectrophotometry (AAS). Based on the results of the analysis, the Pb content in the sample ranged from -6.875 mg/kg to 2.0305 mg/kg, Cd content 0.18 mg/kg to 0.52 mg/kmg/kg, Mn content -4.60 mg/kg up to 149.59 mg/kg. Lead was found in 4 out of 10 lipstick samples, heavy metal Cd was found in all lipstick samples and manganese was found in 8 out of 10 lipstick samples. Based on these results, it can be concluded that the heavy metal content in the electrical sample is below the specified limit so that it meets the requirements of the Regulation of the Head of the POM No. 17 of 2014 concerning requirements for microbial and heavy metal contamination in cosmetics.
Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

In the digital era, online social networks (OSNs) have revolutionized communication, creating spaces for vibrant public discourse. However, these platforms also harbor offensive language that can proliferates hate speech, cyberbullying, and discrimination, significantly undermining the quality of online interactions and posing severe social implications. This research paper introduces a sophisticated approach to offensive language detection on OSNs, employing a novel Hybrid Deep Learning Architecture (HDLA). The urgency of addressing offensive content is juxtaposed with the challenges inherent in accurately identifying nuanced communications, thus necessitating an advanced model that transcends the limitations of traditional natural language processing techniques. The proposed HDLA model synergistically integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, capitalizing on the strengths of both methodologies. While the CNN component excels in the hierarchical extraction of spatial features within text data, identifying offensive patterns often concealed in the structural nuances, the LSTM network, adept in processing sequential data, captures the contextual dependencies in user posts over time. This duality ensures a comprehensive analysis of complex linguistic constructs, enhancing the detection accuracy for both overt and covert offensive content. Our research meticulously evaluates the HDLA model using extensive, multi-source datasets reflective of diverse OSN environments, establishing benchmarks against prevailing deep learning models. Results indicate a substantial improvement in precision, recall, and F1-score, demonstrating the model's efficacy in identifying offensive language amidst varying degrees of subtlety and complexity. Furthermore, the model maintains high interpretability, providing insights into the intricate mechanisms of offensive content propagation. Our findings underscore the potential of HDLA in fostering healthier online communities by efficiently curating digital content, thereby upholding the integrity of digital communication spaces.
Bitcoin Optimized Signal Allocation Strategies using Decomposition

Bitcoin is the first and most famous cryptocurrency. It is a virtual currency that is operated in a decentralized form using cryptographic strategies called blockchains. Although it has experienced significant market acceptance by traders and investors in recent years, it also suffers from volatility and riskiness. Technical analysis is one of the most powerful tools used for trading signals’ allocation using some algorithmic strategies called technical indicators. In this research, a newly proposed multi-objectives decomposition-based particle swarm optimization algorithm is used to find the best parameter values for some technical indicators, which in turn generates the best trading signals for Bitcoin trading. In this context, three conflicting objectives have been used, i.e., the return on investment, the Sortino-ratio, and the number of trades. The proposed algorithm is compared to the original MOEA/D algorithm as well as the indicators using their original parameters. Results showed the superiority of the proposed algorithm during the training and testing periods over the other benchmarks.
A New Method for Revealing Traffic Patterns in Video Surveillance using a Topic Model

Research on video surveillance systems, for instance, in intelligent transportation systems, has advanced due to the growing requirement for monitoring, control, and intelligent management. One of the next issues is extracting patterns and automatically classifying them, given the volume of data produced by these systems. In this study, a theme approach was utilized to translate visual patterns into visual words in order to reveal and extract traffic patterns at crossings. The supplied video is first cut up into segments. The optical flux technique is then used to determine the clips' optical flux characteristics, which are based on a lot of local motion vector data, and translate them into visual words. The thin-group thematic coding method is then used to teach traffic patterns to the proposed system using a non-probable thematic model. By responding to a behavioral query like "Where is a vehicle going?" these patterns convey observable motion that can be utilized to characterize a scene. The results of applying the suggested method to the QM_UL video database demonstrated that the suggested method can accurately identify and depict significant traffic patterns such as left turns, right turns, and intersection crossings.
Deep Speech Recognition System Based on AutoEncoder-GAN for Biometric Access Control

Speech recognition-based biometric access control systems are promising solutions that have resolved many is-sues related to security and convenience. Speech recognition, as a biometric modality, offers unique advantages such as user-friendliness and non-intrusiveness, etc. However, developing robust and accurate speaker identification and authentication systems pose challenges due to variations in speech patterns and environmental factors. Integrating deep learning techniques, especially AutoEncoder and Generative Adversarial Network models, has shown promising results in addressing these chal-lenges. This article presents a novel approach based on the combination of two deep learning models, namely, AE and GAN for speech recognition-based biometric access control. In the model architecture, the AutoEncoder takes the MFCC coefficients as input, and the encoder converts the latter to the latent space, whereas the decoder reconstructs the data. Then, speech features extracted from the latent space are used in the GAN generator to generate additional speech data. The discriminator network has a dual role, serving as both a feature extractor and a classifier. The first extracts relevant features from generated samples, while the latter distinguishes between generated and authentic samples that come from AutoEncoder. This strategy outperforms DNN and LSTM models on VoxCeleb 2, LibriSpeech, and Aishell- 1 datasets. The models are trained to minimize Mean Squared Error (MSE) for both the generator and discriminator, aiming at achieving highly realistic datasets and a robust, interpretable model. This approach addresses challenges in feature extraction, data augmentation, realistic biometric samples generation, data variability handling, and data generalization enhancement, pro-viding therefore, a comprehensive solution.
Evolution of Enterprise Applications through Emerging Technologies

The extensive globalization of services and rapid technological advancements driven by IT have heightened the competitiveness of organizations in introducing innovative products and services. Among the noteworthy innovations is enterprise resource planning (ERP). An integral field in computer science, known as artificial intelligence (AI), is undergoing a transformative integration into various industries. Grasping the concept of artificial intelligence and its application in diverse business applications is crucial, given its broad and intricate nature. The primary focus of this paper is to delve into the realm of artificial intelligence and its utilization within enterprise resource planning. The study not only explores artificial intelligence but also delves into related concepts such as machine learning, deep learning, and neural networks in greater detail. Drawing upon existing literature, this research examines various books and online resources discussing the intersection of artificial intelligence and ERP. The findings reveal that the impact of AI is evident as businesses attain heightened levels of analytical efficiency across different ERP domains, thanks to remarkable advancements in AI, machine learning, and deep learning. Artificial intelligence is extensively employed in numerous ERP areas, with a particular emphasis on customer support, predictive analysis, operational planning, and sales projections.
Examining the Reliability of the International English Language Testing System Design

Background: Despite the evolution of the International English Language Testing System (IELTS) to accommodate the evolving needs of test-takers and institutions, concerns have been raised regarding its reliability in assessing English language proficiency. Purpose: This study aimed to investigate the reliability of the International English Language Testing System (IELTS) design as a measure of English language proficiency. Methodology: To explore this issue, the research reviewed relevant literature and employed various methods, including frequency analyses, data analytics, and machine learning algorithms based on mathematical principles. Unlike previous studies, this work analysed a dataset of 33,505 authentic band scores, allowing for a comprehensive examination of IELTS test score patterns. Findings: The findings revealed that the concept of reliability is complex, and the statistical methods used by IELTS test providers, based on classical test theory, may provide approximate rather than precise scores. Relying solely on the reputation and authority of the IELTS test is insufficient to establish its reliability, as it involves logical fallacies and arguments from authority. Research implications: It is necessary to conduct further independent research to ensure the IELTS test's reliability and address the concerns expressed by researchers and test-takers.
Exploring Nuanced Errors in Bangladeshi Tertiary EFL Students’ Writing Mechanics: A Pedagogical Implication

Background: Top of Form In order to enhance tertiary EFL students' formal writing skills, it is crucial that they develop proficiency in writing mechanics alongside their syntactic maturity. It is crucial because weaknesses in writing mechanics can significantly impact the overall quality of the content. Purpose: the present study aims to examine the nuances of the sub-categories of three major writing mechanics and thus bring up some implications for EFL teachers, syllabus designers, and material designers. Methodology: This study is qualitative in nature and collected 42 narrative essays from 42 tertiary students. The participants comprised both English and non-English majors from seven private and public universities in Bangladesh. The collected data were analyzed using Hsieh and Shannon's (2005) summative content analysis. Findings: The findings of the analysis indicate that the most error-prone mechanics for students are the correct usage of ending punctuation marks (specifically the full stop) and non-ending punctuation marks (such as commas), while the second most challenging mechanics are the correct spellings of di- and polysyllabic words. Further, in capitalization mechanics, they encounter difficulties in the appropriate use of uppercase letters. Research implications: The study concludes with pedagogical implications that tertiary EFL teachers, syllabus designers, and material designers should put more emphasis on the sub-categories of writing mechanics while designing writing tasks for tertiary EFL learners.
Development of BlockScholar as an Educational Mobile Application on Blockchain Technology

In the ever-evolving landscape of technology, blockchain emerges as a transformative force, reshaping digital transactions and decentralized systems. This revolutionary technology, initially synonymous with cryptocurrency, has transcended its roots, becoming a pivotal element in industries spanning finance, supply chain management, healthcare, and more. However, understanding blockchain's intricacies can be daunting, especially for newcomers. This project addresses the need for an accessible and engaging platform to introduce individuals to blockchain technology. Traditional educational resources like textbooks have lost their appeal in today's digital era, leaving learners seeking interactive and mobile-friendly alternatives. While online educational platforms like Udemy and Coursera offer diverse courses, they often lack the immediacy and interactivity that modern learners crave. In the mobile landscape, a noticeable gap exists in educational apps dedicated to blockchain technology. Existing options tend to focus on cryptocurrency, falling short in delivering immersive learning experiences. This project aims to bridge this gap by developing an educational mobile app centered on blockchain technology. The app's primary objective is to offer an interactive and engaging environment for users to explore blockchain's fundamentals, applications, and advantages in a user-friendly manner. By harnessing the ubiquity and convenience of mobile devices, our project seeks to empower learners to delve into the world of blockchain technology. In doing so, we aspire to foster a more informed and tech-savvy community, equipped to comprehend the far-reaching implications of blockchain on various industries. In summary, this project endeavors to unlock the potential of blockchain technology to revolutionize education by creating a mobile app that facilitates the exploration of this transformative field. Through interactive learning experiences and accessible content, we aim to empower individuals to grasp the significance of blockchain and contribute to a more knowledgeable and technologically adept society.
Speaking Disfluency of an English Major Students at One Public University in Indonesia

This study focused on investigating speaking disfluency of an English major students at one public university in Jambi, Indonesia in the academic year 2022/2023. The researcher applied qualitative method with a case study approach to carry out this study and as the way of collecting the data. Four first year students of English education study program consisting of two males and two females were recruited as participants of this research project. To identify the types of disfluencies produced by the students, the researcher gave them a video from YouTube with the title “Ten mistakes to avoid when defending your thesis” from Ranywayz Random YouTube channel and asked them to retell the content of the video using their own words. After listening, audiotaping, and transcribing their speaking, the disfluencies encountered were analyzed by using Gleason and Ratner theory. The students’ disfluencies were then classified into some types such as hesitation, repetition, false starts, and fillers. Each type of speech disfluency would be discussed in the results and discussion sections.
The Use of Technology in Teaching and Learning English through E-Learning for Students College

The purpose of the study is to give information to the teachers on teaching needs using technology, especially in the classroom. For now, we are faced with the Pandemic Covid-19 and teachers continue to be demanded to be more careful in choosing the right technology when teaching online. E-learning is one of the digital platforms for learning in the digital era. This type of research is the methods library research. This article is literature-based in which the data and information were gathered from books, internet postings, journals, social media, etc. Technology includes the use of materials, tools, techniques, and sources of power to make life easier. In education, Technology is really able to improve the teaching and learning process, especially in teaching English during the pandemic.
Deviation-support based fuzzy ensemble of multi-modal deep learning classifiers for breast cancer prognosis prediction

Breast cancer is the fifth leading cause of death in females worldwide. Early detection and treatment are crucial for improving health outcomes and preventing more serious conditions. Analyzing diverse information from multiple sources without errors, particularly with the growing burden of cancer cases, is a daunting task for humans. In this study, our main objective is to improve the accuracy of breast cancer survival prediction using a novel ensemble approach. It is novel due to the consideration of deviation (closeness between predicted classes and actual classes) and support (sparsity between predicted classes and actual classes) of the predicted class with respect to the actual class, a feature lacking in traditional ensembles. The ensemble uses fuzzy integrals on support and deviation scores from base classifiers to calculate aggregated scores while considering how confident or uncertain each classifier is. The proposed ensemble mechanism has been evaluated on a multi-modal breast cancer dataset of breast tumors collected from participants in the METABRIC trial. The proposed architecture proves its efficiency by achieving the accuracy, sensitivity, F1-score, and balanced accuracy of 82.88%, 58.64%, 62.94%, and 74.75% respectively. The obtained results are superior to the performance of individual classifiers and existing ensemble approaches.
AUGMENTED REALITY (AR) AS A COMPONENT OF STEM TECHNOLOGIES IN SCIENTIFIC RESEARCH AND EXPERIMENTAL RESEARCH WORK OF STUDENTS

Formulation of the problem. The article analyzes the problem of using modern trends in education, that is, innovative technologies, such as STEM technology and AR technology, in teaching physics. The analysis of research and publications on this problem makes it possible to state that the issue of visualizing educational information through augmented reality technology in combination with STEM technology, which is a novelty of recent years, has not been sufficiently studied. The problem of revealing the psychological aspects of the use of modern technologies to create a virtual educational space in the context of European integration is insufficiently covered in the scientific literature, which determines the relevance of the study of the problem. The aim of the study. Presentation of the results of the review and generalization of the content analysis of scientific, scientific and methodical publications on the use of AR technologies as a component of STEM education in scientific research and experimental research work in physics. To achieve the goal of the research, we used the following methods: systematic and comparative analysis of pedagogical works, methodical and specialized literature; analysis of the pedagogical experience of using AR for the STEAM approach at school. Materials and methods. To achieve the goal of the research, we used the following methods: systematic and comparative analysis of pedagogical works, methodical and specialized literature; analysis of the pedagogical experience of using AR for the STEAM approach at school. Scientific novelty. It was determined that the use of STEM technology in combination with augmented reality technology allows: to explain the use of physical phenomena and laws by demonstrating AR content and manipulating it; perform natural science, physical and technical laboratory work, demonstrate experiments on models and mock-ups; use AR technical resources; to carry out project activities.It is shown that working with AR content develops the skills of planning and organizing the process, using ready-made AR programs to conduct the experiment, makes it more visible and understandable, and encourages deeper learning of the educational material.