Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Federated Learning for Power Transformer Fault Diagnosis With Unbalanced Data

This article is concerned with developing a privacy-preserving distributed-learning-based fault diagnosis approach for power transformers. Due to the constraints of data privacy, it is not possible to have enough labeled samples for training. Recently, the emergence of federated learning (FL) has provided a secure and distributed learning framework. However, the unbalanced data from multiple power stations may reduce the overall performance of FL while an untrusted central server can threaten the data privacy and security of clients. To address such challenges, a privacy-preserving FL scheme is developed for transformer fault diagnosis, where a multistep data-sharing strategy and an adaptive differential privacy technology are proposed. Specifically, amounts of shared data and noise perturbation will be designed according to the quantity of local data by the central server. The experimental results on the dataset generated according to IEC publication 60599 show that the proposed method has high diagnostic accuracy across various categories of transformer faults and even on training datasets with extremely unbalanced data quantity where the average accuracy is as high as 95.28%.
Does controller sound contain valuable information for video game scene analysis? Case study by character identification of Super Smash Bros. Ultimate

Understanding of gameplay can enhance the experience and entertainment of video game. In this study, we propose to utilize the sound generated by a controller for analyzing the information of gameplay. Controller sound is a user-friendly feature related to gameplay because it can be very easily recorded. As a first step of the research, we performed identification of characters of Super Smash Bros. Ultimate only from controller sound as an example task for examining whether controller sound contains valuable information. The results showed that our model achieved 79% accuracy for identification of five characters only using the controller sound.
PENERAPAN METODE SMART READING UNTUK MENINGKATKAN KEMAMPUAN MEMBACA DI KELAS II SD NEGERI 13 SUNGAI KAWAT TAHUN PELAJARAN 2021/2022

This research was motivated by the researcher at SD Negeri 13 Sungai Kawat regarding students in grade II who were identified as having a deficiency in reading ability. Students in class II find it difficult to recognize letters and have difficulty arranging words in reading. So far, in learning to read at SD Negeri 13 Sungai Kawat, they still use an ineffective method, namely the common (ordinary) reading method. It makes students' reading ability low. The purpose of the study was to describe the improvement of students' reading ability using the smart reading method for class II SD Negeri 13 Sungai Kawat. This study used a qualitative descriptive approach and a form of classroom action research. The implementation process consists of 4 stages: planning, implementing actions, observing, and reflecting. The subjects in this study were second-grade students of SD Negeri 13 Sungai Kawat with six students, consisting of 2 male students and four female students. Data collection techniques used direct observation techniques, performance test techniques, and documentation techniques. The observations in cycle I students obtained an average score of 68.33% with sufficient criteria. Cycle II obtained an average score of 90% with very good criteria. The observations of teacher activities obtained an average score of 88.33 with good criteria, and cycle II obtained an average score of 98.33 with very good criteria. It shows that the application of the smart reading method is going very well. The pre-cycle reading ability test showed students obtained a class average of 52.66 with fewer criteria, the first cycle obtained a grade average of 76.5 with good criteria, then continued in the second cycle obtained a class average of 90.33 with very good criteria. The increase in reading ability increased by 13.83. It shows that the ability to read using the smart reading method is successful and increasing. It can be concluded that the application of the smart reading method can improve reading skills in grade II SD Negeri 13 Sungai Kawat for the 2021/2022 academic year.
Predicting Option Prices and Volatility with High Frequency Data Using Neural Network

Neural network utilizes the huge amount of data for analysis and prediction. This paper predicts option prices and volatility using neutral network based on high frequency intraday data. We focus on short term prediction because option prices and volatility in fact are very volatile and almost impossible to predict. We find that neural network is able to predict option prices and volatility by using predictors constructed from the prices of option and its underlining index, especially in short term which is what practitioners care about more in practice.
Assessing Eye Tracking for Continuous Central Field Loss Monitoring

Eye tracking is increasingly becoming prevalent for health-related interactive systems. Eye tracking can automatically reveal the presence of Central Field Loss (CFL), a dysfunctional visual behavior requiring time-intensive medical assessments. Since CFL typically results in poor fixation stability and more frequent saccades, this work investigates the use of machine learning to estimate the likelihood of CFL based on eye-movement data. We compared random forests, support vector machines, and long-short-term memory (LSTM) neural networks for their ability to discriminate between the presence or absence of an experimentally-induced CFL. We found that the estimation accuracy increases with larger samples of eye-tracking data. However, the computational costs outweigh any increase in accuracy after classifying window sizes of 1600 msec. Here, traditional machine learning approaches outperform the LSTM neural network. We discuss implications for continuous end-user CFL monitoring and processing power to provide an outlook for gaze-based wearable health devices in human-computer interaction.
The Effect of Digital Technological Innovations on Entrepreneurship and Economic Growth of Female Heads of Households in Ilam City

Background &amp; Purpose: The current research aims to provide a model for improving entrepreneurial business based on digital technological innovations for economic growth and empowerment of female heads of households in Ilam city Methodology: This research is applied in terms of purpose and survey in terms of data collection. The statistical population included 309 female heads of entrepreneur households in Ilam city. The external and content validity of the questionnaire was confirmed by specialists, and two criteria, Cronbach's alpha coefficient and composite reliability coefficient, were used to determine the reliability of the questionnaire. Cronbach's alpha coefficients of all variables in this study were higher than the minimum value of 0.7. The collected data were analyzed in the form of descriptive and inferential statistics using SPSS24 software. In the inferential statistics section, among the correlation methods, structural equation modeling (SEM) method with partial least squares approach was used for hypothesis testing and model fit using SmartPLS 2.0 software for this research. Findings: The results of testing the research hypotheses using significant coefficients showed that all the research hypotheses are confirmed. The first hypothesis examines the impact of entrepreneurship on economic growth and this impact has been confirmed. The second hypothesis also examines the effect of using digital technology innovations on entrepreneurship and this issue has also been confirmed. In the third hypothesis, it was stated that the use of digital technological innovations with the mediating role of entrepreneurship has an effect on economic growth. Conclusion: The findings of the research showed that product and service innovations that result from the use of digital technology, regardless of whether they provide new features of the product or service, tend to be fundamental; Therefore, it provides a new path for women heads of household to increase their product and service innovation by using digital technological innovations for new customers.
Anomaly Detection in Machining Centers Based on Graph Diffusion-Hierarchical Neighbor Aggregation Networks

Inlight of the extensive utilization of automated machining centers, the operation and maintenance level and efficiency of machining centers require further enhancement. In our work, an anomaly detection model is proposed to detect the operation execution process by using the anomaly detection method of graph diffusion and graph neighbor hierarchical aggregation. In this paper, six machining center equipment states are defined and modeled, the monitoring sensors are referred to as nodes, and the connections between the sensors are represented as edges. First, we employed the graph diffusion model to enhance data quality within the sensor network model. Then, the node features were extracted using the hierarchical aggregation of neighboring nodes. Finally, after attentional connectivity, the ability of the model to learn global information was further improved. The performance of our model has been rigorously assessed using multiple experimental datasets and benchmarked against various anomaly detection techniques. The empirical findings unequivocally demonstrate the superior performance of our model, in terms of accuracy (96%) and F1 score (94), when compared to baseline models (MLP, GCN, GAT, GraphSAGE, GraphSAINT, GDC, and DiffusAL). The demonstrated effectiveness of the model underscores its versatility for a myriad of application prospects within the realm of manufacturing maintenance management.
PENGGUNAAN METODE PEMBELAJARAN DISCOVERY SEBAGAI UPAYA MENINGKATKAN MOTIVASI DAN PRESTASI BELAJAR SISWA PADA MATA PELAJARAN EKONOMI DI KELAS VII SMP YPK FAKFAK TAHUN AJARAN 2022/2023

ABSTRAK&#x0D; Penelitian ini bertujuan untuk mengetahui penggunaan metode pembelajaran discovery sebagai upaya meningkatkan motivasi dan prestasi belajar siswa pada mata pelajaran Ekonomi di kelas VII SMP YPK Fakfak. Adapun manfaat dari penelitian ini bagi penulis, para guru dan sekolah sebagai masukan yang bersifat ilmiah dalam upaya meningkatkan motivasi dan prestasi belajar siswa pada mata pelajaran Ekonomi di kelas VII SMP YPK Fakfak.&#x0D; Metode yang digunakan dalam penelitian ini adalah Penelitian Tindakan Kelas yang didalam tiap siklusnya terdapat Rencana, Pelaksanaan, Observasi dan Refleksi. Kegiatan penelitian tindakan kelas ini dilaksanakan di Kelas VII SMP YPK Fakfak dengan jumlah siswa 26 orang. Seluruh siswa yang berjumlah 26 orang itu semuanya telah mengikuti siklus I, II, dan III dalam penelitian tindakan kelas.&#x0D; Dari penelitian tersebut dapat disimpulkan bahwa melalui metode discovery, proses belajar mengajar lebih menarik perhatian siswa sehingga merasa senang dan lebih menjadi aktif, meningkatkan daya pikir serta mudah dan mengingatnya dan Prestasi belajar siswa mengalami peningkatan pada mata pelajaran Ekonomi khususnya Kelas VII&#x0D; Kata Kunci : Metode Pembelajaran, Discovery, Motivasi, Prestasi Belajar, Siswa&#x0D; &#x0D; &#x0D; ABSTRACT&#x0D; This research aims to determine the use of the Discovery learning method as an effort to increase student motivation and learning achievement in Economics subjects in class VII YPK Fakfak Middle School. That is the benefit of this research for writers, teachers and schools as scientific input in an effort to increase student motivation and learning achievement in Economics subjects in class VII YPK Fakfak Middle School.&#x0D; The method used in this research is Classroom Action Research, each cycle of which includes Planning, Implementation, Observation and Reflection. This classroom action research activity was carried out in Class VII of YPK Fakfak Middle School, totaling 26 students. All 26 students have participated in cycles I, II and III in classroom action research.&#x0D; From this research it can be concluded that through the discovery method, the teaching and learning process attracts students' attention more so that they feel happy and more active, increase their thinking power and are easy to remember and students' learning achievement in Economics subjects increases. subjects, especially Class VII.&#x0D; Keywords: Learning Methods, Discovery, Motivation, Learning Achievement, Students&#x0D;
INNOVATIVE PEDAGOGICAL APPROACHES TO THE IMPLEMENTATION OF PROJECT ACTIVITIES OF 10-11 GRADE STUDENTS IN TECHNOLOGY LESSONS

The article is devoted to an in-depth study of innovative pedagogical approaches related to the implementation of project activities of 10-11 grade students in technology lessons. The main theoretical aspects of the project approach in education and its essential advantages are carefully studied, emphasizing this with scientifically based research. In particular, the results of current pedagogical research and scientific sources were used to confirm that the project activity contributes to the strengthening of students' creative abilities and their active learning. The article discusses innovative pedagogical approaches that can be effectively implemented to organize project classes. A comprehensive analysis of modern approaches and digital technologies was carried out, using the latest scientific achievements to ensure interactivity and efficiency of project activities. The conclusions of the article make it possible to emphasize the scientific approach to the presented materials. Attention is also focused on the development of critical thinking and problem-based learning, using the implementation of problem-based tasks and interdisciplinary approaches in project activities. It is noted that this approach to projects contributes to the development of students' analytical and critical skills. In particular, the use of project activities in the technology lessons of 10-11 grade students has significant advantages, helping to increase their motivation and interest in learning. By working on specific projects with real application, students show creativity and develop their abilities, which affects self-esteem and self-confidence. In addition, project activities contribute to the development of students' critical thinking and analytical skills, teaching them to justify their decisions and find non-standard approaches to solving problems. It was determined that students show more interest in acquiring knowledge, because project activities contribute to active and practical learning, allowing them to see the connection between theory and practice. Keywords: project activity, innovative pedagogical approaches, students of 10-11 grades, technology lessons, educational process.
Development of Cross-Cutting Skills in Secondary Education Students Through the Use of Textbooks «Exploring Nature»

The article addresses the role of textbooks in shaping cross-cutting skills that are part of all key competencies outlined in the State Standard for Basic Secondary Education. An analysis of recent publications reveals a predominant focus on a competency-based approach, reflecting conceptual principles of textbook creation for the New Ukrainian School. However, these publications insufficiently illuminate the essence and formation of cross-cutting skills through school textbooks.&#x0D; The potential of author-created textbooks for the integrated course 'Exploring Nature' for grades 5-6 in fostering cross-cutting skills outlined in the State Standard for Basic Secondary Education is explored. Examples of tasks and text constructions are demonstrated as tools for developing each cross-cutting skill.&#x0D; It is emphasized that the formation of cross-cutting skills is both a process and a result. As a process, it requires active cognitive engagement from each learner, with textbooks serving as motivators and organizers. As a result, cross-cutting skills ensure the acquisition of key competencies and the realization of the personal potential of students, contributing to the development of the teacher's creative individuality.&#x0D; The article underscores the importance of textbook categorization in the development of cross-cutting skills. It highlights that textbooks serve as guides in shaping cross-cutting skills and do not interfere with teachers' methodological preferences.&#x0D; The methodology described in the article for forming cross-cutting skills can be applied by textbook authors from various school subjects, researchers in textbook creation, and educators aiming to implement the conceptual foundations of the New Ukrainian School. The article outlines perspectives for further research on the development of cross-cutting skills.
Problems in Writing Passive Voice: A Case Study in the EFL Classes

This study addresses EFL students' challenges at a state university in Mataram, Indonesia, in constructing passive voices in the present and past tense. This study was focused on verb tense changes, subject-verb agreement, pronoun usage, and appropriate use of "by" in passive voice constructions. It also aims to explore the causes of these issues and offers insights into potential solutions. With a descriptive qualitative approach, the data were collected using a set of written grammatical tests of passive voices and open interviews. The adopted validated test was completed by thirty students as the participants of this study. The data were analyzed descriptively using an in-depth reading analysis method on the test results and participant responses. The results reveal a need for fundamental grammatical knowledge, limited exposure to passive voice, inadequate practice opportunities, and unclear explanations as contributing factors to the challenges. Language transfer from native languages and a lack of feedback compound the problem. To address these issues, the study recommends basic tense understanding, increased practice, targeted feedback, and exposure to authentic materials to enhance students' proficiency in constructing accurate passive voice sentences.
Advanced Seismic Magnitude Classification Through Convolutional and Reinforcement Learning Techniques

Earthquake Early Warning (EEW) systems are crucial in reducing the dangers associated with earthquakes. This paper delves into the realm of EEWs, focusing on rapidly determining earthquake magnitudes (EMs). Traditional methods for swift magnitude categorization often grapple with challenges such as data disparity and cumbersome processes. Our research introduces an innovative EEW model, employing a 7-second seismic waveform record from three different components provided by the China Earthquake Network Center (CENC). This empirical, quantitative study pioneers a method combining dilated convolutional techniques with a novel mutual learning-based artificial bee colony (ML-ABC) algorithm and reinforcement learning (RL) for EM classification. The proposed model utilizes an ensemble of convolutional neural networks (CNNs) to simultaneously extract feature vectors from input images, which are then amalgamated for classification. To address the imbalances in the dataset, we implement an RL-based algorithm, conceptualizing the training process as a series of decisions with individual samples representing distinct states. Within this framework, the network operates as an agent, receiving rewards or penalties based on its precision in distinguishing between the minority and majority classes. A key innovation in our approach is the initial weight pre-training using the ML-ABC method. This technique dynamically optimizes the "food source" for candidates, integrating mutual learning elements related to the initial weights. Extensive experiments were carried out on the selected dataset to ascertain the most effective parameter values, including the reward function. The findings demonstrate the superiority of our proposed model over other evaluated methods, highlighting its potential as a robust tool for EM classification in seismology. This research provides valuable insights for both seismologists and developers of EEW systems, offering a novel, efficient approach to earthquake magnitude determination.
Separability-based Quadratic Feature Transformation to Improve Classification Performance

Feature transformation is an essential part of data preprocessing to improve the predictive performance of machine learning (ML) algorithms. Box-Cox transformation with the goal of separability is proven to align with the performance improvement of ML algorithms. However, the features mapped using Box-Cox transformation preserve the order of the data, so it is ineffective when used to improve the separability of multimodal distributed features. This research aims to build a feature transformation method using quadratic functions to improve class separability that can adaptively change the order of the data when necessary. Fisher score (Fs) measures the separability level by maximizing the Fisher's Criteria of the quadratic function. In addition to increasing the Fs value of each feature, this method can also make the feature more informative, as evidenced by the increasing value of information gain, information gain ratio, Gini decrease, ANOVA, Chi-Square, reliefF, and FCBF. The increase in Fs is particularly significant for bimodally distributed features. Experiments were conducted on 11 public datasets with two statistical-based machine learning algorithms representing linear and nonlinear ML algorithms to validate the success of this method, namely LDA and QDA. The experimental results show an improvement in accuracy in almost all datasets and ML algorithms, where the highest accuracy improvement is 0.268 for LDA and 0.188 for QDA.
An Edge Computing-based Handgun and Knife Detection Method in IoT Video Surveillance Systems

Real-time handgun and knife detection on edge devices within the Internet of Things (IoT) video surveillance systems hold paramount importance in ensuring public safety and security. Numerous methods have been explored for handgun and knife detection in video-based surveillance systems, with deep learning-based approaches demonstrating superior accuracy compared to other methods. However, the current research challenge lies in achieving high accuracy rates while managing the computational demands to meet real-time requirements. This paper proposes a solution by introducing a single-stage convolutional neural network (CNN) model tailored to address this challenge. The proposed method is developed using a custom dataset, encompassing model generation, training, validation, and testing phases. Extensive experiments and performance evaluations substantiate the efficacy of the proposed approach, which achieves remarkable accuracy results, thus showcasing its potential for enhancing real-time handgun and knife and knife detection capabilities in IoT-based video surveillance systems.
Comparative Study of CatBoost, XGBoost, and LightGBM for Enhanced URL Phishing Detection: A Performance Assessment

Phishing via URLs involves cyber attackers crafting deceptive websites or emails, mimicking genuine entities like banks or social media outlets. The objective is to dupe users into divulging personal data, such as their passwords or card numbers. This study assesses the potential of machine learning in identifying phishing domains by constructing and contrasting three distinct models. These models, crafted using CatBoost, XGBoost, and LightGBM techniques, are then juxtaposed against prior solutions documented in academic literature. We employed the UCI phishing domains dataset, sourced from URLs, as a performance benchmark for our models. Findings indicate that the model built on CatBoost outperforms its counterparts and also surpasses earlier documented methods.