Reviewerly/exHarmony huggingface dataset:
The Impact of the Reading Habit on the Writing Skills of Primary Students

Rosli et al. (2018) suggested that reading is an attempt to comprehend the writer’s message, while Alnahdi and Aftab (2020) stated that it is a gateway to all other information, which may lead to understanding the world outside the text. Hence, Al-Jarf (2021) and Dadzie (2008) asserted that reading is a multifaceted cognitive process of comprehending words written in a textual form that allows readers to enhance their knowledge for personal growth and academic success. This study investigated the impact of the reading habit of the Grade Three students of a selected English Medium Private school, in Kandy, on their writing skills. Since reading lays the foundation for all the other skills in the English language, it is important to inculcate the reading habit from a young age. The research was carried out with a sample population of 30 students (from 03 classes), selected under the random sampling method. The class teachers of the three classes were also included in the study. The students were given a questionnaire that included 10 questions, under 4 criteria which would assess their level of reading habit. A separate questionnaire was given to the teachers that would assess the writing skill of the students under 04 criteria. Each questionnaire was then given scales to identify the level of reading habits as well as writing skills. The major findings of this study were that the students who have a good level of reading habits, have a good level of writing skills. The students who have a low level of reading habits, have a poor level of writing skills. It can be identified that reading is important to improve the writing skill of students. Inculcating reading habits within the student at a young age would help to create better writers for the future. Therefore, teachers should encourage students to improve their reading habits.
Student’ Perception of Using the Instagram Account @jagobahasacom in Improving Student English Vocabulary at SMEA Muhammadiyah 1 Yogyakarta

This research describes the student perception of using Instagram as their supplementary media to improve their vocabulary. The objectives are to figure out how the production of vocabulary was better by using the Instagram account @jagobahasacom based on the result of the pre-test, and post-test, and to analyze the students’ perception of the @jagobahasacom in supporting them in improving their vocabulary based on the theory of Gibson et al. (2019). A qualitative method with in-depth interviews is the method to analyze the research problem. The researcher collected the data at SMEA Muhammadiyah 1 Yogyakarta, specifically in the tenth-grade economics class. The result showed that the students use the Instagram account @jagobahasacom as the primary learning source in supporting their vocabulary ability since Instagram can be used as an enhanced learning experience. The result of the pre-test, and post-test showed the increasing student vocabulary quality was higher than before. Next, most students positively perceive the influence of the Instagram account @jagobahasacom in their learning outcomes.
Exploring Large-Scale 3d Donation Networks: A Visualization System for Immersive and Playful Philanthropic Experiences

Currently, there is a significant rise in the popularity of 3D donation programs, wherein numerous participants actively engage in mutual acts of charitable giving. Drawing upon extensive research conducted within these virtual philanthropic communities, we have developed and implemented a visualization system aimed at providing users with an immersive and playful experience while exploring and navigating large-scale 3D donation networks. Our design leverages familiar three-dimensional representations to introduce novel techniques for comprehending the interconnectedness of complex donation structures, supporting visual analysis and search capabilities, as well as automatically identifying and visualizing philanthropic clusters. Through public installations and controlled studies, our system has demonstrated its usability, ability to facilitate discovery, and potential for fostering enjoyable and socially engaging philanthropic activities.
The Effectiveness of a STEAM-Based Learning Approach in Creative Thinking among Eighth-Grade Students in Jordan

This study aimed to identify the effect of a STEAM-based learning approach in teaching the subject of educational robots on creative thinking among eighth-grade students in Jordan. A purposive sample was selected and divided into two groups. An experimental group (n=33) studied in a STEAM-based learning approach, whereas the control group (n=31) studied in a traditional method. A pre-posttest of Torrance creative thinking was administered, and the data was analyzed using statistical methods to validate the study results, such as the MANCOVA test. The results showed statistically significant differences between the experimental and control groups in creative thinking, favouring the experimental group. In conclusion, the STEAM-based learning approach showed a significant effect on the improvement of students' creative thinking skills. The study ended by some recommendations Keywords: STEAM Approach, Creative Thinking, Eighth Grade Students, Jordan.
Drift Detection in Models Applied to the Recognition of Intentions in Short Sentences Using Convolutional Neural Networks for Classification

Significant advancements have been achieved in natural language processing models for text classification with the emergence of pre-trained transformers and deep learning. Despite promising results, deploying these models in production environments still faces challenges. Classification models are continuously evolving, adapting to new data and predictions. However, changes in data distribution over time can lead to a decline in performance, indicating that the model is outdated. This article aims to analyze the lifecycle of a natural language processing model by employing multivariate statistical methods capable of detecting model drift over time. These methods can be integrated into the training and workflow management of machine learning models. Preliminary results show that the statistical method Maximum Mean Discrepancy performs better in detecting drift in models trained with data from multiple domains through high-dimensional vector spaces after being subjected to an untrained auto-encoder. The classifier model achieved an accuracy rate of 93% in predicting intentions, using accuracy as the evaluation metric.
Multi-Vendor System

Multi-vendor market place allow the vendors sell their products by setting up an exclusive store front. The merchants can oversee co-ordinations, stock, item increments, and so on at their own end. The proprietor of the commercial center can acquire a commission on the offer of each item or by the other income age models. Online e-commerce sites are becoming more. Popular these days and generally meant for online shopping. Every person now-a-days is likely to buy products online as there are more discounts, reviews, ratings of the products. Huge number of alternatives are retrieved for the single user feature input set for a user interested product leading to information overload. This large amount of information will confuse and stop the consumer at some point of purchase. Moreover if the customer is likely to buy the same product with less price, he needs to visit as many sites for the best product. This prompts draw the client’s significant time and exertion. In this paper we separate the information from some online business sites by web rejecting devices. We consider the price, rating, reviews, shipping and cash on delivery basic features from the extraction. Then collect the data from n domains to a single domain apply normalization. Then based on the attributes and features we calculate weight to each product and stored in a sorted order. Based on the user input the top-k products are displayed. Therefore the information overload is reduced and the cross comparison is displayed.
Evaluation of the impact of integrating technology into the learning process of primary education students at Tan Trao University

This article is based on the directives of the State, the Ministry of Education and Training, Tan Trao University, and the practical implementation of applying information technology in teaching in the Primary Education. Its aim is to evaluate the impact of integrating technology into the learning process of Primary Education students at Tan Trao University. By conducting a survey of 100 students majoring in the Primary Education using 11 questions, the author assesses the results, highlights advantages and challenges, and proposes solutions to enhance the integration of technology into the learning process of Primary Education students at Tan Trao University, aiming to ensure the quality of education.
Scalable Blockchain Architecture: Leveraging Hybrid Shard Generation and Data Partitioning

Blockchain technology has gained widespread recognition and adoption in various domains, but its implementation beyond crypto currencies faces a significant challenge - poor scalability. The serial execution of transactions in existing blockchain systems hampers transaction throughput and increases network latency, limiting overall system performance. In response to this limitation, this paper proposes a static analysis-driven data partitioning approach to enhance blockchain system scalability. By enabling parallel and distributed transaction execution through a simultaneous block-level transaction approach, the proposed technique substantially improves transaction throughput and reduces network latency. The study employs a hybrid shard generation algorithm within the Geth node of the blockchain network to create multiple shards or partitions. Experimental results indicate promising outcomes, with miners experiencing a remarkable speedup of 1.91x and validators achieving 1.90x, along with a substantial 35.34% reduction in network latency. These findings provide valuable insights and offer scalable solutions, empowering researchers and practitioners to address scalability concerns and promoting broader adoption of blockchain technology across various industries.
Raising Indonesian EFL Learners’ Intercultural Awareness through Asian Folktales-Mediated Tasks

Evolving learners’ intercultural awareness (ICA) has been one of the goals for 21st century learning competencies. Nevertheless, ICA has not been commonly developed through Asian folktales. To fill this void, the present action research aims to showcase a practical insight into the teaching by the adoption of folktales to raise Indonesian EFL learners’ intercultural awareness. Eleven students joined an EFL learning center in Pekalongan, Indonesia were recruited to participate. Data were garnered through classroom observation and semi-structured interview. The data were analyzed employing a thematic analysis. Framed in Yuanhua and Ying’s (2015) intercultural awareness fundamentals, study findings demonstrate that the students viewed cultural aspects from what they experienced, embedded cultural resources, such as symbols, languages, norms, values, and rituals in learning process, promoted self-regulated learning in intercultural communication, engaged in interpreting and comparing cultural contents among Asian folktales, and communicated cultural resources in their daily lives. This study concludes with some empirical and practical implications for how folktales are incorporated into teaching to foster EFL learners’ intercultural awareness
Analysis of Brain Tumor Meningioma Detection System Development Using Convolutional Neural Network Method Mobilenet Architecture

This research aims to design a brain tumor detection tool using the MobileNet architecture Convolutional Neural Network method. The CNN method with MobileNet can effectively detect brain tumors via CT-Scan, with more accurate diagnostic results and reduced errors. This method also speeds up diagnostic time and can help remote areas. The MobileNet application is standalone but requires a web server; it can detect meningioma and glioma brain tumors. The training data includes contrast and non-contrast images, with an accuracy level of MobileNet version 3 reaching 100% compared to the Anatomical Pathology examination. Evaluation of the effectiveness of the CNN method provides an understanding of the strengths and weaknesses of this method. The CNN method can potentially improve diagnostic accuracy, time efficiency, and the results of detecting meningioma brain tumors. Analysis of differences in diagnoses before and after using the CNN method provides essential information about the benefits and advantages of its use in clinical practice, including improvements in detection accuracy, sensitivity, and specificity in identifying meningioma brain tumors with consistent and reliable results.
High Transaction Rates Performance Evaluation for Secure E-government Based on Private Blockchain Scheme

The implementation of technology in the provision of public services and communication to citizens, which is commonly referred to as e-government, has brought multitude of benefits, including enhanced efficiency, accessibility, and transparency. Nevertheless, this approach also presents particular security concerns, such as cyber threats, data breaches, and access control. One technology that can aid in mitigating the effects of security vulnerabilities within e-government is permissioned blockchain. This work examines the performance of the hyperledger fabric private blockchain under high transaction loads by analyzing two scenarios that involve six organizations as case studies. Several parameters, such as transaction send rate, blockchain size, batch timeout, organization number, and the number of clients, were modified in the two scenarios. The gradual addition of organizations was also observed to determine the impact of multi-organization on the throughput, latency, and scalability of the system. By increasing the block size to approximately 100 transactions per block, acceptable performance and latency results are attained. The throughput and latency findings are accepted for three or four organizations, but when many organizations are added, throughput and latency begin to suffer from poor performance. Also, many tests show that increased block timeout for high sending rates positively affects the throughput and latency.
PQ4R Strategy towards Reading Comprehension Development

This study attempted to find out the effectiveness of PQ4R Strategy in the development of reading comprehension among Grade 7 students. It also sought to determine the results of conducting pre-test and post-test in reading comprehension in terms of identifying fact and opinion, making inferences, getting the main idea, and self-questioning. Thus, it also sought to determine if there is a significant difference in the reading comprehension of respondents before and after the implementation of PQ4R Strategy. This study employed the experimental method of research which made use of pre-test and post-test as the main gathering tools. The respondents of the study were selected 83 Grade 7 students at Malvar School of Arts and Trade for the school year 2022-2023. From the findings, it revealed that the student-respondents got an overall mean of 3.80 with a standard deviation of 1.60 in identifying fact and opinion, overall mean of 4.37 with a standard deviation of 2.21 in making inference, overall mean of 3.11 with a standard deviation of 1.47 in getting the main idea, and overall mean of 2.58 with a standard deviation of 1.65 in self-questioning. Furthermore, it revealed that the post-test scores of the respondents in reading comprehension are higher than their pre-test scores. The null hypothesis stating that “There is no significant difference in the reading comprehension of the respondents before and after the implementation of PQ4R strategy.” is rejected. There is a significant difference in the reading comprehension of the respondents before and after the implementation of PQ4R strategy.
Kontribusi Minat Baca dan Motivasi Belajar Terhadap Kemampuan Menulis Siswa Sekolah Menengah Atas

The aim of this study was to measure the contribution of learning motivation and reading interest to the skills of writing descriptive texts for high school students in Padang City. This type of research is quantitative research through the correlation method. The research population was high school students in the city of Padang as many as 85 people consisting of 3 class groups. The research sample was taken by using total sampling technique. Data collection on reading interest and learning motivation was carried out using a questionnaire technique in the form of a closed questionnaire, while data on the ability to write descriptions was obtained using a test technique in the form of a text description writing performance test. Data analysis was performed with the Pearson Product Moment (PPM) correlation test, multiple correlation tests through the F test and t test. The results of the study show that, first, reading interest contributes to students' skills in writing descriptive text. Second, learning motivation contributes positively to students' writing skills. Third, interest in reading and motivation to learn together contribute to students' skills in writing text descriptions.
The Method for Generating a Set of Reference Images for Assessing the Condition of Critical Infrastructure Facilities Using Mobile Robots

The purpose of this work is to improve the accuracy of critical infrastructure condition assessment using mobile robots by considering the geometric distortions of the current images during the formation of a set of reference images. The goal is achieved by determining the sampling step values by angles and sighting height without loss of accuracy. The most important result is the determination of acceptable discretization values in the range of angles and heights of a correlation-extreme navigation system. The significance of the obtained results is in solving the problem of forming a set of reference images, which will reduce the impact of changes in the geometry of sighting on the accuracy of the evaluation of objects. A special feature of the results obtained is the establishment of maximum permissible sampling steps in angles and heights of sight to ensure the required accuracy of object state estimation. When forming a set of reference images, the sampling step by height should be (0.06....0.11)% and (0.12....0.2)% relative to the initial flight altitude for the sighting surface with normal and high object saturation, respectively. The angular sampling step is 10...17 degrees and 6...10 degrees, respectively, for the same surface types. The difference from known works is that the perspective and scale distortions are considered at the stage of formation of a set of reference images, which ensures high accuracy of the system functioning in conditions of orientation and sighting geometry changes.
Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach

The rapid growth of application development has made applications an integral part of people's lives, offering solutions to societal problems. Health service applications have gained popularity due to their convenience in accessing information on diseases, health, and medicine. However, many of these applications disappoint users with limited features, slow response times, and usability challenges. Therefore, this research focuses on developing a sentiment analysis system to assess user satisfaction with health service applications. The study aims to create a sentiment analysis model using reviews from health service applications on the Google Play Store, including Halodoc, Alodokter, and klikdokter. The dataset comprises 9.310 reviews, with 4.950 positive and 4.360 negative reviews. The IndoBERT pre-training method, a transfer learning model, is employed for sentiment analysis, leveraging its superior context representation. The study achieves impressive results with an accuracy score of 96%, precision of 95%, recall of 96%, and an F1-score of 95%. These findings underscore the significance of sentiment analysis in evaluating user satisfaction with health service applications. By utilizing the IndoBERT pre-training method, this research provides valuable insights into the strengths and weaknesses of health service applications on the Google Play Store, contributing to the enhancement of user experiences.
3D Magnetic Resonance Image Denoising using Wasserstein Generative Adversarial Network with Residual Encoder-Decoders and Variant Loss Functions

Magnetic resonance imaging (MRI) is frequently contaminated by noise during scanning and transmission of images, this deteriorates the accuracy of quantitative measures from the data and limits disease diagnosis by doctors or a computerized system. It is common for MRI to suffer from noise commonly referred to as Rician noise because the uncorrelated Gaussian noise is present in both the real and imaginary parts of a complex K-space image with zero mean and equal standard deviation, the distribution of noise in magnitude MR images typically tends to be related to Rician distributions. To remove the Rician noise from an MRI scan, deep learning has been used in the MRI denoising method to achieve improved performance. The proposed models were inspired by the Residual Encoder-Decoder Wasserstein Generative Adversarial Network (RED-WGAN). Specifically, the generator network is residual autoencoders combined with the convolution and deconvolution operations, and the discriminator network is convolutional layers. As a result of replacing Mean Square Error (MSE) in RED-WGAN with Structurally Sensitive Loss (SSL), RED-WGAN-SSL has been proposed to overcome the loss of important structural details that occurs because of over-smoothing the edges. The RED-WGAN-SSIM model has also been developed using Structural Similarity Loss SSIM. The proposed RED-WGAN-SSL and RED-WGAN-SSIM models are formed by using the SSL, SSIM, Visual Geometry Group (VGG), and adversarial loss that are incorporated to form the new loss function. They preserved the informative details and fine image better than RED-WGAN, so our models could effectively reduce noise and suppress artifacts.
A Yolo-based Violence Detection Method in IoT Surveillance Systems

Violence detection in Internet of Things (IoT)-based surveillance systems has become a critical research area due to their potential to provide early warnings and enhance public safety. There have been many types of research on vision-based systems for violence detection, including traditional and deep learning-based methods. Deep learning-based methods have shown great promise in ameliorating the efficiency and accuracy of violence detection. Despite the recent advances in violence detection using deep learning-based methods, significant limitations and research challenges still need to be addressed, including the development of standardized datasets and real-time processing. This study presents a deep learning method based on You Only Look Once (YOLO) algorithm for the violence detection task to overcome these issues. We generate a model for violence detection using violence and non-violence images in a prepared dataset divided into testing, validation, and training sets. Based on accepted performance indicators, the produced model is assessed. The experimental results and performance evaluation show that the method accurately identifies violence and non-violence classes in real-time.