Reviewerly/exHarmony huggingface dataset:
Improving Students’ Reading Comprehension of Recount Text Through Listen-Read-Discuss Strategy (LRD)

The objective of this study was to improve student’s reading comprehension of recount texts through the use of the Listen-Read-Discuss strategy to address issues that were discovered in the classroom. The eighth-graders in this study were focused on, particularly those in class VIII E, which included 30 students. Action research in the classroom was used for this study. An observation checklist, field notes, and tests were the tools utilized in collecting the data. The researcher combined qualitative and quantitative data to analyze the data and examined the observation checklist with field notes for qualitative data. Moreover, the researcher examined the test that was determined by the mean score for each cycle in qualitative data. Based on qualitative data, this research concluded that students' reading behavior in the classroom improved through the Listen-Read-Discuss teaching and learning process. The student's performance increased from the first cycle to the second cycle, as evidenced by the observation checklist and field notes. Quantitative data results also revealed an improvement in the students' reading comprehension mean scores from the first to the second cycles. Based on the result, the researcher advises teachers to use strategies to teach reading comprehension based on the findings provided in this research report. It can help students who have trouble comprehending the text to understand better and increase their comprehension of the material taught.
Assistive Technology for Enhancing Saudi Female Undergraduate Students’ Reading Cognitive Abilities: An Investigation of Reading Models and Their Impacts on Reading Performance

This study examines the effects of assistive technology, specifically text-to speech (TTS) technology on enhancing Saudi EFL students’ reading cognitive abilities. The study utilized text to speech Natural Reader by Natural Soft Ltd (2022) to carry out a pretest-posttest quasi experiment, to measure students’ reading scores before and after the treatment. Instruments also include two questionnaires. 49 students, enrolled in level III reading course of the English Department at the College of Languages and Translation, at Imam Mohammed Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia, participated in the present study. The experiment lasted 12 weeks during the first semester of the year 2022-2023. The first questionnaire results showed that 66.7% of the students face problems in reading, with 43.8% having problems with loud reading, and 31.3% of students read less than 100 words per minute. The results also showed that 40.81% face problems related to pronunciation, vocabulary, and reading comprehension. The total score of the pre-test and posttest are in favor of posttest with mean score of (74.73 ±10.65) compared to (41.48 ±7.94) for pre-test. The findings reveal that there is an effect of assistive technology on students’ reading cognitive abilities related to reading speed, comprehension, vocabulary, and critical thinking, affirming students’ strong satisfaction with the Reader software, reaching the percentage of 87.5%. The study recommends using the technology-based reading model, proposed by the researcher, in the reading courses at Saudi universities, and investigating the linguistic changes that students experience due to the use of assistive technology in EFL.
لترقية قدرة الطلبة على مهارة الكتابة في المدرسة الثانوية الإسلامية الحكومية 3 أتشية غربية " Word Square" تطبيق وسيلة

This research Aims to determine the application of the Word square media to improve students' ability in writing skills as well as to determine students' responses to the application of the "Word Square" media in learning writing skills, besides that it also aims to determine the influence of the application of the "Word Square" media in increasing students' ability in writing skills at Madrasah Tsanawiyah Negeri 3 Aceh Barat. This research is experimental research with the type One Group Pre test- Post test Design. The research instruments used in this research were interviews, questionnaires and tests. The research results showed that the response of MTSN 3 West Aceh students to Word Square media was very good. This is shown by the T Test results being greater than the T table value This indicates that applying the “Word Square” method to improve students’ ability to write skills in the Government Islamic Secondary School 3, Western Aceh, is effective, and this indicates that the test result T is greater than the result of the table, which means (2.09&lt;25‚51&gt;2.86) Ho is rejected and Ha is accepted
Non-parametric Bayesian Network for Surrogate Data

In recent years, domain experts indicated increasing interest in Bayesian Networks (BN). BN can be represented by a directed acyclic graph, the nodes of which, corresponding to the random variables under study, are connected by edges representing direct relationship between the nodes. For the unknown distributions of the analyzed variables, non-parametric Bayesian networks (NPBN) are used to build a graphical probabilistic model. The current study focuses on the application of MATLAB toolbox BANSHEE for creating NPBN for a set of surrogate random variables with certain cross-correlation coefficients. An acyclic directed graph was constructed, in which links between parent and child nodes were established based on expert assessment. Gaussian copula based on the Spearman's rank correlation was considered as a metric of interrelation between two connected nodes. Two diagnostic tests were carried out for the constructed NPBN. The Cramer-von Mises test showed that the Gaussian copula represents an effective solution for the characterization of all pairwise interactions between variables. Calculation of the d- calibration score confirmed that the Gaussian copula matches the proposed BN structure.
Deep Learning-Based Aerial Object Detection for Unmanned Aerial Vehicles

The proliferation of drones and unmanned aerial vehicles (UAVs) across various sectors, including civil, military, and business applications, has underscored the need for effective collision prevention measures and enhanced surveillance capabilities. Military deployments and civilian applications increasingly rely on UAVs for swift reconnaissance and various tasks. However, the surge in UAV numbers has heightened collision risks, necessitating robust collision prevention measures. In the context of the research's questions and purposes, this research aims to enhance UAV capabilities for efficient and dependable operations in dynamic environments. Specifically, it seeks to improve collision avoidance and surveillance through Charge-Coupled Device (CCD) sensors and deep learning techniques. The research leverages deep learning architectures, including You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (R-CNN), and EfficientDet, in conjunction with CCD sensors for object detection. It conducts a comprehensive comparative analysis to evaluate the performance of these architectures, with a particular focus on YOLOv5, using the UAVDT dataset. The comparative analysis reveals that YOLOv5 outperforms other architectures in terms of accuracy and speed for aerial object detection, especially when applied to the UAVDT dataset. YOLOv5 demonstrates remarkable realtime object detection capabilities, including the identification of diverse objects. While Faster R-CNN and EfficientDet models offer competitive accuracy, they require longer inference times and more training epochs to achieve comparable results. This study showcases the potential of deep learning algorithms to enhance UAV capabilities, making them more efficient and reliable in dynamic environments, thus contributing to the advancement of UAV technology. The research findings presented herein have significant implications for the ongoing development and deployment of UAVs across various sectors.
Year-Round Performance Analysis of PV and PV-TEC Systems Under Different Climate Conditions

The TEC system with its capacity of converting heat into electricity could benefit the overall efficiency of the PV systems through exploiting the lost heat on the PV panel surface-that usually results in a decreased electrical performance of PV cells- to increase the overall production of the system. The study involves an evaluation of the overall performance on real time weather conditions of a PV-only and PV-TEC systems for six different climates conditions in an annual basis. The results of the study have stressed out the need of considering the annual analysis and the real weather conditions rather than limited test periods, or laboratory conditions, in order to deliver a realistic assessment of the systems' performances. As it was found that the PV-TEC system has the same output as the PV-only in Kankan, Hamburg, and Paris; a slightly decreased performance in Cairo, and Abu Dhabi; and a worse performance in Tangier.
Classification of Human Physical Activities and Postures During Everyday Life

Nowadays Wearable sensor-based Human Activity Recognition (HAR) is gaining popularity for its affordability and low computational demands. These sensors are widely used in healthcare and surveillance. However, using smartphone sensors for HAR can be inaccurate due to their non-fixed position. This study uses the publicly available Human Activity Recognition Trondheim dataset (HARTH) to develop a HAR model. The proposed HAR model is capable to recognize diverse human daily life activities in the free-living environment. Identifying individual natural behaviour in the wild (free living) remains challenging because humans engage in unscripted daily activities. While working with controlled conditions (scripted data) can produce optimal results, this system often struggles when applied in real-life scenario. Multiple machine learning classifiers are tested on time domain features extracted from sensor data, with the Multilayer Perceptron (MLP) classifier achieving an impressive 92.92% accuracy, making a significant contribution to the HAR field.
Determining the Utility of Use Case Points and Class Points in Early Software Size Estimation

Software size estimation is considered a pre-requisite for software effort estimation which, in turn, is a pre-requisite for project planning and budgeting. Having the capability to accurately estimate software size at the start of the project is, therefore, expected to be invaluable for software project managers. This paper presents a comparative study of the utility of two well-known software functional sizing techniques - use case points (UCP) and class points (CP) - in predicting physical software size (measured in SLOC) early in the project. A diverse dataset consisting of 30 industrial projects - 15 web apps and 15 mobile apps - is collected. First, the information contained in the relevant project artifacts is analyzed to determine the UCP and CP values of each of these projects. Then, after outlier detection and removal, simple linear regression is used to determine the early size (SLOC) prediction capability of UCP and CP for both web and mobile apps. Results of our analysis reveal that UCP is much better at the early prediction of the size of web apps while CP is much better at the early prediction of the size of mobile apps.
Usability Testing Analysis on Digital Wallet Applications to Measure User Satisfaction

One of the issues that users of digital wallet apps often face is slow loading, which can cause frustration and disrupt the user experience. In addition, lack of app responsiveness due to server errors is also a complaint of users, which can lower their trust in the app. Another problem is the difficulty in the login process, which can make it difficult for users to access the application. From these problems, it is necessary to conduct a "usability testing analysis on digital wallets to measure user satisfaction." a study evaluates user satisfaction using ShopeePay, Dana, and Ovo as digital wallets. In this study, TCR is used as an indicator to measure the level of user satisfaction, and the variables considered are Attractiveness, Understandability, Learnability, and Operability. The results show that ShopeePay has the highest TCR of 78.77%, followed by Ovo at 77.32% and Dana at 75.58%. Attractiveness factors affect user satisfaction in ShopeePay, while in Dana, Learnability and Attractiveness factors influence. In Ovo, Operability and Attractiveness factors affect user satisfaction, while Understandability and Learnability have no significant effect. The findings from this study provide valuable insights for digital wallet service providers to optimize the factors that influence user satisfaction. This can help increase the acceptance and utilization of digital wallets in the growing market.
GWO-Based Workflow Scheduling in Cloud-Fog Environments

The scientific workflows are a set of predefined tasks aiming to achieve a target goal state. Cloud computing provides an appropriate environment for orchestrating such scientific workflows. However, scheduling these workflows in a cloud environment is a big challenge due to the high latency and connectivity on low bandwidth network infrastructure. Nowadays, fog and edge computing devices offer an alternative to cloud computing. As a result, these devices can provide cloud computing capabilities for processing scientific workflows near the source. This paper explores the possibilities of scheduling workflows on both cloud and fog computing devices. In order to schedule workflows on cloud-fog integrated devices, we implemented the Grey Wolf Optimizer (GWO) based algorithm using dynamic weights for updating the grey wolf position on the FogWorkflowSim simulator. The proposed scheme avoids getting into a local optimum and improves the performance of the QoS parameters. We also compare the outcomes of different scheduling algorithms in cloud and hybrid cloud-fog architectures. The simulation results of the proposed approach using well-known scientific workflows suggest that cloud-fog integration for workflow scheduling improves processing performance while reducing energy consumption and cost.
Development of an Ensemble Machine Learning-Based Decision Support System for Patient Admission in Healthcare Unit

This paper introduces a decision support system designed to enhance patient care in healthcare settings. The system employs various decision-making tools, including alerts for emergencies and recommendations for treatment plans, medications, discharge, or transfer to different units, all based on patient data, and medical history. The system achieves an accuracy of 99% by integrating both knowledge-driven and model-driven approaches, seamlessly incorporating user input such as patient demographics and vital signs, along with real-time data on ICU and general ward bed availability. The output is displayed on a user-friendly website that provides suggestions for immediate action in emergencies based on predefined rules. The experiment involves comparing the performance of several classification algorithms, such as decision tree, logistic regression, support vector machine, naive Bayes and with their ensembles. The results indicate that the ensemble models provide the best outcome. The study highlights the importance of accurate decision-making in healthcare and the need for a reliable decision support system. The study concludes with a future scope for extending the system's capabilities, incorporating additional patient data, and further evaluating the system's effectiveness in real-world healthcare settings. Overall, the proposed system is a well-designed machine learning model that can make quick and accurate decisions based on a patient's vital signs.
Injection System Cultiving Machine for Plant Soybean in Panduman Village, Jelbuk District, Jember District

This low soybean production is caused by various factors, including farming technology that is still not good, the readiness and skills of soybean farmers are still lacking, The current conventional/traditional system used by farmers to grow soybeans is to make holes with a small hole or wood. After that put 2-3 soybean seeds in each hole and then cover it with soil, this kind of activity takes a long time, causing discomfort for farmers or farm workers. The target of this activity is farmers by encouraging them planting soybeans in Panduman Village which is still possible from a geographical point of view and directing profit-oriented farming in an effective planting time process. In the planting activity, the design prototype planted soybeans in 2 planting rows with a working capacity of ≤ 18 hours/ha with a seed output of 2-3 seeds/planting hole and a planting depth of 3-5 cm and regular spacing of 40 cm and the seeds were covered with soil cover. The design of the development of this planting machine is considered quite good in planting soybeans.
Modifikasi Skor TIMI sebagai Model Prediksi Mortalitas 30 Hari Pasien STEMI Muda

Introduction. Cardiovascular disease is a leading cause of death worldwide. Three-quarters of these deaths occur in lowand middle-income countries among individuals in their productive years. ST-segment elevation myocardial infarction (STEMI) is a dangerous manifestation of coronary artery disease (CAD) and can lead to sudden death. Smoking and a family history of early CAD are major risk factors for STEMI cases. However, their role in any risk stratification system for patients has not been clearly established. The most widely used score in assessing the prognosis of STEMI patients is the TIMI score, but its accuracy in the young patient population is still unknown. The objectives of this study were to determine the proportion of mortality in young STEMI patients at RSCM, validate the TIMI score in young patients, and develop a risk stratification system for young STEMI patients.
Implementasi K-Medoids Clustering Dalam Pengelompokkan Harga 8 Jenis Minyak Goreng

In everyday life, cooking oil has become a necessity with international sales prices varying depending on the quality and type. The types of cooking oil sold in the international market are very diverse, including coconut, olive, palm kernel oil, palm oil, peanuts, rapeseed, soybeans and sunflower. Therefore, it is necessary to group data on selling prices of cooking oil on the international market to get the best grouping of cooking oil. The data used in this research is historical price data of 8 edible oils kaggle August 1992 to July 2022. Data grouping in this research uses the k-medoids algorithm. The k-medoids algorithm produces a more balanced group, better performance and accuracy than other algorithms. The aim of this research is that the k-medoids algorithm is able to group cooking oil price data into 4 group models, namely group models 2, 3, 4 and 5 and obtain the best group model based on the dbi value. The research results showed that the cooking oil price data was successfully grouped into group 2, 3, 4 and 5 models with the best group based on the lowest dbi performance value being the group 2 model with a dbi value of 0,000 and olive oil being the cooking oil with the highest price in the world while 7 types other cooking oils have more or less the same price (in the same price group).
Survey of High School Students’ Perception of Artificial Intelligence

In this study, we aimed to understand how high school students perceive artificial intelligence (AI) in the context of the influence of AI. We sought to identify what is necessary for the proper implementation of AI education. To achieve this, the study conducted surveys targeting students from five high schools in Region B. The surveys covered their understanding and awareness of AI, perceptions of AI learning, awareness of AI applications, and recognition of the role of AI. A total of 272 respondents were analyzed. Survey results revealed that high school students generally recognized the importance of AI in their lives, however, their interest in learning about AI was relatively low. While there was a high perceived need for education on creating AI application content, there was a negative perception towards utilizing AI in career choices. Although AI was perceived as necessary and having a positive impact on human life, some negative perceptions emerged regarding its influence on human employment. Ultimately, high school students considered their understanding of AI to be at a moderate level, but negative aspects were revealed concerning learning and career choices. This means that education on AI has not been properly integrated, the context of exam-focused school systems. Therefore, it is suggested that educational activities incorporating AI in school curricula as well as providing meaning to AI in career exploration education.
DIGITAL LITERACY AMONG TEACHERS IN SINGKAWANG CITY: AN ANALYSIS OF GENDER, AGE, AND SOCIO-ECONOMIC STATUS

&lt;em&gt;One of the demands of teachers in Generation Z is being able to have digital literacy content that supports their functions and work. There is no research related to the influence of gender, age, and socioeconomic status on digital literacy competency teachers in Singkawang City. This study aims to review whether there are significant differences in teachers' digital literacy abilities in terms of gender, socioeconomic status aspects of those who are able, moderate, or poor, and the age of teachers in different generations. This research method includes the descriptive quantitative inferential survey method. The number of volunteer respondents as research subjects was 200 teachers from 50 elementary and junior high schools who taught in Singkawang City and were selected by the voluntary response sampling technique. Data collection used a questionnaire instrument for teachers that contained 17 items that had been proven valid. Data were analyzed using the Mann-Whitney test for gender and the Kruskal-Wallish H test for age and socioeconomic status. The results showed that (1) the significance value was 0.063 &amp;gt; 0.005, which did not show a significant difference between teachers' digital literacy skills based on gender; (2) the significance value was 0.075 &amp;gt; 0.05, meaning that there was no difference in teachers' digital literacy seen from socioeconomic status (able, moderate, and underprivileged); and (3) the significance value is 0.106 &amp;gt; 0.05, indicating that there is no difference between teachers whose age includes the baby boomers generation, generation X, and the millennial generation&lt;/em&gt;