Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF GOOGLE FORMS AND COMPUTER MODELS IN PHYSICS EDUCATIONAL PROCESS IN THE PEDAGOGICAL COLLEGE

The article is devoted to the features of using Google Forms and computer models, in particular PhET simulations, in Phisics educational process in the pedagogical college. The metodical recomandations are provided increasing the independence of students’ performing tasks to improve knowledge and implement objective control of their educational achievements. The methods of students’ feedback with a teacher in distance learning are considered. Google Forms in the pedagogical college are used for polling, questionnaires, creating tests of different complexity levels, worksheets for conducting thematic control, answer forms for organizing laboratory works. The web application has the ability to create test tasks of different types: 1) by choosing one correct answer from the list; 2) multiple choice, when a student chooses several answers from the options offered to him; 3) tasks to establish compliance or consistency; 4) short answers; 5) a scale where the answer options are presented in the form of the numerical interval; 6) pasted answers as a text line; 7) essay is a text in which the student describes his point of view on the problem under consideration; 8) file upload is a type of task that allows students to download materials from their electronic device. Evaluation of the performed work can be made both automatically and after a teacher’s checking, who can leave his comments for each completed task. Answer forms are sent to the email address. Particular attention is paid to the individual work of students with ussing of computer models. For example, a laboratory work based on PhET simulation on the topic «Determination of the spring stiffness» is proposed. It is indicated that computer simulations contribute to activation of cognitive research interest of students and partially compensate the lack of equipment for the physical experiment in distance learning. To provide feedback during distance learning, we suggest that after completing the Google Forms Automated Test items, students self-track results and submit open-ended problem solutions to Classroom. After checking by the teacher, it is necessary, together with the students, to find out the content of the work done; analyze errors; compare actual results with expected ones; provide samples of correct problem solving; draw up a plan for further action; highlight the success of students and inspire them to work. It has been noted that students' progress and the objectivity of their marks are increased in the systematic use of Google Forms and computer simulations in physics educational process.
Implementation of “Kampus Mengajar" Programs to Improve Learning Motivation

This dedication is carried out to implement various activities of the 3rd Kampus Mengajar Program to motivate students to learn at Middle School 2 Pamarican. After observing the school, multiple problems were found, such as students' lack of enthusiasm during learning due to their lack of extrinsic motivation, students still needing help to read and count correctly, and teachers still using traditional learning media. This study used a qualitative descriptive method with data collection techniques through teacher interviews and school observations. This research was conducted at Middle School 2 Pamarican for four months, from March 2nd, 2022, to June 29th, 2022. This research was aimed at students in grades VII and VIII and was carried out directly. The results of this dedication activity include assisting in teaching, learning multiplication using jarimatika, operating numbers, arithmetic series, and the concept of 3 M with the GASING method, improving reading skills using five methods, learning to recite the Quran, and adapting technology aimed at increasing students' learning motivation. There were many obstacles and challenges during the implementation, especially when learning became limited, so teachers had to have high creativity to improve student learning outcomes.
Comparative Analysis of KNN, Naïve Bayes and SVM Algorithms for Movie Genres Classification Based on Synopsis.

Text classification is a process of categorizing a text into the correct label. Text classification in natural language processing is a challenging task that requires accuracy to get the correct results, manual text classification tends to be inefficient because it requires a lot of time and also experts. The utilization of machine learning for automatic text classification can be a solution to this problem. KNN, Naive Bayes, and SVM are known as some of the most algorithms to solve classification problems, especially text classification. In this study, we are trying to compare the KNN, Naive Bayes, and SVM algorithms for text classification with the problem of classifying movie genres based on a synopsis using datasets obtained from Kaggle.com and IMDB Dataset. The results of this study indicate that of the 12 experiments, Support Vector Machine (SVM) is the bestperforming algorithm with an accuracy of 90%, 93%, 65%, and 63%. It is hoped that this research can help to determine the best algorithm in the text classification process.
The Development of Interactive Games for Covid-19 Prevention Using Indonesian Health Protocols

COVID-19 has become a virus spreading quickly across the world.The government of Indonesia also immediately adjusted theconditions to minimize the spread of the COVID-19 virus byimplementing the health protocols 5M namely, wearing a mask,washing hands, keeping distance, avoiding crowds, and reducingmobility. With the advancement of technology, games can be used aseducational media to support users learning processes and increasetheir knowledge. Therefore, we conducted a study using the learningsupport media, namely interactive games based on a personalcomputer platform with the Windows operating system to help theusers understand the prevention of the COVID-19 virus byimplementing health protocols. This study proposes using a structurefor the iterative cycle process called the ADDIE model framework andusing the Unity game engine for its game development tools. Tomeasure the feasibility of interactive games as educational media, weuse the System Usability Scale method. Based on the applicationtesting of 50 respondents, interactive games get a score of 90 on a scalethat is acceptable. The results also indicate that interactive games aseducational media will improve the users learning process andmotivation about implementing the health protocols.
Artificial Intelligence Based Deep Bayesian Neural Network (DBNN) Toward Personalized Treatment of Leukemia with Stem Cells

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of Deep Bayesian Neural Network (DBNN) for the personalized treatment of leukemia cancer has shown a significant tested accuracy for the model. DBNNs used in this study was able to classify images with accuracy exceeding 98.73%. This study depicts that the DBNN can classify cell cultures only based on unstained light microscope images which allow their further use. Therefore, building a bayesian‐based model to great help during commercial cell culturing, and possibly a first step in the process of creating an automated/semiautomated neural network‐based model for classification of good and bad quality cultures when images of such will be available.
ADVANTAGES AND DISADVANTAGES OF EXTRAORDINARY DISTANCE LEARNING IN HIGHER EDUCATION INSTITUTIONS

The paper analyzes the potential of emergency remote training of higher education applicants. Theoretical analysis of scientific sources and our own experience allowed us to identify technical and technological, psychological and didactic and organizational and managerial advantages and disadvantages of remote learning based on the main aspects of educational process management. It was found out that difficulties of&nbsp; technical and technological nature are associated with the lack of technical and high-tech educational support (reliable access to the Internet, problems with power outages and access to high-quality technical training tools, etc.). The advantages of technical and technological nature were the effective use of «cloud technologies»: online services, file storage, corporate email, video conferences, electronic journals, etc. It is noted that the psychological and didactic difficulties of extreme remote training of higher education applicants are primarily due to the restriction of physical activity, stressful state and lack of coordination of actions of teachers and applicants, as well as the level of self-organization and motivation of subjects of remote training. It was found that didactic difficulties arose more among teachers who perceived the challenges of distance learning as conducting traditional classes in real time on the basis of a certain online platform in the form of video sessions. This means increasing the time spent preparing for synchronous classes, joining students in virtual classes, evaluating students, identifying the independence of completed tasks, and increasing the number of reporting documentation. It is proved that the didactic advantages of remote learning are: a) participation of applicants in the selection of web resources and video materials to create interesting tasks, B) transparency of the assessment system, C) development and stimulation of creativity (encourages them to look for new methods and motivators to activate educational activities). The article substantiates the organizational and managerial shortcomings of emergency remote training of higher education applicants, which include the lack of all possible assistance from the management on remote training and a significant amount of reporting documentation. Organizational and managerial advantages include savings on providing a workplace, the ability to take advanced training courses on the Moodle platform for free, and so on. Attention is focused on technological, pedagogical and organizational prospects for the future regarding the introduction of emergency remote learning in higher education institutions.
METHODICAL FEATURES OF INTRODUCTION INTO THE TRAINING PROCESS OF FUTURE TEACHERS OF THE OPEN REGIONAL STUDENT OLYMPIAD IN PHYSICS OF ZAPORIZHZHIA NATIONAL UNIVERSITY

This article considers the methodological features of the introduction of the open regional student's Olympiad in physics at Zaporizhzhia National University in the process of training future physics teachers. The purpose of the article was to highlight the methodological features of the introduction of the first open regional student's Olympiad in physics at the Zaporizhia National University (ZNU) in the process of training future physics teachers. The University Physics Olympiad was held according to the 11th grade physics program, but the tasks in their content allowed for the possibility of solving them not only by eleventh-graders. Another difference of the Olympiad was the presence of different types of tasks (calculation tasks, tasks that require evaluative considerations, as well as tasks that involve the application of theoretical knowledge to describe a physical experiment). This approach to the preparation of Olympiad tasks was aimed at identifying the level of formation of students in various aspects of the ability to solve problems. Valuable, from a methodological point of view, was the idea of involving students of the subject specialty Secondary Education (Physics) in the organization and conduct of the Olympiad. Students were involved in the preparation of the Olympiad, were on duty during the Olympiad, as well as participated in the discussion by the jury members of the author's solutions. Such immersion of students in the process of the Olympiad was an effective element of their quasi-professional activities. The article presents the tasks and solutions of the problems of the First Open Regional Olympiad in Physics of Zaporizhia National University in 2020/21. The open regional Olympiad in Physics proposed by the authors of the article and introduced at Zaporizhzhya National University plays an important role not only in activating the creative activity of students in teaching physics, but also is an effective organizational form of professional training for students majoring in 014.08 Secondary education (Physics). Students − future teachers of physics are actively involved in this mass event, in particular in organizational and analytical work. Priority areas for further work are related to the development and implementation of the open regional university Olympiad in physics for students in grades 7-11.
Systematic Literature Review of Trend and Characteristic Agile Model

Agile is a methodology and engineering approach for software development that encourages change in collaboration through tasks carried out at various stages of the software development life cycle. Scaled Agile Framework, Kanban, Scrum, Lean, Extreme Programming, Crystal, Dynamic System Development Method, and Feature Driven Development are a few of the approaches that go along with agile. Each of these approaches has distinct traits and qualities of its own. Every engineer and researcher needs to be aware of the benefits and characteristics of each method before deciding to use one. In order to assist engineers and researchers who will use one of these methods, this research will analyze it. The method used in this paper is a systematic literature review, which involved at 52 papers published in the previous eight years, from 2018 to 2022. This method is carried out by determining research questions, determining library initiation and selection, determining inclusion and exclusion criteria, and finally performing data extraction. This essay seeks to establish: (i) Study trends on each agile technique from 2018 to 2022 and (ii) Each agile method's characteristics. The results of this literature review indicate that Scrum and Extreme Programming have overtaken other agile methodologies as the most popular agile techniques over the last eight years. Through an analysis of the characteristics of each methodology, namely the development approach, suggested iteration time period, team communication, project size, project documentation, design, workflow approach, project coordinator, role assignment, coding, testing, and the nature of customer interaction, it is found that Scrum and Extreme Programming do have several advantages over other methodologies.
Hacking as a Cyber Crime, Reviewed from the Perspective of Quran Surah An-Nur Verse 27

Sarcasm is a statement that conveys an opposing viewpoint viapositive or exaggeratedly positive phrases. Due to this intentionalambiguity, sarcasm identification has become one of the importantfactors in sentiment analysis that make many researchers in naturallanguage processing intensively study sarcasm detection. Thisresearch is using multiple channels embedding the attentionbidirectional long-short memory (MCEA-BLSTM) model thatexplored sarcasm detection in news headlines and has differentapproach from previous research-developed models that lexical,semantic, and pragmatic properties. This research found that multiplechannels embedding attention mechanism improve the performance ofBLSTM, making it superior to other models. The proposed methodachieves 96.64% accuracy with an f-measure of 97%
Adaptive Kernel by Layer-wise Convolution

Convolutional Neural Networks (CNN) have demonstrated outstanding performance in computer vision tasks such as image classification, detection, segmentation, and medical image analysis.Adaptive kernels have been successfully used to gain better performance in CNN-based variants.In general, the optimizer updates the weights of the kernel.Recent research suggests that applying a deterministic adaptive kernel after a convolution operation can help a CNN to gain better generalization.However, how to generate the weights of the deterministic adaptive kernel is still a field of active research.To answer this question, we propose an adaptive kernel method that utilizes convoluted data of a layer to construct a set of dynamic Gaussian kernels.Then we perform the convolution operation on the convoluted data and a randomly chosen Gaussian kernel from the set.To validate the proposed adaptive kernel method 1 , we use eighteen different CNN variants and three different datasets (CIFAR10, CIFAR100, and SVHN) on the image classification task.Our method can be applied to any CNN variant as a plugand-play, and it does not introduce any trainable parameter to the network.We also provide a detailed analysis of the impact of our proposed adaptive kernel method.
A Research Based on Genre Analysis into EAP Reading &amp; Writing

EAP English reading and writing teaching is a very important teaching content in current high school English teaching, and the genre teaching method has been applied extremely widely in college English EAP teaching, achieving outstanding teaching effects.In the theoretical system of genre teaching method, genre analysis is the fundamental theoretical core of it.Genre analysis of EAP discourse, on the one hand, can help general academic researchers grasp the macro structure of academic texts, improve their academic communication function and understanding ability of specific texts, and enhance the international academic functionality of general academic workers; On the other hand, genre analysis of academic discourse can be applied to rhetorical writing, translation, cross-cultural and other related general fields, with strong practical value.In this study, an in-depth study of English EAP reading and writing based on genre analysis will be conducted to provide a more adequate reference basis for the further optimization of English reading and writing teaching in China's colleges and universities, and to provide more practical help for the development of English teaching in colleges and universities.
Few shot learning approaches to essay scoring

Automated essay scoring (AES) involves using computer technology to grade written assessments and assigning a score based on their perceived quality.AES has been among the most significant Natural Language Processing (NLP) applications due to its educational and commercial value.Similar to many other NLP tasks, training a model for AES typically involves acquiring a substantial amount of labeled data specific to the essay being graded.This usually incurs a substantial cost.In this study, we consider two recent few-shot learning methods to enhance the predictive performance of machine learning methods for AES tasks.Specifically, we experiment with a prompt-based few-shot learning method, pattern exploiting training (PET), and a prompt-free few-shot learning strategy, SetFit, and compare these against vanilla fine-tuning.Our numerical study shows that PET can provide substantial performance gains over other methods, and it can effectively boost performance when access to labeled data is limited.On the other hand, PET is found to be the most computationally expensive few-shot learning method considered, while SetFit is the fastest method among the approaches.
Pathways to urban sustainability: Design perspectives on a data curation and visualization platform

Figure 1: A screenshot of Curbcut presenting the number of people sharing a tree on the census tract level.The platform consists of three main panels: the left panel shows the variable being visualized, the legend and the zoom level; the middle panel shows an interactive map visualization, where users can click on each of the census tract to retrieve number of people sharing a tree for each census tract; the right panel presents information on the distribution of the selected variable.The compare drop-down menu allows for a bivariate visualization of two selected variables, shown in Figure 2.
TPOT-MTR: A Multiple Target Regression Based on Genetic Algorithm of Automated Machine Learning Systems

The concept that a cross correlation might improve prediction error underpins machine learning algorithms for multi-target regression (MTR). Numerous MTR approaches have been created in recent years, however there are still uncertainties concerning how their performances are impacted by dataset properties such as linearity, number of targets, and cross correlational complexity. In order to contribute to a better understanding of the relationship between dataset properties and MTR methods, authors proposed a new model of TPOT-MTR, which its result will be compared to previously generated 33 synthetic datasets with controlled characteristics and tested their performance against other two MTR methods, Random Forest and SVM. The results demonstrated that TPOT-MTR approaches could enhance performance even in datasets with non-linearly correlated targets, although the prediction improvement varies depending on the method and regressor combinations used.
DVHN: A Deep Hashing Framework for Large-Scale Vehicle Re-Identification

Vehicle re-identification is a pervasive technology in real-world intelligence transportation systems. Conventional methods generally perform re-identification tasks by representing vehicle images as real-valued feature vectors and then ranking the gallery images by computing the corresponding Euclidean distances. Despite achieving remarkable retrieval accuracy, these high-dimensional real-valued feature vectors are not tailored for fast indexing and matching and require tremendous memory and computation when the gallery set is large, making them inapplicable in a large-scale real-world retrieval setting. In light of this limitation, in this paper, we make the very first attempt to develop an efficient vehicle re-identification system (DVHN) for real-world large-scale retrieval tasks with deep hashing learning. It could substantially reduce memory usage and enhances retrieval efficiency while maintaining retrieval accuracy. Concretely, DVHN directly learns discrete compact binary hashing codes for each image by jointly optimizing the feature learning network and the hash code generating module. Specifically, we directly constrain the output from the convolutional neural network to be discrete binary codes and ensure the learned binary codes are optimal for classification. To optimize the deep discrete hashing framework, we further propose an alternating minimization method for learning binary similarity-preserved hashing codes. Extensive experiments on two widely-studied vehicle re-identification datasets- VehicleID and VeRi- have demonstrated the superiority of our method against the state-of-the-art deep hash methods. DVHN of 2048 bits can achieve 13.94% and 10.21% accuracy improvement in terms of mAP and Rank@1 for VehicleID (800) dataset. For VeRi, we achieve 35.45% and 32.72% performance gains for Rank@1 and mAP, respectively.