Reviewerly/exHarmony huggingface dataset:
A proposal for developing third intermediate grade’s computer and information technology textbook in the light of digital citizenship dimensions

The problem and the aim of the study. Digital Citizenship has become a force affecting the construction and the implementation of the curriculum, and the idea of including its knowledge, skills and values in the school curriculum experiences has become an educational goal and a basic standard of the national standards for learning digital technology in the Kingdom of Saudi Arabia, believing in its effective role in preparing learners for life, and preparing them to deal constructively with the transformations that The Kingdom of Saudi Arabia and the world are witnessing it. This research aimed to present a proposal for developing third grade’s Computer and Information Technology Textbook in the light of digital citizenship dimensions. Research methods. To achieve this goal, the researcher used the descriptive approach and designed a content analysis card with digital citizenship dimensions that consists of digital access, digital commerce, digital communication, digital literacy, digital etiquette, digital law, digital rights and responsibility, digital health, and digital security. There are adequate 48 standards that represent the digital citizenship dimensions. The researcher double-checked the card's validity and reliability. Twenty-five experts in the field have reviewed the card, and it has achieved a high score for reliability 96,24%. Results. The content analysis results indicated that the dimension of digital literacy had the highest degree of availability in the computer and information technology book with a percentage of 74,62%, while the rest of the dimensions were not sufficiently covered in the book. Digital law had the lowest availability percentage at 0,38%, followed by digital health at 0,57%, digital etiquette at 0,76%, digital commerce at 1,14%, digital security at 1,23%, digital rights and responsibility at 1,42%, and digital communication at 4,83%. Finally, digital access came in at 15,06 %, in ascending order of percentage availability. Conclusion. According to the results, the researcher presented a proposal to include digital citizenship dimensions in the Computer and Information Technology textbook for third grade intermediate level. The researcher suggested adding educational activities and tasks to the current units of the book to increase the level of availability of digital citizenship dimensions. Furthermore, the researcher created a proposed unit based on digital citizenship dimensions to achieve knowledge continuity. Finally, many recommendations were presented to curriculum designers in Saudi Arabia, those who are responsible for developing the infrastructure of public education schools, and those who are in charge of professional development for teachers.
Entropy Approach in Methods of Electroencephalogram Automatic Analysis

The article explores the possibility of using the entropy characteristics of the time series of the electroencephalogram signal for the task of automatically detecting epileptic seizures by electroencephalogram recording. Because of the brain is a complex distributed active environment, self-oscillating processes take place in it. These processes can be judged by the EEG signal, which is a reflection of the total electrical activity of brain neurons. Based on the assumption that during an epileptic seizure, excessive synchronization of neurons occurs, leading to a decrease in the dynamic complexity of the electroencephalographic signal, entropy can be considered as a parameter characterizing the degree of systemic chaos. The sample entropy method is a robust method for calculating entropy for short time series. In this work, the sample entropy was calculated for an electroencephalographic record of a patient with epilepsy obtained from an open set of clinical data. The calculation was made for different sections of the recording, corresponding to the norm and pathology (generalized epileptic seizure). It has been shown that the entropy characteristics of the electroencephalogram signal can serve as informative features for machine learning algorithms to automatically detect signs of neurological pathology associated with epilepsy.
Artificial Neural Network Architecture Tuning Algorithm

In this paper, we will consider artificial neural networks, one of the most powerful methods of data analysis. For each individual task, the type of neural network changes, and its various parameters are selected, which takes too much time and resources. To avoid these shortcomings, a self-tuning algorithm for the architecture of the neural network was developed and implemented, due to the genetic algorithm. An artificial neural network has been implemented for data classification tasks. This implementation provides the ability to select the number of hidden layers in the artificial neural network, the number of neurons on each of the layers, the type of activation functions for each neuron of the network. Nfr of the implementation of this evolutionary algorithm is the different lengths of individuals in the population and the ability to manipulate it. A genetic algorithm has been implemented that allows coding all the parameters of the neural network discussed above. The algorithm was developed using the modern Keras neural network training library. The efficiency of the developed algorithms was compared with each other.
Hybrid Evolutionary Approach to Decision Trees Ensembles Design

Decision trees are an efficient data analysis tool. Ensembling methods have been developed on the basis of decision trees. These methods make it possible to obtain a data analysis tool in the form of a composition of trees. The paper proposes a new approach since the development of compositions based on decision trees is an urgent problem. The paper proposes a new hybrid approach to designing the composition of decision trees. The approach is based on the idea of the decision tree application built by a genetic programming algorithm as a technique to determine a machine learning method for object classification. Thus, with the help of the proposed approach the authors carry out a hybridization of a self-configuring genetic programming algorithm and a decision tree. The paper treats decision trees built by a modified algorithm with differential evolution considered as data analysis methods that make decisions concerning a sample objects classification. The proposed method is studied on some classification problems with different types of data and dimensions. The comparison with other methods for building compositions of decision trees is made.
Algorithm Development for Implementing the Information Resource Life Cycle Digital Twin

Currently, in many organizations that are engaged in information processing, there is a problem of operational processing of information resources. The daily volume of information processed in the structure of organizations increases every day. Therefore, the problem of processing and analysing an information resource in large volumes in a short time with an approximation to a real information system is very relevant. The problem of increasing the efficiency of information resources processing processes in IT departments is solved by creating a digital twin algorithm using simulation modeling. In this paper, the methods of simulation modeling, digital twin and the life cycle of an information resource are investigated. A digital twin of the information resource life cycle was designed, based on which a model was built in the AnyLogic simulation environment. The developed model will allow the selection of data ordering for the provision of processed data for other departments and the prediction of hardware capacities.
Multi-Swarm PSO With Success-History Based Adaptive Local Search for Dynamic Environments

In this paper, a success-history based parameter adaptation is proposed for the local search procedure used in particle swarm optimization algorithm with multiple swarms. The proposed modified version of the mQSO algorithm is considered within the generalized moving peaks dynamic optimization benchmark set. For the experiments, a set of 12 benchmark problems are chosen from the CEC 2022 competition on dynamic optimization. The experiments involving parameter sensitivity analysis have shown that the adaptive local search with particles generated next to the global best of each swarm with normal distribution allows improving the overall performance of the algorithm in terms of current error and best error before each environment change. An additional set of experiments with increased shifts of peaks between environmental changes has been performed to test the influence of the initial settings of local search with quantum or normally distributed particles. The results have shown that applying adaptive sampling width for normal distribution allows improving performance in cases of bad choice of population size or number of local search particles on every iteration.
The Effect Of Quizizz On Students Reading Comprehension

The aim of this research is to investigate the relationship between mobile-assisted language learning and vocabulary learning integrated with the Quizizz platform. The researcher opted to examine the major effect of employing Mobile-Assisted Language Learning (MALL) coupled with Quizizz on Reading Comprehension of Senior High School students at SMA Negeri 13 Bandar Lampung based on the preceding considerations. This research study used a quantitative approach. The conclusion is based on the presentation and description of the findings and discussions from the data analysis. There was an increase in students' scores on the pre-test and post-test. Online learning using Quizizz towards English students' reading comprehension at SMA Negeri 13 Bandar Lampung is quite effective when viewed from student learning outcomes. Student learning outcomes after lectures using Quizizz are higher than student learning outcomes before studying.Keywords: Mobile-Assisted Language Learning, Quizizz, Reading Comprehension
A Deep Learning Perspective on Connected Automated Vehicle (CAV) Cybersecurity and Threat Intelligence

The automation and connectivity of CAV inherit most of the cyber-physical vulnerabilities of incumbent technologies such as evolving network architectures, wireless communications, and AI-based automation. This book chapter entails the cyber-physical vulnerabilities and risks that originated in IT, OT, and the physical domains of the CAV ecosystem, eclectic threat landscapes, and threat intelligence. To deal with the security threats in high-speed, high dimensional, multimodal data and assets from eccentric stakeholders of the CAV ecosystem, this chapter presents and analyzes some of the state of art deep learning-based threat intelligence for attack detection. The frontiers in deep learning, namely Meta-Learning and Federated Learning, along with their challenges have been included in the chapter. We have proposed, trained, and tested the deep CNN-LSTM architecture for CAV threat intelligence; assessed and compared the performance of the proposed model against other deep learning algorithms such as DNN, CNN, LSTM. Our results indicate the superiority of the proposed model although DNN and 1d-CNN also achieved more than 99% of accuracy, precision, recall, f1-score, and AUC on the CAV-KDD dataset. The good performance of deep CNN-LSTM comes with the increased model complexity and cumbersome hyperparameters tuning. Still, there are open challenges on deep learning adoption in the CAV cybersecurity paradigm due to lack of properly developed protocols and policies, poorly defined privileges between stakeholders, costlier training, adversarial threats to the model, and poor generalizability of the model under out of data distributions.
An ontology for the design of emulsion-based cosmetic products: Development and applications

The design of formulated products, such as cosmetic emulsions, requires handling heterogeneous information such as ingredients data, property models, heuristics, and theoretical knowledge, among others. Because of this, the formulation process is not straightforward, but iterative, highly intuitive, and very experimental. Our proposal to facilitate the design process is an ontology called OntoCosmetic for emulsion-based cosmetic products. An ontology is a conceptual knowledge representation of a domain. It can be interpreted by both designers and machines, allowing extensive exploration of the solution space and a logical understanding of the results of that exploration. This article presents in detail the construction of OntoCosmetic, whose structure is based on a domain ontology for Process System Engineering called OntoCAPE. Two examples show its use: the substitution of an ingredient in a formulation, and the exploration and delimitation of the solution space in a new formulation design problem.
Application of Fuzzy Mamdani Logic in Determining Teacher Performance To The Learning System at Public High School 6 Bengkulu Middle

Nowadays, the process of assessing teacher performance at State High School 6 Bengkulu Middle is still manually, namely by filling in the scores on each criterion consisting of 14 competencies, then the values are added together to get the final result of the teacher performance assessment. However, this takes quite a long time, besides that the assessment of teacher performance is only by looking at teachers who are active in various fields in the school. The application of teacher performance in State High School 6 Bengkulu Middle can help provide the results of teacher performance assessment of the learning system in schools through a fuzzy Mamdani logic approach. The application of teacher performance to the learning system at State High School 6 Bengkulu Middle was created using the Visual Basic .Net programming language and SQL Server 2008r2 Database. Based on the black box testing that has been carried out, it was found that the functionality of the teacher performance application to the learning system at State High School 6 Bengkulu Middle went well as expected and was able to analyze teacher assessment data through the Fuzzy Mamdani Method to determine teacher performance against the learning system in schools
Online Attendance System Website-Based at The Village Hall Office Paya Bakung Using The Waterfall Method

Attendance is a system for recording employee attendance which is carried out in every agency, including government agencies. The Paya Bakung Village Hall Office still applies a manual attendance system so that the recording of employee attendance activities is still relatively minimal. Performing the attendance process manually takes a long time to record incoming hours, outgoing hours, or recap employee attendance reports. From these problems, the author designs and builds a computerized attendance system that can record all attendance activities of employees. Each employee has their own account that can be accessed through the employee's device. Employees can do absent entry and absence every day. The admin records all absent data entered by employees. The web-based attendance system in this study was created using the waterfall method consisting of Requirement Analysis, System and Software Design, Implementation and Unit Testing, Operation and Maintenance. This attendance system provides great benefits for the Paya Bakung Village Hall Office
Sales Mobile Application Design in Handphone Store

In the current era of globalization, technology is advancing rapidly, and computers are one of the technologies which are one of the tools used to make it easier to find problems, of course, it is also supported by hardware, software and brainware. Mobile stores require an information system that supports and provides satisfactory service to buyers. For this reason, the author makes this final project regarding the program for selling cellphones at the cellphone store which currently does not use a computerized system. The system at the cellphone store usually still uses a manual system. Starting from recording sales notes to bookkeeping sales reports that are still recorded in the books. This can allow for errors in recording, ineffectiveness and inefficient calculations in large numbers. And can cause inaccurate reports made. The use of the program is one solution to solving sales problems in the cellphone store. Therefore, with the use of a sales program, it is possible to further assist sales activities in the cellphone store.
Pengembangan Aplikasi Chat Multi Bahasa Berbasis NLP Translation API

Interacting with others is an essential part of human life.. Sometimes users experience problems using chat applications, namely language differences when communicating with foreigners. Based on these problems, the author aims to build a chat application with a mobile-based automatic translator. In this application, the user can choose the language that will be used as needed. This application development uses the React Native framework for mobile applications and uses the NLP translation API for translators. This chat application automatically translates messages into the language used by the user. After doing some testing on the application, it can be concluded that it is according to the design and can make it easier for users to communicate with different languages
Effects of 3-Dimensional Model of Human Digestive System on Students Performance in Biology Concept in Nigeria

This study examined the effects of 3D model of human digestive system (3-DMHDS) on senior secondary school students’ performance in Human digestive system in Ilorin, Kwara State, Nigeria. The research adopted a post-test, control group, non-randomized, and non-equivalent quasi-experimental research design. Two intact classes from two secondary schools were randomly sampled based on institutional ownership. The instruments used were Lesson Note (LN), Biology Performance Test (BPT) for data gathering, and 3D-model of human digestive system (3-DMHDS) as the treatment instrument. The reliability coefficient of BPT was determined using the Pearson product-moment correlation and was found to be 0.84. Three research questions and three research hypotheses were formulated for the study. The mean score was used to answer the research questions while an independent sampled t-test at 0.05 level of significant was used to test the hypotheses. Findings show that 3-DMHDS significantly influence the understanding of the human digestive system among students irrespective of their gender and institutional ownership. It was recommended among others that Biology teachers and students should be motivated to use the 3D model for the teaching and learning of the human digestive system among other biology topics.
The Implementation of Deep Learning Techniques in Developing Conversational Chatbot as The Source of Vaccination Information

The Covid-19 pandemic has hit Indonesia for more than 2 years. To overcome Covid-19, Indonesian government implemented a vaccination program with a target of 70% of the population being vaccinated. However, the recorded population that has been vaccinated to reduce the risk of being exposed to Covid-19 is still low. Several studies have stated that information and invitations to vaccines through mass media are considered insufficient to convince the population to vaccinate. Residents who are still unsure and do not even want to vaccinate need really comprehensive information from experts. To answer this problem, a chatbot that can replace experts in explaining everything related to vaccines can be one solution. This is evidenced by a study which states that the interaction between people who have not been vaccinated with a chatbot that explains about vaccination can reduce the level of doubt of the population about the vaccine by up to 20%. The purpose of this research is to build a chatbot using deep learning technique. Meanwhile, the deep learning technique used to build a conversational chatbot is the Multilayer Perceptron Network (MLP). Based on the result of our study, our chatbot can answer 83% questions correctly out of 30 questions.
PROBLEMS AND CONTRADICTIONS IN THE ORGANIZATION OF INDEPENDENT EDUCATIONAL PROJECT WORK OF STUDENTS IN THE CONDITIONS OF WAR

The article highlights the problems and contradictions in the organization of independent educational project work of students during distance learning (online learning) in war conditions, which affect the quality of providing educational services in these conditions. It has been established that today there are a number of factors that directly affect the activities of higher education institutions and, in particular, subjects of the educational process in the conditions of Russian military aggression against Ukraine. It was determined that among student youth, there are certain problems regarding the assimilation of academic disciplines. Based on the analysis of students' answers to the questions of the anonymous questionnaire, which consists of 48 questions, the following problems were identified: insufficient level of students' ability to work independently on mastering the content of studies, lack of innovative methods of organizing students' independent work in war conditions, low level of use of information technologies and project activities, limited communication between the scientific-pedagogical worker and the student and the students of education among themselves. The results of questionnaires and interviews with students show that the implementation of the competence approach in the organization of their independent project work requires the creation of such organizational and pedagogical conditions under which each of them will have the opportunity to maximally realize not only their knowledge, but also personal and creative abilities, demonstrating a sufficient level awareness of the importance of completed tasks as a prerequisite for the formation of important professional and pedagogical qualities of the future educator. Key words: anonymous survey, future specialists in the field of education, institution of higher education, independent project work of students, methodical guide, online-learning, project technology of learning, scientific and pedagogical worker, war.
PENERAPAN ALGORITMA ECLAT DAN APRIORI PADA DATA MINING UNTUK MARKET BASET ANALISIS PENJUALAN

Growth of the retail business makes competition in implementing better marketing strategies. This research aims to analyze the shopping basket or market basket analysis in a mini market. Using two algorithms, namely the Eclat algorithm and the Apriori algorithm to analyze sales data, the purpose of this study is to find out the best algorithm in finding association rules or association rules from sales data and provide information regarding what items are the most sold as well as to find out what items. which must be displayed on the sales shelf at the same time. Based on the results of the implementation of thealgorithms Eclat and Apriori concluded that the algorithm Eclat works better thanalgorithm Apriori can be seen from the process of seeking the rule of 212 Mart sales data, Eclat algorithm produces a rule as much as 86 items with a time of 0.01s.