Reviewerly/exHarmony huggingface dataset:
INVESTIGATION AND THE ANALYSIS OF MEDICAL BIG DATA AND THE EMPLOYMENT OF ARTIFICIAL INTELLIGENCE FOR DISEASE PREDICTION

The development of technologies like big data, cloud services, Internet+, and artificial intelligence has greatly improved data communication services in the traditional pharmaceutical industry, driving the progress of the healthcare sector in our country. These advancements have facilitated healthcare system reforms, enhanced the quality and efficiency of medical services, and fostered technological innovation. In this context, our research focuses on the following areas and draws the following conclusions: In recent years, the pharmaceutical industry has leveraged technologies such as big data, cloud computing, Internet+, and artificial intelligence to enhance data transmission services. These advancements are considered crucial strategic resources for the pharmaceutical industry and ongoing healthcare system reforms in our country. Their goal is to improve the efficiency and quality of medical services, establish new models of healthcare delivery, and drive economic growth. Given this, our research aims to further explore these advancements. There is an increasing demand for an in-depth exploration of the utilization of logic in the fields of artificial intelligence and computer science. To address this need, the Manual of Logic in Artificial Intelligence and Logic Programming, along with its companion, the Manual of Logic in Computer Science, were developed. These resources combine comprehensive investigations and fundamental research to delve into various underlying topics across multiple disciplines. A foundational understanding of mathematical complexity is assumed as background knowledge, making the material of interest to logicians and mathematicians [1]. However, existing works primarily focus on core concepts and technologies, leaving out extensions, applications, and languages. The handbook aims to provide a comprehensive coverage of constraint programming, offering readers an accurate overview of the entire field and its potential. While each topic is presented in a survey-like manner, some details may be omitted in favor of broader coverage [2].Among various types of uncertainties, randomness and ambiguity are considered crucial and fundamental. The relationship between randomness and ambiguity is discussed, exploring uncertain states and their changes through measures like entropy and hyperentropy. The study investigates uncertainties in chaotic, fractal, and complex networks' evolutions and differentiations. A simple and effective method for simulating uncertainty through knowledge representation is proposed, providing a foundation for automating logical and visual thinking in the presence of uncertainty [4]. The appropriate timing for monitoring implementation remains unclear. Although there is room for improvement, if a data processing algorithm functions correctly, it can be employed separately from the original algorithm. However, there are limitations to its usage [5] Keywords: Big Data, biomedicine, cluster, electronic health cards, healthcare.
Improving Elementary School Students' Understanding of Literacy and Numeracy Through Digital Applications

The challenges of changing times require individuals to have adaptive life skills. Literacy and numeracy skills are two general skills that students need to have. The research aims to analyze elementary school (SD) students' understanding of literacy and numeracy through the use of digital applications in learning. Data collection was carried out through survey techniques using questionnaires, observations, and interviews. The participants in this study were 177 grade 4 (four) elementary school students, four teachers, and two school principals. Processing and analysis of data using descriptive quantitative analysis by combining the results of interviews and observations using a comprehensive analysis technique. The results showed that students' understanding of literacy material reached 39.32, and numeration material reached 34.81. On other aspects, based on the observation of the implementation of learning, it was found that 91% of teachers had delivered literacy and numeracy material in the excellent category. Based on the interviews and field observations, data was obtained that all sample schools had literacy and numeracy strengthening programs and carried them out regularly at school. Strengthening literacy and numeracy using digital application assistance in assessments in schools is currently unable to increase students' understanding of literacy and numeracy significantly.
Powerpoint-Based Pop Up Book Media in Fifth Grade Historical Narrative Text Material in Elementary Schools

The problem was found to be a need for more variety of learning media in the learning process in the classroom, especially the Indonesian language learning content, causing low student interest in learning and difficulty in students understanding the material. This research aims to develop learning media, the suitability of the media, and the effectiveness of Powerpoint-based Pop-Up Book learning media as historical narrative text material for class V elementary schools. This type of research is developed using the ADDIE model. The subjects in this research were class teachers, students, and a team of experts. The data collection method used in this research uses observation, interviews, tests, and questionnaires. The data collection instrument uses a questionnaire sheet. In this research, data analysis techniques used several techniques, namely qualitative descriptive techniques, quantitative descriptive techniques, and inferential statistics. The results of this research were obtained from material expert tests of 96.4%, media experts at 95%, class teacher tests at 95%, and student tests at 91.25%. The results of the effectiveness of the pretest and posttest have increased knowledge competency. The results of the n-gain test stated that the media developed was quite effective for use in teaching and learning activities. It was concluded that the PowerPoint-based Pop-Up Book media on historical narrative text material is very good, effective, and quite suitable for learning and can improve student learning outcomes.
Software GeoGebra: As an Assistance to the Guided Inquiry Model in Improving Students' Numeracy Skills

Students' numeracy skills are still low because mathematics is presented abstractly. Moreover, teachers are less attractive in packaging the learning models and media used, and the application of learning is still conventional and less interactive. The aim of this research is to testThe influence of the guided inquiry model assisted by GeoGebra software on the numeracy abilities of class IV students. The research method is quantitative with a quasi-experimental design with a Non-Equivalent Control Group Design. The study population was 76 students, with sample determination using saturated sampling techniques, 38 students as the control class and 38 students in the experimental class who would be given learning using the guided inquiry model assisted by GeoGebra software. Data was collected using a test method in the form of descriptions obtained after 5 treatments. The instrument uses a set of tests in the form of descriptions. Data analysis techniques use descriptive statistics and inferential statistics (t-test). The results of hypothesis testing showed that the average score of the experimental group was greater than the average score gain of the control group. Based on the results of the data analysis, it can be concluded that there is a significant influenceguided inquiry model assisted by GeoGebra software on the numeracy abilities of class IV students. So, learning activities using GeoGebra software can help to visualize mathematical concepts which can improve students' numeracy skills.
E-Comic Improves Understanding of Indonesian Language Learning Results

The problems currently occurring in elementary schools are limited variations in learning methods and students' difficulties in understanding spelling and the main idea of paragraphs. The aim of this research is to develop E-comics to improve understanding of Indonesian language learning outcomes. This type of research is development through 6 stages. This research involved research subjects consisting of media experts, material experts, and a group of fifth grade students consisting of 33 students. Data was collected through interviews, documentation and value assessments. The data collection instrument is a questionnaire. Data analysis techniques use descriptive qualitative, quantitative and inferential statistical analysis. The research results areThe e-comic media developed has gone through a careful evaluation and revision process and received high validation from media and material experts, exceeding the quality standard threshold. This media is considered appropriate and reliable to support learning and research. In addition, the results of the Wilcoxon test analysis show that there is a significant difference between pretest and post-test data, which shows an increase in students' understanding in the high category. It was concluded that E-comics can improve understanding of Indonesian language learning outcomes. This research is very relevant for advancing education and technology in using e-comics as a medium for learning Indonesian in elementary schools.
Lectora Inspire Interactive Media Based on Android on Class VI Elementary School Proclamation Event Material

Students' opinions regarding the content of social studies learning regarding the meaning of the Proclamation of Independence are prosaic learning content, and the lack of learning models presented to teachers is less interesting. The frequent use of reading-only methods is also an obstacle in delivering material to students. It is what triggers low student learning outcomes. This research aims to develop Android-based Lectora Inspire media on the meaning of the Proclamation of Independence, as well as the influence of student learning outcomes in Class VI elementary schools. This type of research is developed using the ADDIE model. The methods used to collect data are observation, interviews, tests, and questionnaires. Data collection instruments use questionnaires and test questions. The subjects used in this research were teachers, students, learning media experts and learning material experts. Data analysis techniques use descriptive quantitative, qualitative and inferential statistics. The results obtained from this research were that students tested in large groups 84%, small groups 90%, teacher trials 96%, media expert tests 76%, material expert tests 95%, as well as the results of effectiveness tests through pretest and post-test which had a significant increase from small groups to large groups. It was concluded that the Android-based Lectora Inspire media on the meaning of the Proclamation of Independence was very good to use, effective, and very suitable for use in the learning process and also improved student learning outcomes.
Motivation for Salary Increase for Teachers Depending on the Quality of Teaching and Research Results

Abstract In the world market, success is achieved by the countries that have better human capital and allocate sufficient resources for its reproduction. One of the results of the growing enrollment of many universities has been the need for additional qualified teaching staff and advanced training, which has led to the need to retrain existing teachers for staffing the growing number of higher education institutions. The first of the major success factors in this endeavor is to ensure that teachers are motivated to earn a relatively adequate salary. In recent years, a number of RA universities have successfully applied the method of salary calculation, which is based on the principle that a teacher (professor, associate professor, assistant, etc.) receives a salary depending on his category and workload (number of hours per academic year). Another important component of the assessment is the real involvement of the teacher in scientific activities and, based on this, a method for calculating the salary of a teacher is proposed, taking into account, among other things, his scientific activity. In order to encourage the scientific activity of the teacher, an indicator of the effectiveness of his scientific activity is introduced, the measure of which is scientific work during the previous (three to five) years.
Experimental Comparison of Standard and Trajectories Movement Algorithm using Intelligent Camera

With the development of technology, mobile transport robotic systems are increasingly entering various industries. The wide range of movement capabilities of a mobile transport robot with omnidirectional wheels around its central vertical axis and the precision of its movements in small spaces is an important advantage in terms of its controllability when used in logistics warehouses, industrial and production premises. For this reason, it is necessary to use complex algorithms for navigation in transport robots, which can be created with specific tools like Robotic Operating System (ROS) and others similar to it. The aim of the paper is to compare experimentally standard and trajectories movement algorithm using intelligent camera. The achieved results show that for 82.4% of the time the movement accuracy of the AGV is in accordance with the ideal trajectory. From the measurements made for the time to pass the route and the obtained average speed of movement, we can elect the conclusion that, when applying an algorithm using an artificial intelligence camera to determine the upcoming movement trajectory, we get a higher speed of movement and more stable trajectories compared, to a standard trajectories algorithm.
Automated Tracking of Electrical Engineer Competencies through Blockchain Technology

Modern educational systems in recent years combine the advantages of classical and digital education. Through them, basic and specialized preparation of students for work, development skills, and competencies in the practical application of theoretical knowledge for various positions in the industry is carried out. In parallel with this, the users of personnel (the business) impose trends related to the continuous training and improvement of the qualification and competencies (behavioral and technical) of the graduates of electrical engineering. Most often, it is carried out in the form of courses, training seminars, and workshops organized by non-governmental organizations (NGOs), companies, etc. outside the higher education system. The paper suggests a conceptual blockchain model for the automated tracking and digital accreditation of electrical engineers’ technical qualifications and competencies acquired during their academic training (tracking) and throughout their life cycle (work experience). Examples of modifying data in the system are attached. One is certifying the completion of online courses in electrical engineering and electrical machinery through shared resources from a different university than the teaching university. The other is about the possibility for students to enter data into the system when attending a conference. The examples demonstrate that blockchain technology is suitable for data reference requested by an accredited third party. These could be a funding or training structure, a ministry of education, or an employer. In this way, the authenticity of the obtained records of the acquired degrees of education and qualification is guaranteed and the professional development of electrical engineers after the completion of higher education is nonmodifyed tracked. We found that blockchain-based digital control of protected degrees and qualifications certifying competencies is a key factor in promoting self-learning and enhancing practical skills. They are the basis of successful implementation in the labor market and rapid career growth. Therefore, it is necessary to develop uniform educational standards that guarantee high quality of formal and non-formal education and its recognition by both higher education institutions and companies.
Clean Genetic Algorithm Architecture for Improved Modularity and Extensibility

This paper delves into the potential of incorporating SOLID and clean architecture principles into the development of evolutionary algorithms, specifically focusing on a genetic algorithm. The introduced approach presents an innovative design and implementation, offering notable enhancements in extensibility and modularity, thereby contributing to improved testability and maintainability within the domain of evolutionary algorithms. The study demonstrates how the proposed design enables effortless extensions of operations within the genetic algorithm, such as parent selection, crossover, and mutation. These enhancements result from strict adherence to the SOLID and clean architecture principles. To demonstrate the practicality of this approach, the principles have been applied to a Python library called "GAdapt." This paper highlights the constructive influence of introduced concepts on the development of genetic algorithm and the improved flexibility and ease of maintenance inherent in this approach.
Using GPT-3 to Automatically Create RESTful Service Descriptions

There exists a problem of the lack of machine-readable description for most web services that are used today. Most of these services are RESTful services and often do not have formally defined functionality. The aim of this paper is to solve this problem through the automatic generation of descriptions of these services. The practical part of the work includes the application of GPT-3 model for the automatic generation of RESTful web service descriptions through several examples of available application programming interfaces.
Development of Cyber Ranges as a Reference Environment for Digital Transformation

In the last few years, the expansion of information technology in more and more activities is evident and attracting high attention. The improvement of big data processing and the growth of artificial intelligence are the foundations of these processes. However, Cyber security is a lagging element of this technological evolution. Proof of this is the increased Cybercrimes levels for the past year 2022 compared to previous years. To accelerate the resolution of this issue, the study examines the development and use of Cyber range as a major component of the efforts in enhancing Cyber resilience. The different types of solutions in the field, their advantages and disadvantages are considered. The role of Cyber Ranges as a core component of Cyber security endeavor that enables all kinds of research and testing of real-world systems and uncovering their weaknesses. As in recent years, Cyber ranges are establishing themselves as an important tool for training and enhancing the knowledge and skills of Cybersecurity experts. The overview analysis on the topic also draws attention to the benefits of consolidating Cyber polygons as a global service.
Towards a Conceptual Model of a Blockchain System for Automatic Generation of Academic Diplomas: Use Cases and Scenarios

The implementation of a blockchain system for the automatic generation of academic credentials in a higher education institution can be considered as a safeguard against potential instances of abuse and counterfeiting of diplomas. However, the utilization of blockchain technology in the field of education, particularly within higher education institutions, appears to be limited in scope. This article aims to elucidate the advantages and constraints associated with the use of blockchain technology in higher education organizations. In addition, we present the ideas towards developing a blockchain-based prototype model that targets the automatic generation of diplomas using smart contracts and ensures the fast and secure verification of diplomas. In order to provide a more comprehensive understanding of the blockchain system, we aim to expand on its description via the use of graphical representations in the form of use case diagrams for each actor involved in the system. Additionally, we provide use case scenarios, as well as outline the functional as well as the nonfunctional requirements that are essential for the proper functioning of such a system.
Machine Learning Ensembles for Solar Power Forecasting

Solar power forecasting is crucial for better integration of renewable energy, grid stability and energy resources planning and allocation. In this paper, we propose a 24-hour ahead solar power forecasting system by harnessing the power of blending ensemble techniques. Our ensemble framework combines the predictive capabilities of Long ShortTerm Memory (LSTM) and XGBoost models as base learners, further refined by Ridge Regression meta model. We conducted extensive experiments on real-world solar power generation data, assessing the effectiveness of our ensemble methodology. The results demonstrate an improvement in forecasting results compared to individual models based on the Root Mean Square Error (RMSE) score. The blending of LSTM and XGBoost models allows us to capture both the temporal dependencies and nonlinear relationships inherent in solar power generation data, while the Ridge Regression meta model was used for regularization and predictive stability. Our findings highlight the potential of ensemble techniques in advancing solar power forecasting, contributing to the reliable integration of renewable energy sources into the grid.
Application of Artificial Intelligence in ERP System's Fulfillment of the Purchasing Function

Artificial intelligence (AI) has the potential to have an impact on every aspect of a business, including the purchasing division. This article analyzes how AI is used in the fulfillment of the purchasing function in enterprise resource planning (ERP) systems, especially SAP editor (Systems, Applications, and Products in Data Processing), and its impact on the system's performance. First, a review is undertaken of how AI is used in different fields in companies. Thereafter, the paper follows a deep research to collect the information on SAP editor from any sources such as industry reports, article ad information systems site webs. The results indicate that the application of AI can be integrated with the purchasing function of ERP systems almost throughout their entire lifecycle, helping overcome many problems that ERP systems are not able to automate and streamline.
Sentiment analysis in digital learning: Comparing Lexical, Traditional machine learning, and deep learning approaches

Over the last decade, a gradual shift has been observed towards using sentiment analysis approaches to improve the online learning environment. In fact, sentiment analysis can provide us with valuable information about the socio-affective engagement of learners, which is considered a key condition for competences development. Lexical, machine learning, and hybrid approaches have become popular choices in sentiment analysis research and applications. The current study examines the classification accuracy of learners' socio-affective engagement based on their interaction messages using five methods: TextBlob, Naive Bayes, Simple Neural Net, CNN, and LSTM. The experimental results suggest that the LSTM and CNN models perform better, with an accuracy rate on the test set exceeding 0.83.