Reviewerly/exHarmony huggingface dataset:
THE EFFICIENCY AND RELIABILITY OF BACKEND TECHNOLOGIES: EXPRESS, DJANGO, AND SPRING BOOT

Increasing popularity of web applications has led to the development of many technologies that enable their production, both on the client and server side. This article attempts to compare three most popular server-side frameworks – Django, Spring Boot and Express. Each of the selected technologies is based on a different programming language. These frameworks were compared in terms of request processing time and reliability. Within the conducted research three backend applications handling HTTP requests were created, all of them using the same database consisting of employees’ data. Afterwards, a series of load tests was performed to determine levels of efficiency and reliability of created applications for various numbers of virtual users sending requests to the server at the same time. Five test cases with the following number of requests: 1000, 2000, 4000, 8000, and 16000 were planned and performed for each type of HTTP requests handled by the server simultaneously. Based on the obtained results, it was concluded that the Spring Boot framework was the best in terms of request processing time and high reliability. However, it was noted that for many test cases under extreme load, it had a significantly higher percentage of incorrectly processed requests compared to the Express application, even though the application was noticeably slower. The worst results were observed for Django because the test application created for this framework revealed the longest requests processing time and the highest error rate during processing requests out of the three tested applications. The performed studies helped to determine the efficiency and reliability of the tested technologies at various levels of load. Furthermore, the studies were crucial in obtaining knowledge about the evaluated frameworks as well as their properties and formulating conclusions that will be able to help the developers choose technologies before the implementation of their programming projects.
HRYHORII LEVCHENKO’S ARTISTIC AND PEDAGOGICAL ACTIVITY IN THE CONTEXT OF UKRAINIAN MUSIC TEACHER TRAINING DURING THE LAST QUARTER OF THE 20th – BEGINNING OF THE 21st CENTURY

The article highlights the analysis results of the artistic and pedagogical activity of the outstanding Ukrainian teacher, musician, folklorist, professor, Head of the Music Department, and leader of the Ukrainian folk choir “Kalyna” of Poltava V. G. Korolenko National Pedagogical University Hryhorii Levchenko.&#x0D; The analysis has been carried out in the context of future music teacher training in Ukraine during the last quarter of the 20th - the beginning of the 21st century. It is emphasized that with his professional activity, H. Levchenko strengthened the process of providing education to students majoring in spatiality Secondary Education (Music Art) at the institution of higher pedagogical education.&#x0D; Attention is focused on the fact that during the last quarter of the 20th century, the teacher created a classical university department of music with a skilled staff. It is emphasized that during total Russification in the 80s and 90s, teachers led by H. Levchenko managed to adhere to the position of training music teachers based on the best examples of Ukrainian musical heritage. It is indicated that at the end of the 80s of the 20th century, H. Levchenko founded the Ukrainian folk choir “Kalyna.” It became one of the leading student collectives of Ukraine during the last decade of the 20th and the beginning of the 21stcentury. It popularized the folk art of its native land on the territory of Europe. It is established that an essential component of student education during the specified historical period was the combination of formal artistic education with non-formal artistic education not only in the “Kalyna” choir but also in other artistic and creative groups. At the beginning of the 21st century, these groups became creative laboratories of the department. Even today, students undergo musical and performing practice in conducting and choral, vocal and performing, and choirmaster training there. It significantly strengthens their professional competence. It is emphasized that the most valuable thing in Professor H. Levchenko’s work for more than half a century of artistic and pedagogical activity is his ascetic work on the formation of a music teacher as a patriot of his country, capable of preserving and multiplying the musical heritage of our people and educating the young generation on the best examples of musical art of Ukraine.
Output-based algebraic disturbance estimation for a class of T-S fuzzy systems with measurement noises*

The present paper designs an algebraic disturbance estimation method for a class of Takagi-Sugeno (T-S) fuzzy systems with noisy output measurements. The algebraic estimation methods are known by many researchers thanks to the key features like fast-convergence and robustness against measurement noises. To achieve this, the observable canonical form of the disturbed T-S fuzzy system is given at first in this paper. Then, output-based algebraic integral formulas are derived for the disturbance of the system model by applying the modulating functions method. Through a simulation example with an output-feedback control input, the efficiency and robustness of the proposed formulas under noisy measurement environment is verified.
Comparing SVR and Random Forest Forecasting based on Autoregressive Time Series with Application

The accuracy of forecasting the time series of relative humidity in its maximum and minimum cases is important for controlling environmental impacts, damages and risks. In this study, the support vector regression (SVR) method and the random forest (RF) method will be used, depending on the principle of auto regressive (AR) and the autocorrelation (AC), which is the main characteristic of time series in general. The Lags of original time series will be depended as the explanatory (input) variables while the original series will be as target variable. This structure is fitted with the AC principle because the current observation will be depending on time lags in each time step of time series variable. Comparisons of the forecasting results will be performed by using SVR , RF methods and compared to the classical method of analysing time series which is the integrated autoregressive and moving average (ARIMA) model. The SVR and RF methods were employed due to their importance in improving the forecast results, as they are the ideal solution to the problem of non-linearity of the data, as well as the problem of heterogeneity in the climate data, especially as a result of the fact that they contain many seasonal and periodic compounds, which may lead to inaccurate forecast. The forecast of the time series of relative humidity in its minimum and maximum cases was studied in this study for one of the agricultural meteorological stations in the city of Mosul-Iraq. The results of this study reflected the superiority of both SVR method and RF method compared to the classical method represented by the ARIMA model. The results also included the superiority of the RF method in forecasting the training period compared to the SVR method, which was more balanced despite that, as it superiority the results of ARIMA in forecasting the training period and the testing period, while it was its forecast performance is slightly better than the forecast results of the RF method in the test period.
Greek Primary School Teachers' Professional Experience as Digital Actors

The present study investigates teachers’ views on their experience as digital actors in the context of Greek educational policy. The interview was used as a tool for data collection. Findings revealed that the teachers in our survey highlighted the shortcomings of the Greek educational approach in terms of material and technical infrastructure and the training of teachers regarding ICT. Through this new digital context of education, teachers conceptualize their competence and self-efficacy, their self-perception, and their emotional response to integrating new digital tools into teaching practice.
The Study of Genealogical Trees of Bengal Cats Using Machine Learning Methods to Identify Hereditary Diseases

The aim of the work is to investigate, using methods of intelligent analysis, the probability of heritable disease transmission, using a database of Bengal cats, which have a high predisposition to hypertrophic cardiomyopathy. The Neo4j graph database management system was chosen to accomplish the task. The program realizing the collection of information from the web version of the cat database and further processing of the obtained data was implemented in Python. Various approaches have been used to determine an individual's HCM disease status from their pedigree. The analysis used such methods as: random forest method, logistic regression and multilayer perceptron. Experiments have shown that the most effective approach to solving this problem is the prediction of relationships, and the most effective model among the considered candidate models is the random forest method. In practice, methods for solving machine learning problems using graph structure data were considered.
Retracted: Deep-Learning-Guided Student Classroom Action Understanding for Preschool Education

[This retracts the article DOI: 10.1155/2022/9416467.].
The Construction of the Value System of English Language and Literature Courses Based on the Deep Learning Model

With the rapid development of artificial intelligence, big data and other modern technologies, the intelligence of education has become the trend of modern education reform, and the deep learning model, as a representative of the application of artificial intelligence in the field of education, has a natural connection with the construction of the value system of English language and literature courses. This paper analyzes the feasibility and significance of the application of deep learning model in English language and literature from the characteristics of deep learning model, and constructs the value system of English language and literature based on it, aiming to provide more choices for English majors and improve their interest and enthusiasm in learning English language and literature.
The Semosemo: Vehicle Rental Application in Manado City

In everyday human life, there are many aspects that cause a decision to be made. Agreements can be made in writing or unwritten, reciprocal agreements and unilateral agreements, obligatory agreements and one of them is a lease agreement. The lease agreement can help the parties, both from the leaser and the lessee. Car Rental is one of the businesses providing transportation services that involves the use of mobile devices to find out information about the services provided by the company. Car Rental is closely related to transportation services to help people who need car rental for various purposes. To use rental services in Manado City, usually the tenant must go to the rental place, and that is less efficient to do. Therefore, the problem found is how to make the Semosemo Vehicle Rental Application in Manado City. With the aim of making the Semosemo Vehicle Rental Application in the City of Manado. This research uses the prototype method and is also assisted by software such as React Native, Figma, and Visual Studio Code. The result is that the Semosemo application can be made to help rent vehicles in the city of Manado and the application can run accordingly.
The Use of Neural Network Technologies Using the Example of the Domestic Software Platform DeepTalk in the Educational Process of PGUPS at the Department “Information and Computing Systems”

Purpose: To develop a methodology for using artificial intelligence to solve the problem of intensifying the process of developing students’ professional competencies. To show the need to improve teaching technologies due to a reduction in classroom time allocated to general education disciplines when reconfiguring the curricula for training specialists. To consider the issue of rethinking the effectiveness of the existing distance learning system (DLS) as a means of rationalizing educational procedures in connection with the continuing risk of students acquiring only a minimum level of knowledge due to the use of template electronic demonstration materials in the DLS. Methods: To achieve the stated goals, data from the analysis of the results of the implementation of the “Digital Teacher” (DT) product, developed on the basis of the domestic DeepTalk platform, into the educational process of the ICS Department are used. An algorithm for training a neural network as a mathematical core of the DT has been developed. When used at the first stages of loading the DT with text and presentation materials, the principle of randomization of logically related queries, answers and comments from teachers on lectures and practical assignments is implemented. Results: A technique for iterative adjustment of the DT has been developed and tested when increasing the array of situational data with the following elimination of the contradiction between the requirement for representativeness and the initially small volume of the generated training sample. Practical significance: The creation of a developed neural network product is the first step in the deployment of the University’s digital services system, the hierarchical structure of which according to areas of training, qualifications and specializations should include “Digital Teacher”, “Digital Applicant Curator” and other similar products. The introduction of DT into providing teaching in senior years will contribute to the formation of the student’s individual educational trajectory and the development of his cognitive abilities. Further, in the course of obtaining additional professional education, the use of DeepTalk will ensure the acquisition of skills in interacting with AI, used as a means of supporting decision-making under conditions of uncertainty. DeepTalk allows you to assess the predisposition of applicants to the main types of professional activities at JSCo “Russian Railways”.
Web Scraping: Data search and retrieval methodology in information science

Web scraping is a process of automated data collection on websites through the action of bots or programs. It is important nowadays because databases are getting bigger and bigger and, in general, there is an urgent need for information. The technique presented makes it possible to extract texts, numerical data, images, files and tables, available both on the site's home page and in its various tabs. The aim of this chapter is to present the potential of using Web scraping through Python to collect data from websites and its importance for information retrieval in Information Science. We used command lines written by Lisa Tagliaferri to check whether these command lines work and whether we were able to obtain the desired information. The site used to retrieve the desired information about artists' names and the links to their names was Web archive, which lists all the artists whose works are in the National Gallery of Art in the USA. As a result, we realized that command lines are extremely useful for obtaining information, since they allow us to obtain a large amount of information in a short time. In conclusion, we saw that scraping data from websites is perfectly feasible using Python and its code and that the information retrieved was fully satisfactory.
Analysis of Information Security Culture at FMIPA Halu Oleo University Using Partial Least Squares-Structural Equation Modeling Method

This research aims to analyze the information security culture at FMIPA Halu Oleo University. The results of the analysis show that exogenous latent variables, such as information security awareness, the role of faculty leaders, and information security policies, have a significant positive impact on information security culture. The research results show that the security awareness variable has a positive effect (0.221) on the Information Security Culture variable. Apart from that, the top management variable also has a positive effect (0.185) on the Information Security Culture variable. Likewise, the security policy variable has a significant positive influence (0.233) on the Information Security Culture variable. These findings provide an in-depth understanding of the factors that influence the culture of information security in the FMIPA Halu Oleo University environment, which can be the basis for recommending improvements in increasing information system security at the faculty.
Leakage Abuse Attacks on Encrypted Columns Using Lp-Optimization

Our work studies a class of leakage abuse attacks to address the Single-Column Full-Information Leakage Abuse Attack (SF-LAA) problem called the Lp-optimization LAA. Each such attack is defined for some p > 1. In this work, we implement three such attacks (for p = 1, 2, 3) using Python, by reducing the problem first to Lp-norm minimization, then reducing that to the Linear Sum Assignment Problem (LSAP). Alongside the three Lp-optimization attacks, we implement Frequency Analysis as an LAA and compare the effectiveness of the four LAAs by calculating two different scores.
Reorienting Collection Analysis

In public libraries, especially those in rural settings, it is important that every dime of library funding is leveraged effectively into serving the community. As part of a year-long project beginning in January 2023, we are evaluating item-level cost-effectiveness for each circulating item housed at the public library in Lakeville, Indiana. Through the use of big(ish) data, some custom Python scripting, and machine learning algorithms we hope to answer: How much money is saved by library patrons through their use of the public library's physical collection? How much money is saved by the community through the operation of a public library based on the use of the circulating collection? And are there any non-obvious traits which make an item or title a more or less cost-effective circulating asset? In this column, I will describe the scripts, share initial findings, discuss challenges, and investigate next steps.
MENCIPTAKAN KOTA CERDAS: MEMANFAATKAN AI (ARTIFICIAL INTELLIGENCE) UNTUK PENGEMBANGAN RUANG PUBLIK PERKOTAAN YANG CERDAS

As cities continue to grow and face increasing challenges, the concept of smart cities has emerged as a solution to create sustainable, efficient, and livable urban environments. One crucial aspect of smart cities is the development of intelligent urban public spaces that cater to the diverse needs and preferences of residents and visitors. This paper explores the role of AI in the development of such spaces, focusing on how AI technologies can enhance their design, functionality, and user experience. It examines the application of AI in areas such as data analytics, user behavior analysis, sensor networks, and adaptive systems to create dynamic and responsive public spaces. Furthermore, it discusses the benefits, challenges, and potential ethical considerations associated with harnessing AI for intelligent urban public spaces development. Through this exploration, the paper highlights the transformative potential of AI in shaping the future of urban environments and fostering inclusive and sustainable communities.
Efektivitas Kecerdasan Buatan VerasAI sebagai Referensi Desain Fasad Mahasiswa Arsitektur

Digital era development has supported to lighten human work these days. There is a lot of software which is acted in that case. VerasAi is one of the software which support the architecture student to find ideas/reference facade to aplly in a design. Buildang facade has important role in aextethic and building termal performance, In order to an efficient and interesting facade design become crucial in architectural practice. Methode which is used in are kualitatif deduktif that describe plug-in VerasAI application and also sharing quisoner to the architectural students which has been following Studio architecture planning and using VerasAI as a rendering tools in a design process. As results of the research are to measure the efectiveness of using VerasAI in giving facade design concept ideas for architecture students. This research also disclose that VerasAI can help to increasing time effciency in facade design process, student can explore a lot of ideas quikly and effective is possible. However, there are also some constraint of using VerasAI such as algorithm limit and lack of control over some aspects of the design. In other hand, it show how are the advantage and lack of VerasAI and impact to the architecture student.
Data Monopolization by the Tech Industry

Based on Shumaila Shahani's central thesis “that the un­regulated accumulation of data by the tech industry has led to the consolidation of power among a select few, resulting in the erosion of democratic principles” she first explains the data-driven business model of digital platforms providing. She then explains the risks involved in uncontrolled data collection and in data misuse and highlights threats to democracy and national security concerns.
Understanding The EU AI Act: a new era in AI Regulation In Europe

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Retracted: English Word Learning Using Intelligent Massive-Scale Big Data Mining Technique

[This retracts the article DOI: 10.1155/2022/6882922.].
Using Qualtrics XM to Create a Point-of-Use Survey to Assess the Usability of a Local Implementation of Primo

In 2020, Libraries and Cultural Resources (LCR) at the University of Calgary used Qualtrics XM to design and pilot a point-of-use survey to collect user feedback on the usability of our implementation of Primo, Ex Libris's web-scale discovery service. Over a two-week period, users were presented with the pop-up survey while searching and asked to provide feedback. This article summarizes how we designed and implemented this point-of-use survey and the lessons learned from this project.
Implementation of the K-Nearest Neighbor Algorithm to Predict Air Pollution

Air pollution is a serious issue that impacts air quality and human health. In this study, the K-Nearest Neighbor (KNN) algorithm is applied using Rapidminer software to predict air pollution levels. The research aims to predict air pollution levels based on various air quality parameters such as particulates, PM10, PM2.5, CO, NO2, SO2, and O3. By implementing the K-Nearest Neighbor algorithm in Rapidminer, the predicted values for air pollution data resulted in an accuracy of 93.94%. This study concludes that employing the K-Nearest Neighbor algorithm using Rapidminer software can be an effective method for predicting air pollution levels. With a strong accuracy rate of 93.94%, this can have a positive impact on both human health and the environment. The predictive model developed can aid decision-making and enhance awareness among the public regarding the importance of maintaining air quality management.