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A literature review was conducted to develop a clear understanding of the student-centered approach using modern technologies in distance learning. The study aimed to address four research questions: What research experience already exists in the field of the student-centered approach in distance learning? What modern technologies are used in distance learning, and how are they related to the student-centered approach? What are the advantages and limitations of implementing the student-centered approach and modern technologies in distance learning? What recommendations can be derived from existing research for the effective implementation of the student-centered approach and modern technologies in distance learning? The purpose of writing this review article is to provide a comprehensive overview of the student-centered approach using modern technologies in distance learning and its advantages. To conduct this review, a Web of Science and Scopus database was searched using the keywords “student-centered approach,“ “modern technologies,“ and “distance learning.“ The search was limited to articles published between 2012 and 2023. A total of 688 articles were found, which were selected based on their relevance to the topic. After the verification and selection process, 43 articles were included in this review. The main results of the review revealed that the student-centered approach to learning took various forms or was defined individually, and there were significant differences in the main research findings. The review results provide a comprehensive overview of existing studies, advantages and limitations of the student-centered approach using modern technologies in distance learning as well as examples of successful implementation in various educational institutions. The article also discusses the challenges that online and distance learning may pose to the student-centered approach, the modern technologies that support the student-centered approach, and suggests ways to overcome these challenges. The role of technology in facilitating the student-centered approach in online and distance learning is analyzed in the article, along with recommendations and best practices for its implementation. The student-centered approach is gaining increasing attention and popularity as a means to address these issues and improve the quality of online and distance learning.

This article develops a method for analysing sensor data to prevent cyberattacks using a modified LSTM network. This method development is based on the fact that in the context of the rapid increase in sensor devices used in critical infrastructure, it is becoming an urgent task to ensure these systems’ security from various types of attacks, such as data forgery, man-in-the-middle attacks, and denial of service. The method is based on predicting normal system behaviour using a modified LSTM network, which allows for effective prediction of sensor data because the F1 score = 0.90, as well as on analysing anomalies detected through residual values, which makes the method highly sensitive to changes in data. The main result is high accuracy of attack detection (precision = 0.92), achieved through a hybrid approach combining prediction with statistical deviation analysis. During the computational experiment, the developed method demonstrated real-time efficiency with minimal computational costs, providing accuracy up to 92% and recall up to 89%, which is confirmed by high AUC = 0.94 values. These results show that the developed method is effectively protecting critical infrastructure facilities with limited computing resources, which is especially important for cyber police.

People who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their personal data are easily extracted. To solve this problem, a novel browser extension named ClickBaitSecurity is proposed, which helps to evaluate the security of a link. The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm. Both of these algorithms incorporate binary search features to detect malicious content more quickly and more efficiently. Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN). The proposed ClickBaitSecurity solution has greater accuracy in detecting malicious and safe links compared to existing solutions.

Mobile technologies are an essential part of people’s everyday lives since they are utilized for a variety of purposes, such as communication, entertainment, commerce, and education. However, when these gadgets are misused, the human body is exposed to continuous radiation from the electromagnetic field created by them. The communication services available are improving as mobile technologies advance; however, the problem is becoming more severe as the frequency range of mobile devices expands. To solve this complex case, it is necessary to propose a comprehensive approach that combines and processes data obtained from different types of research and sources of information, such as thermal imaging, electroencephalograms, computer models, and surveys. In the present article, a complex model for the processing and analysis of heterogeneous data is proposed based on mathematical and statistical methods in order to study the problem of electromagnetic radiation from mobile devices in-depth. Data science selection/preprocessing is one of the most important aspects of data and knowledge processing aiming at successful and effective analysis and data fusion from many sources. Special types of logic-based binding and pointing constraints are considered for data/knowledge selection applications. The proposed logic-based statistical modeling method provides both algorithmic as well as data-driven realizations that can be evolutionary. As a result, non-anticipated and collateral data/features can be processed if their role in the selected/constrained area is significant. In this research, the data-driven part does not use artificial neural networks; however, this combination was successfully applied in the past. It is an independent subsystem maintaining control of both the statistical and machine-learning parts. The proposed modeling applies to a wide range of reasoning/smart systems.

Decentralization, immutability and transparency make of Blockchain one of the most innovative technology of recent years. This paper presents an overview of solutions based on Blockchain technology for multi-agent robotic systems, and provide an analysis and classification of this emerging field. The reasons for implementing Blockchain in a multi-robot network may be to increase the interaction efficiency between agents by providing more trusted information exchange, reaching a consensus in trustless conditions, assessing robot productivity or detecting performance problems, identifying intruders, allocating plans and tasks, deploying distributed solutions and joint missions. Blockchain-based applications are discussed to demonstrate how distributed ledger can be used to extend the number of research platforms and libraries for multi-agent robotic systems.

Decentralization, immutability and transparency make of Blockchain one of the most innovative technology of recent years. This paper presents an overview of solutions based on Blockchain technology for multi-agent robotic systems, and provide an analysis and classification of this emerging field. The reasons for implementing Blockchain in a multi-robot network may be to increase the interaction efficiency between agents by providing more trusted information exchange, reaching a consensus in trustless conditions, assessing robot productivity or detecting performance problems, identifying intruders, allocating plans and tasks, deploying distributed solutions and joint missions. Blockchain-based applications are discussed to demonstrate how distributed ledger can be used to extend the number of research platforms and libraries for multi-agent robotic systems.