Computer Systems and Information Technologies=Комп'ютерні системи та інформаційні технології

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https://elar.khmnu.edu.ua/handle/123456789/9492

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Перегляд Computer Systems and Information Technologies=Комп'ютерні системи та інформаційні технології за Ключові слова "004.7"

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  • Ескіз
    Документ
    Decision-making method in interdependent computing systems
    (Хмельницький національний університет, 2025) Kryzhanyvskyi, Dmytro; Drozd, Andriy; Besedovskyi, Oleksii
    The relevance of this paper lies in the fact that modern interdependent computing systems are being actively implemented in critical areas ranging from smart energy grids and transportation systems to autonomous robotic platforms and distributed cloud services. These systems are characterized by a complex structure, a large number of interacting agents, and high requirements for real-time decision-making. Despite significant scientific and technological progress, a number of challenges remain unresolved to ensure the sustainability, adaptability, and coherence of all system components. One of the key challenges is the need to ensure rational decision-making in a decentralized environment where each agent has limited information about the state of the system as a whole and operates under conditions of uncertainty and potential distrust of other agents. Classical centralized methods are often ineffective or inapplicable in such cases due to excessive complexity or delays in data exchange. The issue of developing methods that ensure not only the correctness of decisions but also compliance with time constraints is particularly relevant. In interdependent computing environments, where the decision of one agent affects the outcome of the work of others, any delay or error in the strategy can lead to degradation of the performance of the entire system. In such environments, it is crucial to use adaptive, game-based, and reputation-based approaches that allow for dynamic consistency and stability of the system. In this paper, we develop a decision-making method for interdependent computing systems that combines Bayesian reputation updating, log-linear strategy learning, and reinforcement learning mechanisms. The peculiarity of the proposed method is its ability to adapt to changes in the environment and effectively detect unscrupulous agents by dynamically adjusting reputations. The algorithmic implementation of the model allows achieving the Bayesian-Nash equilibrium, which indicates the stability of the system even in complex interaction scenarios. The results of experimental modeling have demonstrated that the proposed method strikes a balance between adaptability, reliability, and efficiency of interactions. The system demonstrates the ability to self-organize, stabilizes in fewer iterations compared to classical approaches, and effectively prevents the influence of sabotaging behavior of individual agents. The prospect of further research is to adapt the model to different types of computing environments, including MEC infrastructures, edge systems, and IoT platforms. Special attention is planned to be paid to the development of new objective functions that would take into account not only the stability and speed of convergence, but also energy consumption, network bandwidth, and quality of service (QoS).
  • Ескіз
    Документ
    Legal and ethical bases for creating representative datasets to detecting manifestations of cyberbullying in text content
    (Хмельницький національний університет, 2025) Sobko, Olena; Chochia, Archil
    The article is devoted to developing the method for creating of representative text data datasets for detecting manifestations of cyberbullying in text content, considering ethical and legal principles. The primary focus is ensuring fair and equal representation of different demographic groups in text samples, which is critical for creating non-discriminatory and socially responsible artificial intelligence models. Emphasis is placed on compliance with key ethical principles – preventing harm, avoiding bias, and ensuring representativeness – and provisions of international law, particularly the General Data Protection Regulation. Proposed method for creating of representative text data datasets for detecting manifestations of cyberbullying in text content, taking into account ethical principles, which includes the following stages: preliminary processing of text data, analysis of distributions according to ethical aspects (age, gender, religion etc.), and representative adjustment through multi-criteria optimization. Machine learning models are trained on prepared balanced samples using appropriate reference datasets to classify text samples according to ethical criteria. The comparison is based on official demographic data for Ukraine, which ensures the reliability of the assessment of deviations. As a result of applying the developed method, a representative sample was created with a deviation of the proportions of ethical groups from the target values within 0.00-0.04%. The statistical metrics obtained confirmed the effectiveness of the selected models and demonstrated a high degree of compliance with the ethical responsibility requirements of the results. The analysis showed that the initial datasets contained imbalances, which were successfully eliminated through multi-criteria optimization and data augmentation. The developed approach can be integrated into preparing training samples for ethically oriented artificial intelligence systems that perform automated detection of cyberbullying manifestations in text content, reducing the risks of reproducing social biases and increasing trust in algorithmic decisions.
  • Ескіз
    Документ
    Method of real-time video stream synchronization in the working environment of an apple orchard
    (Хмельницький національний університет, 2023) Melnychenko, O.; Мельниченко, О.
    Monitoring and analyzing the state of harvest in an apple orchard is essential for efficient horticulture. Unmanned aerial vehicles (UAVs) have been increasingly used for this purpose due to their ability to capture high-resolution images and videos of the orchard from different perspectives. However, synchronizing the video streams from multiple UAVs in real-time presents a significant challenge. The traditional controller-worker architecture used for video stream synchronization is prone to latency issues, which can negatively impact the accuracy of the monitoring system. To address this issue, the authors propose a decentralized method using a consensus algorithm that allows the group of UAVs to synchronize their video streams in real time without relying on a centralized controller device. The proposed method also addresses the challenges of limited network connectivity and environmental factors, such as wind and sunlight. The automated system that utilizes the proposed method was tested in an actual apple orchard. The experimental results show that the proposed approach achieves real-time video stream synchronization with minimal latency and high accuracy. As such, the SSIM index varies from 0.79 to 0.92, with an average value of 0.87, and the PSNR index – varies from 22 to 39, which indicates the decent quality of the received information from combined images. Meanwhile, the effectiveness of the developed system with the proposed approach was proven, which is confirmed by a high average value of 82.69% of the reliability indicator of detecting and calculating the number of fruit fruits and a low average level of type I (14.67%) and II (18.33%) errors. Overall, the proposed method provides a more reliable and efficient approach to real-time video stream synchronization in an apple orchard, which can significantly improve the monitoring and management of apple orchards
  • Ескіз
    Документ
    Monitoring system for critical infrastructure objects based on digital twins
    (Хмельницький національний університет, 2025) Andrieiev, Dmytro; Lyhun, Oleksii; Drozd, Andriy; Ponochovna, Olena
    Critical infrastructures are fundamental to the seamless operation of modern societies, encompassing sectors such as energy, healthcare, transportation, and communications. Ensuring their reliability, performance, continuous operation, safety, maintenance, and protection is a national priority for countries worldwide. The digital twins play a crucial role in critical infrastructure, as they enhance security, resilience, reliability, maintenance, continuity, and operational efficiency across all sectors. Among the benefits offered by digital twins are intelligent and autonomous decision-making, process optimization, improved traceability, interactive visualization, and real-time monitoring, analysis, and prediction. Furthermore, the study revealed that digital twins have the capability to bridge the gap between physical and virtual environments, can be used in combination with other technologies, and can be integrated into various contexts and industries. The use of digital twins was explored as the foundation for developing a modern monitoring system for critical infrastructure facilities enables multi-level assessment of asset conditions in real time, ensuring precise threat detection, anomaly identification, and timely decision-making. Integration with artificial intelligence and big data technologies allows not only the collection and analysis of large volumes of information but also the creation of adaptive behavioral models for systems in emergency situations. Special attention was given to the method of optimizing critical IT infrastructure using digital twins, which combines virtual modeling, predictive algorithms, and automated management. The proposed approach enhances the reliability of digital systems, minimizes downtime, optimizes maintenance costs, and strengthens cybersecurity. This system is especially relevant in the context of growing risks and increasing demands for the stability of strategically important infrastructure assets. The application of digital twins for monitoring and optimizing critical infrastructure demonstrates considerable potential for improving its resilience, safety, and operational efficiency. The approaches discussed in the study confirm the relevance of implementing digital models as tools for timely risk identification, failure prediction, and informed decision-making. By integrating such technologies, organizations can reduce operational costs, minimize downtime, and improve the overall stability of infrastructure operations. Therefore, digital twins represent a vital step toward the digital transformation and modernization of mission-critical systems across various sectors.
  • Ескіз
    Документ
    Task optimisation in multiprocessor embedded systems
    (Хмельницький національний університет, 2025) Martiniuk, Dmytro; Lyhun, Oleksii; Drozd , Andriy; Besedovskyi, Oleksii
    The relevance of this work lies in the fact that the existing task distribution in multiprocessor embedded systems plays a key role in the development of devices used in various industries. Despite the progress made, there are still many research challenges that require in-depth analysis and implementation of effective solutions. One of the main challenges is to ensure the reliability of embedded systems, especially in environments where safety is critical. Although the functionality of such systems is usually defined at the design stage, ensuring their stable operation in real time remains a challenge. It is necessary not only to guarantee the correctness of calculations, but also to adhere to time constraints, which requires new approaches to managing the resources of multiprocessor systems. Another important problem is the need to meet stringent real-time requirements. This is a characteristic feature of embedded systems, which differ from general-purpose systems that have more flexibility in functionality but do not guarantee such predictability and reliability. Therefore, optimization of task scheduling that takes into account the specifics of embedded systems requires further research. It is also important to take into account the variety of embedded systems, which are divided into control systems and streaming systems that have different data processing requirements. Control systems must respond quickly to environmental events while minimizing delays, while streaming systems process continuous data streams, requiring high throughput and efficiency. The development of universal solutions that can optimize the performance of both types of systems is an urgent task for scientists and engineers. Therefore, task optimization in multiprocessor embedded systems has significant potential for development and is relevant for reliability, real-time guarantees, and efficient resource management, which will contribute to the creation of more secure and productive systems. In this paper, we develop a method for optimizing task execution using replication in a multiprocessor system, which allows to effectively minimize the total execution time, ensure load balance, and minimize communication delays. The peculiarity of the method is the implementation of task migration according to replication using the optimization objective function. An experiment with the system demonstrated that the chosen optimization method effectively balances the load, but additional objective functions are needed to optimize energy consumption. The simulation results show that an increase in the number of processors leads to a decrease in the maximum load and the number of migrations, an increase in the number of tasks increases the system load and the number of migrations at the initial stages, and the migration mechanism effectively balances the load, especially at the initial stages of execution. The areas of further research are the detailing of embedded devices and their classification. For each class of embedded devices, it will be necessary to adapt the algorithms and method of task optimization, as well as to develop the target optimization function.