Kharkiv National University of Radio Electronics

This study examines the set of processes aimed at improving the efficiency of steganographic methods for information protection, while ensuring a specified level of integrity and timeliness of transmission in infocommunication systems. The work addresses a scientific and applied task of increasing the level of information security while maintaining a specified level of integrity and timely delivery of data from unmanned aerial systems (UASs). It relates to the existence of a contradiction between ensuring the confidentiality of video information and the efficiency of its delivery with a given integrity. In the process of research, a method for structural steganographic embedding has been devised, based on changing the values of positional basis (PB). The difference is the embedding of information in the process of modifying the PB values, taking into account the detection of structural dependences in the spectrally modified space of a video container (VC). Such transformations are carried out under conditions of reducing the influence of spectral space modifications on the compression level. The above advantages make it possible to build a comprehensive method of steganographic transformations in the process of VC compression. The method is distinguished by two principal aspects. The first is the use of a sequence of steganographic transformations in independent spaces to detect dependences that are used in the process of embedding information. The second is that information is embedded in the structural-spectral space, taking into account its modification by the youngest bits. This enables increasing the level of steganographic capacity by an average of 7% without distorting the integrity of the stegano-containers and removing hidden information on the receiving side. The devised method is intended to be used to improve the confidentiality, efficiency, and integrity of video information delivery from unmanned systems

Community detection in graphs can be viewed as the estimation of a partition map that remains stable under admissible perturbations of graph topology and node attributes. While modern graph neural networks (GNNs) achieve strong empirical accuracy, they often exhibit severe assignment drift under minor perturbations, leading to illusory community structures. In this work, we propose DISPEL-GNN, a stability-aware graph learning framework that integrates spectral operator regularization, Bayesian uncertainty modeling, and risk-aware dynamic attention for perturbation-bounded community detection. The model explicitly constrains graph operators through uniform spectral norm bounds, high-frequency energy suppression, and commutator alignment while dynamically modulating message passing based on node-level spectral risk and epistemic uncertainty. We further formalize instability via assignment of drift functional and establish perturbation bounds linking drift to operator norms and spectral gaps, complemented by a PAC-Bayesian generalization guarantee. Extensive experiments on real-world benchmarks including Cora, Citeseer, Pubmed, Cora-Full, and DBLP demonstrate that DISPEL-GNN consistently reduces assignment drift by 18–35% under feature noise and edge perturbations while improving clustering quality with up to +3.0 NMI and +0.04 ARI compared to strong baselines such as GAT and Bayesian GNNs. The normalized mutual information (NMI), adjusted Rand index (ARI), and PAC-Bayesian (PAC) constraints serve as evaluative and theoretical instruments in this study. Additional studies on synthetic graphs with controlled spectral gaps confirm that the proposed method maintains stable community assignments in low-gap regimes where classical spectral and GNN-based methods degrade sharply. These results establish DISPEL-GNN as a mathematically grounded and practically effective framework for robust and interpretable community detection. A metric-wise dominance analysis shows that DISPEL-GNN achieves metric-wise dominance across most accuracy and robustness criteria, with minor tradeoffs in modularity on selected datasets. These results indicate that explicitly modeling stability and uncertainty provides a principled pathway toward reliable and interpretable community detection in noisy graph environments.

This paper presents a mobile real-time system for diagnostics of the stress state and prediction of the residual life of structural components operating under intensive dynamic loading conditions. The proposed approach is based on the use of operational data throughout the product life cycle and aligns with the Industry 4.0 paradigm, where information obtained during the operational phase is used to support technical inheritance and the structural evolution of engineering products.

In this paper the subject matter is the methods of decomposing conjunctive formulas in the algebra of finite predicates and the possibility of using dependency structures of relational data to build compact logical networks. The work examines the interrelation between operations of relational algebra and predicate algebra, which makes it possible to form a mathematical framework for the rational representation of complex predicate models. The relevance of the study is determined by the need to create high-performance solutions for linguistic information processing and to optimize parallel computational structures. The goal of the work is to develop an effective method of binary predicate decomposition that reduces the number of auxiliary variable values and ensures the transformation of multidimensional predicate formulas into a compact system of binary relations suitable for hardware implementation in the form of logical networks. This approach improves model performance and reduces hardware complexity. To achieve this goal, the following tasks were carried out: analysis of functional, multivalued, and connection dependencies; determination of the conditions for the existence of quantifier-conjunctive decomposition; and investigation of cases in which a predicate can be represented as a system of binary formulas. The limitations of Cartesian decomposition were identified, and it was shown how dependency structures eliminate these drawbacks, forming more compact and structurally justified models. The research methods include the apparatus of finite predicate algebra, the theory of relational dependencies, techniques for constructing auxiliary predicates, variable elimination using existential quantifiers, and modeling processes in the form of logical networks. Special attention is paid to analyzing the structural properties of formulas that influence decomposition efficiency. The results of the research show that the proposed method allows obtaining more compact binary models in comparison to the Cartesian approach. Predicate decomposition applied to a morphological inflection model has confirmed a significant increase in efficiency during hardware implementation of the logical network and improved performance indicators. In the conclusions, it is emphasized that the use of dependency structures is an effective tool for optimizing formal models and opens opportunities for further automation of predicate-structure construction in tasks of logical analysis and knowledge processing.

An automated system for calculating gear transmissions has been developed with the goal of enhancing the accuracy of engineering calculations and minimizing the likelihood of errors in mechanical engineering. The problems of traditional calculation methods, which are characterized by high labor intensity and significant human influence, have been investigated. A modular system has been proposed that integrates a mathematical apparatus for calculating the geometric, kinematic, and energy parameters of transmissions. The system's algorithms provide automatic validation of input data. The software implementation is performed using Windows Forms (.NET), which provides an intuitive user interface. An experimental verification of the accuracy of the calculations was conducted, which demonstrated a maximum error of less than 0,44 % compared to manual calculations. Key advantages of the system: reduction of calculation time, integration of all design stages in a single environment, and adaptability to different load ranges. The results of the study confirm the effectiveness of the proposed approach and its competitiveness compared to existing commercial solutions.

The subject matter of the article is the problem of detecting fabricated information in socially oriented systems characterized by significant user load. The goal of the work is to develop of a two-layer fake information classification model based on a combination of a naive Bayesian classifier and a hybrid recurrent-convolutional neural network. The following tasks were solved in the article: conducting expert evaluation and domain analysis to determine basic classes of fake information; analyzing linguistic markers of disinformation and developing feature vectors for classification; developing models for data segregation using a naive Bayesian classifier; conducting experimental verification of the proposed two-layer model in comparison with the RCNN approach. The following methods used are – analytical method for forming a set of disinformation markers; inductive method for determining the target set of indicators for implementing the second layer of the model; expert evaluation for determining the most influential efficiency factors and feature weight coefficients; experimental and multi-criteria evaluation methods for determining the most effective model. The following results were obtained – a classification structure for types of fake information was formed, including five categories from jokes to globally harmful news. A set of discriminative features characteristic of fabricated information was developed, including primary linguistic markers and secondary stylometric indicators. It was determined that the approach using a two-layer model demonstrated, on average, a 15% improvement in efficiency compared to direct application of a hybrid recurrent-convolutional neural network. Conclusions: the application of a two-layer data classification model successfully expands the capabilities of basic detection of data falsification, including scale assessment and analysis of fabrication intentionality. Empirical analysis shows that implementation of a two-layer model with a naive Bayesian classifier achieves an average 15% performance improvement compared to simple neural network application. This performance difference becomes particularly significant in high-throughput systems where rapid identification and response to fabricated information are critical operational parameters. The obtained result allows us to assert the feasibility of implementing the proposed approach, and accordingly, provides the opportunity to reduce the impact of such information in socially oriented systems, especially during crisis situations.

The subject of the study is the methods of intellectual analysis, namely the construction of a decision tree, associative analysis, the identification of patterns between related events based on data presented by a relational model. The purpose of the study is to analyze the features of information units and data structures, using the example of relational systems that affect the technology of knowledge extraction. Tasks: the article solves the following tasks: to consider the relational data model as the most popular and effective data structure used in intelligent information systems for data processing and storage; to analyze the operations of relational algebra, the operational component of the relational data model regarding the application of aggregate functions; to develop a general formal statement of the problem of knowledge extraction from a relational database; to consider the concept of functional associative rules; the ID3 decision tree generation algorithm focused on data processing in relational systems is analyzed. The following methods are implemented: modern view and trends in the field of data mining; features of building information systems based on relational databases, relational algebra, theory of normalization of relations; analysis of literature on the topic of research; comparative analysis. Results achieved: the relational data model is considered as the most effective data structure used in intelligent information systems for data processing and storage. A group of aggregate functions of relational databases is identified and analyzed with respect to key attributes of the relation, which makes it possible to build logical dependencies between information units of the subject area being analyzed. The task of extracting knowledge from the database is formally formulated. The concept of functional associative rules is introduced. The ID3 decision tree generation algorithm focused on data processing in relational systems is carefully analyzed. The semantic network (SN), built on the basis of the proposed approach, allows to increase the efficiency of decision support systems. Conclusions: the universal approach proposed in the article to build a relational data model of an information system for searching for associative patterns in data allows to solve a whole class of typical tasks in which objects are connected by a "many-to-many" relationship or M →N. The relational database model is proposed as a universal information structure for solving associative analysis tasks and presenting knowledge in the form of a semantic network. The examples given in the article confirm the effectiveness of the developed and considered approaches to solving the problem of data mining in the environment of relational systems. Solving the problem of identifying knowledge in data will allow to improve the quality of management decisions made.

The subject matter of this paper concerns the application of multi-agent systems and software agent technologies for managing and analyzing information resources and task flows within distributed, heterogeneous information environments. It focuses on formal models and execution frameworks that enable intelligent control of complex agent-oriented workflows. The goal is to develop effective methods and models for representing, coordinating, and executing agent-oriented tasks, ensuring correctness and efficient resource use in distributed systems. The tasks addressed include formalizing the information space as an algebraic system comprising objects, relationships, and operations; defining elementary and functional tasks executed by users or agents; organizing interdependent tasks into flows; and modeling agent behavior via frame structures and hierarchical Petri nets incorporating metapositions and metatransitions. The methods employed combine algebraic system modeling, automata theory for agent behavior analysis, and extended predicate Petri nets with modifications supporting hierarchical task management. Coordination constraints and workflow correctness properties are specified using Computation Tree Logic (CTL) and dependency automata to capture inter-task dependencies and atomicity requirements. The work also applies SQL-based operations to exemplify merging of data during task execution. The results include a formal framework distinguishing state-based and task-based planning, a modified frame slot model enabling complex task descriptions with initial conditions and operations, and a hierarchical Petri net scheme with subordinate nets activated conditionally, thereby optimizing computations. A software agent prototype executing a sequence of conditional operations on multiple databases demonstrates the practical application of these concepts. The approach reduces execution time and computational costs by activating only necessary subordinate tasks and avoiding irrational solutions. The conclusions assert that the proposed agent-oriented modeling and hierarchical task execution methods successfully address the complexities of distributed information resource management and workflow control, providing a scalable means for executing fault-tolerant, atomic, and coordinated workflows. This enhances the reliability and efficiency of multi-agent systems in heterogeneous computing environments.