Vol 22, No 5 (2023)

The article is devoted to the original mathematical models of combat operations developed in Russia at the beginning of the XX century. One of the first works in which approaches to mathematical modeling of military operations were outlined can be considered an article by Y. Karpov «Tactics of fortress artillery», published in 1906. It considered the task of defending the fortress from attacking enemy infantry chains. Based on the idea of the attackers overcoming the line of defense, mathematical relations were obtained linking the parameters of the shot of the shrapnel charge with the movements of the infantryman. Similarly, the task of using a machine gun for the defense of the fortress was considered. After analyzing the obtained ratios, Y. Karpov came to the conclusion that all means of defense of the fortress can be correlated through the length of the area defended by this means. P. Nikitin developed Y. Karpov's ideas. He considered a wide range of means of destruction. Based on the results of the research, the author made recommendations on the distribution of forces and means in the defense of fortresses. M. Osipov in 1915 published vivid and original models of two-way combat operations, a year earlier than the well-known Lanchester theory. Summing up the numbers of the fighting sides at infinitesimal intervals of time, and then moving to the limits, he obtains linear and quadratic laws of the influence of the ratio of the number of the fighting sides on their losses, and explores heterogeneous means of destruction. All this is verified by the practice of various battles. M. Osipov showed that the coefficients in the laws of losses depend on the training of personnel, terrain, the presence of fortifications, the moral and psychological state of the troops, etc. Based on the results of mathematical modeling, M. Osipov for the first time substantiated a number of provisions of the art of war. He showed that neither linear nor quadratic laws of losses in general do not correspond to the practice of the battles conducted. For ease of use at that level of computer technology development and to obtain a more reliable result, M. Osipov proposed using the degree of "three second" in the laws of losses, although he himself understood its approximate nature. Much attention is paid to the problem of authorship, the search for a prototype of the creator of the first two-sided model of combat operations, and the application of theory to solve modern applied problems.

Extending smartphone working time is an ongoing endeavour becoming more and more important with each passing year. It could be achieved by more advanced hardware or by introducing energy-aware practices to software, and the latter is a more accessible approach. As the CPU is one of the most power-hungry smartphone devices, Dynamic Voltage Frequency Scaling (DVFS) is a technique to adjust CPU frequency to the current computational needs, and different algorithms were already developed, both energy-aware and energy-agnostic kinds. Following our previous work on the subject, we propose a novel DVFS approach to use simultaneous perturbation stochastic approximation (SPSA) with two noisy observations for tracking the optimal frequency and implementing several algorithms based on it. Moreover, we also address an issue of hardware lag between a signal for the CPU to change frequency and its actual update. As Android OS could use a default task scheduler or an energy-aware one, which is capable of taking advantage of heterogeneous mobile CPU architectures such as ARM big.LITTLE, we also explore an integration scheme between the proposed algorithms and OS schedulers. A model-based testing methodology to compare the developed algorithms against existing ones is presented, and a test suite reflecting real-world use case scenarios is outlined. Our experiments show that the SPSA-based algorithm works well with EAS with a simplified integration scheme, showing CPU performance comparable to other energy-aware DVFS algorithms and a decreased energy consumption.

To provide an accurate and timely response to different types of attacks, intrusion detection systems collect and analyze a large amount of data, which may include information with limited access, such as personal data or trade secrets. Consequently, such systems can be seen as an additional source of risks associated with handling sensitive information and breaching its security. Applying the federated learning paradigm to build analytical models for attack and anomaly detection can significantly reduce such risks because locally generated data is not transmitted to any third party, and model training is done locally - on the data sources. Using federated training for intrusion detection solves the problem of training on data that belongs to different organizations, and which, due to the need to protect commercial or other secrets, cannot be placed in the public domain. Thus, this approach also allows us to expand and diversify the set of data on which machine learning models are trained, thereby increasing the level of detectability of heterogeneous attacks. Due to the fact that this approach can overcome the aforementioned problems, it is actively used to design new approaches for intrusion and anomaly detection. The authors systematically explore existing solutions for intrusion and anomaly detection based on federated learning, study their advantages, and formulate open challenges associated with its application in practice. Particular attention is paid to the architecture of the proposed systems, the intrusion detection methods and models used, and approaches for modeling interactions between multiple system users and distributing data among them are discussed. The authors conclude by formulating open problems that need to be solved in order to apply federated learning-based intrusion detection systems in practice.

2D and 3D digital multimedia files offer numerous benefits like excellent quality, compression, editing, reliable copying, etc. These qualities of the multimedia files, on the other hand, are the cause of fear including the fear of getting access to data during communication. Steganography plays an important role in providing security to the data in communication. Changing the type of cover file from digital multimedia files to protocols improve the security of the communication system. Protocols are an integral part of the communication system and these protocols can also be used to hide secret data resulting in low chances of detection. This paper is intended to help improve existing network steganography techniques by enhancing bandwidth and decreasing detection rates through reviewing previous related work. Recent papers of the last 21 years on network steganography techniques have been studied, analyzed, and summarized. This review can help researchers to understand the existing trends in network steganography techniques to pursue further work in this area for algorithms’ improvement. The paper is divided according to the layers of the OSI model.

In the realm of modern image processing, the emphasis often lies on engineering-based approaches rather than scientific solutions to address diverse practical problems. One prevalent task within this domain involves the skeletonization of binary images. Skeletonization is a powerful process for extracting the skeleton of objects located in digital binary images. This process is widely employed for automating many tasks in numerous fields such as pattern recognition, robot vision, animation, and image analysis. The existing skeletonization techniques are mainly based on three approaches: boundary erosion, distance coding, and Voronoi diagram for identifying an approximate skeleton. In this work, we present an empirical evaluation of a set of well-known techniques and report our findings. We specifically deal with computing skeletons in 2d binary images by selecting different approaches and evaluating their effectiveness. Visual evaluation is the primary method used to showcase the performance of selected skeletonization algorithms. Due to the absence of a definitive definition for the "true" skeleton of a digital object, accurately assessing the effectiveness of skeletonization algorithms poses a significant research challenge. Although researchers have attempted quantitative assessments, these measures are typically customized for specific domains and may not be suitable for our current work. The experimental results shown in this work illustrate the performance of the three main approaches in applying skeletonization with respect to different perspectives.

Difficulties in algorithmic simulation of natural thinking point to the inadequacy of information encodings used to this end. The promising approach to this problem represents information by the qubit states of quantum theory, structurally aligned with major theories of cognitive semantics. The paper develops this idea by linking qubit states with color as fundamental carrier of affective meaning. The approach builds on geometric affinity of Hilbert space of qubit states and color solids, used to establish precise one-to-one mapping between them. This is enabled by original decomposition of qubit in three non-orthogonal basis vectors corresponding to red, green, and blue colors. Real-valued coefficients of such decomposition are identical to the tomograms of the qubit state in the corresponding directions, related to ordinary Stokes parameters by rotational transform. Classical compositions of black, white and six main colors (red, green, blue, yellow, magenta and cyan) are then mapped to analogous superposition of the qubit states. Pure and mixed colors intuitively map to pure and mixed qubit states on the surface and in the volume of the Bloch ball, while grayscale is mapped to the diameter of the Bloch sphere. Herewith, the lightness of color corresponds to the probability of the qubit’s basis state «1», while saturation and hue encode coherence and phase of the qubit, respectively. The developed code identifies color as a bridge between quantum-theoretic formalism and qualitative regularities of the natural mind. This opens prospects for deeper integration of quantum informatics in semantic analysis of data, image processing, and the development of nature-like computational architectures.