Abstract: Since 2015, microcomputers have appeared in the information environment, which are a compact system unit with minimal functionality without peripherals. The article published the results of the analysis of the use of 6 different microcomputers in various fields of activity. The purpose of the study is to determine the limiting factors affecting the efficiency of the targeted use of microcomputers. It has been established that for scientific and educational presentations, office and trading activities, it is currently advisable to use fanless microcomputers with a perforated case and an internal WiFi antenna, at least 4 GB of operational and 64 GB of permanent memory, and a microSD (TF) memory card slot, at least 128 GB, NTFS file system), Intel HD Graphics, USB3.0 and HDMI interfaces. Based on comparative experiments, methodological recommendations were created on optimizing the configuration of the hardware-software environment of microcomputers in stationary and mobile conditions. The problems of major updates to Windows 10, as well as the compatibility of Microsoft Store software and third-party manufacturers, are analyzed. It is recommended to specialize individual microcomputers for working with 32-bit applications; accounting and cryptographic programs; as well as conducting presentations with their video. Options for optimal configuration of the Start menu of the Windows 10 desktop are suggested. It is concluded that specialization in the hardware-software configuration of modern microcomputers allows you to increase the efficiency of using single devices and their paired systems in accordance with BYOD (Bring Your Own Device).

Abstract:

Automatic processing of ionospheric radiosonde ionograms is necessary both for operational monitoring of the ionosphere and for analyzing archived radiosonde data arrays. The central place in this task is occupied by automatic detection and classification of tracks – traces of reflected radio signal modes on ionograms. This paper presents the methodology, results, and comparative analysis of the use of neural network models (neural network architectures) for Mask RCNN and YOLO instance segmentation. The most successful models were the YOLO family, which provide both high accuracy and computational efficiency. At the same time, the exact pixel–by-pixel matching of the masks is not critical – the key requirement is the correct localization of the useful signal. The use of these models to identify tracks on ionograms has demonstrated moderate but sufficient efficiency: the main structures of the tracks are reliably identified. The most successful models were the YOLO family, which provide both high accuracy and computational efficiency. In the future, the set of models used will be expanded, primarily due to visual transformers.

Abstract: Possibilities of query expansion with subject area thesaurus are discussed. The role of the context defined by thesaurus term links is both to refine the query and to increase the size of the sample on the query. Of particular importance is the process of expanding the query for scientific subject areas where the search based on special terminology. In this case, thesauruses of subject areas must be used to minimize the occurrence of information noise. The proposed approach takes into account the application of similar terminology in various subject areas. Examples of the use of thesaurus of separate sections of equations of mathematical physics and related fields demonstrate the effectiveness of the chosen approach of research. By linking to concepts of information resources of other areas of knowledge, the extension of the information query captures search fields of remote subject areas and various types of data, texts, symbolic, audio and video archives. Research shows that expanding the query based on context semantics improves the search quality of scientific publications in digital information and increases the effectiveness of scientific interdisciplinary research.

Abstract: Today the Virtual Observatory tools provide easy access to astronomical data and improve the efficiency of researches. Based on the existing web technologies the astronomical community is developing on realization greater data semantic connectivity. This will put the virtual observatory services to a new level of exchange of not only data but also knowledge. However, there is no development of tools for actualization information collected from web resource, and a means of organizing heterogeneous data prepared by the user for scientific analysis, which could improve further work. We consider the approach to organization of diverse information for a studying object, based on the development of the existing formats of astronomical data and tools to support semantic data connectivity.

Abstract: During the last decades scientometric techniques have been used for research activity stimulation. Number of published articles and number of their citation counts are among the most important scientometric parameters. In an automated environment, when the publications metadata is gathered from various sources, correct linking of original papers with their translations into different languages is extremely important. In the paper we show that the known text similarity measures are inefficient in the context of article linkage problem. We propose a method for semi-automatic article linkage using statistical data on authors publication activities only. This approach may be used for linking articles without training for the language of translation. The method was evaluated on real-world collection of publications metadata of ISTINA information system.

Abstract:

The effectiveness of the ImageNet diffusion model and CLIP models for image generation based on textual descriptions was investigated. Two experiments were conducted using various textual inputs and different parameters to determine the optimal settings for generating images from text descriptions. The results showed that while ImageNet performed well in generating images, CLIP demonstrated better alignment between textual prompts and relevant images. The obtained results highlight the high potential of combining these mentioned models for creating high-quality and contextually relevant images based on textual descriptions.

Abstract:

This paper proposes a new approach to determining the level of internet censorship. Our definition of censorship is based on the principle of accessibility of alternative viewpoints in search engine results and large language models. The calculation method involves compiling a list of 11 questions, the answers to which suggest opposite opinions depending on the country. These answers are evaluated in comparison with the government's point of view. For search engine results, calculate the number of answers to these questions from a pro-government point of view and the proportion of answers that detail an alternative point of view. The difference between these indicators is proposed to be considered an index of internet censorship. Since Internet searches are increasingly performed through chatbots of the most well-known large language models (LLMs), the level of censorship is also assessed for these models. The comparison showed that the responses of the national search engine Baidu in China are closest to the government's point of view, while the level of censorship in the United States is higher than in Russia. The effectiveness of censorship in the United States is explained by a different mechanism, whereby materials expressing alternative opinions restrict access to popular information platforms. Censorship in chatbots is much stronger than in internet search engine results are calculated.

Abstract:

Relevance of the issue under study is due to tenuity of methods proposed by other researchers that use simple heuristics or machine learning algorithms. The purpose of the article is to provide better way to extract titles from scientific PDF documents and offer better and more reasonable approach to title selection generally. The leading approach to the study is regard as many cases and problems appeared during extraction as possible and find an approach to solve all of them. The results showed the efficiency of chosen approach in case of having a document set with all of considered problems. The research highlights that deep analysis of current task problem is a perspective to make the best solutions and tools. The article may be useful for all researchers and developers who often encounter the problem of document structural analysis or title detection as secondary task of a main program workflow.

Abstract:

The article is devoted to the problem of forecasting trends in the prices of financial instruments on the Forex market. The methods of forming forecasts based on business cycle models and fractal self-organization of pricing are considered. Based on historical precedents of crises after 1812 and 1917, the terms of the 2015-2027 crisis are determined, the end of which coincides with the simultaneous end of the 200-year and 40-year trends. It is predicted that the point of technological singularity will be reached in 2039. The methods of integrating fundamental and technical analysis tools for forecasting global events that are absent from the economic calendar are developed. It is proposed to increase the efficiency of forecasting changes in prices of financial instruments using an analytical multitrading system designed to work with 6 strategies: long-term (8 months), medium-term (2 months), short-term (1.5 weeks and 1.5 days) and intraday (8 hours, 2 hours). The choice of strategy depends on the time that a trader is ready to use for analytical activities and control of open transactions, acceptable risks and expected profitability. For all strategies, a set of preferred currency pairs of Forex Club and FxPro brokers is established, recommendations for traders are given. The necessary and sufficient set of technical analysis indicators participating in the formation of a triple signal for positioning the starting point of the Regression Channel is determined, which allows automatic forecasting of tactical levels of the price change trend reversal in the interval of forming groups of 8 oscillations. A regulation has been developed for the creation, publication and verification of tactical forecasts of the duration and amplitude of price oscillations for all strategies and many financial instruments. Forecasts are published in the "Multitrading" channels and groups of the Telegram, Dzen and VKontakte network services. The forecasting tools are supposed to be used in the formation of multitrading system tactics.

Abstract:

This paper introduces RusHallu-RAG, the first comprehensive benchmark for detecting hallucinations in Russian-language RAG systems. The dataset of 1,000 query-answer pairs covers general knowledge and scientific domains with controlled context relevance balancing. A fine-grained taxonomy of six hallucination types is proposed and used for annotation at both response and span levels with human experts and LLMs.

For training a hallucination detector, a balanced dataset of 3,000 examples is collected using synthetic generation methods. A detector based on YandexGPT-5-Lite-8B is trained via SFT. We evaluate 15 open-weight and proprietary models alongside the trained detector. Results show that model size does not guarantee performance: medium-sized models (20–33B) sometimes outperform larger ones. The trained detector achieves an average accuracy of 63.7%, ranking second after proprietary Gemini-2.5-Pro, with a gain of 47.2 p.p. over the baseline. The gap between proprietary and open-weight models is reduced from 20–35 to 8.4 p.p., confirming the effectiveness of the approach. The benchmark, code, and trained model are publicly released.

Abstract:

Recently, such a direction of machine learning as reinforcement learning has been actively developing. As a consequence, attempts are being made to use reinforcement learning for solving computer vision problems, in particular for solving the problem of image classification. The tasks of computer vision are currently one of the most urgent tasks of artificial intelligence.

The article proposes a method for image classification in the form of a deep neural network using reinforcement learning. The idea of ​​the developed method comes down to solving the problem of a contextual multi-armed bandit using various strategies for achieving a compromise between exploitation and research and reinforcement learning algorithms. Strategies such as -greedy, -softmax, -decay-softmax, and the UCB1 method, and reinforcement learning algorithms such as DQN, REINFORCE, and A2C are considered. The analysis of the influence of various parameters on the efficiency of the method is carried out, and options for further development of the method are proposed.

Abstract:

This paper proposes a hybrid Explainable AI architecture that fuses a fully differentiable neuro-fuzzy GD-ANFIS model with the post-hoc SHAP method. The integration is designed to meet XAI 2.0 principles, which call for explanations that are transparent, verifiable, and adaptable at the same time. GD-ANFIS produces human-readable Takagi-Sugeno rules, ensuring structural interpretability, whereas SHAP delivers quantitative feature contributions derived from Shapley theory. To merge these layers, we introduce a comparative-audit mechanism that automatically matches the sets of key features identified by both methods, checks whether the directions of influence coincide, and assesses the consistency between SHAP numerical scores and GD-ANFIS linguistic rules. Such dual-loop on global soil-subsidence mapping, and RMSE 2.30 and 2.36 on Boston Housing and surface-water-quality monitoring respectively, all with full interpretability preserved. In every case, top-feature overlap between the two explanation layers exceeded 60%, demonstrating strong agreement between structural and numerical interpretations. The proposed architecture therefore offers a practical foundation for responsible XAI 2.0 deployment in critical domains ranging from medicine and ecology to geoinformation systems and finance.

Abstract:

n the modern world, streaming data has become widespread in many subject areas. The task of processing streaming data in real time, with minimal delay, is highly relevant.

In stream processing, data processing, various approximate algorithms are often used, which have much higher time and memory efficiency than exact algorithms. In addition, there is often a need to forecast the state of the stream.

Thus, there is currently a need for a tool for sequential snapshotting of aggregated data from streaming data, enabling flow state prediction and approximate algorithms for stream data processing.

The authors of the article have developed such a tool, reviewed its architecture and mechanism of functioning, and evaluated the prospects for its further development.

Abstract:

The problem of effective navigation and search for relevant information within growing volumes of scientific publications necessitates a shift from classical full-text search methods to semantic models. This work proposes an approach to structuring a heterogeneous corpus of scientific texts by constructing a Knowledge Graph (KG). A data processing pipeline is developed that encompasses the extraction of metadata, keywords, and structural elements of articles, followed by their integration into a unified graph. Based on the constructed Knowledge Graph, methods for analyzing explicit connections and extracting implicit connections between publications are implemented. The research results demonstrate the effectiveness of the graph-based representation of scientific information for uncovering hidden patterns within subject domains and supporting intelligent navigation.

Abstract: The article says that quality management is an effective method of managing an organization, and a balanced system of indicators is its tool that allows to increase the effectiveness of activities and systematically measure the effectiveness of the organization based on vision, values and strategy.

Abstract:

This paper addresses the issue of automatic annotation of training datasets in the field of computer vision using machine learning methods. Data annotation is a key stage in the development and training of deep learning models, yet the process of creating labeled data often requires significant time and labor. This paper proposes a mechanism for automatic annotation based on the use of convolutional neural networks (CNN) and active learning methods.

The proposed methodology includes the analysis and evaluation of existing approaches to automatic annotation. The effectiveness of the proposed solutions is assessed on publicly available datasets. The results demonstrate that the proposed method significantly reduces the time required for data annotation, although operator intervention is still necessary.

The literature review includes an analysis of modern annotation methods and existing automatic systems, providing a better understanding of the context and advantages of the proposed approach. The conclusion discusses achievements, limitations, and possible directions for future research in this field.

Abstract:

The article investigates cognitive load in users of a multi-role 3D platform. An experiment (71 participants: 34 designers, 37 customers; 639 observations) using objective metrics and subjective scales revealed different load profiles: designers are slowed down by data entry forms, while customers are hindered by 3D content. Based on 34 problem areas, seven interface design principles and a task classification by cognitive complexity level, accounting for role-specific factors, were formulated. Retesting (21 participants) confirmed their effectiveness: task completion time decreased by 21–28%, errors by 33–44%, frustration by 25–35%, and the use of 3D features increased from 40.5% to 90.9%. The developed three-component model (taxonomic, diagnostic, and design levels) is recommended for designing similar multi-role platforms with 3D functionality.

Abstract:

Cardiovascular diseases remain the leading cause of mortality, and automated electrocardiogram (ECG) analysis can ease clinical workloads but is limited by scarce and imbalanced data. Synthetic ECG can mitigate these issues, and while most methods use Generative Adversarial Networks (GANs), recent work show variational autoencoders (VAEs) perform comparably. We introduce cNVAE-ECG, a conditional Nouveau VAE (NVAE) that generates high-resolution, 12-lead, 10-second ECGs with multiple pathologies. Leveraging a compact channel-generation scheme and class embeddings for multi-label conditioning, cNVAE-ECG improves downstream binary and multi-label classification, achieving up to a 2% AUROC gain in transfer learning over GAN-based models.

Abstract:

This paper presents the experience of verifying client implementations of the TLS cryptographic protocol version 1.3. TLS is a widely used cryptographic protocol today, designed to create secure data transmission channels. The protocol provides the necessary functionality for its tasks: confidentiality of transmitted data, data integrity, and authentication of the parties. In the new version 1.3 of the TLS architecture was significantly redesigned, eliminating a number of shortcomings of previous versions that were identified both during the development of implementations and during their operation. We used a new test suite for verifying client implementations of the TLS 1.3 for compliance with Internet specifications, developed on the basis of the RFC8446, using UniTESK technology and mutation testing methods. To test implementations for compliance with formal specifications, UniTESK technology is used, which provides testing automation tools based on the use of finite state machines. The states of the system under test define the states of the state machine, and the test effects are the transitions of this machine. When performing a transition, the specified impact is passed to the implementation under test, after which the implementation's reactions are recorded and a verdict is automatically made on the compliance of the observed behavior with the specification. Mutational testing methods are used to detect non-standard behavior of the system under test by transmitting incorrect data. Some changes are made to the protocol exchange flow created in accordance with the specification: either the values of the message fields formed on the basis of the developed protocol model are changed, or the order of messages in the exchange flow is changed. The protocol model allows one to make changes to the data flow at any stage of the network exchange, which allows the test scenario to pass through all the significant states of the protocol and in each such state to test the implementation in accordance with the specified program. The presented approach has proven effective in several of our projects when testing network protocols, providing detection of various deviations from the specification and other errors. The current work is part of the TLS 1.3 protocol verification project and covers TLS client implementations.

Abstract:

The study addresses the problem of integrating cognitive and mathematical modeling in research on the development directions of the fuel and energy complex, taking into account energy security requirements. The relevance of the work is due to the fact that in the existing two-level research methodology, the transition from the results of qualitative analysis using cognitive modeling to the parameters of the mathematical model is largely performed manually, which reduces the reproducibility of numerical experiments and limits the efficiency of accumulated knowledge usage. The aim of the work is to develop a software component that ensures the combined use of cognitive and mathematical models within an Energy Knowledge Ecosystem. A software component is proposed, implemented as part of the INTEC‑SAW suite, which provides the transformation of changes in the cognitive model into the parameters of the economic-mathematical model, as well as the reverse interpretation of calculation results. Technology for conducting numerical experiments has been developed, including the construction of semantic (ontological and cognitive) models, formation of computational scenarios, execution of optimization calculations, and presentation of results, distinguished by the automation of the joint use of ontological, cognitive, and economic-mathematical models. To account for uncertainty, a numerical method of stochastic parameter adjustment based on cognitive weights is proposed. The effectiveness of the approach is demonstrated through a numerical experiment investigating the impact of CO₂ emission constraints on the energy balances of the Siberian Federal District. The practical significance of the work lies in increasing the validity and reproducibility of research on the development of the fuel and energy complex through the coordinated use of qualitative and quantitative analysis tools.

Abstract:

The work is aimed at considering the process of modeling a realistic controller of the behavior of groups of objects. The main techniques and principles used to create a realistic controller of the behavior of autonomous agents, united in related groups, are investigated. Based on this data, a behavior controller has been created.

The following works were performed: on the calculation of the effectiveness of the behavior of groups of autonomous agents; on the possibility of using a system of local scalar fields with the aim of constructing the most accurate mathematical model; on the analysis of the possibility of creating a hierarchical system of multi-agent subgroups within the group, with the aim of realizing the movement of these same groups, as an integral part; to use a structured approach to create an efficient controller architecture; on conducting practical experiments to evaluate the correctness of the data obtained.

To achieve the goals set, the computational methods of optimization theory, mathematical statistics, and analytical methods of the mathematical modeling apparatus are involved.

Abstract:

In today's world, digital technologies are penetrating all aspects of human activity, including education and labor. Since 2019, when, in response to global challenges, the world's educational systems have actively started to shift to distance learning, there has been an urgent need to develop and implement reliable identification and authentication technologies. These technologies are necessary to ensure the authenticity of work and protection from falsification of academic achievements, especially in the context of higher education in accordance with the group of specialties and directions (USGS) 10.00.00 - Information Security, where laboratory and practical work play a key role in the educational process.

The problem lies in the need to optimize the flow of incoming data, which, first, can affect the retraining of the neural network core of the recognition system, and second, impose excessive requirements on the network's bandwidth. To solve this problem, efficient preprocessing of gesture data is required to simplify their trajectories while preserving the key features of the gestures.

This article proposes the use of the Douglas–Peucker algorithm for preliminary processing of mouse gesture trajectory data. This algorithm significantly reduces the number of points in the trajectories, simplifying them while preserving the main shape of the gestures. The data with simplified trajectories are then used to train neural networks.

The experimental part of the work showed that the application of the Douglas–Peucker algorithm allows for a 60% reduction in the number of points in the trajectories, leading to an increase in gesture recognition accuracy from 70% to 82%. Such data simplification contributes to speeding up the neural networks' training process and improving their operational efficiency.

The study confirmed the effectiveness of using the Douglas–Peucker algorithm for preliminary data processing in mouse gesture recognition tasks. The article suggests directions for further research, including the optimization of the algorithm's parameters for different types of gestures and exploring the possibility of combining it with other machine learning methods. The obtained results can be applied to developing more intuitive and adaptive user interfaces.

Abstract:

This study focuses on the application of machine learning methods to improve the quality of test items. The research includes a review of the subject area and the implementation of two enhancement methods: similar question retrieval and distractor quality assessment. The first method involves testing five transformer-based models for generating text embeddings and six clustering algorithms. The second method uses the same transformer models in combination with three classification algorithms. Experimental results demonstrated the high effectiveness of the proposed approaches in solving both tasks.

Abstract:

This paper presents the experience of verifying server implementations of the TLS cryptographic protocol version 1.3. TLS is a widely used cryptographic protocol designed to create secure data transmission channels and provides the necessary functionality for this: confidentiality of the transmitted data, data integrity, and authentication of the parties. The new version 1.3 of the TLS protocol was introduced in August 2018 and has a number of significant differences compared to the previous version 1.2. A number of TLS developers have already included support for the latest version in their implementations. These circumstances make it relevant to do research in the field of verification and security of the new TLS protocol implementations. We used a new test suite for verifying implementations of the TLS 1.3 for compliance with Internet specifications, developed on the basis of the RFC8446, using UniTESK technology and mutation testing methods. The current work is part of the TLS 1.3 protocol verification project and covers some of the additional functionality and optional protocol extensions. To test implementations for compliance with formal specifications, UniTESK technology is used, which provides testing automation tools based on the use of finite state machines. The states of the system under test define the states of the state machine, and the test effects are the transitions of this machine. When performing a transition, the specified impact is passed to the implementation under test, after which the implementation's reactions are recorded and a verdict is automatically made on the compliance of the observed behavior with the specification. Mutational testing methods are used to detect non-standard behavior of the system under test by transmitting incorrect data. Some changes are made to the protocol exchange flow created in accordance with the specification: either the values of the message fields formed on the basis of the developed protocol model are changed, or the order of messages in the exchange flow is changed. The protocol model allows one to make changes to the data flow at any stage of the network exchange, which allows the test scenario to pass through all the significant states of the protocol and in each such state to test the implementation in accordance with the specified program. So far, several implementations have been found to deviate from the specification. The presented approach has proven effective in several of our projects when testing network protocols, providing detection of various deviations from the specification and other errors.

Abstract:

The paper addresses the problem of short text request classification under conditions of significant class imbalance and high noise levels in real-world communication flows. The limited effectiveness of synthetic oversampling techniques when dealing with noisy labeling is demonstrated. A hybrid method is proposed, combining preliminary density-based data cleaning and multi-level model ensembling. The application of a density-based clustering algorithm enabled the exclusion of 16.5% of informational noise from the total sample volume. The final model features a two-level architecture and is optimized using Bayesian hyperparameter search. A Recall@3 (R@3) metric of 97.4% was achieved on a hold-out test set. The proposed method allows for the automation of the request distribution process, significantly reducing operator workload and decreasing dispatch time.