Informatics and Automation

"Informatics and Automation" is a scientific, educational, and interdisciplinary journal primarily intended for papers from the fields of computer science, automation, and applied mathematics. The journal is published in both printed and online versions. The printed version has been published since 2002, the online one since 2010. Frequency: 6 times in year. Fee for the processing and publication of an article is not charged. The maximum term of the paper’s reviewing comprises 3 months.

ISSN (print): 2713-3192, ISSN (online): 2713-3206

Media registration certificate: ПИ № ФС 77 - 79228 from 25.09.2020

Founder: St. Petersburg Federal Research Center of the Russian Academy of Sciences

Editor-in-Chief: Rafael Midhatovich Yusupov, Corr. Member of RAS, Ph.D., Dr. Sci.

Frequency / Assess: 6 issues per year / Open

Included in: White List (2nd level), Higher Attestation Commission List, RISC, Scopus

最新一期

卷 24, 编号 1 (2025)

Robotics, automation and control systems

Three-Position Vehicle Control Based on Neural Interface Using Machine Learning
Fradkov A., Babich N.
摘要
The brain-computer interface is a complex system that allows you to control external electronic devices using brain activity. This system includes several elements – a device for reading brain activity signals, a hardware and software complex that processes and analyzes these signals, and a control object. The main challenge here is the development of methods and algorithms that can correctly recognize and predict the intentions of the person who uses this interface to provide solutions to control problems. This paper describes the mathematical formulation of the equipment control problem. Methods for preprocessing EEG signals, analyzing them, and making decisions about issuing a control signal are described; the structure of the software implementation of these methods is described, as well as a plan for experimental testing of the performance of the entire system that forms the brain-computer interface. For classification of EEG signals the methods of machine learning are used. A modification of the k-nearest neighbors method is proposed – the so-called fuzzy almost nearest neighbors method. An algorithm for the adaptive classification of EEG taking into account the drift of the parameters of the subject's model based on the method of recurrent objective inequalities (ROI) has also been developed. The control algorithm was implemented in the Python programming language. A remote-controlled wheelchair is considered as a control object, and turning the chair to the right or left is considered as a control task. To experimentally test the performance of the developed model and algorithms, more than 15 tests were carried out with five subjects in total. The approach developed and described in this article and its software implementation during testing demonstrated its effectiveness in the tasks of controlling the rotation of a wheelchair. Special attention was also paid to the resource intensity of the software implementation. Methods and algorithms were implemented taking into account the requirements that arise when performing calculations on low-performance devices with a limited amount of memory.
Informatics and Automation. 2025;24(1):5-29
pages 5-29 views
Intelligent Agent-Controlled Elevator System: Algorithm and Efficiency Optimization
Gharbi A., Ayari M., El Touati Y.
摘要

The study introduces an innovative intelligent agent-controlled elevator system specially designed to improve passenger wait times and enhance the efficiency of high-rise buildings. By utilizing the classic single-agent planning model, we developed a unique strategy for handling calls from halls and cars, and combined with this strategy we significantly improved the overall performance of the elevator system. Our intelligent control methods are in-depth compared with conventional elevator systems, assessing three important performance indicators: response time, system capacity to handle multiple active elevator cars simultaneously, and average passenger waiting time. The results of the full simulation show that an intelligent agent-based model consistently exceeds conventional elevator systems in all measured criteria. Intelligent control systems have significantly reduced response times, and improved simultaneous elevator management and passenger wait times, especially during high traffic. These advances not only improved traffic flow efficiency, but also greatly contributed to passenger satisfaction and brought smoother and more reliable transport experiences within the building. Furthermore, the increased efficiency of our systems is in line with the goals of building energy management, as it minimizes unnecessary movements and idle time. The results demonstrate the system's ability to meet dynamic, high-occupation environment requirements and mark a significant step forward in intelligent infrastructure management.

Informatics and Automation. 2025;24(1):30-50
pages 30-50 views
Dynamic Foraging in Swarm Robotics: A Hybrid Approach with Modular Design and Deep Reinforcement Learning Intelligence
Hammoud A., Iskandar A., Kovács B.
摘要

This paper proposes a hybrid approach that combines intelligent algorithms and modular design to solve a foraging problem within the context of swarm robotics. Deep reinforcement learning (RL) and particle swarm optimization (PSO) are deployed in the proposed modular architecture. They are utilized to search for many resources that vary in size and exhibit a dynamic nature with unpredictable movements. Additionally, they transport the collected resources to the nest. The swarm comprises 8 E-Puck mobile robots, each equipped with light sensors. The proposed system is built on a 3D environment using the Webots simulator. Through a modular approach, we address complex foraging challenges characterized by a non-static environment and objectives. This architecture enhances manageability, reduces computational demands, and facilitates debugging processes. Our simulations reveal that the RL-based model outperforms PSO in terms of task completion time, efficiency in collecting resources, and adaptability to dynamic environments, including moving targets. Notably, robots equipped with RL demonstrate enhanced individual learning and decision-making abilities, enabling a level of autonomy that fosters collective swarm intelligence. In PSO, the individual behavior of the robots is more heavily influenced by the collective knowledge of the swarm. The findings highlight the effectiveness of a modular design and deep RL for advancing autonomous robotic systems in complex and unpredictable environments.

Informatics and Automation. 2025;24(1):51-71
pages 51-71 views
Collision Avoidance in Circular Motion of a Fixed-Wing Drone Formation Based on Rotational Modification of Artificial Potential Field
Muslimov T.
摘要

In coordinated circular motion of a group of autonomous unmanned aerial vehicles (UAVs or drones), it is important to ensure that collisions between them are avoided. A typical situation occurs when one of the drones in a circular formation needs to overtake the drone ahead. The reason for such an overtake may be due to a given geometry of the UAV formation, when this configuration of a given relative position of the drones has changed for some reason. In this case, the limited maneuverability of UAVs of exactly fixed-wing type requires taking into account the peculiarities of their dynamics in the synthesis of the collision avoidance algorithm. The impossibility of the airspeed for a fixed-wing type UAV to drop below a certain minimum value also plays a role here. In this paper, we propose to use an approach based on vortex vector fields, which are essentially a rotational modification of the artificial potential field (APF) method. In this case, the path following algorithm developed in our previous works provides the circular motion. As a result, a collision avoidance algorithm has been developed that works efficiently by maintaining a coordinated circular motion of the autonomous drone formation without unnecessary turns. The proposed algorithm was named Artificial Potential Field for Circular Motion (abbreviated as APFfCM). Using the direct Lyapunov method, it is shown that the trajectories of the formation system have uniform boundedness (UB) when using the proposed control algorithm. Due to the boundedness of the candidate Lyapunov function, it is guaranteed that no collision event between drones will occur. Thus the control objective of providing coordinated circular motion for an autonomous fixed-wing type drone formation without collisions is achieved. Fixed-wing (“flying wing”) UAV models in MATLAB/Simulink environment demonstrate the effective performance of the proposed algorithm. These models have both full nonlinear dynamics and implementation of tuned autopilots stabilizing angular and trajectory motion.

Informatics and Automation. 2025;24(1):72-98
pages 72-98 views

Information security

Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques
Imamverdiyev Y., Baghirov E., Ikechukwu J.
摘要

In the internet and smart devices era, malware detection has become crucial for system security. Obfuscated malware poses significant risks to various platforms, including computers, mobile devices, and IoT devices, by evading advanced security solutions. Traditional heuristic-based and signature-based methods often fail against these threats. Therefore, a cost-effective detection system was proposed using memory dump analysis and ensemble learning techniques. Utilizing the CIC-MalMem-2022 dataset, the effectiveness of decision trees, gradient-boosted trees, logistic Regression, random forest, and LightGBM in identifying obfuscated malware was evaluated. The study demonstrated the superiority of ensemble learning techniques in enhancing detection accuracy and robustness. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to elucidate model predictions, improving transparency and trustworthiness. The analysis revealed vital features significantly impacting malware detection, such as process services, active services, file handles, registry keys, and callback functions. These insights are crucial for refining detection strategies and enhancing model performance. The findings contribute to cybersecurity efforts by comprehensively assessing machine learning algorithms for obfuscated malware detection through memory analysis. This paper offers valuable insights for future research and advancements in malware detection, paving the way for more robust and effective cybersecurity solutions in the face of evolving and sophisticated malware threats.

Informatics and Automation. 2025;24(1):99-124
pages 99-124 views
Technique for Assessing the Effectiveness of the Functioning of Web Backdoor Detection Systems
Borovkov V., Klyucharev P., Denisenko D.
摘要
Currently, there is a significant increase in information security incidents related to attacks on web resources. Obtaining unauthorized access to web resources remains one of the main methods of penetration into corporate networks of organizations and expanding the capabilities of intruders. In this regard, many studies are aimed at developing web backdoor detection systems (WBDS), but there is a need to assess the effectiveness of these systems. The purpose of this study is to develop an objective approach to assess the effectiveness of the WBDS functioning. In this work, it was found that the effectiveness of web backdoor detection systems is objectively manifested in the process of their use, therefore, testing of such systems should be carried out in conditions as close as possible to real ones. In this regard, the article proposes a new technique for assessing the effectiveness of WBDS. It is based on the calculation of three groups of specific indicators characterizing the potency, resource intensity and responsiveness of the detection tool, as well as the calculation of a generalized effectiveness indicator. Based on an analysis of research in this area, a classification of web backdoors embedded by an attacker into the source code of web applications has been developed. This classification is used when generating test datasets to calculate specific potency indicators. The developed methodology is applicable to tools that work based on the analysis of the source code of web pages. Additionally, its use requires a number of initial data, such as permissible maximum errors of frequent potency indicators and the probability of them being within the confidence interval, as well as weighting coefficients of specific potency indicators, which are selected by expert methods. This work may be useful for information security specialists and researchers who want to conduct a more objective assessment of their WBDS.
Informatics and Automation. 2025;24(1):125-162
pages 125-162 views
Corpus of Privacy Policies for Web Services and Internet of Things Devices for Analyzing the Awareness of Personal Data Subjects
Kuznetsov M., Novikova E.
摘要
Information about what personal data is collected and processed by various devices and digital services is presented in privacy policies, however, as studies show, users rarely read them and, as a result, do not realize which data security risks associated with the processing of personal data arise. The solution to the problem of increasing the awareness of personal data subjects is associated with the development of decision support methods that present privacy policies in a form that is easier to understand, for example, in the form of quantitative risk assessments and pictograms. Their development requires a structured and marked-up corpus of documents. This paper systematizes the corpora of privacy policies that are in open access and shows their distinctive characteristics, such as the year of creation, volume and presence of annotations. A description of a new corpus of documents written in Russian is also presented, the results of a structural and semantic analysis of the collected security policies are given, and a comparison with the corpus of privacy policies written in English is made. It has been shown that the description of scenarios for storing, collecting and processing data in documents in Russian accounts for only 25% of the volume of the document text, which may indicate a lack of details about what types of data are collected, what mechanisms are used for collection, and what are the storage periods, which affects the “transparency” of the use of personal data.
Informatics and Automation. 2025;24(1):163-192
pages 163-192 views
Speaker-Specific Method of Spoofing Attack Detection Based on Anomaly Detection
Evsyukov M.
摘要
Most research in the field of voice presentation attack detection relies on the speaker-independent approach. Nevertheless, several scientific works indicate that using the speaker-specific approach, which involves utilizing prior knowledge about the identity of the claimed speaker to enhance the accuracy of spoofing detection, is likely to be beneficial. Therefore, the goal of this work is to propose a speaker-specific method of spoofing attack detection based on anomaly detection and to evaluate its applicability to the detection of synthesized speech and converted voice. Artificial neural networks pre-trained for the tasks of spoofing detection, speaker recognition, and audio pattern recognition are used for feature extraction. A set of anomaly detection models are used as backend classifiers. Each of them is trained on bonafide data of a target speaker. The experimental evaluation of the proposed method on the ASVspoof 2019 LA dataset shows that the best speaker-specific spoofing detection system, which uses an anomaly detection model and a neural network pre-trained for the task of speaker recognition, achieves an EER of 4.74%. This result suggests that embeddings extracted by networks pre-trained for speaker recognition contain information that can be utilized for spoofing detection. In addition, the proposed method allowed to increase the accuracy of three baseline systems pre-trained for the task of spoofing detection. Experiments with two baseline systems on the ASVspoof 2019 LA dataset showed relative improvement in terms of EER by 7.1% and 9.2%, and in terms of min t-DCF by 4.6%. Experiments with the third baseline system on the ASVspoof 2021 LA dataset showed relative improvement in terms of EER by 3.9% without significant improvement of min t-DCF.
Informatics and Automation. 2025;24(1):193-228
pages 193-228 views

Artificial intelligence, knowledge and data engineering

Analytical Review of Task Allocation Methods for Human and AI Model Collaboration
Ponomarev A., Agafonov А.
摘要
In many practical scenarios, decision-making by an AI model alone is undesirable or even impossible, and the use of an AI model is only part of a complex decision-making process that includes a human expert. Nevertheless, this fact is often overlooked when creating and training AI models – the model is trained to make decisions independently, which is not always optimal. The paper presents a review of methods that allow taking into account the joint work of AI and a human expert in the process of designing (in particular, training) AI systems, which more accurately corresponds to the practical application of the model, allows to increase the accuracy of decisions made by the system “human – AI model”, as well as to explicitly control other important parameters of the system (e.g., human workload). The review includes an analysis of the current literature on a given topic in the following main areas: 1) scenarios of interaction between a human and an AI model and formal problem statements for improving the efficiency of the “human – AI model” system; 2) methods for ensuring the efficient operation of the “human – AI model” system; 3) ways to assess the quality of human-model AI collaboration. Conclusions are drawn regarding the advantages, disadvantages, and conditions of applicability of the methods, as well as the main problems of existing approaches are identified. The review can be useful for a wide range of researchers and specialists involved in the application of AI for decision support.
Informatics and Automation. 2025;24(1):229-274
pages 229-274 views
Automatic Generation of Scientific Articles Abstracts Based on Large Language Models
Golubinskiy A., Tolstykh A., Tolstykh M.
摘要
The concept of automation of the process of annotation of scientific materials (Russian-language scientific articles) is proposed and its practical implementation is carried out by means of machine learning technologies, and additional training of large language models. The relevance of correct and rational compilation of annotations is indicated, and the problems related to establishing a balance between the time-consuming process of annotation and ensuring compliance with key requirements for annotation are highlighted. The basics of annotation presented in the family of standards on information, librarianship, and publishing are analyzed, and the classification of annotations and requirements for their content and functionality is given. The essence and content of the annotation process, and the typical structure of the research object are presented schematically. The issue of integration of digital technologies into the annotation process is analyzed, and special attention is paid to the advantages of introducing machine learning and artificial intelligence technology. The digital toolkit used to generate text in natural language processing applications is briefly described. Its shortcomings for solving the problem posed in this scientific article are noted. The research part substantiates the choice of the machine learning model used to solve the problem of conditional text generation. The existing pre-trained large language models are analyzed and, considering the problem statement and existing limitations of computing resources, the ruT5-base model is selected. A description of the dataset is given, including scientific articles from journals included in the list of peer-reviewed scientific publications in which the main scientific results of dissertations for the degrees of candidate and doctor of science should be published. The data labeling technique based on the operation of the tokenizer of the pre-trained large language model is characterized, and the numerical characteristics of the dataset distributions and the parameters of the training pipeline are presented graphically and in tables. The ROUGE quality metric is used to evaluate the model, and the expert assessment method, including grammar and logic as basic criteria, is used to evaluate the results. The quality of automatic annotation generation is comparable to real texts and meets the requirements of information content, structure and compactness. The article may be of interest to an audience of scientists and researchers seeking to optimize their scientific activities in terms of integrating digitalization tools into the process of writing articles, as well as to specialists involved in training large language models.
Informatics and Automation. 2025;24(1):275-301
pages 275-301 views
HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming
Hung N., Dat P., Tan N., Quan N., Trang L., Nam L.
摘要

360-degree video content has become a pivotal component in virtual reality environments, offering viewers an immersive and engaging experience. However, streaming such comprehensive video content presents significant challenges due to the substantial file sizes and varying network conditions. To address these challenges, view adaptive streaming has emerged as a promising solution, aimed at reducing the burden on network capacity. This technique involves streaming lower-quality video for peripheral views while delivering high-quality content for the specific viewport that the user is actively watching. Essentially, it necessitates accurately predicting the user’s viewing direction and enhancing the quality of that particular segment, underscoring the significance of Viewport Adaptive Streaming (VAS). Our research delves into the application of incremental learning techniques to predict the scores required by the VAS system. By doing so, we aim to optimize the streaming process by ensuring that the most relevant portions of the video are rendered in high quality. Furthermore, our approach is augmented by a thorough analysis of human head and facial movement behaviors. By leveraging these insights, we have developed a reinforcement learning model specifically designed to anticipate user view directions and improve the experience quality in targeted regions. The effectiveness of our proposed method is evidenced by our experimental results, which show significant improvements over existing reference methods. Specifically, our approach enhances the Precision metric by values ranging from 0.011 to 0.022. Additionally, it reduces the Root Mean Square Error (RMSE) by 0.008 to 0.013, the Mean Absolute Error (MAE) by 0.012 to 0.018 and the F1-score by 0.017 to 0.028. Furthermore, we observe an increase in overall accuracy of 2.79 to 16.98. These improvements highlight the potential of our model to significantly enhance the viewing experience in virtual reality environments, making 360-degree video streaming more efficient and user-friendly.

Informatics and Automation. 2025;24(1):302-328
pages 302-328 views
ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest
Ageev A., Konstantinov A., Utkin L.
摘要

In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. The key idea behind ADA-NAF is the incorporation of NAF into an autoencoder structure, where it implements functions of a compressor as well as a reconstructor of input vectors. Our approach introduces several technical advances. First, a proposed end-to-end training methodology over normal data minimizes the reconstruction errors while learning and optimizing neural attention weights to focus on hidden features. Second, a novel encoding mechanism leverages NAF’s hierarchical structure to capture complex data patterns. Third, an adaptive anomaly scoring framework combines the reconstruction errors with the attention-based feature importance. Through extensive experimentation across diverse datasets, ADA-NAF demonstrates superior performance compared to state-of-the-art methods. The model shows particular strength in handling high-dimensional data and capturing subtle anomalies that traditional methods often do not detect. Our results validate the ADA-NAF’s effectiveness and versatility as a robust solution for real-world anomaly detection challenges with promising applications in cybersecurity, industrial monitoring, and healthcare diagnostics. This work advances the field by introducing a novel architecture that combines the interpretability of attention mechanisms with the powerful feature learning capabilities of autoencoders.

Informatics and Automation. 2025;24(1):329-357
pages 329-357 views

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