AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS最新文献

{"title":"Data Processing, Detection and Protection Algorithms to Minimize the Impact of Malware and Phishing Attacks on Users of Digital Platforms","authors":"T. S. Volokitina,&nbsp;M. O. Tanygin","doi":"10.3103/S0005105526700160","DOIUrl":"10.3103/S0005105526700160","url":null,"abstract":"<p>This article is devoted to the development of a scientific and methodological apparatus for improving the effectiveness of protecting digital platforms from cyber threats by creating processing and detection algorithms that take into account the cognitive characteristics of users. A conceptual model of a three-stage protection system is proposed, integrating technical security mechanisms with cognitive decision-making models. A heuristic detection algorithm based on random forest machine learning with analysis of 47 features, including technical URL characteristics and cognitive–semantic content characteristics, has been developed. A methodology for dynamic integration of four threat data sources has been created, reducing response time from 12–14 to 2 h. An algorithm for recursive analysis of redirection chains up to ten levels deep to detect masked threats is proposed. Experimental validation on an empirical base of approximately 1 million records confirmed detection accuracy of 87% when processing 100 thousand records per h. The developed solutions ensure compliance with the requirements of GOST R 57580.1-2017 and Russian legislation in the field of personal data protection.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S74 - S80"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scientific Coauthorship According to RSCI and Scopus Data for 2000–2020: Growth Trends","authors":"S. A. Durnev,&nbsp;E. A. Znamenskaya,&nbsp;A. A. Pechnikov,&nbsp;D. E. Chebukov","doi":"10.3103/S000510552670010X","DOIUrl":"10.3103/S000510552670010X","url":null,"abstract":"<p>Scientific coauthorship is a direct reflection of scientific collaboration. Foreign studies based on Web of Science and Scopus data show that over the past decades there has been an increase in the number of coauthors of scientific publications in international journals in various disciplines. The paper compares the growth trends in the number of coauthors according to the RSCI and Scopus data. The study was conducted in five thematic areas (chemistry, history, mathematics, medicine, and physics) from 2000 to 2020. The article shows the identity of the trends in the growth of the number of coauthors in the cases of publications on history and mathematics and a noticeable difference in other scientific fields.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S20 - S25"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Digital Platform with an Integrated 3D Configurator for Clothing Customization","authors":"E. V. Evdushenko,&nbsp;M. V. Shmatko","doi":"10.3103/S0005105526700172","DOIUrl":"10.3103/S0005105526700172","url":null,"abstract":"<p>In the face of the rapid growth of e-commerce and increasing demand for personalization, the Russian market for customized clothing faces a shortage of technological and widely accessible solutions. This paper presents the results of a research and implementation project focused on developing a multibrand digital platform with an integrated 3D configurator, aimed at transforming the preorder cycle. The solution enables customers to interactively create garment designs in a web environment, while allowing designers to optimize logistics and minimize overproduction. The primary scientific and technical contribution of this work lies in its detailed description of the platform’s target architecture and a scalable 3D model processing pipeline that ensures model optimization and correct browser-based rendering. An additional contribution is the developed methodology for preparing and optimizing 3D garment models for web visualization. Formalized as a set of technical requirements, this methodology achieves a balance between visual quality and performance. As a result of this research, the authors have addressed the challenge of unifying 3D model formats from different designers within a multibrand digital platform—a key distinction from existing single-brand solutions. Furthermore, the implemented technology enables the customization of 3D clothing models with interactive real-time visualization of all design modifications on a single screen. The technological feasibility and effectiveness of the solution are substantiated by a comparative analysis of existing alternatives, a market analysis using the PAM-TAM-SAM-SOM model, and an assessment of functional requirements. The article also outlines a practical strategy for implementing the digital platform, making it a valuable resource for researchers and practitioners working at the intersection of e-commerce, computer graphics, and the digital transformation of business processes.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S81 - S94"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of one Solution When Migrating from Centos to Red OS for a High Availability Cluster","authors":"G. M. Mikhaylov,&nbsp;N. P. Tuchkova,&nbsp;A. M. Chernetsov","doi":"10.3103/S0005105526700147","DOIUrl":"10.3103/S0005105526700147","url":null,"abstract":"<p>This paper presents a brief overview of popular domestic OS distributions that are developed as part of the implementation of import substitution tasks in software and telecommunications. One solution for the transition from CentOS to RED OS is presented for a High Availability cluster based on Pacemaker and the DRBD distributed file system, which ensures the operation of the organization’s website and MySQL database server.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S57 - S59"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forms for Presenting the Results of Comparing Programming Languages Using the Example of Lisp Dialects","authors":"L. V. Gorodnyaya","doi":"10.3103/S0005105526700093","DOIUrl":"10.3103/S0005105526700093","url":null,"abstract":"<div><p>The article is devoted to developing forms for displaying the results of analysis and comparing the features of programming languages, systems, and paradigms. The proposed form is demonstrated using the example of a comparison of the Lisp language, its most successful dialects (Scheme, Common Lisp, Racket, Clojure), and the functional programming paradigm at various levels of language and programming system definitions. The form allowed for a concise demonstration of the inheritance of a number of Lisp language features and their development in dialects at the level of concrete syntax, abstract semantics, and system pragmatics.</p></div>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S7 - S19"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods of Cognitive Modeling and Hybrid Evolutionary Multicriteria Algorithms in a Multiagent Information-Analytical System","authors":"V. B. Chechnev","doi":"10.3103/S000510552670024X","DOIUrl":"10.3103/S000510552670024X","url":null,"abstract":"<div><p>This paper proposes an approach to multicriteria decision support that is based on a cognitively oriented multiagent information-analytical system. Cognitive modeling methods are developed, including a formal ontological representation of knowledge concerning production planning and a coalition–holonic agent architecture that ensures adaptability and transparency of computations. A hybrid evolutionary multicriteria algorithm is introduced, where agents generate alternative plans at the local level using a parallel genetic algorithm that optimizes a combination of several criteria. At a global level, a multistage selection of alternatives is implemented with filtering of resource overloads and similar solutions, followed by final aggregation using the PROMETHEE and ELECTRE multicriteria decision-making methods. An experimental study is carried out that compares manual planning with planning supported by the developed system, as well as analyzing the impact of the dynamic adaptation of the genetic algorithm parameters. The results show that the use of the system makes it possible to reduce plan generation time by a factor of 20–30 while maintaining or improving solution quality. At the same time, resource overloads are completely eliminated, and the early termination of evolutionary computations is ensured without loss of solution quality. The system and proposed algorithms are intended for use in planning project activities at manufacturing enterprises.</p></div>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S139 - S144"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Multimodal Neural Network Monitoring Service for the Surveillance Area","authors":"R. R. Minneakmetov","doi":"10.3103/S0005105526700135","DOIUrl":"10.3103/S0005105526700135","url":null,"abstract":"<p>The article presents an approach to the development of an intelligent multimodal monitoring service for the surveillance area using large neural network models. The proposed solution is capable of analyzing heterogeneous data from video streams, environmental sensor signals (temperature, humidity, etc.), and event logs to obtain a complete picture of what is happening. The main tools used are large language and visual models (for example, LLaMA, MiniCPM‑V, etc.) deployed locally using the Ollama platform, which provides autonomous and secure information processing without the need to transfer data to the cloud. A prototype system has been developed that works offline and is capable of detecting critical situations, abnormal deviations from the norm and contextually significant events in the observed area. The method of forming test scenarios and conducting a qualitative assessment of the model’s performance using the metrics F1-score, Precision, and Recall on a set of various situations is described. The experimental results confirm the applicability of multimodal models for monitoring tasks: the prototype successfully recognizes complex patterns of behavior, demonstrating the potential of large models in building adaptive and scalable surveillance systems.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S48 - S56"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Queries to Nonrelational Data Using Natural Language Based on a Large Language Model","authors":"A. O. Erkimbaev,&nbsp;V. Yu. Zitserman,&nbsp;G. A. Kobzev","doi":"10.3103/S0005105526700111","DOIUrl":"10.3103/S0005105526700111","url":null,"abstract":"<p>The main purpose of this work is to explore new opportunities for the organization of natural language queries in scientific local databases that are not relational. A brief review of recent research shows that there has been an active introduction of natural language queries into databases of various types, and the use of machine learning methods, such as neural algorithms, has also been noted. The widespread use of large language models for query generation in various language settings and fields of expertise over the last two years has been demonstrated. A study was conducted to explore the potential of the AllegroGraph graph database for using large language models for natural language search. The functionality of the database was examined using the example of a metadata system for thermophysical properties in the form of Thermal domain ontology. Testing search queries in a bilingual (English and Russian) database environment revealed some general problems that can be overcome, and it gives us good hope for the future application of new services using large language models.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S26 - S36"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"List of Higher Attestation Commission Journals and Other Russian Indices","authors":"T. A. Polilova","doi":"10.3103/S0005105526700159","DOIUrl":"10.3103/S0005105526700159","url":null,"abstract":"<p>In accordance with the requirement of the Higher Attestation Commission (HAC), metadata for issues of journals from the List of Peer-Reviewed Scientific Publications in which the main scientific results of dissertations for the degree of Candidate of Sciences and for the degree of Doctor of Sciences must be published (HAC List) have been regularly deposited in the Russian Science Citation Index (RSCI) in the bibliographic database eLibrary.ru for more than 20 years. In March 2023, the editorial offices of journals from the HAC List, following a recommendation from the HAC, began uploading information on their 2022 issues to the Russian Scientific Journals (RSJ) database, created by the Russian Research Institute of Economics, Politics and Law in Science and Technology (RIEPL). In April 2025, an order from the Ministry of Science and Higher Education of the Russian Federation added a new requirement: journals in the HAC List must now be registered not only in the RSCI eLibrary.ru but also in the Metaphora Information System (IS), developed by the Russian Center for Scientific Information (RCSI). Journals from the HAC List are recommended to regularly transfer metadata of published issues to the Metaphora IS through specially organized interfaces. What role do the RSJ database and the Metaphora IS play in the infrastructure of scientific publications? In addition to developing the Metaphora IS, the RCSI, by order of the Government of the Russian Federation, acts as the operator of the White List of scientific journals. The White List was formed in 2023 by the Interdepartmental Working Group (IWG) of the Ministry of Science and Higher Education of the Russian Federation. The White List is intended to be used for monitoring and evaluating the publication activity of Russian scientists. Initially, the White List included about 29 000 English-language international journals and about 1000 Russian-language journals from the RSCI database. In September 2025, the Russian-language part of the White List expanded significantly with the inclusion of journals from the HAC List. We would like to receive detailed information from the originators of the White List on how the levels of White List journals (L1, L2, L3, L4) will correspond to the categories of journals in the HAC List (K1, K2, K3).</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S60 - S73"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Types of Embeddings and Their Application in Intellectual Academic Genealogy","authors":"A. Kh. Marinosyan","doi":"10.3103/S0005105526700184","DOIUrl":"10.3103/S0005105526700184","url":null,"abstract":"<p>The paper addresses the problem of constructing interpretable vector representations of scientific texts for intellectual academic genealogy. A typology of embeddings is proposed, comprising three classes: statistical, learned neural, and structured symbolic. The study argues for combining the strengths of neural embeddings (high semantic accuracy) with those of symbolic embeddings (interpretable dimensions). To operationalize this hybrid approach, an algorithm for learned symbolic embeddings is introduced, which utilizes a regression-based mapping from a model’s internal representation to an interpretable vector of scores. The approach is evaluated on a corpus of fragments from dissertation abstracts in educational science. A compact transformer encoder with a regression head was trained to reproduce topic relevance scores produced by a state-of-the-art generative language model. A comparison of six training setups (three regression-head architectures and two encoder settings) shows that fine-tuning the upper encoder layers is the primary driver of quality improvements. The best configuration achieves <i>R</i>² = 0.57 and a Top-3 accuracy of 74% in identifying the most relevant concepts. These results suggest that, for tasks requiring formalized output representations, a compact encoder with a regression head can approximate a generative model’s behavior at substantially lower computational cost. More broadly, the further development of algorithms for constructing learned symbolic embeddings contributes to building a model of formal knowledge representation in which the convergence of neural and symbolic methods ensures both the scalability of scientific text processing and the interpretability of vector representations that encode their content.</p>","PeriodicalId":42995,"journal":{"name":"AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS","volume":"60 1","pages":"S95 - S101"},"PeriodicalIF":0.5,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148193848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}