This article considers the problem of several traveling salesmen. The task is to find a set of a predetermined number of disjoint cycles on a graph with weighted arcs, in which the weight (the sum of the weights of the arcs) of the largest cycle is minimal. An accurate algorithm for solving the problem based on the method of branches and boundaries has been developed. The constructed algorithm, as well as the well-known Balas' and Christofides' algorithm for solving the traveling salesman problem, uses the Hungarian algorithm for solving the assignment problem. Numerical experiments with large-dimensional random graphs have been carried out.
The publishing cycle is currently undergoing significant technological changes: automated publication management systems are being implemented, neural network technologies are being used for content processing, and tools for the intelligent analysis of scientific data are being actively developed. One of the key trends is the automation of the publishing cycle, aimed at accelerating manuscript processing, improving the quality of metadata, and ensuring the interoperability of information resources. In this context, metadata serves as a connecting element for machine processing and navigation within the scientific knowledge space, ensuring the structuring, interpretation, and integration of information into digital library systems. However, metadata for scientific publications often contain errors, inaccuracies, or are incomplete, and their manual creation and refinement are time-consuming and do not ensure high accuracy. The aim of this work is to design and develop a system for the automatic generation, processing, and management of metadata for scientific documents based on data obtained from scientific publication search services and open knowledge bases. The system can be used to automate the process of extracting, refining, and supplementing the metadata of scientific publications for the purpose of subsequently creating electronic collections of scientific documents.
This paper examines modern approaches to subword tokenization of texts as applied to the low-resource Tajik language, which is characterized by a complex morphological structure and a high degree of word-form variability. In the course of the study, a large-scale heterogeneous corpus was compiled and preprocessed, comprising 99 books and 134,497 textual articles of various genres and topics, with a total volume exceeding 33 million tokens. The corpus was cleaned of noise, normalized, and used as a basis for training and subsequent testing of subword models. Based on this corpus, five tokenization models implementing the BPE, WordPiece, and Unigram algorithms were trained and analyzed using the Hugging Face Tokenizers and SentencePiece libraries. Comparative evaluation was conducted using a set of key metrics, including the proportion of out-of-vocabulary (OOV) words, the degree of text representation compression, tokenization speed, as well as characteristics of n-gram distribution, which make it possible to assess the ability of the models to capture the morphological and structural organization of the language. The experimental results made it possible to identify the strengths and weaknesses of different approaches to subword segmentation and to determine the most effective tokenization strategies under conditions of the morphological complexity of the Tajik language. The findings obtained can be used in the development of language models and applied NLP tools for Tajik and other low-resource languages, contributing to the expansion of their presence in the digital environment.
This paper addresses the challenge of developing specialized corpus resources for the automated analysis of political manipulation in Russian-language media discourse. Although semantic text analysis and computational discourse analysis have advanced substantially in recent years, most existing corpora and annotation schemes are designed for English-language data and do not adequately capture the linguistic and discursive characteristics of Russian-language news media. The objective of this study is to construct a specialized corpus of Russian-language news texts and to develop an annotation scheme tailored to the automated analysis of political manipulation, with explicit consideration of the linguistic and discursive features of the Russian-language media environment. The study introduces a corpus of sentence-level fragments extracted from Russian-language news texts published between 2010 and 2019, together with an annotation scheme for manipulative techniques. The scheme is based on an adaptation of established international classifications of manipulative strategies and is reduced to a limited set of interpretable techniques relevant to Russian-language news discourse. The proposed framework covers emotional, argumentative, and contextual forms of manipulative influence. The resulting corpus and annotation scheme provide an empirical foundation for the development and evaluation of automated methods for analyzing political manipulation in Russian-language news media and may also support further research in media and political discourse.
In recent years, interest in knowledge graphs (KGs) has increased exponentially in both the scientific and industrial communities. Integration of various KGs is a pressing problem and is used, for example, to develop complex digital twins of industrial systems. Knowledge graph integration is also necessary when combining KGs extracted from natural language texts using large language models. One component of solving the KG integration problem is entity alignment (EA), which attempts to identify entities in different KGs that describe the same real-world object. In reality, many entities in real KGs have no equivalents in other KGs. In particular, each knowledge graph fragment extracted from a single publication may have its own structure of entity names and identifiers, which significantly complicates the task of identifying entities. This paper describes experiments on entity alignment in the presence of unmatchable entities using a Russian-English dataset as an example.
A computational scheme for the approximate solution of an integral equation with the Grünwald–Letnikov fractional integral has been developed, based on the least squares method. A distinctive feature of this scheme is the use of a neural network to compute the coefficients for the least squares method. The relevance of the study is обусловлена by the fact that, at present, artificial intelligence is increasingly being applied to solve many practical problems related to various physical processes. An estimate of the convergence of approximate solutions to the exact solution has been obtained. Possible directions for the further application of artificial intelligence in solving physical problems are also considered.
This paper is a mosaic of vivid fragments describing the industrial aspects of artificial intelligence (AI). These are sketches of the overall picture, which will likely never be completed, as each day brings information about new achievements, ideas, and threats. Discussions cover issues of civilian AI in short-term workstations, the development of algorithms for intelligent games, the threats and dangers posed by AI, AI ethics, and standards and international norms for artificial intelligence. Each fragment is a review of the latest (mid-January 2026) Russian and international sources, including quotes, translations, screenshots, and links to original documents. This text remains an immense "fragment" on the benefits of AI applications, which was presented with the greatest speed. Perhaps this will be the beginning of a separate, never-ending study.
This paper explores the problem of combining methods in the semantic analysis of scientific data and publications during review. At different stages of data processing in the SciLibRu system, various methods are used, a multi-level ontology is constructed, and a knowledge graph is populated, resulting in the formation of a new data structure distinct from the original. Each method individually serves its purpose in such a system, while their combined use leads to the emergence of new properties, which became the subject of this research. An example of an automatic peer review agent with explainable results is provided.
A study was conducted on searching for similar documents. The goal was to create a recommendation algorithm for finding similar scientific articles in mathematics using a prioritized search of mathematical formulas with textual support. The text was converted from graphical to textual representation using OCR technology for subsequent analysis and indexing. During the analysis process, the text was divided into blocks, followed by the extraction of significant formulas, keywords, and phrases from the text. During the indexing process, a vector database was formed based on vector representations of formulas obtained through the embedding process. The indexing results were used to search for articles that are similar to the document submitted by the user to the algorithm input. A list of similar articles is displayed with results sorted by the metric of closeness of vector representations of formulas. The source data consisted of approximately 5,000 scientific articles devoted to various studies on mathematical topics and presented as PDF files. The experiment was conducted based on data from specific library system content, but the proposed technology can be extended to other library systems, including those containing articles on other topics, such as physics and other exact sciences.
The article presents an ontological model of a control system for a Peltier thermoelectric element. The ontology describes the structure of the system by identifying objects, transformation processes within these objects, and the attributes of the relationships between them. Based on the developed ontological model, a cascade control system has been designed, integrating a PID controller, a fuzzy-digital filter, and an exponential-averaging filter, with its cognitive behavior governed by fuzzy logic rules. Improvement of the dynamic characteristics of transient processes in the Peltier element control system is achieved through the application of the mathematical and ontological solutions specified in the model. The cascade control system reduces the amplitude of the first harmonic of the control signal by 12% and decreases the transient response time by 31.9%.
This article examines key challenges in teaching functional programming to students already familiar with the imperative paradigm. It describes the student models and underlying complexities that arise when teaching functional programming in this context (mutable variables, loops, and sequential computations). An extended example of the transition from the imperative to the functional paradigm is provided. The return of a functional value is examined in detail using examples of numerical differentiation and interpolation. An implementation of deferred evaluation based on anonymous functions is considered. The multiparadigm Lisp language is shown to be a convenient introduction to the functional paradigm.
The electronic library "Scientific Heritage of Russia" (EL SHR ) has been operating in open Internet access since 2010. The library integrates information about scientists who have contributed to the development of Russian science, their scientific publications, related archival materials, online resources and museum objects. The modern version of the NPR EB is developing as a model of a fragment of the Common Digital Space of Scientific Knowledge (CDSSK) and includes a number of functional blocks (metadata generation, publication of digitized documents and museum objects, organization of collections and exhibitions, content administration). The article describes the functionality of the electronic library's administrative block. The block is accessible to authorized users with the appropriate permissions. The block allows you to edit the metadata elements of each object type and the relationships between them, monitor the processing stages of specific objects entered in the electronic library, and export a specified set of related objects.
This article addresses the problem of coordinating heterogeneous software tools in heterogeneous distributed application execution environments. Here, manually configuring launch parameters for newly installed programs on a computing cluster (such as command-line switches, environment variable values, and configuration file settings) poses significant challenges for domain researchers due to the large volume of utility information and the need to store and aggregate information in a fixed format. We propose a method for the automated extraction of launch parameters based on a hybrid neural network training architecture that combines the generation of training samples using large language models with the subsequent fine-tuning of a compact transformer encoder. This approach eliminates the need for expensive graphics accelerators by applying the Low-Rank Adaptation (LoRA) technique to models with up to 1 billion parameters, enabling model execution (inference) on standard CPUs in control nodes. To formalize the quality of extraction, a two-component metric has been developed that aggregates the structural correctness of the output JSON schema (the presence of required fields and program parameter types in the obtained data) and the semantic accuracy of parameter values (correspondence with the description in the documentation). The experimental evaluation of the method focuses on a corpus of software package documentation (man pages, README files). The design results confirm the possibility of approximating the documentation analysis process with a compact model, which contributes to the automation of the software deployment lifecycle and the reduction of task flow management errors in distributed computing systems.
The article reflects the new results of research related to the formation of the Common Digital Space of Scientific Knowledge (CDSSK). This work has been carried out since 2019 in a number of academic organizations, including the Interdepartmental Supercomputer Center of the Russian Academy of Sciences (now the Department of Supercomputer Systems and Parallel Computing at the National Research Center "Kurchatov Institute"). As part of these studies, the structure of the CDSSK ontology, a language for its description, and a number of unified software tools have been developed to ensure the formation of the ontology of individual subspaces and the input of various types and kinds of object attributes and named relationships into the CDSSK. Currently, the formation of the CDSSK content is being modeled using the example of a universal and a number of thematic subspaces. The results of this modeling are presented below. The attributes and relationships of the "Administrative Units" class objects belonging to the "Geography" subspace, the "Organizations and Their Subdivisions" class, and the "Classification Systems" class belonging to the universal subspace are presented. The ability to navigate through the loaded real resources is demonstrated.
This paper presents various aspects of the practical implementation of individualized learning algorithms (based on the results of pedagogical experiments) for both teacher-led instruction (in the classroom, remotely, or in a hybrid mode) and independent student work. The described system simultaneously teaches students course materials and independent learning techniques — that is, educational technologies that shape an individualized educational trajectory. A subset of educational technologies is determined individually for each student in the group. The educational technologies are independent of the course and universal, so they can be applied in subsequent or parallel courses. Teachers can describe new educational technologies as Python scripts without the involvement for developers. The proposed implementation integrates with the Mirera digital educational platform to expand the platform's capabilities.
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.
Universal Decimal Classification (UDC) is a hierarchical indexing system in which a publication may be assigned one or several codes. Manual UDC indexing is labor-intensive and often inconsistent. This paper addresses the automatic assignment of UDC codes to Russian-language mathematical research articles. The aim is to compare combinations of text representations and classification models on a unified corpus and to identify the most effective configurations. A corpus of 4194 articles was collected from Math-Net.Ru, including full texts, abstracts, metadata, and UDC codes. The preprocessing pipeline comprised PDF text extraction, removal of layout artifacts, and normalization of UDC labels. We compared TF-IDF, Word2Vec, SciRus-tiny, and SciRus-tiny3.5 representations combined with logistic regression, Complement Naive Bayes (CNB), and CatBoost. In both the single-label and multi-label settings, the best performance was achieved by TF-IDF + LogReg, while TF-IDF + CNB showed closely competitive results. The proposed approach can be used in automatic subject indexing systems for digital libraries and scientific archives, in UDC recommendation tools for authors and editors, and in metadata quality control workflows.
This paper examines the current challenge of developing an intelligent agent for modeling the characteristics of electronic equipment protection shields. The aim is developing a methodology and software implementation for an intelligent agent that will simplify the analysis of various design solutions and provide decision support for design engineers. An intelligent agent has been developed that automates the process of preparing a description of an alternative design solution for subsequent modeling using the Geant4 software package. Integrating the software module into computing platforms will improve the work of design engineers by reducing routine manual operations, minimizing human error, and ensuring reproducible results.
The article examines current challenges in scientometrics arising from the surge in publication activity and the widespread adoption of generative artificial intelligence. The existing scientometric toolkit for analyzing research activity is reviewed, categorized into quantitative metrics and science mapping methods (citation network analysis, academic genealogy, semantic analysis, etc.). An attempt is made to overcome the limitations of traditional citation analysis, such as “semantic blindness” and vulnerability to manipulation. As a potential solution, a conceptual model is proposed where the unit of analysis shifts from the publication as a whole to an individual “key statement”. This approach involves recording not only the statement’s content but also its type, area of relevance, and its logical relationship with other claims (confirmation, refutation, clarification, generalization, etc.). Within this framework, principles for calculating modified scientometric metrics are introduced. The proposed model was tested on a corpus of 728 articles from the Russian journal Informatics and Education (2016–2025). An analysis conducted using large language models revealed that retrospective extraction of statements faces significant hurdles due to established cultures of scientific communication. Consequently, the study highlights the advantages of having authors formulate key statements themselves as a distinct type of metadata. In conclusion, the paper outlines development paths for the concept of an “embedding space of knowledge,” which could eventually complement existing approaches to analyzing the evolution of scientific ideas and theories.
In the context of digital transformation of organizations and the growing volume of data, there is a demand for more transparent and explainable approaches to employee evaluation. The purpose of the study is to design and validate an ontological model (OWL 2/SHACL) that integrates employees’ cognitive indicators and sociological characteristics into a unified knowledge space to support HR processes. The scientific novelty of the work lies in the development of a unified semantic model linking data from cognitive tests, questionnaires, work context, and performance indicators; in the formulation of competency questions (CQ) that trigger reasoning mechanisms within the knowledge graph; and in the creation of patterns for predicting competency gaps, identifying the risk of overload/burnout, while ensuring ethics and nondiscrimination control. The proposed approach is based on the ontological engineering methodologies METHONTOLOGY and NeOn, semantic web concepts, and psychometric methods.
