The article analyzes the indexing and citation systems of scientific information of the BRICS countries, including the Russian Index of Science Citation (RISC). The possibilities of these digital platforms in processing and presentation of scientific informational document flows, as well as in the implementation of scientometric operations, are revealed. The methodology of the research includes the analyzing of theoretical scientific publications on the problems under consideration in the complex with studying of practical domestic and foreign experience. The relevance and novelty of the work are determined by the absence in the modern scientific literature of profile publications devoted to the joint studying and investigation of science citation indices of the BRICS countries. The main relevant data on content and other characteristics of citation indices of each country is presented. The idea of the possible creation of a common science citation index for the BRICS countries is put forward and the advantages of such creation are described. The authors point out the significance of the common index for ensuring and expanding of equitable access of the BRICS countries to scientific information in the international market of scientific-informational services, as well as for developing of technical and technological contour of the BRICS’ mutual cooperation.
Based on a comprehensive analysis, this article provides a detailed description of the operating principles of bibliographic services, referred to by the author as “global bibliographic platforms.” It also assesses the impact of such platforms on global research information services and outlines the organizational and methodological implications of implementing such systems. Global bibliographic platforms are defined as a type of automatically generated online information resource reflecting the global flow of scholarly documents and satisfying the full range of users’ bibliographic needs. The technological foundation of global bibliographic platforms is formed by a system of international academic standards (DOI and ORCID), the adoption of which has led to the transition of the process of creating initial bibliographic information from libraries and information centers to publishing organizations, and an API interface that enables the fully automated retrieval of bibliographic information. In the future, information support for scientific research and education will shift toward the use of the described bibliographic platforms, which are capable of providing the highest quality service at minimal cost.
A new approach to analyzing the development of scientific knowledge is proposed, based on the concept of information dynamics of evaluative theories. Scientific knowledge is considered as a dynamic system striving to minimize informational tension. A methodology has been developed that uses the average mutual information to aggregate evaluations considering the semantic load and internal consistency of knowledge. Using climate models and evaluations of the age of the universe as examples, the application of the proposed approach regarding the evolution of scientific theories is demonstrated.
This article analyzes current challenges in the effective operation of project management information systems in corporations in the context of digitalization and software import substitution. Key issues include high levels of information asymmetry, rigid management methods, and poor integration of project management system structural elements with the corporate information management system. To address these challenges, a comprehensive approach to business process management with a focus on project management is proposed, which should enable the effective implementation of global transformations in the enterprise’s digital environment. The need to improve the information culture of all stakeholders is argued for to successfully integrate new projects into the corporate information system.
The role and place of intelligent assistants in the architecture of a modern organization are analyzed in the context of applying the TOGAF methodology and architectural engineering principles. The goal of the study is to identify the conceptual and architectural position of an intelligent assistant, define its functional characteristics, and identify mechanisms for interaction with key elements of the enterprise architecture. The developed structural integration model for an intelligent assistant, written in ArchiMate, describes the process of its implementation and operation within the organization’s architecture.
The problem of training highly qualified personnel with competencies in the field of artificial intelligence is considered. The necessity of an integrated approach to the training of qualified specialists in the field of the development and use of artificial intelligence technologies is emphasized. Approaches to improving the educational process are discussed, and features of integrating science and education based on the principle of learning through research are highlighted. An example of a project to create an intelligent decision support system is given.
The problem of ensuring effective adaptive feedback (EAF) in the interaction of subjects of the digital educational environment (DEE) is considered. The purpose of this study is to analyze typical architectural solutions of a university’s DEE, substantiate the concept of effective interaction of subjects and develop a modified architecture with the implementation of the DEE. The research methodology includes a systematic and structural-functional study of the architectures of DEE, methods of modeling, and comparative analysis. A modified DEE architecture is proposed with a theoretical justification of the cause-and-effect relationship between the use of DEE and improving the quality of the educational process. The level of intellectual support for the implementation of EAF in the DEE structure includes a hybrid architecture of language models with elements of social networks and a virtual information and communication asset. The value and novelty of the work lies in an integrated approach to considering the EAF as a system-forming component of the DEE architecture.
A set-theoretic model of the quality of the information resource of the automated system of information support of scientific research is presented. The formalization of the concept of the quality of the information resource is carried out, and the place and role of the developed model for the qualimetry of information resources are determined. The model is developed from the standpoint of the system approach and is presented by a hierarchical mathematical construction. The properties and their indicators are substantiated, adequately characterizing the suitability of the information resource for information support of scientific and technical activities.
More than one million Russian publications indexed in the Web of Science (WoS) database have been analyzed. The study covers the period from 2008 through 2023. The characteristics of author teams are examined through the distribution of publications by the number of authors, as well as the average, median, and maximum number of authors per publication. The analysis was carried out for three major fields of knowledge: Hard Science (HS); Social Sciences (SS); and Humanities and Arts (HA). It is shown that, other factors being equal, publications in the HS field are characterized by the largest author teams. For journal articles, the average number of authors was 18.6 for HS, 6.4 for SS, and 2 for HA. When considering the dependence of author team size on the publication type, the average number of authors in the HS field follows this order: journal papers, 18.6; conference proceedings, 6.6; and books or book chapters, 6.2. In almost all cases, a substantial increase in the size of author teams over time has been observed. For journal papers in the HS field, the average number of authors per publication increased 2.1 times over 16 years (from 9.1 to 19.0); for SS, it tripled (from 2.8 to 8.5); and for HA, it grew 1.5 times (from 1.5 to 2.2).
The article is devoted to the description of a pragmatically oriented information technology for representing cognitive structures based on semiotic universals of an agglutinative language within the framework of creating explanatory artificial intelligence. A decentralized semiotic model of agglutinative natural languages is considered as a means for organizing cognitive processes and interactions between intelligent agents. The software–algorithmic structure of the support system prototype of the semiotic model is presented using the example of the Tatar language.
The algorithm presented in this article makes it possible to efficiently build and modify minimal deterministic finite automata for recognizing a given set of words, including when processing a large amount of information in real time. The key feature of this algorithm is the ability to add new words to the machine and its subsequent minimization on the fly. The algorithm is based on the lexicographic ordering of a set of input words and has a low computational complexity compared to traditional algorithms such as the Hopcroft algorithm or an algorithm using the construction of pairs of distinguishable states. The development of this algorithm is aimed at increasing the speed of constructing minimal deterministic finite automata and their modification for effective natural language processing and real-time web content analysis.
This paper presents a solution to the problem of multiclass flame segmentation with separation by combustion color. The mathematical problems of partial (without separation of the background class into a separate component of the search vector) and full (with separation) segmentation are formulated. A comparison of convolutional neural network methods of UNet, DeepLab and their modern variations, including the wUUNet method, developed specifically for the problem under consideration, is carried out. The paper emphasizes the influence of the size of the computation matrix of segmentation computations with the original frame. Both lossy (compressing the frame to the size of the computation matrix and then decompressing it into the original frame) and lossless (applying a single-window frame sizing scheme or multiwindow schemes for partitioning the frame into a grid of subareas) segmentation schemes are proposed. The best segmentation methods and schemes in terms of quality are selected.
This article discusses well-known methods of knowledge representation and processing. A new model, called notional, is proposed, characterized in that the relations (links) between notions are considered as ordinary notions. The notion is considered as a form of thought expressed by a named set of entities. A specific notion identifies one entity. An abstract notion is formed from other notions by generalization (union) and association (Cartesian product) them. Declarative knowledge is given by enumerable sets of entities, and procedural knowledge is given by solvable ones, where resolving procedures are expressed by formulas of the pure monadic predicate calculus. The description of the applied language of knowledge representation and processing is given. It is proven that queries to the notional model are executed in polynomial time from the logarithm of the average number of entities in notions. The possibility of unsupervised learning of notional models is substantiated. It is shown that the notional model makes it possible to visually represent and effectively process both declarative and procedural knowledge.
This article presents the results that relate to the development of an approach for constructing parametric fuzzy metrics based on additive generators of triangular norms. A family of continuous Archimedean triangular norms is chosen, represented by rational functions with the fractional linear function (LFAG) and the logarithm of the fractional linear function (lnLFAG) acting as generators. The restrictions on the parameters of generators are obtained, under which the corresponding triangular norm is strict. Fuzzy metrics that correspond to these generators are constructed, and their properties are investigated. The approbation of fuzzy metrics was carried out on the clustering problem, and the quality of the solution was evaluated according to various criteria for metric clustering algorithms.
The purpose of this paper is to advance and automate language models for the extraction of statements related to events and factors from text documents using the designed linguistic marker system. The paper presents the outcomes of text-mining models of events and factors extraction approbation on the example of analytical research in the fields of human potential, namely, the social sciences and the humanities. The testing and evaluation of the used linguistic models are performed on the basis of the results comparison obtained in automatic mode, in manual mode (with the participation of expert-analytical validation) and semi-automatic mode (using the implemented system of linguistic markers). The introduced approaches resulted in higher performance in extracting statements containing events and factors.
To increase the transparency of modern computer-aided diagnosis (CAD) systems for assessing the malignancy of lung nodules, an interpretable model based on the application of generalized additive models and concept-based learning is proposed. This model detects a set of clinically significant attributes in addition to a final malignancy regression score and learns the association between the attributes of lung nodules and a final diagnosis decision, as well as their contributions into the decision. The proposed concept-based learning framework provides human-readable explanations in terms of different concepts (numerical and categorical), their values, and their contribution to the final prediction. Numerical experiments with the LIDC-IDRI dataset demonstrate that the diagnosis results obtained using the proposed model, which explicitly explores internal relationships, are in line with similar patterns observed in clinical practice. Additionally, the proposed model shows the competitive classification and the nodule attribute scoring performance, highlighting its potential for effective decision-making in the lung nodule diagnosis.
This study provides a description of the algorithm on the basis of which weights, thresholds, and the number of channels in the layers of a convolutional neural network are analytically calculated. The results of the experiments described in this work showed that the time for calculating the weights of convolutional neural networks is relatively short and amounts to fractions of a second or a minute. The experimental results also showed that using only 10 selected images from the MNIST database, analytically calculated convolutional neural networks are able to recognize more than half of the images of the MNIST test database, without using neural network training algorithms. Preliminary analytical calculation of the value of the weights of a convolutional neural network allows to speed up the training procedure of a convolutional neural network.
The paper presents research materials on the development of mathematical models for machine vision systems using the theory of modified descriptive image algebras. The basic definitions of mathematical objects and operations on them, used in structural synthesis of models, are formulated. The general formulation of the parametric identification of the machine vision system model is given. Mathematical models of machine vision systems for the three tasks of measuring the area of objects of different nature are described. Recommendations on the statistical estimation of values of variation parameters of the model at processing of set of images are given.
Trust in artificial intelligence is a key factor in the widespread introduction of intelligent technologies into the economy and social sphere. The article discusses various aspects of this. These include trust in knowledge and data and in artificial intelligence and machine learning models; the risks and limits of applicability of the methods and technologies used; the explainability of decisions and human-oriented artificial intelligence; the primary validation and secondary validation (verification) of created systems; ChatGPT hallucinations and falsifications; and the ethical, legal, and organizational aspects of the use of artificial intelligence.
This article studies the recognition of special structural segments of genomes called promoters. To solve the problem of promoter recognition machine learning methods based on logical analysis and data classification were used for the first time. These methods are based on searching for informative fragments in feature descriptions of precedents and are focused on processing low-value integer information. The fragments found are well interpretable and allow distinguishing promoters from other regions of the genome. However, searching for them is time-consuming. The results of experiments on an unbalanced sample of a large volume are presented, considering both the traditional method of feature formation using k-mers and the method of direct application of the logical classifier to the original data. It is shown that in the second case, the quality of logical classification is significantly higher and amounts to 94.3
