Vol 73, No 1 (2023)

All over the world there are many both global and local information systems focused on solving various problems. As an integrator that allows you to solve complex information problems at the intersection of sciences and application areas of existing information systems. to the maximum extent using the information resources accumulated in them, the Common Digital Space of Scientific Knowledge (CDSSK) can be considered. The article provides the structure of the CDSSK, the requirements for its functionality and the structure of the software shell, corresponding to the principles of the Semantic WEB. All objects reflected in the CDSSK are divided into two classes – universal and local. Relationships between objects are also divided into two groups – universal and specific. The paper proposes a list of universal classes of objects, defines universal types of relations between them, gives examples of specific relations and approaches to identifying local classes and subclasses of objects in a particular field of science.

The License Management System (LMS) was developed at the JINR Information Technology Laboratory. The purpose of creating an LMS is to automate the management, acquisition, maintenance and use of licensed software products. The system consists of a network licensing system (NLS), databases and a web interface. NLS provides network license management, collects and transmits to the time series database information about which network license was used by the user and at what time. The features of collecting this type of data are given. This data is used in monitoring implemented on the basis of the Grafana platform. The main LMS database stores data related to corporate, private, and other types of licenses. It also stores the necessary data regarding license users. The database is implemented in PostgreSQL. The system provides the ability to process workflows such as ordering new licenses that users need, ordering to add to the catalog of purchased licenses, and other functions. The LMS web interface is implemented in the development environment of the Electronic Document Management System "EDMS Dubna" using the LegoToolkit web application. A website has been developed and created for LMS users.

The paper is devoted to organizing a storage structure for multidimensional reference signals and processes within the framework of digital filtering based on fast simulation algorithms and a universal adaptive matrix transformation operator. The storage contains relevant simulation equations, simulated signals as value tables and images, and original code in Python, C++ and other languages, as well as a supplementary license data. Different approaches to storage creation are analyzed in the paper. The chosen route consists of structuring all the data into a dataset, publishing the dataset on GitHub and building an ontology based upon the dataset. The principles for structuring the data and organizing such an ontology are given. The resulting published dataset is presented.

The article discusses various software implementations of the process of automating the task of using machine learning to solve the linear regression problem. The internal structure and capabilities of a number of existing and widely used automated machine learning tools such as LightAutoML (LAMA), TPOT, AutoSklearn, H2O AutoML, MLJAR are considered. The capabilities of these software systems have been explored to solve the regression problem on multiple datasets.

In recent years, machine learning technologies have become increasingly popular in widespread tasks such as image stylization, black-and-white image coloring, super-resolution of images, fake data searching, voice and image recognition. In this regard, there is a need to implement a set of tools for integrating artificial intelligence systems into applications for mobile devices, smart home devices, and home PCs. The paper describes a solution that allows developers to integrate data analysis and machine learning systems directly into a user application, which will allow to produce a lightweight, portable, and cross-platform monolithic application, which is often not possible with existing solutions. The main features of the proposed solution are the focus on the Microsoft .NET [1] ecosystem and the use of exclusively standard features of BCL and C# programming language. The implemented package of tools is completely cross-platform and hardware independent. The API is similar in many ways to its Python counterparts, which allows to quickly migrate Python codes into a .NET project.

This article provides a detailed overview of the so-called Model Inversion(MI) attacks. These attacks aim at Machine-Learning-as-a-Service (MLaaS) platforms, and the goal is to use some well-prepared adversarial samples to attack target models and gain sensitive information from ML models, such as items from the dataset on which ML model was trained or ML model's parameters. This kind of attack now becomes an enormous threat to ML models, therefore, it is necessary to research this attack, understand how it will affect ML models, and based on this knowledge, we can propose some strategies that may improve the robustness of ML models.

The long-term development of the world economy is characterized by cyclical development. To date, there is no single accepted approach to describe the nature of the economic cycle. Therefore, studies of economic and political cycles are one of the key areas of economic theory. Econometrics and machine learning have a common goal: to build a predictive model, for a target variable, using explanatory variables. This research aims to identify economic cycle in Russian Federation using collective factors. It uses a different approach, concerning classical econometric techniques, and shows how machine learning (ML) techniques can improve the accuracy of forecasts. We used three machine learning algorithms such as k-Nearest Neighbors (kNN), Random Forests (RF) and Support vector machines (SVM). The research is based on 30 economic factors for the period 1990-2020 from FRED, World Bank, WTO, Federal State Statistics Service, Bank of Russia etc. The results indicate that the Russian economy would be very active (peak) in the next quarters. This result could be a new approach to provide policy recommendations to authorities and financial institutions in particular.

This article proposes an algorithm for solving the problem of extracting information from biomedical patents and scientific publications. The introduced algorithm is based on machine learning methods. Experiments were carried out on patents from the USPTO database. Experiments have shown that the best extraction quality was achieved by a model based on BioBERT.

The article is devoted to an approach to building a conceptual modeling system, which includes text recognition in a conceptual structure and text generation based on a conceptual structure. The metagraph is used as a conceptual structure. The architecture of the conceptual modeling system is proposed. The metagraph model is considered as a data model for conceptual modeling. The main ideas of the work of the text parsing module and text generation module are considered.

The structure and evolution of open star clusters (OSCs) are considered using the Pleiades OSCs and the OSC group in the Orion Sword region as examples. The stars were selected according to the Gaia data. The relationship between the Orion Sword clusters and molecular clouds is traced according to the data of the Herschel spacecraft. The place of the considered objects in the general scheme of evolution compiled by us earlier is shown. It is concluded that there is an urgent need to expand the OSC classification. The considered Pleiades star system showed the presence of an extensive stellar halo. The stellar stream Pisces Eridanus found in the vicinity of the Pleiades is probably genetically related to the Pleiades and, together with it, represents the remnants of the disintegrated OB association. In the Orion Sword region, the observed young OSCs are most likely associated with molecular clouds. Young clusters stand out associated with dust (15 35 K) and hot (10000 K) gas. Data on OSCs are rapidly replenishing, and the number of OSCs is growing due to their detection in the Gaia surveys. Analysis in this area can be iterated and extended over time with proven methodologies to fit data management concepts in data-intensive areas.

The undoubted challenge for science is the extraction of knowledge from fast growing heterogeneous datasets. Particularly, details of experimental setups are insufficiently formalized and cannot be easily inserted into databases. Thus, there is a problem of using these details in the process of data integration and meta-analyses. For this purpose, we developed a scheme of formalization for object descriptions with its origination, protocols for field and laboratory measurements (including instruments and experimental conditions). It allows the integration of larger amounts of data accounting for its specifics of acquisition, for example, by applying adjustments, assigning weights to data sources (based on its reliability, method precision and experimental uncertainty) or directly accounting for experimental conditions in models. This formalization is currently used to develop an electronic laboratory journal for soil research, intended for detailed description of a conducted or planned experiment. The study aims to: increase the re-producibility of scientific research results; allow automatic data processing and error detection, and most importantly; effective soil data mining for decision support systems.