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News
July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Система поддержки принятия решений «ELECTRE»

Прикладная информатика. 2017. Т. 12. № 1 (67). С. 102–113.
Kravchenko T. K.

Problem statement: solutions analytical justification by using a decision support system (DSS) significantly increases the quality of decisions. The existing DSS generally employs methods analytic hierarchy process and analytical networks. It does not always lead to the desired results as each method is based on certain assumptions and is not universal. The creation of the DSS, including ELECTRE collection methods, will allow to obtain additional information about the relative importance of different variants of the solution for the task. Methodological basis for the creation the DSS «ELECTRE»: the family of ELECTRE methods, which include ELECTRE I, ELECTRE Iv, ELECTRE IS, ELECTRE II, ELECTRE III and others. Their key feature consists in the fact that they do not use the convolution operation of evaluation of the alternatives specified in different scales on different criteria. Results: developed software of DSS «ELECTRE», allowing to use any of the methods of this family to select the preferred alternatives based on multiple criteria and evaluations of their relative importance. Practical relevance: unlike other developments, the research results allow to analytically justify solutions using a variety of methods of collection in one software environment.

Research target: Computer Science Economics and Management
Priority areas: management business informatics
Language: Russian
Full text
Keywords: системы поддержки принятия решенийэкспертная система поддержки принятия решенийdecision support systemsExpert Decision Support System (EDSS)ELECTRE family methodsметоды семейства ELECTREdecision support system «ELECTRE»the task of choosing a platform for business intelligence for the enterpriseсистема поддержки принятия решений «ELECTRE»задача выбора платформы бизнес-интеллекта для предприятия
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