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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 поиска документов, релевантных заданному тексту

С. 266–269.
Полушин Г. В.
RELEVANT DOCUMENTS SEARCH SYSTEM   The article is dedicated to the relevant documents search process automation. Relevant literature search is a non-trivial task, as it requires time and imposes certain requirements to the reader. The paper proposes methods to minimize efforts required to find relevant literatureand to automatize the search process. The analysis of existing software solutions for extracting keywords from texts in Russian is given. Method of extracting key phrases fromRussian text using metrics TF-IDF is described. Method of automatic search of relevant documents with the extracted keywords using a free search engine for full texts of scientific publications of all formats and disciplines Google Scholar is also described. In practice, the system can be used by students, teachers and all who are engaged in scientific activities.   Key words: keywords extraction, automatic search, text processing
Language: Russian
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Text on another site
Keywords: keywords extractionautomatic searchtext processingизвлечение ключевых словавтоматизированный поискобработка текста

In book

Математика и междисциплинарные исследования – 2016
Пермь: Пермский государственный национальный исследовательский университет, 2016.
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