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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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Архитектура DSM-платформы, основанной на знаниях

С. 304–311.
Lyadova L. N., Suvorov N. M., Василюк В. А.

The goal is developing the architecture of the DSM platform, the main functions of which are based on the use of multifaceted ontology. Language toolkits automating the model editors development, code generation and documentation reduce the difficulty of creating and using new domain specific languages (DSL), but DSL development requires not only deep knowledge in the field of modeling, but also an understanding of the domain areas features. The architecture includes tools that automate the designing DSL metamodels on the basis of existing languages and domain ontologies intended for the modeling of areas languages are created for. DSL development is based on performing a mapping of the domain ontology into the ontology of modeling languages, the result of which is a metamodel of the new language. Ontology is also the basis for the implementation of semantic model transformations.

Language: Russian
Full text
Keywords: языки моделированияLanguage toolkitsпредметно-ориентированное моделированиеязыковой инструментариймногоуровневое моделированиеdomain specific modelingmodeling languagesmultifaceted ontologymulti-level modelingDSL development automationмногоаспектная онтологияавтоматизация разработки DSL

In book

Технологии разработки информационных систем (ТРИС-2020): материалы X Международной научно-технической конференции
Таганрог: Южный федеральный университет, 2020.
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