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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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Value Proposition Analysis of Network Business During Digital Adaptation in COVID-19 Conditions

Smart Innovation, Systems and Technologies. 2021. Vol. 205. P. 145–155.
Fainshtein E.

This paper explores how digital technology has forced network enterprises to reconsider and develop value proposition, evaluating how digital queries of potential consumers influence it during the period of business restrictions caused by the COVID-19. This is theoretical and empirical research in equal measure. Study was conducted on 1000 keyword semantic analysis using Google Keyword Planner analytic big data on customer queries for restaurant delivery services in St. Petersburg (Russia) to analyze key preferences that should be included in the value proposition. Data were divided into five major categories: delivery time, promotions, food processing, menu, and aggregators’ delivery companies. MANOVA analysis showed all five were significant while ANOVA—significance for menu and aggregators’ delivery companies. Findings indicate that value proposition competitive advantage is driven by big data analysis and customer needs operative reaction under COVID-19 conditions. Value proposition components generated from big data analysis have a positive business performance impact.

Language: English
DOI
Text on another site
Keywords: big data e-commercestrategic marketingvalue propositiondynamic content analysisCOVID-19
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