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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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Extending the “Extended Privacy Calculus model”: Relating privacy behavior and well-being in a representative sample of Vkontakte SNS users

Current Psychology. 2026. Vol. 45. Article 728.
Sinyavskaya Y., Koltsova O.

The privacy calculus model has been widely applied to examine how social networking site (SNS) users balance perceived risks and benefits in their privacy decisions. However, it remains unclear how this risk–benefit evaluation, after translating into corresponding privacy behaviors, subsequently affects users’ well-being. This study proposes an extended model in which several measures of well-being serve as outcomes of this privacy calculus process. Using a cross-sectional survey and digital trace data on privacy settings from a demographically stratified sample of Russian VKontakte SNS users (N = 895), we found that users who anticipated greater benefits from SNS use disclosed more personal information, whereas those perceiving higher privacy risks engaged in stronger protective behaviors. As expected, higher self-disclosure further translated into greater psychological well-being and perceived social capital. In contrast, although privacy concerns were positively related to protective behavior, they showed an association with only one component of well-being —perceived bonding social capital —and this link was not mediated by privacy protection. Overall, by integrating motivational and behavioral dimensions and considering both self-disclosure and protection within a single framework, this cross-sectional study demonstrates an asymmetric role of the two privacy-calculus components in shaping well-being in online contexts.

Research target: Social Sciences
Language: English
DOI
Text on another site
Keywords: social capitalstructural equation modeling (SEM) satisfaction with life well-beingprivacy behaviorprivacy calculussocial networking sites (SNS)
Publication based on the results of:
Analysis of Human Interaction with Information and Improvement of Algorithms of Information Processing (2025)
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