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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Киберагрессия и киберпреступления в современной России: факторы риска

Социологические исследования. 2025. Т. 51. № 10. С. 39–51.
Shcherbakov R.

The study investigates social groups vulnerable to cyber aggression and cybercrime. The main objective is to examine how socio-demographic conditions, as well as cultural capital in the form of digital skills and everyday internet use shape the distribution of both types of cyber risks. The analysis relies on survey data from the Monitoring of Digital Transformation of Economy and Society, collected by ISSEK NRU HSE in April – May 2024 through face-to-face interviews. The representative sample (N = 10038) reflects the key socio-demographic characteristics of the Russian population. The survey results revealed that approximately one-third of respondents have experienced cyber aggression or cybercrime. Intrusive communication and tactless questions were identified as the most common forms of cyber aggression, while fraudulent emails requesting personal data were а most frequently reсorded type of cybercrime. Regression analysis demonstrated that elderly individuals and those with higher education are more likely to experience a broader range of cybercrime. Younger respondents and individuals without higher education were found to be more exposed to a broader range of cyber aggression. The intensity of internet use in everyday activities significantly predicted vulnerability to both cyber aggression and cybercrime. Users engaged in online communication and gaming face higher exposure to cyber aggression, whereas online communication and online shopping were the strongest predictors of cybercrime. Higher levels of online privacy concerns, security skills, and information literacy were also associated with greater exposure to both threats. Overall, the findings highlight importance of considering both socio-demographic and behavioral factors in understanding digital risks.

Research target: Sociology (including Demography and Anthropology
Language: Russian
Full text
DOI
Text on another site
Keywords: рискиriskscybercrimeцифровизацияdigitalizationcyber aggressionкиберагрессиякиберпреступлениякибервиктимность
Publication based on the results of:
Monitoring of artificial intelligence technologies and digital transformation of economy and society (2025)
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