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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
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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.
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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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Представления о мигрантах сквозь призму теории когнитивного диссонанса: результаты квазиэксперимента

Мониторинг общественного мнения: Экономические и социальные перемены. 2025. № 4. С. 288–314.
Egorova T.

The article attempts to reconstruct the diversity of tactics used by people to reduce emotional tension in situations where cognitive dissonance may arise during discussions of issues related to migrants and migration. The research is based on the qualitative data collected via a series of in-depth interviews (N = 21) in Moscow, Chelyabinsk, and Kazan. The interviews included a quasi-experimental section in which the interviewer attempted to induce cognitive dissonance in informants, who then attempted to reduce it in various ways.

The analysis of the data has shown that there are three main ways that the informants were drawn to. In the case of the first one ― the search for additional consonant information ― people tend to seek additional, never-mentioned conditions that would make the dissonant case possible without any radical changes to their expressed point of view. In the second case, trivialization of either their own opinion (through the emphasis on the lack of expertise) or the dissonant case (through the emphasis on its exclusivity) happens. Lastly, some people tended to avoid or distract themselves from the dissonant information altogether by changing the topic. In general, however, the ways that people tend to reduce cognitive dissonance are rather inconsistent and situational, which may be explained by the inconsistency and situational nature of the views that are expressed in conversation.

The results are interpreted as elements of cognitive and cultural schemas that people might have in relation to migration, and are indicative of the topic not being deeply reflected upon or internalized by the informants. The results also indicate that, to the extent that the issues of migration, migrants and their presence in Russia are not personal, the opinions on the subject remain quite discursive in nature.

Research target: Sociology (including Demography and Anthropology
Language: Russian
DOI
Text on another site
Keywords: collective representationsотношение к мигрантамcognitive sociologyкогнитивная социологияколлективные представленияcognitive schemaкогнитивный поворотcognitive turnattitudes to migrantsкогнитивные схемы
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