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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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Сквозь призму BERT: тематическое моделирование трансформации коллективных идентичностей участников движения Black Lives Matter под влиянием контрпротеста

Политическая наука. 2025. № 1. С. 219–239.
Koncha V.

Counter-protests have become a common response to protest movements around the world. Understanding this process is often based on collective identities that are formed in opposition to an out-group. In the context of the Black Lives Matter (BLM) movement, the initial image of the “other” was mainly attributed to the authorities. However, the emergence of the All Lives Matter (ALM) counter-protest movement has changed the way how collective identities are constructed - now the image of the “other” is also attributed to participants in the counter-protest movement, introducing a new layer of complexity into the dynamics of identities and raising questions about how these identities are transformed when faced with counter-protest. Applying a new approach in BERT topic modeling, the author analyzes thematic shifts in the posts of BLM protesters between 2013 and 2014 on the social network Twitter (X). The application of the BERT topic modeling model allowed conducting a detailed analysis of online messages, capturing contextual dependencies and revealing the complexities of the transformation of collective identity in response to the emergence of the ALM counter-protest movement. The study contributes to the existing literature by filling two gaps: demonstrating the methodological advantage of using BERT on empirics and exploring how the collective identities of protest movement participants are transformed in the context of protest and counter-protest dynamics. BERT topic modeling is a powerful tool for studying the transformation of collective identities, offering a promising methodological basis for further research at the intersection of protest, counter-protest, and collective identities in the digital age.

Research target: Political Science, International Relations, and Public Administration
Language: Russian
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Keywords: коллективная идентичностьtwitterпротестBlackLivesMatterAllLivesMatterконтрпротест
Publication based on the results of:
Institutions, human capital and regional Development: historical analysis and contemporary challenges (2025)
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