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May 25, 2026
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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.
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Russian Anti-corruption Protests: How Russian Twitter Sees It?

P. 270–281.
Goncharov Dmitry, Nechai Valerii

This article explores the map of political preferences of Russian Twitter users in the wake of March 2017 anti-corruption protests. So far there is little research on the political aspects of Twitter in Russia and our paper seeks to fill this gap in the scholarship. It is based on content analysis of over 45,000 tweets published during a week after March 26 events. According to the project preliminary results, political attitudes of Russians remain fairly moderate, though evidence points to some polarization among the politically involved. The research also reveals a variety of value patterns shared by politically active users and investigates corresponding clusters of users that are taking shape in the ongoing online discussion and networking. The article concludes with an interpretation of how these clusters might relate to menu of political participation during current electoral cycle in Russia.

Language: English
Full text
DOI
Keywords: российская политикаRussian politicstwitterтвиттерtopic modellingprotest politicsпротестная политика

In book

Digital Transformation and Global Society. Second International Conference, DTGS 2017, St. Petersburg, Russia, June 21–23, 2017, Revised Selected Papers
Cham: Springer, 2017.
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