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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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The Effects of Opinion Leader Radicalization under Different User Tolerance Levels: Simulating Political Communications on Twitter

P. 1–7.
Akhremenko A. S., Ilya Filippov, Yureskul E.

In this paper we present the results of computational experiments based on a novel agent-based communication model of Twitter activity. The model was designed specifically for analyzing the dynamics of communication between competing ideological positions, which sets the model apart from existing modelling literature. The model incorporates network structures into an agent-based framework; the nodes of the network represent regular users and political leaders. We also introduce network characteristics specific to Twitter, such as following and recommendations, the ability to view and retweet messages etc. Our model allows us to evaluate the effect of opinion and preference polarization, network homophily, radicalization of opinion leaders and user tolerance. In this study we specifically focus on radicalization and user tolerance. Based on 30 000 computational experiments, we show that changing user tolerance toward competing ideological positions changes substantively the propensity of opinion leaders to radicalize

Language: English
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Keywords: социальные сетиполяризацияpolarizationagent-based modelingsocial mediatwitterтвиттерагентно-ориентированное моделированиерадикализацияradicalization

In book

2020 International Conference on Engineering Management of Communication and Technology (EMCTECH), 20-22 October 2020, Vienna, Austria
IEEE, 2020.
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