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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
'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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«Вавилонская башня»: теоретический обзор детерминант обыденных представлений о межличностном насилии

Социология власти. 2025. Т. 37. № 3. С. 13–35.
Rodionova M.

Despite its high prevalence and increasing popularity, the concept of violence remains ambiguous and contested in both everyday and academic discourse. This article focuses on interpersonal violence and aims to identify the key factors that influence what people will define as violence. Based on a systematic theoretical review of existing empirical research, this paper shows how people perceive and categorize violent acts depending on the characteristics of the evaluated conflict interaction. It is demonstrated that individual characteristics of assessors have less influence on lay evaluations than factors describing the situation being assessed. Among these situational factors, the perception of the intent of the violent agent plays a crucial role: intentional acts aimed at causing harm are more likely to be classified as violent compared to unintentional acts. Another important factor is the type of violence and the harm associated with it: physical violence is rated as more severe than psychological violence. In addition, the existence of justifying reasons and the moral evaluation of the act influence the perception of violence. In lay perceptions, normalization narratives surrounding an action tend to reduce how violent it is judged to be. Furthermore, the perceived violence of an action is closely linked to its lay moral evaluation. The study highlights the differences between lay and scientific conceptualizations of violence. It shows that lay conceptualizations of violence are as multifaceted as scientific conceptualizations, yet are more robust.

Research target: Sociology (including Demography and Anthropology Psychology
Language: Russian
Text on another site
Keywords: преднамеренное действиеharmвредпсихологическое насилиесистематический обзорсоциальное насилиеintentional action systematic reviewlay perceptionssocial violencephysical violencepsychological violencejustification of violencemoral evaluation of violenceфизическое насилиеоправдание насилияморальная оценка насилия
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