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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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Признаки и приемы, используемые для определения настроения собеседника при текстовой коммуникации в мессенджерах

Психологические исследования: электронный научный журнал. 2024. Т. 17. № 98. Статья 6.
Sysoeva T., Красницкая К. В.

Instant messaging is becoming highly important in modern daily life. This type of communication differs significantly from face-to-face communication: non-verbal signals typically used to understand the interlocutor’s emotions (facial expressions, gestures, etc.) are inaccessible to observation, and the non-verbal signals that replace them in text messages are intentional. At the same time, recognition of other’s emotions is also available in this communication format, but the specific cues used for such recognition remain poorly studied. A few studies have shown that some message characteristics change depending on the sender’s mood, and message recipients are sensitive to the presence of some cues, making conclusions about the interlocutor’s mood. However, no attempts have been made to fully describe all the cues used for the interlocutor’s emotion perception in instant messaging. We surveyed 353 respondents (Mage = 18.07, 85.84% female), asking them to describe the cues they rely on to understand the mood of their interlocutor, as well as to rate the possibility to recognize the other’s mood when communicating via instant messaging and the frequency with which such recognition is correct. Overall, respondents reported that they often successfully interpret the mood of their interlocutors, while perceiving others as less accurate in recognizing their own emotions. Among the most frequently mentioned cues by which one can determine the mood of an interlocutor in text-based communication are the use of emoticons, punctuation marks, the length of messages, and changes in the interlocutor’s typical way of messaging. The importance of preliminary acquaintance with the interlocutor is perhaps fundamental to perception of her emotions in instant messaging.

Research target: Psychology
Language: Russian
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Keywords: текстовая коммуникацияfamiliarityвосприятие эмоцийdigital communicationemotion perceptiontext-based communicationinstant messagingverbal emotional cuesnonverbal emotional cuesцифровое общениемгновенный обмен сообщениямивербальные признаки эмоцийневербальные признаки эмоцийзнакомство с собеседником
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