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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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Взаимосвязь принятия студентами информационных технологий в обучении и удовлетворенности онлайн-дисциплинами в условиях тотального перехода на удаленное образование

Педагогика и психология образования. 2022. № 2. С. 144–160.
Е.А. Воронина, М.Л. Курьян

The paper is  concerned with students’ attitude to  transformation of educational process to online format in the pandemic conditions that led to a ubiquitous use of technologies. The present paper is aimed at revealing a  possible relationship between students’ acceptance of  e-learning technologies and their satisfaction with online courses. Also, the  research seeks to analyze learners’ attitude to digital educational tools and satisfaction with distant learning with respect to their characteristics. As a methodological base of research, the Technology Acceptance Model (TAM) and an instrument to assess satisfaction with online courses were used. The results obtained demonstrated that there is  interrelation between technology acceptance in e-learning and online courses satisfaction. This is likely to demonstrate that the higher the level of technology acceptance is, the higher is the satisfaction with online classes. Also, it was found that for learners the use of IT tools while studying online does not present much difficulty, however for many of them their usefulness is not quite evident. The findings showed that the attitude to e-learning varies depending on the gender of respondents, year of study and their educational profile. These findings may be  used for adaptation of  the  existing off-line courses to  online format and when designing new educational programs. They signal the necessity of diagnosing and monitoring students’ perception of  IT, which, as  this work showed, may be  a  predictor of students’ satisfaction with e-learning and influence its effectiveness

Research target: Education
Language: Russian
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Keywords: e-learningэлектронное обучениеучастники образовательного процессаTAM (Technology Acceptance Model)digital learning toolseducation stakeholdersмодель принятия технологийцифровые инструменты обучения
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