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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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Восприятие новых технологий населением как показатель открытости к инновациям

Форсайт. 2021. Т. 15. № 1. С. 39–54.
Pishnyak A., Khalina N.

Rapid technological progress is one of the basic processes in the modern world. It is an integral part both in the field of labor and employment and in leisure and recreation. The request for an accelerated implementation of digital technologies in the economy and social sphere, which is inherent in one of the national development goals of the Russian Federation, makes this topic more important. In the presence of technological challenges, people have to adapt to constantly emerging innovations. Meanwhile the perception of innovations together with other individual characteristics and socioeconomic characteristics of different social groups could be considered determinants of openness to technological innovations. Based on this assumption, the authors of this article set the following objectives: they evaluate the openness of the population to innovation (through the construction of the index), examine the perception of innovation (by identifying factors of the perception of new technologies), and test the relationship between openness to innovation and the perception of new technologies. The multi-domain index of openness to innovation combines assessments of individual attitude, acceptance, and trust in innovations related to various spheres of the population’s life. The perception of innovation is revealed through the Technology Acceptance Model and includes: perceived ease of use of new technologies; the perceived usefulness of new technologies; perceived security and reliability of new technologies; and perceived elitist features of new technologies. This study demonstrates that openness to innovation depends upon the perception of new technologies and is differentiated among population groups.

Research target: Economics and Management Sociology (including Demography and Anthropology
Priority areas: economics sociology
Language: Russian
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Keywords: инновациитехнологический прогрессinnovationВосприятие инновацийmultidimensional indexмногомерный индексtechnological progressPerception of innovationTechnology Acceptance ModelМодель принятия инноваций
Publication based on the results of:
Poverty, inequality and social mobility in modern Russia: An Interdisciplinary Analysis (2020)
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