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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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Construction of Universities' Typology via DEA

P. 25–33.
Abankina I., Aleskerov F. T., Belousova V., Gokhberg L., Zinkovsky K., Kiselgof S. G., Petrushchenko S., Shvydun S.

In recent decades, increased economic pressure and growing societal expectations have led to the introduction of performance-based funding models for universities. In this respect, a great scholarly attention has paid to how to evaluate universities performance correctly. This allows national governments to design and apply various taxonomies to facilitate the development of efficient programmes for the advancement of higher education. The wide spread approach used for that purpose is DEA. This paper provides a review of different approaches how to take into account universities heterogeneity when applying DEA to construct the typologies of  university by showing statistically their similarities and differences. The authors use the modified DEA proposed by Aleskerov & Petrushchenko (2013) to evaluate performance scores of Russian technical universities. This proposed typology divides universities into specific groups with a description taking into account their heterogeneity.

Language: English
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DOI
Text on another site
Keywords: efficiencyperformancehigher education institutions (HEIs)research and educational activities of HEIsData envelopment analysis (DEA)
Publication based on the results of:
Development and research of new mathematical models in socio-economic and political spheres (2016)

In book

Data Envelopment Analysis and its Applications. Proceedings of the 13th International Conference of DEA
Braunschweig: [б.и.], 2016.
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