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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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Тотальная прозрачность: рейтинги и индексы в отечественном образовании и науке

Экономический анализ: теория и практика. 2014. № 31. С. 13–22.
Volkova O. N.

The purpose of this paper was to find out how the ideas of transparency and accountability were mirrored in Russian universities’ practices within last decade. Quantification and Scientometrics are shown as the main instruments of these ideas' implementation. The case-study was designed to show the dynamics of Russian scientists’ interest to the universities activity metrics, by the means of National electronic library (e-library.ru). Two main trends for the transparency technologies in Russian education were revealed: “how much to pay” and “for what to pay”. We discussed the purposes' understanding insufficiency and imitation nature of some reforms.

JEL Codes A12, A13, I21, I23, M4, Z13

Research target: Education Sociology (including Demography and Anthropology Economics and Management
Priority areas: economics
Language: Russian
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Keywords: индекс цитированияпрозрачностьквантификацияподотчетностьratingaccountingучетscientometricsнаукометрияtransparencyrating of universitiesакадемический рейтингaccountabilitycitation indexрейтинги вузовquantificationрейтинги в образованииUniversity ratingиндекс публикационной активности
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