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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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Построение рейтингов журналов по экономике с помощью методов теории коллективного выбора

Издательский дом ВШЭ , 2013. № 3.
Subochev A., Aleskerov F. T., Pislyakov V.
The data on 212 economic journals are used to produce qualitative estimates of (in)consistency of evaluations based on the main bibliometric indices (2- and 5-year impact-factors, immediacy index, SNIP, SJR, Hirsch index, article influence). Aggregated rankings are calculated. These rankings aggregate the information on journals’ comparative values contained in separate index rankings. A new approach for the construction of aggregated rankings is proposed, based on several methods of social choice theory. Rankings are aggregated by simple majority rule. The result of the aggregation is a binary relation showing which journal from a given pair is better than the other one with respect to majority of indices used for their evaluation. A social choice solution concept based on majority relation (Copeland rule, Markov ranking, the uncovered set and the minimal externally stable set) is applied to determine journals to be considered as the best ones in a given set of journals. An aggregated ranking is produced as a result of a multistage procedure of selection and exclusion of the best journals.
Research target: Mathematics Economics and Management Sociology (including Demography and Anthropology Computer Science
Priority areas: economics sociology IT and mathematics
Language: Russian
Full text
Keywords: экономикабиблиометрические индикаторыэкономические наукинаучный журналимпакт-фактор журналанепокрытое множествовнешнеустойчивое множествобиблиометрияагрегированный рейтингправило большинстваправило Коупландарейтинг журналовмарковский метод ранжирования
Publication based on the results of:
An investigation of new methods of mathematical modelling and mechanism design in the social, economic and political sciences (2013)
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