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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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A Searching Tool for Russian Error-Annotated Learner English Corpus

NRU HSE , 2016.
Fenogenova A., Kuzmenko E.
Learner corpora constitute an effective resource for specialists in fields of second language acquisition, foreign language teaching and corpus linguistics. They tend to get significant scholarly help from statistical tools of various kinds. However, for valuable usage of a corpus it should provide convenient and powerful tools for searching and manipulating data. In this paper we focus on searching tools, presented in \textit{Russian Error-Annotated Learner English Corpus (REALEC)}, report our attempts to improve the format of the searching tools in our corpora. We also provide evidences that database search is much more efficient than common text search and demonstrate that search functionality in corpora is of great importance for research efficiency and extensive facilities.
Research target: Philology and Linguistics
Priority areas: humanitarian IT and mathematics
Language: English
Full text
Keywords: информационный поискlearner corporaанглийский язык как иностранныйEnglish as a second languageучебные корпусыautomatic search in corporaпоиск в корпусах
Publication based on the results of:
Лексикологические исследования на базе учебного корпуса REALEC (Learner corpus REALEC: Lexicological observations) (2016)
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