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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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Неологизмы в социальной сети Фейсбук

Компьютерная лингвистика и интеллектуальные технологии. 2014. С. 440–454.
Obiedkov S., Панченко А. И., Muraviev N.

*Реализация соц. сети Facebook запрещена на территории России по основаниям осуществления экстремистской деятельности.

In this paper, we present a study of neologisms and loan words frequently occurring in Facebook user posts. We have collected a dataset of over 573 million posts written during 2006-2013 by Russian-speaking Facebook users. From these, we have built a vocabulary of most frequent lemmatized words missing from the Opencorpora dictionary (http://opencorpora.org/dict.php) the assumption being that many such words have entered common use only recently. This assumption is certainly not true for all the words extracted in this way; for that reason, we manually filtered the automatically obtained list in order to exclude non-Russian or incorrectly lemmatized words, as well as words recorded by other dictionaries or those occurring in pre-2000 texts from the Russian National Corpus (http:// www.ruscorpora.ru). The result is a list of 168 words that can potentially be considered neologisms. We present an attempt at an etymological classification of these neologisms (unsurprisingly, most of them have recently been borrowed from English, but there are also quite a few new words composed of previously borrowed stems) and identify various derivational patterns. We also classify words into several large thematic areas, "internet", "marketing", and "multimedia" being among those with the largest number of words. We consider our results preliminary, but believe that, together with the word base collected in the process, they can serve as a starting point in further studies of neologisms and lexical processes that lead to their acceptance into the mainstream language.

Priority areas: IT and mathematics
Language: Russian
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Keywords: социальная сетьсловообразованиеneologismзаимствованиенеологизмsocial networkloan wordnon-vocabularyderivationнесловарное слово
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