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  • (Не)известные злодеи: анализ литературных мемов в тематических пабликах "Вконтакте"
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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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(Не)известные злодеи: анализ литературных мемов в тематических пабликах "Вконтакте"

С. 83–103.
Klimova M., Fomina E.

This article is devoted to the analysis of memes on literary topics on “Vkontakte” social website called “Literary memes” and “Literaturniye memy”. The focus is on the characters of Russian and foreign literature, depicted in the memes as negative ones. The study examines the specifics of precedence and humorous components, techniques that contribute to the negative perception of characters in memes as well as it  attempts to explain the reasons for referring certain characters to“villains”. In addition, the comments of  users of the website are interpreted, some of which allow the ambivalence of literary villains and concentrate on the positive features of these characters.

Language: Russian
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Keywords: прецедентные феноменыprecedent phenomenaмемыmemesfictional charactersliterary villainsперсонажилитературные злодеи

In book

Зло в культуре и культура зла
Зло в культуре и культура зла
Fomina E., Лукманова О. Б., Шалимова Н. С., Валяева Н. Ю., Klimova M., Куликова В. А., Плотникова М. А., Нахман Ф. Г., Khusyainov T., Костригин А. А., Штрикер Ю. Д. Н. Новгород: Издательство Тимура Хусяинова, 2022.
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