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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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Языково-ориентированный подход к разработке средств визуализации данных: автоматизация разработки DSL

С. 195–206.
Ермаков И. Д., Lyadova L. N.

Abstract: The purpose of the project is approbation of approach to the developing automation tools for designing domain-specific languages (DSL) to create data visualization tools customized to user needs. The main idea of the language-oriented approach is that visual domain-specific languages should be developed to describe new visualization models, the use of which should ensure a reduction in the requirements for user skills in the field of programming when solving data visualization problems. The basis of the solution is a multifaceted ontology that describes the languages and models of the domains, the tasks solved by users, as well as the rules for generating new DSLs. Examples are given demonstrating the possibilities of the presented approach for the development of new visualization models.

Language: Russian
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Keywords: Language toolkitsметамоделированиепредметно-ориентированные языкиmetamodelingпредметно-ориентированное моделированиеdata visualizationязыковой инструментарийвизуализация данныхdomain specific modelingmultifaceted ontologyмногоаспектная онтологияdomain specific languageгенерация метамоделейmetamodels generation

In book

Технологии разработки инструментальных средств (ТРИС-2024) : Материалы XIV Международной научно-технической конференции
Таганрог: Издательство ЮФУ, 2024.
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