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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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Метрики центральности сетевого анализа как инструмент определения уровня абстракции понятий внутри понятийной структуры обучающихся

Психологические исследования: электронный научный журнал. 2025. Т. 18. № 102. Статья 3.
Andronova E., Kapuza A.

This study examines concepts as systemic elements within a conceptual structure, drawing on the theoretical framework of L.S. Vygotsky’s cultural-historical psychology. A key characteristic of concepts is their level of abstraction, which reflects their degree of generalization within a system. However, existing classification methods lack a unified approach, complicating empirical research. The current study aimed to explore network analysis metrics as a tool for distinguishing concepts of different levels within a concept structure. We analysed 242 concepts from conceptual maps created by experts and students in data analysis. First, a qualitative analysis was conducted using formal definitions and expert evaluations. Then, betweenness centrality and degree centrality metrics were applied to assess their effectiveness in distinguishing conceptual levels. The findings showed that betweenness centrality and normalized betweenness centrality significantly differed across levels: higher-level concepts had higher values compared to the basic and subordinate ones. Degree centrality varied depending on the classification method. When classified by definition, all group differences were shown to be significant. When classified by maps, differences were found only for the subordinate level. However, the number of incoming connections did not show distinguishing capability. Based on the results, it can be assumed that network analysis demonstrates potential not only in assessing the systemic characteristics of a conceptual structure but also in evaluating the role of individual concepts within it.

Research target: Psychology Education
Language: Russian
DOI
Keywords: сетевой анализnetwork analysisconcept structureкультурно-историческая теорияконцептуальные картыconcept mapsL. Vygotsky’s cultural-historical theory levels of abstractionпонятийная структурауровни абстракции понятий
Publication based on the results of:
Using digital traces to study learners' behavioral strategies (2025)
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