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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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Speech and Computer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 11658 LNAI

Vol. 11658: Speech and Computer 21st International Conference, SPECOM 2019, Istanbul, Turkey, August 20–25, 2019, Proceedings. Springer, 2019.

Speech and Computer

Chapters
Author Clustering with and Without Topical Features
Panicheva P., Litvinova O., Litvinova T., , in: Speech and Computer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 11658 LNAIVol. 11658: Speech and Computer 21st International Conference, SPECOM 2019, Istanbul, Turkey, August 20–25, 2019, Proceedings.: Springer, 2019. P. 348–358.
Typically, the task of authorship attribution has been solved using supervised machine learning methods. It is only recently that unsupervised methods have been applied to authorship attribution, namely author clustering. Clustering could be useful in realistic scenario as it represents natural grouping of documents without a priori authorship information, although the problem of feature selection ...
Added: October 28, 2019
Language: English
DOI
Text on another site
Keywords: speech processingcomputational linguistics
Speech and Computer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 11658 LNAI
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