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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.
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Success Factors of Electronic Petitions at Russian Public Initiative Project: The Role of Informativeness, Topic and Lexical Information

P. 243–250.
Porshnev A.

Online petitions are usually regarded as one of the most popular channels to involve citizens in the political process. In our paper we have analyzed texts and voting data (pro and against) from 9705 e-petitions submitted from 2013 until 2017 at Russian Public Initiative project. Analysis of dynamics showed stabilization of interest to this resource (emergence of a new authors, growth of “strong” petitions etc.). Studying success factors of electronic petitions at the Russian public initiative project we found out that the topic and lexical information are significant factors, as well as the level of petitions.

Language: English
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Keywords: text analysistopic modeling InformativenessVoting dynamicsCitizens’ interestElectronic petitionsE-petitionsRussian Public Initiative project

In book

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10th International Conference on Social Informatics, SocInfo 2018; St.Petersburg
Springer, 2018.
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