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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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Restricted Domains of Dichotomous Preferences with Possibly Incomplete Information

P. 2023–2025.
Karpov A., Obraztsova S., Terzopoulou Z.

Restricted domains have been extensively studied within computational social choice, initially for voters’ preferences that are total orders over the set of alternatives and subsequently for preferences that are dichotomous—i.e., that correspond to approved and disapproved alternatives. We contribute to the latter stream of work. We obtain forbidden subprofile characterisations for various important dichotomous domains, and we also study profiles with incomplete information about the voters’ preferences. Specifically, we design polynomial algorithms to determine whether such incomplete profiles admit completions within certain restricted domains.

Language: English
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Keywords: voter preferences
Publication based on the results of:
Decision making and data analysis in socio-economic and political systems (2020)

In book

AAMAS'2020: Proceedings of the 2020 International Conference on Autonomous Agents & Multiagent Systems
IFAAMAS, 2020.
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