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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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Two Cognitive Systems, Two Implications, and Selection Tasks

P. 195–205.
Bobrova A., Pietarinen A.

Dual-process theories of reasoning take for granted the fundamental difference between the two cognitive systems, Systems 1 and 2. This paper, in contrast, argues that System 1, which is responsible for fast, intuitive, associative, and effortless reasoning, can be explained to be just as logical as System 2, which is said to draw consequences in rulebased, rational and criticised fashions. The only difference between the two systems is argued to be that the former draws conclusions in a logic which is diagrammatic, and moreover a positive and implicational fragment of ordinary, classical logic. Such a fundamental connection between the two systems is then applied to explain away cognitive biases in the Wason card selection task. The selection task thus ceases to represent a paradigm case of confirmation bias, because both systems of reasoning exhibit important processes of logical inferences.

Language: English
DOI
Keywords: Existential GraphsPositive implicationWason Selection Task

In book

Software Engineering and Formal Methods: SEFM 2019 Collocated Workshops: CoSim-CPS, ASYDE, CIFMA, and FOCLASA, Oslo, Norway, September 16–20, 2019, Revised Selected Papers
Vol. 12226: Software Engineering and Formal Methods. , Champaign: Springer, 2020.
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