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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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?

Event-Related Potentials in Response to Fake News Correction: Pilot Study

P. 124–127.
Morozova A., Monahhova E., Gorodnicheva Y., Zinchenko O., Shestakova A., Klucharev V.

Fake news has become a serious problem with the development of the Internet and social networks. Due to their rapid spread and influence on people's opinions and decision-making, the need to combat media fakes become evident. This pilot study investigated behavioral and neuropsychological responses to fake news corrections that indicate the presence of the fake. We used medical- and healthcare-related news headlines, both fake and accurate, and employed a short-format debunking correction (Truth/Fake) from authoritative and non-authoritative medical sources. 22 participants took part in the pilot study, assessing their trust in news headlines and the willingness to share them with others before and after the correction, while the electroencephalography (EEG) data were recorded. We hypothesized that the trust in the news and the willingness to share them would increase after the Truth-correction (indicating that the statement is correct) from the authoritative source, and would decrease after the Fake- correction (indicating that the statement is false) from the same source. On the neuropsychological level we assumed that the correction would elicit the Feedback-Related Negativity (FRN), and its amplitude would be larger in response to the Fake-correction, than to the Truth- correction, and would correlate with the magnitude of an individual opinion change. The results showed the significant effect of the corrections on the trust in headlines and willingness to share them. Authoritative Truth-corrections increased trust and willingness to share, while authoritative Fake-corrections decreased trust and willingness to share headlines. The EEG data showed the FRN/ P600 responses to the corrections. However, no significant differences between the FRN amplitudes across experimental conditions were found, and the component only correlated with the magnitude of willingness to share changes.

Language: English
DOI
Text on another site
Keywords: EEGmisinformationfake newsERP (event-related potential)

In book

2024 Sixth International Conference Neurotechnologies and Neurointerfaces (CNN)
IEEE, 2024.
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