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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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Raising the value of research studies in psychological science by increasing the credibility of research reports: the transparent Psi project

Royal Society Open Science. 2023. Vol. 10. No. 2. Article 191375.
Kekecs Z., Palfi B., Szecsi P., Zrubka M., Kovacs M., Bakos B., Cousineau D., Tressoldi P., Schmidt K., Grassi M., Evans T. R., Yamada Y., Miller J., Liu H., Yonemitsu F., Dubrov D., Röer J. P., Becker M., Schnepper R., Ariga A., Arriaga P., Oliveira R., Põldver N., Kreegipuu K., Hall B., Wiechert S., Verschuere B., Girán K., Aczel B.

The low reproducibility rate in social sciences has produced hesitation among researchers in accepting published findings at their face value. Despite the advent of initiatives to increase transparency in research reporting, the field is still lacking tools to verify the credibility of research reports. In the present paper, we describe methodologies that let researchers craft highly credible research and allow their peers to verify this credibility. We demonstrate the application of these methods in a multi-laboratory replication of Bem's Experiment 1 (Bem 2011J. Pers. Soc. Psychol.100, 407–425. (doi:10.1037/a0021524)) on extrasensory perception (ESP), which was co-designed by a consensus panel including both proponents and opponents of Bem's original hypothesis. In the study we applied direct data deposition in combination with born-open data and real-time research reports to extend transparency to protocol delivery and data collection. We also used piloting, checklists, laboratory logs and video documented trial sessions to ascertain as-intended protocol delivery, and external research auditors to monitor research integrity. We found 49.89% successful guesses, while Bem reported 53.07% success rate, with the chance level being 50%. Thus, Bem's findings were not replicated in our study. In the paper, Raising the value of research studies in psychological science by increasing the credibility of research reports: the transparent Psi project 1 we discuss the implementation, feasibilityand perceived usefulness of the credibility-enhancing methodologies used throughout the project.

Research target: Social Sciences
Language: English
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Keywords: transparencyresearch methodsmetasciencereplicationcredibilityreal-time procedures
Publication based on the results of:
Socio-cultural and psychological mechanisms of unity and polarization of Russian society in the context of global challenges (2023)
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