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.
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.
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.
Chernyavskiy A., Shomova S., Dushakova I. et al., , in: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).: ELRA and ICCL, 2024. P. 17795–17807.
The topic of automatic detection of manipulation and propaganda in the media is not a novel issue; however, it remains an urgent concern that necessitates continuous research focus. The topic is studied within the framework of various papers, competitions and shared tasks, which provide different techniques definitions and include the analysis of text data, images, ...
Chernyavskiy A., Ilvovsky D., Nakov P., , in: Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: (Volume 1: Long Papers).: Association for Computational Linguistics, 2024. P. 1452–1462.