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News
June 2, 2026
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
June 2, 2026
Discovering Science through Russian Language: HSE Prep Year Students Present at International Conference in Kazan
On May 23, 2026, the V International Scientific and Practical Conference ‘Discovering the World of Science’ took place in Kazan at the Preparatory Faculty for International Students of Kazan Federal University. Four students of the HSE International Preparatory Year took part in the event: two delivered their presentations in person, while two participated online. Their work was supervised by Acting Director of the International Prep Year Irina Isaeva and lecturer Ekaterina Kozhemyakova.
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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?

Concept Relation Discovery and Innovation Enabling Technology (CORDIET)

P. 53–62.
Poelmans J., Elzinga P., Neznanov A., Viaene S., Kuznetsov S., Ignatov D. I., Dedene G.

Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and Hidden Markov Models (HMM) as main artifacts in the analysis process. The user can define temporal, text mining and compound attributes. The text mining attributes are used to analyze the unstructured text in documents, the temporal attributes use these document’s timestamps for analysis. The compound attributes are XML rules based on text mining and temporal attributes. The user can cluster objects with object-cluster rules and can chop the data in pieces with segmentation rules. The artifacts are optimized for efficient data analysis; object labels in the FCA lattice and ESOM map contain an URL on which the user can click to open the selected document.

Language: English
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Keywords: анализ текстованализ формальных понятийFormal Concept Analysistext miningknowledge discoveryобнаружение знанийrule miningtaxonomy buildingвывод правилпостроение таксономий

In book

CDUD'11 – Concept Discovery in Unstructured Data Workshop co-located with the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC-2011), June 2011, Moscow, Russia
CDUD'11 – Concept Discovery in Unstructured Data Workshop co-located with the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC-2011), June 2011, Moscow, Russia
Issue 757. , M.: Higher School of Economics Publishing House, 2011.
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