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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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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.
Academic editor: Ignatov D. I., Kuznetsov S., Poelmans J.

Concept discovery is a Knowledge Discovery in Databases (KDD) research field that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often textual, form. Compared to traditional data mining techniques, human-centered instruments actively engage the domain expert in the discovery process. This volume contains the contributions to CDUD 2011, the International Workshop on Concept Discovery in Unstructured Data (CDUD) held in Moscow. The main goal of this workshop was to provide a forum for researchers and developers of data mining instruments working on issues with analyzing unstructured data. We are proud that we could welcome 13 valuable contributions to this volume. The majority of the accepted papers described innovative research on data discovery in unstructured texts. Authors worked on issues such as transforming unstructured into structured information by amongst others extracting keywords and opinion words from texts with Natural Language Processing methods. Multiple authors who participated in the workshop used methods from the conceptual structures field including Formal Concept Analysis and Conceptual Graphs. Applications include but are not limited to text mining police reports, sociological definitions, movie reviews, etc.

Chapters
Recommender System Based on Algorithm of Bicluster Analysis RecBi
Ignatov D. I., Poelmans J., Zaharchuk V. V., , in: 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, RussiaIssue 757.: M.: Higher School of Economics Publishing House, 2011. P. 122–126.
In this paper we propose two new algorithms based on biclustering analysis, which can be used at the basis of a recommender system for educational orientation of Russian School graduates. The first algorithm was designed to help students make a choice between different university faculties when some of their preferences are known. The second algorithm ...
Added: December 3, 2012
Concept Relation Discovery and Innovation Enabling Technology (CORDIET)
Poelmans J., Elzinga P., Neznanov A. et al., , in: 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, RussiaIssue 757.: M.: Higher School of Economics Publishing House, 2011. P. 53–62.
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 ...
Added: December 3, 2012
Research Challenges of Dynamic Socio-Semantic Networks
Yavorskiy R., , in: 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, RussiaIssue 757.: M.: Higher School of Economics Publishing House, 2011. P. 119–122.
A general model of socio-semantic network is presented in terms of state-transitions systems. We provide some examples and indicate research directions, which seem to us the most important from the application point of view. ...
Added: September 28, 2014
Research target: Electronics and Electrical Engineering Philology and Linguistics Mathematics Philosophy, Ethics, and Religious Studies
Priority areas: mathematics engineering science
Language: English
Full text
Text on another site
Keywords: математикабикластеризацияразработка данных (Data Mining)информатикаnatural language processingнеструктурированная информацияанализ формальных понятийdata miningвычислительная техникаFormal Concept Analysisbiclusteringtext miningknowledge discoveryunstructured dataобнаружение знанийрекомендательные системыанализ текстовых данныхкибернетикаавтоматикаRecommender Systems
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
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