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News
October 19, 2026
'Cultural Studies Seeks to Understand What Lies Beneath the Surface of Everyday Things'
Maxim Zhiganov has been exploring different aspects of sound for many years—first in relation to his native Perm and then on a broader, global scale. In this interview for the HSE Young Scientists project, he talks about sound maps, the special issue of the journal Logos, and why it is important to make the familiar less clear and obvious.
October 14, 2026
At HSE University–St Petersburg, Experts Discuss How AI Changes Tourist Routes and Cultural Heritage
Neural networks are no longer simple travel assistants; now they decide what to include on the route and what to skip. Artificial intelligence changes how people perceive a place and shapes the image of tourist centres. At the II International Scientific and Practical Conference ’Information Technologies in the Preservation and Promotion of Cultural Heritage,’ researchers discussed who controls algorithmic solutions and in what ways, and whether authentic traditions get lost in them.
October 12, 2026
Road Injuries Leading Cause of Death Among Men Aged 10 to 39
Worldwide, road injuries are the leading cause of death among males aged 10–39. An international team of researchers reached this conclusion as part of the Global Burden of Disease Study 2023, analysing data from 204 countries between 1990 and 2023. Their findings have been published in The Lancet Public Health, with HSE University Professor Vasily Vlassov listed among the collaborators.

 

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EC '19 Proceedings of the 2019 ACM Conference on Economics and Computation

Association for Computing Machinery (ACM), 2019.
Under the general editorship: A. Karlin, N. Immorlica, R. Johari

The papers in these Proceedings were presented at the 20th ACM Conference on Economics and Computation (EC'19), held between June 24 and 28, 2019, as part of the 2019 ACM Federated Computing Research Conference in Phoenix, Arizona, USA. Since 1999 the ACM Special Interest Group on Electronic Commerce (SIGecom) has sponsored EC, the leading scientific conference on advances in theory, artificial intelligence, empirics and applied modeling at the interface of economics and computation.

The program committee selected 106 papers from among 382 submissions that were received by February 14, 2019 (an overall acceptance rate of 27.7%). Each paper was reviewed by at least two program committee members and one senior program committee member on the basis of significance, scientific novelty, technical quality, readability, and relevance to the conference. The authors were asked to align their submission with one of four tracks: Applied Modeling, Artificial Intelligence, Empirics, and Theory. The next table summarizes the number of submissions and the number of accepted papers for each track.

Of the 16 accepted papers, 40 are published in these Proceedings. The remaining 66, at the authors' request, are included as abstracts with pointers to full working papers that the authors guarantee to be reliable for at least two years. This option accommodates the practices of fields outside of computer science in which conference publishing can preclude journal publishing. We expect that many of the papers in these Proceedings will appear at a later date in more complete form in scientific journals.

Papers were presented in up to three parallel sessions. To emphasize commonalities among the problems studied at EC, and to encourage discussion at the conference, sessions were organized by topic rather than by track, and no indication of a paper's track was given at the conference or appears in these proceedings.

Chapters
Identifying Bid Leakage in Procurement Auctions: Machine Learning Approach
Dmitry Ivanov, Alexander Nesterov, , in: EC '19 Proceedings of the 2019 ACM Conference on Economics and Computation.: Association for Computing Machinery (ACM), 2019. P. 69–70.
We propose a novel machine-learning-based approach to detect bid leakage in first-price sealed-bid auctions. We extract and analyze the data on more than 1.4 million Russian procurement auctions between 2014 and 2018. As bid leakage in each particular auction is tacit, the direct classification is impossible. Instead, we reduce the problem of bid leakage detection ...
Added: November 11, 2020
Research target: Mathematics Computer Science Economics and Management
Priority areas: economics IT and mathematics business informatics mathematics
Language: English
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DOI
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Keywords: коррупциягосзакупкизапрос котировокFirst-price auctionМашинное обучение и анализ данныхcorruption in public procurementbid leakageprocurement auctionspositive unlabeled learning
Publication based on the results of:
Allocation and social choice mechanisms: axioms, incentives, algorithms (2020)
EC '19 Proceedings of the 2019 ACM Conference on Economics and Computation
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