Zobrazeno 1 - 10
of 7 924
pro vyhledávání: '"Large-scale event"'
Event Factuality Detection (EFD) task determines the factuality of textual events, i.e., classifying whether an event is a fact, possibility, or impossibility, which is essential for faithfully understanding and utilizing event knowledge. However, du
Externí odkaz:
http://arxiv.org/abs/2407.15352
Akademický článek
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Autor:
Koen M.F. Gorgels, Nicole H.T.M. Dukers-Muijrers, Ymke J. Evers, Volker H. Hackert, Paul H.M. Savelkoul, Christian J.P.A. Hoebe
Publikováno v:
Public Health in Practice, Vol 8, Iss , Pp 100523- (2024)
Objectives: The COVID-19 pandemic highlights the importance of understanding facilitators for disease transmission. Events such as Carnival, characterized by large gatherings and extensive social interactions, have the potential to become ‘super sp
Externí odkaz:
https://doaj.org/article/247d10eecf904a1db7870cae3c12625c
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence [IEEE Trans Pattern Anal Mach Intell] 2024 Oct 07; Vol. PP. Date of Electronic Publication: 2024 Oct 07.
We introduce EventNarrative, a knowledge graph-to-text dataset from publicly available open-world knowledge graphs. Given the recent advances in event-driven Information Extraction (IE), and that prior research on graph-to-text only focused on entity
Externí odkaz:
http://arxiv.org/abs/2111.00276
We introduce the first very large detection dataset for event cameras. The dataset is composed of more than 39 hours of automotive recordings acquired with a 304x240 ATIS sensor. It contains open roads and very diverse driving scenarios, ranging from
Externí odkaz:
http://arxiv.org/abs/2001.08499
Autor:
Guo, Zhihui1 (AUTHOR), Chen, Hongbin1 (AUTHOR) chbscut@guet.edu.cn, Li, Shichao1 (AUTHOR) chbscut@guet.edu.cn
Publikováno v:
Sensors (14248220). Mar2023, Vol. 23 Issue 6, p3237. 26p.
Click through rate (CTR) prediction is very important for Native advertisement but also hard as there is no direct query intent. In this paper we propose a large-scale event embedding scheme to encode the each user browsing event by training a Siames
Externí odkaz:
http://arxiv.org/abs/1804.09133
Autor:
Yang, Ming1 (AUTHOR) FordDte@yahoo.com, Balas, Valentina E. (AUTHOR), Hong, Jer Lang (AUTHOR), Gu, Jason (AUTHOR), Lin, Tsung-Chih (AUTHOR)
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 2019, Vol. 37 Issue 4, p4753-4761. 9p.