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Demo Data from EDBT 2026 / reviewers in the wild / expert

Wang Dong

dblp:348/0670 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Vision and language · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Vision and language › multimodal understanding
multimodal document understanding		 1.012026
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding · ACL (1) 2026
Computer vision › Vision and language
visual question answering		 1.012026
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding · ACL (1) 2026

Methods — techniques the papers use, named apart from their topics

reasoning · 1.0multimodal large language model · 1.0
YearPublicationVenuePosition
2026 From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding
abstract
Yandi Wang, Libin Zhan, Ziwei Huang, Tiancheng Luo, Yuxuan Jiang, Wang Dong, Leilei Gan, Jun Chen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Yandi Wang, Libin Zhan, Ziwei Huang 0005, Tiancheng Luo, Wang Dong, Leilei Gan
 ACL (1)6
2008 Reliability Analysis of Component-Based Software Based on Relationships of Components
abstract
Recent development in daily business life indicates demand for higher reliability of SOA software. After analyzing the traditional software reliability models (Markov chain based), it was found that many software with complicated relationships of components did not satisfy the Markov property. This paper analyzed various complex components' relationships (parallel, loops, backup, fault- tolerance, request and response, etc.) how to influence the reliability of the whole system, and solved these complicated relationships to satisfy the Markov property. This work extended the scope of the application of the Markov Model and improved the accuracy of reliability estimation.
Wang Dong, Huang Ning, Ye Ming
 ICWS1