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Changzheng Wang

dblp:119/4636 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · conflict

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

Artificial intelligence and machine learning · 1 · 1 since 2021Computer networks · 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.

Computer networks
1 paper
Internet architecture and protocols · 87% Network performance modeling · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet architecture and protocols
ATM networks
0.011995
ATM data transmission systems based on N-ISDN · IEEE Trans. Commun. 1995
Internet architecture and protocols
ISDN
0.011995
ATM data transmission systems based on N-ISDN · IEEE Trans. Commun. 1995

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

simulation · 0.0
YearPublicationVenuePosition
2024 Multi-Granularity Dual-Aware Contrastive Learning for Few-shot Named Entity Recognition
abstract
Few-shot Named Entity Recognition aims to identify named entities from unstructured texts in various domains using a minimal amount of training samples and classify them into predefined categories. Many popular approaches decompose this process into two tasks: Span Detection and Entity Classification. However, they still have some issues: (1) Neglecting presentation optimization. Most of these methods emphasize classification, neglecting the optimization of span presentation during Span Detection. (2) Missing label semantics. They have not fully leveraged semantic information of entity type labels during Entity Classification. To address these issues, this paper proposes Multi-Granularity Dual-Aware Contrastive Learning (MGDAC) for few-shot NER. Specifically, we introduce multi-granularity contrastive learning for solve neglecting presentation optimization, focusing on both the overall vector and internal vector granularity to enhance features beneficial for span detection in token vector representations. Additionally, we design dual-aware contrastive learning for solve missing label semantics, effectively utilizing semantic information from both entity tokens and entity type labels during prototype construction to jointly optimize prototype representations. Finally, extensive experiments on the FewNERD dataset demonstrate that our proposed method exhibits improvements in both Span Detection and Entity Classification, outperforming other competitive baseline methods.
Boxiang Ma, Changzheng Wang, Shaoru Guo, Xuefeng Su, Zhichao Yan 0002, Wenyuan Shao, Zezheng Zhang, Ru Li 0001
IJCNN2
1995 ATM data transmission systems based on N-ISDN
abstract
A novel approach for data transmission in N-ISDN (narrow band integrated services digital network) is proposed. This approach uses the ATM (asynchronous transfer mode) technology normally applied to B-ISDN (broad band ISDN) and provides high performance for the simultaneous transmission of several types of burst information using a single N-ISDN line. It will be compatible to B-ISDN in the future. In the paper, the proposed transmission model is first outlined, including its system description, the layer construction and the functional elements. Then, the transmission protocol is described in detail. Finally, a simulation system is realized, and the performance of the transmission system is confirmed by the simulation results which show the usefulness of the proposed data transmission mode.>
Changzheng Wang, Masahiro Numa, Kotaro Hirano
IEEE Trans. Commun.1