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Shenping Xiong

dblp:369/9336 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0006-1706-0511ORCID · corroborated

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

Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

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
Graph learning · 50% Representation and self-supervised learning · 50%
Network and information security
1 paper
Privacy and data protection · 100%

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

TopicWeightPapersLastEvidence papers
Machine learning › Graph learning › graph neural network › attention-based graph neural network
graph attention network
1.012026
TGNN: Enhancing Pixel Tracking Detection via LLM-driven Annotation and GAT-powered Structural Representation · WWW 2026
Machine learning › Representation and self-supervised learning
structured representation
1.012026
TGNN: Enhancing Pixel Tracking Detection via LLM-driven Annotation and GAT-powered Structural Representation · WWW 2026
Privacy and data protection
web tracking
1.012026
TGNN: Enhancing Pixel Tracking Detection via LLM-driven Annotation and GAT-powered Structural Representation · WWW 2026

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

large language model · 2.0graph attention network · 2.0
YearPublicationVenuePosition
2026 TGNN: Enhancing Pixel Tracking Detection via LLM-driven Annotation and GAT-powered Structural Representation
abstract
Web tracking is increasingly pervasive, raising serious concerns about user privacy and security. Among existing techniques, pixel tracking is particularly stealthy and cost-effective, embedding invisible images that exfiltrate user activities to third-party servers. Current defenses, including filter list blocking and conventional machine learning, often fail to capture the cross-site associations that enable pixel tracking to evade detection.
Shenping Xiong, Xutong Wang, Ze Jin, Xinyu Liu 0019, Haoqiang Wang, Ru Tan, Qixu Liu
WWW1
2025 MTAD-Kanformer: multivariate time-series anomaly detection via kan and transformer
Shenping Xiong
Appl. Intell.3
2025 MemFlow-AD: An anomaly detection and localization model based on memory module and normalizing flow
Shenping Xiong, Tijian Cai
J. Vis. Commun. Image Represent.3