25 citations · 29 across the 12 of their papers we have counts for
14 papers
Rethinking Multi-View Fusion for Urban Representation Learning
Sean Bin Yang, Zongyi Xu, Jilin Hu +5
Urban region representation learning encodes heterogeneous urban signals, such as mobility flows, points of interest, and land-use patterns, into unified embeddings for downstream…
When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning
Sean Bin Yang, Ying Sun, Zongyi Xu +6
Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use information, to support tasks including…
REFINE: Trajectory Representation Learning via Closed-Loop Transcription -- Extended Version
Sean Bin Yang, Ying Sun, Jilin Hu +5
Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether discriminative or generative, ad…
VTRQ: Enabling Verifiable Trajectory Range Queries in Hybrid-Storage Blockchains
Zhongming Yao, Junchang Xin, Yumeng Song +7
Due to their increasingly large volumes, outsourcing of trajectory storage and querying to third-party service providers has become attractive. However, in such outsourced environm…
Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective
Hengyu Liu, Tianyi Li, Zhihong Cui +5
This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (papers, documentation, struc…
CLEAR: A Knowledge-Centric Vessel Trajectory Analysis Platform
Hengyu Liu, Tianyi Li, Haoyu Wang +5
Vessel trajectory data from the Automatic Identification System (AIS) is used widely in maritime analytics. Yet, analysis is difficult for non-expert users due to the incompletenes…
VISTA: Knowledge-Driven Vessel Trajectory Imputation with Repair Provenance
Hengyu Liu, Tianyi Li, Haoyu Wang +4
Repairing incomplete trajectory data is essential for downstream spatio-temporal applications. Yet, existing repair methods focus solely on reconstruction without documenting the r…
Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directions
Sean Bin Yang, Ying Sun, Yunyao Cheng +3
Foundation models (FMs) have emerged as a powerful paradigm, enabling a diverse range of data analytics and knowledge discovery tasks across scientific fields. Inspired by the succ…
Advancing Knowledge Tracing by Exploring Follow-up Performance Trends
Hengyu Liu, Yushuai Li, Minghe Yu +6
Intelligent Tutoring Systems (ITS), such as Massive Open Online Courses, offer new opportunities for human learning. At the core of such systems, knowledge tracing (KT) predicts st…
MH-GIN: Multi-scale Heterogeneous Graph-based Imputation Network for AIS Data (Extended Version)
Hengyu Liu, Tianyi Li, Yuqiang He +3
Location-tracking data from the Automatic Identification System, much of which is publicly available, plays a key role in a range of maritime safety and monitoring applications. Ho…
ACTIVE: Continuous Similarity Search for Vessel Trajectories
Tiantian Liu, Hengyu Liu, Tianyi Li +2
Publicly available vessel trajectory data is emitted continuously from the global AIS system. Continuous trajectory similarity search on this data has applications in, e.g., mariti…
A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction
Haomin Yu, Tianyi Li, Kristian Torp +1
Accurate vessel trajectory prediction facilitates improved navigational safety, routing, and environmental protection. However, existing prediction methods are challenged by the ir…
Towards Mobility Data Science (Vision Paper)
Mohamed Mokbel, Mahmoud Sakr, Li Xiong +45
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracki…
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network
Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen
Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often…