13 citations · 16 across the 3 of their papers we have counts for
8 papers
ReAD: A Regional Anomaly Detection Framework Based on Dynamic Partition
Huaishao Luo, Chuishi Meng, Bowen Wu +3
The detection of the abnormal area from urban data is a significant research problem. However, to the best of our knowledge, previous methods designed on spatio-temporal anomalies…
Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
Yuxuan Liang, Kun Ouyang, Yiwei Wang +4
Citywide crowd flow analytics is of great importance to smart city efforts. It aims to model the crowd flow (e.g., inflow and outflow) of each region in a city based on historical…
Federated Extra-Trees with Privacy Preserving
Yang Liu, Mingxin Chen, Wenxi Zhang +2
It is commonly observed that the data are scattered everywhere and difficult to be centralized. The data privacy and security also become a sensitive topic. The laws and regulation…
Urban flows prediction from spatial-temporal data using machine learning: A survey
Peng Xie, Tianrui Li, Jia Liu +3
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors,…
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
Huaishao Luo, Tianrui Li, Bing Liu +1
This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity…
Federated Forest
Yang Liu, Yingting Liu, Zhijie Liu +3
Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the Eu…