Summary of Uncertainty-aware Human Mobility Modeling and Anomaly Detection, by Haomin Wen et al.
Uncertainty-aware Human Mobility Modeling and Anomaly Detection
by Haomin Wen, Shurui Cao, Leman Akoglu
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to modeling human mobility behavior using GPS data without labeled training data, focusing on detecting anomalies such as malicious or bad-actor behavior. The authors utilize sequence models like Transformers for unsupervised learning and inference, incorporating aleatoric (data) uncertainty estimation due to inherent stochasticity in individual behaviors. Additionally, epistemic (model) uncertainty is incorporated to handle data sparsity and robustly detect anomalies. The proposed model outperforms forecasting and anomaly detection baselines on large expert-simulated datasets with tens of thousands of agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to track people’s movements using GPS data without knowing which behaviors are good or bad. It uses special computer models to learn patterns in the data, but instead of just predicting where someone will go next, it also tries to detect unusual behavior that might be suspicious. The model is designed to handle the fact that different people behave differently and that there’s always some uncertainty when trying to predict what they’ll do. By using this approach, the paper shows how we can better identify unusual or malicious behavior. |
Keywords
» Artificial intelligence » Anomaly detection » Inference » Unsupervised
