Summary of Crimealarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction, by Kaixi Hu et al.
CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction
by Kaixi Hu, Lin Li, Qing Xie, Xiaohui Tao, Guandong Xu
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel fine-grained sequential crime prediction framework called CrimeAlarm is proposed to capture comprehensive criminal intents. The framework equips a mutual distillation strategy inspired by curriculum learning, which includes two phases: early training captures spot-shared criminal intents through high-confidence sequence samples, and later phase learns spot-specific intents by increasing the contribution of low-confidence sequences. This approach models unobserved criminal intents through output probability distributions learned reciprocally between prediction networks. CrimeAlarm outperforms state-of-the-art methods in terms of NDCG@5, achieving improvements of 4.51% for NYC16 and 7.73% for CHI18 in accuracy measures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CrimeAlarm is a new way to predict crime events. It tries to guess what kind of crimes will happen next by looking at past crimes. The problem is that there are many different types of crimes, and they can be hard to recognize. CrimeAlarm uses a special learning method that helps it learn about all these different crime types. This makes it better at predicting the right type of crime than other methods. It even does better on tests! |
Keywords
* Artificial intelligence * Curriculum learning * Distillation * Probability
