SAPE: A System for Situation-Aware Public Security Evaluation
Authors: Shu Wu, Qiang Liu, Ping Bai, Liang Wang, Tieniu Tan
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This model can achieve better performance than the compared state-of-the-art methods. SAPE has two demonstrations, i.e., global public security evaluation and China public security evaluation. In the global part, based on Global Terrorism Database from UMD, for each country, SAPE can predict risk level and top-n potential terrorist organizations which might attack the country. |
| Researcher Affiliation | Academia | Shu Wu, Qiang Liu, Ping Bai, Liang Wang, Tieniu Tan Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences, China {shu.wu, qiang.liu, ping.bai, wangliang, tnt}@nlpr.ia.ac.cn |
| Pseudocode | No | The paper provides a mathematical formulation of its model but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links to source code or statements indicating that the code for their methodology is publicly available. |
| Open Datasets | Yes | Global public security evaluation is conducted on the global terrorism dataset1. This dataset includes more than 125,000 terrorist incidents that have occurred around the world since 1970, which contains 168 countries and 998 terrorist organizations. 1http://www.start.umd.edu/gtd/ |
| Dataset Splits | No | The paper describes the datasets used and the time windows considered (e.g., 'one month with a recurrent structure'), but it does not explicitly provide specific percentages, sample counts, or methodologies for training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or programming language versions) used in the implementation. |
| Experiment Setup | No | The paper describes architectural choices like the sigmoid activation function and time window/interval settings ('one month with a recurrent structure and each day of the month with a temporal transition matrix'). However, it does not provide specific hyperparameter values such as learning rate, batch size, optimizer details, or number of training epochs. |