Modelling Regional Crime Risk using Directed Graph of Check-ins

Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"

We propose a generic spatiotemporal event forecasting method, which we developed for the National Institute of Justice's (NIJ) Real-Time Crime Forecasting Challenge. Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation (KDE) and self-exciting point process (SEPP) mod…

结合签到、犯罪与事件响应数据联合学习的多警员高风险区域巡逻路由方法

Multi-officer Routing for Patrolling High Risk Areas Jointly Learned from Check-ins, Crime and Incident Response Data

针对现有警察巡逻规划研究对移动签到与POI数据利用不足、多警员场景复杂度高的问题，本文提出联合学习与非随机优化的多警员高风险区域优先巡逻方法，结合元启发式算法求解最优路线，在真实数据集上验证了性能优势。

分析美国大都会社区在Reddit上的犯罪 discourse：趋势、影响与见解

Analyzing Crime Discourse in U.S. Metropolitan Communities on Reddit: Trends, Influences, and Insights

本文研究Reddit上384个美国大都会社区的犯罪讨论，发现大型社区和政治倾向更自由的社区犯罪讨论更频繁，但犯罪率与讨论频率无强相关性。

Towards Federated Graph Learning for Collaborative Financial Crimes Detection

Financial crime is a large and growing problem, in some way touching almost every financial institution. Financial institutions are the front line in the war against financial crime and accordingly, must devote substantial human and technology resources to this effort. Current processes to detect financial misconduct have limitations in their ability to effectively differentiate between malicious behavior and ordinary financial activity. These limitations tend to result in gross over-reporting of suspicious activity that necessitate time-intensive and costly manual review. Advances in technolo…

Regionalized models for Spanish language variations based on Twitter

Spanish is one of the most spoken languages in the globe, but not necessarily Spanish is written and spoken in the same way in different countries. Understanding local language variations can help to improve model performances on regional tasks, both understanding local structures and also improving the message's content. For instance, think about a machine learning engineer who automatizes some language classification task on a particular region or a social scientist trying to understand a regional event with echoes on social media; both can take advantage of dialect-based language models to …