ATD: Scanning Dynamic Spatial-Temporal Discrete Events for Threat Detection
ATD:扫描动态时空离散事件以进行威胁检测
基本信息
- 批准号:1830210
- 负责人:
- 金额:$ 27.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-10-01 至 2022-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The overarching research objective of this project is to develop a statistical framework for detecting anomalies from spatial-temporal discrete event data. Nowadays, a large volume of such event data dispersed over space and time are becoming increasingly available in a wide variety of applications, such as human activity data, social network data, and crime data. The observations of the discrete events can occur in continuous time and locations, and there can be a complex text description of such events. The discrete event data contain rich correlation and causal information, which can potentially be used to infer the dynamics of the underlying systems and detect threats. The project aims to develop statistical methods to harvest this potential in threat detection using discrete events and address the algorithmic and computational challenges. The developed methods will go beyond the status-quo model estimation by considering more general statistical inference problems such as hypothesis tests and likelihood-based inference. The developed methods are general and can be used for various discrete event data. The project will specifically demonstrate the effectiveness of the developed methods on a large-scale crime dataset collected by the Atlanta Police Department. Recently, point process models have been proven an effective model for capturing the correlation structure in discrete events. While much success has been achieved in estimating the self-exciting spatial-temporal point process models, it remains unclear how one can perform anomaly detection leveraging these models, since (1) detection (which can be cast as hypothesis test) is inherently different from estimation, which involves different kinds of statistics and performance metrics; (2) in various situations, there is a large number of discrete events over broad spatial areas, and the goal is to detect a small cluster of related events, which amounts to "finding a needle in a haystack", thus there is a need to develop powerful and computationally efficient statistics; (3) the normal or reference state can be complex and dynamic and methods need to adapt to the slowly time-varying normal state. The project will address these challenges and provide answers to two related fundamental questions: how to detect clusters of correlated events from a large amount of data using the point process model, and how to estimate time-varying background normal pattern. The proposed work will advance the state-of-art for scan statistic research and build a novel connection between pseudo-likelihood estimation and reinforcement learning. The developed methods will be tested in a specific application of crime data analysis. The proposed education activities will involve students at all levels in rigorous mathematical and statistical training and gain hands-on data analysis skills.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该项目的总体研究目标是开发一个统计框架,用于从时空离散事件数据中检测异常。如今,大量分散在空间和时间上的事件数据越来越多地应用于各种应用中,例如人类活动数据、社交网络数据和犯罪数据。对离散事件的观察可以发生在连续的时间和地点,并且可以有此类事件的复杂文本描述。离散事件数据包含丰富的相关性和因果信息,可用于推断底层系统的动态并检测威胁。该项目旨在开发统计方法,以利用离散事件挖掘威胁检测的潜力,并解决算法和计算挑战。所开发的方法将通过考虑更一般的统计推理问题(例如假设检验和基于可能性的推理)来超越现状模型估计。所开发的方法具有通用性,可用于各种离散事件数据。该项目将具体展示所开发方法在亚特兰大警察局收集的大规模犯罪数据集上的有效性。最近,点过程模型已被证明是捕获离散事件中相关结构的有效模型。虽然在估计自激时空点过程模型方面已经取得了很大成功,但仍不清楚如何利用这些模型进行异常检测,因为(1)检测(可以称为假设检验)本质上不同于估计,涉及不同类型的统计数据和绩效指标; (2)在各种情况下,在广阔的空间区域内存在大量的离散事件,而目标是检测一小群相关事件,这相当于“大海捞针”,因此需要开发强大且计算高效的统计数据; (3)正常或参考状态可能是复杂的和动态的,并且方法需要适应缓慢随时间变化的正常状态。该项目将解决这些挑战,并为两个相关的基本问题提供答案:如何使用点过程模型从大量数据中检测相关事件的集群,以及如何估计随时间变化的背景正常模式。拟议的工作将推进扫描统计研究的最新水平,并在伪似然估计和强化学习之间建立新颖的联系。所开发的方法将在犯罪数据分析的具体应用中进行测试。拟议的教育活动将使各级学生接受严格的数学和统计培训,并获得实践数据分析技能。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力优点和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(21)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Tensor Kernel Recovery for Discrete Spatio-Temporal Hawkes Processes
离散时空霍克斯过程的张量核恢复
- DOI:10.1109/tsp.2022.3229642
- 发表时间:2022
- 期刊:
- 影响因子:5.4
- 作者:Sheen, Heejune;Zhu, Xiaonan;Xie, Yao
- 通讯作者:Xie, Yao
Sequential Change-Point Detection for Mutually Exciting Point Processes
- DOI:10.1080/00401706.2022.2054862
- 发表时间:2021-02
- 期刊:
- 影响因子:2.5
- 作者:Haoyun Wang;Liyan Xie;Yao Xie;Alex Cuozzo;Simon Mak
- 通讯作者:Haoyun Wang;Liyan Xie;Yao Xie;Alex Cuozzo;Simon Mak
Crime Event Embedding with Unsupervised Feature Selection
- DOI:10.1109/icassp.2019.8682285
- 发表时间:2018-06
- 期刊:
- 影响因子:0
- 作者:Shixiang Zhu;Yao Xie
- 通讯作者:Shixiang Zhu;Yao Xie
Conformal prediction interval for dynamic time-series
- DOI:
- 发表时间:2020-10
- 期刊:
- 影响因子:0
- 作者:Chen Xu;Yao Xie
- 通讯作者:Chen Xu;Yao Xie
Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling
- DOI:10.1109/tits.2021.3068139
- 发表时间:2020-05
- 期刊:
- 影响因子:8.5
- 作者:Shixiang Zhu;Ruyi Ding;Minghe Zhang;P. V. Hentenryck;Yao Xie
- 通讯作者:Shixiang Zhu;Ruyi Ding;Minghe Zhang;P. V. Hentenryck;Yao Xie
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Yao Xie其他文献
Conformal prediction set for time-series
时间序列的共形预测集
- DOI:
10.48550/arxiv.2206.07851 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Chen Xu;Yao Xie - 通讯作者:
Yao Xie
Conformal prediction for multi-dimensional time series by ellipsoidal sets
椭球集多维时间序列的共形预测
- DOI:
10.48550/arxiv.2403.03850 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Chen Xu;Hanyang Jiang;Yao Xie - 通讯作者:
Yao Xie
Co-transport of negatively charged nanoparticles in saturated porous media: Impacts of hydrophobicity and surface O-functional groups.
带负电纳米颗粒在饱和多孔介质中的共传输:疏水性和表面 O 官能团的影响。
- DOI:
10.1016/j.jhazmat.2020.124477 - 发表时间:
2020-11 - 期刊:
- 影响因子:13.6
- 作者:
Tianjiao Xia;Yixuan Lin;Shunli Li;Ni Yan;Yao Xie;Mengru He;Xuetao Guo;Lingyan Zhu - 通讯作者:
Lingyan Zhu
Poisson matrix completion
泊松矩阵完成
- DOI:
10.1109/isit.2015.7282774 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Yang Cao;Yao Xie - 通讯作者:
Yao Xie
Quantitative Prediction of Air Entrainment Defects in Casting Filling Process
铸件充型过程中夹气缺陷的定量预测
- DOI:
10.24425/afe.2023.146686 - 发表时间:
2023 - 期刊:
- 影响因子:0.6
- 作者:
Y. Yin;Yao Xie;Yingchen Song;Xu Shen;Xiao;Jianxin Zhou - 通讯作者:
Jianxin Zhou
Yao Xie的其他文献
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{{ truncateString('Yao Xie', 18)}}的其他基金
Collaborative Research: ATD: a-DMIT: a novel Distributed, MultI-channel, Topology-aware online monitoring framework of massive spatiotemporal data
合作研究:ATD:a-DMIT:一种新颖的分布式、多通道、拓扑感知的海量时空数据在线监测框架
- 批准号:
2220495 - 财政年份:2023
- 资助金额:
$ 27.5万 - 项目类别:
Standard Grant
Bridging Statistical Hypothesis Tests and Deep Learning for Reliability and Computational Efficiency
连接统计假设检验和深度学习以提高可靠性和计算效率
- 批准号:
2134037 - 财政年份:2022
- 资助金额:
$ 27.5万 - 项目类别:
Continuing Grant
Collaborative Research: IMR: MM-1A: MapQ: Mapping Quality of Coverage in Mobile Broadband Networks using Latent Gaussian Process Models
合作研究:IMR:MM-1A:MapQ:使用潜在高斯过程模型映射移动宽带网络的覆盖质量
- 批准号:
2220387 - 财政年份:2022
- 资助金额:
$ 27.5万 - 项目类别:
Standard Grant
Sequential Detection and Prediction for Solar Situation Awareness in Power Networks
电力网络中太阳态势感知的顺序检测和预测
- 批准号:
1938106 - 财政年份:2019
- 资助金额:
$ 27.5万 - 项目类别:
Standard Grant
CAREER: Quick Detection for Streaming Data Over Dynamic Networks
职业:快速检测动态网络上的流数据
- 批准号:
1650913 - 财政年份:2017
- 资助金额:
$ 27.5万 - 项目类别:
Continuing Grant
CyberSEES: Type 2: Collaborative Research: Real-time Ambient Noise Seismic Imaging for Subsurface Sustainability
CyberSEES:类型 2:协作研究:用于地下可持续性的实时环境噪声地震成像
- 批准号:
1442635 - 财政年份:2015
- 资助金额:
$ 27.5万 - 项目类别:
Standard Grant
NSF Student Travel Grant for the 10th ACM International Conference on Underwater Networks and System (WUWNet'15)
NSF 学生旅费资助第十届 ACM 国际水下网络和系统会议 (WUWNet15)
- 批准号:
1551297 - 财政年份:2015
- 资助金额:
$ 27.5万 - 项目类别:
Standard Grant
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