Collaborative Research: CISE-MSI: DP: IIS: Event Detection and Knowledge Extraction via Learning and Causality Analysis for Resilience Emergency Response

协作研究：CISE-MSI：DP：IIS：通过学习和因果关系分析进行事件检测和知识提取，以实现弹性应急响应

• 批准号: 2219615
• 负责人: Sanjay Madria
• 金额: $ 27.96万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219615&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; DP

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project utilizes information gleaned from social media about upcoming events to inform designated authorities in a timely manner so they can prepare mitigating action plans in case of emergency. Besides the extracted events themselves, harvested information may include (but is not limited to) images, posted messages, people’s sentiments and other surrounding context which will improve relevancy and trust of the information in understanding emergency situations. Extracted events become the source for investigating and analyzing spatial-temporal influences between events and cross-domain events to derive further insights. Potential applications are real-time tracking and monitoring of events for disaster relief, and forecasting of events for mitigation plans. Project outcomes will benefit researchers in information extraction and integration with interests in graph models and transfer learning; in addition to providing practical studying materials in areas such as deep learning, spatio-temporal data causality and analysis for students about disaster resilience and progressing towards community resilience in the long term. Moreover, the work will increase research capacity and collaborations to generate new research opportunities for students from underrepresented communities to pursue advanced degrees in computer science. Social media data provides a means to identify happening events prior, during, and post disasters. It provides signals for designed authorities for reactions and mitigation planning. This research will use social media posts, machine learning, and transfer learning techniques in three thrusts: 1) Extract local and global events; 2) Embed surrounding context such as relevance and trust; 3) Analyze spatial-temporal relationship between events and cross-domain events for further insights. This project puts forth a novel approach to events analysis under the umbrella of graph neural network and transfer learning, leveraging recent advances and opportunities in deep learning. The resulting data-driven algorithms will be modelled emphasizing the socio-economic aspects of the consequences and cascading losses by allowing the system to adapt according to the community-based variables and the dynamics of the disasters. The findings will be disseminated via publications, source code, and data to reach diverse communities of researchers and students.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.

该奖项是根据2021年《美国救援计划法》的全部或部分资助的（公共法117-2）。本项目利用从社交媒体中收集的有关即将发生的事件的信息，以及时向指定当局通知指定当局，以便在出现的情况下可以准备缓解行动计划。除了提取的事件本身外，收获的信息还可能包括（但不限于）图像，已发布消息，人们的情感和其他周围的上下文提取的事件成为调查和分析事件和跨体事件之间的时空影响的来源，以获得进一步的见解。潜在的应用程序是对救灾事件的实时跟踪和监视，以及对缓解计划的事件的预测。项目成果将使研究人员在信息提取和集成与图形模型和转移学习的兴趣方面受益；除了在深度学习，时空数据因果关系和分析灾难弹性和促进社区韧性方面的分析等领域提供实际学习材料外。此外，这项工作将提高研究能力和合作，为来自代表性不足社区的学生创造新的研究机会，以购买计算机科学领域的高级学位。社交媒体数据提供了一种在灾难期间，期间和之后识别发生事件的方法。它为设计当局提供了反应和缓解计划的信号。这项研究将使用三个推力的社交媒体帖子，机器学习和转移学习技术：1）提取本地和全球事件； 2）嵌入周围的环境，例如相关性和信任； 3）分析事件与跨域事件之间的时空关系，以获取进一步的见解。该项目在图形中性网络的保护下和转移学习的伞下提出了一种新颖的事件分析方法，利用了深度学习的最新进展和机会。将通过允许系统根据基于社区的变量和灾难的动态来适应后果和级联损失的社会经济方面的社会经济方面进行建模。这些发现将通过出版物，源代码和数据传播，以覆盖研究人员和学生的潜水社区。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估诚实地认为通过评估。