HDR DSC: Collaborative Research: Creating and Integrating Data Science Corps to Improve the Quality of Life in Urban Areas

HDR DSC：协作研究：创建和整合数据科学团队以提高城市地区的生活质量

• 批准号: 2321574
• 负责人: Sharad Sharma
• 金额: $ 18万
• 依托单位: University of North Texas
• 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: 2023-01-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321574&HistoricalAwards=false
• 关键词: HDR; DSC; Collaborative; Research; Creating

The goal of this project is to develop a team-based data science corps program for undergraduate students from Computer Science, Information Systems, and Business integrating both academic training as well as hands-on experience through real-world data science projects. This project is a collaborative effort with the University of Maryland Baltimore County as the coordinating as well as an implementing organization, and the University of Baltimore, Towson University, and Bowie State University as implementing organizations. This project focuses on the city of Baltimore as an exemplar for other cities in the US and across the globe. The project team will collaborate with a number of communities in the city of Baltimore to integrate real-world data science projects into classroom instruction in data science. The specific objectives of this project are as follows: (i) Develop the technical, analytical, modeling, and critical thinking skills that are key to success as a data science professional; (ii) Connect a cohort of students to communities, organizations, and projects that can benefit from the power of data science; (iii) Nurture and support innovative thinking in solving some of the key challenges facing the real world; (iv) Promote a better understanding of the power and pitfalls of data-driven discoveries to improve the quality of life in urban communities; (v) Increase the data science workforce capacity to support this critical area that is of growing importance in society; and finally, (vi) Evaluate the effect of the proposed data science corps on student learning. This project will create a core set of knowledge that will be valuable in developing solutions for real-world urban settings with the understanding that not all projects will require the application or use of every topic covered in the data science corps program. The core set of knowledge includes data collection and cleaning, data analysis using machine learning and deep learning techniques, data visualization including geospatial data and virtual reality, data privacy and security, and infrastructure for smart cities including IoT-based sensor networks. The proposed data science corps program will have two main phases: instructional phase (10 modules in total) and real-world team projects (5 modules in total). The project teams consist of students who have taken a course in at least one of the following areas: data collection and analysis, big data, machine learning including deep learning, smart cities, cybersecurity, geospatial data analysis and visualization, and virtual reality. Examples of team projects include: (i) developing community-based indicators that are compiled from open data portals and parametric and non-parametric statistical techniques to understand the relationship between urban sustainability and a range of factors including cleanliness and environment, crime and safety, business and economics, social and political, housing, health, and education; (ii) combining deep learning models such as convolutional neural networks (CNN) and long term short term memory recurrent neural networks (LSTM-RNN) to develop prediction models for derelict buildings that are likely to become vacant; (iii) combining sensor data and social media for automated information extraction, validation, and quality checks that can be beneficial to both citizens and emergency managers in crisis situations such as flash floods; (iv) developing smart streetlights that are networked LED systems that can be adjusted based on time of day and motion and can report outages back to central operations; and (v) developing augmented reality-based systems that leverage systems such as Microsoft HoloLens and mobile devices for building evacuation.NSF's Harnessing the Data Revolution Data Science Corps program focuses on building capacity for harnessing the data revolution at the local, state, national, and international levels to help unleash the power of data in the service of science and society. Projects in this program are being jointly funded by the NSF's Harnessing the Data Revolution Big Idea; the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems; the Directorate for Education and Human Resources, Division of Undergraduate Education; the Directorate for Mathematical and Physical Sciences, Division of Mathematical Sciences; and the Directorate for Social, Behavioral and Economic Sciences, Office of Multidisciplinary Activities and Division of Behavioral and Cognitive Sciences.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.

该项目的目的是为来自计算机科学，信息系统和业务的本科生开发基于团队的数据科学兵团计划，并通过现实世界中的数据科学项目整合学术培训以及动手经验。该项目是与马里兰州巴尔的摩大学作为协调和实施组织以及巴尔的摩大学，陶森大学和鲍伊州立大学作为实施组织的合作。 该项目的重点是巴尔的摩市，作为美国其他城市和全球其他城市的典范。 项目团队将与巴尔的摩市的许多社区合作，将现实世界数据科学项目整合到数据科学的课堂教学中。该项目的具体目标如下：（i）发展技术，分析，建模和批判性思维能力，这些技巧是数据科学专业人员成功的关键； （ii）将一组学生与可以从数据科学的力量中受益的社区，组织和项目联系起来； （iii）培养和支持创新思维，以解决现实世界所面临的一些关键挑战； （iv）促进对数据驱动发现的力量和陷阱的更好理解，以改善城市社区的生活质量； （v）提高数据科学劳动力的能力，以支持这个关键领域，该领域在社会中越来越重要；最后，（vi）评估拟议的数据科学兵团对学生学习的影响。该项目将创建一组核心知识，这些知识将在为现实世界中的城市环境开发解决方案方面有价值，并了解并非所有项目都需要应用或使用数据科学司部计划中所涵盖的每个主题。知识的核心集包括数据收集和清洁，使用机器学习和深度学习技术的数据分析，数据可视化，包括地理空间数据和虚拟现实，数据隐私和安全性以及包括基于物联网的传感器网络在内的智能城市的基础架构。 拟议的数据科学兵团计划将有两个主要阶段：教学阶段（总共10个模块）和现实世界的团队项目（总共5个模块）。项目团队由在以下至少一个领域中学习的学生组成：数据收集和分析，大数据，机器学习，包括深度学习，智能城市，网络安全，地理空间数据分析和可视化以及虚拟现实。团队项目的示例包括：（i）开发基于社区的指标，这些指标是根据开放数据门户以及参数和非参数统计技术汇编而成的，以了解城市可持续性与一系列因素之间的关系，包括清洁度和环境，犯罪和安全，商业和经济，社会和经济，社会和政治，住房，住房，健康和教育； （ii）结合深度学习模型，例如卷积神经网络（CNN）和长期记忆复发的神经网络（LSTM-RNN），以开发可能已空置的废弃建筑物的预测模型； （iii）将传感器数据和社交媒体结合起来，以进行自动化信息提取，验证和质量检查，这在危机情况（例如山洪）的情况下对公民和急救人员都有益； （iv）开发可以根据一天中的时间和运动进行调整的网络LED系统的智能路灯，并可以向中央操作报告中断； （v）开发基于增强的基于现实的系统，该系统利用Microsoft Hololens和移动设备等系统来构建疏散。NSF利用数据革命数据科学部的侧重于建立在本地，州，国家和国际层次上利用数据革命的能力，以帮助释放数据在科学和社会中释放数据的能力。该计划中的项目是由NSF利用数据革命的重大想法共同资助的；计算机和信息科学与工程局，信息和智能系统部；教育和人力资源局，本科教育司；数学和物理科学局，数学科学部；以及社会，行为和经济科学局，多学科活动办公室以及行为和认知科学的划分。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。

项目成果

Mobile augmented reality system for object detection, alert, and safety

用于物体检测、警报和安全的移动增强现实系统

• DOI: 10.2352/ei.2023.35.12.ervr-218
• 发表时间: 2023
• 期刊: Electronic Imaging
• 作者: Sharma, Sharad;Engel, Don
• 通讯作者: Engel, Don

Mobile AR Application for Navigation and Emergency Response

用于导航和应急响应的移动 AR 应用程序

• 发表时间: 2022
• 期刊: 2022 International Conference on Computational Science and Computational Intelligence (CSCI
• 作者: Mannuru, Nishith Reddy;Kanumuru, Mounica;Sharma, Sharad
• 通讯作者: Sharma, Sharad