OAC Core: Small: Collaborative Research: Conversational Agents for Supporting Sustainable Implementation and Systemic Diffusion of Cyberinfrastructure and Science Gateways

OAC 核心：小型：协作研究：支持网络基础设施和科学网关可持续实施和系统扩散的对话代理

• 批准号: 2007100
• 负责人: Kerk Kee
• 金额: $ 20万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007100&HistoricalAwards=false
• 关键词: OAC; Core; Small; Collaborative; Research

By designing a chatbot that harnesses the power of machine learning to connect scientists/educators with existing tools, datasets, and other experts, this sociotechnical project aims to remove significant user support barriers in the US STEM community. A chatbot called Vidura will be developed to provide personalized user support to novice and expert users in the form of a conversational agent to facilitate interdisciplinary research and education collaborations. The chatbot investigations in the project benefit the investments in science gateways (SG) and cyberinfrastructure (CI) made by the NSF and other federal agencies for over two decades. Vidura chatbot works around the clock and makes the greatest impacts when user support by human agents (i.e., domain specialists or CI support persons) is not available and/or is too costly as a science gateway surges in user uptake. The Vidura chatbot will initially be prototyped in CyNeuro, a neuroscience SG, but will be made accessible to be adapted in SGs across multiple domains to benefit the broader research/education communities.This project integrates human communication science, conversational agent design, recommender algorithms, machine learning techniques, domain topic modeling in a synergistic way that advances social science, computer science, and neuroscience. In addition, this sociotechnical project provides research opportunities to benefit undergraduate and graduate students in both social and computer sciences and creates new interdisciplinary courses. The project activities will benefit students with diverse backgrounds as it will be carried out at two large public universities, one of which is a Hispanic serving institution. The project objectives and activities will focus on answering three main research questions: (i) How to design a chatbot for gathering user requirements and creating user profiles with proficiency in order to provide personalized expert service support and maintain adoption? (ii) How to equip the chatbot communication and custom dialogue flows during support actions with underlying recommender algorithms using un-supervised machine learning within/across science domains? (iii) How to implement a chatbot framework in research and education workflows of data-intensive/computation-intensive application communities (such as the neuroscience community, as well as in CI provider communities of SGCI, XSEDE, NSG, CyVerse, and JetStream) to evaluate utility across domains and identify best practices for ongoing relevance? The project findings will ultimately advance knowledge on how to enable conversational agents implemented as chatbot interfaces with pertinent underlying recommender algorithms in order to provide expert services to scientific domain users with reduced cost and increased convenience.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.

通过设计一个聊天机器人，利用机器学习的力量将科学家/教育工作者与现有工具、数据集和其他专家联系起来，这个社会技术项目旨在消除美国 STEM 社区中重大的用户支持障碍。将开发名为 Vidura 的聊天机器人，以对话代理的形式为新手和专家用户提供个性化用户支持，以促进跨学科研究和教育合作。该项目中的聊天机器人研究有利于 NSF 和其他联邦机构二十多年来对科学网关 (SG) 和网络基础设施 (CI) 的投资。 Vidura 聊天机器人全天候工作，当人类代理（即领域专家或 CI 支持人员）无法提供用户支持和/或随着科学网关用户使用量激增而成本过高时，Vidura 聊天机器人会产生最大的影响。 Vidura 聊天机器人最初将在 CyNeuro（一个神经科学 SG）中进行原型设计，但将可以在多个领域的 SG 中进行调整，以使更广泛的研究/教育社区受益。该项目集成了人类通信科学、对话代理设计、推荐算法、机器学习技术、领域主题建模以协同方式推进社会科学、计算机科学和神经科学。此外，这个社会技术项目还提供研究机会，使社会科学和计算机科学领域的本科生和研究生受益，并创建新的跨学科课程。该项目活动将在两所大型公立大学进行，其中一所是西班牙裔服务机构，因此将使具有不同背景的学生受益。该项目的目标和活动将重点回答三个主要研究问题：（i）如何设计一个聊天机器人来收集用户需求并熟练地创建用户档案，以提供个性化的专家服务支持并保持采用？ (ii) 如何在支持行动期间使用底层推荐算法在科学领域内/跨科学领域使用无监督机器学习来装备聊天机器人通信和自定义对话流？ (iii) 如何在数据密集型/计算密集型应用社区（例如神经科学社区，以及 SGCI、XSEDE、NSG、CyVerse 和 JetStream 等 CI 提供商社区）的研究和教育工作流程中实现聊天机器人框架评估跨领域的效用并确定持续相关性的最佳实践？该项目的研究成果最终将促进人们对如何将对话代理实现为具有相关底层推荐算法的聊天机器人接口的知识，从而以降低的成本和增加的便利性为科学领域用户提供专家服务。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。

项目成果

Measurement of Utility in User Access of COVID-19 Literature via AI-powered Chatbot

通过 AI 驱动的聊天机器人测量用户访问 COVID-19 文献的效用

• DOI: 10.1109/aipr52630.2021.9762160
• 发表时间: 2021-10
• 期刊: IEEE AIPR
• 作者: Oruche, Roland;Milman, Eric D.;Cheng, Xiyao;Joish, Megha;Kulkarni, Chaitra;Sharma, Agrim;Kee, Kerk;Regunath, Hariharan;Calyam, Prasad
• 通讯作者: Calyam, Prasad

Science gateway adoption using plug‐in middleware for evidence‐based healthcare data management

采用科学网关，使用插件中间件进行基于证据的医疗保健数据管理

• DOI: 10.1002/cpe.7195
• 发表时间: 2022-01
• 期刊: Concurrency and Computation: Practice and Experience
• 作者: Oruche, Roland;Milman, Eric;Lemus Alarcon, Mauro;Cheng, Xiyao;Pandey, Ashish;Wang, Songjie;Calyam, Prasad;Kee, Kerk
• 通讯作者: Kee, Kerk