CHS: Small: Collaborative Research: Optimizing the Human-Machine System for Citizen Science

CHS：小型：协作研究：优化公民科学的人机系统

• 批准号: 2006894
• 负责人: Lucy Fortson
• 金额: $ 33.89万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006894&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Optimizing

This project investigates optimal human-machine collaboration for analysis of large, complex data sets. The project uses advances in machine learning to develop new citizen science system infrastructure to be built into Zooniverse --- a large, open-source platform for online citizen science. Citizen Science is an established method for distributed analysis of large quantities of data in which online volunteers help with tasks requiring human pattern recognition. An example is identifying morphology of galaxies in the Galaxy Zoo project. Much larger data sets are looming on the horizon. Designing a human-machine system to accelerate labeling of known classes at the same time as solving the problem of detecting interesting anomalies (suggesting new phenomena) requires answering several crucial research questions about how humans and machines best complement one another. Since the project's new techniques will be incorporated into Zooniverse, they will be available to all for use in citizen science projects across many disciplines. Additional benefits of this project include engaging over 2 million members of the public who participate in citizen science through Zooniverse, engaging young women in University of Minnesota computer science coding camps, and providing year-long capstone projects for Data Science Masters program students to engage in real-world research while preparing them for careers in data science. This project will carry out a detailed investigation of load balancing between human and machine classifiers, optimizing for the speed, accuracy, completeness or purity required by the domain research for a given task. The research program follows two thrusts: (1) Classification Efficiency Studies to optimize the classification efficiency of known classes across multiple domains and task types; and (2) Systematized Serendipity Studies to increase the efficiency of discovery, including detection of rare instances, unusual findings, and new classes. The project develops new infrastructure that builds on the existing capabilities of the Zooniverse citizen science platform through two modules: (1) Machine Integration Infrastructure to readily incorporate and combine machines on projects to increase classification efficiency as well as explore the machine-driven component of systematized serendipity; and (2) Leveling-up Strategies for Volunteers to enable human-driven identification of emergent classes. Ultimately, the human- and machine-driven mechanisms will be joined to form a combined human-machine system that will be tested for its ability to identify unknown, rare, or difficult to identify classes.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.

该项目研究了最佳的人机协作，以分析大型，复杂的数据集。 该项目利用机器学习的进步来开发新的公民科学系统基础架构，该基础设施内置在世界公民科学的大型开源平台中。 公民科学是一种既定方法，用于分布大量数据分析，在线志愿者在其中帮助需要人类模式识别的任务。一个例子是识别星系动物园项目中星系的形态。 更大的数据集迫在眉睫。设计人机系统来加速已知类别的标记，同时解决检测有趣异常的问题（暗示新现象）需要回答有关人类和机器如何最好地补充彼此的几个关键研究问题。 由于该项目的新技术将被纳入周刊，因此它们将用于所有学科的公民科学项目中的所有人。 该项目的其他好处包括与超过200万的公众成员通过Zooniverse参与公民科学，在明尼苏达大学计算机科学编码营中吸引年轻妇女，并为数据科学硕士课程学生提供为期一年的Capstone项目，以便在为现实世界中的研究中参与现实世界中的研究。该项目将对人与机器分类器之间的负载平衡进行详细研究，以优化域研究所需的速度，准确性，完整性或纯度。该研究计划遵循两个推力：（1）分类效率研究，以优化跨多个领域和任务类型的已知类别的分类效率； （2）系统化的偶然性研究以提高发现效率，包括对罕见实例，异常发现和新类别的检测。 该项目开发了新的基础架构，该基础架构通过两个模块建立在Zooniverse Citizen Science平台的现有功能上：（1）机器集成基础架构，可以随时在项目上合并并结合机器，以提高分类效率，并探索系统驱动的Serendipity的机器驱动组件； （2）为志愿者制定策略，以使以人为导向的新兴阶级识别。最终，将连接人类和机器驱动的机制，形成一个组合的人机系统，该系统将因其识别未知，罕见或难以识别的班级的能力而进行测试。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影响的评估来审查审查的审查标准，以评估值得通过评估。

项目成果

From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model

从脂肪滴到漂浮森林：使用基于 PatchGAN 的分割模型进行跨域迁移学习

• 发表时间: 2022
• 期刊: CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management: Workshop on Human-in-the-loop Data Curation
• 作者: Mantha, K. B.

Harnessing the Hubble Space Telescope Archives: A Catalog of 21,926 Interacting Galaxies

• DOI: 10.3847/1538-4357/acc0ff
• 发表时间: 2023-03
• 期刊: The Astrophysical Journal
• 作者: David O’Ryan;B. Merín;B. Simmons;Ant'onia Vojtekov'a;Anna Anku;Mike Walmsley;I. Garland;T. Géron

A Spatially Resolved Analysis of Star Formation Burstiness by Comparing UV and Hα in Galaxies at z ∼ 1 with UVCANDELS

通过使用 UVCANDELS 比较 z ≤ 1 处星系中的 UV 和 Hα 来对恒星形成爆发进行空间分辨分析

• DOI: 10.3847/1538-4357/acd9cf
• 发表时间: 2023
• 期刊: The Astrophysical Journal
• 作者: Mehta, Vihang;Teplitz, Harry I.;Scarlata, Claudia;Wang, Xin;Alavi, Anahita;Colbert, James;Rafelski, Marc;Grogin, Norman;Koekemoer, Anton;Prichard, Laura
• 通讯作者: Prichard, Laura

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning

• DOI: 10.1093/mnras/stac525
• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: Mike Walmsley;A. Scaife;C. Lintott;M. Lochner;Verlon Etsebeth;Tobias G'eron;H. Dickinson;L. Fortson;S. Kruk;K. Masters;K. Mantha;B. Simmons

Galaxy Zoo: kinematics of strongly and weakly barred galaxies

星系动物园：强和弱棒星系的运动学

• DOI: 10.1093/mnras/stad501
• 发表时间: 2023
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Géron, Tobias;Smethurst, Rebecca J;Lintott, Chris;Kruk, Sandor;Masters, Karen L;Simmons, Brooke;Mantha, Kameswara Bharadwaj;Walmsley, Mike;Garma-Oehmichen, L;Drory, Niv
• 通讯作者: Drory, Niv