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.

该项目研究了用于分析大型复杂数据集的最佳人机协作。 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设施，并将其内置到Zooniverse中，Zooniverse是一个大型的在线公民科学开源平台。 公民科学是一种分布式分析大量数据的方法，在线志愿者帮助完成需要人类模式识别的任务。一个例子是在银河动物园项目中识别星系的形态。 更大的数据集即将出现。设计一个人机系统来加速已知类别的标记，同时解决检测有趣异常（暗示新现象）的问题，需要回答几个关于人类和机器如何最好地互补的关键研究问题。 由于该项目的新技术将被纳入Zooniverse，它们将可供所有人用于跨学科的公民科学项目。 该项目的其他好处包括通过Zooniverse吸引超过200万参与公民科学的公众，吸引明尼苏达大学计算机科学编码营的年轻女性，并为数据科学硕士课程的学生提供为期一年的顶点项目，让他们在为数据科学职业做准备的同时从事现实世界的研究。该项目将对人类和机器分类器之间的负载平衡进行详细调查，优化给定任务的领域研究所需的速度，准确性，完整性或纯度。该研究计划遵循两个重点：（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，以提高发现的效率，包括检测罕见的实例，不寻常的发现和新的类别。 该项目通过两个模块开发新的基础设施，建立在Zooniverse公民科学平台的现有能力基础上：（1）机器集成基础设施，以便随时将项目上的机器纳入和联合收割机，以提高分类效率，并探索系统化意外发现的机器驱动组件;（2）志愿者升级策略，以实现人工驱动的新兴类别识别。最终，人类和机器驱动的机制将结合起来，形成一个人机结合的系统，该系统将被测试其识别未知、稀有或难以识别的类别的能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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

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

Galaxy Zoo: Clump Scout – Design and first application of a two-dimensional aggregation tool for citizen science

Galaxy Zoo：Clump Scout – 设计并首次应用公众科学的二维聚合工具

• DOI: 10.1093/mnras/stac2919
• 发表时间: 2022
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Dickinson, Hugh;Adams, Dominic;Mehta, Vihang;Scarlata, Claudia;Fortson, Lucy;Serjeant, Stephen;Krawczyk, Coleman;Kruk, Sandor;Lintott, Chris;Mantha, Kameswara Bharadwaj
• 通讯作者: Mantha, Kameswara Bharadwaj

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