Help Me to Help You: Machine Augmented Citizen Science
Help Me to Help You: Machine Augmented Citizen Science
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帮助我来帮助你：机器增强公民科学
DOI：
10.1145/3362741
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Walmsley, Mike
中科院分区：
文献类型：
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作者：
Wright, Darryl E.;Fortson, Lucy;Lintott, Chris;Laraia, Michael;Walmsley, Mike
不同领域的研究人员所面临的数据集的规模不断扩大，导致了一系列创造性的反应，包括部署现代机器学习技术，以及大规模“公民科学项目”的出现。然而，随着问题的规模(以及项目之间对注意力的竞争)的增长，后者为前者提供适当大训练集的能力被拉大了。我们探索了无监督学习的应用程序，以利用最初未标记的数据集中存在的结构。我们模拟对相似的点进行分组，然后将这些组呈现给志愿者进行标记。公民科学标记分组数据的效率更高，收集的标记可用于进一步提高标记未来数据的效率。为了演示这些想法，我们使用Zooniverse项目、超新星猎人项目和一个使用MNIST手写数字数据集的模拟项目收集的志愿者标记进行实验，这些数据来自泛STARRS瞬变调查(Psst)。我们的结果显示，在最好的情况下，我们可以预期将两个数据集的志愿者工作量分别减少87.0%和92.8%。这些结果说明了机器学习和公民科学家之间的共生关系，两者相互促进，对未来公民科学项目的设计具有重要意义。
The increasing size of datasets with which researchers in a variety of domains are confronted has led to a range of creative responses, including the deployment of modern machine learning techniques and the advent of large scale “citizen science projects.” However, the ability of the latter to provide suitably large training sets for the former is stretched as the size of the problem (and competition for attention amongst projects) grows. We explore the application of unsupervised learning to leverage structure that exists in an initially unlabelled dataset. We simulate grouping similar points before presenting those groups to volunteers to label. Citizen science labelling of grouped data is more efficient, and the gathered labels can be used to improve efficiency further for labelling future data.To demonstrate these ideas, we perform experiments using data from the Pan-STARRS Survey for Transients (PSST) with volunteer labels gathered by the Zooniverse project, Supernova Hunters and a simulated project using the MNIST handwritten digit dataset. Our results show that, in the best case, we might expect to reduce the required volunteer effort by 87.0% and 92.8% for the two datasets, respectively. These results illustrate a symbiotic relationship between machine learning and citizen scientists where each empowers the other with important implications for the design of citizen science projects in the future.