SI2-SSE: Human- and Machine-Intelligent Software Elements for Cost-Effective Scientific Data Digitization

在数据密集型科学发现的时代，所有社区的大数据科学家在实际执行发现分析之前，将大部分时间和精力用于收集、整合、整理、转换和评估质量。一些努力甚至可能从不能以数字形式获得和访问的信息开始，当它可用时，它通常是非结构化的形式，与需要结构化和统一格式数据的分析工具不兼容。处理数据量和种类以及加速数字化速度的两种主要方法是应用众包或机器学习解决方案。然而，在同时利用这两种解决方案，并使不同的努力更容易共享和重用开发的软件元素方面做得很少。人类和机器智能网络（humanain）项目的愿景是通过人与机器处理之间的集成和相互合作的根本性进展来加速科学数据数字化，以解决科学数据数字化中存在的实际障碍和瓶颈。尽管HuMaIN专注于生物多样性社区面临的数字化任务，但正在开发的软件元素本质上是通用的，预计将适用于其他科学领域（例如，探索月球表面的陨石坑需要与在文本中查找单词相同类型的众包工具，以及机器学习工具是否可以提供类似结果的相同问题可以进行测试）。HuMaIN项目建议进行以下软件元素的研究和开发：(a)用于科学数据数字化的可配置机器学习应用程序（例如，光学字符识别和自然语言处理），这些应用程序将作为RESTful服务自动提供，以提高HuMaIN软件元素与其他元素互操作的能力，同时通过新的应用规范语言减少软件开发时间；(b)导致网络-人协调系统的工作流程，该系统将利用反馈回路（例如，基于众包数据及其质量的共识）自适应变化，提高整个系统的可持续性；(c)新的众包微任务，能够在各种场景中重复使用，并包含用于研究时效性用户界面的用户活动传感器；(d)支持按需自动创建和配置众包工作流程的服务，以满足个人群体的需求。将部署一个基于云的系统，以提供必要的执行环境，并对网络-人类工作流程中涉及的服务执行进行可追溯性，并且对该项目中开发的所有软件元素进行成本效益分析，将提供与人类和机器智能任务相关的长期假设场景的评估和评估。众包活动将吸引广泛的用户来完成对专业知识要求不高的任务，同时它将使志愿者接触到应用科学和工程，可能会吸引K-12教师和学生的兴趣。

In the era of data-intensive scientific discovery, Big Data scientists in all communities spend the majority of their time and effort collecting, integrating, curating, transforming, and assessing quality before actually performing discovery analysis. Some endeavors may even start from information not being available and accessible in digital form, and when it is available, it is often in non-structured form, not compatible with analytics tools that require structured and uniformly-formatted data. Two main methods to deal with the volume and variety of data as well as to accelerate the rate of digitization have been to apply crowdsourcing or machine-learning solutions. However, very little has been done to simultaneously take advantage of both types of solutions, and to make it easier for different efforts to share and reuse developed software elements. The vision of the Human- and Machine-Intelligent Network (HuMaIN) project is to accelerate scientific data digitization through fundamental advances in the integration and mutual cooperation between human and machine processing in order to handle practical hurdles and bottlenecks present in scientific data digitization. Even though HuMaIN concentrates on digitization tasks faced by the biodiversity community, the software elements being developed are generic in nature, and expected to be applicable to other scientific domains (e.g., exploring the surface of the moon for craters require the same type of crowdsourcing tool as finding words in text, and the same questions of whether machine-learning tools could provide similar results can be tested).The HuMaIN project proposes to conduct research and develop the following software elements: (a) configurable Machine-Learning applications for scientific data digitization (e.g., Optical Character Recognition and Natural Language Processing), which will be made automatically available as RESTful services for increasing the ability of HuMaIN software elements to interoperate with other elements while decreasing the software development time via a new application specification language; (b) workflows leading to a cyber-human coordination system that will take advantage of feedback loops (e.g., based on consensus of crowdsourced data and its quality) for self-adaptation to changes and increased sustainability of the overall system, (c) new crowdsourcing micro-tasks with ability of being reusable for a variety of scenarios and containing user activity sensors for studying time-effective user interfaces, and (d) services to support automated creation and configuration of crowdsourcing workflows on demand to fit the needs of individual groups. A cloud-based system will be deployed to provide the necessary execution environment with traceability of service executions involved in cyber-human workflows, and cost-effectiveness analysis of all the software elements developed in this project will provide assessment and evaluation of long standing what-if scenarios pertaining human- and machine-intelligent tasks. Crowdsourcing activities will attract a wide range of users with tasks that require low expertise, and at the same time it will expose volunteers to applied science and engineering, potentially attracting interest of K-12 teachers and students.