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

Principal Investigator: José Fortes

Sponsor: NSF

Start Date: August 1, 2015

End Date: July 31, 2020

Amount: $488,048

Abstract

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.

More Information: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1535086&HistoricalAwards=false