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Products for grant HAA-266472-19

SnowVision: A Machine Learning-Based Image Processing Tool for the Study of Archaeological Collections
Karen Smith, South Carolina Department of Natural Resources

Grant details:

Building Science Gateways for Humanities (Conference Paper/Presentation)
Title: Building Science Gateways for Humanities
Author: Zhou, Jun
Author: Karen Smith
Author: Greg Wilsbacher
Author: Paul Sagona
Author: David Reddy
Author: Ben Torkian
Abstract: Building science gateways for humanities content poses new challenges to the science gateway community. Compared with science gateways devoted to scientific content, humanities-related projects usually require 1) processing data in various formats, such as text, image, video, etc., 2) constant public access from a broad audience, and therefore 3) reliable security, ideally with low maintenance. Many traditional science gateways are monolithic in design, which makes them easier to write, but they can be computationally inefficient when integrated with numerous scientific packages for data capture and pipeline processing. Since these packages tend to be single-threaded or nonmodular, they can create traffic bottlenecks when processing large numbers of requests. Moreover, these science gateways are usually challenging to resume development on due to long gaps between funding periods and the aging of the integrated scientific packages. In this paper, we study the problem of building science gateways for humanities projects by developing a service-based architecture, and present two such science gateways: the Moving Image Research Collections (MIRC) – a science gateway focusing on image analysis for digital surrogates of historical motion picture film, and SnowVision - a science gateway for studying pottery fragments in southeastern North America. For each science gateway, we present an overview of the background of the projects, and some unique challenges in their design and implementation. These two science gateways are deployed on XSEDE’s Jetstream academic clouding computing resource and are accessed through web interfaces. Apache Airavata middleware is used to manage the interactions between the web interface and the deep-learning-based (DL) backend service running on the Bridges graphics processing unit (GPU) cluster.
Date: 07/28/2020
Primary URL:
Primary URL Description: AMC Digital Library
Conference Name: Practice and Experience in Advanced Research Computing: PEARC '20