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: https://dl.acm.org/doi/10.1145/3311790.3396628
Primary URL Description: AMC Digital Library
Conference Name: Practice and Experience in Advanced Research Computing: PEARC '20
Snowvision: The Promise of Algorithmic Methods in Southeastern Archaeological Research (Article)
Title: Snowvision: The Promise of Algorithmic Methods in Southeastern Archaeological Research
Author: Colin Wilder
Author: Sam T. McDorman
Author: Jun Zhou
Author: Adam King
Author: Yuhang Lu
Author: Karen Y. Smith
Author: Song Wang
Author: W. Matthew J. Simmons
Abstract: This article presents the contexts, methods, contributions, and preliminary findings of Snowvision, a digital archaeology project developed by faculty and students at the University of South Carolina and the South Carolina Department of Natural Resources. Snowvision uses computer vision to reconstruct southeastern Native American paddle designs from the Swift Creek period, ca. 100-850 CE. In this essay, we first present the context of the Swift Creek culture of the southeastern United States, along with broader related issues in prehistoric archaeology. Then, the relevant methods from archaeology and computer vision are introduced and discussed. We also introduce World Engraved, our public-facing digital archive of sherd designs and distributions, and explain its role in our overall project. We then explore, in some level of technical detail, the ways in which our work refines existing pattern-matching algorithms used in the field of computer vision. Finally,
we discuss our accomplishments and findings to date and the possibilities for future research that Snowvision provides.
Year: 2020
Primary URL: https://paas.org.pl/wp-content/uploads/2020/12/PJAS_14_autumn_2020.pdf
Access Model: open access
Format: Journal
Periodical Title: Polish Journal for American Studies
Publisher: Polish Association for American Studies
Snowvision: Segmenting, Identifying, and Discovering Stamped Curve Patterns from Fragments of Pottery (Article)
Title: Snowvision: Segmenting, Identifying, and Discovering Stamped Curve Patterns from Fragments of Pottery
Author: Yuhang Lu
Author: Jun Zhou
Author: Sam T. McDorman
Author: Canyu Zhang
Author: Deja Scott
Author: Jake Bukuts
Author: Colin Wilder
Author: Karen Y. Smith
Author: Song Wang
Abstract: In southeastern North America, Indigenous potters and woodworkers carved complex, primarily abstract, designs into wooden pottery paddles, which were subsequently used to thin the walls of hand-built, clay vessels. Original paddle designs carry rich historical and cultural information, but pottery paddles from ancient times have not survived. Archaeologists have studied design fragments stamped on sherds to reconstruct complete or nearly complete designs, which is extremely laborious and time-consuming. In Snowvision, we aim to develop computer vision methods to assist archaeologists to accomplish this goal more efficiently and effectively. For this purpose, we identify and study three computer vision tasks: (1) extracting curve structures stamped on pottery sherds; (2) matching sherds to known designs; (3) clustering sherds with unknown designs. Due to the noisy, highly fragmented, composite-curve patterns, each task poses unique challenges to existing methods. To solve them, we propose (1) a weakly-supervised CNN-based curve structure segmentation method that takes only curve skeleton labels to predict full curve masks; (2) a patch-based curve pattern matching method to address the problem of partial matching in terms of noisy binary images; (3) a curve pattern clustering method consisting of pairwise curve matching, graph partitioning and sherd stitching. We evaluate the proposed methods on a set of collected sherds and extensive experimental results show the effectiveness of the proposed algorithms.
Year: 2022
Primary URL: https://trebuchet.public.springernature.app/get_content/dfc427fe-5f36-4d45-b2e9-e673e8f36eb5
Secondary URL: https://link.springer.com/article/10.1007/s11263-022-01669-7
Access Model: Public for 30 days; Subscription only after 30 days
Format: Journal
Periodical Title: International Journal of Computer Vision
Publisher: Springer