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Virtual Wrap-Up Presentation: Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack
Virtual Wrap-Up Presentation: Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack
CSE Conference and Workshop Papers
Includes framing, overview, and discussion of the explorations pursued as part of the Digital Libraries, Intelligent Data Analytics, and Augmented Description demonstration project, pursued by members of the Aida digital libraries research team at the University of Nebraska-Lincoln through a research services contract with the Library of Congress. This presentation covered: Aida research team and background for the demonstration project; broad outlines of “Digital Libraries, Intelligent Data Analytics, and Augmented Description”; what changed for us as a research team over the collaboration and why; deliverables of our work; thoughts toward “What next”; and deep-dives into the explorations. The machine learning …
Document Images And Machine Learning: A Collaboratory Between The Library Of Congress And The Image Analysis For Archival Discovery (Aida) Lab At The University Of Nebraska, Lincoln, Ne, Yi Liu, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang
Document Images And Machine Learning: A Collaboratory Between The Library Of Congress And The Image Analysis For Archival Discovery (Aida) Lab At The University Of Nebraska, Lincoln, Ne, Yi Liu, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang
CSE Conference and Workshop Papers
This presentation summarized and presented preliminary results from the first weeks of work conducted by the Aida research team in response to Library of Congress funding notice ID 030ADV19Q0274, “The Library of Congress – Pre-processing Pilot.” It includes overviews of projects on historic document segmentation, document classification, document quality assessment, figure and graph extraction from historic documents, text-line extraction from figures, subject and objective quality assesments, and digitization type differentiation.
A First Practical Algorithm For High Levels Of Relational Consistency, Shant Karakashian, Robert J. Woodward, Christopher Reesons, Berthe Y. Choueiry, Christian Bessiere
A First Practical Algorithm For High Levels Of Relational Consistency, Shant Karakashian, Robert J. Woodward, Christopher Reesons, Berthe Y. Choueiry, Christian Bessiere
CSE Conference and Workshop Papers
Consistency properties and algorithms for achieving them are at the heart of the success of Constraint Programming. In this paper, we study the relational consistency property R(∗,m)C, which is equivalent to m-wise consistency proposed in relational databases. We also define wR(∗,m)C, a weaker variant of this property. We propose an algorithm for enforcing these properties on a Constraint Satisfaction Problem by tightening the existing relations and without introducing new ones. We empirically show that wR(∗,m)C solves in a backtrack-free manner all the instances of some CSP benchmark classes, thus hinting at the tractability of those classes.