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Full-Text Articles in Library and Information Science

The Flow Less Traveled: Documenting Independent Original Research On Fluid Flow Interactions In The Laurentian Great Lakes And Immediate Surroundings, Thomas F. Hansen May 2021

The Flow Less Traveled: Documenting Independent Original Research On Fluid Flow Interactions In The Laurentian Great Lakes And Immediate Surroundings, Thomas F. Hansen

Theses and Dissertations

This work is a compilation of several research projects undertaken by the author. Each research effort identifies a problem that has been addressed traditionally using methods that are significantly costly, to such an extent that, in general, funding, convenience, and practicality are primary limiting factors to their effective implementation. In each case, the author has been able to either build upon existing, less expensive alternatives, or even invent novel approaches. The fundamental recurring research question is, can creative, even novel, computational approaches make more efficient use of resources to interpret or present data in such a way as to make …


Large Scale Subject Category Classification Of Scholarly Papers With Deep Attentive Neural Networks, Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles Jan 2021

Large Scale Subject Category Classification Of Scholarly Papers With Deep Attentive Neural Networks, Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few …


Digital Libraries, Intelligent Data Analytics, And Augmented Description: A Demonstration Project, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack Jan 2020

Digital Libraries, Intelligent Data Analytics, And Augmented Description: A Demonstration Project, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

UNL Libraries: Faculty Publications

From July 16-to November 8, 2019, the Aida digital libraries research team at the University of Nebraska-Lincoln collaborated with the Library of Congress on “Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project.“ This demonstration project sought to (1) develop and investigate the viability and feasibility of textual and image-based data analytics approaches to support and facilitate discovery; (2) understand technical tools and requirements for the Library of Congress to improve access and discovery of its digital collections; and (3) enable the Library of Congress to plan for future possibilities. In pursuit of these goals, we focused our …


Final Presentation To The Library Of Congress On Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack Jan 2020

Final Presentation To The Library Of Congress On Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

University of Nebraska-Lincoln Libraries: Conference Presentations and Speeches

This presentation to Library of Congress staff, delivered onsite on January 10, 2020, presents a tour through the demonstration project pursued by the Aida digital libraries research team with the Library of Congress in 2019-2020. In addition to providing an overview and analysis of the specific machine learning projects scoped and explored, this presentation includes a number of high-level take-aways and recommendations designed to influence and inform the Library of Congress's machine learning efforts going forward.


Virtual Wrap-Up Presentation: Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack Nov 2019

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 Aug 2019

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.


Application Of The Image Analysis For Archival Discovery Team’S First- Generation Methods And Software To The Burney Collection Of British Newspapers, Elizabeth Lorang, Leen-Kiat Soh, Chulwoo Pack, Yi Liu, Delaram Rahimighazikalayeh, John O'Brien May 2019

Application Of The Image Analysis For Archival Discovery Team’S First- Generation Methods And Software To The Burney Collection Of British Newspapers, Elizabeth Lorang, Leen-Kiat Soh, Chulwoo Pack, Yi Liu, Delaram Rahimighazikalayeh, John O'Brien

CDRH Grant Reports

The current study, “Application of the Image Analysis for Archival Discovery Team’s First- Generation Methods and Software to the Burney Collection of British Newspapers,” is the first test of our approaches—methods and software—to a different newspaper corpus, specifically the 17th and 18 Century Burney Newspapers Collection. This study stands as the first complete attempt at applying Aida’s software and methods to non-Chronicling America newspapers, as a step toward understanding the potential of our approaches across digitized historic newspapers. In taking this step, our goals were (1) to test how well the software and a classifier model developed on Chronicling America …


Improved Evolutionary Support Vector Machine Classifier For Coronary Artery Heart Disease Prediction Among Diabetic Patients, Narasimhan B, Malathi A Dr Apr 2019

Improved Evolutionary Support Vector Machine Classifier For Coronary Artery Heart Disease Prediction Among Diabetic Patients, Narasimhan B, Malathi A Dr

Library Philosophy and Practice (e-journal)

Soft computing paves way many applications including medical informatics. Decision support system has gained a major attention that will aid medical practitioners to diagnose diseases. Diabetes mellitus is hereditary disease that might result in major heart disease. This research work aims to propose a soft computing mechanism named Improved Evolutionary Support Vector Machine classifier for CAHD risk prediction among diabetes patients. The attribute selection mechanism is attempted to build with the classifier in order to reduce the misclassification error rate of the conventional support vector machine classifier. Radial basis kernel function is employed in IESVM. IESVM classifier is evaluated through …


Work-In-Progress Reports Submitted To The Library Of Congress As Part Of Digital Libraries, Intelligent Data Analytics, And Augmented Description, Chulwoo Pack, Yi Liu, Leen-Kiat Soh, Elizabeth Lorang Jan 2019

Work-In-Progress Reports Submitted To The Library Of Congress As Part Of Digital Libraries, Intelligent Data Analytics, And Augmented Description, Chulwoo Pack, Yi Liu, Leen-Kiat Soh, Elizabeth Lorang

CSE Technical Reports

This document includes work-in-progress reports submitted to the Library of Congress as part of the Aida digital libraries research team's work on Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project. These work-in-progress reports provide a snapshot glimpse, as well as underlying rationale and decision-making, at various points in the development of the project and its machine learning explorations. Reports cover explorations on historic newspapers, minimally-processed manuscript collections, materials digitized from physical originals and those digitized from microform surrogates, and investigate challenges related to image segmentation and document zoning, classification, document image quality analysis, metadata generation, and more.


Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles Jan 2019

Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles

Computer Science Faculty Publications

We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …