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Online e-journal databases enable scholars to search the literature in a research domain or to cross-search an interdisciplinary field. The key literature can thereby be efficiently mapped. This study builds a web-based citation analysis system consisting of four modules: (1) literature search; (2) statistics; (3) articles analysis; and (4) co-citation analysis. The system focuses on the PubMed Central dataset and facilitates specific keyword searches in each research domain for authors, journals, and core issues. In addition, we use data mining techniques for co-citation analysis. The results could help researchers develop an in-depth understanding of the research domain. An automated system for co-citation analysis promises to facilitate understanding of the changing trends that affect the journal structure of research domains. The proposed system has the potential to become a value-added database of the healthcare domain, which will benefit researchers. [ABSTRACT FROM AUTHOR]

This paper details an approach for accurately machine sorting Library of Congress (LC) call numbers which improves considerably upon other methods reviewed. The authors have employed this sorting method in creating an open-source software tool for library stacks maintenance, possibly the first such application capable of sorting the full range of LC call numbers. The method has potential application to any software environment that stores and retrieves LC call number information. [ABSTRACT FROM AUTHOR]

In this paper we investigate the possibility of improving the efficiency of data compression, and thus reducing storage requirements, for seven widely used text document formats. We propose an open-source text compression software library, featuring an advanced word-substitution scheme with static and semidynamic word dictionaries. The empirical results show an average storage space reduction as high as 78 percent compared to uncompressed documents, and as high as 30 percent compared to documents compressed with the free compression software gzip. [ABSTRACT FROM AUTHOR]

The field of explainable artificial intelligence (XAI) advances techniques, processes, and strategies that provide explanations for the predictions, recommendations, and decisions of opaque and complex machine learning systems. Increasingly academic libraries are providing library users with systems, services, and collections created and delivered by machine learning. Academic libraries should adopt XAI as a tool set to verify and validate these resources, and advocate for public policy regarding XAI that serves libraries, the academy, and the public interest. [ABSTRACT FROM AUTHOR]

Plots and figures play an important role in the process of understanding a scientific publication, providing overviews of large amounts of data or ideas that are difficult to intuitively present using only the text. State-of-the-art digital libraries, which serve as gateways to knowledge encoded in scholarly writings, do not yet take full advantage of the graphical content of documents. Enabling machines to automatically unlock the meaning of scientific illustrations would allow immense improvements in the way scientists work and the way knowledge is processed. In this paper, we present a novel solution for the initial problem of processing graphical content, obtaining figures from scholarly publications stored in PDF. Our method relies on vector properties of documents and, as such, does not introduce additional errors, unlike methods based on raster image processing. Emphasis has been placed on correctly processing documents in high-energy physics. The described approach distinguishes different classes of objects appearing in PDF documents and uses spatial clustering techniques to group objects into larger logical entities. Many heuristics allow the rejection of incorrect figure candidates and the extraction of different types of metadata. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is powerful, complex, ubiquitous, often opaque, sometimes invisible, and increasingly consequential in our everyday lives. Navigating the effects of AI as well as utilizing it in a responsible way requires a level of awareness, understanding, and skill that is not provided by current digital literacy or information literacy regimes. Algorithmic literacy addresses these gaps. In arguing for a role for libraries in algorithmic literacy, the authors provide a working definition, a pressing need, a pedagogical strategy, and two specific contributions that are unique to libraries. [ABSTRACT FROM AUTHOR]

The article aims to introduce a gold-standard Pashto dataset and a segmentation app. The Pashto dataset consists of 300 line images and corresponding Pashto text from three selected books. A line image is simply an image consisting of one text line from a scanned page. To our knowledge, this is one of the first open access datasets which directly maps line images to their corresponding text in the Pashto language. We also introduce the development of a segmentation app using textbox expanding algorithms, a different approach to OCR segmentation. The authors discuss the steps to build a Pashto dataset and develop our unique approach to segmentation. The article starts with the nature of the Pashto alphabet and its unique diacritics which require special considerations for segmentation. Needs for datasets and a few available Pashto datasets are reviewed. Criteria of selection of data sources are discussed and three books were selected by our language specialist from the Afghan Digital Repository. The authors review previous segmentation methods and introduce a new approach to segmentation for Pashto content. The segmentation app and results are discussed to show readers how to adjust variables for different books. Our unique segmentation approach uses an expanding textbox method which performs very well given the nature of the Pashto scripts. The app can also be used for Persian and other languages using the Arabic writing system. The dataset can be used for OCR training, OCR testing, and machine learning applications related to content in Pashto. [ABSTRACT FROM AUTHOR]

This research compares automatic subject metadata generation when the pre-1800s Long-S character is corrected to a standard < s >. The test environment includes entries from the third edition of the Encyclopedia Britannica, and the HIVE automatic subject indexing tool. A comparative study of metadata generated before and after correction of the Long-S demonstrated an average of 26.51 percent potentially relevant terms per entry omitted from results if the Long-S is not corrected. Results confirm that correcting the Long-S increases the availability of terms that can be used for creating quality metadata records. A relationship is also demonstrated between shorter entries and an increase in omitted terms when the Long-S is not corrected. [ABSTRACT FROM AUTHOR]

This article presents the results of a pilot project that tested the application of algorithmic topic modeling to chat reference conversations. The outcomes for this project included determining if this method could be used to identify the most common chat topics in a semester and whether these topics could inform library services beyond chat reference training. After reviewing the literature, four topic modeling algorithms were successfully implemented using Python code: (1) LDA, (2) phrase-LDA, (3) DMM, and (4) NMF. Analysis of the top ten topics from each algorithm indicated that LDA, phrase- LDA, and NMF show the most promise for future analysis on larger sets of data (from three or more semesters) and for examining different facets of the data (fall versus spring semester, different time of day, just the patron side of the conversation). [ABSTRACT FROM AUTHOR]

This article will describe how permissionless metadata blockchains could be created to overcome two significant limitations in current cataloging practices: centralization and a lack of traceability. The process would start by creating public and private keys, which could be managed using digital wallet software. After creating a genesis block, nodes would submit either a new record or modifications to a single record for validation. Validation would rely on a Federated Byzantine Agreement consensus algorithm because it offers the most flexibility for institutions to select authoritative peers. Only the top tier nodes would be required to store a copy of the entire blockchain thereby allowing other institutions to decide whether they prefer to use the abridged version or the full version. [ABSTRACT FROM AUTHOR]