mv: ‘./input-file.zip’ and ‘./input-file.zip’ are the same file Creating study carrel named johnson-machine-2021 Initializing database Unzipping Archive: input-file.zip creating: ./tmp/input/johnson-machine-2021/ inflating: ./tmp/input/johnson-machine-2021/11-prudhomme-taking.pdf inflating: ./tmp/input/johnson-machine-2021/07-kim-ai.pdf inflating: ./tmp/input/johnson-machine-2021/03-plumb-humanities.pdf inflating: ./tmp/input/johnson-machine-2021/.DS_Store inflating: ./tmp/input/johnson-machine-2021/12-cohen-machine.pdf inflating: ./tmp/input/johnson-machine-2021/08-altman-building.pdf inflating: ./tmp/input/johnson-machine-2021/05-wiegand-cultures.pdf inflating: ./tmp/input/johnson-machine-2021/09-lesk-fragility.pdf inflating: ./tmp/input/johnson-machine-2021/00-johnson-preface.pdf inflating: ./tmp/input/johnson-machine-2021/04-janco-machine.pdf inflating: ./tmp/input/johnson-machine-2021/13-lucic-towards.pdf inflating: ./tmp/input/johnson-machine-2021/14-hansen-can.pdf inflating: ./tmp/input/johnson-machine-2021/metadata.csv inflating: ./tmp/input/johnson-machine-2021/01-hintze-artificial.pdf inflating: ./tmp/input/johnson-machine-2021/06-jiang-cross.pdf inflating: ./tmp/input/johnson-machine-2021/10-morgan-bringing.pdf inflating: ./tmp/input/johnson-machine-2021/02-harper-generative.pdf === updating bibliographic database Building study carrel named johnson-machine-2021 FILE: cache/00-johnson-preface.pdf OUTPUT: txt/00-johnson-preface.txt FILE: cache/11-prudhomme-taking.pdf OUTPUT: txt/11-prudhomme-taking.txt FILE: cache/12-cohen-machine.pdf OUTPUT: txt/12-cohen-machine.txt FILE: cache/08-altman-building.pdf OUTPUT: txt/08-altman-building.txt FILE: cache/14-hansen-can.pdf OUTPUT: txt/14-hansen-can.txt FILE: cache/09-lesk-fragility.pdf OUTPUT: txt/09-lesk-fragility.txt FILE: cache/13-lucic-towards.pdf OUTPUT: txt/13-lucic-towards.txt FILE: cache/07-kim-ai.pdf OUTPUT: txt/07-kim-ai.txt FILE: cache/03-plumb-humanities.pdf OUTPUT: txt/03-plumb-humanities.txt FILE: cache/05-wiegand-cultures.pdf OUTPUT: txt/05-wiegand-cultures.txt FILE: cache/10-morgan-bringing.pdf OUTPUT: txt/10-morgan-bringing.txt FILE: cache/01-hintze-artificial.pdf OUTPUT: txt/01-hintze-artificial.txt FILE: cache/02-harper-generative.pdf OUTPUT: txt/02-harper-generative.txt FILE: cache/06-jiang-cross.pdf OUTPUT: txt/06-jiang-cross.txt FILE: cache/04-janco-machine.pdf OUTPUT: txt/04-janco-machine.txt === file2bib.sh === id: 00-johnson-preface author: Johnson title: Preface date: 2021 pages: 3 extension: .pdf txt: ./txt/00-johnson-preface.txt cache: ./cache/00-johnson-preface.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:38:26Z Last-Modified 2021-02-23T19:38:38Z Last-Save-Date 2021-02-23T19:38:38Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 94 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:38:26Z date 2021-02-23T19:38:38Z dc:format application/pdf; version=1.3 dcterms:created 2021-02-23T19:38:26Z dcterms:modified 2021-02-23T19:38:38Z meta:creation-date 2021-02-23T19:38:26Z meta:save-date 2021-02-23T19:38:38Z modified 2021-02-23T19:38:38Z pdf:PDFVersion 1.3 pdf:charsPerPage ['2727', '3115', '239'] pdf:docinfo:created 2021-02-23T19:38:26Z pdf:docinfo:creator_tool LaTeX with hyperref pdf:docinfo:modified 2021-02-23T19:38:38Z pdf:docinfo:producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 pdf:encrypted false pdf:hasMarkedContent false pdf:hasXFA false pdf:hasXMP false pdf:unmappedUnicodeCharsPerPage ['0', '0', '0'] producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 resourceName b'00-johnson-preface.pdf' xmp:CreatorTool LaTeX with hyperref xmpTPg:NPages 3 === file2bib.sh === id: 04-janco-machine author: Janco title: Machine Learning in Digital Scholarship date: 2021 pages: 6 extension: .pdf txt: ./txt/04-janco-machine.txt cache: ./cache/04-janco-machine.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:42:59Z Last-Modified 2021-02-23T19:43:05Z Last-Save-Date 2021-02-23T19:43:05Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 289 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:42:59Z date 2021-02-23T19:43:05Z dc:format application/pdf; version=1.3 dcterms:created 2021-02-23T19:42:59Z dcterms:modified 2021-02-23T19:43:05Z meta:creation-date 2021-02-23T19:42:59Z meta:save-date 2021-02-23T19:43:05Z modified 2021-02-23T19:43:05Z pdf:PDFVersion 1.3 pdf:charsPerPage ['1761', '3241', '3580', '3731', '3266', '1253'] pdf:docinfo:created 2021-02-23T19:42:59Z pdf:docinfo:creator_tool LaTeX with hyperref pdf:docinfo:modified 2021-02-23T19:43:05Z pdf:docinfo:producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 pdf:encrypted false pdf:hasMarkedContent false pdf:hasXFA false pdf:hasXMP false pdf:unmappedUnicodeCharsPerPage ['0', '0', '0', '0', '0', '0'] producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 resourceName b'04-janco-machine.pdf' xmp:CreatorTool LaTeX with hyperref xmpTPg:NPages 6 === file2bib.sh === id: 11-prudhomme-taking author: Prudhomme title: Taking a Leap Forward: Machine Learning for New Limits date: 2021 pages: 9 extension: .pdf txt: ./txt/11-prudhomme-taking.txt cache: ./cache/11-prudhomme-taking.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:55:02Z Last-Modified 2021-02-23T19:55:06Z Last-Save-Date 2021-02-23T19:55:06Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 178 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:55:02Z date 2021-02-23T19:55:06Z dc:format application/pdf; version=1.3 dcterms:created 2021-02-23T19:55:02Z dcterms:modified 2021-02-23T19:55:06Z meta:creation-date 2021-02-23T19:55:02Z meta:save-date 2021-02-23T19:55:06Z modified 2021-02-23T19:55:06Z pdf:PDFVersion 1.3 pdf:charsPerPage ['1663', '3168', '2905', '3394', '1436', '2975', '3027', '2659', '547'] pdf:docinfo:created 2021-02-23T19:55:02Z pdf:docinfo:creator_tool LaTeX with hyperref pdf:docinfo:modified 2021-02-23T19:55:06Z pdf:docinfo:producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 pdf:encrypted false pdf:hasMarkedContent false pdf:hasXFA false pdf:hasXMP false pdf:unmappedUnicodeCharsPerPage ['0', '0', '0', '0', '0', '0', '0', '0', '0'] producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 resourceName b'11-prudhomme-taking.pdf' xmp:CreatorTool LaTeX with hyperref xmpTPg:NPages 9 === file2bib.sh === id: 13-lucic-towards author: Lucic title: Towards a Chicago place name dataset: From back-of-the-book index to a labeled dataset date: 2021 pages: 7 extension: .pdf txt: ./txt/13-lucic-towards.txt cache: ./cache/13-lucic-towards.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:56:30Z Last-Modified 2021-02-23T19:56:35Z Last-Save-Date 2021-02-23T19:56:35Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 273 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:56:30Z date 2021-02-23T19:56:35Z dc:format application/pdf; version=1.3 dcterms:created 2021-02-23T19:56:30Z dcterms:modified 2021-02-23T19:56:35Z meta:creation-date 2021-02-23T19:56:30Z meta:save-date 2021-02-23T19:56:35Z modified 2021-02-23T19:56:35Z pdf:PDFVersion 1.3 pdf:charsPerPage ['1012', '2777', '3424', '1458', '1325', '3261', '2950'] pdf:docinfo:created 2021-02-23T19:56:30Z pdf:docinfo:creator_tool LaTeX with hyperref pdf:docinfo:modified 2021-02-23T19:56:35Z pdf:docinfo:producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 pdf:encrypted false pdf:hasMarkedContent false pdf:hasXFA false pdf:hasXMP false pdf:unmappedUnicodeCharsPerPage ['0', '0', '0', '0', '0', '0', '0'] producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 resourceName b'13-lucic-towards.pdf' xmp:CreatorTool LaTeX with hyperref xmpTPg:NPages 7 === file2bib.sh === id: 14-hansen-can author: Hansen title: Can a Hammer Categorize Highly Technical Articles? date: 2021 pages: 8 extension: .pdf txt: ./txt/14-hansen-can.txt cache: ./cache/14-hansen-can.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:57:06Z Last-Modified 2021-02-23T19:57:11Z Last-Save-Date 2021-02-23T19:57:11Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 222 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:57:06Z date 2021-02-23T19:57:11Z dc:format application/pdf; version=1.3 dcterms:created 2021-02-23T19:57:06Z dcterms:modified 2021-02-23T19:57:11Z meta:creation-date 2021-02-23T19:57:06Z meta:save-date 2021-02-23T19:57:11Z modified 2021-02-23T19:57:11Z pdf:PDFVersion 1.3 pdf:charsPerPage ['1679', '3465', '3316', '3818', '3393', '3462', '2610', '427'] pdf:docinfo:created 2021-02-23T19:57:06Z pdf:docinfo:creator_tool LaTeX with hyperref pdf:docinfo:modified 2021-02-23T19:57:11Z pdf:docinfo:producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 pdf:encrypted false pdf:hasMarkedContent false pdf:hasXFA false pdf:hasXMP false pdf:unmappedUnicodeCharsPerPage ['0', '0', '0', '0', '0', '0', '0', '0'] producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 resourceName b'14-hansen-can.pdf' xmp:CreatorTool LaTeX with hyperref xmpTPg:NPages 8 === file2bib.sh === id: 06-jiang-cross author: Jiang title: Cross-Disciplinary ML Research is like Happy Marriages: Five Strengths and Two Examples date: 2021 pages: 10 extension: .pdf txt: ./txt/06-jiang-cross.txt cache: ./cache/06-jiang-cross.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:51:37Z Last-Modified 2021-02-23T19:51:44Z Last-Save-Date 2021-02-23T19:51:44Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 353 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:51:37Z date 2021-02-23T19:51:44Z dc:format application/pdf; version=1.3 dcterms:created 2021-02-23T19:51:37Z dcterms:modified 2021-02-23T19:51:44Z meta:creation-date 2021-02-23T19:51:37Z meta:save-date 2021-02-23T19:51:44Z modified 2021-02-23T19:51:44Z pdf:PDFVersion 1.3 pdf:charsPerPage ['1568', '2994', '3143', '236', '1927', '2986', '1941', '2732', '2893', '373'] pdf:docinfo:created 2021-02-23T19:51:37Z pdf:docinfo:creator_tool LaTeX with hyperref pdf:docinfo:modified 2021-02-23T19:51:44Z pdf:docinfo:producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 pdf:encrypted false pdf:hasMarkedContent false pdf:hasXFA false pdf:hasXMP false pdf:unmappedUnicodeCharsPerPage ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0'] producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 resourceName b'06-jiang-cross.pdf' xmp:CreatorTool LaTeX with hyperref xmpTPg:NPages 10 === file2bib.sh === id: 01-hintze-artificial author: Hintze title: Artificial Intelligence in the Humanities: Wolf in Disguise, or Digital Revolution? date: 2021 pages: 10 extension: .pdf txt: ./txt/01-hintze-artificial.txt cache: ./cache/01-hintze-artificial.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:39:22Z Last-Modified 2021-02-23T19:39:48Z Last-Save-Date 2021-02-23T19:39:48Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 287 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:39:22Z date 2021-02-23T19:39:48Z dc:format application/pdf; version=1.3 dcterms:created 2021-02-23T19:39:22Z dcterms:modified 2021-02-23T19:39:48Z meta:creation-date 2021-02-23T19:39:22Z meta:save-date 2021-02-23T19:39:48Z modified 2021-02-23T19:39:48Z pdf:PDFVersion 1.3 pdf:charsPerPage ['1331', '3418', '3364', '3591', '3404', '3645', '3332', '2918', '2663', '309'] pdf:docinfo:created 2021-02-23T19:39:22Z pdf:docinfo:creator_tool LaTeX with hyperref pdf:docinfo:modified 2021-02-23T19:39:48Z pdf:docinfo:producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 pdf:encrypted false pdf:hasMarkedContent false pdf:hasXFA false pdf:hasXMP false pdf:unmappedUnicodeCharsPerPage ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0'] producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 resourceName b'01-hintze-artificial.pdf' xmp:CreatorTool LaTeX with hyperref xmpTPg:NPages 10 === file2bib.sh === id: 09-lesk-fragility author: Lesk title: Fragility and Intelligibility of Deep Learning for Libraries date: 2021 pages: 11 extension: .pdf txt: ./txt/09-lesk-fragility.txt cache: ./cache/09-lesk-fragility.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:53:44Z Last-Modified 2021-02-23T19:53:51Z Last-Save-Date 2021-02-23T19:53:51Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 521 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:53:44Z date 2021-02-23T19:53:51Z dc:format application/pdf; version=1.3 dcterms:created 2021-02-23T19:53:44Z dcterms:modified 2021-02-23T19:53:51Z meta:creation-date 2021-02-23T19:53:44Z meta:save-date 2021-02-23T19:53:51Z modified 2021-02-23T19:53:51Z pdf:PDFVersion 1.3 pdf:charsPerPage ['1254', '3210', '2506', '1393', '3171', '1852', '2607', '3047', '2661', '2974', '362'] pdf:docinfo:created 2021-02-23T19:53:44Z pdf:docinfo:creator_tool LaTeX with hyperref pdf:docinfo:modified 2021-02-23T19:53:51Z pdf:docinfo:producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 pdf:encrypted false pdf:hasMarkedContent false pdf:hasXFA false pdf:hasXMP false pdf:unmappedUnicodeCharsPerPage ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0'] producer macOS Version 11.2.1 (Build 20D74) Quartz PDFContext, AppendMode 1.1 resourceName b'09-lesk-fragility.pdf' xmp:CreatorTool LaTeX with hyperref xmpTPg:NPages 11 === file2bib.sh === id: 10-morgan-bringing author: Morgan title: Bringing Algorithms and Machine Learning Into Library Collections and Services date: 2021 pages: 13 extension: .pdf txt: ./txt/10-morgan-bringing.txt cache: ./cache/10-morgan-bringing.pdf Content-Type application/pdf Creation-Date 2021-02-23T19:54:29Z Last-Modified 2021-02-23T19:54:36Z Last-Save-Date 2021-02-23T19:54:36Z X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.pdf.PDFParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 340 access_permission:assemble_document true access_permission:can_modify true access_permission:can_print true access_permission:can_print_degraded true access_permission:extract_content true access_permission:extract_for_accessibility true access_permission:fill_in_form true access_permission:modify_annotations true created 2021-02-23T19:54:29Z date 2021-02-23T19:54:36Z dc:format application/pdf; 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Reducing johnson-machine-2021 === reduce.pl bib === id = 07-kim-ai author = Kim title = AI and Its Moral Concerns date = 2021 pages = 13 extension = .pdf mime = application/pdf words = 7293 sentences = 784 flesch = 55 summary = does not provide an easy answer to the question of how one should program moral decisionmaking into intelligent machines. Described below are some of the significant ethical challenges that autonomous AI systems such as military robots present. 11Note that this moral decision-making process can be modeled with a rule-based symbolic AI approach, a machine 13(Kahn 2012) also argues that the resulting increase in the number of wars by the use of military robots will be morally 15This black-box nature of AI systems powered by machine learning has raised great concern among many AI researchers in recent years. agency in the AI -powered automated information environment presents an ethical challenge In this chapter, I discussed four significant ethical challenges that automating decisions and actions with AI presents: (a) moral desensitization; (b) unintended outcomes; (c) surrender of are at an early stage in developing AI applications and applying machine learning and deep learning techniques to improve library services, systems, and operations. cache = ./cache/07-kim-ai.pdf txt = ./txt/07-kim-ai.txt === reduce.pl bib === id = 11-prudhomme-taking author = Prudhomme title = Taking a Leap Forward: Machine Learning for New Limits date = 2021 pages = 9 extension = .pdf mime = application/pdf words = 3910 sentences = 387 flesch = 51 summary = Combining automatic processes to assist in supporting inventory management with a focus on descriptive metadata, a machine learning solution could help alleviate time-consuming and relatively expensive metadata tagging tasks, Deep learning neural networks are more effective in feature detection as they are able to solve complex problems such as image classification with greater accuracy when trained with large datasets. For images, how can archives build a data-labeling pipeline into their digital curation workflow that enables machine learning of collections? machine learning is only good so long as value is added, archives and libraries will need to think As deep learning applications will only be as effective as the data, archives and libraries should expand their Along with greater computing capabilities, artificial intelligence could be an opportunity for libraries and archives to boost the discovery of their digital collections by pushing text and image cache = ./cache/11-prudhomme-taking.pdf txt = ./txt/11-prudhomme-taking.txt === reduce.pl bib === id = 03-plumb-humanities author = Plumb title = Humanities and Social Science Reading through Machine Learning date = 2021 pages = 14 extension = .pdf mime = application/pdf words = 7195 sentences = 599 flesch = 45 summary = Respondents such as Mark Algee-Hewitt pointed out that literary scholars employ computational statistical models in order to reveal something about texts that human readers Machine learning, and word embedding algorithms in particular, may have a unique ability to shift this conversation into new territory, where scholars Acknowledging this helps contextualize machine learning algorithms for text analysis tasks in the humanities, but also highlights data curation challenges This naturally raises questions about how machine learning algorithms like word embeddings are implemented for text analysis, and how they Based on the potential for word embeddings to model semantic spaces for different corpora and compare the distribution of terms, the next step was to build a corpus of non-canonical Designing humanities research with novel word embedding models stands to widen the territory where machine learning engineers look for conceptual concepts Systematic data curation, combined with word embedding algorithms, represent a new interpretive system for literary scholars. cache = ./cache/03-plumb-humanities.pdf txt = ./txt/03-plumb-humanities.txt === reduce.pl bib === id = 12-cohen-machine author = Cohen title = Machine Learning + Data Creation in a Community Partnership for Archival Research date = 2021 pages = 13 extension = .pdf mime = application/pdf words = 7542 sentences = 474 flesch = 54 summary = archivally focused project that emerged from a partnership between the Pine Mountain Settlement School (PMSS)1 in Harlan County, Kentucky, and scholars and students at Berea College. a latent social network of historical families represented by the images held in one local archive, curricula for use in Kentucky public schools with PMSS archival materials. That decision led a team of Berea College undergraduate and faculty researchers to scrape the data from the PMSS archive site and supplement the images and transcriptions it contains with available textual metadata drawn from the site.9 Alongside the WordPress facial recognition software to identify the persons in historic photographs in the PMSS archives. We demonstrated to the local members at Pine Mountain how our use case and its constraints for digital archives fit with the current standards for the fair use of copyrighted materials cache = ./cache/12-cohen-machine.pdf txt = ./txt/12-cohen-machine.txt === reduce.pl bib === id = 05-wiegand-cultures author = Wiegand title = Cultures of Innovation: Machine Learning as a Library Service date = 2021 pages = 14 extension = .pdf mime = application/pdf words = 7014 sentences = 761 flesch = 49 summary = traditional role, librarians in the 20th century added a new function—discovery—teaching people to find and use the library's collected scholarship. learning in the library as the next step beyond collecting, with librarians instructing on information infrastructure with the goal of empowering library users to find, evaluate, and use scholarly go far beyond local library collections to a global perspective and normative practice of participation at scale in innovative emerging technologies such as Machine Learning. start by using Machine Learning tools to automate alerts of new content in a narrow area of interest and help researchers at all levels find and focus on problem-solving. A library that adapted Machine Learning as an innovation technology would improve its practices; add new services; choose, use, and license collections differently; utilize all spaces for learning; and role model innovative leadership. opening local collections to discovery and use in order to create new knowledge through digitization and semantic linking, with cross-disciplinary technologies to augment traditional research cache = ./cache/05-wiegand-cultures.pdf txt = ./txt/05-wiegand-cultures.txt === reduce.pl bib === id = 08-altman-building author = Altman title = Building a Machine Learning Pipeline date = 2021 pages = 11 extension = .pdf mime = application/pdf words = 6148 sentences = 361 flesch = 63 summary = As you begin ingesting and preparing data, you'll want to explore possible machine learning algorithms to perform on your dataset. Start by determining what general type of learning algorithm you need, and proceed from there to research and select one that While the final output of a machine learning workflow is some sort of intelligent model, The pipeline for a machine learning project generally comprises five stages: data acquisition, data preparation, model training and testing, evaluation and analysis, and application of results. good idea to save a copy in the rawest possible form and treat that copy as immutable, at least during the initial phase of testing different algorithms or configurations. algorithm uses the training data to "learn" a set of rules that it can subsequently apply to new, Immutable data storage can benefit the batch-processing ML pipeline, especially during the initial research and development phase. cache = ./cache/08-altman-building.pdf txt = ./txt/08-altman-building.txt === reduce.pl bib === id = 09-lesk-fragility author = Lesk title = Fragility and Intelligibility of Deep Learning for Libraries date = 2021 pages = 11 extension = .pdf mime = application/pdf words = 4796 sentences = 474 flesch = 63 summary = Machine learning systems have a set of data for training. of the real problem (if you train a machine translation program solely on engineering documents, there may be a lot of training data, including many noisy points, and the program may decide on Many popular magazines have discussed this problem; Forbes, for example, had an explanation of how the choice of datasets can produce a biased result without any deliberate attempt to used to suggest malicious creation of training data or examples of data designed to deceive machine learning systems. blood pressure, and lower blood pressure decreases the risk of heart attacks." Then I have to explain that the paper evaluates 32 possibilities (prior/current ownership ⇥ cats/dogs ⇥ 4 medical compare the performance of machine learning systems for medical diagnosis with actual doctors If a program is constantly learning from new data, there is no list of previously fixed failures to cache = ./cache/09-lesk-fragility.pdf txt = ./txt/09-lesk-fragility.txt === reduce.pl bib === id = 13-lucic-towards author = Lucic title = Towards a Chicago place name dataset: From back-of-the-book index to a labeled dataset date = 2021 pages = 7 extension = .pdf mime = application/pdf words = 3073 sentences = 285 flesch = 63 summary = Reading Chicago Reading1 is a grant-supported digital humanities project that takes as its object the "One Book One Chicago" (OBOC) program2 of the Chicago Public Library. A related question is the focus of this paper: by associating place names with sentiment scores in Chicago-themed OBOC The HathiTrust research portal permits the extraction of non-consumptive features of the works included in the digital library, even those that are still under copyright. The place names extracted from our three Chicago-setting OBOC books allowed us to focus Our interest in creating a dataset of Chicago place names extracted from literature led us to Kaser's book contains several indexes that can serve as sources of labeled data or instances in which Chicago locations are mentioned. the index as a source of already-labeled data for Chicago place names. associated sentiment scores for Chicago place names in the three OBOC selections centered on cache = ./cache/13-lucic-towards.pdf txt = ./txt/13-lucic-towards.txt === reduce.pl bib === id = 00-johnson-preface author = Johnson title = Preface date = 2021 pages = 3 extension = .pdf mime = application/pdf words = 1090 sentences = 72 flesch = 47 summary = The plan called for a survey and a series of workshops hosted across the country to explore, originally, "the national need for library based topic modeling tools in support of cross-disciplinary libraries ran concurrently with our grant — Cordell 2020 and Padilla 2019, which were commissioned by major players in the field, the Library of Congress and OCLC, respectively — and vi Machine Learning, Libraries, and Cross-Disciplinary Research We would like to thank the IMLS for providing essential funding support for the grant and the Thank you to the members of the Notre Dame IMLS grant team who, at of course, thanks to the 95 participants in our 2019 IMLS Grant Workshops (too many to enumerate here) and to the essay authors for sharing their expertise and perspectives in growing our collective knowledge of machine learning and its use in research, scholarship, and cultural heritage organizations. https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html cache = ./cache/00-johnson-preface.pdf txt = ./txt/00-johnson-preface.txt === reduce.pl bib === id = 04-janco-machine author = Janco title = Machine Learning in Digital Scholarship date = 2021 pages = 6 extension = .pdf mime = application/pdf words = 3101 sentences = 245 flesch = 54 summary = Tools like RunwayML, the Teachable Machine, and Google AutoML allow researchers to train project-specific Since 2014, dramatic innovations in machine learning have occurred, providing new capabilities in computer vision, natural language processing, and other areas of applied artificial intelligence. deliberately and identify how machine learning methods can benefit a scholar's research? for identifying basic tasks that can be completed by computers in ways that advance humanities research (2000). When working with texts or images, machine learning models are presently capable of making simple annotations and associations. Google's Teachable Machine offers an intuitive web application that humanities faculty and students can use to train classification models for images, sounds, and poses. Machine learning models offer a variety of ways to identify similarity and difference with research materials. goals of academic researchers in the humanities with the technical possibilities of machine learning. "Scholarly Primitives: What Methods Do Humanities Researchers Have cache = ./cache/04-janco-machine.pdf txt = ./txt/04-janco-machine.txt === reduce.pl bib === id = 14-hansen-can author = Hansen title = Can a Hammer Categorize Highly Technical Articles? date = 2021 pages = 8 extension = .pdf mime = application/pdf words = 4339 sentences = 394 flesch = 63 summary = I would use the Mathematical Subject Classification (MSC) values assigned to the publications in MathSciNet1 to create a temporal citation network which would allow me to visualize Machine-learning-based categorization needs data to classify, which in our case automated categorization of mathematics, we were dilettantes in the world of machine learning. what happens when smarter and more capable minds tackle the problem of classifying mathematics and other highly technical subjects using advanced machine learning techniques. 9Mathematical Subject Classification (MSC) values in MathSciNet and zbMath are a particularly interesting categorization set to work with as they are assigned and reviewed by a subject area expert editor and an active researcher in the 16See ?iiTb,ff�+�/2KB+XKB+'QbQ7iX+QKf. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 One really interesting part of the machine learning method used by Microsoft was that it did not rely only on information from the article being replace the work of humans categorizing mathematics articles indexed in a database, which for cache = ./cache/14-hansen-can.pdf txt = ./txt/14-hansen-can.txt === reduce.pl bib === id = 01-hintze-artificial author = Hintze title = Artificial Intelligence in the Humanities: Wolf in Disguise, or Digital Revolution? date = 2021 pages = 10 extension = .pdf mime = application/pdf words = 5069 sentences = 357 flesch = 56 summary = Artificial Intelligence, with its ability to machine learn coupled to an almost human-like understanding, sounds like the ideal tool to the humanities. But are these technologies imbued with intuition or understanding, and do they learn like humans? In the 80s and 90s, as home computers were becoming more common, Hollywood was sensationalizing the idea of smart or human-like Artificial Intelligent machines (AI) through movies Machine learning allows us to learn from these data sets in ways that exceed human capabilities, while an artificial brain will eventually allow us to objectively describe a subjective experience (through quantifying neural activations or positively and negatively associated memories). The following paragraphs will explore current Machine Learning and Artificial Intelligence learning, to the point where our whole identity as human could be generously defined as the Just because humans and machine learning are both black Currently, machines do not learn but must be trained, typically with human-labeled data. cache = ./cache/01-hintze-artificial.pdf txt = ./txt/01-hintze-artificial.txt === reduce.pl bib === id = 06-jiang-cross author = Jiang title = Cross-Disciplinary ML Research is like Happy Marriages: Five Strengths and Two Examples date = 2021 pages = 10 extension = .pdf mime = application/pdf words = 3623 sentences = 425 flesch = 55 summary = Cross-disciplinary research matters, because (1) it provides an understanding of complex problems that require a multifaceted approach to solve; (2) it combines disciplinary breadth with the ability to collaborate One of the most popular cross-disciplinary research topics/programs is Machine Learning + top strengths of conducting cross-disciplinary ML research and give two examples based on my marriages, just like collaborators expect to have successful project outcomes (Robinson and Blanton 1993; Pettigrew 2000; Xu et al. The history professor Liang Cai and I have collaborated on an international research project titled "Digital Empires: Structured Biographical and Social Network Analysis of Early Chinese We have enjoyed our collaboration and the power of cross-disciplinary research. Specifically, I presented the top strengths of producing successful cross-disciplinary ML research: (1) Partners are satisfied with communication. "The Challenges of Cross lj Disciplinary Research." Social Research Collaboration." Social Studies of Science 33, no. "Building Cross-Disciplinary Research Collaborations." cache = ./cache/06-jiang-cross.pdf txt = ./txt/06-jiang-cross.txt === reduce.pl bib === id = 10-morgan-bringing author = Morgan title = Bringing Algorithms and Machine Learning Into Library Collections and Services date = 2021 pages = 13 extension = .pdf mime = application/pdf words = 5793 sentences = 739 flesch = 74 summary = advent of computers, the idea of sharing cataloging data as MARC (machine readable cataloging) the full text of its collections to enhance bibliographic description and resulting public service. ability to save, organize, and retrieve data; on the whole, the library profession does not understand the concept of a "data structure." For example, tab-delimited files, CSV (comma-separated the use of data structures, computers store and retrieve information. Libraries use computers to store, organize, preserve, and disseminate the gray literature of our time, and we call these systems "institutional repositories." In all Using such a process, there are really only four different types of machine learning: classification, clustering, regression, and dimension reduction. Given a set of previously classified menus, one could create a model There are many possible ways to enhance library collections and services through the use of machine learning. of plain text files and an integer, Topic Modeling Tool will create a weighted list of latent themes cache = ./cache/10-morgan-bringing.pdf txt = ./txt/10-morgan-bringing.txt === reduce.pl bib === id = 02-harper-generative author = Harper title = Generative Machine Learning date = 2021 pages = 15 extension = .pdf mime = application/pdf words = 5935 sentences = 662 flesch = 59 summary = Reddit have each issued their own bans on the category of machine-generated or -altered content that is commonly termed "deep fakes" (Cohen 2020; Romm, Harwell, and Stanley-Becker TV because of their dystopian implications, deep fakes are just one application of generative machine learning. Figure 2.2: Images generated with a simple statistical model appear as noise as the model is insufficient to capture the structure of the real data (Markov chains trained using wine bottles and 1In many examples, I have used the Google QuickDraw Dataset to highlight features of generative machine learning. (?iiTb,ff;Bi?m#X+QKf;QQ;H2+'2�iBp2H�#f[mB+F/'�r@/�i�b2i) shows the generator learning how to produce better sketches over time. built a GAN that generates high-quality photo-realistic images of people (Karras, Laine, and Aila Beyond medicine and autonomous vehicles, generative data augmentation will progressively impact other imaging-heavy fields (Shorten and Khoshgoftaar 2019) like GANs in Action: Deep Learning with Generative Adversarial Networks. cache = ./cache/02-harper-generative.pdf txt = ./txt/02-harper-generative.txt Building ./etc/reader.txt 05-wiegand-cultures 01-hintze-artificial 07-kim-ai 05-wiegand-cultures 11-prudhomme-taking 07-kim-ai number of items: 15 sum of words: 75,921 average size in words: 5,061 average readability score: 56 nouns: data; machine; learning; research; �; library; information; process; libraries; model; example; text; time; images; work; word; systems; results; project; tools; use; knowledge; training; way; place; people; algorithms; system; researchers; set; models; words; collections; materials; methods; problem; algorithm; language; dataset; scholars; image; applications; ways; problems; humanities; analysis; questions; examples; network; history verbs: is; are; be; have; was; were; do; using; has; used; learning; make; based; use; given; see; been; help; learn; generated; find; does; create; trained; had; need; build; provide; identify; ff; work; generate; did; called; including; being; become; produce; known; include; ’s; working; making; found; know; edited; take; existing; understand; get adjectives: new; such; other; many; different; digital; more; computational; large; moral; human; deep; important; possible; -; historical; literary; able; social; local; good; specific; ethical; �; same; similar; cultural; available; own; neural; high; real; first; traditional; final; common; better; library; disciplinary; technical; artificial; multiple; full; simple; particular; intelligent; unique; original; likely; recent adverbs: not; also; more; only; then; well; as; even; very; out; now; so; however; most; often; n’t; up; just; together; still; already; instead; here; rather; first; highly; always; especially; perhaps; much; far; too; really; morally; fully; better; back; similarly; increasingly; down; yet; previously; on; generally; easily; thus; sometimes; simply; long; likely pronouns: we; it; you; their; they; our; i; its; your; them; us; one; my; itself; her; themselves; he; his; me; she; ourselves; yourself; ours; ibqm; `ikr?qh2f; #f[mb+f/`; ’s; ၯஒ,ࡢᄝࡢმ; zbmath,19; qbxq`;fryxryydfbrr3jr@yrn@ynj; mfvqm`2@; ibqmbx?ikh?/`4tm#v2; hxpj3brxrynd9; hvib+bfk; https://www.aclweb.org/anthology/p14-5010/; https://radimrehurek.com/gensim/; http://www.minedminds.org/; http://read.gov/resources/; him; hh@/b;bi; hbx2; hbbkf; fr?v@kyr8@r; ff/?h; de-; byry; bhf1pb/2m+2@amkk; b;m; `b; +ibfi proper nouns: �; learning; machine; ai; libraries; disciplinary; cross; -; al; researchǔchapter; ml; library; chicago; university; ff; digital; et; intelligence; artificial; new; data; google; research; m; science; information; ieee; york; press; n.d; review; gan; journal; marc; microsoft; ing; generative; conference; adversarial; technology; may; international; figure; humanities; computer; march; .; markov; january; congress keywords: learning; machine; research; datum; system; libraries; word; university; scholar; reading; process; pmss; place; notre; nakazawa; msc; moral; model; microsoft; material; markov; literary; library; kentucky; information; image; ieee; human; gan; disciplinary; computational; cohen; chinese; chicago; balke; algorithm; adversarial one topic; one dimension: learning file(s): ./cache/11-prudhomme-taking.pdf titles(s): Taking a Leap Forward: Machine Learning for New Limits three topics; one dimension: learning; library; word file(s): ./cache/07-kim-ai.pdf, ./cache/05-wiegand-cultures.pdf, ./cache/03-plumb-humanities.pdf titles(s): AI and Its Moral Concerns | Cultures of Innovation: Machine Learning as a Library Service | Humanities and Social Science Reading through Machine Learning five topics; three dimensions: learning machine data; 2019 https learning; learning machine data; research ml disciplinary; chicago place book file(s): ./cache/07-kim-ai.pdf, ./cache/02-harper-generative.pdf, ./cache/12-cohen-machine.pdf, ./cache/06-jiang-cross.pdf, ./cache/13-lucic-towards.pdf titles(s): AI and Its Moral Concerns | Generative Machine Learning | Machine Learning + Data Creation in a Community Partnership for Archival Research | Cross-Disciplinary ML Research is like Happy Marriages: Five Strengths and Two Examples | Towards a Chicago place name dataset: From back-of-the-book index to a labeled dataset Type: zip2carrel title: johnson-machine-2021 date: 2021-02-23 time: 21:20 username: emorgan patron: Eric Morgan email: emorgan@nd.edu input: Y7z2ihXDL5.zip ==== make-pages.sh htm files ==== make-pages.sh complex files ==== make-pages.sh named enities ==== making bibliographics id: 08-altman-building author: Altman title: Building a Machine Learning Pipeline date: 2021 words: 6148 sentences: 361 pages: 11 flesch: 63 cache: ./cache/08-altman-building.pdf txt: ./txt/08-altman-building.txt summary: As you begin ingesting and preparing data, you''ll want to explore possible machine learning algorithms to perform on your dataset. Start by determining what general type of learning algorithm you need, and proceed from there to research and select one that While the final output of a machine learning workflow is some sort of intelligent model, The pipeline for a machine learning project generally comprises five stages: data acquisition, data preparation, model training and testing, evaluation and analysis, and application of results. good idea to save a copy in the rawest possible form and treat that copy as immutable, at least during the initial phase of testing different algorithms or configurations. algorithm uses the training data to "learn" a set of rules that it can subsequently apply to new, Immutable data storage can benefit the batch-processing ML pipeline, especially during the initial research and development phase. id: 12-cohen-machine author: Cohen title: Machine Learning + Data Creation in a Community Partnership for Archival Research date: 2021 words: 7542 sentences: 474 pages: 13 flesch: 54 cache: ./cache/12-cohen-machine.pdf txt: ./txt/12-cohen-machine.txt summary: archivally focused project that emerged from a partnership between the Pine Mountain Settlement School (PMSS)1 in Harlan County, Kentucky, and scholars and students at Berea College. a latent social network of historical families represented by the images held in one local archive, curricula for use in Kentucky public schools with PMSS archival materials. That decision led a team of Berea College undergraduate and faculty researchers to scrape the data from the PMSS archive site and supplement the images and transcriptions it contains with available textual metadata drawn from the site.9 Alongside the WordPress facial recognition software to identify the persons in historic photographs in the PMSS archives. We demonstrated to the local members at Pine Mountain how our use case and its constraints for digital archives fit with the current standards for the fair use of copyrighted materials id: 14-hansen-can author: Hansen title: Can a Hammer Categorize Highly Technical Articles? date: 2021 words: 4339 sentences: 394 pages: 8 flesch: 63 cache: ./cache/14-hansen-can.pdf txt: ./txt/14-hansen-can.txt summary: I would use the Mathematical Subject Classification (MSC) values assigned to the publications in MathSciNet1 to create a temporal citation network which would allow me to visualize Machine-learning-based categorization needs data to classify, which in our case automated categorization of mathematics, we were dilettantes in the world of machine learning. what happens when smarter and more capable minds tackle the problem of classifying mathematics and other highly technical subjects using advanced machine learning techniques. 9Mathematical Subject Classification (MSC) values in MathSciNet and zbMath are a particularly interesting categorization set to work with as they are assigned and reviewed by a subject area expert editor and an active researcher in the 16See ?iiTb,ff�+�/2KB+XKB+''QbQ7iX+QKf. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 One really interesting part of the machine learning method used by Microsoft was that it did not rely only on information from the article being replace the work of humans categorizing mathematics articles indexed in a database, which for id: 02-harper-generative author: Harper title: Generative Machine Learning date: 2021 words: 5935 sentences: 662 pages: 15 flesch: 59 cache: ./cache/02-harper-generative.pdf txt: ./txt/02-harper-generative.txt summary: Reddit have each issued their own bans on the category of machine-generated or -altered content that is commonly termed "deep fakes" (Cohen 2020; Romm, Harwell, and Stanley-Becker TV because of their dystopian implications, deep fakes are just one application of generative machine learning. Figure 2.2: Images generated with a simple statistical model appear as noise as the model is insufficient to capture the structure of the real data (Markov chains trained using wine bottles and 1In many examples, I have used the Google QuickDraw Dataset to highlight features of generative machine learning. (?iiTb,ff;Bi?m#X+QKf;QQ;H2+''2�iBp2H�#f[mB+F/''�r@/�i�b2i) shows the generator learning how to produce better sketches over time. built a GAN that generates high-quality photo-realistic images of people (Karras, Laine, and Aila Beyond medicine and autonomous vehicles, generative data augmentation will progressively impact other imaging-heavy fields (Shorten and Khoshgoftaar 2019) like GANs in Action: Deep Learning with Generative Adversarial Networks. id: 01-hintze-artificial author: Hintze title: Artificial Intelligence in the Humanities: Wolf in Disguise, or Digital Revolution? date: 2021 words: 5069 sentences: 357 pages: 10 flesch: 56 cache: ./cache/01-hintze-artificial.pdf txt: ./txt/01-hintze-artificial.txt summary: Artificial Intelligence, with its ability to machine learn coupled to an almost human-like understanding, sounds like the ideal tool to the humanities. But are these technologies imbued with intuition or understanding, and do they learn like humans? In the 80s and 90s, as home computers were becoming more common, Hollywood was sensationalizing the idea of smart or human-like Artificial Intelligent machines (AI) through movies Machine learning allows us to learn from these data sets in ways that exceed human capabilities, while an artificial brain will eventually allow us to objectively describe a subjective experience (through quantifying neural activations or positively and negatively associated memories). The following paragraphs will explore current Machine Learning and Artificial Intelligence learning, to the point where our whole identity as human could be generously defined as the Just because humans and machine learning are both black Currently, machines do not learn but must be trained, typically with human-labeled data. id: 04-janco-machine author: Janco title: Machine Learning in Digital Scholarship date: 2021 words: 3101 sentences: 245 pages: 6 flesch: 54 cache: ./cache/04-janco-machine.pdf txt: ./txt/04-janco-machine.txt summary: Tools like RunwayML, the Teachable Machine, and Google AutoML allow researchers to train project-specific Since 2014, dramatic innovations in machine learning have occurred, providing new capabilities in computer vision, natural language processing, and other areas of applied artificial intelligence. deliberately and identify how machine learning methods can benefit a scholar''s research? for identifying basic tasks that can be completed by computers in ways that advance humanities research (2000). When working with texts or images, machine learning models are presently capable of making simple annotations and associations. Google''s Teachable Machine offers an intuitive web application that humanities faculty and students can use to train classification models for images, sounds, and poses. Machine learning models offer a variety of ways to identify similarity and difference with research materials. goals of academic researchers in the humanities with the technical possibilities of machine learning. "Scholarly Primitives: What Methods Do Humanities Researchers Have id: 06-jiang-cross author: Jiang title: Cross-Disciplinary ML Research is like Happy Marriages: Five Strengths and Two Examples date: 2021 words: 3623 sentences: 425 pages: 10 flesch: 55 cache: ./cache/06-jiang-cross.pdf txt: ./txt/06-jiang-cross.txt summary: Cross-disciplinary research matters, because (1) it provides an understanding of complex problems that require a multifaceted approach to solve; (2) it combines disciplinary breadth with the ability to collaborate One of the most popular cross-disciplinary research topics/programs is Machine Learning + top strengths of conducting cross-disciplinary ML research and give two examples based on my marriages, just like collaborators expect to have successful project outcomes (Robinson and Blanton 1993; Pettigrew 2000; Xu et al. The history professor Liang Cai and I have collaborated on an international research project titled "Digital Empires: Structured Biographical and Social Network Analysis of Early Chinese We have enjoyed our collaboration and the power of cross-disciplinary research. Specifically, I presented the top strengths of producing successful cross-disciplinary ML research: (1) Partners are satisfied with communication. "The Challenges of Cross lj Disciplinary Research." Social Research Collaboration." Social Studies of Science 33, no. "Building Cross-Disciplinary Research Collaborations." id: 00-johnson-preface author: Johnson title: Preface date: 2021 words: 1090 sentences: 72 pages: 3 flesch: 47 cache: ./cache/00-johnson-preface.pdf txt: ./txt/00-johnson-preface.txt summary: The plan called for a survey and a series of workshops hosted across the country to explore, originally, "the national need for library based topic modeling tools in support of cross-disciplinary libraries ran concurrently with our grant — Cordell 2020 and Padilla 2019, which were commissioned by major players in the field, the Library of Congress and OCLC, respectively — and vi Machine Learning, Libraries, and Cross-Disciplinary Research We would like to thank the IMLS for providing essential funding support for the grant and the Thank you to the members of the Notre Dame IMLS grant team who, at of course, thanks to the 95 participants in our 2019 IMLS Grant Workshops (too many to enumerate here) and to the essay authors for sharing their expertise and perspectives in growing our collective knowledge of machine learning and its use in research, scholarship, and cultural heritage organizations. https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html https://www.oclc.org/research/publications/2019/oclcresearch-responsible-operations-data-science-machine-learning-ai.html id: 07-kim-ai author: Kim title: AI and Its Moral Concerns date: 2021 words: 7293 sentences: 784 pages: 13 flesch: 55 cache: ./cache/07-kim-ai.pdf txt: ./txt/07-kim-ai.txt summary: does not provide an easy answer to the question of how one should program moral decisionmaking into intelligent machines. Described below are some of the significant ethical challenges that autonomous AI systems such as military robots present. 11Note that this moral decision-making process can be modeled with a rule-based symbolic AI approach, a machine 13(Kahn 2012) also argues that the resulting increase in the number of wars by the use of military robots will be morally 15This black-box nature of AI systems powered by machine learning has raised great concern among many AI researchers in recent years. agency in the AI -powered automated information environment presents an ethical challenge In this chapter, I discussed four significant ethical challenges that automating decisions and actions with AI presents: (a) moral desensitization; (b) unintended outcomes; (c) surrender of are at an early stage in developing AI applications and applying machine learning and deep learning techniques to improve library services, systems, and operations. id: 09-lesk-fragility author: Lesk title: Fragility and Intelligibility of Deep Learning for Libraries date: 2021 words: 4796 sentences: 474 pages: 11 flesch: 63 cache: ./cache/09-lesk-fragility.pdf txt: ./txt/09-lesk-fragility.txt summary: Machine learning systems have a set of data for training. of the real problem (if you train a machine translation program solely on engineering documents, there may be a lot of training data, including many noisy points, and the program may decide on Many popular magazines have discussed this problem; Forbes, for example, had an explanation of how the choice of datasets can produce a biased result without any deliberate attempt to used to suggest malicious creation of training data or examples of data designed to deceive machine learning systems. blood pressure, and lower blood pressure decreases the risk of heart attacks." Then I have to explain that the paper evaluates 32 possibilities (prior/current ownership ⇥ cats/dogs ⇥ 4 medical compare the performance of machine learning systems for medical diagnosis with actual doctors If a program is constantly learning from new data, there is no list of previously fixed failures to id: 13-lucic-towards author: Lucic title: Towards a Chicago place name dataset: From back-of-the-book index to a labeled dataset date: 2021 words: 3073 sentences: 285 pages: 7 flesch: 63 cache: ./cache/13-lucic-towards.pdf txt: ./txt/13-lucic-towards.txt summary: Reading Chicago Reading1 is a grant-supported digital humanities project that takes as its object the "One Book One Chicago" (OBOC) program2 of the Chicago Public Library. A related question is the focus of this paper: by associating place names with sentiment scores in Chicago-themed OBOC The HathiTrust research portal permits the extraction of non-consumptive features of the works included in the digital library, even those that are still under copyright. The place names extracted from our three Chicago-setting OBOC books allowed us to focus Our interest in creating a dataset of Chicago place names extracted from literature led us to Kaser''s book contains several indexes that can serve as sources of labeled data or instances in which Chicago locations are mentioned. the index as a source of already-labeled data for Chicago place names. associated sentiment scores for Chicago place names in the three OBOC selections centered on id: 10-morgan-bringing author: Morgan title: Bringing Algorithms and Machine Learning Into Library Collections and Services date: 2021 words: 5793 sentences: 739 pages: 13 flesch: 74 cache: ./cache/10-morgan-bringing.pdf txt: ./txt/10-morgan-bringing.txt summary: advent of computers, the idea of sharing cataloging data as MARC (machine readable cataloging) the full text of its collections to enhance bibliographic description and resulting public service. ability to save, organize, and retrieve data; on the whole, the library profession does not understand the concept of a "data structure." For example, tab-delimited files, CSV (comma-separated the use of data structures, computers store and retrieve information. Libraries use computers to store, organize, preserve, and disseminate the gray literature of our time, and we call these systems "institutional repositories." In all Using such a process, there are really only four different types of machine learning: classification, clustering, regression, and dimension reduction. Given a set of previously classified menus, one could create a model There are many possible ways to enhance library collections and services through the use of machine learning. of plain text files and an integer, Topic Modeling Tool will create a weighted list of latent themes id: 03-plumb-humanities author: Plumb title: Humanities and Social Science Reading through Machine Learning date: 2021 words: 7195 sentences: 599 pages: 14 flesch: 45 cache: ./cache/03-plumb-humanities.pdf txt: ./txt/03-plumb-humanities.txt summary: Respondents such as Mark Algee-Hewitt pointed out that literary scholars employ computational statistical models in order to reveal something about texts that human readers Machine learning, and word embedding algorithms in particular, may have a unique ability to shift this conversation into new territory, where scholars Acknowledging this helps contextualize machine learning algorithms for text analysis tasks in the humanities, but also highlights data curation challenges This naturally raises questions about how machine learning algorithms like word embeddings are implemented for text analysis, and how they Based on the potential for word embeddings to model semantic spaces for different corpora and compare the distribution of terms, the next step was to build a corpus of non-canonical Designing humanities research with novel word embedding models stands to widen the territory where machine learning engineers look for conceptual concepts Systematic data curation, combined with word embedding algorithms, represent a new interpretive system for literary scholars. id: 11-prudhomme-taking author: Prudhomme title: Taking a Leap Forward: Machine Learning for New Limits date: 2021 words: 3910 sentences: 387 pages: 9 flesch: 51 cache: ./cache/11-prudhomme-taking.pdf txt: ./txt/11-prudhomme-taking.txt summary: Combining automatic processes to assist in supporting inventory management with a focus on descriptive metadata, a machine learning solution could help alleviate time-consuming and relatively expensive metadata tagging tasks, Deep learning neural networks are more effective in feature detection as they are able to solve complex problems such as image classification with greater accuracy when trained with large datasets. For images, how can archives build a data-labeling pipeline into their digital curation workflow that enables machine learning of collections? machine learning is only good so long as value is added, archives and libraries will need to think As deep learning applications will only be as effective as the data, archives and libraries should expand their Along with greater computing capabilities, artificial intelligence could be an opportunity for libraries and archives to boost the discovery of their digital collections by pushing text and image id: 05-wiegand-cultures author: Wiegand title: Cultures of Innovation: Machine Learning as a Library Service date: 2021 words: 7014 sentences: 761 pages: 14 flesch: 49 cache: ./cache/05-wiegand-cultures.pdf txt: ./txt/05-wiegand-cultures.txt summary: traditional role, librarians in the 20th century added a new function—discovery—teaching people to find and use the library''s collected scholarship. learning in the library as the next step beyond collecting, with librarians instructing on information infrastructure with the goal of empowering library users to find, evaluate, and use scholarly go far beyond local library collections to a global perspective and normative practice of participation at scale in innovative emerging technologies such as Machine Learning. start by using Machine Learning tools to automate alerts of new content in a narrow area of interest and help researchers at all levels find and focus on problem-solving. A library that adapted Machine Learning as an innovation technology would improve its practices; add new services; choose, use, and license collections differently; utilize all spaces for learning; and role model innovative leadership. opening local collections to discovery and use in order to create new knowledge through digitization and semantic linking, with cross-disciplinary technologies to augment traditional research ==== make-pages.sh questions ==== make-pages.sh search ==== make-pages.sh topic modeling corpus Zipping study carrel