Search (113 results, page 1 of 6)

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Classification

ME 3150

RVK

ME 3150

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Classification

ME 3000

RVK

ME 3000

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Abstract

In the 21st century, the image has remained a common sight in classrooms and on TV, including a feature on Silicon Valley in 2014. Pushback towards the use of the image also grew in the 2010s leading up to 2019, when the Losing Lena documentary was released. Forsén shares her side of the story and asks for her image to be retired: "I retired from modelling a long time ago. It's time I retired from tech, too. We can make a simple change today that creates a lasting change for tomorrow. Let's commit to losing me." After the film's release, many of my female colleagues shared stories about their own encounters with the image throughout their careers. When one of the only women this well referenced, respected, and remembered in your field is known for a nude photo that was taken of her and is now used without her consent, it inevitably shapes the perception of the position of women in tech and the value of our contributions. The film called on the engineering community to stop their spread of the image and use alternatives instead. This led to efforts to remove the image from textbooks and production code and a slow, but noticeable decline in the image's use for research.

Content

"Having known Lenna for almost a decade, I have struggled to understand what the story of the image means for what tech culture is and what it is becoming. To me, the crux of the Lenna story is how little power we have over our data and how it is used and abused. This threat seems disproportionately higher for women who are often overrepresented in internet content, but underrepresented in internet company leadership and decision making. Given this reality, engineering and product decisions will continue to consciously (and unconsciously) exclude our needs and concerns. While social norms are changing towards non-consensual data collection and data exploitation, digital norms seem to be moving in the opposite direction. Advancements in machine learning algorithms and data storage capabilities are only making data misuse easier. Whether the outcome is revenge porn or targeted ads, surveillance or discriminatory AI, if we want a world where our data can retire when it's outlived its time, or when it's directly harming our lives, we must create the tools and policies that empower data subjects to have a say in what happens to their data. including allowing their data to die."

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Abstract

He doesn't dismiss the potential of LLMs to become useful assistants in all sorts of ways-Google and Microsoft have already announced that they will be integrating LLMs into their search and office productivity software. But he talked me through some of his criticisms of the technology's apparent capabilities. At the heart of Dr Marcus's thoughtful critique is an attempt to put LLMs into proper context. Deep learning, the underlying technology that makes LLMs work, is only one piece of the puzzle in the quest for machine intelligence. To reach the level of artificial general intelligence (AGI) that many tech companies strive for-i.e. machines that can plan, reason and solve problems in the way human brains can-they will need to deploy a suite of other AI techniques. These include, for example, the kind of "symbolic AI" that was popular before artificial neural networks and deep learning became all the rage.

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Source

https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

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Abstract

Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These "exclusive journals" are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy-makers, funding, and research institutions-via more accurate academic performance evaluations-, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories.

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Content

"Die Englischsprachige Wikipedia verfügt jetzt über mehr als 6 Millionen Artikel. An zweiter Stelle kommt die deutschsprachige Wikipedia mit 2.3 Millionen Artikeln, an dritter Stelle steht die französischsprachige Wikipedia mit 2.1 Millionen Artikeln (via Researchbuzz: Firehose <https://rbfirehose.com/2020/01/24/techcrunch-wikipedia-now-has-more-than-6-million-articles-in-english/> und Techcrunch <https://techcrunch.com/2020/01/23/wikipedia-english-six-million-articles/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&guccounter=1&guce_referrer=aHR0cHM6Ly9yYmZpcmVob3NlLmNvbS8yMDIwLzAxLzI0L3RlY2hjcnVuY2gtd2lraXBlZGlhLW5vdy1oYXMtbW9yZS10aGFuLTYtbWlsbGlvbi1hcnRpY2xlcy1pbi1lbmdsaXNoLw&guce_referrer_sig=AQAAAK0zHfjdDZ_spFZBF_z-zDjtL5iWvuKDumFTzm4HvQzkUfE2pLXQzGS6FGB_y-VISdMEsUSvkNsg2U_NWQ4lwWSvOo3jvXo1I3GtgHpP8exukVxYAnn5mJspqX50VHIWFADHhs5AerkRn3hMRtf_R3F1qmEbo8EROZXp328HMC-o>). 250120 via digithek ch = #fineBlog s.a.: Angesichts der Veröffentlichung des 6-millionsten Artikels vergangene Woche in der englischsprachigen Wikipedia hat die Community-Zeitungsseite "Wikipedia Signpost" ein Moratorium bei der Veröffentlichung von Unternehmensartikeln gefordert. Das sei kein Vorwurf gegen die Wikimedia Foundation, aber die derzeitigen Maßnahmen, um die Enzyklopädie gegen missbräuchliches undeklariertes Paid Editing zu schützen, funktionierten ganz klar nicht. *"Da die ehrenamtlichen Autoren derzeit von Werbung in Gestalt von Wikipedia-Artikeln überwältigt werden, und da die WMF nicht in der Lage zu sein scheint, dem irgendetwas entgegenzusetzen, wäre der einzige gangbare Weg für die Autoren, fürs erste die Neuanlage von Artikeln über Unternehmen zu untersagen"*, schreibt der Benutzer Smallbones in seinem Editorial <https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2020-01-27/From_the_editor> zur heutigen Ausgabe."

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Content

Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.

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Abstract

This article presents the history, contents, structures, functions, and applications of the International Classification of Diseases (ICD), which is a global standard maintained by the World Health Organization (WHO). The article aims to present ICD from the knowledge organization perspective and focuses on the current versions, ICD-10 and ICD-11. It also introduces the relationship between ICD and other health knowledge organization systems (KOSs), plus efforts in research and development reported in health informatics. The article concludes that the high-level effort of promoting a unified classification system such as ICD is critical in providing a common language for systematic recording, reporting, analysis, interpretation, and comparison of mortality and morbidity data. It greatly enhances the constancy of coding across languages, cultures, and healthcare systems around the world.

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Footnote

Derived from the article of similar title in the ISKO Encyclopedia of Knowledge Organization Version 1.1 (= 1.0 plus details on electrophones and Wikipedia); version 1.0 published 2019-01-17, this version 2019-05-29. Article category: KOS, specific (domain specific). The author would like to thank the anonymous reviewers for their useful comments, as well as the editor, Professor Birger Hjørland, for all of his insightful comments and ideas.

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Abstract

Purpose To provide a close, detailed analysis of the frequency, nature, and depth of visible use of Michel Foucault's works by library and information science/studies (LIS) scholars. Design/methodology/approach The study conducted extensive full-text searches in a large number of electronically available LIS journal databases to find citations of Foucault's works, then examined each cited article to evaluate the nature and depth of use. Findings Most uses of Foucault are brief or in passing. In-depth explorations of Foucault's works are comparatively rare and relatively little-used by other LIS scholars. Yet the relatively brief uses of Foucault encompass a wide array of different topics spread across a wide spectrum of LIS journal literature. Research limitations/implications The study was limited to articles from particular relatively prominent LIS journals. Results might vary if different journals or non-journal literature were studied. More sophisticated bibliometric techniques might reveal different relative performance among journals and might better test, confirm, or reject various patterns and relationships found here. Other research approaches, such as discourse analysis, social network analysis, or scholar interviews, might reveal patterns of use and influence not visible in this literature sample. Originality/value This intensive study of both quality and quantity of citations may challenge some existing assumptions regarding citation analysis, plus illuminating Foucault scholarship. It also indicates possible problems for future application of artificial intelligence (AI) approaches to similar depth-of-use studies.

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Date

27. 1.2023 16:22:55

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Date

2. 3.2020 14:08:22

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Source

Spektrum der Wissenschaft. 2023, H.10, S.22-24

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Date

17. 1.2020 22:15:11

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Date

4. 5.2020 17:22:40

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Date

4. 5.2020 17:22:28

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Date

22. 6.2020 19:11:24