Search (225 results, page 1 of 12)

  • × year_i:[2020 TO 2030}
  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.11
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  2. Gabler, S.: Vergabe von DDC-Sachgruppen mittels eines Schlagwort-Thesaurus (2021) 0.09
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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.
  3. Xie, J.; Lu, H.; Kang, L.; Cheng, Y.: Citing criteria and its effects on researcher's intention to cite : a mixed-method study (2022) 0.04
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1079-1091
  4. Qiu, J.; Zuo, M.; Wang, J.; Cai, C.: Knowledge order in an online knowledge community : group heterogeneity and two paths mediated by group interaction (2021) 0.03
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.8, S.1075-1091
  5. Krüger, N.; Pianos, T.: Lernmaterialien für junge Forschende in den Wirtschaftswissenschaften als Open Educational Resources (OER) (2021) 0.03
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    Date
    22. 5.2021 12:43:05
  6. Luyt, L.: Representation and the problem of bibliographic imagination on Wikipedia (2022) 0.03
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    Source
    Journal of documentation. 78(2022) no.5, S.1075-1091
  7. Wang, H.; Song, Y.-Q.; Wang, L.-T.: Memory model for web ad effect based on multimodal features (2020) 0.02
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    Abstract
    Web ad effect evaluation is a challenging problem in web marketing research. Although the analysis of web ad effectiveness has achieved excellent results, there are still some deficiencies. First, there is a lack of an in-depth study of the relevance between advertisements and web content. Second, there is not a thorough analysis of the impacts of users and advertising features on user browsing behaviors. And last, the evaluation index of the web advertisement effect is not adequate. Given the above problems, we conducted our work by studying the observer's behavioral pattern based on multimodal features. First, we analyze the correlation between ads and links with different searching results and further assess the influence of relevance on the observer's attention to web ads using eye-movement features. Then we investigate the user's behavioral sequence and propose the directional frequent-browsing pattern algorithm for mining the user's most commonly used browsing patterns. Finally, we offer the novel use of "memory" as a new measure of advertising effectiveness and further build an advertising memory model with integrated multimodal features for predicting the efficacy of web ads. A large number of experiments have proved the superiority of our method.
  8. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.02
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    Abstract
    Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
  9. Tay, A.: ¬The next generation discovery citation indexes : a review of the landscape in 2020 (2020) 0.02
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    Abstract
    Conclusion There is a reason why Google Scholar and Web of Science/Scopus are kings of the hills in their various arenas. They have strong brand recogniton, a head start in development and a mass of eyeballs and users that leads to an almost virtious cycle of improvement. Competing against such well established competitors is not easy even when one has deep pockets (Microsoft) or a killer idea (scite). It will be interesting to see how the landscape will look like in 2030. Stay tuned for part II where I review each particular index.
    Date
    17.11.2020 12:22:59
  10. Barth, T.: Inverse Panopticon : Digitalisierung & Transhumanismus [Transhumanismus II] (2020) 0.02
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    Content
    Vgl.: Barth, T.: Digitalisierung und Lobby: Transhumanismus I. [12. Januar 2020]. Unter: https://www.heise.de/tp/features/Digitalisierung-und-Lobby-Transhumanismus-I-4633314.html?view=print.
  11. Bergman, O.; Israeli, T.; Whittaker, S.: Factors hindering shared files retrieval (2020) 0.02
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    Date
    20. 1.2015 18:30:22
  12. Gorichanaz, T.: Sanctuary : an institutional vision for the digital age (2021) 0.02
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    Date
    22. 1.2021 14:20:55
  13. Huang, T.; Nie, R.; Zhao, Y.: Archival knowledge in the field of personal archiving : an exploratory study based on grounded theory (2021) 0.02
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    Date
    22. 1.2021 14:20:27
  14. Haimson, O.L.; Carter, A.J.; Corvite, S.; Wheeler, B.; Wang, L.; Liu, T.; Lige, A.: ¬The major life events taxonomy : social readjustment, social media information sharing, and online network separation during times of life transition (2021) 0.02
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    Date
    10. 6.2021 19:22:47
  15. Guo, T.; Bai, X.; Zhen, S.; Abid, S.; Xia, F.: Lost at starting line : predicting maladaptation of university freshmen based on educational big data (2023) 0.02
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    Date
    27.12.2022 18:34:22
  16. Krattenthaler, C.: Was der h-Index wirklich aussagt (2021) 0.02
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    Abstract
    Diese Note legt dar, dass der sogenannte h-Index (Hirschs bibliometrischer Index) im Wesentlichen dieselbe Information wiedergibt wie die Gesamtanzahl von Zitationen von Publikationen einer Autorin oder eines Autors, also ein nutzloser bibliometrischer Index ist. Dies basiert auf einem faszinierenden Satz der Wahrscheinlichkeitstheorie, der hier ebenfalls erläutert wird.
    Content
    Vgl.: DOI: 10.1515/dmvm-2021-0050. Auch abgedruckt u.d.T.: 'Der h-Index - "ein nutzloser bibliometrischer Index"' in Open Password Nr. 1007 vom 06.12.2021 unter: https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzM3NCwiZDI3MzMzOTEwMzUzIiwwLDAsMzQ4LDFd.
    Object
    h-index
  17. Geras, A.; Siudem, G.; Gagolewski, M.: Should we introduce a dislike button for academic articles? (2020) 0.02
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    Abstract
    There is a mutual resemblance between the behavior of users of the Stack Exchange and the dynamics of the citations accumulation process in the scientific community, which enabled us to tackle the outwardly intractable problem of assessing the impact of introducing "negative" citations. Although the most frequent reason to cite an article is to highlight the connection between the 2 publications, researchers sometimes mention an earlier work to cast a negative light. While computing citation-based scores, for instance, the h-index, information about the reason why an article was mentioned is neglected. Therefore, it can be questioned whether these indices describe scientific achievements accurately. In this article we shed insight into the problem of "negative" citations, analyzing data from Stack Exchange and, to draw more universal conclusions, we derive an approximation of citations scores. Here we show that the quantified influence of introducing negative citations is of lesser importance and that they could be used as an indicator of where the attention of the scientific community is allocated.
    Date
    6. 1.2020 18:10:22
  18. Dederke, J.; Hirschmann, B.; Johann, D.: ¬Der Data Citation Index von Clarivate : Eine wertvolle Ressource für die Forschung und für Bibliotheken? (2022) 0.02
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    Abstract
    Der Data Citation Index (DCI) stellt eine durchsuchbare Sammlung bibliografischer Metadaten zu Forschungsdaten in Datensätzen und Datenstudien ausgewählter Repositorien dar. Der DCI deckt alle wissenschaftlichen Disziplinen ab.
    Object
    Data Citation Index
  19. Rae, A.R.; Mork, J.G.; Demner-Fushman, D.: ¬The National Library of Medicine indexer assignment dataset : a new large-scale dataset for reviewer assignment research (2023) 0.02
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    Abstract
    MEDLINE is the National Library of Medicine's (NLM) journal citation database. It contains over 28 million references to biomedical and life science journal articles, and a key feature of the database is that all articles are indexed with NLM Medical Subject Headings (MeSH). The library employs a team of MeSH indexers, and in recent years they have been asked to index close to 1 million articles per year in order to keep MEDLINE up to date. An important part of the MEDLINE indexing process is the assignment of articles to indexers. High quality and timely indexing is only possible when articles are assigned to indexers with suitable expertise. This article introduces the NLM indexer assignment dataset: a large dataset of 4.2 million indexer article assignments for articles indexed between 2011 and 2019. The dataset is shown to be a valuable testbed for expert matching and assignment algorithms, and indexer article assignment is also found to be useful domain-adaptive pre-training for the closely related task of reviewer assignment.
    Date
    22. 1.2023 18:49:49
  20. Menczer, F.; Hills, T.: ¬Die digitale Manipulation (2021) 0.01
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