Search (134 results, page 1 of 7)

  • × year_i:[2020 TO 2030}
  • × language_ss:"e"
  1. Bergman, O.; Israeli, T.; Whittaker, S.: Factors hindering shared files retrieval (2020) 0.03 
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    Date
    20. 1.2015 18:30:22
  2. Gorichanaz, T.: Sanctuary : an institutional vision for the digital age (2021) 0.03 
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    Date
    22. 1.2021 14:20:55
  3. Huang, T.; Nie, R.; Zhao, Y.: Archival knowledge in the field of personal archiving : an exploratory study based on grounded theory (2021) 0.03 
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    Date
    22. 1.2021 14:20:27
  4. 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) 0.03 
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    Date
    10. 6.2021 19:22:47
  5. 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) 0.03 
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    Date
    27.12.2022 18:34:22
  6. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.03 
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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.
  7. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.03 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  8. Babcock, K.; Lee, S.; Rajakumar, J.; Wagner, A.: Providing access to digital collections (2020) 0.02 
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    Abstract
    The University of Toronto Libraries is currently reviewing technology to support its Collections U of T service. Collections U of T provides search and browse access to 375 digital collections (and over 203,000 digital objects) at the University of Toronto Libraries. Digital objects typically include special collections material from the university as well as faculty digital collections, all with unique metadata requirements. The service is currently supported by IIIF-enabled Islandora, with one Fedora back end and multiple Drupal sites per parent collection (see attached image). Like many institutions making use of Islandora, UTL is now confronted with Drupal 7 end of life and has begun to investigate a migration path forward. This article will summarise the Collections U of T functional requirements and lessons learned from our current technology stack. It will go on to outline our research to date for alternate solutions. The article will review both emerging micro-service solutions, as well as out-of-the-box platforms, to provide an overview of the digital collection technology landscape in 2019. Note that our research is focused on reviewing technology solutions for providing access to digital collections, as preservation services are offered through other services at the University of Toronto Libraries.
  9. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.02 
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  10. Moore, S.M.; Kiser, T.; Hodge, C.: Classification of print-based cartographic materials : a survey and analysis (2022) 0.02 
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  11. Kleiner, J.; Ludwig, T.: If consciousness is dynamically relevant, artificial intelligence isn't conscious. (2023) 0.02 
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  12. Fugmann, R.: What is information? : an information veteran looks back (2022) 0.02 
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    Date
    18. 8.2022 19:22:57
  13. Reichmann, S.; Klebel, T.; Hasani-Mavriqi, I.; Ross-Hellauer, T.: Between administration and research : understanding data management practices in an institutional context (2021) 0.01 
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  14. Zaitseva, E.M.: Developing linguistic tools of thematic search in library information systems (2023) 0.01 
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    Abstract
    Within the R&D program "Information support of research by scientists and specialists on the basis of RNPLS&T Open Archive - the system of scientific knowledge aggregation", the RNPLS&T analyzes the use of linguistic tools of thematic search in the modern library information systems and the prospects for their development. The author defines the key common characteristics of e-catalogs of the largest Russian libraries revealed at the first stage of the analysis. Based on the specified common characteristics and detailed comparison analysis, the author outlines and substantiates the vectors for enhancing search inter faces of e-catalogs. The focus is made on linguistic tools of thematic search in library information systems; the key vectors are suggested: use of thematic search at different search levels with the clear-cut level differentiation; use of combined functionality within thematic search system; implementation of classification search in all e-catalogs; hierarchical representation of classifications; use of the matching systems for classification information retrieval languages, and in the long term classification and verbal information retrieval languages, and various verbal information retrieval languages. The author formulates practical recommendations to improve thematic search in library information systems.
  15. Xie, B.; He, D.; Mercer, T.; Wang, Y.; Wu, D.; Fleischmann, K.R.; Zhang, Y.; Yoder, L.H.; Stephens, K.K.; Mackert, M.; Lee, M.K.: Global health crises are also information crises : a call to action (2020) 0.01 
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  16. Aalberg, T.; O'Neill, E.; Zumer, M.: Extending the LRM Model to integrating resources (2021) 0.01 
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  17. Al-Khatib, K.; Ghosa, T.; Hou, Y.; Waard, A. de; Freitag, D.: Argument mining for scholarly document processing : taking stock and looking ahead (2021) 0.01 
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  18. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.01 
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    Source
    Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]
  19. Diken, T.: Cataloging psychological tests in an academic library (2021) 0.01 
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  20. Heng, G.; Cole, T.W.; Tian, T.(C.); Han, M.-J.: Rethinking authority reconciliation process (2022) 0.01 
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