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Journal articles on the topic 'Information content'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 May 2022

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1

Shankaranarayanan, G., and Roger Blake. "From Content to Context." Journal of Data and Information Quality 8, no. 2 (February 27, 2017): 1–28. http://dx.doi.org/10.1145/2996198.

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2

Rus, Ioan. ""Audit Information Content "." Annales Universitatis Apulensis Series Oeconomica 1, no. 14 (June 30, 2012): 119–27. http://dx.doi.org/10.29302/oeconomica.2012.14.1.10.

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3

Pineda, David. "Information and Content." Philosophical Issues 9 (1998): 381. http://dx.doi.org/10.2307/1522987.

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4

Gao, Ge, Sun Young Hwang, Gabriel Culbertson, Susan R. Fussell, and Malte F. Jung. "Beyond Information Content." Proceedings of the ACM on Human-Computer Interaction 1, CSCW (December 6, 2017): 1–18. http://dx.doi.org/10.1145/3134683.

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5

TÖRNEBOHM, HÅKAN. "Content of information." Theoria 21, no. 2-3 (February 11, 2008): 146–57. http://dx.doi.org/10.1111/j.1755-2567.1955.tb00255.x.

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6

Su, Ja-Hwung, Hsin-Ho Yeh, Philip S. Yu, and Vincent S. Tseng. "Music Recommendation Using Content and Context Information Mining." IEEE Intelligent Systems 25, no. 1 (January 2010): 16–26. http://dx.doi.org/10.1109/mis.2010.23.

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7

Müller, Henning, and Jayashree Kalpathy-Cramer. "Putting the Content Into Context." International Journal of Healthcare Information Systems and Informatics 4, no. 1 (January 2009): 88–98. http://dx.doi.org/10.4018/jhisi.2009010106.

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8

Gradmann, Stefan. "From containers to content to context." Journal of Documentation 70, no. 2 (March 4, 2014): 241–60. http://dx.doi.org/10.1108/jd-05-2013-0058.

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Purpose – The aim of this paper is to reposition the research library in the context of the changing information and knowledge architecture at the end of the “Gutenberg Parenthesis” and as part of the rapidly emerging “semantic” environment of the Linked Open Data paradigm. Understanding this process requires a good understanding of the evolution of the “document” notion in the passage from print based culture to the distributed hypertextual and RDF based information architecture of the WWW. Design/methodology/approach – These objectives are reached using literature study and a descriptive historical approach as well as text mining techniques using Google nGrams as a data source. Findings – The paper presents a proposal for effectively repositioning research libraries in the context of eScience and eScholarship as well as clear indications of the proposed repositioning already taking place. Furthermore, a new perspective of the “document” notion is provided. Practical implications – The evolution described in the contribution creates opportunities for libraries to reposition themselves as aggregators and selectors of content and as contextualising agents as part of future Linked Data based scholarly research environments provided they are able and ready to operate the related cultural changes. Originality/value – The paper will be useful for practitioners in search of strategic guidance for repositioning their librarian institutions in a context of ever increasing competition for scarce funding resources.
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9

Lidén, Erik R. "Swedish Stock Recommendations: Information Content or Price Pressure?" Multinational Finance Journal 11, no. 3/4 (December 1, 2007): 253–85. http://dx.doi.org/10.17578/11-3/4-4.

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10

Kazantsev, I. G. "Information content of projections." Inverse Problems 7, no. 6 (December 1, 1991): 887–98. http://dx.doi.org/10.1088/0266-5611/7/6/010.

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11

Bogdan, Radu J. "Mind, content and information." Synthese 70, no. 2 (February 1987): 205–27. http://dx.doi.org/10.1007/bf00413936.

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12

Zhu, Qiusha, Lin Lin, Mei-Ling Shyu, and Dianting Liu. "Utilizing Context Information to Enhance Content-Based Image Classification." International Journal of Multimedia Data Engineering and Management 2, no. 3 (July 2011): 34–51. http://dx.doi.org/10.4018/jmdem.2011070103.

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Traditional image classification relies on text information such as tags, which requires a lot of human effort to annotate them. Therefore, recent work focuses more on training the classifiers directly on visual features extracted from image content. The performance of content-based classification is improving steadily, but it is still far below users’ expectation. Moreover, in a web environment, HTML surrounding texts associated with images naturally serve as context information and are complementary to content information. This paper proposes a novel two-stage image classification framework that aims to improve the performance of content-based image classification by utilizing context information of web-based images. A new TF*IDF weighting scheme is proposed to extract discriminant textual features from HTML surrounding texts. Both content-based and context-based classifiers are built by applying multiple correspondence analysis (MCA). Experiments on web-based images from Microsoft Research Asia (MSRA-MM) dataset show that the proposed framework achieves promising results.
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13

Porcic, Nebojsa. "Information on travel of Nemanjic embassies: Content and context." Balcanica, no. 47 (2016): 97–118. http://dx.doi.org/10.2298/balc1647097p.

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The paper offers an overview of available information on travel of medieval Serbian embassies in the Nemanjic dynasty period. This content is contextualized into the wider picture of regional embassy travel, presented by the far better documented embassies of Dubrovnik, Venice and the Byzantine Empire. The information is sorted by the key determining factors of an embassy?s journey - diplomatic and auxiliary personnel, representational accessories such as gifts and adornments, transport over land and sea, sustenance, lodging, expenses and obstacles presented by nature and men.
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14

Hu, Shizhe, Bo Wang, Zhengzheng Lou, and Yangdong Ye. "Multi-view content-context information bottleneck for image clustering." Expert Systems with Applications 183 (November 2021): 115374. http://dx.doi.org/10.1016/j.eswa.2021.115374.

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15

Drake, Michael S., Bret A. Johnson, Darren T. Roulstone, and Jacob R. Thornock. "Is There Information Content in Information Acquisition?" Accounting Review 95, no. 2 (July 1, 2019): 113–39. http://dx.doi.org/10.2308/accr-52498.

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ABSTRACT In this study, we examine whether investors' actions to acquire accounting information are predictive of future firm performance because these actions partially reveal investors' private expectations of this performance. Using a database of EDGAR downloads, we find some evidence that information acquisition of accounting reports by EDGAR users is, on average, predictive of future firm performance. We then determine the identity of the EDGAR user and examine whether different users' private expectations will be relatively more predictive of subsequent performance. We find that the information acquisition activities of more sophisticated institutional users (e.g., hedge funds, investment banks) are more strongly associated with future performance than are those of less sophisticated retail users. Finally, we find that information acquisition by sophisticated institutions is a leading indicator of their equity holdings. In summary, this study provides evidence of predictive information embedded in sophisticated investors' actions to acquire accounting information.
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16

Elsukov, P. Yu. "INFORMATION, REDUCING UNCERTAINTY AND INFORMATION, INCREASING CONTENT." Educational resources and technology, no. 3 (2017): 62–68. http://dx.doi.org/10.21777/2500-2112-2017-3-62-68.

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17

Mayernik, Matthew S., Jillian C. Wallis, Christine L. Borgman, and Alberto Pepe. "Adding context to content: The CENS deployment center." Proceedings of the American Society for Information Science and Technology 44, no. 1 (October 24, 2008): 1–7. http://dx.doi.org/10.1002/meet.1450440388.

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18

Fox, Robert. "Information economy." Digital Library Perspectives 34, no. 2 (May 14, 2018): 78–83. http://dx.doi.org/10.1108/dlp-10-2017-0040.

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Purpose This paper aims to describe several methods to expose website information to Web crawlers for providing value-added services to patrons. Design/methodology/approach This is a conceptual paper exploring the areas of search engine optimization (SEO) and usability in the context of search engines. Findings Not applicable Originality/value This paper explains several methods that can be used to appropriately expose website content and library services to the Web crawlers in such a way that services and content can be syndicated via those search engines.
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19

Tsay, Ruey S., Yi-Mien Lin, and Hsiao-Wen Wang. "Residual income, non-earnings information, and information content." Journal of Forecasting 28, no. 6 (September 2009): 487–511. http://dx.doi.org/10.1002/for.1104.

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20

Symons, V. J. "A review of information systems evaluation: content, context and process." European Journal of Information Systems 1, no. 3 (August 1991): 205–12. http://dx.doi.org/10.1057/ejis.1991.35.

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21

Oliveira, Eva, Teresa Chambel, and Nuno Magalhães Ribeiro. "Sharing Video Emotional Information in the Web." International Journal of Web Portals 5, no. 3 (July 2013): 19–39. http://dx.doi.org/10.4018/ijwp.2013070102.

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Video growth over the Internet changed the way users search, browse and view video content. Watching movies over the Internet is increasing and becoming a pastime. The possibility of streaming Internet content to TV, advances in video compression techniques and video streaming have turned this recent modality of watching movies easy and doable. Web portals as a worldwide mean of multimedia data access need to have their contents properly classified in order to meet users’ needs and expectations. The authors propose a set of semantic descriptors based on both user physiological signals, captured while watching videos, and on video low-level features extraction. These XML based descriptors contribute to the creation of automatic affective meta-information that will not only enhance a web-based video recommendation system based in emotional information, but also enhance search and retrieval of videos affective content from both users’ personal classifications and content classifications in the context of a web portal.
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22

Stuessy, T. F. "Information content for biological classifications." Gardens' Bulletin Singapore 71, no. 2 (December 16, 2019): 365–76. http://dx.doi.org/10.26492/gbs71(2).2019-04.

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23

Seo Gab Soo. "Information Content of Taxable Income." Korea International Accounting Review ll, no. 47 (February 2013): 113–30. http://dx.doi.org/10.21073/kiar.2013..47.006.

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24

Sullivan, David C., Tiba Aynechi, Vincent A. Voelz, and Irwin D. Kuntz. "Information Content of Molecular Structures." Biophysical Journal 85, no. 1 (July 2003): 174–90. http://dx.doi.org/10.1016/s0006-3495(03)74464-6.

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25

Morgan, Cliff. "Journals metadata: information about content." Learned Publishing 12, no. 3 (July 1999): 191–95. http://dx.doi.org/10.1087/09531519950145779.

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26

Hyötyniemi, H. "Information Content Weighting of Algorithms." IFAC Proceedings Volumes 27, no. 8 (July 1994): 1351–56. http://dx.doi.org/10.1016/s1474-6670(17)47898-0.

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27

Hayden, James E. "Monophyletic classification and information content." Cladistics 36, no. 4 (March 2020): 424–36. http://dx.doi.org/10.1111/cla.12410.

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28

Stegmann, Ulrich E. "Genetic Information as Instructional Content*." Philosophy of Science 72, no. 3 (July 2005): 425–43. http://dx.doi.org/10.1086/498472.

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29

Ковалев and Aleksey Kovalev. "Information Content of Double Entry." Auditor 2, no. 10 (October 25, 2016): 42–49. http://dx.doi.org/10.12737/22272.

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The paper presents the interpretation of double-entry accounting as a system consisting of three elements (a double record structure, accounting, accounts, equality of numerical values of the two parts of the record), shows the location of double entry in the process of obtaining and transforming information about economic processes.
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30

Dauter, Zbigniew, Manfred S. Weiss, Howard Einspahr, and Edward N. Baker. "Expectation bias and information content." Acta Crystallographica Section D Biological Crystallography 69, no. 2 (January 19, 2013): 141. http://dx.doi.org/10.1107/s0907444913000255.

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31

Shaffer, Sherrill. "Information content of forecast errors." Economics Letters 59, no. 1 (April 1998): 45–48. http://dx.doi.org/10.1016/s0165-1765(98)00025-1.

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32

Pasadeos, Yorgo, Glenn Key, Steven Hall, and Cynthia Morvillo. "Information Content of Newspaper Advertisements." Newspaper Research Journal 8, no. 3 (March 1987): 1–9. http://dx.doi.org/10.1177/073953298700800301.

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This study is a content analysis of a sample of newspaper advertisements, 98% of which contained at least one information cue; one-third contained four or more cues. The average number of cues per ad was 2.8. Compared with earlier studies, these findings suggest newspaper ads are more “informative” than television and magazine ads. This would be expected because of the “local” and copy-heavy nature of newspaper advertising.
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33

Schuster, H. G. "Information content of chaotic signals." Physica Scripta 40, no. 3 (September 1, 1989): 367–72. http://dx.doi.org/10.1088/0031-8949/40/3/019.

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34

Sánchez, David, Montserrat Batet, and David Isern. "Ontology-based information content computation." Knowledge-Based Systems 24, no. 2 (March 2011): 297–303. http://dx.doi.org/10.1016/j.knosys.2010.10.001.

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35

BIDDLE, GARY C., GIM S. SEOW, and ANDREW F. SIEGEL. "Relative versus Incremental Information Content." Contemporary Accounting Research 12, no. 1 (September 1995): 1–23. http://dx.doi.org/10.1111/j.1911-3846.1995.tb00478.x.

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36

Blacknell, D., and C. J. Oliver. "Information content of coherent images." Journal of Physics D: Applied Physics 26, no. 9 (September 14, 1993): 1364–70. http://dx.doi.org/10.1088/0022-3727/26/9/004.

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37

Ebadi, H., Meysam Bolgorian, and G. R. Jafari. "Inverse statistics and information content." Physica A: Statistical Mechanics and its Applications 389, no. 23 (December 2010): 5439–46. http://dx.doi.org/10.1016/j.physa.2010.08.006.

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38

Brody, Dorje C., and Lane P. Hughston. "Information content for quantum states." Journal of Mathematical Physics 41, no. 5 (May 2000): 2586–92. http://dx.doi.org/10.1063/1.533260.

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39

Lewis, Paul O., Ming-Hui Chen, Lynn Kuo, Louise A. Lewis, Karolina Fučíková, Suman Neupane, Yu-Bo Wang, and Daoyuan Shi. "Estimating Bayesian Phylogenetic Information Content." Systematic Biology 65, no. 6 (May 6, 2016): 1009–23. http://dx.doi.org/10.1093/sysbio/syw042.

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40

Lew, Michael S., Nicu Sebe, Chabane Djeraba, and Ramesh Jain. "Content-based multimedia information retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 2, no. 1 (February 2006): 1–19. http://dx.doi.org/10.1145/1126004.1126005.

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41

Chu, Quentin C., Deborah N. Pittman, and Linda Q. Yu. "Information Content of Maturing TIIS." Journal of Fixed Income 13, no. 4 (March 31, 2004): 90–99. http://dx.doi.org/10.3905/jfi.2004.391031.

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42

Liang, Pierre Jinghong. "Recognition: An Information Content Perspective." Accounting Horizons 15, no. 3 (September 1, 2001): 223–42. http://dx.doi.org/10.2308/acch.2001.15.3.223.

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This essay offers a synthesis of the varied accounting literature on recognition. First, in a review of the historical evolution of accounting thoughts, two debates on accounting measurements stand out: historical cost as the basis for asset valuation and realization as the basic test for income determination. These debates have specific implications for the recognition issue. Second, I examine the shift from the traditional measurement perspective to an information content perspective in both rhetorical policy debates and academic accounting thoughts. Under the information content perspective, the (strategic) decision-making context becomes particularly important. Last, I advocate information content as a combining perspective, not as a replacement of the measurement perspective. In particular, I draw attention to the recent academic studies with an explicit or implicit theme of recognition. These studies combine modern information economics with the accounting measurement structure. This line of research has the potential to provide further understanding of the comparative advantage of accounting as a source of information.
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43

Neal, J. j., and P. Figueira. "Eniric: Extended NIR Information Content." Journal of Open Source Software 4, no. 37 (May 8, 2019): 1053. http://dx.doi.org/10.21105/joss.01053.

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44

Heery, Rachel. "Information gateways: collaboration on content." Online Information Review 24, no. 1 (February 2000): 40–45. http://dx.doi.org/10.1108/14684520010320077.

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45

Dauter, Zbigniew, Manfred S. Weiss, Howard Einspahr, and Edward N. Baker. "Expectation bias and information content." Acta Crystallographica Section F Structural Biology and Crystallization Communications 69, no. 2 (January 19, 2013): 83. http://dx.doi.org/10.1107/s1744309113001486.

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46

WEISS, OLAF, MIGUEL A. JIMÉNEZ-MONTAÑO, and HANSPETER HERZEL. "Information Content of Protein Sequences." Journal of Theoretical Biology 206, no. 3 (October 2000): 379–86. http://dx.doi.org/10.1006/jtbi.2000.2138.

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47

Vardarajan, Badri N., Gary W. Beecham, and Jonathan L. Haines. "Pedigree Selection and Information Content." Current Protocols in Human Genetics 97, no. 1 (April 2018): e56. http://dx.doi.org/10.1002/cphg.56.

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48

"Content Based Document Information Retrieval System." International Journal of Science and Research (IJSR) 6, no. 1 (January 5, 2017): 198–200. http://dx.doi.org/10.21275/art20163876.

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49

"Information content criteria." Government Information Quarterly 16, no. 3 (January 1999): 305–7. http://dx.doi.org/10.1016/s0740-624x(99)80036-7.

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50

Cerbus, R. T., and W. I. Goldburg. "Information content of turbulence." Physical Review E 88, no. 5 (November 13, 2013). http://dx.doi.org/10.1103/physreve.88.053012.

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