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Статті в журналах з теми "CONTENT ACCESS"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Moffett, Jonathan D., and Morris S. Sloman. "Content-dependent access control." ACM SIGOPS Operating Systems Review 25, no. 2 (April 1991): 63–70. http://dx.doi.org/10.1145/122120.122125.

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2

Jalil Piran, Md, S. M. Riazul Islam, and Doug Young Suh. "CASH: Content- and Network-Context-Aware Streaming Over 5G HetNets." IEEE Access 6 (2018): 46167–78. http://dx.doi.org/10.1109/access.2018.2864790.

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3

Fan, Yuqi, Bing Yang, Donghui Hu, Xiaohui Yuan, and Xiong Xu. "Social- and Content-Aware Prediction for Video Content Delivery." IEEE Access 8 (2020): 29219–27. http://dx.doi.org/10.1109/access.2020.2972920.

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4

Kim, Guhyun, Vladimir Kornijcuk, Jeeson Kim, Dohun Kim, Cheol Seong Hwang, and Doo Seok Jeong. "Combination-Encoding Content-Addressable Memory With High Content Density." IEEE Access 7 (2019): 137620–28. http://dx.doi.org/10.1109/access.2019.2942150.

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5

Li, Chengming, Shimin Gong, Xiaojie Wang, Lei Wang, Qingshan Jiang, and Koji Okamura. "Secure and Efficient Content Distribution in Crowdsourced Vehicular Content-Centric Networking." IEEE Access 6 (2018): 5727–39. http://dx.doi.org/10.1109/access.2017.2778502.

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6

Ryu, Seung-Kwan, Chang-Hee Park, and Jong-Bae Hong. "Content Analysis of Public Access Programs." Journal of the Korea Contents Association 12, no. 1 (January 28, 2012): 146–53. http://dx.doi.org/10.5392/jkca.2012.12.01.146.

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7

Geißler, Stefan. "New methods to access scientific content." Information Services & Use 28, no. 2 (October 2, 2008): 141–46. http://dx.doi.org/10.3233/isu-2008-0563.

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8

Schewe, Klaus Dieter, N. A. Kinshuk, and Tiong Goh. "Content adaptivity in wireless web access." International Journal of Mobile Communications 2, no. 3 (2004): 260. http://dx.doi.org/10.1504/ijmc.2004.005164.

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9

Desai, Rutvik H. "Access and content of abstract concepts." Physics of Life Reviews 29 (July 2019): 166–68. http://dx.doi.org/10.1016/j.plrev.2019.03.010.

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10

Koumpis, K., and S. Renals. "Content-based access to spoken audio." IEEE Signal Processing Magazine 22, no. 5 (September 2005): 61–69. http://dx.doi.org/10.1109/msp.2005.1511824.

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11

Bieri, Tamberlyn, Darin Blasiar, Philip Ozersky, Igor Antoshechkin, Carol Bastiani, Payan Canaran, Juancarlos Chan, et al. "WormBase: new content and better access." Nucleic Acids Research 35, suppl_1 (November 11, 2006): D506—D510. http://dx.doi.org/10.1093/nar/gkl818.

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12

Linares, Rocío, and Santiago Pelegrina. "Focus Switching in Working Memory." Experimental Psychology 65, no. 3 (May 2018): 115–27. http://dx.doi.org/10.1027/1618-3169/a000397.

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Анотація:

Abstract. Focus switching in working memory involves accessing an object in the focus of attention in order to retrieve its content. Objects in working memory can be viewed as consisting of two types of information: contents (e.g., numerical information) and contexts (e.g., cues to retrieve the contents). This study examined the extent to which content retrieval and context access may be separated. Three experiments were carried out in which object switching and content retrieval were manipulated. In addition, the alternation between the retrieval operations was also manipulated. The main result was that content retrieval required time over and above that needed to access the object. This finding supports the idea that contexts and their contents may be accessed independently when an object is brought into the focus.

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13

Salahuddin, Mohammad A., Jagruti Sahoo, Roch Glitho, Halima Elbiaze, and Wessam Ajib. "A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks." IEEE Access 6 (2018): 91–114. http://dx.doi.org/10.1109/access.2017.2754419.

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14

Tran, Thinh Duy, and Long Bao Le. "Joint Resource Allocation and Content Caching in Virtualized Content-Centric Wireless Networks." IEEE Access 6 (2018): 11329–41. http://dx.doi.org/10.1109/access.2018.2804902.

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15

Xu, Xiaogeng, Chunyan Feng, Siyang Shan, Tiankui Zhang, and Jonathan Loo. "Proactive Edge Caching in Content-Centric Networks With Massive Dynamic Content Requests." IEEE Access 8 (2020): 59906–21. http://dx.doi.org/10.1109/access.2020.2983068.

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16

Liu, Yujie, Dianjie Lu, Guijuan Zhang, Jie Tian, and Weizhi Xu. "Q-Learning Based Content Placement Method for Dynamic Cloud Content Delivery Networks." IEEE Access 7 (2019): 66384–94. http://dx.doi.org/10.1109/access.2019.2917564.

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17

Lyaskovsky, Ivan Ivanovich. "CONTENT OF THE RIGHT TO INFORMATION IN THE CONTEXT OF OPEN ACCESS AND RESTRICTED ACCESS INFORMATION MODE." Information Society, no. 1 (2021): 42–48. http://dx.doi.org/10.52605/16059921_2021_01_42.

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18

Badsha, Shahriar, Ibrahim Khalil, Xun Yi, and Mohammed Atiquzzaman. "Designing Privacy-Preserving Protocols for Content Sharing and Aggregation in Content Centric Networking." IEEE Access 6 (2018): 42119–30. http://dx.doi.org/10.1109/access.2018.2856299.

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19

Zhang, Tiankui, Xinyuan Fang, Yuanwei Liu, and Arumugam Nallanathan. "Content-Centric Mobile Edge Caching." IEEE Access 8 (2020): 11722–31. http://dx.doi.org/10.1109/access.2019.2962856.

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20

Xu, Kai, Xiang Li, Sanjay Kumar Bose, and Gangxiang Shen. "Joint Replica Server Placement, Content Caching, and Request Load Assignment in Content Delivery Networks." IEEE Access 6 (2018): 17968–81. http://dx.doi.org/10.1109/access.2018.2817646.

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21

Yang, Steven J., and Norman W. Shao. "An Ontology-Based Content Model for Intelligent Web Content Access Services." International Journal of Web Services Research 3, no. 2 (April 2006): 61–80. http://dx.doi.org/10.4018/jwsr.2006040103.

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22

Htet Hlaing, Htet, Yuki Funamoto, and Masahiro Mambo. "Secure Content Distribution with Access Control Enforcement in Named Data Networking." Sensors 21, no. 13 (June 30, 2021): 4477. http://dx.doi.org/10.3390/s21134477.

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Анотація:

NDN is one of the new emerging future internet architectures which brings up new solutions over today’s internet architecture, facilitating content distribution, in-network caching, mobility support, and multicast forwarding. NDNs ubiquitous in-network caching allows consumers to access data directly from the intermediate router’s cache. However, it opens content privacy problems since data packets replicated in the router are always accessible by every consumer. Sensitive contents in the routers should be protected and accessed only by authorized consumers. Although the content protection problem can be solved by applying an encryption-based access control policy, it still needs an efficient content distribution scheme with lower computational overhead and content retrieval time. We propose an efficient and secure content distribution (ES_CD), by combining symmetric encryption and identity-based proxy re-encryption. The analysis shows that our proposed scheme achieves content retrieval time reduction up to 20% for the cached contents in our network simulation environment and a slight computational overhead of less than 19 ms at the content producer and 9 ms at the consumer for 2 KB content. ES_CD provides content confidentiality and ensures only legitimate consumers can access the contents during a predefined time without requiring a trusted third party and keeping the content producer always online.

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23

McLaughlin, Brian P., and Michael Tye. "Is Content-Externalism Compatible with Privileged Access?" Philosophical Review 107, no. 3 (July 1998): 349. http://dx.doi.org/10.2307/2998442.

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24

Marsden, Christopher. "Net Neutrality and Consumer Access to Content." SCRIPT-ed 4, no. 4 (December 15, 2007): 407–35. http://dx.doi.org/10.2966/scrip.040407.407.

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25

Mohan, R., J. R. Smith, and Chung-Sheng Li. "Adapting multimedia Internet content for universal access." IEEE Transactions on Multimedia 1, no. 1 (March 1999): 104–14. http://dx.doi.org/10.1109/6046.748175.

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26

Guarino, N., C. Masolo, and G. Vetere. "OntoSeek: content-based access to the Web." IEEE Intelligent Systems 14, no. 3 (May 1999): 70–80. http://dx.doi.org/10.1109/5254.769887.

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27

Yang Li. "Gesture Search: Random Access to Smartphone Content." IEEE Pervasive Computing 11, no. 1 (2012): 10–13. http://dx.doi.org/10.1109/mprv.2012.9.

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28

Jeannin, S., and B. Mory. "Video motion representation for improved content access." IEEE Transactions on Consumer Electronics 46, no. 3 (2000): 645–55. http://dx.doi.org/10.1109/30.883425.

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29

Ricci, Fernanda, Aishwarya Subramanian, and Mark Wade. "Open Access to High-Content Clonogenic Analysis." Journal of Biomolecular Screening 20, no. 3 (November 7, 2014): 391–401. http://dx.doi.org/10.1177/1087057114557775.

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Анотація:

Image-processing programs are used to identify and classify eukaryotic cell colonies as spots following seeding at low density on dishes or in multiwell plates. The output from such approaches, however, is generally limited to 1–2 parameters, and there is no ability to extract phenotypic information at the single colony level. Furthermore, there is a lack of user-friendly pipelines for analysis of clonogenicity in the context of high-content analysis. This article describes an experimental and multiparametric image analysis workflow for clonogenic assays in multiwell format, named the Colony Assay Toolbox (CAT). CAT incorporates a cellular-level resolution of individual colonies and facilitates the extraction of phenotypic information, including the number and size of colonies and nuclei, as well as morphological parameters associated with each structure. Furthermore, the pipeline is capable of discriminating between colonies composed of senescent and nonsenescent cells. We demonstrate the accuracy and flexibility of CAT by interrogating the effects of 2 preclinical compounds, Nutlin-3a and ABT-737, on the growth of human osteosarcoma cells. CAT is accessible to virtually all laboratories because it uses common wide-field fluorescent microscopes, the open-source CellProfiler program for colony image analysis, and a single fluorescent dye for all the segmentation steps.

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30

Burrows, Sandra. "Online Access to Newspaper Content in Canada." Serials Librarian 53, no. 1-2 (September 2007): 151–61. http://dx.doi.org/10.1300/j123v53n01_12.

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31

Bruhn, Allison Leigh, and Ted S. Hasselbring. "Increasing Student Access to Content Area Textbooks." Intervention in School and Clinic 49, no. 1 (March 18, 2013): 30–38. http://dx.doi.org/10.1177/1053451213480030.

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32

Worring, Marcel, and Arnold W. M. Smeulders. "Content based internet access to paper documents." International Journal on Document Analysis and Recognition 1, no. 4 (May 1, 1999): 209–20. http://dx.doi.org/10.1007/s100320050020.

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33

Thomas, Lisa Carlucci. "Subscriptions Are Us: Content, Access, & Collections." Journal of Web Librarianship 6, no. 1 (January 2012): 56–58. http://dx.doi.org/10.1080/19322909.2012.648493.

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34

Hemanathan, V., and N. Anusha. "Role based content access control in NDN." Journal of Innovative Technology and Education 2 (2015): 65–73. http://dx.doi.org/10.12988/jite.2015.51016.

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35

Segalowitz, Sidney J., and Korri C. Lane. "Lexical Access of Function versus Content Words." Brain and Language 75, no. 3 (December 2000): 376–89. http://dx.doi.org/10.1006/brln.2000.2361.

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36

Hijazi, Haytham, Ricardo Couceiro, Joao Castelhano, Paulo De Carvalho, Miguel Castelo-Branco, and Henrique Madeira. "Intelligent Biofeedback Augmented Content Comprehension (TellBack)." IEEE Access 9 (2021): 28393–406. http://dx.doi.org/10.1109/access.2021.3058664.

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37

Tu, Wang, Xin Jin, Lingjun Li, Chenggang Yan, Yaoqi Sun, Mang Xiao, Weidong Han, and Jiyong Zhang. "Efficient Content Adaptive Plenoptic Video Coding." IEEE Access 8 (2020): 5797–804. http://dx.doi.org/10.1109/access.2020.2964056.

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38

Chen, Rong-Huei, and Shi-Chung Chang. "Modeling Content and Membership Growth Dynamics of User-Generated Content Sharing Networks With Two Case Studies." IEEE Access 6 (2018): 4779–96. http://dx.doi.org/10.1109/access.2017.2789334.

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39

Sharma, Abhishek. "CP-CDN: Content Delivery Powered By Context and User Intelligence." Mathematical Statistician and Engineering Applications 70, no. 1 (January 31, 2021): 155–63. http://dx.doi.org/10.17762/msea.v70i1.2295.

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Анотація:

In order to improve end users' Web access experiences, today's Internet content providers generally employ Content Delivery Networks (CDNs) to provide material to end users. To comprehend reasons, methods, and amounts of CP-level intelligences may aid content distribution, we employ a measurement-based approach. The largest content provider in China, Tencent, constructed a CDN, which we first offer as a measurement study. Contrarily, CPCDN may leverage context intelligence—which captures how contents are constantly manipulated then synthesized in various circumstances —in order to optimise the user experience and service quality throughout content delivery since it is aware of the precise environment in which material is consumed. In this study, we use a measurement-based approach to reasons, methods, and amounts of CP-level information may aid content delivery in order to support the quick transition to CPCDN. We proactively replicate content based on interest groups based on crowd patterns so that users can access their desired content from nearby CPCDN servers. CPCDN is able to raise the level of both content delivery efficiency and user experience by using the potent user intelligence that captures how contents are constantly exchanged and disseminated across social networked users.

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40

Raicevic, Maja, and Amulya K. Saxena. "Assessment of public access YouTube™ content for pediatric minimal access surgery education." Journal of Pediatric Endoscopic Surgery 1, no. 1 (April 26, 2019): 41–43. http://dx.doi.org/10.1007/s42804-019-00010-3.

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41

Zhong, Zhenyu, Jianmin Qin, Zeyu Zhong, and Zhang Li. "Fog Radio Access Networks With Hierarchical Content Delivery." IEEE Access 7 (2019): 20950–60. http://dx.doi.org/10.1109/access.2019.2898349.

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42

Zhang, Xiaoli. "Content-Based E-Commerce Image Classification Research." IEEE Access 8 (2020): 160213–20. http://dx.doi.org/10.1109/access.2020.3018877.

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43

Zhang, Guanglin, Jian Liu, Jiajie Ren, Lin Wang, and Jinbei Zhang. "Capacity of Content-Centric Hybrid Wireless Networks." IEEE Access 5 (2017): 1449–59. http://dx.doi.org/10.1109/access.2017.2669111.

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44

Li, Bing, Maode Ma, and Xinbin Yang. "Advanced Perceptive Forwarding in Content-Centric Networks." IEEE Access 5 (2017): 4595–605. http://dx.doi.org/10.1109/access.2017.2676341.

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45

Chen, Hao, Yi He, Ling Wei, Xiqi Li, Jinsheng Yang, and Yudong Zhang. "A Content-Adaptive Method for Image Denoising." IEEE Access 7 (2019): 183222–35. http://dx.doi.org/10.1109/access.2019.2958697.

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46

Soni, Rajkumar, Prasun Chakrabarti, Zbigniew Leonowicz, Micha Jasinski, Krzysztof Wieczorek, and Vadim Bolshev. "Estimation of Life Cycle of Distribution Transformer in Context to Furan Content Formation, Pollution Index, and Dielectric Strength." IEEE Access 9 (2021): 37456–65. http://dx.doi.org/10.1109/access.2021.3063551.

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47

Antani, S., Rodney Long, and T. M. Deserno. "Content-based Image Retrieval for Scientific Literature Access." Methods of Information in Medicine 48, no. 04 (2009): 371–80. http://dx.doi.org/10.3414/me0561.

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Анотація:

Summary Objectives: An increasing number of articles are published electronically in the scientific literature, but access is limited to alphanumerical search on title, author, or abstract, and may disregard numerous figures. In this paper, we estimate the benefits of using content-based image retrieval (CBIR) on article figures to augment traditional access to articles. Methods: We selected four high-impact journals from the Journal Citations Report (JCR) 2005. Figures were automatically extracted from the PDF article files, and manually classified on their content and number of sub-figure panels. We make a quantitative estimate by projecting from data from the Cross-Language Evaluation Forum (Image-CLEF) campaigns, and qualitatively validate it through experiments using the Image Retrieval in Medical Applications (IRMA) project. Results: Based on 2077 articles with 11,753 pages, 4493 figures, and 11,238 individual images, the predicted accuracy for article retrieval may reach 97.08%. Conclusions: Therefore, CBIR potentially has a high impact in medical literature search and retrieval.

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48

Bourne, Philip E., J. Lynn Fink, and Mark Gerstein. "Open Access: Taking Full Advantage of the Content." PLoS Computational Biology 4, no. 3 (March 28, 2008): e1000037. http://dx.doi.org/10.1371/journal.pcbi.1000037.

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49

Deserno, TM, B. Molander, MO Güld, C. Thies, and H.-G. Gröndahl. "Content-based access to oral and maxillofacial radiographs." Dentomaxillofacial Radiology 36, no. 6 (September 2007): 328–35. http://dx.doi.org/10.1259/dmfr/11645252.

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50

Ho, Dean. "ALA Expands Open Access to JALA Scientific Content." Journal of the Association for Laboratory Automation 14, no. 4 (August 2009): A6. http://dx.doi.org/10.1016/j.jala.2009.04.005.

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