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Thematische Bibliographien / Extreme user

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Zeitschriftenartikel

Auswahl der wissenschaftlichen Literatur zum Thema „Extreme user“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

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Zeitschriftenartikel zum Thema "Extreme user"

1

Lee, Seoung Eun and 김승인. "Mobile Fitness Application’s User Experience Study - Analytic focus on Extreme users -." Journal of Digital Design 14, no. 3 (2014): 769–77. http://dx.doi.org/10.17280/jdd.2014.14.3.076.

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2

Basak, A., and S. T. Roy. "Visual Ergonomics for Colourblindness: Applying Universal Design Principles in Graphical User Interface to Provide Affordance to the Colourblind Users." Proceedings of the Design Society 2 (May 2022): 2055–66. http://dx.doi.org/10.1017/pds.2022.208.

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AbstractWith evolution of Graphical User Interface, the access to the computer interface was expanded to cater to the extreme user categories by providing accessibility features and making the computer interface more inclusive. One such extreme user category is colourblind, which has a special accessibility requirement from the GUI. This paper studies the iconography of the Microsoft Windows operating system interface with an objective to develop an inclusive icon design solution that is visually ergonomic for colourblind users to include them as a mainstream user category in a computer interf

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3

Johansson, Stefan, Per-Olof Hedvall, Mia Larsdotter, Thomas P. Larsson, and Catharina Gustavsson. "Co-Designing with Extreme Users: A Framework for User Participation in Design Processes." Scandinavian Journal of Disability Research 25, no. 1 (2023): 418–30. http://dx.doi.org/10.16993/sjdr.952.

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4

Cheng, Zhi Gang, Jie Fan, Xiunian Jing, and Li Lu. "Sub-millikelvin station at Synergetic Extreme Condition User Facility." Chinese Physics B 27, no. 7 (2018): 070702. http://dx.doi.org/10.1088/1674-1056/27/7/070702.

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5

Gamage, Dilrukshi, Indika Perera, and Shantha Fernando. "Exploring MOOC User Behaviors Beyond Platforms." International Journal of Emerging Technologies in Learning (iJET) 15, no. 08 (2020): 161. http://dx.doi.org/10.3991/ijet.v15i08.12493.

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MOOC user behavior is generally studied using the data collected within platform interactions in the learning system or via outside social media platforms. It is important to understand the root causes of anomalies in MOOCs, such as the 80% attrition, less interactions within platforms and what causing the reflected behaviors beyond platforms. We study MOOC student behaviors outside the platform using ethnographic methods, mainly focusing on diary study and interviews. Two groups, 11 extreme users who have completed many MOOCs and 10 who never completed MOOC have been used to collect data. The

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6

Sim, Kok Swee, Desmond Teck Kiang Kho, Mohsen Esmaeilinia, Yang Lee, and Chung Sheng Ee. "Graphic User Interface for Extreme Level Eliminating Adaptive Histogram Equalization." Journal of Image and Graphics 4, no. 1 (2016): 42–45. http://dx.doi.org/10.18178/joig.4.1.42-45.

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7

Mantoro, Teddy, Akeem Olowolayemo, Sunday O. Olatunji, Media A. Ayu, and Abu Osman. "Extreme learning machine for user location prediction in mobile environment." International Journal of Pervasive Computing and Communications 7, no. 2 (2011): 162–80. http://dx.doi.org/10.1108/17427371111146446.

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8

Lwambuka, Ladislaus, and Primus V. Mtenga. "Bridge Management Strategy Based on Extreme User Costs for Bridge Network Condition." Advances in Civil Engineering 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/390359.

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This paper presents a practical approach for prioritization of bridge maintenance within a given bridge network. The maintenance prioritization is formulated as a multiobjective optimization problem where the simultaneous satisfaction of several conflicting objectives includes minimization of maintenance costs, maximization of bridge deck condition, and minimization of traffic disruption and associated user costs. The prevalence of user cost during maintenance period is twofold; the first case refers to the period of dry season where normally the traffic flow is diverted to alternative routes

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9

Li, Yuancheng, Yaqi Cui, and Xiaolong Zhang. "User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 5 (2020): 641–49. http://dx.doi.org/10.2174/2352096512666191004130655.

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Background: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. Objective: The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. Methods: In order to improve the clustering accuracy, this paper proposes

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10

Chua, Tat-Seng, Huanbo Luan, Maosong Sun, and Shiqiang Yang. "NExT: NUS-Tsinghua Center for Extreme Search of User-Generated Content." IEEE Multimedia 19, no. 3 (2012): 81–87. http://dx.doi.org/10.1109/mmul.2012.39.

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