Relevant bibliographies by topics / Adaptive interface

Academic literature on the topic 'Adaptive interface'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Adaptive interface"

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Hasan, Sajib. "Adaptive Fitts for Adaptive Interface." AIUB Journal of Science and Engineering (AJSE) 17, no. 2 (July 31, 2018): 51–58. http://dx.doi.org/10.53799/ajse.v17i2.9.

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Adaptive interface would enable Human Computer Interaction apply machine learning to cope with human carelessness (mistakes), understand user performance level and provide an interaction interface accordingly. This study tends to translate the theoretical issues of human task into working model by investigating and implementing the predicting equation of human psychomotor behavior to a rapid and aimed movement, developed by Paul Fitt in 1954. The study finds logarithmic speed-accuracy trade-off and predict user performance in a common task “point-select” using common input device mouse. The performance of user is visualized as an evidence and this visualization make a valuable step toward understanding the change required in user interface to make the interface adaptive and consistent. It proposed a method of calculating the amount of change required through learning; add extension to the theory of machine intelligence and increase knowledge of Fitts applicability in terms of machine learning.
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Gong, Qing, and Gavriel Salvendy. "Design of Skill-Based Adaptive Interface: The Effect of a Gentle Push." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 4 (October 1994): 295–99. http://dx.doi.org/10.1177/154193129403800423.

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To accommodate individual skill differences in using a computer interface, a skill adaptive interface was designed and tested. Current human-computer interaction modes can be classified into two types, recall and recognition based interfaces. They have different memory requirements and generally allow different operating speeds and learning time. However, a static combination of the two interface modes has drawbacks. The dynamic skill adaptive interface introduced in this study tries to eliminate these problems without sacrificing the advantages from either interface modes by gently ‘pushing’ users to switch to the appropriate interface mode. Experiment showed that the adaptive interface can yield significant better performances than the static hybrid interface for certain groups of users.
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Marsh, William E., Jonathan W. Kelly, Julie Dickerson, and James H. Oliver. "Fuzzy Navigation Engine: Mitigating the Cognitive Demands of Semi-Natural Locomotion." Presence: Teleoperators and Virtual Environments 23, no. 3 (October 1, 2014): 300–319. http://dx.doi.org/10.1162/pres_a_00195.

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Many interfaces exist for locomotion in virtual reality, although they are rarely considered fully natural. Past research has found that using such interfaces places cognitive demands on the user, with unnatural actions and concurrent tasks competing for finite cognitive resources. Notably, using semi-natural interfaces leads to poor performance on concurrent tasks requiring spatial working memory. This paper presents an adaptive system designed to track a user's concurrent cognitive task load and adjust interface parameters accordingly, varying the extent to which movement is fully natural. A fuzzy inference system is described and the results of an initial validation study are presented. Users of this adaptive interface demonstrated better performance than users of a baseline interface on several movement metrics, indicating that the adaptive interface helped users manage the demands of concurrent spatial tasks in a virtual environment. However, participants experienced some unexpected difficulties when faced with a concurrent verbal task.
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Eray, Ekin, Benjamin Sanchez, and Carl Haas. "Usage of Interface Management System in Adaptive Reuse of Buildings." Buildings 9, no. 5 (April 30, 2019): 105. http://dx.doi.org/10.3390/buildings9050105.

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Adaptive reuse of buildings is considered a superior alternative for new construction in terms of sustainability and a disruptive practice in the current capital project delivery model for the renewal of today’s built environment. In comparison to green-field construction projects, adaptive reuse projects require distinct stages, definition of interfaces, decision gates, and planning methods in order to secure the success of the building project. Unfortunately, little research has been done regarding establishing feasible systems for the planning, assessment, and management of adaptive reuse projects, leading to underperforming building projects outcomes. Interface management (IM) can improve renovation projects outcomes by defining appropriate ways to identify, record, monitor, and track project interfaces. IM has the potential of bringing cost and time benefits during adaptive reuse projects execution. The aim of this study is to develop a reference framework for implementing IM for adaptive reuse projects. First, the inefficiencies of redevelopment projects are explained inside of a circular economy (CE) context. Second, an ontology of IM for adaptive reuse projects is defined based on the current barriers to adaptive reuse and the most common interface problems in construction projects. Third, the defined ontology is expanded through a case study by showing examples of adaptive reuse barriers on a case project, and how IM could have been part of the solution for these problems. Finally, this study concludes with the suggestions on interface management systems (IMS) implementation for future adaptive reuse projects.
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OHTA, Tokio. "Adaptive Complexity and Interface Science." Hyomen Kagaku 18, no. 8 (1997): 510–11. http://dx.doi.org/10.1380/jsssj.18.510.

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Dusan, Sorin, and James Flanagan. "Adaptive interface for spoken dialog." Journal of the Acoustical Society of America 111, no. 5 (2002): 2481. http://dx.doi.org/10.1121/1.4778628.

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Popovic, Jelena, and Olof Runborg. "Adaptive fast interface tracking methods." Journal of Computational Physics 337 (May 2017): 42–61. http://dx.doi.org/10.1016/j.jcp.2017.02.017.

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Zhao, Meng, Wenjun Ying, John Lowengrub, and Shuwang Li. "An Efficient Adaptive Rescaling Scheme for Computing Moving Interface Problems." Communications in Computational Physics 21, no. 3 (February 7, 2017): 679–91. http://dx.doi.org/10.4208/cicp.oa-2016-0040.

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AbstractIn this paper, we present an efficient rescaling scheme for computing thelong-timedynamics of expanding interfaces. The idea is to design an adaptive time-space mapping such that in the new time scale, the interfaces evolves logarithmically fast at early growth stage and exponentially fast at later times. The new spatial scale guarantees the conservation of the area/volume enclosed by the interface. Compared with the original rescaling method in [J. Comput. Phys. 225(1) (2007) 554–567], this adaptive scheme dramatically improves the slow evolution at early times when the size of the interface is small. Our results show that the original three-week computation in [J. Comput. Phys. 225(1) (2007) 554–567] can be reproduced in about one day using the adaptive scheme. We then present the largest and most complicated Hele-Shaw simulation up to date.
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Zaman, Atif, Mudassar Ahmad, Shafique Ahamd, and Tasleem Mustafa. "Adaptive Graphical User Interface for Web Applications Using Aspect Oriented Component Engineering." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 2 (August 5, 2013): 1384–92. http://dx.doi.org/10.24297/ijct.v10i2.3304.

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Graphical User Interface (GUI) is considered to be an essential part in any web applications development. Aspect-Oriented Component Engineering (AOCE) is new approach for developing more and higher quality reusable and adaptable software or web applications components. AOCE uses the idea of providing and requiring services. Adaptable user interface for AOCE based development has not yet been considered to web applications. Simple and easy user interface facilitate users by which application or web interface can be operated effectively. The purpose of this study is to discuss popular user interfaces and suggestions for adaptable GUI designing. The example of adaptation at different levels includes architecture, presentation, extension and composition. Furthermore the AOCE common systemic aspects are discussed for web interfaces adaptivity.
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Bunt, Andrea, Cristina Conati, and Joanna McGrenere. "Mixed-Initiative Interface Personalization as a Case Study in Usable AI." AI Magazine 30, no. 4 (January 2, 2010): 58. http://dx.doi.org/10.1609/aimag.v30i4.2264.

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Interface personalization aims to streamline the process of working in a feature-rich application by providing the user with an adapted interface tailored specifically to his/her needs. The MICA (Mixed-Initiative Customization Assistance) system explores a middle ground between two opposing approaches to personalization: (1) an adaptable approach, where personalization is fully user controlled and (2) and adaptive approach, where personalization is fully system controlled. We overview MICA’s strategy for providing user-adaptive recommendations to help users decide how to personalize their interfaces. In doing so, we focus primarily on how MICA handles threats to usability that are often found in adaptive interfaces including obtrusiveness and lack of understandability and control. We also describe how we evaluated MICA and highlight results from these evaluations.
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Dissertations / Theses on the topic "Adaptive interface"

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Keeble, Richard John. "An adaptive environment for personal information management." Thesis, Brunel University, 1999. http://bura.brunel.ac.uk/handle/2438/6509.

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This dissertation reports the results of research into the provision of adaptive user interfaces to support individuals in the management of their personal information. Many individuals find that they have increased responsibility for managing aspects of their own lives, including the information associated with their jobs. In contrast with traditional approaches to information management, which are generally driven by organisational or business requirements, the requirements of personal information management systems tend to be less rigidly defined. This dissertation employs research from the areas of personal information management and adaptive user interfaces - systems which can monitor how they are used, and adapt on a personal level to their user - to address some of the particular requirements of personal information management systems. An adaptive user interface can be implemented using a variety of techniques, and this dissertation draws on research from the area of software agents to suggest that reactive software agents can be fruitfully applied to realise the required adaptivity. The reactive approach is then used in the specification and development of an adaptive interface which supports simple elements of personal information management tasks. The resulting application is evaluated by means of user trials and a usability inspection, and the theoretical architectures and techniques used in the specification and development of the software are critically appraised. The dissertation demonstrates an application of reactive software agents in adaptive systems design and shows how the behaviour of the system can be specified based on the analysis of some representative personal information management tasks.
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Tam, Hing Sang Derek. "Adaptive equalization for serial digital interface." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0010/MQ34140.pdf.

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Xu, Yong. "Developing Mutually Adaptive Human Agent Interface." 京都大学 (Kyoto University), 2010. http://hdl.handle.net/2433/120378.

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Cavanaugh, Curtis. "AN ADAPTIVE ELECTRONIC INTERFACE FOR GAS SENSORS." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-20020108-121219.

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CAVANAUGH, CURTIS C. An Adaptive Electronic Interface for Gas Sensors (Under the direction of H. Troy Nagle).This thesis focuses on the development of an adaptive electronic interface for gas sensors that are used in the NC State electronic nose. We present an adaptive electronic interface that allows for the accurate mapping of the sensor?s voltage output to sensor resistance profiles. The adaptive interface uses a linearized Wheatstone bridge in a constant current configuration. The balancing of the bridge and the adjustment of the subsequent gain stage is performed using programmable variable resistors. The programmable resistors are controlled by a LabVIEW® program. The same control program also determines and records all the resistor values in the interface circuit. The resistance of each sensor is accurately computed by LabVIEW® using the interface-circuit, resistor values, and the voltage output of the circuit. Compensating for sensor drift can be done in LabVIEW® by adjusting the programmable resistor values so that a zero-voltage output is produced during the reference cycle. By doing this zero adjustment between each ?sniff? of an odorant, the baseline drift can be minimized.A single channel of the adaptive electronic interface has been designed and tested. The interface can be calibrated so that it is 99% accurate when performing sensor resistance measurements.A new conducting polymer sensor chamber has also been designed and tested. The new radial flow sensor chamber was minimizes the dead volume in the chamber and also deliver the odorant to each sensor at the same time. Two operating modes were compared: continuous-flow and sniff-and-hold. Both modes gave good classification performance while testing four different coffee samples. Experimental testing indicates that sensor response is highly correlated with the sample flow rate. Future work to more fully characterize this correlation is recommended.

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Noriega, Atala Enrique. "An Evaluation Framework for Adaptive User Interface." Thesis, The University of Arizona, 2014. http://hdl.handle.net/10150/323226.

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With the rise of powerful mobile devices and the broad availability of computing power, Automatic Speech Recognition is becoming ubiquitous. A flawless ASR system is still far from existence. Because of this, interactive applications that make use of ASR technology not always recognize speech perfectly, when not, the user must be engaged to repair the transcriptions. We explore a rational user interface that uses of machine learning models to make its best effort in presenting the best repair strategy available to reduce the time in spent the interaction between the user and the system as much as possible. A study is conducted to determine how different candidate policies perform and results are analyzed. After the analysis, the methodology is generalized in terms of a decision theoretical framework that can be used to evaluate the performance of other rational user interfaces that try to optimize an expected cost or utility.
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Sabawi, Younis Abid. "Adaptive discontinuous Galerkin methods for interface problems." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/39386.

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The aim of this thesis is to derive adaptive methods for discontinuous Galerkin approximations for both elliptic and parabolic interface problems. The derivation of adaptive method, is usually based on a posteriori error estimates. To this end, we present a residual-type a posteriori error estimator for interior penalty discontinuous Galerkin (dG) methods for an elliptic interface problem involving possibly curved interfaces, with flux-balancing interface conditions, e.g., modelling mass transfer of solutes through semi-permeable membranes. The method allows for extremely general curved element shapes employed to resolve the interface geometry exactly. Respective upper and lower bounds of the error in the respective dG-energy norm with respect to the estimator are proven. The a posteriori error bounds are subsequently used to prove a basic a priori convergence result. Moreover, a contraction property for a standard adaptive algorithm utilising these a posteriori bounds, with a bulk refinement criterion is also shown, thereby proving that the a posteriori bounds can lead to a convergent adaptive algorithm subject to some mesh restrictions. This work is also concerned with the derivation of a new L1∞(L2)-norm a posteriori error bound for the fully discrete adaptive approximation for non-linear interface parabolic problems. More specifically, the time discretization uses the backward Euler Galerkin method and the space discretization uses the interior penalty discontinuous Galerkin finite element method. The key idea in our analysis is to adapt the elliptic reconstruction technique, introduced by Makridakis and Nochetto [48], enabling us to use the a posteriori error estimators derived for elliptic interface models and to obtain optimal order in both L1∞(L2) and L1∞(L2) + L2(H¹) norms. The effectiveness of all the error estimators and the proposed algorithms is confirmed through a series of numerical experiments.
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Hanumansetty, Reena Gowri. "Model based approach for context aware and adaptive user interface generation." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/10087.

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User interface design and development for ubiquitous software applications is challenged by the presence of varying contexts. Context comprises of user'­s computing platform, the environment in which the user is interacting with the application and user characteristics which comprise of user's behavior during interaction and user preferences for interface display and interaction. We present a framework for adaptive user interface generation where adaptation occurs when context changes. This framework introduces three new concepts. First, formalization for representing context is introduced. Our design of context specification is unique since it reflects the association of context with level and nature of user interface adaptation. Secondly, user interface generation life cycle is studied and we define a context model on top of task model to introduce the contextual conditions into user interface generation process. Using the context model, user interface designer can specify contextual requirements and its effect on the user interface. Third, context aware adaptation of user interfaces is achieved by mapping context specifications to various levels of user interface generation life cycle. We designed a specification language called rule specification using which the user interface designer can specify the mapping. With the new design of context representation, context model, and rule specification, we demonstrate how changes in contexts adapts task model which in turn adapts the user interface.
Master of Science
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Hassell, Jonathan Philip. "Automatic macro extraction : generalisation and the adaptive interface." Thesis, University of York, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297112.

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Umapathy, Vijay. "An adaptive user interface for open educational content." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66706.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 67).
For my Masters of Engineering thesis project, I have developed a framework of meta- data around open educational resources that are used by students to master a given concept. Based on the assumption that a course can be broken down into specfic concepts and that there are several resources available to students both on sites such as OpenCourseWare and also peer-managed resources like Wikipedia, I develop a model for "relevance" of a resource to the learning of a given concept, where relevance is defined by assignment to a concept by a teacher as well as usage data from students viewing the resources. I combine both active feedback (clicking a "like" button) and passive feedback (view duration and count across all users) to score each resource for a given concept. This score is then used to create an adaptive user interface that indicates which resources may be the most relevant for students learning a given concept based on their peers' feedback. In addition to designing this framework, I provide an implementation of the adaptive user interface on Apple's iPad device, and I provide an evaluation of the educational benefits of this adaptive interface by designing and executing a controlled user study on students in MIT's introductory physics course.
by Vijay Umapathy.
M.Eng.
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Oskoei, Mohammadreza Asghari. "Developing adaptive myoelectric human-machine interface for rehabilitation." Thesis, University of Essex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502190.

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Books on the topic "Adaptive interface"

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Tam, Hing Sang Derek. Adaptive equalization for serial digital interface. Ottawa: National Library of Canada, 1998.

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Eccles, Simon R. PSI, a personalised system interface: An investigation into an adaptive interface as a means to acquiring operatingsystem proficiency. (s.l: The Author), 1988.

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Doherty, Marian A. C. Can interface design significantly improve the on-line tutorial by making it adaptive and interactive?. [S.l: The author], 1990.

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Kokswijk, Jacob van. Hum@n: Telecoms and Internet as interface to interreality: a search for adaptive technology and defining users. Zwolle: Bergboek, 2003.

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Kokswijk, Jacob van. Hum@n : telecoms and internet as interface to interreality: A search for adaptive technology and defining users. [Zwolle]: Bergboek.nl, 2005.

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Lehman, Jill Fain. Adaptive parsing: Self-extending natural language interfaces. Boston: Kluwer Academic, 1992.

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Lehman, Jill Fain. Adaptive Parsing: Self-Extending Natural Language Interfaces. Boston, MA: Springer US, 1992.

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Bezold, Matthias. Adaptive multimodal interactive systems. New York: Springer, 2011.

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Herbordt, Wolfgang. Sound capture for human/machine interfaces: Practical aspects of microphone array signal processing. Berlin: Springer, 2005.

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Herbordt, Wolfgang. Sound capture for human/machine interfaces: Practical aspects of microphone array signal processing. Berlin: Springer, 2005.

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Book chapters on the topic "Adaptive interface"

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Treu, Siegfried. "Adaptive Design." In User Interface Design, 291–325. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2429-8_13.

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Fukuda, Toshio. "Intelligent Adaptive Interface." In Active Media Technology, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45336-9_1.

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Langley, Pat. "User Modeling in Adaptive Interface." In CISM International Centre for Mechanical Sciences, 357–70. Vienna: Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-2490-1_48.

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Duvallet, Claude, Hadhoum Boukachour, and Alain Cardon. "Intelligent and Self-Adaptive Interface." In Intelligent Problem Solving. Methodologies and Approaches, 711–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45049-1_86.

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Magee, John, and Margrit Betke. "HAIL: Hierarchical Adaptive Interface Layout." In Lecture Notes in Computer Science, 139–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14097-6_24.

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Santha, Sunil D. "Actor interface analysis and reflective practice." In Climate Change and Adaptive Innovation, 150–69. 1 Edition. | New York : Routledge, 2020. | Series: Indigenous and environmental social work: Routledge, 2020. http://dx.doi.org/10.4324/9780429203138-7.

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Takahashi, Natalie Mie, and Plinio Thomaz Aquino Junior. "Adaptive Interface Combined to Interface Patterns in Human Factors." In Advances in Intelligent Systems and Computing, 76–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60011-6_8.

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Sánchez, Antonio J., Elena Hernández, Fernando de la Prieta, Juan Manuel Corchado, and Sara Rodríguez. "Describing Interfaces in the Framework of Adaptive Interface Ecosystems." In Progress in Artificial Intelligence, 38–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30244-3_4.

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Mitrović, Nikola, and Eduardo Mena. "Adaptive User Interface for Mobile Devices." In Interactive Systems:Design, Specification, and Verification, 29–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36235-5_3.

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Berry, Dianne C., and Fiorella de Rosis. "Designing an Adaptive Interface for EPIAIM." In AIME 91, 306–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-48650-0_25.

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Conference papers on the topic "Adaptive interface"

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Girouard, Audrey. "Adaptive brain-computer interface." In the 27th international conference extended abstracts. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1520340.1520436.

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Sukaviriya, Piyawadee Noi, and James D. Foley. "Supporting adaptive interfaces in a knowledge-based user interface environment." In the 1st international conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/169891.169922.

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Du, Dingkun, and Kofi Odame. "A bandwidth-adaptive sensor interface." In 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2015. http://dx.doi.org/10.1109/biocas.2015.7348304.

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Bier, Agnieszka, and Zdzisław Sroczyski. "Adaptive Math-To-Speech Interface." In the Mulitimedia, Interaction, Design and Innnovation. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2814464.2814471.

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Tomlinson, Bill, Eric Baumer, Man Lok Yau, Paul Mac Alpine, Lorenzo Canales, Andrew Correa, Bryant Hornick, and Anju Sharma. "Dreaming of adaptive interface agents." In CHI '07 extended abstracts. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1240866.1240940.

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Liang, Ruquan, and Satoru Komori. "Computation of a Propagating Interface in Multiphase Flows Using an Adaptive Coupled Level Set Method." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-39044.

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We present a numerical strategy for a propagating interface in multiphase flows using a level set method combined with a local mesh adaptative technique. We use the level set method to move the propagating interface in multiphase flows. We also use the local mesh adaptative technique to increase the grid resolution at regions near the propagating interface and additionally at the regions near points of high curvature with a minimum of additional expense. For illustration, we apply the adaptive coupled level set method to a collection of bubbles moving under passive transport. Good agreement has been obtained in the comparision of the numerical results for the collection of bubbles using an adaptative grid with those using a single grid. We also apply the adaptive coupled level set method to a droplet falling on a step where it is important to accurately model the effect of surface tension force and the motion of the free-surface, and the numerical results agree very closely with available data.
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Bo Hong, Bin Lou, Jing Guo, and Shangkai Gao. "Adaptive active auditory brain computer interface." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5334133.

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Stenger, B., T. E. Woodley, T. K. Kim, C. Hernandez, and R. Cipolla. "AIDIA - Adaptive Interface for Display InterAction." In British Machine Vision Conference 2008. British Machine Vision Association, 2008. http://dx.doi.org/10.5244/c.22.78.

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Roummieh, Youssef, and Parisa Ghodous. "Adaptive Interface for Collaborative Environments: AICE." In 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing. IEEE, 2009. http://dx.doi.org/10.1109/uic-atc.2009.67.

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"AN ADAPTIVE INTERFACE FOR ACTIVE LOCALIZATION." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003317302480258.

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Reports on the topic "Adaptive interface"

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Hudlicka, Eva, and John Billingsley. ABAIS: Affect and Belief Adaptive Interface System. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada373270.

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Allain, Jean Paul. Adaptive complex nanocomposite alloys for burning plasma-material interface tunability. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1740016.

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Aragones, Amy, Jeanette Bruno, Andrew Crapo, and Marc Garbiras. An Ontology-Based Architecture for Adaptive Work-Centered User Interface Technology. Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada445885.

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Voruganti, Ram R., and Jacob Cohen. DTM-9000 Adaptive Digital Modem AN/TRC-170 Radio Interface Tests. Revision 1. Fort Belvoir, VA: Defense Technical Information Center, March 1987. http://dx.doi.org/10.21236/ada179986.

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Armstrong, Jerawan C., and Jeffrey A. Favorite. Applications of Mesh Adaptive Direct Search Algorithms to Solve Inverse Transport Problem: Unknown Interface Location. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1095220.

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Tyler, Sherman W. Adaptive Interfaces. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada229751.

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Ross, Alexander. Assessing Adaptive Capacity to Climate and Population Change at the Urban-Rural Interface: Human-Water System Dynamics in the Hood River Valley, Oregon. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7358.

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Norcio, A. F., and J. Stanley. Adaptive Human-Computer Interfaces. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada200930.

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Bennett, Kevin B. Dynamically Adaptive Interfaces: A Preliminary Investigation. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada334286.

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Hansberger, Jeffrey T. Development of the Next Generation of Adaptive Interfaces. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada614163.

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