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Libros sobre el tema "Interactive computation"

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Publicado: 27 de febrero de 2024

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1

Goldin, Dina, Scott A. Smolka y Peter Wegner, eds. Interactive Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-34874-3.

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2

Garg, Akash. Interactive, Computation Assisted Design Tools. [New York, N.Y.?]: [publisher not identified], 2020.

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3

France, Société mathématique de, ed. Interactive models of computation and program behavior. Paris: Société mathématique de France, 2009.

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4

Paulson, Lawrence C. Logic and Computation: Interactive Proof with Cambridge LCF. Cambridge: Cambridge University Press, 1987.

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5

Paulson, Lawrence C. Logic and computation: Interactive proof with Cambridge LCF. Cambridge: Cambridge University Press, 1987.

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6

Barkey, Derek A. Manual for program PSTRESS: Peel stress computation. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1987.

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7

C, Madan Ram y Langley Research Center, eds. Manual for program PSTRESS: Peel stress computation. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1987.

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8

M, Vose Emily, ed. Oracle Hyperion interactive reporting 11 expert guide: Master advanced dashboards, JavaScript and computation features of Oracle Hyperion Interactive Reporting 11 and much more. Birmingham, U.K: Packt Pub., 2011.

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9

Aman, Bogdan. Mobility in Process Calculi and Natural Computing. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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10

E, Taplin John H., ed. Cost-benefit analysis and evolutionary computing: Optimal scheduling of interactive road projects. Cheltenham, UK: E. Elgar Pub., 2005.

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11

Crowdsourcing. Cambridge, Massachusetts: The MIT Press, 2013.

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12

Amy, Felty y SpringerLink (Online service), eds. Interactive Theorem Proving: Third International Conference, ITP 2012, Princeton, NJ, USA, August 13-15, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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13

Bazilevs, Yuri, Kenji Takizawa y Tayfun E. Tezduyar. Computational Fluid-Structure Interaction. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118483565.

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14

Kajler, N., ed. Computer-Human Interaction in Symbolic Computation. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6461-7.

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15

N, Kajler, ed. Computer-human interaction in symbolic computation. Wien: Springer-Verlag, 1998.

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16

Protein interaction networks: Computational analysis. Cambridge: Cambridge University Press, 2009.

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17

M, Baecker Ronald y University of Toronto at Mississauga. Dept. of Computer Science., eds. The design of interactive computational media : CSC 318S. Mississauga: University of Toronto at Mississauga, 2005.

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18

Jaiman, Rajeev Kumar y Vaibhav Joshi. Computational Mechanics of Fluid-Structure Interaction. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5355-1.

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19

Haller, Susan, Alfred Kobsa y Susan McRoy, eds. Computational Models of Mixed-Initiative Interaction. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-1118-0.

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20

Haase, Werner, Vittorio Selmin y Bengt Winzell, eds. Progress in Computational Flow-Structure Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45489-2.

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21

Susan, Haller, Kobsa Alfred y McRoy Susan, eds. Computational models of mixed-initiative interaction. Dordrecht: Kluwer Academic, 1999.

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22

Philip, Agre y Rosenschein Stanley J, eds. Computational theories of interaction and agency. Cambridge, Mass: MIT Press, 1996.

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23

1932-, Miller C. A., U.S. Nuclear Regulatory Commission. Office of Nuclear Reactor Regulation. Division of Engineering., City University of New York. City College. Earthquake Research Center. y Viking Systems International, eds. Consideration of uncertainties in soil-structure interaction computations. Washington, DC: Division of Engineering, Office of Nuclear Reactor Regulation, U.S. Nuclear Regulatory Commission, 1992.

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24

1932-, Miller C. A., U.S. Nuclear Regulatory Commission. Office of Nuclear Reactor Regulation. Division of Engineering., City University of New York. City College. Earthquake Research Center. y Viking Systems International, eds. Consideration of uncertainties in soil-structure interaction computations. Washington, DC: Division of Engineering, Office of Nuclear Reactor Regulation, U.S. Nuclear Regulatory Commission, 1992.

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25

Computational cancer biology: An interaction network approach. London: Springer, 2012.

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26

Vidyasagar, Mathukumalli. Computational Cancer Biology: An Interaction Network Approach. London: Springer London, 2012.

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27

A, Tsihrintzis George y Jain L. C, eds. Multimedia services in intelligent environments: Advanced tools and methodologies. Berlin: Springer, 2008.

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28

Jankowski, Andrzej. Interactive Granular Computations in Networks and Systems Engineering: A Practical Perspective. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57627-5.

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29

Karapanos, Evangelos. Modeling Users' Experiences with Interactive Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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30

United States. National Aeronautics and Space Administration., ed. Interactive computer graphics applications for compressible aerodynamics. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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31

Interactive Computation: The New Paradigm. Springer, 2006.

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32

Wegner, Peter, Scott A. Smolka y Dina Goldin. Interactive Computation: The New Paradigm. Springer, 2010.

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33

Wegner, Peter, Scott A. Smolka y Dina Goldin. Interactive Computation: The New Paradigm. Springer London, Limited, 2006.

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34

Paulson, Lawrence C. Logic and Computation: Interactive Proof with Cambridge LCF. Cambridge University Press, 2011.

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35

McDill, Jean Marie, Beverly West, Steven H. Strogatz y John Cantwell. Interactive Differential Equations. 2a ed. Addison Wesley, 1999.

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36

McDill, Jean Marie, Beverly West, Hubert Hohn, Steven H. Strogatz y John Cantwell. Interactive Differential Equations. Addison Wesley Longman, 1996.

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37

McDill, Jean Marie, Beverly West, Hubert Hohn, Steven H. Strogatz y John Cantwell. Interactive Differential Equations. 2a ed. Addison-Wesley, 1997.

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38

Wang, Jay y Adam Wang. Introduction to Computation in Physical Sciences: Interactive Computing and Visualization with Python. Springer International Publishing AG, 2022.

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39

Logic and Computation: Interactive Proof with Cambridge LCF (Cambridge Tracts in Theoretical Computer Science). Cambridge University Press, 1990.

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40

Makedon, Fillia y Peter A. Gloor. Parallel Computation: Practical Implementation of Algorithms and Machines : Interactive Conference Proceedings/Booklet and Cd-Rom. Springer-Verlag Telos, 1993.

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41

Aman, Bogdan y Gabriel Ciobanu. Mobility in Process Calculi and Natural Computing. Springer, 2014.

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42

Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi y Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.

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Resumen
This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design more expressive, efficient and versatile interaction.
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43

Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi y Andrew Howes. Computational Interaction. Oxford University Press, 2018.

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44

Computational Interaction. Oxford University Press, 2018.

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45

Brabham, Daren C. Crowdsourcing. MIT Press, 2013.

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46

Taplin, John H. E., Min Qiu y Renlong Han. Cost-Benefit Analysis And Evolutionary Computing: Optimal Scheduling of Interactive Road Projects (Transport Economics, Management and Policy). Edward Elgar Publishing, 2005.

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47

Law, Edith y Luis von Ahn. Human Computation. Morgan & Claypool Publishers, 2011.

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48

Computational Fluid-Structure Interaction. Elsevier, 2019. http://dx.doi.org/10.1016/c2017-0-00711-5.

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49

Thomaz, Andrea, Maya Cakmak y Guy Hoffman. Computational Human-Robot Interaction. Now Publishers, 2016.

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50

Brumby, Duncan P., Christian P. Janssen, Tuomo Kujala y Dario D. Salvucci. Computational Models of User Multitasking. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0013.

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When users interact with computers and technology ‘in the wild’, multitasking is a practically ubiquitous part of their interactions. Human-computer interaction (HCI) researchers and practitioners have increasingly used computational models to better understand these multitasking behaviours and to build new interactive technologies that facilitate interaction and/or mitigate the problems that arise from multitasking and distraction. This chapter outlines three approaches for modelling: cognitive architectures, cognitive constraint modelling, and uncertainty modelling. These approaches are some of the most common and powerful approaches to computational models of user multitasking, and have complementary strengths. It draws on examples from several domains for which multitasking is a central component, giving a particular focus to in-car multitasking and driver distraction.
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