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Books on the topic 'User modelling'

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Published: 4 June 2021
Last updated: 29 January 2023

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1

Jones, K. Sparck. Realism about user modelling. Cambridge: University of Cambridge, Computer Laboratory, 1987.

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2

User modelling in text generation. London: Pinter Publishers, 1993.

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3

Kuflik, Tsvi, Shlomo Berkovsky, Francesca Carmagnola, Dominikus Heckmann, and Antonio Krüger, eds. Advances in Ubiquitous User Modelling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05039-8.

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4

Cook, Steven. Modelling generic user-interface with functional programs. London: Queen Mary College, Department of Computer Science and Statistics, 1987.

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5

Kuflik, Tsvi. Advances in ubiquitous user modelling: Revised selected papers. Berlin: Springer, 2009.

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6

Integrated graphic and computer modelling. London: Springer, 2008.

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7

Mooney, Gabrielle Joanne. Intelligent information retrieval from the world wide web using fuzzy user modelling. Leicester: De Montfort University, 1999.

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8

Jaume, Agustí, ed. Software blueprints: Lightweight uses of logic in conceptual modelling. Reading, MA: Addison-Wesley, 1999.

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9

Borsboom van Beurden, J. A. M. (Judith A. M.) and SpringerLink (Online service), eds. Land-Use Modelling in Planning Practice. Dordrecht: Springer Science+Business Media B.V., 2011.

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10

Ender, Konukoglu, Pop Mihaela, Rhode Kawal, Sermesant Maxime, Young Alistair, and SpringerLink (Online service), eds. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: Second International Workshop, STACOM 2011, Held in Conjunction with MICCAI 2011, Toronto, ON, Canada, September 22, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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11

Regoczei, Stephen. On 'Extracting knowledge from text': Modelling the architecture of language users. Toronto: Computer Systems Research Institute, University of Toronto, 1989.

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12

Lammont, Norma. The computer in the workplace: Cognitive and sociological modelling of users. Manchester: University of Manchester, 1995.

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13

Koomen, Eric. Land-Use Modelling in Planning Practice. Dordrecht: Springer Nature, 2011.

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14

Singh, Aditya Kumar, and Ram Sakal Singh. Crop modelling for land use planning. Udaipur: Agrotech Pub. Academy, 2011.

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15

Shuen, Alex Pak Chin. A critique of the financial modelling capability of commonly-used spreadsheet packages. [s.l: The Author], 1990.

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16

Carter, S. P. Review of atmospheric corrosion data and their uses in standards and modelling. Manchester: UMIST, 1996.

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17

Users guide to physical modelling and experimentation: Experience of the HYDRALAB network. Leiden, The Netherlands: CRC Press/Balkema, 2011.

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18

Creaser, Claire. Modelling use at individual service points. Loughborough [England]: Library & Information Statistics Unit, Dept. of Information and Library Studies, Loughborough University, 1998.

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19

Landscape modelling: Geographical space, transformation and future scenarios. Dordrecht: Springer, 2010.

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20

Bardazzi, Rossella, and Leonardo Ghezzi, eds. Macroeconomic modelling for policy analysis. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-396-0.

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Over the last 30 years, the Inforum approach to macro modelling has been shared by economists worldwide. Researchers have focussed much of their efforts to developing a linked system of international interindustry models with a consistent methodology. A world-wide network of research associates use the same methods and software obtaining comparable results. The XXth Inforum World Conference was held in Florence in September 2012 and this book contains a selection of papers presented during that Conference. All these contributions are aimed at policymakers, stakeholders, and applied economists. Some papers are devoted to specific topics (total factor productivity, energy issues, external linkages, demographic changes) and some others are oriented to macro model building and simulations.
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21

Camara, Oscar. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: Third International Workshop, STACOM 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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22

Barra, Tomás de la. Integrated land use and transport modelling: Decision chains and hierarchies. Cambridge: Cambridge University Press, 1989.

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23

South Saskatchewan regional plan: Water quantity and quality modelling results. [Edmonton]: Alberta Environment, 2010.

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24

IMACS International Conference on Mathematical Modelling and Applied Mathematics (1990 Moscow, R.S.F.S.R.). Mathematical modelling and applied mathematics: Proceedings of the IMACS International Conference on Mathematical Modelling and Applied Mathematics, Moscow, USSR, 18-23 June 1990. Amsterdam: North-Holland, 1992.

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25

Hagoort, M. J. The neighbourhood rules: Land-use interactions, urban dynamics and cellular automata modelling. Utrecht: Universiteit Utrecht, 2006.

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26

de, Nijs Ton. Modelling land use change: Improving the prediction of future land use patterns. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, 2009.

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27

Modelling land use change: Improving the prediction of future land use patterns. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, 2009.

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28

Pejtersen, Annelise Mark. The Book House: Modelling users' needs and search strategies as a basis for system design. Roskilde, Denmark: Risø National Laboratory, 1989.

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29

Environment, Alberta Alberta. The Southern Rockies landscape planning pilot study: Visual resource modelling. [Edmonton]: Alberta Environment, Land and Forest Service, Integrated Resource Management Division, 2000.

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30

Soil hydrology, land use and agriculture: Measurement and modelling. Cambridge, MA: CABI, 2011.

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31

McMurry, J. Preliminary modelling study of geochemical interactions between a used-fuel disposal vault and the surrounding geosphere. Pinawa, Man: Whiteshell Laboratories, 1995.

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32

Paris, Cecile. User Modelling in Text Generation. Bloomsbury Publishing Plc, 2015.

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33

Airports: SPASM Rules and Modelling and User Guide. Department for Transport (DFT), 2002.

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34

Heckmann, Dominikus, Tsvi Kuflik, Shlomo Berkovsky, Antonio Krüger, and Francesca Carmagnola. Advances in Ubiquitous User Modelling: Revised Selected Papers. Springer London, Limited, 2009.

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35

Brumby, Duncan P., Christian P. Janssen, Tuomo Kujala, and 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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36

Scardi, Paolo, and Robert E. Dinnebier. Extending the Reach of Powder Diffraction Modelling: By User Defined Macros. Trans Tech Publications, Limited, 2010.

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37

SCONUL, ed. MA/HEM methodology for access/holdings economic modelling: Acquisitions decision support tool : user manual. (s.l.): SCONUL, 1996.

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38

Missaoui, Rokia, Talel Abdessalem, and Matthieu Latapy. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment. Springer International Publishing AG, 2017.

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39

Virvou, Maria, and Christos Troussas. Advances in Social Networking-based Learning: Machine Learning-based User Modelling and Sentiment Analysis. Springer, 2020.

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40

Virvou, Maria, and Christos Troussas. Advances in Social Networking-Based Learning: Machine Learning-Based User Modelling and Sentiment Analysis. Springer International Publishing AG, 2021.

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41

Interactive Modelling and Simulation in Business System Design. Springer International Publishing AG, 2016.

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42

Hoeweler, U. MOBY: Molecular Modelling on the PC: Single User: Version 1.5 with a Handbook in English. Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 1992.

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43

Butler, N., and A. J. Neilson. Bolt Modelling in DYNA3D: (Paper Presented at the 3rd DYNA3D User Group Conference, London, 1989) (Reports). AEA Technology Plc, 1989.

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44

Williamson, John H., Antti Oulasvirta, Per Ola Kristensson, and Nikola Banovic, eds. Bayesian Methods for Interaction and Design. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108874830.

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Intended for researchers and practitioners in interaction design, this book shows how Bayesian models can be brought to bear on problems of interface design and user modelling. It introduces and motivates Bayesian modelling and illustrates how powerful these ideas can be in thinking about human-computer interaction, especially in representing and manipulating uncertainty. Bayesian methods are increasingly practical as computational tools to implement them become more widely available, and offer a principled foundation to reason about interaction design. The book opens with a self-contained tutorial on Bayesian concepts and their practical implementation, tailored for the background and needs of interaction designers. The contributed chapters cover the use of Bayesian probabilistic modelling in a diverse set of applications, including improving pointing-based interfaces; efficient text entry using modern language models; advanced interface design using cutting-edge techniques in Bayesian optimisation; and Bayesian approaches to modelling the cognitive processes of users.
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45

Young, Alistair, Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, and Maxime Sermesant. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Springer, 2014.

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46

Young, Alistair, Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, and Maxime Sermesant. Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Springer, 2016.

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47

Practical R for biologists: an introduction. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0000.

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Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.
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48

O'Donoghue, Cathal. Practical Microsimulation Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198852872.001.0001.

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The purpose of this book is to bring together for the first time a description, with examples, of the main methods used in microsimulation modelling, used in the field of income-distribution analysis. The book provides a practical complement to the Handbook of Microsimulation Modelling, published in 2014. It is structured to develop and use the different types of models used in the field, with a focus on household-targeted policy. The book aims to fill a gap in the literature in providing a greater degree of codified knowledge through a practical guide to developing and using microsimulation models. At present, the training of researchers and analysts that use and develop microsimulation modelling is done on a relatively ad-hoc basis through occasional training programmes and lecture series, built around lecture notes. This book would enable a more formalized and organized approach. Each chapter addresses a separate modelling approach in a similar, consistent way, describing in practical terms the key methodological skills for each approach: · It provides some policy context to each modelling approach so as to understand the modelling choices made and structures developed. · As a very data-intensive modelling approach, each chapter describes key data analysis and data-preparation methods. · As a modelling approach that is used extensively for deciding policy, often involving huge budgets, validation is key. Each chapter describes an approach to validating the model. · Depending upon the policy context, the analysis is assessed in different ways. Each chapter contains a section devoted to measurement issues and tabulating output from the models. · Last, each chapter contains an example simulation of a policy analysis using the chapter’s methodological approach.
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49

Frostick, Lynne E., Stuart J. McLelland, and T. G. Mercer, eds. Users Guide to Physical Modelling and Experimentation. CRC Press, 2011. http://dx.doi.org/10.1201/b11335.

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50

Koomen, Eric, Aldrik Bakema, and John Stillwell. Modelling Land-Use Change. Springer, 2009.

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