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Adaptive Hypermedia and Adaptive Web-Based Systems: Third International Conference, AH 2004, Eindhoven, The Netherlands, August 23-26, 2004, Proceedings (Lecture Notes in Computer Science #3137)

Adaptive Hypermedia and Adaptive Web-Based Systems: 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008, Proceedings (Lecture Notes in Computer Science #5149)

Adaptive Hypermedia has emerged as an important area of both academic and deployed research. It encompasses a broad range of research that will enable personalized, adaptive hypermedia systems to play an even more e?ective role in people’s lives. The Web has enabled the widespread use of many person- ized systems, such as recommenders, personalized ?lters and retrieval systems, e-learning systems and various forms of collaborative systems. Such systems have been widely deployed in diverse domains such as e-Commerce, e-Health, e-Government, digital libraries, personalized travel planning as well as tourist and cultural heritage services. They are particularly promising for users with special needs. The exciting possibilities of such deployed adaptive hypermedia systems rely on research progress in a broad range of areas such as: user pro- ing and modeling; acquisition, updating and management of user models; group modeling and community-based pro?ling;recommender systems and recomm- dation strategies; data mining for personalization; the Semantic Web; adaptive multimedia content authoring and delivery; ubiquitous computing environments and Smart Spaces; personalization for the plethora of mobile devices, such as PDAs, mobile phones and other hand-held devices; and pragmatics such as p- vacy, trust and security. Empirical studies of adaptive hypermedia and Web systems are also critical to informing future directions. The AdaptiveHypermediaconferenceshavebecomethe majorforumsforthe scienti?c exchange and presentation of research results on adaptive hypermedia and adaptive Web-based systems.

Adaptive Hypermedia and Adaptive Web-Based Systems: 4th International Conference, AH 2006, Dublin, Ireland, June 21-23, 2006, Proceedings (Lecture Notes in Computer Science #4018)

Here are the refereed proceedings of the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH 2006, held in Dublin, Ireland, June 2006. The book presents 22 revised full papers and 19 revised short papers together with abstracts of 3 keynotes, 12 poster papers, and 14 doctoral consortium posters. Topics include pioneering theories, techniques, and innovative technologies to provide dynamic personalization, adaptation, and contextualization of hypermedia resources and services.

Adaptive Hypertext and Hypermedia

Hypertext/hypermedia systems and user-model-based adaptive systems in the areas of learning and information retrieval have for a long time been considered as two mutually exclusive approaches to information access. Adaptive systems tailor information to the user and may guide the user in the information space to present the most relevant material, taking into account a model of the user's goals, interests and preferences. Hypermedia systems, on the other hand, are `user neutral': they provide the user with the tools and the freedom to explore an information space by browsing through a complex network of information nodes. Adaptive hypertext and hypermedia systems attempt to bridge the gap between these two approaches. Adaptation of hypermedia systems to each individual user is increasingly needed. With the growing size, complexity and heterogeneity of current hypermedia systems, such as the World Wide Web, it becomes virtually impossible to impose guidelines on authors concerning the overall organization of hypermedia information. The networks therefore become so complex and unstructured that the existing navigational tools are no longer powerful enough to provide orientation on where to search for the needed information. It is also not possible to identify appropriate pre-defined paths or subnets for users with certain goals and knowledge backgrounds since the user community of hypermedia systems is usually quite inhomogeneous. This is particularly true for Web-based applications which are expected to be used by a much greater variety of users than any earlier standalone application. A possible remedy for the negative effects of the traditional `one-size-fits-all' approach in the development of hypermedia systems is to equip them with the ability to adapt to the needs of their individual users. A possible way of achieving adaptivity is by modeling the users and tailoring the system's interactions to their goals, tasks and interests. In this sense, the notion of adaptive hypertext/hypermedia comes naturally to denote a hypertext or hypermedia system which reflects some features of the user and/or characteristics of his system usage in a user model, and utilizes this model in order to adapt various behavioral aspects of the system to the user. This book is the first comprehensive publication on adaptive hypertext and hypermedia. It is oriented towards researchers and practitioners in the fields of hypertext and hypermedia, information systems, and personalized systems. It is also an important resource for the numerous developers of Web-based applications. The design decisions, adaptation methods, and experience presented in this book are a unique source of ideas and techniques for developing more usable and more intelligent Web-based systems suitable for a great variety of users. The practitioners will find it important that many of the adaptation techniques presented in this book have proved to be efficient and are ready to be used in various applications.

Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations (T-Labs Series in Telecommunication Services)

This book treats the topic of extending the adaptive filtering theory in the context of massive multichannel systems by taking into account a priori knowledge of the underlying system or signal. The starting point is exploiting the sparseness in acoustic multichannel system in order to solve the non-uniqueness problem with an efficient algorithm for adaptive filtering that does not require any modification of the loudspeaker signals.The book discusses in detail the derivation of general sparse representations of acoustic MIMO systems in signal or system dependent transform domains. Efficient adaptive filtering algorithms in the transform domains are presented and the relation between the signal- and the system-based sparse representations is emphasized. Furthermore, the book presents a novel approach to spatially preprocess the loudspeaker signals in a full-duplex communication system. The idea of the preprocessing is to prevent the echoes from being captured by the microphone array in order to support the AEC system. The preprocessing stage is given as an exemplarily application of a novel unified framework for the synthesis of sound figures. Finally, a multichannel system for the acoustic echo suppression is presented that can be used as a postprocessing stage for removing residual echoes. As first of its kind, it extracts the near-end signal from the microphone signal with a distortionless constraint and without requiring a double-talk detector.

Adaptive Image Processing Algorithms for Printing (Signals and Communication Technology)

This book presents essential algorithms for the image processing pipeline of photo-printers and accompanying software tools, offering an exposition of multiple image enhancement algorithms, smart aspect-ratio changing techniques for borderless printing and approaches for non-standard printing modes. All the techniques described are content-adaptive and operate in an automatic mode thanks to machine learning reasoning or ingenious heuristics. The first part includes algorithms, for example, red-eye correction and compression artefacts reduction, that can be applied in any photo processing application, while the second part focuses specifically on printing devices, e.g. eco-friendly and anaglyph printing. The majority of the techniques presented have a low computational complexity because they were initially designed for integration in system-on-chip. The book reflects the authors’ practical experience in algorithm development for industrial R&D.

Adaptive Information Processing: An Introductory Survey (Monographs in Computer Science)

This book began as a series of lecture notes for a course called Introduc­ tion to Adaptive Systems which I developed for undergraduate Computing Science majors at the University of Alberta and first taught in 1973. The objective of the course has been threefold: (l) to expose undergraduate computer scientists to a variety of subjects in the theory and application of computation, subjects which are too often postponed to the graduate level or never taught at all; (2) to provide undergraduates with a background sufficient to make them effective participants in graduate level courses in Automata Theory, Biological Information Processing, and Artificial Intelligence; and (3) to present a personal viewpoint which unifies the apparently diverse aspects of the subject matter covered. All of these goals apply equally to this book, which is primarily designed for use in a one semester undergraduate computer science course. I assume the reader has a general knowledge of computers and programming, though not of particular machines or languages. His mathematical background should include basic concepts of number systems, set theory, elementary discrete probability, and logic.

Adaptive Instructional Systems: Second International Conference, AIS 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings (Lecture Notes in Computer Science #12214)

This volume constitutes the refereed proceedings of the Second International Conference on Adaptive Instructional Systems, AIS 2020, which was due to be held in July 2020 as part of HCI International 2020 in Copenhagen, Denmark. The conference was held virtually due to the COVID-19 pandemic.A total of 1439 papers and 238 posters have been accepted for publication in the HCII 2020 proceedings from a total of 6326 submissions. The 41 papers presented in this volume were organized in topical sections as follows: designing and developing adaptive instructional systems; learner modelling and methods of adaptation; evaluating the effectiveness of adaptive instructional systems.Chapter "Exploring Video Engagement in an Intelligent Tutoring System" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Adaptive Instructional Systems: First International Conference, AIS 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings (Lecture Notes in Computer Science #11597)

This book constitutes the refereed proceedings of the First International Conference on Adaptive Instructional Systems, AIS 2019, held in July 2019 as part of HCI International 2019 in Orlando, FL, USA. HCII 2019 received a total of 5029 submissions, of which 1275 papers and 209 posters were accepted for publication after a careful reviewing process. The 50 papers presented in this volume are organized in topical sections named: Adaptive Instruction Design and Authoring, Interoperability and Standardization in Adaptive Instructional Systems, Instructional Theories in Adaptive Instruction, Learner Assessment and Modelling, AI in Adaptive Instructional Systems, Conversational Tutors.

Adaptive Instructional Systems: 5th International Conference, AIS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings (Lecture Notes in Computer Science #14044)

This book constitutes the refereed proceedings of the 5th International Conference, AIS 2023, held as part of the 25th International Conference, HCI International 2023, which was held virtually in Copenhagen, Denmark in July 2023.The total of 1578 papers and 396 posters included in the HCII 2023 proceedings was carefully reviewed and selected from 7472 submissions. The AIS 2023 proceeding helps to understand the theory and enhance the state-of-practice for a set of technologies (tools and methods) called adaptive instructional systems (AIS). AIS are defined as artificially intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, preferences, and interests of each individual learner or team in the context of domain learning objectives.

Adaptive Instructional Systems: 4th International Conference, AIS 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings (Lecture Notes in Computer Science #13332)

This book constitutes the refereed proceedings of the 4th International Conference on Adaptive Instructional Systems, AIS 2022, held as part of the 23rd International Conference, HCI International 2022, which was held virtually in June/July 2022.The total of 1271 papers and 275 posters included in the HCII 2022 proceedings was carefully reviewed and selected from 5487 submissions. The AIS 2022 proceedings were organized in the following topical sections: Learner Modeling and State Assessment for Adaptive Instructional Decisions; Adaptation Design to Individual Learners and Teams; Design and Development of Adaptive Instructional Systems; Evaluating the Effectiveness of Adaptive Instructional Systems.

Adaptive Instructional Systems. Adaptation Strategies and Methods: Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part II (Lecture Notes in Computer Science #12793)

This two-volume set LNCS 12774 and 12775 constitutes the refereed proceedings of the 12th International Conference on Social Computing and Social Media, SCSM 2021, held as part of the 23rd International Conference, HCI International 2021, which took place in July 2021. Due to COVID-19 pandemic the conference was held virtually. The total of 1276 papers and 241 poster papers included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The regular papers of AIS 2021, Part II, focus on Learner Modelling and State Assessment in AIS.

Adaptive Instructional Systems. Design and Evaluation: Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I (Lecture Notes in Computer Science #12792)

This two-volume set LNCS 12792 and 12793 constitutes the refereed proceedings of the Third International Conference on Adaptive Instructional Systems, AIS 2021, held as Part of the 23rd International Conference, HCI International 2021, which took place in July 2021. Due to COVID-19 pandemic the conference was held virtually.The total of 1276 papers and 241 poster papers included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The regular papers of AIS 2021, Part I, are organized in topical sections named: Conceptual Models and Instructional Approaches for AIS; Designing and Developing AIS; Evaluation of AIS; Adaptation Strategies and Methods in AIS.

Adaptive Intelligent Systems: Proceedings of the BANKAI workshop, Brussels, Belgium, 12-14 October 1992

Dedicated to the consideration of advanced I.T. technologies and their financial applications, this volume contains contributions from an international group of system developers and managers from academia, the financial industry and their suppliers: all actively involved in the development and practical introduction of these technologies into banking and financial organisations.Concentrating on real experience and present needs, rather than theoretical possibilities or limited prototype applications, it is hoped the publication will give a better insight into advanced I.T. practice and potential as it currently exists and motivate today's developers and researchers.In addition to the discussion of a wide range of technologies and approaches to ensure adaptivity, three other major topics are explored in the book: neural networks, classical software engineering techniques and rule-based systems.

Adaptive Interaction: A Utility Maximization Approach to Understanding Human Interaction with Technology (Synthesis Lectures on Human-Centered Informatics)

This lecture describes a theoretical framework for the behavioural sciences that holds high promise for theory-driven research and design in Human-Computer Interaction. The framework is designed to tackle the adaptive, ecological, and bounded nature of human behaviour. It is designed to help scientists and practitioners reason about why people choose to behave as they do and to explain which strategies people choose in response to utility, ecology, and cognitive information processing mechanisms. A key idea is that people choose strategies so as to maximise utility given constraints. The framework is illustrated with a number of examples including pointing, multitasking, skim-reading, online purchasing, Signal Detection Theory and diagnosis, and the influence of reputation on purchasing decisions. Importantly, these examples span from perceptual/motor coordination, through cognition to social interaction. Finally, the lecture discusses the challenging idea that people seek to find optimal strategies and also discusses the implications for behavioral investigation in HCI.

Adaptive Internal Model Control (Advances in Industrial Control)

Written in a self-contained tutorial fashion, this monograph successfully brings the latest theoretical advances in the design of robust adaptive systems to the realm of industrial applications. It provides a theoretical basis for verifying some of the reported industrial successes of existing adaptive control schemes and enables readers to synthesize adaptive versions of their own robust internal model control schemes.

Adaptive Learning Agents: Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009. Revised Selected Papers (Lecture Notes in Computer Science #5924)

ThisbookpresentsselectedandrevisedpapersoftheSecondWorkshoponAd- tive and Learning Agents 2009 (ALA-09), held at the AAMAS 2009 conference in Budapest, Hungary, May 12. The goalof ALA is to provide an interdisciplinaryforum for scientists from a variety of ?elds such as computer science, biology, game theory and economics. This year’s edition of ALA was the second after the merger of the former wo- shops ALAMAS and ALAg. In 2008 this joint workshop was organized for the ?rst time under the ?ag of both events. ALAMAS was a yearly returning Eu- pean workshop on adaptive and learning agents and multi-agent systems (held eight times). ALAg was the international workshop on adaptive and learning agents, which was usually held at AAMAS. To increase the strength, visibility and quality of the workshop it was decided to merge both workshops under the ?ag of ALA and to set up a Steering Committee as an organizational backbone. This book contains six papers presented during the workshop, which were carefully selected after an additional review round in the summer of 2009. We therefore wish to explicitly thank the members of the Program Committee for the quality and sincerity of their e?orts and service. Furthermore we would like to thank all the members of the senior Steering Committee for making this workshop possible and supporting it with sound advice. We also thank the AAMAS conference for providing us a platform for holding this event. Finally we also wish to thank all authors who responded to our call-for-papers with interesting contributions.

Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models

The fact that I have the opportunity to present a second edition of this monograph is an indicator for the growing size of the community concerned with agent-based computational economics. The rapid developments in this field make it very difficult to keep a volume like this, which is partly devoted to surveying the literature, up to date. I have done my best to incorporate the relevant new developments in this revised edition but it is in the nature of such a work that the selection of material covered is biased by the authors personal interest and his informational constraints. My apologies go to all researchers in this field whose work is not or not adequately represented in this book. Besides the correction of some errors and typos several additions have been made. In the literature survey sections 2.4 (which was also reorganized) and 3.5 new material was added. I have also added a new section in chapter 3 which deals with the question how well empirically observed phenomena can be explained by GA simulations. A new section in chapter 6 presents a rather extensive analysis of the behavior of a two population GA in the framework of a sealed bid double auction market. Further minor additions and changes were made throughout the text.

Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economical Models (Lecture Notes in Economics and Mathematical Systems #441)

An analysis of the learning behavior of genetic algorithms in economic systems with mutual interaction, such as markets. These systems are characterized by a state-dependent fitness function and - for the first time - mathematical results characterizing the long-term outcome of genetic learning in such systems are provided. The usefulness of such results is illustrated by many simulations in evolutionary games and economic models.

Adaptive Learning Environments: Foundations and Frontiers (NATO ASI Subseries F: #85)

Adaptive Learning Environments (ALEs) can be viewed as the intersection of two traditionally distinct areas of research: instructional science and computer science. They encompass intelligent tutoring systems, interactive learning environments, and situated learning environments. There is increasing interest in effective instructional systems from education, industry, military and government sectors. Given recent advances in hardware architecture and reduction of hardware costs, the time is right to define the next steps in research and development of ALEs. This book is an outgrowth of the presentations and discussions that took place at the NATO Advanced Study Institute held at the University of Calgary in July 1990. It contains chapters from both researchers in instructional science and researchers in computer science on the following topics: - Systems and architectures for instruction - Representing curriculum and designing instructional tasks - Environments to support learning - Diagnosing students' learning and adjusting plans for instruction - Models of students' metacognition, motivation and learning strategies - Student-system interactions. The book containsintroductions/critiques of each pair of chapters, and a final chapter discusses the synthesis of instructional science and computer science.

Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods (Genetic and Evolutionary Computation)

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The book further facilitates the discovery of polynomial models for time-series prediction.

Adaptive Logics for Defeasible Reasoning: Applications in Argumentation, Normative Reasoning and Default Reasoning (Trends in Logic #38)

This book presents adaptive logics as an intuitive and powerful framework for modeling defeasible reasoning. It examines various contexts in which defeasible reasoning is useful and offers a compact introduction into adaptive logics. The author first familiarizes readers with defeasible reasoning, the adaptive logics framework, combinations of adaptive logics, and a range of useful meta-theoretic properties. He then offers a systematic study of adaptive logics based on various applications. The book presents formal models for defeasible reasoning stemming from different contexts, such as default reasoning, argumentation, and normative reasoning. It highlights various meta-theoretic advantages of adaptive logics over other logics or logical frameworks that model defeasible reasoning. In this way the book substantiates the status of adaptive logics as a generic formal framework for defeasible reasoning.

Adaptive Machine Learning Algorithms with Python: Solve Data Analytics and Machine Learning Problems on Edge Devices

Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment. What You Will Learn Apply adaptive algorithms to practical applications and examplesUnderstand the relevant data representation features and computational models for time-varying multi-dimensional dataDerive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real dataSpeed up your algorithms and put them to use on real-world stationary and non-stationary dataMaster the applications of adaptive algorithms on critical edge device computation applications Who This Book Is ForMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.

Adaptive Mesh Refinement - Theory and Applications: Proceedings of the Chicago Workshop on Adaptive Mesh Refinement Methods, Sept. 3-5, 2003 (Lecture Notes in Computational Science and Engineering #41)

Advanced numerical simulations that use adaptive mesh refinement (AMR) methods have now become routine in engineering and science. Originally developed for computational fluid dynamics applications these methods have propagated to fields as diverse as astrophysics, climate modeling, combustion, biophysics and many others. The underlying physical models and equations used in these disciplines are rather different, yet algorithmic and implementation issues facing practitioners are often remarkably similar. Unfortunately, there has been little effort to review the advances and outstanding issues of adaptive mesh refinement methods across such a variety of fields. This book attempts to bridge this gap. The book presents a collection of papers by experts in the field of AMR who analyze past advances in the field and evaluate the current state of adaptive mesh refinement methods in scientific computing.

Adaptive Methods — Algorithms, Theory and Applications: Proceedings of the Ninth GAMM-Seminar Kiel, January 22–24, 1993 (Notes on Numerical Fluid Mechanics)