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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)

Adaptive Middleware for the Internet of Things: The GAMBAS Approach

Over the past years, a considerable amount of effort has been devoted, both in industry and academia, towards the development of basic technology as well as innovative applications for the Internet of Things. Adaptive Middleware for the Internet of Things introduces a scalable, interoperable and privacy-preserving approach to realize IoT applications and discusses abstractions and mechanisms at the middleware level that simplify the realization of services that can adapt autonomously to the behavior of their users. Technical topics discussed in the book include:Behavior-driven Autonomous ServicesGAMBAS Middleware ArchitectureGeneric and Efficient Data AcquisitionInteroperable and Scalable Data ProcessingAutomated Privacy PreservationAdaptive Middleware for the Internet of Things summarizes the results of the GAMBAS research project funded by the European Commission under Framework Programme 7. It provides an in-depth description of the middleware system developed by the project consortium. In addition, the book describes several innovative mobility and monitoring applications that have been built, deployed and operated to evaluate the middleware under realistic conditions with a large number of users. Adaptive Middleware for the Internet of Things is ideal for personnel in the computer and communication industries as well as academic staff and research students in computer science interested in the development of systems and applications for the Internet of Things.

Adaptive Middleware for the Internet of Things: The GAMBAS Approach

Over the past years, a considerable amount of effort has been devoted, both in industry and academia, towards the development of basic technology as well as innovative applications for the Internet of Things. Adaptive Middleware for the Internet of Things introduces a scalable, interoperable and privacy-preserving approach to realize IoT applications and discusses abstractions and mechanisms at the middleware level that simplify the realization of services that can adapt autonomously to the behavior of their users. Technical topics discussed in the book include:Behavior-driven Autonomous ServicesGAMBAS Middleware ArchitectureGeneric and Efficient Data AcquisitionInteroperable and Scalable Data ProcessingAutomated Privacy PreservationAdaptive Middleware for the Internet of Things summarizes the results of the GAMBAS research project funded by the European Commission under Framework Programme 7. It provides an in-depth description of the middleware system developed by the project consortium. In addition, the book describes several innovative mobility and monitoring applications that have been built, deployed and operated to evaluate the middleware under realistic conditions with a large number of users. Adaptive Middleware for the Internet of Things is ideal for personnel in the computer and communication industries as well as academic staff and research students in computer science interested in the development of systems and applications for the Internet of Things.

Adaptive Mobile Computing: Advances in Processing Mobile Data Sets (Intelligent Data-Centric Systems: Sensor Collected Intelligence)

Adaptive Mobile Computing: Advances in Processing Mobile Data Sets explores the latest advancements in producing, processing and securing mobile data sets. The book provides the elements needed to deepen understanding of this trend which, over the last decade, has seen exponential growth in the number and capabilities of mobile devices. The pervasiveness, sensing capabilities and computational power of mobile devices have turned them into a fundamental instrument in everyday life for a large part of the human population. This fact makes mobile devices an incredibly rich source of data about the dynamics of human behavior, a pervasive wireless sensors network with substantial computational power and an extremely appealing target for a new generation of threats.Offers a coherent and realistic image of today’s architectures, techniques, protocols, components, orchestration, choreography and development related to mobile computingExplains state-of-the-art technological solutions for the main issues hindering the development of next-generation pervasive systems including: supporting components for collecting data intelligently, handling resource and data management, accounting for fault tolerance, security, monitoring and control, addressing the relation with the Internet of Things and Big Data and depicting applications for pervasive context-aware processingPresents the benefits of mobile computing and the development process of scientific and commercial applications and platforms to support themFamiliarizes readers with the concepts and technologies that are successfully used in the implementation of pervasive/ubiquitous systems

Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach (Advanced Information Processing)

This book brings together for the first time the complete theory of data based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data based modelling new concepts including extended additive and multiplicative submodels are developed. All of these algorithms are illustrated with benchmark examples to demonstrate their efficiency. The book aims at researchers and advanced professionals in time series modelling, empirical data modelling, knowledge discovery, data mining and data fusion.

Adaptive Motion of Animals and Machines

• Motivation It is our dream to understand the principles of animals’ remarkable ability for adaptive motion and to transfer such abilities to a robot. Up to now, mechanisms for generation and control of stereotyped motions and adaptive motions in well-known simple environments have been formulated to some extentandsuccessfullyappliedtorobots.However,principlesofadaptationto variousenvironmentshavenotyetbeenclari?ed,andautonomousadaptation remains unsolved as a seriously di?cult problem in robotics. Apparently, the ability of animals and robots to adapt in a real world cannot be explained or realized by one single function in a control system and mechanism. That is, adaptation in motion is induced at every level from thecentralnervoussystemtothemusculoskeletalsystem.Thus,weorganized the International Symposium on Adaptive Motion in Animals and Machines(AMAM)forscientistsandengineersconcernedwithadaptation onvariouslevelstobebroughttogethertodiscussprinciplesateachleveland to investigate principles governing total systems. • History AMAM started in Montreal (Canada) in August 2000. It was organized by H. Kimura (Japan), H. Witte (Germany), G. Taga (Japan), and K. Osuka (Japan), who had agreed that having a small symposium on motion control, with people from several ?elds coming together to discuss speci?c issues, was worthwhile. Those four organizing committee members determined the scope of AMAM as follows.

Adaptive Multilevel Solution of Nonlinear Parabolic PDE Systems: Theory, Algorithm, and Applications (Lecture Notes in Computational Science and Engineering #16)

Nowadays there is an increasing emphasis on all aspects of adaptively gener­ ating a grid that evolves with the solution of a PDE. Another challenge is to develop efficient higher-order one-step integration methods which can handle very stiff equations and which allow us to accommodate a spatial grid in each time step without any specific difficulties. In this monograph a combination of both error-controlled grid refinement and one-step methods of Rosenbrock-type is presented. It is my intention to impart the beauty and complexity found in the theoretical investigation of the adaptive algorithm proposed here, in its realization and in solving non-trivial complex problems. I hope that this method will find many more interesting applications. Berlin-Dahlem, May 2000 Jens Lang Acknowledgements I have looked forward to writing this section since it is a pleasure for me to thank all friends who made this work possible and provided valuable input. I would like to express my gratitude to Peter Deuflhard for giving me the oppor­ tunity to work in the field of Scientific Computing. I have benefited immensly from his help to get the right perspectives, and from his continuous encourage­ ment and support over several years. He certainly will forgive me the use of Rosenbrock methods rather than extrapolation methods to integrate in time.

Adaptive Multimedia Retrieval: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007, Revised Selected Papers (Lecture Notes in Computer Science #4918)

This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Adaptive Multimedia Retrieval, AMR 2007, held in Paris, France, in July 2007. The 18 revised full papers presented together with 2 invited papers were carefully selected during two rounds of reviewing and improvement. The papers are organized in topical sections on image annotation, feedback and user modelling, music retrieval, fusion, P2P and middleware, databases and summarization, as well as ontology and semantics.

Adaptive Multimedia Retrieval: Third International Workshop, AMR 2005, Glasgow, UK, July 28-29, 2005, Revised Selected Papers (Lecture Notes in Computer Science #3877)

This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Adaptive Multimedia Retrieval, held in September 2005. The 18 revised full papers presented were carefully selected during two rounds of reviewing and improvement. Also included are three invited papers by leading researchers in the area to illustrate the core topics of the workshop: User, Context and Feedback. The papers are organized in topical sections on ranking, systems, spatio-temporal relations, using feedback, using context, and meta data.

Adaptive Multimedia Retrieval: 6th International Workshop, AMR 2008, Berlin, Germany, June 26-27, 2008. Revised Selected Papers (Lecture Notes in Computer Science #5811)

Adaptive Multimedia Retrieval: First International Workshop, AMR 2003, Hamburg, Germany, September 15-16, 2003, Revised Selected and Invited Papers (Lecture Notes in Computer Science #3094)

This book is an extended collection of contributions that wereoriginally subm- ted to the 1st International Workshop on Adaptive Multimedia Retrieval (AMR 2003), which was organized as part of the 26th German Conference on Arti?cial Intelligence (KI 2003),and held during September 15–18,2003at the University of Hamburg, Germany. Motivated by the overall success of the workshop – as revealed by the stimulating atmosphere during the workshop and the number of very interested and active participants – we ?nally decided to edit a book based on revised papers that were initially submitted to the workshop. Furthermore, we invited some more introductory contributions in order to be able to provide a conclusive book on current topics in the area of adaptive multimedia retrieval systems. We hope that we were able to put together a stimulating collection of articles for the interested reader. We like to thank the organizationcommittee of the 26th German Conference on Arti?cial Intelligence (KI 2003) for providing the setting and the admin- trative support in realizing this workshop as part of their program. Especially, we like to thank Christopher Habel for promoting the workshop as part of the conference program and Andreas Gun ¨ ther for his kind support throughout the organization process.