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Learning and Collaboration Technologies. Human and Technology Ecosystems: 7th International Conference, LCT 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II (Lecture Notes in Computer Science #12206)
by Panayiotis Zaphiris Andri IoannouThis two-volume set LNCS 12205 and LNCS 12206 constitutes the proceedings of the 7th International Conference on Learning and Collaboration Technologies, LCT 2020, held as part of the 22nd International Conference, HCI International 2020, which took place in Copenhagen, Denmark, in July 2020.The total of 1439 papers and 238 posters included in the 37 HCII 2020 proceedings volumes was carefully reviewed and selected from 6326 submissions.The papers in this volume are organized in the following topical sections: communication and conversation in learning; cognition, emotions and learning; games and gamification in learning; VR, robot and IoT in learning; and collaboration technology and collaborative learning. As a result of the Danish Government's announcement, dated April 21, 2020, to ban all large events (above 500 participants) until September 1, 2020, the HCII 2020 conference was held virtually.
Learning and Collaboration Technologies. Learning and Teaching: 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018, Proceedings, Part II (Lecture Notes in Computer Science #10925)
by Panayiotis Zaphiris Andri IoannouThis two-volume set LNCS 10924 and 10925 constitute the refereed proceedings of the 5th International Conference on Learning and Collaboration Technologies, LCT 2018, held as part of the 20th International Conference on Human-Computer Interaction, HCII 2018, in Las Vegas, NV, USA in July 2018. The 1171 papers presented at HCII 2018 conferences were carefully reviewed and selected from 4346 submissions. The papers cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of applications areas. The papers in this volume are organized in the following topical sections: designing and evaluating systems and applications, technological innovation in education, learning and collaboration, learners, engagement, motification, and skills, games and gamification of learning, technology-enhanced teaching and assessment, computing and engineering education.
Learning and Collaboration Technologies. Novel Learning Ecosystems: 4th International Conference, LCT 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part I (Lecture Notes in Computer Science #10295)
by Panayiotis Zaphiris Andri IoannouThe two-volume set LNCS 10295 and 10296 constitute the refereed proceedings of the 4th International Conference on Learning and Collaboration Technologies, LCT 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCII 2017, in Vancouver, BC, Canada, in July 2017, in conjunction with 15 thematically similar conferences. The 1228 papers presented at the HCII 2017 conferences were carefully reviewed and selected from 4340 submissions.The papers cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The papers included in this volume are organized in the following topical sections: multimodal and natural interaction for learning; learning and teaching ecosystems; e-learning, social media and MOOCs; beyond the classroom; and games and gamification for learning.
Learning and Collaboration Technologies. Novel Technological Environments: 9th International Conference, LCT 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings, Part II (Lecture Notes in Computer Science #13329)
by Panayiotis Zaphiris Andri IoannouChapter “Developing a VR Tool to Support Repeat Pattern Design Learning ” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Learning and Collaboration Technologies. Technology in Education: 4th International Conference, LCT 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part II (Lecture Notes in Computer Science #10296)
by Panayiotis Zaphiris Andri IoannouThe two-volume set LNCS 10295 and 10296 constitute the refereed proceedings of the 4th International Conference on Learning and Collaboration Technologies, LCT 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCII 2017, in Vancouver, BC, Canada, in July 2017, in conjunction with 15 thematically similar conferences. The 1228 papers presented at the HCII 2017 conferences were carefully reviewed and selected from 4340 submissions.The papers cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The papers included in this volume are organized in the following topical sections: STEM education; diversity in learning; learning analytics; and improving the learning and collaboration experience.The chapter 'The Quality of MOOCs: How to Improve the Design of Open Education and Online Courses for Learners?' is Open Access under a CC BY 4.0 license.
Learning and Collaboration Technologies. Ubiquitous and Virtual Environments for Learning and Collaboration: 6th International Conference, LCT 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part II (Lecture Notes in Computer Science #11591)
by Panayiotis Zaphiris Andri IoannouThis two-volume set LNCS 11590 and 11591 constitutes the refereed proceedings of the 6th International Conference on Learning and Collaboration Technologies, LCT 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCII 2019, in Orlando, FL, USA in July 2019. The 1274 full papers 209 posters presented at the HCII 2019 conferences were carefully reviewed and selected from 5029 submissions. The papers cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of applications areas. The papers in this volume are organized in the following topical sections: mobile and ubiquitous learning; virtual reality and augmented reality systems for learning; and collaborative technology.
Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making (Intelligent Systems, Control and Automation: Science and Engineering #13)
by S.H. KimIntelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are required to manage the resulting complexity of design and behavior. This book presents a general framework for adaptive systems. The utility of the comprehensive framework is demonstrated by tailoring it to particular models of computational learning, ranging from neural networks to declarative logic. The key to robustness lies in distributed decision making. An exemplar of this strategy is the neural network in both its biological and synthetic forms. In a neural network, the knowledge is encoded in the collection of cells and their linkages, rather than in any single component. Distributed decision making is even more apparent in the case of independent agents. For a population of autonomous agents, their proper coordination may well be more instrumental for attaining their objectives than are their individual capabilities. This book probes the problems and opportunities arising from autonomous agents acting individually and collectively. Following the general framework for learning systems and its application to neural networks, the coordination of independent agents through game theory is explored. Finally, the utility of game theory for artificial agents is revealed through a case study in robotic coordination. Given the universality of the subjects -- learning behavior and coordinative strategies in uncertain environments -- this book will be of interest to students and researchers in various disciplines, ranging from all areas of engineering to the computing disciplines; from the life sciences to the physical sciences; and from the management arts to social studies.
Learning and Decision-Making from Rank Data (Synthesis Lectures on Artificial Intelligence and Machine Learning)
by Lirong CostaThe ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state-of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.
Learning and Generalisation: With Applications to Neural Networks (Communications and Control Engineering)
by Mathukumalli VidyasagarHow does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Learning and Geometry: Computational Approaches (Progress in Computer Science and Applied Logic #14)
by David Kueker Carl SmithThe field of computational learning theory arose out of the desire to for mally understand the process of learning. As potential applications to artificial intelligence became apparent, the new field grew rapidly. The learning of geo metric objects became a natural area of study. The possibility of using learning techniques to compensate for unsolvability provided an attraction for individ uals with an immediate need to solve such difficult problems. Researchers at the Center for Night Vision were interested in solving the problem of interpreting data produced by a variety of sensors. Current vision techniques, which have a strong geometric component, can be used to extract features. However, these techniques fall short of useful recognition of the sensed objects. One potential solution is to incorporate learning techniques into the geometric manipulation of sensor data. As a first step toward realizing such a solution, the Systems Research Center at the University of Maryland, in conjunction with the Center for Night Vision, hosted a Workshop on Learning and Geometry in January of 1991. Scholars in both fields came together to learn about each others' field and to look for common ground, with the ultimate goal of providing a new model of learning from geometrical examples that would be useful in computer vision. The papers in the volume are a partial record of that meeting.
Learning and Instruction in the Digital Age
by J. Michael Spector Dirk Ifenthaler Pedro Isaias Kinshuk Demetrios SampsonInstruction tailored to the individual student, learning and teaching outside the limits of time and space—ideas that were once considered science fiction are now educational reality, with the prospect of an intelligent Web 3.0 not far distant. Alongside these innovations exists an emerging set of critical-thinking challenges, as Internet users create content and learners (and teachers) take increased responsibility in their work. Learning and Instruction in the Digital Age nimbly balances the technological and pedagogical aspects of these rapid changes, gathering papers from noted researchers on a wealth of topics relating to cognitive approaches to learning and teaching, mental models, online learning, communications, and innovative educational technologies, among them: Cognition and student-centered, Web-based learning, The progression of mental models throughout a course of instruction, Experiencing education with 3D virtual worlds, Expanding educational boundaries through multi-school collaboration, Adapting e-learning to different learning styles, The student blog as reflective diary. With its blend of timely ideas and forward thinking, Learning and Instruction in the Digital Age will enrich the work of researchers in educational psychology, educational technology, and cognitive science.
Learning and Instructional Technologies for the 21st Century: Visions of the Future (Studies Of Brain Function #Vol. 3)
by Douglas M. Harvey Leslie MollerLearning and Instructional Technologies for the 21st Century gathers research which identify models and approaches to improve learning through the inclusion of technology. These papers, from leading researchers and thinkers in instructional technology, begin by refuting the idea that education can be improved through more or better technology. Instead, the contributors emphasize specific, research-based ideas, which re-evaluate learning, reorganize schools, redirect technology, and provide instruction. Acknowledging the critical role of technology, these contributions explore technology's main advantage--its ability to enable advanced learning designs and emerging paradigms as well as to evolve learning interactions. While each paper explores a specific aspect of the role of technology, the collection shares this common theme. Without sufficient consideration to the process of learning and its many facets, technological availability alone will not provide a sustained impact on the educational process. Originating from the first AECT Research Symposium, Learning and Instructional Technologies for the 21st Century will be of interest to researchers and practitioners alike.
Learning and Intelligent Optimization: 12th International Conference, LION 12, Kalamata, Greece, June 10–15, 2018, Revised Selected Papers (Lecture Notes in Computer Science #11353)
by Roberto Battiti Mauro Brunato Ilias Kotsireas Panos M. PardalosThis book constitutes the thoroughly refereed post-conference proceedings of the 12th International Conference on Learning and Intelligent Optimization, LION 12, held in Kalamata, Greece, in June 2018. The 28 full papers and 12 short papers presented have been carefully reviewed and selected from 62 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.
Learning and Intelligent Optimization: 11th International Conference, LION 11, Nizhny Novgorod, Russia, June 19-21, 2017, Revised Selected Papers (Lecture Notes in Computer Science #10556)
by Roberto Battiti Dmitri E. Kvasov Yaroslav D. SergeyevThis book constitutes the thoroughly refereed post-conference proceedings of the 11th International Conference on Learning and Intelligent Optimization, LION 11, held in Nizhny,Novgorod, Russia, in June 2017. The 20 full papers (among these one GENOPT paper) and 15 short papers presented have been carefully reviewed and selected from 73 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.
Learning and Intelligent Optimization: 4th International Conference, LION 4, Venice, Italy, January 2010. Selected Papers (Lecture Notes in Computer Science #6073)
by Christian Blum Roberto Battiti
Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011, Selected Papers (Lecture Notes in Computer Science #6683)
by Carlos A. Coello-CoelloThis book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Learning and Intelligent Optimization, LION 5, held in Rome, Italy, in January 2011. The 32 revised regular and 3 revised short papers were carefully reviewed and selected from a total of 99 submissions. In addition to the contributions to the general track there are 11 full papers and 3 short papers presented at the following four special sessions; IMON: Intelligent Multiobjective OptimizatioN, LION-PP: Performance Prediction Self* EAs: Self-tuning, self-configuring and self-generating evolutionary algorithms LION-SWAP: Software and Applications.
Learning and Intelligent Optimization: 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected Papers (Lecture Notes in Computer Science #8994)
by Clarisse Dhaenens Laetitia Jourdan Marie-Eléonore MarmionThis book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Learning and Optimization, LION 9, which was held in Lille, France, in January 2015.The 31 contributions presented were carefully reviewed and selected for inclusion in this book. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Special focus is given to algorithm selection and configuration, learning, fitness landscape, applications, dynamic optimization, multi-objective, max-clique problems, bayesian optimization and global optimization, data mining and - in a special session - also on dynamic optimization.
Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016, Revised Selected Papers (Lecture Notes in Computer Science #10079)
by Paola Festa Meinolf Sellmann Joaquin VanschorenThis book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Learning and Optimization, LION 10, which was held on Ischia, Italy, in May/June 2016. The 14 full papers presented together with 9 short papers and 2 GENOPT papers were carefully reviewed and selected from 47 submissions. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Special focus is given to new ideas and methods; challenges and opportunities in various application areas; general trends, and specific developments.
Learning and Intelligent Optimization: 7th International Conference, LION 7, Catania, Italy, January 7-11, 2013, Revised Selected Papers (Lecture Notes in Computer Science #7997)
by Giuseppe Nicosia and Panos PardalosThis book constitutes the proceedings of the 7th International Conference on Learning and Optimization, LION 7, which was held in Catania, Italy, in January 2013. The 49 contributions presented in this volume were carefully reviewed and selected from 101 submissions. They explore the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems.
Learning and Intelligent Optimization: 6th International Conference, LION 6, Paris, France, January 16-20, 2012, Revised Selected Papers (Lecture Notes in Computer Science #7219)
by Youssef Hamadi Marc SchoenauerThis book constitutes the thoroughly refereed post-conference proceedings of the 6th International Conference on Learning and Intelligent Optimization, LION 6, held in Paris, France, in January 2012. The 23 long and 30 short revised papers were carefully reviewed and selected from a total of 99 submissions. The papers focus on the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. In addition to the paper contributions the conference also included 3 invited speakers, who presented forefront research results and frontiers, and 3 tutorial talks, which were crucial in bringing together the different components of LION community.
Learning and Intelligent Optimization: 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers (Lecture Notes in Computer Science #12096)
by Ilias S. Kotsireas Panos M. PardalosThis book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 14, held in Athens, Greece, in May 2020. The 37 full papers presented together with one invited paper have been carefully reviewed and selected from 75 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. Due to the COVID-19 pandemic, LION 14 was not held as a physical meeting.
Learning and Intelligent Optimization: Second International Conference, LION 2007 II, Trento, Italy, December 8-12, 2007. Selected Papers (Lecture Notes in Computer Science #5313)
by Vittorio Maniezzo Roberto Battiti Jean-Paul WatsonThis volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8–12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is "in the loop" as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of "learning" about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.
Learning and Intelligent Optimization: 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers (Lecture Notes in Computer Science #11968)
by Nikolaos F. Matsatsinis Yannis Marinakis Panos PardalosThis book constitutes the thoroughly refereed pChania, Crete, Greece, in May 2019. The 38 full papers presented have been carefully reviewed and selected from 52 submissions. The papers focus on advancedresearch developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence and describe advanced ideas, technologies, methods, and applications in optimization and machine learning.
Learning and Intelligent Optimization: 8th International Conference, Lion 8, Gainesville, FL, USA, February 16-21, 2014. Revised Selected Papers (Lecture Notes in Computer Science #8426)
by Panos M. Pardalos Mauricio G. C. Resende Chrysafis Vogiatzis Jose L. WalterosThis book constitutes the thoroughly refereed post-conference proceedings of the 8th International Conference on Learning and Optimization, LION 8, which was held in Gainesville, FL, USA, in February 2014. The 33 contributions presented were carefully reviewed and selected for inclusion in this book. A large variety of topics are covered, such as algorithm configuration; multiobjective optimization; metaheuristics; graphs and networks; logistics and transportation; and biomedical applications.
Learning and Intelligent Optimization: 15th International Conference, LION 15, Athens, Greece, June 20–25, 2021, Revised Selected Papers (Lecture Notes in Computer Science #12931)
by Dimitris E. Simos Panos M. Pardalos Ilias S. KotsireasThis book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 15, held in Athens, Greece, in June 2021. The 30 full papers presented have been carefully reviewed and selected from 35 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components.