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New Trends and Technologies in Computer-Aided Learning for Computer-Aided Design ; IFIP International Working Conference: EduTech 2005, Perth, Australia, October 20-21, 2005
Computation and communication technologies underpin work and development in many different areas. Among them, Computer-Aided Design of electronic systems and E-Learning technologies are two areas which are different but share many concerns. The design of CAD and E-Learning systems already touches on a number of parallels, such as system interoperability, user interfaces, standardization, EML-based formats, reusability aspects (of content or designs), and intellectual property rights. Furthermore, the teaching of Design Automation tools and methods is particularly amenable to a distant or blended learning setting, and implies the interconnection of typical CAD tools, such as simulators or synthesis tools, with e-learning tools. There are many other aspects in which synergy can be found when using E-Learning technology for teaching and learning technology. This workshop, sponsored by IFIP WG 10.5 Design and Engineering of Electronic Systems in cooperation with IFIP WG 3.6 Distance Education, will explore the interrelationship between these two subjects, where Computer-Aided Design meets Computer-Aided Learning. New Trends and Technologies in Computer-Aided Learning for Computer-Aided Design documents recent approaches and results presented at the EduTech 2005 Workshop, which was held in October 2005 in Perth, Australia and sponsored by the International Federation for Information Processing (IFIP). The topics chosen for this working conference are very timely: learning environments, tools and applications for education, education technologies and trends, and teaching in the hardware design area.
Modern deep learning for tabular data : Novel approaches to common modeling problems
Synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their 'default' usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability.
Hybrid Learning and Education ; 1st International Conference, ICHL 2008 Hong Kong, China, August 13-15, 2008 Proceedings
This book constitutes the refereed proceedings of the First International Conference on Hybrid Learning, ICHL 2008, held in Hong Kong, China, in August 2008.The 38 revised full papers presented together with 3 keynote lectures were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on hybrid education, model and pedagogies for hybrid learning, trends, pervasive learning, mobile and ubiquitous learning, hybrid learning experiences, hybrid learning systems, technologies, as well as contextual attitude and cultural effects.
Evolving Connectionist Systems : The Knowledge Engineering Approach
Evolving Connectionist Systems is aimed at all those interested in developing and using intelligent computational models and systems to solve challenging real world problems in computer science, engineering, bioinformatics and neuroinformatics. The book challenges scientists and practitioners with open questions about future creation of new information models inspired by Nature. This edition includes new methods for adaptive, knowledge-based learning, such as online incremental feature selection, spiking neural networks, transductive neuro-fuzzy inference, adaptive data and model integration, cellular automata and artificial life systems, particle swarm optimisation, ensembles of evolving systems, and quantum inspired neural networks. New applications to gene and protein interaction modelling, brain data analysis and brain model creation, computational neuro-genetic modelling, adaptive speech, image and multimodal recognition, language modelling, adaptive robotics, modelling dynamic financial and socio-economic systems, and ecological modelling, are covered. An important new feature of the book is the attempt to connect different structural and functional levels of a complex, intelligent system, looking for inspiration from functional relationships in natural systems, such as the genetic and the brain activity.
E-Learning Methodologies : Fundamentals, technologies and applications
Covers state of the art topics including user modeling for e-learning systems and cloud, IOT, and mobile-based frameworks. It also considers security challenges and ethical conduct using Blockchain technology. E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies.
Designing machine learning systems : An iterative process for production-ready applications
Machine learning systems are both complex and unique. Each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. The book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Computational intelligence and security ; International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised selected papers
It covers bio-inspired computing, evolutionary computation, learning systems and multi-agents, cryptography, information processing and intrusion detection, systems and security, image and signal processing, and pattern recognition.
Learning and Adaption in Multi-Agent Systems ; 1st International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers
Contains selected and revised papers of the International Workshop on Lea- ing and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference in Utrecht, The Netherlands, July 26. An important aspect in multi-agent systems (MASs) is that the environment evolves over time, not only due to external environmental changes but also due to agent int- actions. For this reason it is important that an agent can learn, based on experience, and adapt its knowledge to make rational decisions and act in this changing environment autonomously. Machine learning techniques for single-agent frameworks are well established. Agents operate in uncertain environments and must be able to learn and act - tonomously. This task is, however, more complex when the agent interacts with other agents that have potentially different capabilities and goals. The single-agent case is structurally different from the multi-agent case due to the added dimension of dynamic interactions between the adaptive agents. Multi-agent learning, i.e., the ability of the agents to learn how to cooperate and compete, becomes crucial in many domains. Autonomous agents and multi-agent systems (AAMAS) is an emerging multi-disciplinary area encompassing computer science, software engineering, biology, as well as cognitive and social sciences. A t- oretical framework, in which rationality of learning and interacting agents can be - derstood, is still under development in MASs, although there have been promising ?
Classification and Learning Using Genetic Algorithms : Applications in Bioinformatics and Web Intelligence
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains.
Big Data : Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used.
Anticipatory Behavior in Adaptive Learning Systems : From Brains to Individual and Social Behavior
Anticipatory behavior in adaptive learning systems is steadily gaining the - terest of scientists, although many researchers still do not explicitly consider the actual anticipatory capabilities of their systems.The introductory chapter of this volume therefore does not only provide an overview of the contributions included in this volume but also proposes a taxonomy of how anticipatory mechanisms can improve adaptive behavior and learning in cognitive systems. During the workshop it became clear that ant- ipations are involved in various cognitive processes that range from individual anticipatory mechanisms to social anticipatory behavior.
Advances in web-based learning - ICWL 2005 ; 4th international conference, Hong Kong, China, July 31 - August 3, 2005, proceedings
With the rapid development of Web-based learning, a new set of learning - vironments including virtual classrooms, virtual laboratories and virtual universities are being developed. These new learning environments, however, also introduce new problems that need to be addressed. On the technical side, there is a need for the deployment of effective technologies on Web-based education. On the learning side, the cyber mode of learning is very different from tra- tional classroom-based learning. On the management side, the establishment of a cyber university imposes very different requirements for the set up. ICWL 2005, the 4th International Conference on Web-Based Learning, was held in Hong Kong, China from July 31 to August 3, 2005, as a continued - tempt to address many of the above-mentioned issues. Following the great success of ICWL
Advances in web based learning -- ICWL 2006 ; 5th International conference, Penang, Malaysia, July 19-21, 2006, Revised Papers
The conference program was organized in a single-track 3-day workshop. It included a tutorial, a keynote talk, and oral/poster paper presentations in several sessions dedicated to specific topics. Session topics included “Personalization in E-Learning,” “Designs, Model and Framework of E-Learning Systems,” “Implementations and Evaluations of E-Learning Systems,” “Tools in E-Learning,” and “Learning Resource Deployment, Organization and Management. ” We received a total of 99 submissions from all over the world.
Advances in intelligent data analysis XVIII ; 18th International symposium on intelligent data analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings
This book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.
AdvancED Flash Interface Design
Flash allows users to create some amazing interactive interfaces to interact with rich Internet applications, e-learning systems, and simple web sites. In this book, two of the most talented Flash designers in the world will show you how to use them effectively to create breathtaking visuals for your Flash web sites. You'll also learn how to take advantage of Flash's powerful built-in vector-based drawing tools.
A Matrix Algebra Approach to Artificial Intelligence
The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines
