Intelligent Adaptation and Personalization Techniques in Computer-supported Collaborative Learning

While many of us are concerned with the loss of communal spaces and ties that broaden one's sense of self beyond the 'me' or 'I' and into the 'we' and 'us', less clear are the educational advantages of a community approach in terms of learning curricular content. The chapters in this 2004 volume explore the theoretical, design, learning, and methodological questions with respect to designing for and researching web-based communities to support learning. The authors, coming from diverse academic backgrounds (computer science, information science, instructional systems technology, educational psychology, sociology, and anthropology), are frank in examining what we do and do not know about the processes and practices of designing communities to support learning. Taken as a collection, these manuscripts point to the challenges and complex tensions that emerge when designing for web-supported community, especially when the focal practice of the community is learning.

While many of us are concerned with the loss of communal spaces and ties that broaden one's sense of self beyond the 'me' or 'I' and into the 'we' and 'us', less clear are the educational advantages of a community approach in terms of learning curricular content. The chapters in this 2004 volume explore the theoretical, design, learning, and methodological questions with respect to designing for and researching web-based communities to support learning. The authors, coming from diverse academic backgrounds (computer science, information science, instructional systems technology, educational psychology, sociology, and anthropology), are frank in examining what we do and do not know about the processes and practices of designing communities to support learning. Taken as a collection, these manuscripts point to the challenges and complex tensions that emerge when designing for web-supported community, especially when the focal practice of the community is learning.

Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.

Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how t

Games Programming has been written for the personal computer owner who is setting out to write fams programs. It charts the territory which lies beyond the simplistic arcade game and which leads to the more intellectually exciting game moderators and learning programs. This is not another book of program listings, nor does it present game algorithms in isolation from the computer methods which make them work. The aim is to equip the reader with an awareness of modern techniques of structured programming, a robust attitude to computer languages, and a source of valuable 'know how'.

This 1997 book examines recent changes in the design of intelligent machines. New computer models of vision and navigation in animals suggest a different way to build machines. Cognition is viewed not just in terms of high-level 'expertise,' but in the ability to find one's way around the world, to learn new ways of seeing things, and to coordinate activity. This approach is called situated cognition. Situated Cognition differs from other purely philosophical treatises in that Clancey, an insider who has built expert systems for twenty years, explores the limitations of existing computer programs and compares them to human memory and learning capabilities. Clancey examines the implications of 'situated action' from the perspective of artificial intelligence specialists interested in building robots.

This 1997 book examines recent changes in the design of intelligent machines. New computer models of vision and navigation in animals suggest a different way to build machines. Cognition is viewed not just in terms of high-level 'expertise,' but in the ability to find one's way around the world, to learn new ways of seeing things, and to coordinate activity. This approach is called situated cognition. Situated Cognition differs from other purely philosophical treatises in that Clancey, an insider who has built expert systems for twenty years, explores the limitations of existing computer programs and compares them to human memory and learning capabilities. Clancey examines the implications of 'situated action' from the perspective of artificial intelligence specialists interested in building robots.

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

Marketing and sales prioritize AI and machine learning more than any other business department, yet often struggle with how to scale and strategize the opportunities it presents.AI Strategy for Sales and Marketing presents a framework for understanding how AI can boost customer-centricity and sales by creating a connected strategy that delivers value today and into the future. Supported by practical tips and advice throughout, it covers topics including personalization, upskilling, customer experience for both on and offline shopping channels and the importance of using AI responsibly to create consumer trust. Featuring original research and interviews with leading practitioners, it also contains global case studies from organizations in a range of sectors, including Samsung, LinkedIn, PwC, Rolls Royce and Hilton, with insights into the various stages of their adoption journeys. Written by a recognized industry expert, it is an invaluable resource for those wanting to benefit from usin

Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques.Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/un

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors,

Written specifically for dentists, White and Pharoah's Oral Radiology, Ninth Edition features more than 1,500 high-quality radiographic images and illustrations to demonstrate the foundational principles, core concepts, and techniques of oral and maxillofacial radiology. This bestselling book delivers state-of-the-art information about oral and maxillofacial radiology principles and techniques, and image interpretation. You will gain a solid foundation in radiation physics, radiation biology, and radiation safety and protection before learning the imaging techniques used in dentistry, including specialized techniques such as MRI and CT. You'll also learn how to recognize the key radiographic features of pathologic conditions and interpret radiographs accurately. This edition includes new chapters on Computed Tomography, MRI, Nuclear Medicine, and Ultrasound Imaging, as well as the latest information on quality assurance standards, 3D printing, computer aided treatments, and AI in oral

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning,

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. • Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry • A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, f

For 80 years, mathematics has driven fundamental innovation in computing and communications. This timely book provides a panorama of some recent ideas in mathematics and how they will drive continued innovation in computing, communications and AI in the coming years. It provides a unique insight into how the new techniques that are being developed can be used to provide theoretical foundations for technological progress, just as mathematics was used in earlier times by Turing, von Neumann, Shannon and others. Edited by leading researchers in the field, chapters cover the application of new mathematics in computer architecture, software verification, quantum computing, compressed sensing, networking, Bayesian inference, machine learning, reinforcement learning and many other areas.

Intelligent technical systems, which combine mechanical, electrical and software engineering with methods from control engineering and advanced mathematics, go far beyond the state of the art in mechatronics and open up fascinating perspectives. Among these systems are so-called self-optimizing systems, which are able to adapt their behavior autonomously and flexibly to changing operating conditions. The Collaborative Research Center 614 "Self-optimizing concepts and structures in mechanical engineering" pursued the long-term aim to enable others to develop dependable self-optimizing systems. Assuring their dependability poses new challenges. However, self-optimization also offers the possibility to adapt the system's behavior to improve dependability during operation. The aim of this book is to provide methods and techniques to master the challenges and to exploit the possibilities given by self-optimization. The reader will be able to develop self-optimizing systems that fulfill and

In dentistry today, heavy focus is given to learning the techniques of fixed or implant-supported restorations at the expense of learning how to make removable dentures. As a result, many talented clinicians lack the training and experience necessary to provide functional and dependable complete denture therapy to their patients, even when this could be the best treatment option for certain patients. This book seeks to solve that problem, taking the reader step by step through the process of fabricating removable complete dentures from the very first appointment through impressions, delivery, and maintenance. Sections alternate between clinical and laboratory steps (color coded for quick reference), and technical aspects of denture making, such as master cast fabrication and baseplate and occlusal rim formation, are covered in detail for clinicians who choose to perform them in-house rather than outsourcing to a laboratory. With sections devoted to jaw and occlusion evaluation, how to

Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic. As a reference on a core problem in computer science, this book will appeal to theoreticians and practitioners alike.

This is the first self-contained text to consider security and non-cooperative behavior in wireless networks. Major networking trends are analyzed and their implications explained in terms of security and cooperation, and potential malicious and selfish misdeeds are described along with the existing and future security techniques. Fundamental questions of security including user and device identification; establishment of security association; secure and cooperative routing in multi-hop networks; fair bandwidth distribution; and privacy protection are approached from a theoretical perspective and supported by real-world examples including ad hoc, mesh, vehicular, sensor, and RFID networks. Important relationships between trust, security, and cooperation are also discussed. Contains homework problems and tutorials on cryptography and game theory. This text is suitable for advanced undergraduates and graduate students of electrical engineering and computer science, and researchers and pr

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional str

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to kee

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to kee

Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Similarly, link detection – a rapidly evolving approach to the analysis of text that shares and builds upon many of the key elements of text mining – also provides new tools for people to better leverage their burgeoning textual data resources. The Text Mining Handbook presents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection in such varied fields a

Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed matter physics and statistical mechanics, this book provides an introduction to computer simulations in physics. The 5th edition contains extensive new material describing numerous powerful algorithms and methods that represent recent developments in the field. New topics such as active matter and machine learning are also introduced. Throughout, there are many applications, examples, recipes, case studies, and exercises to help the reader fully comprehend the material. This book is ideal for graduate students and researchers, both in academia and industry, who want to learn techniques that have become a third tool of physical science, complementing experiment and analytical theory.

Malicious hackers utilize the World Wide Web to share knowledge. Analyzing the online communication of these threat actors can help reduce the risk of attacks. This book shifts attention from the defender environment to the attacker environment, offering a new security paradigm of 'proactive cyber threat intelligence' that allows defenders of computer networks to gain a better understanding of their adversaries by analyzing assets, capabilities, and interest of malicious hackers. The authors propose models, techniques, and frameworks based on threat intelligence mined from the heart of the underground cyber world: the malicious hacker communities. They provide insights into the hackers themselves and the groups they form dynamically in the act of exchanging ideas and techniques, buying or selling malware, and exploits. The book covers both methodology - a hybridization of machine learning, artificial intelligence, and social network analysis methods - and the resulting conclusions, det

Many processes in materials science and engineering, such as the load deformation behaviour of certain structures, exhibit nonlinear characteristics. The computer simulation of such processes therefore requires a deep understanding of both the theoretical aspects of nonlinearity and the associated computational techniques. This book provides a complete set of exercises and solutions in the field of theoretical and computational nonlinear continuum mechanics and is the perfect companion to Nonlinear Continuum Mechanics for Finite Element Analysis, where the authors set out the theoretical foundations of the subject. It employs notation consistent with the theory book and serves as a great resource to students, researchers and those in industry interested in gaining confidence by practising through examples. Instructors of the subject will also find the book indispensable in aiding student learning.

Mobile Robotics offers comprehensive coverage of the essentials of the field suitable for both students and practitioners. Adapted from Alonzo Kelly's graduate and undergraduate courses, the content of the book reflects current approaches to developing effective mobile robots. Professor Kelly adapts principles and techniques from the fields of mathematics, physics and numerical methods to present a consistent framework in a notation that facilitates learning and highlights relationships between topics. This text was developed specifically to be accessible to senior level undergraduates in engineering and computer science, and includes supporting exercises to reinforce the lessons of each section. Practitioners will value Kelly's perspectives on practical applications of these principles. Complex subjects are reduced to implementable algorithms extracted from real systems wherever possible, to enhance the real-world relevance of the text.

State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure. Machine learning develops intelligent computer systems that are able to genera

Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting se

The world of machine learning is changing all the time. It is so amazing the idea that we are able to take a computer and let it learn as it goes. Without having to write out all of the codes that we need for every situation out there or every input that the user may pick, we are able to write out codes in machine learning, even with Python, in order to let the computer or device learn and make decisions on its own.This guidebook is going to take a closer look at how Python machine learning is able to work, as well as how you can use some of the tools and techniques that come with this process for your own needs. When you are interested in learning more about what machine learning is all about, as well as how you can use a part of the coding from Python inside of this process, then this guidebook is the tool for you Some of the topics that we will explore when we go through this guidebook will include: Understanding some of the basics of machine learning;Some of the different parts tha