The focus of these conference proceedings is on research, development, and applications in the fields of numerical geometry, scientific computing and numerical simulation, particularly in mesh generation and related problems. In addition, this year's special focus is on Delaunay triangulations and their applications, celebrating the 130th birthday of Boris Delaunay. In terms of content, the book strikes a balance between engineering algorithms and mathematical foundations. It presents an overview of recent advances in numerical geometry, grid generation and adaptation in terms of mathematical foundations, algorithm and software development and applications. The specific topics covered include: quasi-conformal and quasi-isometric mappings, hyperelastic deformations, multidimensional generalisations of the equidistribution principle, discrete differential geometry, spatial and metric encodings, Voronoi-Delaunay theory for tilings and partitions, duality in mathematical programming and numerical geometry, mesh-based optimisation and optimal control methods. Further aspects examined include iterative solvers for variational problems and algorithm and software development. The applications of the methods discussed are multidisciplinary and include problems from mathematics, physics, biology, chemistry, material science, and engineering.

In this book the author presents some techniques for exploring trees and graphs. He illustrates the linear search technique and the backtracking technique, and as instances of tree exploration methods he presents various algorithms for parsing subclasses of context-free languages. He also illustrates some tree and graph exploration and manipulation methods by presenting, among others, algorithms for visiting trees, evaluating Boolean expressions, proving propositional formulas, computing paths in graphs, and performing string matching. This book has been used for advanced undergraduate and graduate courses on automata and formal languages, and assumes some prior exposure to the basic notions in that area. Sample programs are presented in Java and Prolog.

Communities of Computing is the first book-length history of the Association for Computing Machinery (ACM), founded in 1947 and with a membership today of 100,000 worldwide. It profiles ACM's notable SIGs, active chapters, and individual members, setting ACM's history into a rich social and political context. The book's 12 core chapters are organized into three thematic sections. "Defining the Discipline" examines the 1960s and 1970s when the field of computer science was taking form at the National Science Foundation, Stanford University, and through ACM's notable efforts in education and curriculum standards. "Broadening the Profession" looks outward into the wider society as ACM engaged with social and political issues - and as members struggled with balancing a focus on scientific issues and awareness of the wider world. Chapters examine the social turbulence surrounding the Vietnam War, debates about the women's movement, efforts for computing and community education, and international issues including professionalization and the Cold War. "Expanding Research Frontiers" profiles three areas of research activity where ACM members and ACM itself shaped notable advances in computing, including computer graphics, computer security, and hypertext. Featuring insightful profiles of notable ACM leaders, such as Edmund Berkeley, George Forsythe, Jean Sammet, Peter Denning, and Kelly Gotlieb, and honest assessments of controversial episodes, the volume deals with compelling and complex issues involving ACM and computing. It is not a narrow organizational history of ACM committees and SIGS, although much information about them is given. All chapters are original works of research. Many chapters draw on archival records of ACM's headquarters, ACM SIGs, and ACM leaders. This volume makes a permanent contribution to documenting the history of ACM and understanding its central role in the history of computing.

While doctors and physicians are more than capable of detecting diseases of the brain, the most agile human mind cannot compete with the processing power of modern technology. Utilizing algorithmic systems in healthcare in this way may provide a way to treat neurological diseases before they happen. Early Detection of Neurological Disorders Using Machine Learning Systems provides innovative insights into implementing smart systems to detect neurological diseases at a faster rate than by normal means. The topics included in this book are artificial intelligence, data analysis, and biomedical informatics. It is designed for clinicians, doctors, neurologists, physiotherapists, neurorehabilitation specialists, scholars, academics, and students interested in topics centered on biomedical engineering, bio-electronics, medical electronics, physiology, neurosciences, life sciences, and physics.

With the growing use of new technologies and artificial intelligence (AI) applications, intelligent systems can be used to manage large amounts of existing data in healthcare domains. Having more intelligent methods for accessing data allows medical professionals to more efficiently identify the best medical practices and more concrete solutions for diagnosing and treating a multitude of rare diseases. Intelligent Systems for Healthcare Management and Delivery provides relevant and advanced methodological, technological, and scientific approaches related to the application of sophisticated exploitation of AI, as well as providing insight into the technologies and intelligent applications that have received growing attention in recent years such as medical imaging, EMR systems, and drug development assistance. This publication fosters a scientific debate for new healthcare intelligent systems and sophisticated approaches for enhanced healthcare services and is ideally designed for medical professionals, hospital staff, rehabilitation specialists, medical educators, and researchers.

Set your students on track to achieve the best grade possible with My Revision Notes: AQA A-level Computer Science. Our clear and concise approach to revision will help students learn, practise and apply their skills and understanding. Coverage of key content is combined with practical study tips and effective revision strategies to create a guide that can be relied on to build both knowledge and confidence. With My Revision Notes: AQA A-level Computer Science, students can: > Consolidate knowledge with clear, focused and relevant content coverage, based on what examiners are looking for > Develop understanding with self-testing - our regular 'Now test yourself,' tasks and answers will help commit knowledge to memory > Improve technique through exam-style practice questions, expert tips and examples of typical mistakes to avoid > Identify key connections between topics and subjects with our 'Learning links' focus > Plan and manage a successful revision programme with our topic-by-topic planner, new exam breakdown feature, user-friendly definitions throughout and questions and answers online

Great handbook to get you going with Ruby Programming! Skip your traditional technical books and dive right in so your proficient with programming instantly! Need to learn fast, tired of spending too much time trying to get through your standard technical books? Just want to get started and begin all your desired program development by the end of the day? Learn to set up with Ruby now All the Ruby Syntax you need immediately at your fingertips Access to all different statements And even Object oriented programming within this read! One click equals all of Ruby Programming! Get it now!

Containing selected papers on the fundamentals and applications of Complexity Science, this multi-disciplinary book presents new approaches for resolving complex issues that cannot be resolved using conventional mathematical or software models. Complex Systems problems can occur in a variety of areas such as physical sciences and engineering, the economy, the environment, humanities and social and political sciences. Complexity Science problems, the science of open systems consisting of large numbers of diverse components engaged in rich interaction, can occur in a variety of areas such as physical sciences and engineering, the economy, the environment, humanities and social and political sciences. The global behaviour of these systems emerges from the interaction of constituent components and is unpredictable but not random. The key attribute of Complex Systems is the ability to self-organise and adapt to unpredictable changes in their environment. Renown complexity thinkers and practitioners as well as those who are new to the area of complexity will find interest in this book.

An intellectual property discussion is central to qualitative research projects, and ethical guidelines are essential to the safe accomplishment of research projects. Undertaking research studies without adhering to ethics may be dangerous to researchers and research subjects. Therefore, it is important to understand and develop practical techniques for handling ethics with a specific focus on qualitative projects so that researchers conducting this type of research may continue to use ethical practices at every step of the project. Data Analysis and Methods of Qualitative Research: Emerging Research and Opportunities discusses in detail the methods related to the social constructionist paradigm that is popular with qualitative research projects. These methods help researchers undertake ideal qualitative projects that are free from quantitative research techniques/concepts all while acquiring practical skills in handling ethics and ethical issues in qualitative projects. The chapters each contain case studies, learning outcomes, question and answer sections, and discuss critical research philosophies in detail along with topics such as ethics, research design, data gathering and sampling methods, research outputs, data analysis, and report writing. Featuring a wide range of topics such as epistemology, probability sampling, and big data, this book is ideal for researchers, practitioners, computer scientists, academicians, analysts, coders, and students looking to become competent qualitative research specialists.

The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces: user input involving new media (speech, multi-touch, hand and body gestures, facial expressions, writing) embedded in multimodal-multisensor interfaces that often include biosignals. This edited collection is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This second volume of the handbook begins with multimodal signal processing, architectures, and machine learning. It includes recent deep learning approaches for processing multisensorial and multimodal user data and interaction, as well as context-sensitivity. A further highlight is processing of information about users' states and traits, an exciting emerging capability in next-generation user interfaces. These chapters discuss real-time multimodal analysis of emotion and social signals from various modalities, and perception of affective expression by users. Further chapters discuss multimodal processing of cognitive state using behavioral and physiological signals to detect cognitive load, domain expertise, deception, and depression. This collection of chapters provides walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this rapidly expanding field. In the final section of this volume, experts exchange views on the timely and controversial challenge topic of multimodal deep learning. The discussion focuses on how multimodal-multisensor interfaces are most likely to advance human performance during the next decade.

High-performance computing (HPC) describes the use of connected computing units to perform complex tasks. It relies on parallelization techniques and algorithms to synchronize these disparate units in order to perform faster than a single processor could, alone. Used in industries from medicine and research to military and higher education, this method of computing allows for users to complete complex data-intensive tasks. This field has undergone many changes over the past decade, and will continue to grow in popularity in the coming years. Innovative Research Applications in Next-Generation High Performance Computing aims to address the future challenges, advances, and applications of HPC and related technologies. As the need for such processors increases, so does the importance of developing new ways to optimize the performance of these supercomputers. This timely publication provides comprehensive information for researchers, students in ICT, program developers, military and government organizations, and business professionals.

From the chaos of the early DARPA, ARPANET and NSF-funded NSFNET has emerged a globe-spanning communications facility we today call simply "The Internet." It has become so commonplace and so taken for granted that Wired News has decreed that writers should no longer capitalize it. This tale is not singularly focused on the past. It tells not only how we got here, but where we think the Commercial Internet must go. For all its greatness, today's Internet has serious shortcomings. Theft of personal data, identity theft, online scams, and advertising fraud run rampant, with online dollars diverted to organized crime. Insecure systems, poor security practices and an attitude of secrecy and reluctance to acknowledge failings inhibit real solutions. We propose a way forward, a networking future that is bright, optimistic, and secure.