A decision procedure is an algorithm that, given a decision problem, terminates with a correct yes/no answer. Here, the authors focus on theories that are expressive enough to model real problems, but are still decidable. Specifically, the book concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research. The techniques described in the book draw from fields such as graph theory and logic, and are routinely used in industry. The authors introduce the basic terminology of satisfiability modulo theories and then, in separate chapters, study decision procedures for each of the following theories: propositional logic; equalities and uninterpreted functions; linear arithmetic; bit vectors; arrays; pointer logic; and quantified formulas.
During task composition, such as can be found in distributed query processing, workflow systems and AI planning, decisions have to be made by the system and possibly by users with respect to how a given problem should be solved. Although there is often more than one correct way of solving a given problem, these multiple solutions do not necessarily lead to the same result. Some researchers are addressing this problem by providing data provenance information. Others use expert advice encoded in a supporting knowledge-base. However, users do not usually trust complete automation during decision-making for certain domains with natural variation, like biology; they need a way to be able to control and/or intervene with the system´s reasoning to verify parts of the process. This book provides a thorough analysis of the problem and presents a data-centric methodology of measuring decision criticality and describe its potential use. We argue that agent technology is a natural fit for the design of distributed heterogeneous integration systems, particularly in bioinformatics, and we propose a multi-agent system design and architecture as the basis of our framework.
Over time, technical debt affects virtually every significant software project. As software systems evolve, earlier design or code decisions prove to be ´´not quite right,´´ gradually becoming impediments that slow down the evolution of the system, or even grind it to a halt. Most software practitioners have experienced this phenomenon, but many feel helpless to address it. In this guide, three leading software engineering experts introduce empirically validated principles and practices for managing and mitigating technical debt in any software system. Using real-life examples, the authors explain the forms of technical debt that afflict software-intensive systems, their root causes, and their impacts. Next, they introduce a palette of proven approaches, strategies, methods, and tools for: Identifying sources of technical debt in any software system Assessing the magnitude of technical debt Limiting the introduction of technical debt in the first place Reducing the impact of technical debt over time As software systems mature, the challenge of technical debt has grown, and it has become increasingly urgent for software professionals and their managers to address it head-on. Managing Technical Debt shows them how.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of ´´boosting,´´ how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
El objetivo es favorecer a todos los grupos sociales (discapacitados, adulto mayor, niños, pueblos indígenas, etc.) en el acceso a las nuevas tecnologías de información y comunicaciones (NTICs) y la oportunidad de participar en la toma de decisiones en condiciones de igualdad mediante el uso de ellas. Analizando una muestra de 180 sitios web peruanos, en cuanto al cumplimiento de las normas peruanas en temas de accesibilidad web, y los mecanismos implementados para reducir la barrera de accesibilidad en los portales y páginas web peruanas.
This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.
Publisher´s Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Discover how to write your own fun and useful Julia programs quickly and easily-no prior experience required! This engaging guide shows, step by step, how to build custom programs using Julia, the new dynamic and intuitive programming language. The first title in a programming and computer technology series from 15-year-old technology phenom, IBM Champion and Google Developer Expert, Tanmay Bakshi, the book is an essential starting point for beginners of all ages. You will learn the basics of Julia programming language and take a look towards the future, including programming for next-generation technologies such as machine learning! Tanmay Teaches Julia for Kids and Beginners starts with a fun, easy-to-follow introduction to the programming language. From there, you will find out how to work with variables, make decisions with ´´if´´ statements, put it all together using arrays and dictionaries, and call code with functions. It´ll even take you to a point where you can take code you´ve written in the Python programming language, and import it directly into your Julia applications. This book is every beginner´s essential guide to Julia, one of today´s most popular open-source computer programming languages. .Presented in an accessible style that makes learning easy .Demonstrates calling Python code within Julia .Demonstrates the future of technology through Flux, an intuitive machine learning library written in pure Julia .Written by a 15-year-old media personality and coding expert
Follow step-by-step guidance to craft a successful security program. You will identify with the paradoxes of information security and discover handy tools that hook security controls into business processes. Information security is more than configuring firewalls, removing viruses, hacking machines, or setting passwords. Creating and promoting a successful security program requires skills in organizational consulting, diplomacy, change management, risk analysis, and out-of-the-box thinking. What You Will Learn: Build a security program that will fit neatly into an organization and change dynamically to suit both the needs of the organization and survive constantly changing threats Prepare for and pass such common audits as PCI-DSS, SSAE-16, and ISO 27001 Calibrate the scope, and customize security controls to fit into an organization´s culture Implement the most challenging processes, pointing out common pitfalls and distractions Frame security and risk issues to be clear and actionable so that decision makers, technical personnel, and users will listen and value your advice Who This Book Is For: IT professionals moving into the security field; new security managers, directors, project heads, and would-be CISOs; and security specialists from other disciplines moving into information security (e.g., former military security professionals, law enforcement professionals, and physical security professionals)