A common misconception in the Mac community is that the Mac is more secure than any other operating system on the market. Although this might be true in most side-by-side analyses of security features right out of the box, what this isn’t taking into account is that security tends to get overlooked once the machine starts to be configured for its true purposes. For example, when sharing is enabled or remote control applications are installed, then a variety of security threats are often established—no matter what the platform is.

In the security sector, the principle of least privilege is a philosophy that security professionals abide by when determining security policies. This principle states that if you want to be secure, you need to give every component of your network the absolute minimum permissions required to do its job. But what are those permissions? What are the factors that need to be determined when making that decision? No two networks are the same; therefore, it’s certainly not a decision that can be made for you. It’s something you will need to decide for yourself based on what kinds of policies are implemented to deal with information technology security.

Do you spend a lot of time during the design process wondering what users really need? Do you hate those endless meetings where you argue how the interface should work? Have you ever developed something that later had to be completely redesigned?

Paper Prototyping can help. Written by a usability engineer with a long and successful paper prototyping history, this book is a practical, how-to guide that will prepare you to create and test paper prototypes of all kinds of user interfaces. You'll see how to simulate various kinds of interface elements and interactions. You'll learn about the practical aspects of paper prototyping, such as deciding when the technique is appropriate, scheduling the activities, and handling the skepticism of others in your organization. Numerous case studies and images throughout the book show you real world examples of paper prototyping at work.

Learn how to use this powerful technique to develop products that are more useful, intuitive, efficient, and pleasing:

• Save time and money - solve key problems before implementation begins
• Get user feedback early--use it to focus the development process
• Communicate better--involve development team members from a variety of disciplines
• Be more creative--experiment with many ideas before committing to one

About the Author

Carolyn Snyder is an internationally recognized usability consultant with 10 years of experience in usability and another 10 as a software engineer and project manager. She has taught usability testing and paper prototyping to development teams at dozens of companies. She is co-author of Web Site Usability: A Designer's Guide and E-Commerce User Experience.

Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.

"This is a clear, insightful book that takes an alternative look at some of the broader issues surrounding software development.... The commentary on the work of Christopher Alexander, currently being taken up so fervently by the software architecture community, is particularly appealing."--Sanjiv Gossain, Associate Director, Cambridge Technology Partners

"The essays are both entertaining and insightful.... Gabriel has an outstanding command of English which makes his writing readable and entertaining."--Steve Bilow, Journal of Object-Oriented Programming

"...Gabriel is an illuminating guide, providing fresh and invigorating perspectives guaranteed to stick with you long after you boot up."--San Francisco Sunday Examiner and Chronicle

In our homes, our schools, and our businesses, computers play an ever-increasing role. But while most of us today can work a computer--albeit with the help of the ever-present computer software manual--we know little about what goes on inside the box and virtually nothing about software design or the world of computer programming.

In Patterns of Software, the respected software pioneer and computer scientist, Richard Gabriel, gives us an informative inside look at the world of software design and computer programming and the business that surrounds them. In this wide-ranging volume, Gabriel discusses such topics as what makes a successful programming language, how the rest of the world looks at and responds to the work of computer scientists, how he first became involved in computer programming and software development, what makes a successful software business, and why his own company, Lucid, failed in 1994, ten years after its inception.

Perhaps the most interesting and enlightening section of the book is Gabriel's detailed look at what he believes are the lessons that can be learned from architect Christopher Alexander, whose books--including the seminal A Pattern Language--have had a profound influence on the computer programming community. Gabriel illuminates some of Alexander's key insights--"the quality without a name," pattern languages, habitability, piecemeal growth--and reveals how these influential architectural ideas apply equally well to the construction of a computer program.

Gabriel explains the concept of habitability, for example, by comparing a program to a New England farmhouse and the surrounding structures which slowly grow and are modified according to the needs and desires of the people who live and work on the farm. "Programs live and grow, and their inhabitants--the programmers--need to work with that program the way the farmer works with the homestead."

Although computer scientists and software entrepreneurs will get much out of this book, the essays are accessible to everyone and will intrigue anyone curious about Silicon Valley, computer programming, or the world of high technology.

About the Author

About the Author: Richard P. Gabriel is Distinguished Computer Scientist at ParcPlace-Digitalk, Inc. and Consulting Professor of Computer Science at Stanford University.

This book includes selected papers of the First International Workshop on Model-Based Software and Data Integration 2008, held in Berlin, Germany, in April 2008 as a part of the Berlin Software Integration Week 2008.

The 9 revised full papers presented together with 3 invited lectures were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data integration, software architectures, services and migration, as well as on model-based and semantic approaches.

A classic -- offering comprehensive and unified coverage with a balance between theory and practice!

Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition and audio classification, communications, computer-aided diagnosis, data mining. The authors, leading experts in the field of pattern recognition, have once again provided an up-to-date, self-contained volume encapsulating this wide spectrum of information.

Each chapter is designed to begin with basics of theory progressing to advanced topics and then discusses cutting-edge techniques. Problems and exercises are present at the end of each chapter with a solutions manual provided via a companion website where a number of demonstrations are also available to aid the reader in gaining practical experience with the theories and associated algorithms.

This edition includes discussion of Bayesian classification, Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering.

Key Features:
* Up-to-date results on support vector machines including í-SVMs and their geometric interpretation
* Classifier combinations including the Boosting approach
* Feature generation for image analysis, speech recognition and audio classification
* Up-to-date material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics
* Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification

About the Author

Sergios Theodoridis acquired a Physics degree with honors from the University of Athens, Greece in 1973 and a MSc and a Ph.D. degree in Signal Processing and Communications from the University of Birmingham, UK in 1975 and 1978 respectively. Since 1995 he has been a Professor with the Department of Informatics and Communications at the University of Athens. Konstantinos Koutroumbas acquired a degree from the University of Patras, Greece in Computer Engineering and Informatics in 1989, a MSc in Computer Science from the University of London, UK in 1990, and a Ph.D. degree from the University of Athens in 1995. Since 2001 he has been with the Institute for Space Applications and Remote Sensing of the National Observatory of Athens.