Many types of web applications are running on the Internet today. There are also as many ways to manage and maintain the infrastructure that powers those applications. IBM® BluemixTM delivers quick and easy cloud capabilities to deploy and maintain your web application, with minimal hassle and overhead. As you follow along with four lab-style scenarios, this IBM RedpaperTM publication demonstrates how to create and deploy a web-based collaboration application on IBM Bluemix. The application chosen for these scenarios is Etherpad Lite, an open-source web-based collaboration application. Each lab extends the functionality of the Etherpad Lite application and to give you a good foundation for discovering the additional powerful capabilities that are available on Bluemix. The target audience for this paper is technical cloud specialists who are familiar with the technology of enterprise applications, but might be new to Bluemix.
Many types of web applications are running on the Internet today. There are also as many ways to manage and maintain the infrastructure that powers those applications. IBM® BluemixTM delivers quick and easy cloud capabilities to deploy and maintain your web application, with minimal hassle and overhead. As you follow along with two lab-style scenarios, this IBM RedpaperTM publication demonstrates how to create and deploy a web-based collaboration application on IBM Bluemix. Lab 1 features a Java Liberty Profile application that uses the Delivery Pipeline, Data Cache, and Monitoring and Analytics services. The lab focuses on quickly getting an application started, importing some existing code, and using a Data Cache service from IBM Bluemix, Delivery Pipeline, and Monitoring and Analytics services. Lab 2 extends functionality of Lab 1 by adding Auto-Scaling and Load Impact services to load-test the application and watch the behavior of auto-scaling service in action. The target audience for this paper is technical cloud specialists who are familiar with technology of enterprise applications, but might be new to IBM Bluemix. This paper provides a good foundation to help you discover some of the powerful application development capabilities that are available in IBM Bluemix.
Presents an overview of the history of computer crime as well as case studies to show the affect various events had on shaping the views of computer crime in the United States.
The second edition of Research Methods for Criminology and Criminal Justice is a core text for criminology and criminal justice research methods courses. This text offers a general foundation of knowledge that transcends particular topics or subject areas, allowing students to apply the methods and concepts discussed to a multitude of scenarios. Within the first five chapters, students learn (a) the philosophy behind scientific research, (b) the role of theory and hypotheses in the research process, (c) ethical issues in conducting research in our field, and (d) how research reports are structured. Thereafter, each new chapter will add information and examples that help students move toward a further understanding of research design and methodology that can be applied across the social and behavioral sciences to better understand social phenomena.
With masterful storytelling, Bergland and Hayes demonstrate how Lapham blended his ravenous curiosity with an equable temperament and a passion for detail to create a legacy that is still relevant today." --John Gurda In this long overdue tribute to Wisconsin's first scientist, authors Martha Bergland and Paul G. Hayes explore the remarkable life and achievements of Increase Lapham (1811-1875). Lapham's ability to observe, understand, and meticulously catalog the natural world marked all of his work, from his days as a teenage surveyor on the Erie Canal to his last great contribution as state geologist. Self-taught, Lapham mastered botany, geology, archaeology, limnology, mineralogy, engineering, meteorology, and cartography. A prolific writer, his 1844 guide to the territory was the first book published in Wisconsin. Asked late in life which field of science was his specialty, he replied simply, "I am studying Wisconsin." Lapham identified and preserved thousands of botanical specimens. He surveyed and mapped Wisconsin's effigy mounds. He was a force behind the creation of the National Weather Service, lobbying for a storm warning system to protect Great Lakes sailors. Told in compelling detail through Lapham's letters, journals, books, and articles, Studying Wisconsin chronicles the life and times of Wisconsin's pioneer citizen-scientist.
This impressive compilation offers a nearly complete listing of sound recordings made by American minority artists prior to mid-1942. Organized by national group or language, the seven-volume set cites primary and secondary titles, composers, participating artists, instrumentation, date and place of recording, master and release numbers, and reissues in all formats. Because of its clear arrangements and indexes, it will be a unique and valuable tool for music and ethnic historians, folklorists, and others.
The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.
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