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"In the dynamic and interactive academic learning environment, students are required to have qualified information literacy competencies while critically reviewing print and electronic information. However, many undergraduates encounter difficulties in searching peer-reviewed information resources. Scholarly Information Discovery in the Networked Academic Learning Environment is a practical guide for students determined to improve their academic performance and career development in the digital age. Also written with academic instructors and librarians in mind who need to show their students how to access and search academic information resources and services, the book serves as a reference to promote information literacy instructions. This title consists of four parts, with chapters on the search for online and printed information via current academic information resources and services: part one examines understanding information and information literacy; part two looks at academic information delivery in the networked world; part three covers searching for information in the academic learning environment; and part four discusses searching and utilizing needed information in the future in order to be more successful beyond the academic world."

"Machine Learning (ML) algorithms have opened up new possibilitiesfor the acquisition and processing of documents in InformationRetrieval (IR) systems. Indeed, it is now possible to automate severallabor-intensive tasks related to documents such as categorization andentity extraction. Consequently, the application of machine learning techniquesfor various large-scale IR tasks has gathered significant researchinterest in both the ML and IR communities. This tutorial provides areference summary of our research in applying machine learning techniquesto diverse tasks in Digital Libraries (DL). Digital library portalsare specialized IR systems that work on collections of documentsrelated to particular domains. We focus on open-access, scientific digitallibraries such as CiteSeerx, which involve several crawling, ranking,content analysis, and metadata extraction tasks. We elaborate on thechallenges involved in these tasks and highlight how machine learningmethods can successfully address these challenges."

"This book, provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment. After an introduction, in its second part the authors describe the NeOn Methodology framework. The book’s third part details the key activities relevant to the ontology engineering life cycle. For each activity, a general introduction, methodological guidelines, and practical examples are provided. The fourth part then presents a detailed overview of the NeOn Toolkit and its plug-ins. Lastly, case studies from the pharmaceutical and the fishery domain round out the work."

"This comprehensive textbook/reference presents a focused review of the state of the art in privacy research, encompassing such diverse topics as cloud computing, crowdsourcing platforms, vehicular ad-hoc networks, big data, mobile devices, location-based systems, smart grid technology, databases, social networks, healthcare, behavioral economics, and peer-to-peer networks. The first book of its kind designed specifically to cater to courses on privacy, this authoritative volume provides technical, legal, and ethical perspectives on privacy issues from a global selection of renowned experts."