This book constitutes the proceedings of the 7th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2011, held in Karlsruhe, Germany in September 2011. The 17 full papers and 7 short papers presented were carefully reviewed and selected from 54 submissions. In addition the book contains two keynote speeches and 8 poster papers. They cover cutting-edge results from the wide range of disciplines concerned with context, including the cognitive sciences (linguistics, psychology, philosophy, computer science, neuroscience), the social sciences and organization sciences, and all application areas.

This book constitutes the refereed proceedings of the 10th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems, DAIS 2010, held in Amsterdam, The Netherlands, in June 2009. The DAIS conference was held as part of the federated event on Distributed Computing Techniques (DisCoTec), together with the 12th International Conference on Coordination Models and Languages (Coordination 2010), the 12th Formal Methods for Open Object-Based Distributed Systems and the 30th Formal Techniques for Networked and Distributed Systems (FMOODS/FORTE 2010). The 17 revised full papers presented were carefully reviewed and selected from53 submissions. The papers are organized in topical sections on ubiquitous services and applications, grid computing, sensor networks, context awareness, service orientation, distributed fault tolerant controllers, cloud and cluster computing, adaptive and (re)configurable systems, and collaborative systems.

Individuals with disabilities often have difficulty accomplishing tasks, living independently, and utilizing information technologies; simple aspects of daily life taken for granted by non-disabled individuals. Assistive Technologies: Concepts, Methodologies, Tools, and Applications presents a comprehensive collection of research, developments, and knowledge on technologies that enable disabled individuals to function effectively and accomplish otherwise impossible tasks. These volumes serve as a crucial reference source for experts in fields as diverse as healthcare, information science, education, engineering, and human-computer interaction, with applications bridging multiple disciplines.

Pervasive computing enables users to interact with information resources in their everyday lives. The development of computational technologies that can exist in ever smaller devices while simultaneously increasing processing power allows such devices to blend seamlessly into tangible environments. Intelligent Technologies and Techniques for Pervasive Computing provides an extensive discussion of such technologies, theories and practices in an attempt to shed light on current trends and issues in the adaption of pervasive systems. Within its pages, students and practitioners of computer science will find both recent developments and practical applications—an overview of the field and how intelligent techniques can help to improve user experience in the distribution and consumption of pertinent, timely information. This book is part of the Advances in Computational Intelligence and Robotics series collection.

Just like any new technology, what perplexes many is the question of how to apply Deep Learning in a business context. Technology that is disruptive does not automatically imply that the development of valuable use cases are apparent. For years, many people could not figure out how to monetize the World Wide Web. We are in that same situation with Deep Learning AI. The developments are mind-boggling but the monetization is far from being obvious.Deep Learning Artificial Intelligence involves the interplay of Computer Science, Physics, Biology, Linguistics and Psychology. In addition to that, it is technology that can be extremely disruptive. Furthermore, the ramifications to society and even our own humanity can be immense. There are few subjects that are as captivating and as consequential as this. Surprisingly, there is very little that is written about this new technology in a more comprehensive and cohesive way. This book is an opinionated take on the developments of Deep Learning AI.

By using various data inputs, ubiquitous computing systems detect their current usage context, automatically adapt their services to the user’s situational needs and interact with other services or resources in their environment on an ad-hoc basis. Designing such self-adaptive, context-aware knowledge processing systems is, in itself, a formidable challenge. This book presents core findings from the VENUS project at the Interdisciplinary Research Center for Information System Design (ITeG) at Kassel University, where researchers from different fields, such as computer science, information systems, human-computer interaction and law, together seek to find general principles and guidelines for the design of socially aware ubiquitous computing systems. To this end, system usability, user trust in the technology and adherence to privacy laws and regulations were treated as particularly important criteria in the context of socio-technical system design. During the project, a comprehensive blueprint for systematic, interdisciplinary software development was developed, covering the particular functional and non-functional design aspects of ubiquitous computing at the interface between technology and human beings. The organization of the book reflects the structure of the VENUS work program. After an introductory part I, part II provides the groundwork for VENUS by presenting foundational results from all four disciplines involved. Subsequently, part III focuses on methodological research funneling the development activities into a common framework. Part IV then covers the design of the demonstrators that were built in order to develop and evaluate the VENUS method. Finally, part V is dedicated to the evaluation phase to assess the user acceptance of the new approach and applications. The presented findings are especially important for researchers in computer science, information systems, and human-computer interaction, but also for everyone working on the acceptance of new technologies in society in general.

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.