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This book analyses the transformation of European Education Policy from 1973 to 2020. In doing so, it offers a unique insight into the changes of European education from a predominantly national concern to a supranational policy framework, driven by an economic discourse concerning productivity and employability. The book shows that the idea of the ¿Europe of Knowledge¿ did not originate in the Lisbon Strategy of 2000, but rather was the result of a gradual development that started in the mid-1980s. This begun with the establishment of a specific problem definition of education as a solution for Europe¿s lack of competitiveness, a definition that was incrementally constructed by the European Commission and the European business community. Highlighting significant and unexplored questions such as the role of European transnational business in education and the role of the ¿problem entrepreneur¿ in defining policy issues, this book will provide a comprehensive perspective on European Education Policy that will be of interest to all students of European Politics, Education Policy, and Public Policy.

The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this work criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature.This work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis proposed is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal.Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. As such, it will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.

This book presents a comprehensive reference source for dynamic and innovative research in the field of cyber security, focusing on nature-inspired research and applications. The authors present the design and development of future-ready cyber security measures, providing a critical and descriptive examination of all facets of cyber security with a special focus on recent technologies and applications. The book showcases the advanced defensive cyber security mechanism that is a requirement in the industry and highlights measures that provide efficient and fast solutions. The authors explore the potential of AI-based and nature-inspired based computing compatibilities in establishing an adaptive defense mechanism system. The book focuses on current research while highlighting the empirical results along with theoretical concepts to provide a reference for students, researchers, scholars, professionals, and practitioners in the field of cyber security and analytics. This book featurescontributions from leading scholars from all over the world.

This book provides a comprehensive overview of the current state of the art of practical applications of neuroprosthesis based on functional electrical stimulation for restoration of motor functions lost by spinal cord injury and discusses the use of brain-computer interfaces for their control.The book covers numerous topics starting with basics about spinal cord injury, electrical stimulation, electrical brain signals and brain-computer interfaces. It continues with an overview of neuroprosthetic solutions for different purposes and non-invasive and invasive brain-computer interface implementations and presents clinical use cases and practical applications of BCIs. Finally, the authors give an outlook on cutting edge research with a high potential for clinical translation in the near future.All authors committed themselves to use easy-to-understand language and to avoid very specific information, focusing instead on the essential aspects. This makes this bookan ideal choice not only for researchers and clinicians at all stages of their education interested in the topic of brain-computer interface-controlled neuroprostheses, but also for end users and their caregivers who want to inform themselves about the current technological possibilities to improve paralyzed motor functions.

RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack mapsthat aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.