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Federated Learning
Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-th...
Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development
This book focuses on different algorithms and models related to AI, big data and IoT used for various domains. It enables the reader to have a broader and deeper understanding of several perspectives regarding the dynamics, challenges, and opportunities for sustainable development using artificial intelligence, big data and IoT. Applications of Artificial Intelligence, Big Data and Internet of Things (IoT) in Sustainable Development focuses on IT-based advancements in multidisciplinary fields such as healthcare, finance, bioinformatics, industrial automation, and environmental science. The authors discuss the key issues of security, management, and the realization of possible solutions to hu...
From Trustworthy AI Principles to Public Procurement Practices
This book is an early warning to public officials, policymakers, and procurement practitioners on the impact of AI on the public sector. Many governments have established national AI strategies and set ambitious goals to incorporate AI into the public infrastructure, while lacking AI-specific procurement guidelines. AI is not traditional software, and traditional processes are not sufficient to meet the challenges AI brings. Today’s decisions to embed AI and algorithmic systems into public system infrastructure can – and will – have serious repercussions in the future. The promise of AI systems is to make the public sector more efficient, effective, fair, and sustainable. However, AI s...
Process Mining Workshops
This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included.
Federated Learning
Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for dri...
Malware Analysis Using Artificial Intelligence and Deep Learning
This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
Security, Privacy, and Digital Forensics in the Cloud
In a unique and systematic way, this book discusses the security and privacy aspects of the cloud, and the relevant cloud forensics. Cloud computing is an emerging yet revolutionary technology that has been changing the way people live and work. However, with the continuous growth of cloud computing and related services, security and privacy has become a critical issue. Written by some of the top experts in the field, this book specifically discusses security and privacy of the cloud, as well as the digital forensics of cloud data, applications, and services. The first half of the book enables readers to have a comprehensive understanding and background of cloud security, which will help the...
Information Systems Engineering in Responsible Information Systems
This book constitutes the thoroughly refereed proceedings of the CAiSE Forum 2019 held in Rome, Italy, as part of the 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019, in June 2019. The CAiSE Forum - one of the traditional tracks of the CAiSE conference - aims to present emerging new topics and controversial positions, as well as demonstration of innovative systems, tools and applications related to information systems engineering. This year’s theme was “Responsible Information Systems”. The 19 full papers and 3 short papers presented in this volume were carefully reviewed and selected from 14 direct submissions (of which 7 full papers were selected), plus 15 transfers from the CAiSE main conference (which resulted in another 12 full and 3 short papers).
Collaborative Networks of Cognitive Systems
This book constitutes the refereed proceedings of the 19th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2018, held in Cardiff, UK, in September 2018. The 57 revised full papers were carefully reviewed and selected from 143 submissions. They provide a comprehensive overview of identified challenges and recent advances in various collaborative network (CN) domains and their applications, with a strong focus on the following areas: blockchain in collaborative networks, industry transformation and innovation, semantics in networks of cognitive systems, cognitive systems for resilience management, collaborative energy services in smart cities, cognitive systems in agribusiness, building information modeling, industry 4.0 support frameworks, health and social welfare services, risk, privacy and security, collaboration platform issues, sensing, smart and sustainable enterprises, information systems integration, dynamic logistics networks, collaborative business processes, value creation in networks, users and organizations profiling, and collaborative business strategies.
