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This book establishes the foundations needed to realize the ultimate goals for artificial intelligence, such as autonomy and trustworthiness.Aimed at scientists, researchers, technologists, practitioners, and students, it brings together contributions offering the basics, the challenges and the state-of-the-art on trusted autonomous systems in a single volume.The book is structured in three parts, with chapters written by eminent researchers and outstanding practitioners and users in the field. The first part covers foundational artificial intelligence technologies, while the second part covers philosophical, practical and technological perspectives on trust. Lastly, the third part presents advanced topics necessary to create future trusted autonomous systems.The book augments theory with real-world applications including cybersecurity, defence and space.

Trusted Autonomy --- Automation Technology --- Autonomous Systems --- Self-Governance --- Trusted Autonomous Systems --- Design of Algorithms and Methodologies

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Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.

3D Bildanalyse, Entwicklungsbiologie, Algorithmen, Data Mining, Software3D Image Analysis, Developmental Biology, Algorithms, Data Mining, Software

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In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

short term load forecasting --- statistical forecasting models --- artificial neural networks (ANNs) --- knowledge-based expert systems --- evolutionary algorithms --- meta-heuristic algorithms --- support vector regression/support vector machines --- seasonal mechanism --- novel intelligent technologies

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This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more.

Complex Scheduling Problems --- Scheduling Problems in Segments of a Supply Chain --- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management etc. --- Vehicle Routing --- Scheduling under Uncertainty --- Scheduling under Resource Constraints --- Just-in-Time Scheduling --- Assembly Scheduling --- Agent Based Scheduling --- Real-Time Scheduling --- Scheduling Heuristics and Metaheuristics --- Evolutionary Algorithms --- Approximation Algorithms --- Enumerative Algorithms --- Complexity Issues

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This book examines the impact of the "Big Five" technology companies – Apple, Alphabet/Google, Amazon, Facebook and Microsoft – on journalism and the media industries. It looks at the current role of algorithms and artificial intelligence in curating how we consume media and their increasing influence on the production of the news.Exploring the changes that the technology industry and automation have made in the past decade to the production, distribution and consumption of news globally, the book considers what happens to journalism once it is produced and enters the media ecosystems of the internet tech giants – and the impact of social media and AI on such things as fake news in the post-truth age.

Media & Communications --- Apple --- Alphabet --- Google --- Facebook --- Amazon --- Microsoft --- journalism --- media industry --- algorithms --- artificial intelligence --- curation --- news production --- media ecosystem --- social media --- fake news --- post-truth --- digital media