Bo Jiang – författare

This book introduces embedded software engineering and management methods, proposing the relevant testing theory and techniques that promise the final realization of automated testing of embedded systems.The quality and reliability of embedded systems have become a great concern, faced with the rising demands for the complexity and scale of system hardware and software. The authors propose and expound on the testing theory and techniques of embedded software systems and relevant environment construction technologies, providing effective solutions for the automated testing of embedded systems. Through analyzing typical testing examples of the complex embedded software systems, the authors verify the effectiveness of the theories, technologies and methods proposed in the book.In combining the fundamental theory and technology and practical solutions, this book will appeal to researchers and students studying computer science, software engineering, and embedded systems, as well as professionals and practitioners engaged in the development, verification, and maintenance of embedded systems in the military and civilian fields.

This book introduces embedded software engineering and management methods, proposing the relevant testing theory and techniques that promise the final realization of automated testing of embedded systems.The quality and reliability of embedded systems have become a great concern, faced with the rising demands for the complexity and scale of system hardware and software. The authors propose and expound on the testing theory and techniques of embedded software systems and relevant environment construction technologies, providing effective solutions for the automated testing of embedded systems. Through analyzing typical testing examples of the complex embedded software systems, the authors verify the effectiveness of the theories, technologies and methods proposed in the book.In combining the fundamental theory and technology and practical solutions, this book will appeal to researchers and students studying computer science, software engineering, and embedded systems, as well as professionals and practitioners engaged in the development, verification, and maintenance of embedded systems in the military and civilian fields.

This book offers an in-depth exploration of explainable learner models, presenting theoretical foundations and practical applications in the context of educational AI. It aims to provide readers with a comprehensive understanding of how these models can enhance adaptive learning systems.Chapters cover a wide range of topics, including the development and optimization of explainable learner models, the integration of these models into adaptive learning systems, and their implications for educational equity. It also discusses the latest advancements in AI explainability techniques, such as pre-hoc and post-hoc explainability, and their application in intelligent tutoring systems. Lastly, the book provides practical examples and case studies to illustrate how explainable learner models can be implemented in real-world educational settings.This book is an essential resource for researchers, educators, and practitioners interested in the intersection of AI and education. It offers valuable insights for those looking to integrate explainable AI into their educational practices, as well as for policymakers focused on promoting equitable and transparent learning environments.

This book offers an in-depth exploration of explainable learner models, presenting theoretical foundations and practical applications in the context of educational AI. It aims to provide readers with a comprehensive understanding of how these models can enhance adaptive learning systems.Chapters cover a wide range of topics, including the development and optimization of explainable learner models, the integration of these models into adaptive learning systems, and their implications for educational equity. It also discusses the latest advancements in AI explainability techniques, such as pre-hoc and post-hoc explainability, and their application in intelligent tutoring systems. Lastly, the book provides practical examples and case studies to illustrate how explainable learner models can be implemented in real-world educational settings.This book is an essential resource for researchers, educators, and practitioners interested in the intersection of AI and education. It offers valuable insights for those looking to integrate explainable AI into their educational practices, as well as for policymakers focused on promoting equitable and transparent learning environments.

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods.  For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark.   This book contains three parts.  The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data recovery, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks.  Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.

This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods.

The two-volume set LNAI 15497 and LNAI 15498 constitutes the refereed proceedings of the 14th International Conference on Brain Inspired Cognitive Systems, BICS 2024, held in Hefei, China, during December 6–8, 2024.