Bo Jiang

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 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.

Explore an in-depth explanation of buddha nature and self-emptiness. The original Sublime Continuum Explanatory Commentary was written by Noble Asanga to explain the verses received from the bodhisattva Maitreya in the late fourth century CE in northern India. Here it is introduced and presented in an original translation from Sanskrit and Tibetan, with the translation of an extensive Tibetan Supercommentary by Gyaltsap Darma Rinchen (1364-1432), whose work closely followed the view of his teacher, Tsong Khapa (1357-1419). Contemporary scholars have widely misunderstood the Buddhist Centrist (Madhyamaka) teaching of emptiness, or selflessness, as either a form of nihilism or a radical skepticism. Yet Buddhist philosophers from Nagarjuna on have shown that the negation of intrinsic reality, when accurately understood, affirms the supreme value of relative realities. Gyaltsap Darma Rinchen, in his Supercommentary, elucidates a highly positive theory of the buddha nature, showing how the wisdom of emptiness empowers the compassionate life of the enlightened, as it is touched by its oneness with the truth body of all buddhas. With his clear study of Gyaltsap's insight and his original English translation, Bo Jiang completes his historic project of studying and presenting these works from Sanskrit and Tibetan in both Chinese and, now, English translations, in linked publications.

Futugrammi la quarta antologia di fantascienza contemporanea cinese in doppia lingua dopo Nebula, Sinosfera e Artificina. Le storie selezionate provengono da autori affermati e gi noti nel panorama cinese e internazionale come Chen Qiufan, Yang Ping, Zhan Ran e Jiang Bo e da voci nuove ma altrettanto interessanti come quelle delle scrittrici Bella Han e Su Min. Nel suo incredibile sviluppo tecnologico, la Cina rappresenta, da almeno dieci anni, una fucina di narrazioni futuribili che questi autori hanno declinato in vario modo: dalla ricerca e profilazione del partner perfetto tramite algoritmi e Big Data, al progressivo allungamento della vita umana mediante tecniche di ibernazione, da un virus ematico che anticipa la pandemia globale da covid19 alle disparit sociali legate all'accesso di trattamenti anti-invecchiamento, passando per la contraffazione dell'identit personale e le nevrosi dell'era post-capitalistica di cui la Cina incarna ormai la versione pi attuale e al tempo stesso avveniristica. Traduzioni dal cinese di Chiara Rizzo, Domenica Recupero, Maria Teresa Trucillo, Francesca Bistocchi, Noemi Vetta, Louise Machetti, Andrea Chiara Palmerini. Introduzione di Fei DaoAmore in cloud di Chen QiufanCaos ematico di Yang PingA Helen di Bella HanUn altro volto di Jiang BoL'era delle bare di ghiaccio di Zhang RanL'era della post-coscienza di Su Min《未来文字》是继《星云》、《汉字文化圈》和《赛博格中国》之后出版的第四 本双语中国当代科幻选集。其中不仅包含了许多在中国和国际科幻界知名作家如陈楸 帆、杨平、张冉和江波的作品，还有白乐寒以及苏民等新人作家的创作。近十年间，中国的科技飞速发展，与此同时，在这片土地上也涌现出一批风格各 异的作家，他们以不同的方式讲述着可能的未来：从利用算法和大数据分析来寻找完 美伴侣到利用休眠技术来延长人类平均寿命；从一种可以说是预示了2019年新冠病毒 的血液病毒到根据阶层来进行抗衰老疗法的社会；最后还有伪造个人身份和后资本主 义时代的神经失衡。上述的情景有些已经在当今越来越具有未来主义特点的中国出现 了。这些作品由Chiara Rizzo, Domenica Recupero, Maria Teresa Trucillo, Francesca Bistocchi, Noemi Vetta, Louise Machetti, Andrea Chiara Palmerini译成中文。目录现实-滤镜-科幻, 飞氘云爱人, 陈楸帆血乱, 杨平致海伦, 白乐寒变脸, 江波2065：冰棺时代, 张冉后意识时代, 苏民

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. 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.