Longbing Cao Livres

Focusing on the global scientific response to COVID-19, this book chronicles four years of research efforts aimed at understanding and mitigating the pandemic. It compiles and analyzes a vast array of scientific literature, examining the roles of mathematics, AI, and epidemiology in quantifying the virus's impact. The findings highlight publication trends, collaboration efforts, and economic correlations, offering a comprehensive study that aims to enhance preparedness for future health emergencies. It serves as a vital resource for policymakers, researchers, and funding authorities in public health.

This book constitutes the thoroughly refereed proceedings of the International Workshops on Behavior and Social Informatics and Computing, BSIC 2013, held as collocated event of IJCAI 2013, in Beijing, China in August 2013 and the International Workshop on Behavior and Social Informatics, BSI 2013, held as satellite workshop of PAKDD 2013, in Gold Coast, Australia, in April 2013. The 23 papers presented were carefully reviewed and selected from 58 submissions. The papers study a wide range of techniques and methods for behavior/social-oriented analyses including behavioral and social interaction and network, behavioral/social patterns, behavioral/social impacts, the formation of behavioral/social-oriented groups and collective intelligence and behavioral/social intelligence emergence.

This book offers a thorough introduction to metasynthetic computing and engineering, covering concepts, methodologies, analysis, design, and applications. It explores complex systems, particularly open complex giant systems like the Internet, and addresses complex behavioral and social issues while emphasizing actionable knowledge discovery in the era of big data. The author discusses the role of ubiquitous intelligence in these systems, including human, domain, social, network, data, and machine intelligence, highlighting their synergy through metasynthetic engineering. A key focus is on a human-centered methodology for qualitative-to-quantitative metasynthesis, aimed at understanding and managing open complex giant systems, alongside the computing approach known as metasynthetic computing. The book introduces various techniques and tools for analyzing and designing solutions for open complex problems. Utilizing systematological methodology, it addresses the complexities inherent in these systems, suggesting that neither reductionism nor holism alone may suffice. The work encourages discussions, design, implementation, and reflection on effective methodologies and tools for computing and engineering complex systems. It serves as a valuable resource for researchers, students, and practitioners across fields such as complex systems, artificial intelligence, data science, computer science, cognitive science, behavioral science,

This book constitutes the thoroughly refereed post-conference proceedings of five international workshops held in conjunction with PAKDD 2011 in Shenzhen, China, in May 2011: the International Workshop on Behavior Informatics (BI 2011), the Workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2011), the Workshop on Biologically Inspired Techniques for Data Mining (BDM 2011), the Workshop on Advances and Issues in Traditional Chinese Medicine Clinical Data Mining (AI-TCM 2011), and the Second Workshop on Data Mining for Healthcare Management (DMGHM 2011). The book also includes papers from the First PAKDD Doctoral Symposium on Data Mining (DSDM 2011). The 42 papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics discussing emerging techniques in the field of knowledge discovery in databases and their application domains extending to previously unexplored areas such as data mining based on optimization techniques from biological behavior of animals and applications in Traditional Chinese Medicine clinical research and health care management.

The 2009 International Workshop on Agents and Data Mining Interaction (ADMI 2009), held alongside AAMAS 2009, emerged as a key event in the promising research field of agent mining. Following successful previous workshops in Hong Kong, San Jose, and Sydney, ADMI 2009 in Budapest served as a premier forum for sharing research, engineering results, and addressing challenges in the integration of agents and data mining. The workshop aimed to promote both theoretical and applied research, focusing on several key objectives: exploiting agent-driven data mining to enhance critical data mining problems, improving data mining-driven agents to bolster agent intelligence, exploring the integration of agents and data mining for advanced information processing, and identifying future research directions in this synergy. The event featured two invited talks—one on “Agents and Data Mining in Bioinformatics” and another on “Knowledge-Based Reinforcement Learning”—alongside twelve selected papers from seven countries, predominantly from Europe, highlighting the growth of ADMI research in the region. The proceedings will be published in the LNAI series by Springer, with special thanks to Alfred Hofmann for their support.