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Uniquely aimed at teams that think together to solve problems and make decisions, this book explains how to enhance the collective intelligence of a team-size group and combine it with the artificial intelligence (AI) of generative AI to create a hybrid intelligence that is smarter than either one on its own. Boards, committees, and other team-size groups of 5-20 people are the primary problem-solving and decision-making units within organizations, and they form the bridges between organizations, industries, and nations that collaborate on projects. So how can leaders exponentially improve their teams’ capabilities? Assemble the right people, arm them with the right processes, and execute ...
This book constitutes the proceedings of the 5th International Conference on Algorithmic Aspects in Information Management, AAIM 2009, held in San Francisco, CA, USA, in June 2009. The 25 papers presented together with the abstracts of two invited talks were carefully reviewed and selected for inclusion in this book. While the areas of information management and management science are full of algorithmic challenges, the proliferation of data (Internet, biology, finance etc) has called for the design of efficient and scalable algorithms and data structures for their management and processing. This conference is intended for original algorithmic research on immediate applications and/or fundamental problems pertinent to information management and management science, broadly construed. The conference aims at bringing together researchers in Computer Science, Operations Research, Economics, Game Theory, and related disciplines.
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
The research in content-based indexing and retrieval of visual information such as images and video has become one of the most populated directions in the vast area of information technologies. Social networks such as YouTube, Facebook, FileMobile, and DailyMotion host and supply facilities for accessing a tremendous amount of professional and user generated data. The areas of societal activity, such as, video protection and security, also generate thousands and thousands of terabytes of visual content. This book presents the most recent results and important trends in visual information indexing and retrieval. It is intended for young researchers, as well as, professionals looking for an algorithmic solution to a problem.
This book constitutes the refereed proceedings of the 17th International Conference on Database and Expert Systems Applications, DEXA 2006. The book presents 90 revised full papers together with 1 invited paper. The papers are organized in topical sections on XML, data and information, data mining and data warehouses, database applications, WWW, bioinformatics, process automation and workflow, knowledge management and expert systems, database theory, query processing, and privacy and security.
The Fifth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. Advances in information technology and data collection methods have led to the availability of large data sets in commercial enterprises and in a wide variety of scientific and engineering disciplines. The field of data mining draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high performance computing to discover interesting and previously unknown information in data. This conference results in data mining, including applications, algorithms, software, and systems.
The MRS Symposium Proceeding series is an internationally recognised reference suitable for researchers and practitioners.