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The Third Branch
A bulletin of the federal courts.
Handbook of Data Intensive Computing
Data Intensive Computing refers to capturing, managing, analyzing, and understanding data at volumes and rates that push the frontiers of current technologies. The challenge of data intensive computing is to provide the hardware architectures and related software systems and techniques which are capable of transforming ultra-large data into valuable knowledge. Handbook of Data Intensive Computing is written by leading international experts in the field. Experts from academia, research laboratories and private industry address both theory and application. Data intensive computing demands a fundamentally different set of principles than mainstream computing. Data-intensive applications typically are well suited for large-scale parallelism over the data and also require an extremely high degree of fault-tolerance, reliability, and availability. Real-world examples are provided throughout the book. Handbook of Data Intensive Computing is designed as a reference for practitioners and researchers, including programmers, computer and system infrastructure designers, and developers. This book can also be beneficial for business managers, entrepreneurs, and investors.
Judicial Applications of Artificial Intelligence
The judiciary is in the early stages of a transformation in which AI (Artificial Intelligence) technology will help to make the judicial process faster, cheaper, and more predictable without compromising the integrity of judges' discretionary reasoning. Judicial decision-making is an area of daunting complexity, where highly sophisticated legal expertise merges with cognitive and emotional competence. How can AI contribute to a process that encompasses such a wide range of knowledge, judgment, and experience? Rather than aiming at the impossible dream (or nightmare) of building an automatic judge, AI research has had two more practical goals: producing tools to support judicial activities, i...
11th Annual Conference Cognitive Science Society Pod
First published in 1989. This Program discusses The Eleventh Annual Conference of the Cognitive Science Society, August 1989 in Ann Arbor, Michigan. The book begins with 66 paper presentations and concludes with 59 poster presentations across over 1000 pages. This program also includes a comprehensive author listing with affiliations and titles.
The Cambridge Handbook of Intellectual Property and Social Justice
Protection for intellectual property has never been absolute; it has always been limited in the public interest. The benefits of intellectual property protection are meant to flow to everyone, not just a limited population of creators and the corporations that represent them. Given this social-utility function, intellectual property regimes must address issues of access, inclusion, and empowerment for marginalized and excluded groups. This handbook defines an approach to considering social justice in intellectual property law and regulation. Top scholars in the field offer surveys of social justice implementation in patents, copyright, trademarks, trade secrets, rights of publicity, and other major IP areas. Chapters define Intellectual Property Social Justice theory and include recommendations for reforming aspects of IP law and administration to further social justice by providing better access, more inclusion, and greater empowerment to marginalized groups.
Machine Learning and Data Mining in Pattern Recognition
This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001. The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and associative memory; rule induction and grammars; clustering and conceptual clustering; data mining on signals, images, and spatio-temporal data; nonlinear function learning and neural net based learning; learning for handwriting recognition; statistical and evolutionary learning; and content-based image retrieval.
Case-Based Reasoning Research and Development
The biennial International Conference on Case-Based Reasoning (ICCBR) - ries, which began in Sesimbra, Portugal, in 1995, was intended to provide an international forum for the best fundamental and applied research in case-based reasoning (CBR). It was hoped that such a forum would encourage the g- wth and rigor of the eld and overcome the previous tendency toward isolated national CBR communities. The foresight of the original ICCBR organizers has been rewarded by the growth of a vigorous and cosmopolitan CBR community. CBR is now widely recognized as a powerful and important computational technique for a wide range of practical applications. By promoting an exchange of ideas among CBR rese...
Legal Knowledge and Information Systems
The range of topics addressed in this volume is broader than in previous JURIX volumes. All the main legal functions are covered: legal drafting, legal negotiating, legal decision making and legal argumentation.
Legal Knowledge and Information Systems
Artificial intelligence as applied to the legal domain has gained momentum thanks to the large, annotated corporate legal and case-law collections, human chats, and social media information now available in open data. Often represented in XML or other Semantic Web technologies, these now make it possible to use the AI theory developed by the JURIX community in over thirty years of research. Innovative machine and deep-learning techniques with which to classify legal texts and detect terms, principles, concepts, evidence, named entities, and rules are also emerging, and the last five years have seen a gradual increase in their practical application. This book presents papers from the 31st Int...
Computational Legal Studies
Featuring contributions from a diverse set of experts, this thought-provoking book offers a visionary introduction to the computational turn in law and the resulting emergence of the computational legal studies field. It explores how computational data creation, collection, and analysis techniques are transforming the way in which we comprehend and study the law, and the implications that this has for the future of legal studies.
