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E-raamat: Computational Methods for Counterterrorism

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  • Ilmumisaeg: 18-Jun-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783642011412
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 18-Jun-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783642011412

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Modern terrorist networks pose an unprecedented threat to international security. This book includes computational research that addresses critical issues for countering terrorism. It describes research on discovering relevant information buried in vast amounts of unstructured data.

Modern terrorist networks pose an unprecedented threat to international security. Their fluid and non-hierarchical structures, their religious and ideological motivations, and their predominantly non-territorial objectives all radically complicate the question of how to neutralize them. As governments and militaries work to devise new policies and doctrines to combat terror, new technologies are desperately needed to make these efforts effective. This book collects a wide range of the most current computational research addressing critical issues for counterterrorism in a dynamic and complex threat environment:finding, summarizing, and evaluating relevant information from large and dynamic data stores;simulation and prediction of likely enemy actions and the effects of proposed counter-efforts; andproducing actionable intelligence by finding meaningful patterns hidden in masses of noisy data items.The contributions are organized thematically into four sections. The first section concerns efforts to provide effective access to small amounts of relevant information buried in enormous amounts of diverse unstructured data. The second section discusses methods for the key problem of extracting meaningful information from digitized documents in various languages. The third section presents research on analyzing graphs and networks, offering new ways of discovering hidden structures in data and profiles of adversaries' goals and intentions. Finally, the fourth section of the book describes software systems that enable analysts to model, simulate, and predict the effects of real-world conflicts. The models and methods discussed in this book are invaluable reading for governmental decision-makers designing new policies to counter terrorist threats, for members of the military, intelligence, and law enforcement communities devising counterterrorism strategies based on new technologies, and for academic and industrial researchers devising more effective methods for knowledge discovery in complicated and diverse datasets.
Foreword
James A. Hendler
VII
Preface IX
Part I Information Access
1 On Searching in the "Real World"
Ophir Frieder
3
2 Signature-Based Retrieval of Scanned Documents Using Conditional Random Fields
Harish Srinivasan and Sargur Srihari
17
3 What Makes a Good Summary?
Qunhua Zhao, Eugene Santos, Jr., Hien Nguyen, and Ahmed Mohamed
33
4 A Prototype Search Toolkit
Margaret M. Knepper, Kevin L. Fox, and Ophir Frieder
51
Part II Text Analysis
5 Unapparent Information Revelation: Text Mining for Counterterrorism
Rohini K. Srihari
67
6 Identification of Sensitive Unclassified Information
Kazem Taghva
89
7 Rich Language Analysis for Counterterrorism
Mathieu Guidere, Newton Howard, and Shlomo Argamon
109
Part III Graphical Models
8 Dicliques: Finding Needles in Haystacks
Robert M. Haralick
123
9 Information Superiority via Formal Concept Analysis
Bjoern Koester and Stefan E. Schmidt
143
10 Reflexive Analysis of Groups
Vladimir A. Lefebvre
173
11 Evaluating Self-Reflexion Analysis Using Repertory Grids
James Grice and Brenda L. McDaniel
211
Part IV Conflict Analysis
12 Anticipating Terrorist Safe Havens from Instability Induced Conflict
Robert Shearer and Brett Marvin
229
13 Applied Counterfactual Reasoning
Noel Hendrickson
249
14 Adversarial Planning in Networks
Sviatoslav Braynov
263
15 Gaming and Simulating Ethno-Political Conflicts
Barry G. Silverman, Gnana K. Bharathy, and Benjamin D. Nye
275
Index 303
Shlomo Argamon is Associate Professor of Computer Science at the Illinois Institute of Technology, Chicago, IL, USA, since 2002. Prior to that, he had held academic positions at Bar-Ilan University, where he held a Fulbright Postdoctoral Fellowship (1994-96), and at the Jerusalem College of Technology. Dr. Argamon received his B.S. (1988) in Applied Mathematics from Carnegie-Mellon University, and his M.Phil. (1991) and Ph.D. (1994) in Computer Science from Yale University, where he was a Hertz Foundation Fellow. His current research interests lie mainly in the use of machine learning methods to aid in functional analysis of natural language, with particular focus on questions of style. During his career, Dr. Argamon has worked on a variety of problems in experimental machine learning, including robotic map-learning, theory revision, and natural language processing, and has published numerous research papers in these areas.