This book shows how open source intelligence can be a powerful tool for combating crime by linking local and global patterns to help understand how criminal activities are connected. Readers will encounter the latest advances in cutting-edge data mining, machine learning and predictive analytics combined with natural language processing and social network analysis to detect, disrupt, and neutralize cyber and physical threats. Chapters contain state-of-the-art social media analytics and open source intelligence research trends. This multidisciplinary volume will appeal to students, researchers, and professionals working in the fields of open source intelligence, cyber crime and social network analytics. Chapter Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks-networks of offenders who have committed crimes together-have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.

ABZ 2010 was held in the beautiful natural setting of Orford in the Eastern Townships of Qu ebec, during February22-25,2010, midwaythroughthe Ca- dian winter and the 21st Winter Olympics, bringing participants from all over the world to brave this rigorous climate. ABZcoversrecentadvancesinfourequallyrigorousmethodsforsoftwareand hardware development: Abstract State Machines (ASM), Alloy, B and Z. They shareacommonconceptualframework, centeredaroundthe notionsofstateand operation, andpromotemathematicalprecisioninthemodeling, veri?cation, and construction of highly dependable systems. These methods have continuously matured over the past decade, reaching a stage where they have been successfully integrated into industrial practice in various areas like trains, automobiles, aerospace, smart cards, virtual machines, and business processes. Their development is in?uenced by both research and practice, which mutually nurture each other. ABZ has both a long and a short history. With the aim of stimulating cro- fertilization between these four methods, it has merged their individual conf- ence and workshopseries which started in 1986 for Z, 1994 for ASM, 1996 for B, and 2006 for Alloy. The ?rst ABZ conference was held in London in 2008; ABZ 2010 is the second edition. The conference remains organized as four separate Program Committe

This book focuses on applications of social network analysis in predictive policing. Data science is used to identify potential criminal activity by analyzing the relationships between offenders to fully understand criminal collaboration patterns. Co-offending networks-networks of offenders who have committed crimes together-have long been recognized by law enforcement and intelligence agencies as a major factor in the design of crime prevention and intervention strategies. Despite the importance of co-offending network analysis for public safety, computational methods for analyzing large-scale criminal networks are rather premature. This book extensively and systematically studies co-offending network analysis as effective tool for predictive policing. The formal representation of criminological concepts presented here allow computer scientists to think about algorithmic and computational solutions to problems long discussed in the criminology literature. For each of the studied problems, we start with well-founded concepts and theories in criminology, then propose a computational method and finally provide a thorough experimental evaluation, along with a discussion of the results. In this way, the reader will be able to study the complete process of solving real-world multidisciplinary problems.

This book shows how open source intelligence can be a powerful tool for combating crime by linking local and global patterns to help understand how criminal activities are connected. Readers will encounter the latest advances in cutting-edge data mining, machine learning and predictive analytics combined with natural language processing and social network analysis to detect, disrupt, and neutralize cyber and physical threats. Chapters contain state-of-the-art social media analytics and open source intelligence research trends. This multidisciplinary volume will appeal to students, researchers, and professionals working in the fields of open source intelligence, cyber crime and social network analytics. Chapter Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Egon B.. orger Tribute to Egon B.. orger on th the Occasion of his 60 Birthday 1 2 Jean-Raymond Abrial and Uwe Glasser .. 1 jrabrial@neuf. fr 2 glaesser@cs. sfu. ca Egon B.. orger was born on May 13, 1946, in Westfalia (Germany). After the cl- sic baccalaur' eat, from 1965-1971 he studied philosophy, logic and mathematics at the Sorbonne (Paris, France), Institut Sup' erieur de Philosophie de Louvain (Belgium), Universit' e de Louvain and Universitat .. Munster .. (Germany), where he got his doctoral degree and in 1976 his "Habilitation" in mathematics. The themes of his doctoral dissertation,ReductionclassesinKromandHornfor- lae,andofhis"Habilitationsschrift,"Asimple method for determining thedegree of unsolvability of decision problems for combinatorial systems,havetheirroot inthe computationalviewofmathematicallogicheldatthe time atthe Institute for Logic and Foundations of Mathematics at the University of Mu ..nster, a t- dition going back to (among others) Leibniz, Ackermann, G.. odel, Post, Turing, Kleene, and associated in Munster .. with the names of the founder of the ins- tute, Heinrich Scholz, and his followers Hans Hermes, Gisbert Hasenj. ager and Dieter R.. odding. This heritage determined the focus of B.. orger's logical inves- gations in what nowadaysis called computability and computationalcomplexity theory and his early interest in applying methods from logic to solve problems in computer science.