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Mathematical Methods in Counterterrorism: Tools and Techniques for a New Challenge |
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1 | (8) |
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1 | (1) |
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2 | (2) |
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Conclusion and Acknowledgements |
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4 | (5) |
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Modeling Criminal Activity in Urban Landscapes |
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9 | (24) |
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9 | (2) |
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Background and Motivation |
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11 | (3) |
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Computational Criminology |
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11 | (2) |
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13 | (1) |
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13 | (1) |
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14 | (5) |
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15 | (2) |
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Rapid Prototyping with Core ASM |
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17 | (1) |
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Interactive Design with Control State ASMs |
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18 | (1) |
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Mastermind: Modeling Criminal Activity |
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19 | (9) |
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19 | (1) |
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20 | (2) |
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22 | (2) |
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Space Evolution Module: ASM Model |
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24 | (2) |
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26 | (2) |
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28 | (5) |
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29 | (4) |
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Extracting Knowledge from Graph Data in Adversarial Settings |
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33 | (22) |
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Characteristics of Adversarial Settings |
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33 | (1) |
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34 | (1) |
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Eigenvectors and the Global Structure of a Graph |
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35 | (1) |
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36 | (1) |
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Computation of Node Properties |
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37 | (2) |
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Social Network Analysis (SNA) |
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37 | (1) |
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Principal eigenvector of the adjacency matrix |
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38 | (1) |
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Embedding Graphs in Geometric Space |
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39 | (13) |
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The Walk Laplacian of a graph |
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39 | (1) |
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40 | (1) |
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The rightmost eigenvectors |
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41 | (3) |
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The leftmost eigenvectors |
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44 | (2) |
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The `middle' eigenvectors |
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46 | (3) |
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Working in a lower-dimensional space |
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49 | (1) |
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Overlays of eigenvectors and edges |
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50 | (1) |
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Using correlation rather than connection |
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51 | (1) |
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52 | (3) |
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53 | (2) |
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Mathematically Modeling Terrorist Cells: Examining the Strength of Structures of Small Sizes |
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55 | (14) |
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``Back to Basics'': Recap of the Poset Model of Terrorist Cells |
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55 | (2) |
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Examining the Strength of Terrorist Cell Structures - Questions Involved and Relevance to Counterterrorist Operations |
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57 | (1) |
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Definition of ``Strength'' in Terms of the Poset Model |
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58 | (1) |
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59 | (1) |
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59 | (2) |
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Structures of Posets of Size 7: Observations and Patterns |
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61 | (4) |
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Implications and Applicability |
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65 | (1) |
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Ideas for Future Research |
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66 | (1) |
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67 | (2) |
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67 | (2) |
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Combining Qualitative and Quantitative Temporal Reasoning for Criminal Forensics |
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69 | (22) |
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69 | (2) |
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Temporal Knowledge Representation and Reasoning |
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71 | (1) |
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72 | (10) |
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Language and Point Graph Representation |
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73 | (3) |
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Operations on Point Graphs |
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76 | (1) |
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77 | (2) |
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79 | (1) |
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80 | (2) |
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Using Temper for Criminal Forensics - The London Bombing |
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82 | (6) |
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88 | (3) |
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89 | (2) |
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Two Theoretical Research Questions Concerning the Structure of the Perfect Terrorist Cell |
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91 | (16) |
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102 | (5) |
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Understanding Terrorist Organizations with a Dynamic Model |
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107 | (20) |
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107 | (2) |
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109 | (2) |
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111 | (3) |
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114 | (3) |
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Nascent terrorist organizations |
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114 | (1) |
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115 | (2) |
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117 | (1) |
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Counter-Terrorism Strategies |
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117 | (3) |
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117 | (2) |
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119 | (1) |
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Minimization of Strength S |
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120 | (1) |
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120 | (1) |
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121 | (6) |
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122 | (1) |
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Concrete Example of Strength Minimization |
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123 | (1) |
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124 | (3) |
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Inference Approaches to Constructing Covert Social Network Topologies |
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127 | (14) |
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127 | (1) |
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128 | (1) |
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A Bayesian Inference Approach |
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129 | (2) |
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131 | (3) |
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134 | (4) |
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138 | (3) |
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139 | (2) |
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A Mathematical Analysis of Short-term Responses to Threats of Terrorism |
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141 | (20) |
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141 | (4) |
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145 | (3) |
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148 | (4) |
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152 | (4) |
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Interaction between warnings and physical deployments |
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152 | (2) |
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Effect of intelligence on defensive measures |
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154 | (2) |
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Illustrative numerical experiments |
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156 | (2) |
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158 | (3) |
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160 | (1) |
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161 | (24) |
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161 | (4) |
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Random Intersection Graphs |
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165 | (4) |
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Induced edge clique covers; exact quantities |
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166 | (1) |
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Expected subgraph counts in the constant-μ limit |
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166 | (3) |
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169 | (3) |
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172 | (1) |
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172 | (1) |
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The dynamic Erdos-Renyi process |
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173 | (1) |
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173 | (4) |
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The LRDT Framework for Static Networks |
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174 | (3) |
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Hierarchical Hypothesis Management |
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177 | (3) |
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178 | (1) |
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178 | (1) |
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179 | (1) |
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180 | (5) |
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180 | (5) |
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Part III Communication/Interpretation |
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Security of Underground Resistance Movements |
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185 | (20) |
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185 | (1) |
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Best defense against optimal subversive strategies |
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186 | (4) |
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Best defense against random subversive strategies |
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190 | (3) |
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Maximizing the size of surviving components |
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193 | (3) |
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Ensuring that the survivor graph remains connected |
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196 | (9) |
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203 | (2) |
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Intelligence Constraints on Terrorist Network Plots |
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205 | (10) |
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205 | (1) |
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Tipping Point in Conspiracy Size |
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206 | (3) |
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209 | (3) |
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Stopping Rule for Terrorist Attack Multiplicity |
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212 | (1) |
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Preventing Spectacular Attacks |
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213 | (2) |
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214 | (1) |
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On Heterogeneous Covert Networks |
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215 | (14) |
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216 | (1) |
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217 | (1) |
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Secrecy and Communication in Homogeneous Covert Networks |
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218 | (2) |
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Jemaah Islamiya Bali bombing |
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220 | (3) |
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A First Approach to Heterogeneity in Covert Networks |
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223 | (6) |
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The Optimal High Risk Interaction Pair |
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223 | (3) |
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Approximating Optimal Heterogeneous Covert Networks |
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226 | (2) |
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228 | (1) |
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Two Models for Semi-Supervised Terrorist Group Detection |
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229 | (24) |
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229 | (1) |
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Terrorist Group Detection from Crime and Demographics Data |
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230 | (5) |
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COPLINK CrimeNet Explorer |
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230 | (4) |
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234 | (1) |
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Offender Group Representation Model (OGRM) |
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235 | (1) |
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Group Detection Model (GDM) |
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236 | (1) |
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Offender Group Detection Model (OGDM) |
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237 | (5) |
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Computing Similarity Score |
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239 | (1) |
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Using Terrorist Modus Operandi Ontology |
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239 | (1) |
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240 | (1) |
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241 | (1) |
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Experiments and Evaluation |
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242 | (2) |
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242 | (1) |
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Testbed: Terrorist Groups Detected in Bursa |
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243 | (1) |
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244 | (9) |
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247 | (6) |
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CAPE: Automatically Predicting Changes in Group Behavior |
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253 | (18) |
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253 | (2) |
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255 | (1) |
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256 | (2) |
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258 | (2) |
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260 | (6) |
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260 | (2) |
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Learning Predictive Conditions from the Change Table |
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262 | (3) |
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The CAPE-Forecast Algorithm |
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265 | (1) |
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266 | (1) |
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267 | (1) |
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268 | (3) |
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269 | (2) |
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Interrogation Methods and Terror Networks |
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271 | (20) |
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271 | (3) |
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274 | (1) |
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274 | (4) |
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275 | (1) |
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276 | (1) |
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277 | (1) |
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278 | (3) |
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281 | (4) |
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Investigation Budget Allocation |
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282 | (1) |
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Legal Environment and Interrogation Methods |
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283 | (2) |
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Extensions and Conclusions |
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285 | (6) |
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290 | (1) |
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Terrorists and Sponsors. An Inquiry into Trust and Double-Crossing |
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291 | (18) |
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State-Terrorist Coalitions |
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291 | (4) |
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295 | (2) |
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297 | (3) |
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300 | (2) |
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302 | (1) |
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303 | (5) |
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Conclusion. External Shocks |
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308 | (1) |
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308 | (1) |
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Simulating Terrorist Cells: Experiments and Mathematical Theory |
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309 | (10) |
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309 | (1) |
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The Question of Theory versus Real-Life Applications |
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310 | (1) |
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311 | (1) |
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312 | (1) |
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313 | (6) |
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316 | (3) |
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A Brinkmanship Game Theory Model of Terrorism |
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319 | (14) |
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319 | (3) |
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The Extensive Form of the Brinkmanship Game |
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322 | (3) |
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Incentive Compatibility (``Credibility'') Constraints |
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325 | (3) |
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The Effectiveness Constraint |
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326 | (2) |
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The Acceptability Constraint |
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328 | (1) |
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Equilibrium Solution and Interpretation of the Results |
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328 | (2) |
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330 | (3) |
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332 | (1) |
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Strategic Analysis of Terrorism |
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333 | (16) |
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333 | (2) |
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Strategic Substitutes and Strategic Complements in the Study of Terrorism |
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335 | (7) |
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Proactive Counterterrorism Measures |
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336 | (2) |
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Defensive Countermeasures: Globalized Threat |
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338 | (1) |
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Defensive Measures: No Collateral Damage |
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339 | (1) |
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340 | (1) |
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341 | (1) |
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Terrorist Signaling: Backlash and Erosion Effects |
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342 | (5) |
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347 | (2) |
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347 | (2) |
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Underfunding in Terrorist Organizations |
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349 | (36) |
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349 | (4) |
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353 | (6) |
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355 | (1) |
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356 | (3) |
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359 | (2) |
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359 | (1) |
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360 | (1) |
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361 | (9) |
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362 | (4) |
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366 | (4) |
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370 | (5) |
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375 | (10) |
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380 | (5) |
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Part VI History of the Conference on Mathematical Methods in Counterterrorism |
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Personal Reflections on Beauty and Terror |
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385 | (4) |
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385 | (1) |
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The ``Thinking Man's Game'' |
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385 | (2) |
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387 | (2) |
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Toward a Mathematical Theory of Counterterrorism |
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389 | |