Robert L. Popp

Countering terrorism through information technology

Developing the information-analysis tools for an effective multi-agency information-sharing effort.

Emergent information technologies and enabling policies for counter-terrorism

Foreword. Preface. Contributors. Chapter 1: Utilizing Information and Social Science Technology to Understand and Counter the Twenty-First Century Strategic Threat 1 (Robert L. Popp, David Allen, and Claudio Cioffi-Revilla). Chapter 2: Hidden Markov Models and Bayesian Networks for Counter-Terrorism (Krishna Pattipati, Peter Willett, Jeffrey Allanach, Haiying Tu, and Satnam Singh). Chapter 3: Anticipatory Models for Counter-Terrorism (Mark Lazaroff and David Snowden). Chapter 4: Information Processing at Very High Speed Data Ingestion Rates (J. Brian Sharkey, Doyle Weishar, John W. Lockwood, Ron Loui, Richard Rohwer, John Byrnes, Krishna Pattipati, Stephen Eick, David Cousins, and Michael Nicoletti). Chapter 5: Analysis of Heterogeneous Data in Ultrahigh Dimensions (R. A. Ammar, S. A. Demurjian , Sr., I. R. Greenshields, Krishna Pattipati, and S. Rajasekaran). Chapter 6: Semantic Web Technologies for Terrorist Network Analysis (Jennifer Golbeck, Aaron Mannes, and James Hendler). Chapter 7: Improving National and Homeland Security Through Context Knowledge Representation and Reasoning Technologies (Nazli Choucri, Stuart E. Madnick, and Michael D. Siegel). Chapter 8: Anonymized Semantic Directories and a Privacy-Enhancing Architecture for Enterprise Discovery (Jeff Jonas and John Karat). Chapter 9: Facilitating Information Sharing Across Intelligence Community Boundaries Using Knowledge Management and Semantic Web Technologies (Brian Kettler, Gary Edwards, and Mark Hoffman). Chapter 10: Applying Semantic Web Reasoning to Counter-Terrorism (Paul Kogut, Yui Leung, Kathleen M. Ryan, Linda Gohari, Mieczyslaw M. Kotar, and Jerzy J. Letkowski). Chapter 11: Schemer: Consensus-Based Knowledge Validation and Collaboration Services for Virtual Teams of Intelligence Experts (Clifford Behrens, Hyong-Sop Shim, and Devaisis Bassu). Chapter 12: Sharing Intelligence Using Information Supply Chains (Shuang Sun, Xiaocong Fan, and John Yen). Chapter 13: Supporting Knowledge Management In Emergency Crisis Management Domains: Envisioned Designs for Collaborative Work (Michael D. McNeese, Isaac Brewer, Rashaad E. T. Jones, and Erik S. Connors). Chapter 14: Agent-Based Simulations for Disaster Rescue Using the DEFACTO Coordination System (Janusz Marecki, Nathan Schurr, and Milind Tambe). Chapter 15: Transcending the Tower of Babel: Supporting Access to Multilingual Information with Cross-Language Information Retrieval (Douglas W. Oard). Chapter 16: Journey from Analysis to Inquiry: Technology and Transformation of Counter-Terrorism Analysis (Aaron B. Frank and Desmond Saunders-Newton). Chapter 17: Behavioral Network Analysis for Terrorist Detection (Seth A. Greenblatt, Thayne Coffman, and Sherry E. Marcus). Chapter 18: Detecting Terrorist Activities in the Twenty-First Century: A Theory of Detection for Transactional Networks (Tom Mifflin, Chris Boner, Greg Godfrey, and Michael Greenblatt). Chapter 19: Social Network Analysis Via Matrix Decompositions (D. B. Skillicorn). Chapter 20: Legal Standards for Data Mining (Fred H. Cate). Chapter 21: Privacy and Consequences: Legal and Policy Structures for Implementing New Counter-Terrorism Technologies and Protecting Civil Liberty (Paul Rosenzweig). Chapter 22: Designing Technical Systems to Support Policy: Enterprise Architecture, Policy Appliances, and Civil Liberties (K. A. Taipale). Index. About the Editors.

Emergent Information Technologies and Enabling Policies for Counter-Terrorism: Popp/Emergent Information Technologies and Enabling Policies for Counter-Terrorism

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Normative framework and computational models for simulating and assessing command and control processes

Abstract We present a normative methodology and computational framework to assess the effectiveness and efficiency of command and control (C2) organizations. Our process is based on quantitative representations of the organization and mission, and utilizes normative models of team and individual decision making. Our assessment methodology has been applied to evaluate the benefits of the Sensing and Patrolling Enablers Yielding Effective Security system—a ground-based decentralized C3I system comprised of emerging and existing sensing, SA/C2, and Shaping technologies. To facilitate the assessment analysis, our models have been implemented using a computational agent framework for the Distributed Dynamic Decision making (DDD) virtual simulation platform.

An adaptive m-best SD assignment algorithm and parallelization for multitarget tracking

In this paper we describe a novel data association algorithm and parallelization, termed m-best SD, that determines in O(mSkn/sup 3/) time (m assignments, S lists of size n, k relaxations) the m-best solutions to an SD assignment problem. The significance of this work is that the m-best SD assignment algorithm (in a sliding window mode) provides for an efficient implementation of an (S-1)-scan Multiple Hypothesis Tracking (MHT) algorithm by obviating the need for a brute force enumeration of an exponential number of joint hypotheses. Initially, given a static SD assignment problem, sets of complete position measurements are extracted, namely, the 1-st, 2-nd, ..., m-th best (in terms of likelihood) sets of composite measurements are determined based on the line of sight (LOS) (i.e., incomplete position) measurements. Using the joint likelihood functions used to determine the m-best SD assignment solutions, the composite measurements are then quantified with a probability of being correct using a JPDA-like technique. Lists of composite measurements, along with their corresponding probabilities, are then used in turn with a state estimator in a dynamic 2D assignment algorithm to estimate the states of the moving targets over time. The 2D assignment cost coefficients are based on a likelihood function that incorporates the true composite measurement probabilities obtained from the (static) m-best SD assignment solutions. We demonstrate m-best SD on a simulated passive sensor track formation and maintenance problem, consisting of multiple time samples of LOS measurements originating from multiple (S=7) synchronized high frequency direction finding sensors.

Assessing nation-state instability and failure

DARPA initiated a six-month Pre-Conflict Anticipation and Shaping (PCAS) initiative to demonstrate the utility of quantitative and computational social science models (Q/CSS) applied to assessing the instability and failure of nation-states. In this program ten different teams of Q/CSS researchers and practitioners developed nation state instability models and then applied them to two different countries to assess their current stability levels as well as forecast their stability levels 6-12 months hence. The models developed ranged from systems dynamics, structural equations, cellular automata, Bayesian networks and hidden Markov models, scale-invariant geo-political distributions, and multi agent-based systems. In the PCAS program we also explored a mechanism for sensitivity analysis of Q/CSS model results to selected parameters, and we also implemented a mechanism to automatically categorize, parse, extract and auto-populate a bank of Q/CSS models from large-scale open source text streams. Preliminary yet promising results were achieved, and the utility of the results can provide added value for decision-making problems around planning, intelligence analysis, information operations and training. This paper describes the motivation and rationale for the program, the Q/CSS models and mechanisms, and presents results from some of the models. In addition, future research and key challenges in using these Q/CSS models within an operational decision making environment will be discussed

Collaborative Tools for Counter-Terrorism Analysis

One of the major challenges in counter-terrorism analysis involves connecting the relatively few and sparse terrorism-related dots embedded within massive amounts of data flowing into the governments intelligence and counter-terrorism agencies. Information technologies have the potential to empower intelligence agencies or analysts with the ability to find pertinent data faster, conduct more efficient and effective analysis, share information with others if necessary, relay concerns to the appropriate decision-makers, and ultimately put the data into a form that allows senior decision-makers to understand and act on it so that they can anticipate and preempt terrorist plots or attacks from occurring. Advanced collaboration among multiple analysts or tools is one such crucial technology. In this paper, we introduce NEMESIS (network modeling environment for structural intervention strategies), a collaborative environment to integrate and share information among different counter-terrorism analysis tools. Two component tools, ASAM (adaptive safety analysis and monitoring system) and ORA (organizational risk analysis), are described in this paper. The functionality of these two tools, along with the NEMESIS collaboration is illustrated via a real world example gleaned from open sources

Collaboration and modeling tools for counter-terrorism analysis

One of the major challenges in counter-terrorism analysis today involves connecting the relatively few and sparse terrorism-related dots embedded within massive amounts of data flowing into the governments intelligence and counter-terrorism agencies. Information technologies have the potential to empower analysts with a superior ability to process and analyze the data, disseminate and share it, and ultimately put the data into a form that allows senior decision-makers to understand and act on it so that they can anticipate and ultimately preempt terrorist plots or attacks from occurring. Advanced collaboration among multiple analysts or tools is one such crucial technology. We introduce NEMESIS, a collaborative environment to integrate and share information among different modeling tools. Two component-modeling tools, ASAM System and ORA, are described in this paper. The functionality of these two tools along with the NEMESIS system is illustrated via a real world example gleaned from open sources.

Distributed‐ and shared‐memory parallelizationsof assignment‐based data association formultitarget tracking

To date, there has been a lack of efficient and practical distributed‐ and shared‐memoryparallelizations of the data association problem for multitarget tracking. Filling this gap is oneof the primary focuses of the present work. We begin by describing our data association algorithmin terms of an Interacting Multiple Model (IMM) state estimator embedded into anoptimization framework, namely, a two‐dimensional (2D) assignment problem (i.e., weightedbipartite matching). Contrary to conventional wisdom, we show that the data association (oroptimization) problem is not the major computational bottleneck; instead, the interface to theoptimization problem, namely, computing the rather numerous gating tests and IMM stateestimates, covariance calculations, and likelihood function evaluations (used as cost coefficientsin the 2D assignment problem), is the primary source of the workload. Hence, for both ageneral-purpose shared‐memory MIMD (Multiple Instruction Multiple Data) multiprocessorsystem and a distributed‐memory Intel Paragon high‐performance computer, we developedparallelizations of the data association problem that focus on the interface problem. For theformer, a coarse‐grained dynamic parallelization was developed that realizes excellent performance(i.e., superlinear speedups) independent of numerous factors influencing problemsize (e.g., many models in the IMM, denseycluttered environments, contentious target‐measurementdata, etc.). For the latter, an SPMD (Single Program Multiple Data) parallelization wasdeveloped that realizes near‐linear speedups using relatively simple dynamic task allocationalgorithms. Using a real measurement database based on two FAA air traffic control radars, weshow that the parallelizations developed in this work offer great promise in practice.

Transcending the Tower of Babel: Supporting Access to Multilingual Information with CrossLanguage Information Retrieval

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