NC-BSI: TASK 3.5: Reduction of False Alarm Rates from Fused Data Problem Statement/Objectives Research Objectives Intelligent fusing of data from hybrid sensors Minimize false alarms when detecting changes and anomalies to improve reliability Extending the solutions to current and new border security data applications Methodology (A) Cliques of neighboring sensors will be formed to discover reliable local patterns via data fusion, cross validation, and level-0 Bayesian network inference. (B) Clique outputs will be integrated at the next level with level-i Bayesian network inference about global patterns for intelligent monitoring. (C) Tools and techniques will be developed to select complements by minimizing the number and considering features from different sources based on canonical correlation analysis. Benefits to DHS Sensors and state-of-the-art video technology have been deployed and generate enormous quantities of data. Inability to identify meaningful patterns and relationships within this mass of data results in false alarms, real alerts lost in background data, inability to identify “big picture” relationships and duplication of effort. More effective tools and techniques to find meaning in the large, heterogeneous datasets will improve efficiency of human responses, increase agent confidence and provide more effective tactical and strategic information to guide operations. Deliverables and Timelines Year 1 – Identify user population and identify current sources, uses of sensor data and examples of false alarms Obtain sample data sets, apply existing algorithms and identify (or insert) false alarms as ground truth for testing during development Develop initial cliques of neighborhood sensors and begin to develop/refine algorithms. Year 2 - Conduct feature-level data fusion study for a hybrid data fusion system using cliques of neighboring sensors. 1

NC-BSI: TASK 3.5: Reduction of False Alarm Rates from Fused Data Elevator speech We will use efficient feature-level data fusion techniques to represent the massive, hybrid sensor data in reconfigurable sub-graphs at different levels and apply techniques to accurately and efficiently discover and verify critical patterns, detecting deceptive variants, and predicting new trends. Ongoing/leveraged research Data Mining and Information Fusion Hierarchical Bayesian reasoning about uncertainty with noisy information Canonical correlation analysis Feature level data fusion Costs and Special Equipment Personnel Travel Total project funding : $70,588 Investigators The Arizona State University Task 3.5 Team Huan Liu George Runger Jeremy Rowe 2

Presentation and use of the models and simulations Collaborative Research core of Computer Science/Informatics, Data Mining, Modeling and Analysis to inform scenarios, planning and policy Data Description and Management Physical Technology Infrastructure Data Foundation Schematic Representation NC-BSI: TASK 3.5: Reduction of False Alarm Rates from Fused Data

Embedded Intelligence, DataMining Detail Evaluator of baseline Hierarchical system with neighborhood, complementary and surrogate features Sensors … Hierarchical Bayesian integration, complementary and surrogate features False Alarm Filters, Detection Ensembles Context and Environment Information Clique neighborhoods and zooms: Level 0 Clique neighborhoods and zooms: Level 1 Evaluator: Baseline System and Simulator Retrieval and Analysis NC-BSI: TASK 3.5: Reduction of False Alarm Rates from Fused Data