Maj. Kelly Bullock’s presentation on a “Mathematical Framework for Measuring Effectiveness” received best paper in the Measure of Effectiveness WG. Kelly’s research presented a domain independent approach to measuring effectiveness derived from Measurement Theory, Measure Theory, and new effect paradigms. In addition, an integrated framework for addressing error and uncertainty in effectiveness measurement was introduced. Finally, implementation of the approach was demonstrated through a scenario involving a terrorist attack on a CONUS USAF base.

Maj. Todd Hamill’s presentation on “A Reach-Based Approach to Screening Actors of Interest in Organizations” received best paper in the Information Operations WG. Given the current efforts to reduce the threat of terrorism, Todd’s research focused on improved methods to understand the adversary. Partially motivated by criticisms of existing measures used to characterize the most influential or most revered member of a group, the study discussed the development of a new, reach-based measure. This measure has the potential to support the investigation of adversarial networks despite the oftentimes limited availability of descriptive information. Relationships to pertinent social network analysis measures, underlying assumptions, and theoretical bases for the new measure were discussed. The presentation concluded with an analysis of a variety of exemplar networks and an exploration of the results, benefits, and limitations of this measure.

Maj. Tim Smetek’s presentation on “Improved Hyper-spectral Target Detection Using Optimization and Applied Statistical Methods” received best paper in the Air Power and Combat Identification WG. Hyper-spectral imagery (HSI) provides a unique means for automatically detecting targets on the battlefield or in other operational settings. However, current HSI detection methods require the user to have considerable technical knowledge of the sensor, image collection conditions, and data analysis methods. This extensive knowledge often precludes practical use of HSI in an operational environment. Tim’s research investigated autonomous methods for accurate hyper-spectral target detection that alleviate the technical knowledge burden for the user, thereby making HSI more relevant to the warfighter. Specifically, multivariate outlier detection methods, to include the use of nonlinear optimization, are developed to automatically detect anomalous objects in a hyper-spectral scene. Performance of the proposed anomaly detection methods is assessed using benchmark hyper-spectral data sets found in the technical literature as well as in synthetic imagery.