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Characterizing Human Performance: Detecting Targets at High False Alarm Rates [Slides]

Speed, Ann S.; Wheeler, Jason W.; Russell, John L.; Oppel, Fred O.; Sanchez, Danielle; Silva, Austin R.; Chavez , Anna C.

Analysts develop a “no threat” bias with high false alarms. If only shown alarms for actual attacks, may never actually see an alarm. We see this in the laboratory, but not often studied in applied environments. (TSA is an exception.) In this work, near-operational paradigms are useful, but difficult to construct well. Pilot testing is critical before engaging time-limited professionals. Experimental control is difficult to balance with operational realism. Grounding near-operational experiments in basic research paradigms has both advantages and disadvantages. Despite shortcomings in our second experiment, we now have a platform for experimental investigations into the human element of physical security systems.

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Sparse coding for N-gram feature extraction and training for file fragment classification

IEEE Transactions on Information Forensics and Security

Wang, Felix W.; Quach, Tu-Thach Q.; Wheeler, Jason W.; Aimone, James B.; James, Conrad D.

File fragment classification is an important step in the task of file carving in digital forensics. In file carving, files must be reconstructed based on their content as a result of their fragmented storage on disk or in memory. Existing methods for classification of file fragments typically use hand-engineered features, such as byte histograms or entropy measures. In this paper, we propose an approach using sparse coding that enables automated feature extraction. Sparse coding, or sparse dictionary learning, is an unsupervised learning algorithm, and is capable of extracting features based simply on how well those features can be used to reconstruct the original data. With respect to file fragments, we learn sparse dictionaries for n-grams, continuous sequences of bytes, of different sizes. These dictionaries may then be used to estimate n-gram frequencies for a given file fragment, but for significantly larger n-gram sizes than are typically found in existing methods which suffer from combinatorial explosion. To demonstrate the capability of our sparse coding approach, we used the resulting features to train standard classifiers, such as support vector machines over multiple file types. Experimentally, we achieved significantly better classification results with respect to existing methods, especially when the features were used in supplement to existing hand-engineered features.

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An elastomeric insole for 3-axis ground reaction force measurement

Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

Lincoln, Lucas S.; Bamberg, Stacy J.Morris; Parsons, Erin; Salisbury, Curt M.; Wheeler, Jason W.

Measurement of the ground reaction force vector is important in clinical gait analysis and biomechanics research, for example to enable inverse dynamic calculations. Instrumented insoles allow biomechanical data to be collected outside of the motion analysis laboratory in many environments. However, current insole-based approaches typically measure only the vertical component of the reaction force and the plantar center of pressure. This work describes the development and evaluation of a silicone insole capable of measuring the complete three dimensional reaction force vector. The insole is optically based and low-cost with no complex manufacturing requirements. Accuracy over five nominal gait trails is shown to be on the order of 10% of the force range, with mean errors of 10.7 N in the shear directions and 68.1 N in normal. The insole can provide a simple mobile platform that allows kinetic gait data to be collected in many environments while minimally affecting the wearer's gait. © 2012 IEEE.

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28 Results
28 Results