The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns.

Computational Red teaming (CRT) is a simulation-based optimization application utilized by defense analysts to uncover vulnerabilities of operational plans. In CRT, agent-based simulation models of military scenarios are automatically analyzed and modeled using evolutionary computation techniques. The CRT optimization process aims at identifying simulation models which exhibit emergent system behaviors of interest, e.g., when the adversary (called “Red”) breaks the defensive (“Blue”) strategies. Numerous multiobjective evolutionary algorithms (MOEAs) have been applied to CRT; however, the elitist and converging nature of these Pareto-based optimization algorithms typically leads to the generation of optimal, with respect to the Pareto front, but poorly diversified adversarial operational plans. As a result, the near-optimal alternative strategies are omitted; this considerably limits the applicability of CRT when considering the decision makers point of view. We propose a diversity enhancement scheme for MOEAs which uses the diversity contribution of individual solutions in the aggregated (combining both the objective and decision variable spaces) space to compute the fitness assignment. This feature enables both the exploitation of Pareto-optimal solutions whilst promoting diversification of the solutions in the decision variable space. Our experimental results indicate that this diversity enhancement mechanism can effectively resolve the diversification issue and, ultimately, enhance CRT to assist decision making.