Abstract: A health experience detection system comprises an eye-tracking device and a health experience detection device disposed in electrical communication with the eye-tracking device. The health experience detection device comprises a controller having a memory and a processor. The controller is configured to: receive eye-movement data from the eye-tracking device, the eye-movement data associated with eye-movement of a user and comprising at least one of saccade event data and fixation event data, apply the eye-movement data to a health experience identification engine to generate a health experience identifier associated with the user, and output a notification regarding the health experience identifier of the user as associated with the eye-movement data.

Abstract: A method of detecting an anomaly in a sensor network for diagnosing a network attack may include receiving a data set comprising a plurality of vector-valued measurements from a plurality of sensors, and decomposing the data set into a low-rank component L and a sparse component S using an Augmented Lagrange Multiplier (ALM) method. In one embodiment, at least one of L or S can be determined using an exact minimizer of a Lagrangian in the ALM method, L can represent patterns that occur in a relatively large number of the plurality of sensors, and S can represent patterns that occur in a relatively small number of the plurality of sensors. The method may also include ascertaining, using the computer system, the anomaly in the data set based on the patterns in the sparse component S.