Abstract: Systems, methods, and other embodiments associated with autonomous situation awareness based on ambient intelligence and permuted binary-state predicate classification are described. In one embodiment, a method includes accessing a stream of multivariate observations of system status and command variables. The method supplements the multivariate observations with ML estimates of ambient variables based on the system variables and command variables. The method determines anomalies of the system variables based on residuals between observed values and ML estimates of the system variables based on the supplemented observations. The method evaluates the anomalies with predicates to select one of the command variables to be adjusted. The method generates a suggestion for the selected command variable based on ML predictions of future values for the system variables. And, the method generates an electronic alert to adjust the controls of the asset to match the suggestion for the selected command variable.

Abstract: Systems, methods, and other embodiments associated with auditing the results of a machine learning model are described. In one embodiment, a method accesses original time series data and machine learning estimates of the original time series data. The method generates reconstituted time series data from the machine learning estimates by reversing operations of a machine learning model trained for generating the machine learning estimates from the original time series data. The method detects tampering (or corruption) in the original time series data based on a difference between the original time series data and reconstituted time series data. And, the method generates an electronic verification report that indicates whether the tampering (or corruption) is detected in the original time series data.

Abstract: Systems, methods, and other embodiments associated with auditing the results of a machine learning model are described. In one embodiment, a method accesses original time series data and machine learning estimates of the original time series data. The method generates reconstituted time series data from the machine learning estimates by reversing operations of a machine learning model trained for generating the machine learning estimates from the original time series data. The method detects tampering (or corruption) in the original time series data based on a difference between the original time series data and reconstituted time series data. And, the method generates an electronic verification report that indicates whether the tampering (or corruption) is detected in the original time series data.