A technological approach can be employed to protect data. Datasets from distinct computing environments of an organization can be scanned to identify data elements subject to protection, such as sensitive data. The identified elements can be automatically protected such as by masking, encryption, or tokenization. Data lineage including relationships amongst data and linkages between computing environments can be determined along with data access patterns to facilitate understanding of data. Further, personas and exceptions can be determined and employed as bases for access recommendations.

Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.

A fault resilient airborne network includes a plurality of aircraft system components installed within an aircraft and at least one agent in communication with the plurality of aircraft system components during in-flight operation of the aircraft. The at least one agent is configured to monitor an aircraft system component for a fault, observe a fault within the aircraft system component, and provide reconfiguration instructions to the aircraft system component in response to the observed fault. The at least one agent is further configured to predict a life expectancy of the aircraft system component using machine learning models while monitoring the aircraft system component for a fault, and provide reconfiguration instructions to the aircraft system component when the life expectancy of the aircraft system component meets a threshold. The reconfiguration instructions are configured to cause an adjustment in at least some of the plurality of aircraft system components.

A fault resilient airborne network includes a plurality of aircraft system components installed within an aircraft and at least one agent in communication with the plurality of aircraft system components during in-flight operation of the aircraft. The at least one agent is configured to monitor an aircraft system component for a fault, observe a fault within the aircraft system component, and provide reconfiguration instructions to the aircraft system component in response to the observed fault. The at least one agent is further configured to predict a life expectancy of the aircraft system component using machine learning models while monitoring the aircraft system component for a fault, and provide reconfiguration instructions to the aircraft system component when the life expectancy of the aircraft system component meets a threshold. The reconfiguration instructions are configured to cause an adjustment in at least some of the plurality of aircraft system components.

A method of determining whether a user has fallen comprises detecting a potential fall using a motion sensing device, updating a probability of the potential fall being an actual fall based on an additional sensor, and updating the probability of the potential fall being an actual fall based on user context, the user context including an identified activity prior to the potential fall.

The present disclosure describes improvements in optimization systems. During an optimization loop, an advanced objective function is used to determine an objective value, a specification metric, and a rule coverage metric for a particular solution. The specification metric characterizes compliance of the solution with certain formal specifications. The rule coverage metric characterizes the degree to which all rules (or a particular rule) are tested during testing of the system. The objective value and metrics may influence future operation of the optimization loop.

A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.

A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.

A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.

A system and method are presented in which known genetic attributes associated with a condition are used to seed the determination of additional attributes which are associated with the condition. Based on the learning, the additional attributes (genetic, behavioral, or both) provide for an increased correlation between the combined attributes and the condition. For behavioral attributes, a measure of the impact of the behavioral attribute on the risk of the condition can be transmitted to another device or system.