Abstract: Systems, device and techniques are disclosed for implementing a security configuration change based on one or more base events and a current security configuration. An inference module may identify a security configuration change based on receiving base events from a state storage/event listener and analyzing the base events to determine if a current security configuration is optimal given the base events.

Abstract: Systems, device and techniques are disclosed for implementing a security configuration change based on one or more base events and a current security configuration. An inference module may identify a security configuration change based on receiving base events from a state storage/event listener and analyzing the base events to determine if a current security configuration is optimal given the base events.

Abstract: Systems, device and techniques are disclosed for implementing a security configuration change based on one or more base events and a current security configuration. An inference module may identify a security configuration change based on receiving base events from a state storage/event listener and analyzing the base events to determine if a current security configuration is optimal given the base events.

Abstract: Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Abstract: Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Abstract: Systems, device and techniques are disclosed for implementing a security configuration change based on one or more base events and a current security configuration. An inference module may identify a security configuration change based on receiving base events from a state storage/event listener and analyzing the base events to determine if a current security configuration is optimal given the base events.

Abstract: Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Abstract: Systems, device and techniques are disclosed for implementing a security configuration change based on one or more base events and a current security configuration. An inference module may identify a security configuration change based on receiving base events from a state storage/event listener and analyzing the base events to determine if a current security configuration is optimal given the base events.

Abstract: Systems, device and techniques are disclosed for implementing a security configuration change based on one or more base events and a current security configuration. An inference module may identify a security configuration change based on receiving base events from a state storage/event listener and analyzing the base events to determine if a current security configuration is optimal given the base events.

Abstract: Systems, device and techniques are disclosed for implementing a security configuration change based on one or more base events and a current security configuration. An inference module may identify a security configuration change based on receiving base events from a state storage/event listener and analyzing the base events to determine if a current security configuration is optimal given the base events.

Abstract: Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Abstract: A mechanism for a user to navigate a list on a touch sensitive screen by making on-screen gestures is described. Items in the list are sorted based on a common attribute, and grouped into different categories associated with the attribute. A portion of the items are displayed in the sorted sequence in a graphical user interface (UI) on a touch sensitive screen. When the user makes an on-screen gesture, one or more corresponding categories are identified and their indicators are dynamically and prominently displayed as feedback. Once the gesture is completed, the graphical UI scrolls to the portion of the list including items in the desired category.