Abstract: Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

Abstract: Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

Abstract: Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

Abstract: Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

Abstract: Context-based coordinated data retrieval for mobile devices is provided. An interface between application code of a mobile device and an operating system of the mobile device receives a declarative language based data-retrieval request made by the application code for data from an I/O device of the mobile device. Based on operating context of the mobile device, opportunistic coordination rule(s) are selected from a local rules cache, the rule(s) representative of at least one power-saving option for retrieving data for responding to the request. The rule(s) are evaluated to determine how to retrieve data for responding to the request, and action(s) are performed based on the evaluating, the action(s) including responding to the request.

Abstract: An approach is provided for auto-adjusting instructional video playback based on cognitive user activity detection analysis. The approach includes, for instance, providing for playback an instructional video, including a series a steps to accomplish one or more tasks, and monitoring, during playback of the instructional video, progress of a user through the series of steps. The monitoring includes, at least in part, video monitoring the user, and using an activity detection analysis to detect, based on the monitoring, actions by the user as the user progresses through the series of steps. Playback of the instructional video is automatically adjusted based on the activity detection analysis to match the progress of the user through the series of steps.

Abstract: Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

Abstract: An approach is provided for auto-adjusting instructional video playback based on cognitive user activity detection analysis. The approach includes, for instance, providing for playback an instructional video, including a series a steps to accomplish one or more tasks, and monitoring, during playback of the instructional video, progress of a user through the series of steps. The monitoring includes, at least in part, video monitoring the user, and using an activity detection analysis to detect, based on the monitoring, actions by the user as the user progresses through the series of steps. Playback of the instructional video is automatically adjusted based on the activity detection analysis to match the progress of the user through the series of steps.

Abstract: Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

Abstract: Context-based coordinated data retrieval for mobile devices is provided. An interface between application code of a mobile device and an operating system of the mobile device receives a declarative language based data-retrieval request made by the application code for data from an I/O device of the mobile device. Based on operating context of the mobile device, opportunistic coordination rule(s) are selected from a local rules cache, the rule(s) representative of at least one power-saving option for retrieving data for responding to the request. The rule(s) are evaluated to determine how to retrieve data for responding to the request, and action(s) are performed based on the evaluating, the action(s) including responding to the request.

Abstract: Context-based coordinated data retrieval for mobile devices is provided. An interface between application code of a mobile device and an operating system of the mobile device receives a declarative language based data-retrieval request made by the application code for data from an I/O device of the mobile device. Based on operating context of the mobile device, opportunistic coordination rule(s) are selected from a local rules cache, the rule(s) representative of at least one power-saving option for retrieving data for responding to the request. The rule(s) are evaluated to determine how to retrieve data for responding to the request, and action(s) are performed based on the evaluating, the action(s) including responding to the request.

Abstract: Context-based coordinated data retrieval for mobile devices is provided. An interface between application code of a mobile device and an operating system of the mobile device receives a declarative language based data-retrieval request made by the application code for data from an I/O device of the mobile device. Based on operating context of the mobile device, opportunistic coordination rule(s) are selected from a local rules cache, the rule(s) representative of at least one power-saving option for retrieving data for responding to the request. The rule(s) are evaluated to determine how to retrieve data for responding to the request, and action(s) are performed based on the evaluating, the action(s) including responding to the request.