Abstract: Embodiments described herein are directed to implementing compliance settings by a computing device for bringing the computing device into compliance with a configuration scenario. For instance, a computing device may receive, from a server, configuration information describing compliance settings for implementing by the computing device to bring the computing device into compliance with a configuration scenario. Moreover, the computing device may identify a state machine indicated by the configuration information that describes a configuration process for implementing the compliance settings and execute the state machine to configure the computing device with the compliance settings.

Abstract: Embodiments described herein are directed to implementing compliance settings by a computing device for bringing the computing device into compliance with a configuration scenario. For instance, a computing device may receive, from a server, configuration information describing compliance settings for implementing by the computing device to bring the computing device into compliance with a configuration scenario. Moreover, the computing device may identify a state machine indicated by the configuration information that describes a configuration process for implementing the compliance settings and execute the state machine to configure the computing device with the compliance settings.

Abstract: Machine-learned prediction of a blame frame of a crash stack. Specifically, a crash stack associated with a crash is parsed into a sequence of frames. The blame frame of the crash stack is estimated by, for each of a plurality of the sequence of frames, identifying a plurality of features of the corresponding frame, feeding the plurality of features to a neural network, and using the output of the neural network to make a prediction on whether the corresponding frame is a blame frame of the crash. If this is done during training time, the predicted blame frame can be compared against the actual blame frame, resulting in an adjustment of the neural network. Through appropriate featurization of the frames, and by use of the neural network, the prediction can be made cross-application and considering the context of the frame within the crash stack.

Abstract: Machine-learned prediction of a blame frame of a crash stack. Specifically, a crash stack associated with a crash is parsed into a sequence of frames. The blame frame of the crash stack is estimated by, for each of a plurality of the sequence of frames, identifying a plurality of features of the corresponding frame, feeding the plurality of features to a neural network, and using the output of the neural network to make a prediction on whether the corresponding frame is a blame frame of the crash. If this is done during training time, the predicted blame frame can be compared against the actual blame frame, resulting in an adjustment of the neural network. Through appropriate featurization of the frames, and by use of the neural network, the prediction can be made cross-application and considering the context of the frame within the crash stack.

Abstract: Embodiments described herein are directed to implementing compliance settings by a computing device for bringing the computing device into compliance with a configuration scenario. For instance, a computing device may receive, from a server, configuration information describing compliance settings for implementing by the computing device to bring the computing device into compliance with a configuration scenario. Moreover, the computing device may identify a state machine indicated by the configuration information that describes a configuration process for implementing the compliance settings and execute the state machine to configure the computing device with the compliance settings.

Abstract: Embodiments described herein are directed to implementing compliance settings by a computing device for bringing the computing device into compliance with a configuration scenario. For instance, a computing device may receive, from a server, configuration information describing compliance settings for implementing by the computing device to bring the computing device into compliance with a configuration scenario. Moreover, the computing device may identify a state machine indicated by the configuration information that describes a configuration process for implementing the compliance settings and execute the state machine to configure the computing device with the compliance settings.

Abstract: Embodiments described herein are directed to implementing compliance settings by a computing device for bringing the computing device into compliance with a configuration scenario. For instance, a computing device may receive, from a server, configuration information describing compliance settings for implementing by the computing device to bring the computing device into compliance with a configuration scenario. Moreover, the computing device may identify a state machine indicated by the configuration information that describes a configuration process for implementing the compliance settings and execute the state machine to configure the computing device with the compliance settings.

Abstract: Techniques are described for configuring a multivariant mobile operating system. The current state of a mobile computing device can be determined by determining keys to use for obtaining configuration settings. The determined keys can be used to retrieve configuration settings. The configuration settings can be prioritized. The prioritized configuration settings can be applied to configure the multivariant mobile operating system.

Abstract: Techniques are described for configuring a multivariant mobile operating system. The current state of a mobile computing device can be determined by determining keys to use for obtaining configuration settings. The determined keys can be used to retrieve configuration settings. The configuration settings can be prioritized. The prioritized configuration settings can be applied to configure the multivariant mobile operating system.