Abstract: Systems and methods for application analytics reporting include comparing metrics regarding the use of the application to a usage model for the application. The usage model indicates an expected set of states of the application and transitions between the states during execution of the application. A determined difference between the metrics and the expected states and transitions indicated by the usage model that exceeds a predetermined threshold is provided as analytics data.

Abstract: A computing device may identify one or more wireless broadcast signals that are each associated with a name that conforms to a specified wireless naming scheme. Responsive to identifying a particular wireless broadcast signal that is associated with a respective name conforming to the specified wireless naming scheme, the computing device may determine a signal strength of the particular wireless broadcast signal. The computing device may determine a likelihood that a remote device broadcasting the particular wireless signal is within a specified proximity of the computing device based at least in part on the signal strength of the particular wireless broadcast signal. Responsive to determining that the likelihood that the remote device is within the specified proximity of the computing device exceeds a specified threshold, the computing device may initiate an establishment of a wireless connection with the remote device.

Abstract: A computing device may identify one or more wireless broadcast signals that are each associated with a name that conforms to a specified wireless naming scheme. Responsive to identifying a particular wireless broadcast signal that is associated with a respective name conforming to the specified wireless naming scheme, the computing device may determine a signal strength of the particular wireless broadcast signal. The computing device may determine a likelihood that a remote device broadcasting the particular wireless signal is within a specified proximity of the computing device based at least in part on the signal strength of the particular wireless broadcast signal. Responsive to determining that the likelihood that the remote device is within the specified proximity of the computing device exceeds a specified threshold, the computing device may initiate an establishment of a wireless connection with the remote device.

Abstract: A computing device may identify one or more wireless broadcast signals that are each associated with a name that conforms to a specified wireless naming scheme. Responsive to identifying a particular wireless broadcast signal that is associated with a respective name conforming to the specified wireless naming scheme, the computing device may determine a signal strength of the particular wireless broadcast signal. The computing device may determine a likelihood that a remote device broadcasting the particular wireless signal is within a specified proximity of the computing device based at least in part on the signal strength of the particular wireless broadcast signal. Responsive to determining that the likelihood that the remote device is within the specified proximity of the computing device exceeds a specified threshold, the computing device may initiate an establishment of a wireless connection with the remote device.

Abstract: A computing device may identify one or more wireless broadcast signals that are each associated with a name that conforms to a specified wireless naming scheme. Responsive to identifying a particular wireless broadcast signal that is associated with a respective name conforming to the specified wireless naming scheme, the computing device may determine a signal strength of the particular wireless broadcast signal. The computing device may determine a likelihood that a remote device broadcasting the particular wireless signal is within a specified proximity of the computing device based at least in part on the signal strength of the particular wireless broadcast signal. Responsive to determining that the likelihood that the remote device is within the specified proximity of the computing device exceeds a specified threshold, the computing device may initiate an establishment of a wireless connection with the remote device.

Abstract: A computing device may identify one or more wireless broadcast signals that are each associated with a name that conforms to a specified wireless naming scheme. Responsive to identifying a particular wireless broadcast signal that is associated with a respective name conforming to the specified wireless naming scheme, the computing device may determine a signal strength of the particular wireless broadcast signal. The computing device may determine a likelihood that a remote device broadcasting the particular wireless signal is within a specified proximity of the computing device based at least in part on the signal strength of the particular wireless broadcast signal. Responsive to determining that the likelihood that the remote device is within the specified proximity of the computing device exceeds a specified threshold, the computing device may initiate an establishment of a wireless connection with the remote device.

Abstract: Systems and methods for application analytics reporting include comparing metrics regarding the use of the application to a usage model for the application. The usage model indicates an expected set of states of the application and transitions between the states during execution of the application. A determined difference between the metrics and the expected states and transitions indicated by the usage model that exceeds a predetermined threshold is provided as analytics data.

Abstract: Systems and methods for application analytics reporting include comparing metrics regarding the use of the application to a usage model for the application. The usage model indicates an expected set of states of the application and transitions between the states during execution of the application. A determined difference between the metrics and the expected states and transitions indicated by the usage model that exceeds a predetermined threshold is provided as analytics data.

Abstract: Systems and methods for application analytics reporting include comparing metrics regarding the use of the application to a usage model for the application. The usage model indicates an expected set of states of the application and transitions between the states during execution of the application. A determined difference between the metrics and the expected states and transitions indicated by the usage model that exceeds a predetermined threshold is provided as analytics data.

Abstract: A performance-efficient activity-modeling system generates a group-activity model for a population group using information from an optimal subset of users of the population group. During operation, the system computes utility scores for a set of users based on a utility-scoring function, such that a respective utility score indicates a usefulness or penalty of collecting a corresponding user's contextual information. The system then selects, from the set of users, a subset of users with highest utility scores, and receives user information from each of the selected users. The system generates the group-activity model based on the user information received from the selected users.

Abstract: A performance-efficient activity-modeling system generates a group-activity model for a population group using information from an optimal subset of users of the population group. During operation, the system computes utility scores for a set of users based on a utility-scoring function, such that a respective utility score indicates a usefulness or penalty of collecting a corresponding user's contextual information. The system then selects, from the set of users, a subset of users with highest utility scores, and receives user information from each of the selected users. The system generates the group-activity model based on the user information received from the selected users.

Abstract: A client device can receive information about a population to which a user belongs. During operation, the client device determines information about a user, determines a group identifier for the user, and communicates the determined information about the local user and the group identifier to a group-modeling server. The client device then receives a group-activity model that corresponds to the group identifier, and generates a user-activity model for the local user based on the group-activity model and the determined information about the local user. The client device uses the user-activity model to compute an activity probability for a corresponding target activity. The group-modeling server receives user information from a plurality of client devices of a group, and generates a group-activity model for the group based on the user information. The server then sends the group-activity model to users of the identified group.

Abstract: A sensing system includes a set of sensors and a data-fusing mechanism coupled to at least one of these sensors. In the set of sensors, at least one sensor is configured to store one or more measurement models for one or more phenomenon states. Furthermore, at least one sensor in the set of sensors is configured to sample a measurement value and generate a likelihood function based on the sampled measurement and the measurement models. The data-fusing mechanism coupled to a respective sensor in the set of sensors is configured to collect one or more likelihood functions generated by the one or more sensors and use the collected likelihood functions to compute an aggregate probability of a phenomenon state.

Abstract: A sensing system includes a set of sensors and a data-fusing mechanism coupled to at least one of these sensors. In the set of sensors, at least one sensor is configured to store one or more measurement models for one or more phenomenon states. Furthermore, at least one sensor in the set of sensors is configured to sample a measurement value and generate a likelihood function based on the sampled measurement and the measurement models. The data-fusing mechanism coupled to a respective sensor in the set of sensors is configured to collect one or more likelihood functions generated by the one or more sensors and use the collected likelihood functions to compute an aggregate probability of a phenomenon state.