Abstract: Methods, computer-readable media, and apparatuses for composing a video in accordance with a user goal and an audience preference are described. For example, a processing system having at least one processor may obtain a plurality of video clips of a user, determine at least one goal of the user for a production of a video from the plurality of video clips, determine at least one audience preference of an audience, and compose the video comprising at least one video clip of the plurality of video clips of the user in accordance with the at least one goal of the user and the at least one audience preference. The processing system may then upload the video to a network-based publishing platform.

Abstract: A processing system including at least one processor may receive a task request for a user from a requesting party, identify a trust profile for the requesting party, identify a first automated system to access to fulfill the task request, determine whether the trust profile for the requesting party permits an access to the first automated system, and fulfill the task request via the first automated system when it is determined that the trust profile for the requesting party permits the access to the first automated system.

Abstract: Aspects of the subject disclosure may include, for example, a method including obtaining an advertisement package, wherein the advertisement package defines an interactive extended reality advertisement and includes a plurality of optional features; obtaining information about a user, their equipment, and their environment; creating an interactive extended reality advertisement by choosing a selected feature of the plurality of optional features according to the user information; and presenting the interactive extended reality advertisement to the user equipment. Other embodiments are disclosed.

Abstract: The concepts and technologies disclosed herein are directed, in part, to a system that can monitor traffic traversing a virtualized network that includes a plurality of virtual network functions (“VNFs”) that provide, at least in part, a service. The system can capture an event from the traffic. The event can involve at least one VNF, and can negatively affect at least one operational aspect of the virtualized network in providing the service. The system can create snapshot that represents a network state of the virtualized network during the event. The system can create, based upon the snapshot, a shadow network. The shadow network can include a network emulation of the network state of the virtualized network during the event. The system can determine, from the shadow network, at least one modification to at least a portion of the virtualized network that would at least mitigate negative effects of the event.

Abstract: Concepts and technologies disclosed herein are directed to machine learning model understanding as-a-service. According to one aspect of the concepts and technologies disclosed herein, a model understanding as-a-service system can receive, from a user system, a service request that includes a machine learning model created for a user associated with the user system. The model understanding as-a-service system can conduct an analysis of the machine learning model in accordance with the service request. The model understanding as-a-service system can compile, for the user, results of the analysis of the machine learning model in accordance with the service request. The model understanding as-a-service system can create a service response that includes the results of the analysis. The model understanding as-a-service system can provide the service response to the user system.

Abstract: Methods, computer-readable media, and devices are disclosed for improving an object model based upon measurements of physical properties of an object via an unmanned vehicle using adversarial examples. For example, a method may include a processing system capturing measurements of physical properties of an object via at least one unmanned vehicle, updating an object model for the object to include the measurements of the physical properties of the object, where the object model is associated with a feature space, and generating an example from the feature space, where the example comprises an adversarial example. The processing system may further apply the object model to the example to generate a prediction, capture additional measurements of the physical properties of the object via the at least one unmanned vehicle when the prediction fails to identify that the example is an adversarial example, and update the object model to include the additional measurements.

Abstract: An example method may include a processing system including at least one processor detecting an interaction of a first user and a second user, providing a temporary authorization to the second user to access a data set based upon an authorization of the first user to access the data set, wherein the providing the temporary authorization is in response to the detecting the interaction, generating a record of an access of the second user to the data set, wherein the record includes a notation of the temporary authorization of the second user to access the data set based upon the authorization of the first user, detecting an end to the interaction of the first user and the second user, and revoking the temporary authorization of the second user to access the data set in response to the detecting of the end of the interaction.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for monitoring a user's food intake. For instance, in one example, a user's food intake is monitored based on data collected from a sensor. The user's current nutrient consumption is estimated based on the monitoring. A recommendation is presented to the user based on the estimating, where the recommendation is designed to help the user achieve a target nutrient consumption.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for performing autonomous multi-pass data acquisition using unmanned aerial vehicle. For instance, in one example, a method includes obtaining a first set of sensor data collected by a fleet of unmanned aerial vehicles comprising at least one unmanned aerial vehicle, wherein the first set of sensor data depicts a target area at a first granularity, constructing a three-dimensional map of hierarchical unit representations of the first set of sensor data, sending a signal to the fleet of unmanned aerial vehicles to obtain a second set of sensor data at a second granularity that is finer than the first granularity, based at least in part on an examination of the three-dimensional map, and aggregating the second set of sensor data to form a high-resolution composite of the target area.