Abstract: Devices, computer-readable media, and methods are disclosed for assigning a service to a vehicle-based mobile node. For example, a processor deployed in a communication network may receive a request for the service from a mobile endpoint device, determine a route of the mobile endpoint device, determine a route of the vehicle-based mobile node, and assign the service to the vehicle-based mobile node when the route of the mobile endpoint device and the route of the vehicle-based mobile node coincide.

Abstract: Aspects of the subject disclosure may include, for example, systems and methods aggregating video content and adjusting the aggregate video content according to a training model. The adjusted aggregate video content comprises a first subset of the images and does not comprise a second subset of the images. The first subset of the images is determined by the training model based on a plurality of categories corresponding to a plurality of events. The illustrative embodiments also include presenting the adjusted aggregate video content and receiving identifications for the first subset of the images in the aggregate video content. Further, the illustrative embodiments include adjusting the training model according to the identifications and providing the adjusted training model to a network device. Other embodiments are disclosed.

Abstract: A method includes receiving, at a server from a user device, a request for media content. The request identifies a first personal quality profile. The method includes identifying, at the server, a version of the media content to send to the user device based on first media quality preferences indicated by the first personal quality profile. The method includes, in response to a determination of unavailability of the version from a content source: retrieving, at the server from the content source, a particular version of the media content; and modifying, at the server, a visual attribute of the particular version to generate the version. The method further includes sending the version from the server to the user device.

Abstract: A system embodying the subject disclosure includes a memory to store instructions. A controller coupled to the memory, responsive to executing the instructions, can perform operations including obtaining performance characteristics for segments of a network; the segments are selected based on a trajectory of a mobile device coupled to the network. The controller can predict future transport and segment characteristics based on the performance characteristics. The controller can receive a request from the mobile device for transmission of a data packet over the network. The controller can monitor a power state of the mobile device and predict a future power state of the mobile device. The controller can determine a target time for fulfilling the request, based on the future power state and the future transport and segment characteristics. The controller can schedule a time for fulfilling the request according to the target time. Other embodiments are disclosed.

Abstract: Management and configuration of internet of things network connected devices is facilitated herein. A proxy device comprises a memory that stores executable instructions that, when executed by a processor, facilitate performance of operations that comprise determining a first identity and a first operational parameter of a first device and a second identity and a second operational parameter of a second device. The first device and the second device can be associated with a defined communication network. The proxy device can be provisioned within the defined communication network and can operate as a security update proxy node for the first device and the second device. The operations can also comprise facilitating a first security update at the first device and a second security update at the second device based on a determination that the first device and the second device have delegated responsibility for security synchronization to the proxy device.

Abstract: A method and apparatus for providing a recommended action are disclosed. For example, the method receives first data that is measured via at least one sensor deployed at a venue, receives second data from at least one user endpoint device present at the venue, wherein the second data comprises data that is measured via a sensor associated with the at least one user endpoint device, applies a perception model of the venue to the first data and the second data that are received, generates at least one recommended action when a threshold is reached, wherein the threshold is established for generating the at least one recommended action, and provides an update to the perception model of the venue based on the at least one recommended action that is generated.

Abstract: A method may include a processing system having at least one processor receiving a data processing task from a user device, determining a plurality of sub-tasks of the data processing task, determining a plurality of machine learning models for performing the plurality of sub-tasks, and arranging the plurality of machine learning models into a plurality of candidate solutions for performing the data processing task. The processing system may further evaluate the plurality of candidate solutions using a test data set to provide measures of a plurality of performance metrics for each of the plurality of candidate solutions, and provide a solution comprising one of the plurality of candidate solutions to the user device, where the solution is selected based upon the measures of the plurality of performance metrics.

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: Aspects of the subject disclosure may include, for example, systems and methods aggregating video content and adjusting the aggregate video content according to a training model. The adjusted aggregate video content comprises a first subset of the images and does not comprise a second subset of the images. The first subset of the images is determined by the training model based on a plurality of categories corresponding to a plurality of events. The illustrative embodiments also include presenting the adjusted aggregate video content and receiving identifications for the first subset of the images in the aggregate video content. Further, the illustrative embodiments include adjusting the training model according to the identifications and providing the adjusted training model to a network device. Other embodiments are disclosed.

Abstract: A system and method are provided for content-based non-linear control of video data playback. A multimedia database having multimedia data including multimedia content data is searched based on a user query to determine a first set of multimedia data. The multimedia data includes indexes to and condensed representations of corresponding video data stored in a video database. A portion of the first set of multimedia data is displayed at a control device in response to the user query. A user of the control device selects an element of the first set of multimedia data for video playback and video data corresponding to the element delivered to a video device for playback. A user of the control device selects an element of the first set of multimedia data for additional information and a second set of multimedia data corresponding to the element delivered to the control device.

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: A method includes receiving, at a server computing device, a request to stream media content to a user device. The request is associated with data identifying a personal quality profile. The method includes retrieving, via the server computing device, the personal quality profile associated with the data. The personal quality profile indicates media quality preferences. The method includes determining, via a processor of the server computing device, a version of the media content to send to the user device based on the media quality preferences of the personal quality profile and based on a delivery bit rate. The method also includes sending the version from the server computing device to the user device in response to the request.

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: Aspects of the subject disclosure may include, for example, analyzing content flow data in a communication network to obtain flow statistics; the statistics are aggregated to construct a model of the content flow based on the aggregated flow statistics. Operations of network nodes are predicted based on the model; a content delivery path in the communication network is identified. A quality of service is detected for network nodes on the content delivery path. The detected quality of service is compared with a predicted quality of service based on the model; a network anomaly is detected based on the comparing. The content flow on the content delivery path is adjusted responsive to detection of the network anomaly. Other embodiments are disclosed.

Abstract: A method and apparatus for providing a recommended action are disclosed. For example, the method receives first data that is measured via at least one sensor deployed at a venue, receives second data from at least one user endpoint device present at the venue, wherein the second data comprises data that is measured via a sensor associated with the at least one user endpoint device, applies a perception model of the venue to the first data and the second data that are received, generates at least one recommended action when a threshold is reached, wherein the threshold is established for generating the at least one recommended action, and provides an update to the perception model of the venue based on the at least one recommended action that is generated.

Abstract: A method includes determining, at an image processing device, object quality values for a plurality of objects represented in an image. The object quality values are based on portions of image data for the image. The object quality values include a blurriness value for each object and a color value for each object. The method includes accessing, via the image processing device, object category metrics associated with an object category. The object category metrics include a blurriness metric for each object and a color metric for each object. The method also includes performing, with the image processing device, a particular image processing operation for the image based on comparisons of the object quality values for each object to corresponding object category metrics.

Abstract: A mobile device that incorporates the subject disclosure may perform, for example, operations including requesting transport characteristics of a network coupled to a server and to the device according to a trajectory of the device. Data packets are transported to the mobile device according to a dynamic adaptive streaming over hypertext transfer protocol. The operations include obtaining the current transport characteristics of the network and predicting a future transport characteristic of the network, according to the trajectory of the device. A time is scheduled for sending a request to the server for transmission over the network of a data packet. The time can be scheduled according to the current and future transport characteristics of the network, to avoid degradation in quality of media content presented by the device. The data packet received responsive to sending the request is buffered for a future presentation of the media content. Other embodiments are disclosed.

Abstract: A machine-readable medium may include a group of reusable components for building a spoken dialog system. The reusable components may include a group of previously collected audible utterances. A machine-implemented method to build a library of reusable components for use in building a natural language spoken dialog system may include storing a dataset in a database. The dataset may include a group of reusable components for building a spoken dialog system. The reusable components may further include a group of previously collected audible utterances. A second method may include storing at least one set of data. Each one of the at least one set of data may include ones of the reusable components associated with audible data collected during a different collection phase.

Abstract: A method includes accessing, at a base station of a wireless communications network, information identifying a plurality of user devices in a coverage area of the base station. The method includes determining a forecasted content request for media content at a particular time for delivery to a first user device of the plurality of user devices based on media consumption data for the first user device. The method includes receiving, from a second user device of the plurality of user devices, a request for the media content at a time before the particular time. The method includes sending an instruction to the first user device that directs the first user device store the media content received via a multicast transmission of the media content. The method also includes sending the media content via the multicast transmission to the first user device and to the second user device responsive to the request.