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: 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: Aspects of the subject disclosure may include, for example, a method comprising obtaining, by a processing system including a processor, first and second models for a structure of an object, based respectively on ground-level and aerial observations of the object. Model parameters are determined for a three-dimensional (3D) third model of the object based on the first and second models; the determining comprises a transfer learning procedure. Data representing observations of the object is captured at an airborne unmanned aircraft system (UAS) operating at an altitude between that of the ground-level observations and the aerial observations. The method also comprises dynamically adjusting the third model in accordance with the operating altitude of the UAS; updating the adjusted third model in accordance with the data; and determining a 3D representation of the structure of the object, based on the updated adjusted third model. Other embodiments are disclosed.

Abstract: A wireless communication device includes a processor, a display device coupled to the processor, and a memory accessible to the processor. The memory includes instructions executable by the processor to perform operations. The operations include receiving a user input to play a media content item. The operations include sending a request for a summary of the media content item to a network device in response to the user input in lieu of sending a particular request for the media content item. The operations also include, in response to the reply including media content, sending the media content to the display device for display.

Abstract: Concepts and technologies disclosed herein are directed to image prediction. According to one aspect disclosed herein, an image prediction system can receive a training data set that includes a plurality of training images. The image prediction system can define N-dimensional feature vectors corresponding to the plurality of training images in the training data set, parameterize the N-dimensional feature vectors to obtain a plurality of parameterized curves corresponding the plurality of training images in the training data set, obtain a square root velocity representation for each parameterized curve of the plurality of parameterized curves, rescale the plurality of parameterized curves to remove scaling variability among the plurality of parameterized curves, define a pre-shape space for the plurality of parameterized curves, and obtain shape space points pertaining to each parameterized curve of the plurality of parameterized curves on a shape space that inherits a structure from the pre-shape space.

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: Aspects of the subject disclosure may include, for example, a method for determining a first set of features in first images of first media content, generating a similarity score by processing the first set of features with a favorability model derived by identifying generative features and discriminative features of second media content that is favored by a viewer, and providing the similarity score to a network for predicting a response by the viewer to the first media content. Other embodiments are disclosed.

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.

Abstract: Aspects of the subject disclosure may include, for example, a method comprising obtaining, by a processing system including a processor, first and second models for a structure of an object, based respectively on ground-level and aerial observations of the object. Model parameters are determined for a three-dimensional (3D) third model of the object based on the first and second models; the determining comprises a transfer learning procedure. Data representing observations of the object is captured at an airborne unmanned aircraft system (UAS) operating at an altitude between that of the ground-level observations and the aerial observations. The method also comprises dynamically adjusting the third model in accordance with the operating altitude of the UAS; updating the adjusted third model in accordance with the data; and determining a 3D representation of the structure of the object, based on the updated adjusted third model. Other embodiments are disclosed.

Abstract: Concepts and technologies disclosed herein are directed to image prediction. According to one aspect disclosed herein, an image prediction system can receive a training data set that includes a plurality of training images. The image prediction system can define N-dimensional feature vectors corresponding to the plurality of training images in the training data set, parameterize the N-dimensional feature vectors to obtain a plurality of parameterized curves corresponding the plurality of training images in the training data set, obtain a square root velocity representation for each parameterized curve of the plurality of parameterized curves, rescale the plurality of parameterized curves to remove scaling variability among the plurality of parameterized curves, define a pre-shape space for the plurality of parameterized curves, and obtain shape space points pertaining to each parameterized curve of the plurality of parameterized curves on a shape space that inherits a structure from the pre-shape space.

Abstract: Aspects of the subject disclosure may include, for example, a system and method for selecting media content for a group of persons located at a venue. The system and method identify a plurality of viewers in a vicinity of a venue that has one or more display devices from location data and extracts a content viewing preference of each viewer from their profile data. The process includes aggregating the content viewing preference of each of the plurality of viewers to generate an aggregated content profile. Based on the aggregated content profile, a list of content is generated. Next, a first group of viewers approaching a viewing range of a display device are sensed by imaging data. Any conflicts between content viewing preferences of the first group of viewers are detected and resolved based on weighting the viewing preferences of the group. Other embodiments are disclosed.

Abstract: A method includes receiving, at a network device, a request from a device for a summary of media content. The method includes accessing the media content in response to the request and performing an analysis of the media content based on a user profile associated with the device. The user profile indicates preferences, and the analysis determines whether the media content includes a feature indicated by a preference of the preferences. In response to the analysis indicating that the media content includes the feature, the method includes selecting representative frames of the media content based on the analysis and sending the representative frames to the device.

Abstract: A system that incorporates the subject disclosure may perform operations, for example, including determining a first correlation value between a behavior of a first audience member and a subject matter being presented at a display processor, wherein the subject matter being presented at the display is observable by the first audience member. A second correlation value is determined between the behavior of the first audience member and a characteristic of an environment in a vicinity of the display and the first audience member. Responsive to the second correlation value exceeding the first correlation value, a determination is made as to whether a present channel selection resulting from user input initiated by the first audience member is responsive to the characteristic of the environment influencing the audience member more than the subject matter being presented at the display. Other embodiments are disclosed.

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: In one example, the present disclosure describes a device, computer-readable medium, and method for performing event monitoring in an unconstrained manner using a network of drones. For instance, in one example, a first video and a second video are obtained. The first video is captured by a first drone monitoring a first field of view of a scene, while the second video is captured by a second drone monitoring a second field of view of the scene. Both the first video and the second video are unlabeled. A deep learning technique is applied to the first video and the second video to learn a model of the scene. The model identifies a baseline for the scene, and the deep learning technique is unsupervised. The model is stored.

Abstract: Aspects of the subject disclosure may include, for example, a method for determining a first set of features in first images of first media content, generating a similarity score by processing the first set of features with a favorability model derived by identifying generative features and discriminative features of second media content that is favored by a viewer, and providing the similarity score to a network for predicting a response by the viewer to the first media content. Other embodiments are disclosed.

Abstract: A processor may generate initial subspaces by reducing data of a plurality of domains to a same dimensionality, cluster data within each initial subspace into clusters, create an input data matrix by projecting data from each cluster to an associated one of a plurality of generative subspaces associated with the plurality of clusters, perform a parallel transport between first and second sets of generative subspaces to identify a set of directions between pairs of subspaces of the first and second sets, sample points comprising a plurality of intermediate representations along the directions via an exponential mapping, create an expanded set of prior information by projecting the data from the first and second sets to the plurality of intermediate representations, perform a sparse coding with the input data matrix and the expanded set of prior information, and perform a cross domain inference using the output of the sparse coding.

Abstract: A method includes accessing information identifying user devices, and accessing media consumption data indicating content requests corresponding to the user devices. The method also includes determining expected locations of the user devices based on the information, and determining forecasted content requests based on the media consumption data. The method further includes generating a pre-caching schedule based on the forecasted content requests and the expected locations, and determining that the pre-caching schedule indicates that particular media data is to be provided via a multicast transmission to a first user device and to a second user device. The method also includes sending an instruction to the first user device directing the first user device to store the particular media data. The method includes sending, responsive to a request from the second user device, the particular media data via the multicast transmission to the first user device and to the second user device.