Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of media items, each including an object having a plurality of variables, performing adaptive analysis and transfer analysis on the media items, generating a model based on both the adaptive analysis and the transfer analysis and recognizing, using the model, the object in media content. The model may also provide a description of at least one of the variables of the object in the media content, and/or how that variable relates to the object. The variables may be known in the media items and unknown in the media content. The adaptive analysis and/or the transfer analysis may be performed iteratively and/or simultaneously. The variables may include an intensity of the object, a viewpoint from which the object is observed, an illumination of the object, a blur of the object, and/or other variables. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, generating narrative descriptions corresponding to visual features, visual events, and interactions there between for media content, where the narrative descriptions are associated with time stamps of the media content, and presenting the media content and an audio reproduction of the narrative descriptions, wherein the audio reproduction is synchronized to video of the media content according to the time stamps. Other embodiments are disclosed.

Abstract: A method includes receiving media data corresponding to a media content item. The method includes analyzing the media data to determine characteristics of the media content item based on first visual information of the media content item. The method includes analyzing the characteristics of the media content item based on a media popularity data structure to generate a prediction of whether the media content item is likely to exceed a popularity threshold within a particular period of time. The method further includes determining a location to store the media data based on the prediction.

Abstract: Methods, computer-readable media, and devices are disclosed for providing a notification of an anomaly in a media content that is associated with an event type. For example, a method may include a processing system including at least one processor for detecting a first anomaly from a first media content, generating a first anomaly signature for the first anomaly, obtaining a notification of a first event, the notification including an event type, time information, and location information of the first event, correlating the first anomaly to the notification of the first event, and labeling the first anomaly signature with the event type. The processing system may further detect a second anomaly from a second media content that matches the first anomaly signature and transmit a notification of a second event of the event type when it is detected that the second anomaly matches the first anomaly signature.

Abstract: Aspects of the subject disclosure may include, for example, a method including obtaining media content and an identification of a plurality of points of interest in the media content, receiving a request from a user to view the media content, obtaining information about the user, identifying one or more highlight and/or degrade points based on the information about the user, modifying the media content to create highlighted content, the highlighted content being the media content modified to attract attention of the user to the highlight point and drive the attention of the user away from the degrade point, presenting the highlighted content to the user, and monitoring the user's consumption of the content. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, modifying a user profile associated with a user associated with a content service to generate an updated user profile according to consumption of media content by the user and user feedback information associated with the consumption of the media content, determining a user context according to information associated with user device, where the user context includes current activity of the user, modifying a set of media content according to the user context that is determined to generate an updated set of media content, where a type of media content is eliminated from the set of media content in the updated set of media content according to the user context, and presenting the updated set of content at a presentation device of the user via a personal media channel of the user associated with the content service. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, embodiments for determining a media profile for media content. The media profile comprises a media metric for each of a plurality of portions of the media content. Further, embodiments include determining a price for advertising associated with the media content. In addition, embodiments include identifying an advertisement profile for each of a plurality of advertisements. The advertisement profile comprises an ad metric for each of a plurality of portions of an advertisement. Also, embodiments include selecting a first advertisement associated with a first advertisement profile from the plurality of advertisements according to the media profile, the first advertisement profile, and the price for the advertising associated with the media content. Further, embodiments include providing the first advertisement to be presented with the media content at a playback device. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, generating narrative descriptions corresponding to visual features, visual events, and interactions there between for media content, where the narrative descriptions are associated with time stamps of the media content, and presenting the media content and an audio reproduction of the narrative descriptions, wherein the audio reproduction is synchronized to video of the media content according to the time stamps. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, embodiments include receiving training data comprising historical states for network resources of a communication network. Further embodiments include generating a Riemannian geometry of the communication network according to the historical states for network resources. Each network resource is one of a vertex or an edge within the first Riemannian geometry. Additional embodiments include determining paths along the Riemannian geometry. Each path comprises at least one network resource. Also, embodiments include determining a velocity vector of each path according to at least one historical state. Embodiments include identifying a group of velocity vectors that generate a sub-geometry within the Riemannian geometry and provisioning a portion of network resources according to the group of velocity vectors and the sub-geometry. Other embodiments are disclosed.

Abstract: A method includes analyzing media data to determine characteristics of a media content item corresponding to the media data based on first visual information of the media content item. The method includes analyzing the characteristics of the media content item based on a media popularity data structure to generate a prediction of whether the media content item is likely to exceed a popularity threshold within a particular period of time. The method further includes generating a suggested content change to the media content item in response to the prediction indicating that the media content item is not likely to exceed the popularity threshold within the particular period of time.

Abstract: A method, computer-readable medium, and apparatus for modeling data of a plurality of services for providing a new service are disclosed. For example, a method may include a processor for generating a first policy from a first service by a first policy model using machine learning for processing first data of the first service, generating a second policy from a second service by a second policy model using machine learning for processing second data of the second service, wherein the first service and the second service are different, and implementing one or more functions for a new service using the first policy and the second policy.

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: 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 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: 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: 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: In one example, the present disclosure describes a device, computer-readable medium, and method for adaptively clustering media content from multiple different domains in the presence of domain shift. For instance, in one example, a plurality of data content items is acquired from a plurality of different domains, wherein at least some data content items of the plurality of data content items are unlabeled. The plurality of data content items is encoded with a feature representing a domain shift variation that is assumed to be present in the plurality of data content items, wherein the domain shift variation comprises variation in a characteristic of the plurality of data content items. The plurality of data content items is clustered into a predefined number of content categories subsequent to the encoding.