Abstract: In one example, a method performed by a processing system including at least one processor includes acquiring a first item of media content from a user, where the first item of media content depicts a subject, acquiring a second item of media content, where the second item of media content depicts the subject, compositing the first item of media content and the second item of media content to create, within a metaverse of immersive content, an item of immersive content that depicts the subject, presenting the item of immersive content on a device operated by the user, and adapting the presenting of the item of immersive content in response to a choice made by the user.

Abstract: Aspects of the subject disclosure may include, for example, training a decision scoring model for an e-sport based on historical data for the e-sport. In various embodiments, the decision scoring model may be trained based on the historical data and on metadata associated with the e-sport. Some embodiments can include identifying a plurality of candidate in-game decision sequences based on decision parameters for a gameplay decision of an ongoing gaming session and a gaming session history for the ongoing gaming session. Various embodiments can include applying the decision scoring model to rank the plurality of candidate in-game decision sequences. Other embodiments are disclosed.

Abstract: An example method includes obtaining a plurality of candidate media segments for possible inclusion in a single stream of media segments that is personalized for a first user, wherein at least one candidate media segment of the plurality of candidate media segments comprises an excerpt from a media asset, selecting, based on a known media consumption behavior of the first user, a subset of the plurality of candidate media segments, wherein the subset includes candidate media segments of the plurality of candidate media segments that are to be included in the single stream of media segments, modifying at least one candidate media segment in the subset based on the known media consumption behavior of the first user, and compiling the subset into the single stream of media segments, wherein the single stream of media segments includes the at least one candidate media segment in the subset that was modified.

Abstract: A system for embedding users' preferences and behaviors based on interactions with an array of devices.

Abstract: In one example, a method performed by a processing system including at least one processor includes acquiring a first item of media content from a user, where the first item of media content depicts a subject, acquiring a second item of media content, where the second item of media content depicts the subject, compositing the first item of media content and the second item of media content to create, within a metaverse of immersive content, an item of immersive content that depicts the subject, presenting the item of immersive content on a device operated by the user, and adapting the presenting of the item of immersive content in response to a choice made by the user.

Abstract: A method performed by a processing system including at least one processor includes obtaining an output of a machine learning algorithm, identifying a vulnerability in the output of the machine learning algorithm, wherein the vulnerability relates to a bias in the output, integrating auxiliary data from an auxiliary data source of a plurality of auxiliary data sources into the machine learning algorithm to try to compensate for the vulnerability, determining whether the integrating has compensated for the vulnerability, and generating a runtime output using the machine learning algorithm when the processing system determines that the integrating has compensated for the vulnerability.

Abstract: In one example, a method includes identifying a target performance metric of a machine learning algorithm, wherein the target performance metric is to be improved, obtaining a set of auxiliary data from a plurality of auxiliary data sources, wherein the plurality of auxiliary data sources is separate from a training data set used to train the machine learning algorithm, selecting a candidate attribute type from the set of auxiliary data, identifying a quality metric for the candidate attribute type, calculating a change in the target performance metric when data values associated with the candidate attribute type are included in the training data set, determining that a tradeoff between the target performance metric and the quality metric of the candidate attribute type is satisfied by inclusion of the data values in the training data set, and training the machine learning algorithm using the training data set augmented with the data value.

Abstract: Aspects of the subject disclosure may include, for example, training a decision scoring model for an e-sport based on historical data for the e-sport. In various embodiments, the decision scoring model may be trained based on the historical data and on metadata associated with the e-sport. Some embodiments can include identifying a plurality of candidate in-game decision sequences based on decision parameters for a gameplay decision of an ongoing gaming session and a gaming session history for the ongoing gaming session. Various embodiments can include applying the decision scoring model to rank the plurality of candidate in-game decision sequences. Other embodiments are disclosed.

Abstract: Techniques for knowledge discovery based on indirect inference of association are presented. A data management component (DMC) can determine and extract, in a structured format, entities, relationships between entities, and concepts relating thereto in documents, based on analysis of information in the documents and/or keywords relating to concepts, to generate an association inference model. Using artificial intelligence techniques, DMC can embed the entities and relationships to a common representation to generate and train a scoring model that can be used to evaluate and score similarity strength between entities, including entities that do not have a known relationship, and can predict or infer relationships, including indirect relationships, between entities or between concepts. In that regard, DMC or user can evaluate concept-level scores to determine a level of relationship between concepts. DMC can feedback information from the scoring model or evaluation to update the association inference model.

Abstract: An example method includes obtaining a plurality of candidate media segments for possible inclusion in a single stream of media segments that is personalized for a first user, wherein at least one candidate media segment of the plurality of candidate media segments comprises an excerpt from a media asset, selecting, based on a known media consumption behavior of the first user, a subset of the plurality of candidate media segments, wherein the subset includes candidate media segments of the plurality of candidate media segments that are to be included in the single stream of media segments, modifying at least one candidate media segment in the subset based on the known media consumption behavior of the first user, and compiling the subset into the single stream of media segments, wherein the single stream of media segments includes the at least one candidate media segment in the subset that was modified.

Abstract: A system for embedding users' preferences and behaviors based on interactions with an array of devices.

Abstract: Aspects of the subject disclosure may include, for example, a machine-readable medium that has recorded executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, including analyzing media content for video complexity and audio characteristics, thereby creating a content classification; determining a user context for a user observing the media content; determining a content manipulation based on the content classification, the user context, or a combination thereof; and presenting the media content by applying the content manipulation determined. Other embodiments are disclosed.

Abstract: Techniques for determining, modifying, and correcting data elements of documents, tables, and databases are presented. A data management component (DMC) can determine and extract entities of a group of entities, and relationships between entities, in documents, tables, and databases based on analysis of the entities and information relating thereto. DMC can determine a trained model representative of the entities and their relationships based on the relationships. For a subsequently received entity, DMC can predict a relationship between the subsequent entity and an entity of the entity group based on the model. DMC can determine candidate data modifications associated with the subsequent entity based on the relationship between the subsequent entity and the entity. DMC can rank the candidate data modifications based on probabilities that the candidate data modifications are a correct data modification, wherein data modification information relating to the ranking can be presented as an output.

Abstract: A method includes constructing an information graph based on a set of training data provided to a machine learning algorithm, identifying an area of the information graph in which to increase an inclusion of the information graph, wherein the inclusion comprises a consideration of a population that is underrepresented in the information graph, collecting, from an auxiliary data source, auxiliary data about the population for use in increasing the inclusion of the information graph, utilizing the auxiliary data to increase the inclusion of the information graph, to generate an updated information graph, using the updated information graph to generate a test output that incorporates information from the auxiliary data, generating, when the test output satisfies an inclusion criterion, a runtime output using the updated information graph, receiving user feedback regarding the runtime output, and determining, in response to the user feedback, whether to further increase inclusion of the runtime output.

Abstract: Aspects of the subject disclosure may include, for example, a machine-readable medium that has recorded executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, including analyzing media content for video complexity and audio characteristics, thereby creating a content classification; determining a user context for a user observing the media content; determining a content manipulation based on the content classification, the user context, or a combination thereof; and presenting the media content by applying the content manipulation determined. Other embodiments are disclosed.

Abstract: An example method includes recording a copy of an item of media content, comparing the copy of the item of media content to a stored fingerprint for the item of media content, determining that the copy of the item of media content is missing a portion of the item of media content, based on the comparing, retrieving the portion of the item of media content that is missing, incorporating the portion of the item of media content that is missing into the copy of the item of media content, and storing the copy of the item of media content, including the portion of the item of media content that is missing.

Abstract: An example method includes extracting a plurality of candidate content segments from a first item of media content, wherein the plurality of candidate content segments is extracted over a first window of time and a second window of time, determining, for a first candidate content segment of the plurality of candidate segments that is extracted during both the first window of time and the second window of time, that user interest in the first candidate content segment is increasing, and generating a single stream of content segments, where the single stream of content segments includes a subset of the plurality of candidate content segments including the first candidate content segment.

Abstract: Aspects of the subject disclosure may include, for example, a device that includes a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations such as collecting historical data on viewership of media content; gathering metadata of events in the media content; training a scoring model on the metadata and historical data, wherein the scoring model ranks placement choices and provide a score for each placement choice for advertisements within the media content; and evaluating a placement choice for ad completion rate, thereby generating an evaluation of the placement choice. Other embodiments are disclosed.

Abstract: An example method includes obtaining a plurality of candidate media segments for possible inclusion in a single stream of media segments that is personalized for a first user, wherein at least one candidate media segment of the plurality of candidate media segments comprises an excerpt from a media asset, selecting, based on a known media consumption behavior of the first user, a subset of the plurality of candidate media segments, wherein the subset includes candidate media segments of the plurality of candidate media segments that are to be included in the single stream of media segments, modifying at least one candidate media segment in the subset based on the known media consumption behavior of the first user, and compiling the subset into the single stream of media segments, wherein the single stream of media segments includes the at least one candidate media segment in the subset that was modified.

Abstract: Aspects of the subject disclosure may include, for example, a device that includes a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations such as collecting historical data on viewership of media content; gathering metadata of events in the media content; training a scoring model on the metadata and historical data, wherein the scoring model ranks placement choices and provide a score for each placement choice for advertisements within the media content; and evaluating a placement choice for ad completion rate, thereby generating an evaluation of the placement choice. Other embodiments are disclosed.