Abstract: According to a first aspect of the present invention, there is provided a computer implemented method, a computer system and a computer program product, including training a set of exploitation models, training a set of exploration models, generating a combined exploitation and exploration heat map, and inputting the combined exploitation and exploration heat map into a convoluted neural network.

Abstract: The present invention may include a computing device receives a location data from a client device, wherein the client device comprises one or more location sensors to generate the location data. The computing device determines an altitude of the client device above a floor from the location data. The computing device determines an age and a height of a user using a trained neural network from the client device and the location data and assigns a wireless network channel to the client device based on the age and the height of the user.

Abstract: A computer-implemented method, system and computer program product for embedding contextual information in an image or video frames. A generative adversarial network (GAN) is trained to provide contextual information to be embedded in an image or video frames, where the contextual information includes text, sound and/or video frames that provides context to the image or video frames. After training the GAN, an image or video frames are received to be embedded with contextual information if necessary. Features are then extracted from the received image/video frames. An image(s) or video frame(s) are identified in a database using the GAN associated with features with a similarity to the extracted features of the received image/video frames that exceeds a threshold value. Such identified images and/or video frames are associated with “references” containing contextual information which are extracted. The received image/video frames are then augmented with the extracted references to provide context.

Abstract: A method and a system for dataset evaluation are provided. The method can include acquiring a new dataset for integration with an existing dataset. The existing dataset is used to train a machine learning model. The method can also include calculating a baseline of variation for the existing dataset. The baseline of variation can be determined by evaluating various characteristics of the existing dataset. The method can further include determining an output value for the new dataset by also evaluating characteristics of the new dataset. The method can also include comparing the output value to the baseline of variation to generate a variance between the output value and the baseline of variation. The method can further include determining whether the variance is within an acceptable range of the baseline of variation.

Abstract: An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.

Abstract: A system for executing a video summary is provided. One or more video segments for a video based on one or more digital media is generated. A video summary is generated based on a user request.

Abstract: A method, system, and computer program product for context-based machine learning model generation are provided. The method collects ground data for a set of machine learning model deployments associated with a set of problems. A knowledge graph is generated for the set of machine learning models based on the ground data. An initial set of hyperparameters are determined for a new problem based on the knowledge graph. A modified set of hyperparameters are generated for the new problem based on the initial set of hyperparameters. The method generates a machine learning model for the new problem based on the modified set of hyperparameters.

Abstract: According to one embodiment, a method, computer system, and computer program product for customizing a mixed reality experience based on the experiential state of users in a queue to join the mixed reality experience or immersed in the mixed reality experience is provided. The present invention may include modeling the experiential state of the at least one user participating in the mixed-reality experience; modeling one or more relationships between the experiential state of the at least one user and one or more physical or virtual experience parameters comprising the mixed-reality experience; based on the one or more modeled relationships, predicting one or more alterations to the one or more physical or virtual experience parameters to enhance the experiential state of the at least one user; and operating a mixed reality system to perform one or more remedial actions to execute the one or more predicted alterations.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: building, by one or more DNA processor, a DNA strand corresponding to a conditional expectation. The methods include, for instance: obtaining, by one or more DNA processor, a conditional expectation having a regularization metric.

Abstract: A computer captures a voice of a user. The computer determines a frequency spectrum and a voice pattern of the voice. The computer identifies one or more topics of the voice by transcribing the voice by a natural language processing. The computer identifies the user based on matching the frequency spectrum of the voice to the frequency spectrum of the conversation and the pattern of the voice to the pattern of the conversation when a conversation is intercepted and determines a trust score based on comparing the one or more topics to the one or more topics extracted from the conversation.

Abstract: Using a first trained residual neural network, an event analysis is generated from a set of event description encodings, an event description encoding in the set of event description encodings encoding a natural language description of an event, the event analysis comprising a first set of coordinates in an event space. Using a second trained residual neural network, the event analysis and a user description encoding, a personalization analysis is generated, the user description encoding a user interaction with the event, the personalization analysis comprising a component of a second set of coordinates in the event space. Using a combination of the event analysis and the personalization analysis, a first event component is activated, the first event component comprising a component of the event.

Abstract: A processor may monitor frequency data related to a user metric of a user during a measurement window. The user metric may relate to the user's use of a computer implemented environment. The processor may simplify the frequency data related to the user metric, resulting in a set of simplified frequency data. The processor may input the set of simplified frequency data into a neural network. The neural network may determine a likelihood of a negative user experience for the user. The processor may alter a parameter of the first user environment based on the likelihood.

Abstract: A computer-implemented method includes: presenting, by a computing device, a virtual event user interface to an attendee of a virtual event; obtaining, by the computing device, data about actions performed by the attendee within the virtual event; creating, by the computing device, a tensile sphere kernel that is specific to the attendee using the obtained data; determining, by the computing device, an event recommendation of the virtual event using the tensile sphere kernel; and presenting, by the computing device, the event recommendation to the attendee by changing the virtual event user interface to show the event recommendation.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: ascertaining a user state of a user based on user state data for a cognitive menu selection service from an eatery by use of an electronic menu system. Selection criteria to request one or more items from menus of the eatery is determined based on the user state. A request including the selection criteria is sent and the one or more items corresponding to the selection criteria is obtained and presented to the user by use of a customized virtual reality interface on a personal device of the user.

Abstract: Using a model executing on a classical processor, a set of classical features is scored. The scored set of classical features is divided into a set of feature groups, a number of classical features in a group determined according to a qubit capability of a quantum processor. Using a model executing on the quantum processor and a group of the scored set of classical features, a set of quantum features is scored. The score of a quantum feature is adjusted according to an accuracy of the quantum data model. The scored set of classical features and the scored set of quantum features are combined according to a measure of differences between the scored set of classical features and the scored set of quantum features. Using the combined set of scored features and a first set of input data of a resource, a valuation of a resource is calculated.

Abstract: An approach is provided in which the approach receives a request to upload a file at a server system that includes metadata encoded with a non-invertible key. The metadata includes contact information corresponding to an owner of the file. The approach establishes both a photon channel and a classical channel between the server system and a client system, which are both secured using one or more shared secret keys. The approach interfaces with the client system over the photon channel and the classical channel to decode the contact information at the server system, and sends an upload request from the server system to the owner of the file using the contact information. The approach authorizes the upload request at the server system in response to receiving an upload approval from the owner.

Abstract: Using a logistic regression classification model executing on a processor, a topic is classified into an interaction type in a set of predefined interaction types. A set of documents corresponding to the topic is extracted from a document repository. Using a generative adversarial model executing on a processor, a sentiment corresponding to a reaction to a previous presentation is scored, the scoring resulting in a scored sentiment. Using a trained attention layer model, the interaction type, the set of documents, and the scored sentiment are weighted, the weighting generating a weighted interaction type, a weighted set of documents, and a weighted scored sentiment. Using a natural language generation transformer model executing on the processor, a document in the weighted set of documents is weighted according to the weighted interaction type and the weighted scored sentiment.

Abstract: In an embodiment, a correlated forecast is computer generated by a processor that receives input data for historic values of a first input variable, creates forecast data for future values of the first input variable using the historic values of the first input variable, generates diagnostic data based on a diagnostic analysis of the forecast data, creates a first diagnostic variable that includes a first diagnostic value from a first cognitive process, generates a feature vector based on a second cognitive process that determines the feature vector by identifying a correlation between the first diagnostic variable and a second diagnostic variable, and generates a final forecast using the feature vector as an input for a cognitive forecasting process, where the first cognitive process determines the first diagnostic value based on the diagnostic data.

Abstract: In an approach for derivation of progressively variant dark data utility for legacy system candidate microservices, a processor analyzes a variability of data stored in a legacy system for a plurality of data metrics. A processor measures, for each data metric, a utility of the data using an intra-analysis and a meta-analysis. A processor correlates the plurality of data metrics to candidate microservices. A processor generates insights for the candidate microservices based on the utility of the data.

Abstract: Edge function-guided artificial intelligence (AI) request routing is provided by applying a machine learning model to predictors of cloud endpoint hydration to determine hydration levels of cloud endpoints, of a hybrid cloud environment, that provide AI processing, determining, for each edge component of a plurality of edge components of the hybrid cloud environment and each cloud endpoint of the cloud endpoints, alternative flow paths between the edge component and the cloud endpoint, the alternative flow paths being differing routes for routing data between the edge component and the cloud endpoint, and the alternative flow paths being of varying flow rates determined based on the hydration levels of the cloud endpoints, and dynamically deploying edge functions on edge component(s), the edge functions configuring the edge component(s) to alternate among the alternative flow paths available in routing AI processing requests from the edge component(s) to target cloud endpoints of the cloud endpoints.