Abstract: A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.

Abstract: Approaches for optimizing network demand forecasting models and network topology using hyperparameter selection are provided. An approach includes defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature. The approach also includes generating, using a computer device, an optimal model using a subset of features selected from the pool of features. The approach further includes predicting future demand on a network using the optimal model. The approach additionally includes allocating resources in the network based on the predicted future demand.

Abstract: A price for use of a digital asset in a set of digital assets is determined. The set of digital assets stored in a digital asset repository. A time slot during which the digital asset is available for use is determined. The digital asset is leased out at the price and during the time slot, the leasing allowing use of the digital asset during the time slot in return for payment of the price. the leased digital asset is integrated with a set of base characteristics of a virtualized user. The integrated leased digital asset is presented in a virtual environment during the time slot.

Abstract: An embodiment includes accessing captured video of a first physical object in a physical environment. The embodiment also includes detecting a feature of the first physical object in a first frame of the video. The embodiment encodes a first qubit with a first quantum state based on a first value of the feature of the first physical object, and then entangles the first qubit with a second qubit forming an entangled qubit pair. The embodiment detects a second quantum state of the second qubit of the entangled qubit pair and renders computer-generated content of a virtual environment that includes a computer-generated first virtual object that corresponds to the first physical object, where the rendering comprises determining a property of the first virtual object based on the detected second quantum state.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: determining a current transportation mode of a user with use of data of a user passenger vehicle associated to the user and data of a user auxiliary passenger vehicle associated to the user, wherein the user passenger vehicle is capable of carrying the user auxiliary vehicle, and wherein the user auxiliary vehicle is configured to be hand carried by the user; evaluating a current route of the user in dependence on the current transportation mode of the user as determined by the determining; and providing one or more output in dependence on the evaluating.

Abstract: Simulating uncertainty in an artificial neural network is provided. Aleatoric uncertainty is simulated to measure what the artificial neural network does not understand from sensor data received from an object operating in a real-world environment by adding random values to edge weights between nodes in the artificial neural network during backpropagation of output data of the artificial neural network and measuring impact on the output data by the added random values to the edge weights between the nodes. Epistemic uncertainty is simulated to measure what the artificial neural network does not know by dropping out a selected node from each respective layer of the artificial neural network during forward propagation of the sensor data and measuring impact of dropped out nodes on the output data of the artificial neural network. An action corresponding to the object is performed based on the impact of simulating the aleatoric and epistemic uncertainty.

Abstract: Methods, computer program products, and systems are presented. The methods can include, for instance: generating a plurality of deep transfer learning networks. Further, the methods can include, for instance: encoding one or more transfer layers.

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: Provided are a computer program product, system, and method for training and using a machine learning module to determine locales and augmented reality representations of information on locales to render in an augmented reality display. A determination is made of qualifying locales accessible to a visiting user during a user available time from a current location. Values for locale features of the qualifying locales and values of user features in a user profile of the visiting user are provided as input to a locale attraction machine learning module to determine local attraction scores for the qualifying locales. A determination is made of qualifying locales based on locale attraction scores of the qualifying locales to transmit to the visiting user computing device to cause an augmented reality display to render augmented reality representations of information on the qualifying locales.

Abstract: A processor may analyze, using an AI system, an application, where the application includes one or more application modules. The processor may determine, using the AI system, that an application module is critical based on a contextual scenario. The AI system may be trained utilizing data regarding heat generation of hardware on which the application module is operating. The processor may identify, using the AI system, required resources of the hardware for the application module to function during the contextual scenario. The processor may allocate an availability of the required resources for the application module.

Abstract: A server for regulating power from a Power-over-Ethernet switch to a plurality of vision sensors includes the following operations. An evaluation of individual contributions that each of the plurality of vision sensors makes to operational efficiency of the vision system is performed. Information indicative of a degradation condition to the plurality of vision sensors is received from the plurality of vision sensors. An amount of power needed to be supplied from the switch to alleviate the degradation condition of the plurality of vision sensors is estimated based upon the information indicative of the degradation condition. A determination is made that a total amount of power supplied from the switch exceeds an available amount of power capable of being supplied from the switch. Power from the switch to the plurality of vision sensors is individually regulated in real-time using a prioritization determined according to the evaluation.

Abstract: Using a digital twin model of a user, a digital twin model of a geographical location, a digital twin model of an event venue located at the geographical location, and a plurality of digital twin models of clothing items, a product recommendation customized to the user and a planned event is generated, the planned event planned to occur at the event venue. A product recommendation depiction is generated, the product recommendation depiction comprising a depiction of the product recommendation being worn by the user at the planned event. An answer to a natural language query regarding the product recommendation depiction is generated.

Abstract: A first plurality of machine learning operations are performed on Internet of Things (IoT) input data in an IoT ecosystem. The first plurality of machine learning operations are performed using a first machine learning level. One or more first machine learning outputs are received from the first machine learning level. A second plurality of machine learning operations are executed on the one or more first machine learning outputs. The second plurality of machine learning operations are executed using a second machine learning level. One or more second machine learning outputs are obtained from the second machine learning level. A third plurality of machine learning operations run on the one or more second machine learning outputs. The third plurality of machine learning operations run using a third machine learning level. An IoT output is identified from the third machine learning level.

Abstract: Using a sentiment analysis model, a reaction to a presentation of a first content is measured. Using the first content and the reaction, a content generation parameter is determined. Using a first content generation model and the content generation parameter, second content corresponding to the first content is generated. Using a second content generation model and the content generation parameter, third content corresponding to the first content is generated. The first content is adjusted by combining the first content with the second content and the third content, the adjusting resulting in an adjusted content.

Abstract: The present invention includes an embodiment that may determine an access level within an organization. The embodiment may generate a simulated scenario based on the access level. The embodiment may identify responses of the user to the generated simulated scenario. The embodiment may capture one or more input frames. The embodiment may analyze the responses and the one or more input frames and generate education for the user based on the responses and the one or more input frames.

Abstract: A computer-implemented method for detecting a change in a meta-environment is disclosed. The method comprises collecting historic parameter values from at least one actor being active in the meta-environment, wherein each of the at least one actors has a set of abilities represented by parameters relating to the collected parameter values, collecting current parameter values from the at least one actor being active in the meta-environment, determining weight values of the collected historic parameters and weight values of the collected current parameters, and upon a value of a function of the determined weight values of the historic parameters and the weight values of the collected current parameters being greater than a predefined threshold value, determining a detection of the change in the meta-environment.

Abstract: Synchronizing a sensor network and an ontology by analyzing outputs of a set of sensors, analyzing relationships of an ontology, mapping the outputs to the relationships, identifying a correlation among the outputs, and modifying the ontology according to the correlation.

Abstract: A computer-implemented method, a computer system and a computer program product refine service inquiries through an understanding of user context. The method includes receiving a service inquiry from a user on a computing device. The method also includes capturing movement data within a user environment. The movement data is selected from a group consisting of audio data, video data and telemetry data extracted from a second device in the user environment. The method further includes determining a user context by correlating motion and position of the user with respect to the user environment. In addition, the method includes generating a set of predicted modifications to the service inquiry based on the user context. Lastly, the method includes displaying the set of predicted modifications to the service inquiry on the computing device.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: generating a plurality of deep transfer learning networks including a source deep transfer learning network for a source domain and a target deep transfer learning network for a target domain. Transfer layers of the source deep transfer learning network are encoded to a chromosome, diversified, and integrated with the target deep transfer learning network and the target deep transfer learning network passing a predefined fitness threshold condition is produced.

Abstract: A mechanism is provided in a data processing system for question answering using time weighted evidence. The mechanism receives an input question. The mechanism determines a time focus for the input question and defines a weighting function. The weighting function is a bell curve having a peak at the time focus on a time axis. The mechanism decomposes the input question into one or more queries and applies the one or more queries to a corpus of information to obtain a set of hypothesis evidence. Each item of information within the hypothesis evidence has an associated time value. The mechanism weights the set of hypothesis evidence based on the associated time values according to the weighting function to form time weighted evidence and generates hypotheses for answering the input question based on the time weighted evidence.