Abstract: A method includes: determining, by the computing device, an activity associated with a physical object in a physical environment, the physical object being a physical object about which a user lacks knowledge; retrieving, by the computing device and from a digital library, a digital object that is associated with the activity; personalizing, by the computing device, the digital object, the personalizing being based on feedback from prior interactions of the user with the digital object; generating, by the computing device, an augmented reality activity including the determined activity, the augmented reality activity comprising a reactive association between the personalized digital object and the physical object; and generating, by the computing device, an augmented reality animation that comprises the augmented reality activity, the physical object, and the personalized digital object.

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: Using a classical data model executing on a classical processor, a set of classical features is scored. A score of a classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving a resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The quantum data model is executed a number of times previously determined using a set of results of executing the quantum data model on a set of annotated training data. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.

Abstract: Using a classical data model executing on a classical processor, a set of classical features is scored. A classical feature comprises a first attribute of a resource, and a score of the classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving the resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.

Abstract: A computer-implemented method and system for autonomic cloud to edge compute allocations in a sports fantasy league trade transaction. The computer implemented method includes, in one embodiment, calculating, based on an expected return associated with each player on each of a plurality of teams in a fantasy sports league, a valuation for each player. An embodiment includes determining a positional need for each of the plurality of teams in the fantasy sports league, and determining, based on the valuation for each player and the positional need for each team, one or more players to include in a trade with another team of the plurality of teams.

Abstract: The disclosure includes using dilation of speech content from a separated audio input for speech recognition. An audio input from a speaker and predicted changes for the audio input based on an external noise are received at a CNN (Convolutional Neural Network). In the CNN, diarization is applied to the audio input to predict how a dilation of speech content from the speaker changes the audio input to generate a CNN output. A resulting dilation is determined from the CNN output. A word error rate is determined for the dilated CNN output to determine an accuracy for speech to text outputs. An adjustment parameter is set to change a range of the dilation based on the word error rate, and the resulting dilation of the CNN output is adjusted based on the adjustment parameter to reduce the word error rate.

Abstract: A method, apparatus, computer system, and computer program product for managing a device. The method detects, by a computer system, a physical handling of the device to form a physical handling pattern for the device. The method determines, by the computer system, a baseline physical handling pattern for the device, wherein the baseline physical handling pattern for the device meets a set of handling metrics for the device. The method initiates, by the computer system, a set of actions in response to the physical handling pattern for the device deviating from the baseline physical handling pattern for the device.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: identifying a training data set and defining a window for an initial beta value representing bias tolerated in formulating expectation conditional to each feature vector from the training data set. The conditional expectations are parallelly regularized by use of DNA computer. Amongst numerous combinations of candidate models, a best fit ensemble is produced as the machine learning model for predicting targeted outcomes based on inputs other than the training data set.

Abstract: A method includes: determining, by the computing device, an activity associated with a physical object in a physical environment, the physical object being a physical object about which a user lacks knowledge; retrieving, by the computing device and from a digital library, a digital object that is associated with the activity; personalizing, by the computing device, the digital object, the personalizing being based on feedback from prior interactions of the user with the digital object; generating, by the computing device, an augmented reality activity including the determined activity, the augmented reality activity comprising a reactive association between the personalized digital object and the physical object; and generating, by the computing device, an augmented reality animation that comprises the augmented reality activity, the physical object, and the personalized digital object.

Abstract: A computer-implemented method and system for autonomic cloud to edge compute allocations in a sports fantasy league trade transaction. The computer implemented method includes, in one embodiment, calculating, based on an expected return associated with each player on each of a plurality of teams in a fantasy sports league, a valuation for each player. An embodiment includes determining a positional need for each of the plurality of teams in the fantasy sports league, and determining, based on the valuation for each player and the positional need for each team, one or more players to include in a trade with another team of the plurality of teams.

Abstract: Techniques for padding audiovisual clips (for example, audiovisual clips of sporting events) for the purpose of causing the clip to have a predetermined duration so that the padded clip can be evaluated for viewer interest by a machine learning (ML) algorithm. The unpadded clip is padded with audiovisual segment(s) that will cause the padded clip to have a level of viewer interest that it would have if the unpadded clip had been longer. In some embodiments the padded segments are synthetic images generated by a generative adversarial network such that the synthetic images would have the same level of viewer interest (as adjudged by an ML algorithm) as if the unpadded clip had been shot to be longer.

Abstract: An approach for altering alter training data and training process associated with a neural network to emulate environmental noise and operational instrument error by using the concepts of shots to sample within a squeezed space model, wherein shots are an uncertainty index that is the average of all shots from a sampling, is disclosed. The approach leverages a squeeze theorem to create a squeezed space model based on the regression of the upper and lower bound associated with the environmental noise and instrument error. The approach calculates an average noise index based on the squeezed space model, wherein the index is used to alter the training data and process.

Abstract: A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.

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: A method, a structure, and a computer system for tangible mixed reality. Exemplary embodiments may include identifying one or more real objects within an environment and identifying one or more interactions between the one or more real objects, one or more reactive objects, and one or more animations therebetween. The exemplary embodiments may further include generating one or more learning activities for a user of a mixed reality headset that include at least one of the one or more interactions, and deploying the one or more learning activities to the mixed reality headset.

Abstract: The disclosure includes using dilation of speech content from an interlaced audio input for speech recognition. A learning model is initiated to determine dilation parameters for each of a plurality of audible sounds of speech content from a plurality of speakers received at a computer as an audio input. As part of the learning model, a change of each of a plurality of independent sounds is determined in response to an audio stimulus, the independent sounds being derived from the audio input. The disclosure applies the dilation parameters, respectively, based on the change of each of the independent sounds. A voice print is constructed for each of the speakers based on the independent sounds and the dilation parameters, respectively. Speech content is attributed to each of the plurality of speakers based at least in part on the voice print, respectively, and the independent sounds.

Abstract: Distributing computation workload among computing nodes of differing computing paradigms is provided. Compute gravity of each computing node in a cloud computing paradigm and each computing node in a client network computing paradigm within an Internet of Systems is calculated. Each component part of an algorithm is distributed to an appropriate computing node of the cloud computing paradigm and client network computing paradigm based on calculated compute gravity of each respective computing node within the Internet of Systems. Computation workload of each component part of the algorithm is distributed to a respective computing node of the cloud computing paradigm and the client network computing paradigm having a corresponding component part of the algorithm for processing.

Abstract: A method includes: determining an amount of available storage in a user mobile device; predicting an amount of storage in the device that will be required for a future operation of the device; identifying an amount of data stored on the device that has not been previously backed up to an external storage device that is external to the device; backing up to an external backup device, a portion of the data that has not been previously backed up, the external backup device being external to the device; and deleting from the device the data that is backed up to the external backup device. A sum of an amount of the data deleted and the amount of available storage in the device is greater than the predicted amount of storage, and the backing up is performed after the predicting and automatically while the device is connected to a network.

Abstract: An embodiment for cognitively enhancing a search query is provided. The embodiment may include receiving a voice query from a user. The embodiment may also include analyzing the voice query. The embodiment may further include identifying an object within a focus area of the user based on the voice query. The embodiment may also include determining whether the identification of the object is confident, and in response to determining the identification of the object is not confident, receiving feedback from the user. In response to determining the identification of the object is confident, the embodiment may further include generating a relationship between a word in the voice query and the identified object. The embodiment may also include delivering an enhanced response to the user based on the identified object and the received feedback.

Abstract: A live event virtual experience is provided. Emotions of virtual spectators to a current situation occurring in a live event at a physical venue are determined from received data regarding reactions of the virtual spectators to the live event using bias detection. A historical sound clip that matches the emotions of the virtual spectators to the current situation occurring in the live event is retrieved. The historical sound clip that matches the emotions of the virtual spectators to the current situation occurring in the live event is input into a machine learning model that performs inverse bias mitigation to amplify bias and applies in-process adversarial fairness debiasing. The historical sound clip after performing the inverse bias mitigation to amplify the bias and applying the in-process adversarial fairness debiasing is converted to a standardized historical sound segment length. A sound representation is generated from the standardized historical sound segment length.