Abstract: Creating movement in a physical environment includes extracting, by computer hardware, digital features from interactions of a user with a digital environment that models a physical environment. Physical features are extracted by the computer hardware from sensor data generated from detected interactions of the user with the physical environment. A recommendation is generated by the computer hardware for the user within the physical environment based on a correlation of the digital features with the physical features. The recommendation is provided to the user. Providing the recommendation may include generating haptic signals by decoding, using a decoder stage executed by the computer hardware, the physical features and the recommendation, wherein the haptic signals specify a movement of the user within the physical environment in furtherance of the recommendation.

Abstract: An example operation may include one or more of executing queries on one or more external data stores to retrieve domain data of one or more ranked lists of assets and variables corresponding to the domain data, generating a plurality of feature groups based on the retrieved domain data and variables, wherein the plurality of feature groups correspond to a plurality of features used to generate the one or more ranked lists, converting the plurality of features groups into a plurality of values via execution of an optimization engine, transforming the plurality of values into a plurality of sentences describing the plurality of feature groups, and displaying the plurality of sentences via a user interface.

Abstract: A method, a structure, and a computer system for diverse natural language generation. The exemplary embodiments may include training a data-to-text neural network (D2T NN) and training a text-to-text neural network (T2T NN), wherein the D2T NN and the T2T NN have identical transformer architectures, and wherein the training of the D2T NN and the T2T NN are indexed by time. The exemplary embodiments may further include interleaving weights between the D2T NN and the T2T NN, as well as generating a sentence based on the interleaved D2T NN and T2T NN.

Abstract: A method for detecting conflation errors in an augmented reality interface is disclosed. In one embodiment, such a method includes receiving an actual screenshot of an augmented reality interface. The method generates, using a generative adversarial network (GAN), a replica screenshot of the augmented reality interface that indicates how the actual screenshot is expected to appear. The replica screenshot is non-identical to the actual screenshot. The method receives the actual screenshot and the replica screenshot into a fractal-based convolutional neural network (CNN) to determine a correlation between the actual screenshot and the replica screenshot. In the event the correlation is below a designated threshold, the method flags the actual screenshot as having a potential conflation error. This conflation error may involve conflating a digital component for a physical component or vice versa. A corresponding system and computer program product are also disclosed.