Abstract: A computer-implemented method includes receiving a communication between first and second users via a communication channel associated with a communication space., and identifying the first user having a first role and the second user having a second role, a formality of the communication is determined based on the second role. The method includes identifying a transformer model for the communication space and monitoring the communication for an agreement clause via the transformer model by deriving an agreement clause based on the communication and classifying the derived agreement clause.

Abstract: Provided are techniques for enhancing silent features with adversarial networks for improved model versions. Input features are obtained. Hidden features are identified. Quantum feature importance scoring is performed to assign an importance score to each of the hidden features. Silent features are identified as the hidden features with the importance score below a first threshold. Important features are identified as the input features and as the hidden features with the importance score above a second threshold. A silent feature model is built using the silent features. An important feature model is built using the important features. An ensemble model is built with the silent feature model and the important feature model. The ensemble model is used to generate one or more predictions and one or more prescriptions.

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: Methods and systems for cohort selection for agile processes are disclosed. A method includes: assigning, by a computing device, each of a plurality of entities to one of a plurality of groups; and for each of the plurality of groups: for each of the entities in the group, determining, by the computing device, information about the entity and encoding the determined information about the entity using a genetic algorithm; determining a ranking, by the computing device, of each of the entities in the group based on the encoded information about each of entity in the group; crossing over, by the computing device, portions of the encoded information of pairs of entities occupying adjacent positions in the ranking; and measuring, by the computing device, fitness of each of the entities in the group.

Abstract: A method, computer system, and a computer program product for speech synthesis is provided. The present invention may include generating one or more final voiceprints. The present invention may include generating one or more voice clones based on the one or more final voiceprints. The present invention may include classifying the one or more voice clones into a grouping using a language model, wherein the language model is trained using manually classified uncloned voice samples. The present invention may include identifying a cluster within the grouping, wherein the cluster is identified by determining a difference between corresponding vectors of the one or more voice clones below a similarity threshold. The present invention may include generating a new archetypal voice by blending the one or more voice clones of the cluster where the difference between the corresponding vectors is below the similarity threshold.

Abstract: Described are techniques for a trustworthy artificial intelligence (AI) service. The techniques include identifying a user experience (UX) component in a front-end UX containing information that conveys a trustworthy AI factor. The techniques further include evaluating the information contained in the UX component to determine a trust score for the UX component that indicates a degree to which the information contained in the UX component conveys the trustworthy AI factor. The techniques further include determining, based on the trust score for the UX component, that the information contained in the UX component does not meet a threshold of disclosure of the trustworthy AI factor. The techniques further include obtaining an alternative UX component containing additional information that meets the threshold of disclosure of the trustworthy AI factor and providing the alternative UX component for incorporation into the front-end UX of the application.

Abstract: A computer-implemented method, a computer system and a computer program product control participant interaction within remote meeting sessions. The method includes capturing participant data using a device, where the participant data is associated with a participant within the remote meeting session and is selected from a group consisting of: audio data, image data and text data. The method also includes identifying an event in the participant data. The method further includes determining that the participant has not modified a participant interaction mode. Lastly, the method includes modifying the participant interaction mode of the participant when the event is classified as causing a change of the participant interaction mode.

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: 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 computer-implemented method includes generating several crypto tokens, each token includes a content portion that is encrypted. For each pair of tokens, a first token and a second token, in response to a correlation score between content portions of the first token and the second token exceeding a predetermined correlation threshold, creating a grid link between the first token and the second token. The tokens are added into a ledger. A group of tokens is created, based on a similarity score. A series of forecasting equations is distributed to the group of tokens. A forecast reveal rate is generated based on the series of forecasting equations, and in response to the forecast reveal rate meeting a forecast threshold, a key is generated for revealing at least one content portions embedded in the group of tokens. The key is released to decrypt the at least one content portion.

Abstract: An embodiment includes identifying, from among the plurality of digital content datasets, a set of candidate textual items based on relevance to a specified subtopic using one or more natural language processing techniques. The embodiment groups candidate textual items into a predetermined number of groups using relevance scores and feature vectors. The embodiment trains a pre-trained encoder-decoder model using a designated group of selected textual items, where the pre-trained encoder-decoder model is pretrained to generate textual content according to a particular style of writing. The embodiment generates, using the pre-trained encoder-decoder model, machine-authored textual content in the particular style of writing resulting in an article about the specified subtopic based on the designated group of selected textual items. The embodiment also includes transmitting the article to a remote web server as an update for a website.

Abstract: In an approach for optimizing the enhancement of media content using a Generative Adversarial Network (GAN), a processor identifies a degree of enhancement to be performed on the media content to attain the defined quality. A processor calculates a length of time required to enhance the media content to the degree of enhancement identified considering one or more factors. A processor identifies a buffer required to enhance the media content to the degree of enhancement identified, wherein the buffer prevents an interruption in the media content. A processor identifies a distribution to enhance the media content between a cloud hosted server and a user computing device. A processor enhances the media content according to the distribution identified.

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: Video of a plurality of fields of view of a scene is captured, each field of view comprising data of the scene from a different vantage point. An excitement level is determined by analyzing a portion of the captured video. Using the excitement level, a time series of future excitement levels is forecast. Using the time series of future excitement levels, a virtual field of view path of the scene is forecast. An insert image is determined to be included in the virtual field of view path. Captured data from at least two video cameras in the plurality of video cameras is composited into a virtual field of view of the scene. A rendering of the insert image is inserted into the virtual field of view.

Abstract: Using a first trained generative adversarial network, a first multimedia content is transformed into a text description of the first multimedia content. The text description is adjusted according to a constraint using a trained attention layer, the adjusting creating an adjusted text description. Using a trained model, the adjusted text description is transformed into a second multimedia content, the second multimedia content comprising an adjustment of the first multimedia content according to the constraint.

Abstract: From a set of natural language text documents, a concept tree is constructed. For a node in the concept tree a polarity of the subset represented by the node is scored. A second set of natural language text documents is added to the subset, the adding resulting in a modified subset of natural language text documents having a polarity score within a predefined neutral polarity score range. From the modified subset, a bin of sentences is selected according to a sentence selection parameter, a sentence in the bin of sentences being extracted from a selected document in the modified subset. A sentence having a factuality score below a threshold factuality score is removed from the bin of sentences. From the filtered bin of sentences a new natural language text document corresponding to the filtered bin of sentences is generated using a transformer deep learning narration generation model.

Abstract: A computer receives determines a mobile device requires a recharge, where the mobile device have a solar cell and an imaging device. The computer identifies an object with a low diffusion rate. The computer recharges the mobile device, based on determining that the mobile device receiving the solar energy from the identified object.

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: 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: Providing a hybrid neural network architecture by training a plurality of models using a set of training data, the plurality comprising quantum models and classical models, evaluating each model using a common test data set, assigning one or more evaluation metrics to each model according to the evaluation, generating a plurality of networks, each network comprising a combination of the models, evaluating a flow of each network, selecting a network according to the flow, and providing the selected network to a user.