Abstract: Predicting infection risk by generating a first temporal graph of a first set of disease progression data, generating a second temporal graph of a second set of disease progression data, combining a first temporal graph node embedding and a second temporal graph node embedding, and generating a predicted infection risk according to the first temporal graph node embedding and the second temporal graph node embedding.

Abstract: A system, method, and computer program product for verifying signatures. The system includes at least one processing component, at least one memory component, and a reference storage comprising a set of reference signatures. The system also includes a model generator configured to generate a signature model based on the set of reference signatures. Further, the system includes a verification component configured to receive a signature, and determine whether the signature is valid.

Abstract: Embodiments for managing user concentration in a work environment are described. Information associated with a user is received. At least one characteristic of a task of the user is identified based on the information associated with the user. A concentration risk level associated with the user performing the task is determined based on the at least one characteristic of the task. A computing environment associated with the user performing the task is provided based on based on the concentration risk level.

Abstract: Smart speaker system mechanisms, associated with a smart speaker device comprising an audio capture device, are provided for processing audio sample data captured by the audio capture device. The mechanisms receive, from the audio capture device of the smart speaker device, an audio sample captured from a monitored environment. The mechanisms classify a sound in the audio sample data as a type of sound based on performing a joint analysis of a plurality of different characteristics of the sound and matching results of the joint analysis to criteria specified in a plurality of sound models. The mechanisms determine, based on the classification of the sound, whether a responsive action is to be performed based on the classification of the sound. In response to determining that a responsive action is to be performed, the mechanisms initiate performance of the responsive action by the smart speaker system.

Abstract: An exemplary method includes intercepting data in a virtual presence solution; classifying the intercepted data against machine-readable conduct guidelines by processing the intercepted data in a neural network; and intervening in the virtual presence solution in response to a component of the intercepted data being classified to a match with at least one of the machine-readable conduct guidelines.

Abstract: A computer-implemented method for correcting muffled speech caused by facial coverings is disclosed. The computer-implemented method includes monitoring a user's speech for speech distortion. The computer-implemented method further includes determining that the user's speech is distorted. The computer-implemented method further includes determining that a cause of the user's speech distortion is based, at least in part, on a presence of a particular type of facial covering. The computer-implemented method further includes automatically correcting the speech distortion of the user based, at least in part, on the particular type of facial covering causing the speech distortion.

Abstract: Members of an audience of a visually dynamic event are clustered using a plurality of sources. A current point of interest (POI) of the visually dynamic event and a future POI of the visually dynamic event are identified across the member clusters. An effectiveness score for given content and a given member cluster is computed for the current point of interest and an effectiveness score for a given content and a given member cluster is computed for the future POI by tracking a position, a speed, and a direction of movement of the current POI. A location of a background area is determined and ranked for each member cluster based on the current point of interest. Electronic displays that correspond to the ranked background areas are identified and ranked. Content is distributed across the electronic displays for each current time period based on the ranked display areas.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises generating a baseline prediction for each database in a plurality of databases, wherein the generated baseline prediction includes datapoints for each performance feature of a respective database in the plurality of databases; generating a linkage matrix using a dimension reduction algorithm in an equal-dimensional feature space based on a measured difference between actual performance datapoints of each respective database and respective datapoints associated with generated baseline prediction for each database in the plurality of databases; linking one or more databases in the plurality of databases based on the measured difference within the generated linkage matrix reaching a predetermined threshold of measured performance; and generating a design using the linked databases for a computer system capable of being displayed through a user interface.

Abstract: A search query can be received. User parameters can be identified based on the search query. The search query can be refined to include the user parameters. A search result from a search for media content using the refined search query can be received. Based on at least one search result received from the search and based on the user parameters, an augmented media content can be generated. Playing of the augmented media content can be synchronized with a user's activity by controlling playing of the augmented media content while detecting the user's activity pace.

Abstract: In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.

Abstract: A request for use of an application programming interface (API) is received. Context associated with the request is determined. Based on the context, a challenge is generated, which can be used for determining whether to permit the use of the API. A response to the challenge is received. Based on the response, the request can be facilitated for using the API.

Abstract: A database comprising data associated with one or more routes is maintained. The data associated with the one or more routes comprises difficulty level data for utilizing one or more mobility assistive tools to traverse the one or more routes. In response to receiving a query from a given computing device, one or more amounts of physical exertion for a given user to traverse at least a portion of the one or more routes utilizing a given mobility assistive tool are predicted. One or more routes for the given user to traverse are selected based at least in part on the predicted amounts of physical exertion. One or more contextual factors of the given user are estimated to at least one of optimize and prioritize the selected one or more routes for the given user based on analyzing user data.

Abstract: Receive a transaction generated by a user of a non-internet application; identify transaction life cycle steps of previous similar transactions; and generate a transaction risk score for the transaction using machine learning models and a blockchain record of the previous similar transactions. In response to the transaction risk score exceeding a threshold value, authenticate the transaction and the user using two-step authentication. The two-step authentication uses challenge/answer templates derived from the blockchain record of previous transactions.

Abstract: Configuration and dynamic profiling of storage class memory (SCM) devices is provided. Information is retrieved that includes historical SCM device configurations, historical SCM device utilization, functional and non-functional properties of a plurality of SCM devices on a host node, current real time utilization of the plurality of SCM devices by an application workload of a customer running on the host node, and relationships between the plurality of SCM devices, needs of the customer, and resource capabilities and real time resource utilization on the host node. A configuration of each respective SCM device is determined based on retrieved information and an artificial intelligence-predicted SCM device future utilization trajectory of the customer. Each respective SCM device is dynamically configured with a set of SCM device partitions according to a corresponding SCM device profile based on the determined configuration of each respective SCM device of the plurality of SCM devices.

Abstract: A signal representing a sound can be received. A machine learning model can be run to identify that the sound triggers a reaction in a user hearing the sound. A preventive action can be automatically activated to mitigate the reaction. The user's reactions can be monitored. Responsive to determining that the user's reaction has been mitigated or suppressed, the preventive action can be deactivated. The machine learning model can be retrained using at least the signal as new training data.

Abstract: An embodiment for simulating a live video presentation in a recorded video is provided. The embodiment may include receiving a previously recorded online meeting. The embodiment may also include transcribing and indexing the transcription of the previously recorded online meeting. The embodiment may further include receiving audio content from a user. The embodiment may also include searching for a response to the audio content in the transcription. The embodiment may further include in response to determining the response is found in the transcription, generating a solution for the audio content from the transcription. The embodiment may also include integrating the generated solution into the previously recorded online meeting. The embodiment may further include updating one or more video frames of the previously recorded online meeting based on the generated solution.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of code datasets prior to a data migration; analyzing the identified code datasets for a plurality of parameters; dynamically predicting a carbon footprint associated with the analyzed code datasets based on the plurality of parameters for each analyzed code dataset; and automatically optimizing the analyzed code datasets based on the predicted carbon footprint for data migration.

Abstract: Train a machine learning model, using an image-based knowledge graph of tropical cyclone data, for implementing a surface field modeling architecture that produces images of at least surface wind fields and surface rainfall fields from images of at least tropical cyclone tracks and pressure intensities. Generate model images of a modeled surface wind field and a modeled surface rainfall field by providing images of at least a user-generated tropical cyclone track and pressure intensity to the trained machine learning model.

Abstract: A method, computer system, and a computer program product for interpreting conference call interruptions is provided. The present invention may include analyzing a conference call including at least two participants. The present invention may also include identifying at least one interrupted segment in the analyzed conference call. The present invention may further include reconstructing the identified at least one interrupted segment of the analyzed conference call and integrating it with the uninterrupted (heard) portion of the conference call.

Abstract: In an aspect, during a presentation of a presentation material, viewers of the presentation material can be monitored. Based on the monitoring, new content can be determined for insertion into the presentation material. The new content can be automatically inserted to the presentation material in real time. In another aspect, during the presentation, a presenter of the presentation material can be monitored. The presenter's speech can be intercepted and analyzed to detect a level of confidence. Based on the detected level of confidence, the presenter's speech can be adjusted and the adjusted speech can be played back automatically, for example, in lieu of the presenter's original speech that is intercepted.