Abstract: A method, computer system, and a computer program product for integrating uncertainty quantifications into a long-term risk based calendar is provided. The present invention may include receiving data for an organization. The present invention may include generating a long-term risk based calendar using the data received for the organization. The present invention may include presenting the long-term risk based calendar to a plurality of user associated with the organization. The present invention may include providing one or more recommendations to at least one of the plurality of users.

Abstract: One or more computer processors discover an anomalous subset through sparsity-based automatic feature selection.

Abstract: With a trained, computerized discontinuation predictor machine learning component (MLC), predict, based on an input time series, a time when a subject will discontinue a course of medical treatment; with a trained, computerized pattern behavior extractor MLC, extract from said input time series the top k discriminatory sequences via discriminatory sub-sequence mining (said top k discriminatory sequences differentiate between first and second classes of interest to provide a hypothesis for downstream analysis of a cause of discontinuing said course of treatment). With a trained, causal effect estimator computerized MLC, determine a reason why said subject will discontinue said course of medical treatment, based on said top k discriminatory sequences and additional data; and with a computerized user interface, provide said time and said reason why to a responsible party to initiate an intervention.

Abstract: A computer-implemented method for differentiating patterns of care (DPoC) to detect anomalous subsets in any given population with a defined set of outcomes and features. The computer-implemented method includes detecting the anomalous subsets, ranking the anomalous subsets based on a score of each anomalous subset that is reflective of an anomaly thereof, specifying whether each of the anomalous subsets overlaps with another one of the anomalous subsets, whether each of the anomalous subsets is unique and whether each of the anomalous subsets is conditional and specifying as to whether the detecting of each of the anomalous subsets has a higher or lower outcome than expected.

Abstract: Augmenting a live broadcast of a sporting event includes receiving or generating a data stream of a live broadcast of a sporting event, generating commentary on the live broadcast in real time at a plurality of different levels of knowledge of the sport of the sporting event being broadcast, determining a knowledge level of the sport of a user, and providing the generated commentary at one of the plurality levels of knowledge of the sport to a user in real time based on the determined knowledge level of the sport of the user.

Abstract: Context-dependent memory stimulation is provided. A context-dependent task resumption memory cue personalized to a user is sent to a client device of the user via a network during a task interruption lag period corresponding to a primary task prior to the user starting a set of secondary tasks to support a context-dependent memory of the user associated with the primary task. It is determined that the user started and then stopped performing the set of secondary tasks based on monitoring the client device. The context-dependent task resumption memory cue personalized to the user is resent to the client device via the network during a task resumption lag period corresponding to the primary task to stimulate the context-dependent memory of the user associated with the primary task in response to the computer determining that the user stopped performing the set of secondary tasks and resumed the primary task.

Abstract: A plurality of key features that contribute to a level of anomalousness of an anomalous subgroup are identified. One or more minimal perturbations to a set of features of the anomalous subgroup that result in a reduction of the level of anomalousness are identified. The application of one or more minimal perturbations to members of the anomalous subgroup is facilitated.

Abstract: In an approach to improve platform modification by dynamically modifying content based on user feedback. Embodiments suggest one or more solutions and recommendations, via a user interface, to one or more users that comply with one or more standards, regulations, and resources of an organization. Further, embodiments verify the solutions and recommendations that comply with the one or more standards, regulations, and resources of the organization, and automatically generate a prototype solution. Additionally, embodiments provide, by the user interface, one or more augmented solutions for the one or more users, wherein augmented solutions are provided to the one or more users requiring a temporary solution; and label and cluster user feedback and associated solutions of the one or more users to provide effective solutions for future users based on a similarity of the feedback of the one or more users.

Abstract: An embodiment associates a virtual-meeting host designation with a first user. During the virtual meeting, the embodiment generates meeting context data that includes user characteristic data and meeting characteristic data. The embodiment uses the meeting context data to generate a predicted end time for the virtual meeting and predicts a meeting run-over condition indicative of the virtual meeting continuing past a scheduled end time by comparing the predicted end time and a scheduled end time. Responsive to predicting the meeting run-over condition, the embodiment uses the user characteristic data to generate a ranked list of other users and renders, as a display to the first user, the ranked list and an indication of the meeting run-over condition. The embodiment receives a selection of a second user as a new host of the virtual meeting and, responsive to receiving the selection, associates the host designation with the second.

Abstract: Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.

Abstract: A method for regulating emotions in conversations is disclosed. In one embodiment, such a method includes monitoring a conversation between participants. The method further divides the conversation into a plurality of utterances and calculates an emotion score for each utterance. The method further determines whether an emotion score of an utterance exceeds a threshold. In the event the emotion score exceeds the threshold, the method intervenes in the conversation in an attempt to return the conversation to a more constructive path. In certain embodiments, this intervention may include taking one or more actions that are specifically tailored to returning the conversation to a more constructive path. In other or the same embodiments, responses or reactions of the participants to the actions are audited to determine if the actions were successful in returning the conversation to a more constructive path. A corresponding system and computer program product are also disclosed.

Abstract: A computer-implemented method for predicting electric vehicle (EV) charging behavior of a driver. The method includes predicting when an EV needs to be charged, where the EV needs to be charged, and for how long the EV needs to be charged based on individualized characteristics of the driver or one or more passengers, weather conditions, and geospatial characteristics. The method further includes evaluating an availability of one or more EV charging stations located within a given radius of the EV and comparing a location of the one or more available EV charging stations, located within the given radius of the EV, to one or more desired locations of the driver of the EV. The method further includes determining an estimated waiting time at the one or more EV charging stations and scheduling an EV charging time at the one or more EV charging stations, based on the location and duration.

Abstract: One or more computer processors discover an anomalous subset through sparsity-based automatic feature selection.

Abstract: A method, computer system, and a computer program product for integrating uncertainty quantifications into a long-term risk based calendar is provided. The present invention may include receiving data for an organization. The present invention may include generating a long-term risk based calendar using the data received for the organization. The present invention may include presenting the long-term risk based calendar to a plurality of user associated with the organization. The present invention may include providing one or more recommendations to at least one of the plurality of users.

Abstract: A supervised discriminator for detecting bio-markers in an audio sample dataset is trained and a denoising autoencoder is trained to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample of the audio sample dataset. A conditional auxiliary generative adversarial network (GAN) trained to generate the output audio sample with the same fidelity as the input audio sample, wherein the output audio sample is void of the bio-markers. The conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder are deployed in an audio processing system.

Abstract: A computer system may create a language model corpus including multilingual alignment for training a combined language model and train (or pre-train) the combined language model. The computer system may create an adverse medication reaction corpus to include adverse medication reaction utterances and label an N-gram of an utterance in the adverse medication reaction utterances as a response to query, for multiple N-grams. The computer system may generate a code-mixed utterance model to perform code-mixed utterances in a turn by turn dialogue, by at least adding additional output layer including at least a start vector, language vector, and a query vector including at least the labeled N-gram, which are additional to the combined language model's predicted next words.

Abstract: Protected features from media that enable bias in a decision-making process of an artificial intelligence (AI) system are identified and ranked. Adversarial artifacts are generated, using a generative adversarial network, to obfuscate at least a portion of the ranked protected features and the adversarial artifacts are integrated into the media.

Abstract: A method including: receiving multiple time series of data, each time series representing a health-related temporal impact of a historical set of elements on an adverse health-related condition; automatically deconvolving the health-related temporal impact, to determine an individual contribution of each element of each of the historical sets to the health-related temporal impact of the respective historical set, wherein the deconvolving is performed respectively of an additive impact and/or a multiplicative impact of elements; receiving a selection of a new set of elements which is different from any one of the historical sets; and based on the determined individual contributions, automatically predicting a temporal impact of the new set of elements, wherein the new set consists of elements selected from said group but which do not jointly constitute any one of the historical sets.

Abstract: A modeling problem can be received. A database of prior calibrated models can be searched to identify a similar problem having features similar to the received modeling problem. The modeling problem can be calibrated using information of the identified similar problem. The accuracy of calibrated modeling problem can be monitored. The modeling problem can be recalibrated until a performance criterion is met. Calibrated modeling problem can be stored in the database.

Abstract: Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.