Abstract: Computer-implemented methods selecting a cloud computing system for deployment of a workload are provided. Aspects include obtaining an input function that characterizes the workload and obtaining a plurality of performance models, wherein each of the plurality of performance models corresponds to one of a plurality of cloud computing systems including the cloud computing system. Aspects also include applying the input function into each of the plurality of performance models, calculating one or more of a gain, a phase shift, and a stability margin for each of the plurality of performance models, and selecting the cloud computing system from the plurality of cloud computing systems based at least in part on the one or more of the gain, the phase shift, and the stability margin.

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: 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: Methods, systems and computer program products for workload summarization and congestion avoidance are provided. Aspects include generating a workload summary associated with the workload request responsive to receiving a workload request. Aspects also include detecting potential congestion associated with the workload request by predicting resource usage based on the workload summary and a knowledge base model. Aspects further include performing an action to avoid the potential congestion, the action comprising holding off the workload request for at least a predetermined duration.

Abstract: A computer-implemented method, in accordance with one aspect of the present invention, includes analyzing timing data for stages in a pipeline running in an edge system for detecting starvation of one or more of the stages in the pipeline. In response to detecting one or more of the stages being starved, starvation avoidance is performed for mitigating the starvation of the starving stage(s). A computer-implemented method, in accordance with another aspect of the present invention, includes analyzing timing data for pipelines running in parallel in an edge system for detecting starvation of one or more of the pipelines. In response to detecting one or more of the pipelines being starved, starvation avoidance is performed for mitigating the starvation of the starving pipeline(s).

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: Methods, systems, and computer program products for predicting states from patient-caregiver dyadic biomarker data using artificial intelligence are provided herein. A computer-implemented method includes obtaining biomarker data derived from one or more dyads, each dyad comprising at least one patient and at least one caregiver associated with the at least one patient; determining, based on processing the obtained biomarker data, data-based representations of mental distress and/or social rhythm disruption among at least one of the one or more dyads; predicting mental distress and/or social rhythm disruption among a given dyad of at least one patient and at least one caregiver associated with the at least one patient by processing input biomarker data, derived from the given dyad, using artificial intelligence techniques in connection with at least a portion of the data-based representations; and performing automated actions based on the predicting.

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 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: 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: 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: A translation-engine suggestion method, system, and computer program product include identifying probes for third-party translation-engines for an input text, segmenting sections of the input text into a plurality of segments according to the identified probes, fragmenting the input text into fragments according to the segments, applying each fragment to the identified probe using the corresponding third-party translation-engine, and outputting a translation by combining each fragment.

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: A bias detection method, system, and computer program product include creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer, predicting a probability of overlapping data points between the applicant and the reviewer, building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer, and calculating a bias score via a deep learning model run over the enriched embeddings.

Abstract: A translation-engine suggestion method, system, and computer program product include identifying probes for third-party translation-engines for an input text, segmenting sections of the input text into a plurality of segments according to the identified probes, fragmenting the input text into fragments according to the segments, applying each fragment to the identified probe using the corresponding third-party translation-engine, and outputting a translation by combining each fragment.

Abstract: An apparatus obtains participant sentiment data including at least one of: a text conversation between a participant and an investigator in a clinical trial; a video conversation between the participant and the investigator; and the participant's social network information; extracts and normalizes a sentiment score from the participant sentiment data; generates a compliance score for the participant by using a trained regressor on at least the sentiment score; compares the compliance score to a lower threshold and to a higher threshold; selects an action from a decision tree in response to the compliance score; and facilitates the action.

Abstract: A bias detection method, system, and computer program product include creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer, predicting a probability of overlapping data points between the applicant and the reviewer, building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer, and calculating a bias score via a deep learning model run over the enriched embeddings.

Abstract: A method includes: receiving a diagnosis of a medical condition; retrieving encoded guidelines for pharmaceutical treatment corresponding to the diagnosis; in response to the encoded guidelines, generating a list of recommended medications with related information; modifying the list of recommended medications, in response to personal context data obtained from at least one of a wearable device, a smart pill bottle, a patient health wallet, and an electronic health record; further modifying the list of recommended medications in response to environmental context data; generating an ordered list of recommended medications by yet further modifying the list of recommended medications in response to a patient similarity analysis; providing to a prescriber the ordered list of recommended medications; developing a logistical plan including a mode of delivery for one of the recommended medications; and programming an autonomous device to implement the mode of delivery.

Abstract: A guideline annotation method, system, and computer program product, include extracting a medical data point from a medical guideline, determining an attribute context of the medical data point, finding a related literature to the medical guideline, determining an expert practice as a difference between the medical guideline and an expert operating procedure, and annotating and outputting a new medical guideline including the related literature and the expert practice.