Abstract: Managing notifications is provided. Personal monitoring system inputs corresponding to each member of a defined group performing a common task are contextually analyzed to identify a notification sequence for each respective member enabling task performance in a synchronized manner. Progress of each respective member while performing activities corresponding to the common task is analyzed using the personal monitoring system inputs to enable dynamic modification of the notification sequence and content to the members in accordance with the progress. Existence of any problem is identified during performance of activities corresponding to the common task to accordingly modify the notification sequence and content to target members for mitigation of an existing problem.

Abstract: An article is automatically augmented. The article and one or more comments are received. Comment elements are extracted from the one or more comments, and article elements are extracted from the article. Alignment scores are generated for comment-article pairs based on the extracted comment and article elements. Further, it is determined that at least one comment-article pair has an alignment score at or above a threshold alignment score. At least one augmentation feature is then generated.

Abstract: Approaches presented herein enable optimization of a developing application to a user base. More specifically, application-centric data is gathered during a cultivation phase of the developing application. Substantially concurrently with the cultivation phase of the developing application, the application-centric data is analyzed according to static code of the developing application, a testing of the developing application, or a user experience (UX) design of the developing application. A machine learning model is applied to the analyzed application-centric data. This machine learning model is trained on historic application feedback data from applications available to the user base. Based on the machine learning model, a recommended change to optimize the developing application to the user base is generated.

Abstract: Managing notifications is provided. Personal monitoring system inputs corresponding to each member of a defined group performing a common task are contextually analyzed to identify a notification sequence for each respective member enabling task performance in a synchronized manner. Progress of each respective member while performing activities corresponding to the common task is analyzed using the personal monitoring system inputs to enable dynamic modification of the notification sequence and content to the members in accordance with the progress. Existence of any problem is identified during performance of activities corresponding to the common task to accordingly modify the notification sequence and content to target members for mitigation of an existing problem.

Abstract: Methods, systems, and computer program products for determining data representative of bias within a model are provided herein. A computer-implemented method includes obtaining a first dataset on which a model was trained, wherein the first dataset contains protected attributes, and a second dataset on which the model was trained, wherein the protected attributes have been removed from the second dataset; identifying, for each of the one or more protected attributes in the first dataset, one or more attributes in the second dataset correlated therewith; determining bias among at least a portion of the identified correlated attributes; and outputting, to at least one user, identifying information pertaining to the one or more instances of bias.

Abstract: Methods, systems, and computer program products for determining model-related bias associated with training data are provided herein. A computer-implemented method includes obtaining, via execution of a first model, class designations attributed to data points used to train the first model; identifying any of the data points associated with an inaccurate class designation and/or a low-confidence class designation; training a second model using the data points from the dataset, but excluding the identified data points; determining bias related to at least a portion of those data points used to train the second model by: modifying one or more of the data points used to train the second model; executing the first model using the modified data points; and identifying a change to one or more class designations attributed to the modified data points as compared to before the modifying; and outputting identifying information pertaining to the determined bias.

Abstract: Methods, systems, and computer program products for domain aware explainable anomaly and drift detection for multi-variate raw data using a constraint repository are provided herein. A computer-implemented method includes obtaining a set of data and information indicative of a domain of said set of data; obtaining constraints from a domain-indexed constraint repository based on said set of data and said information, wherein the domain-indexed constraint repository comprises a knowledge graph having a plurality of nodes, wherein each node comprises an attribute associated with at least one of a plurality of domains and constraints corresponding to the attribute; detecting anomalies in said set of data based on whether portions of said set of data violate said retrieved constraints; generating an explanation corresponding to each of the anomalies that describe the attributes corresponding to the violated constraints; and outputting an indication of the anomalies and the corresponding explanation.

Abstract: Aspects of the present invention provide an approach for reducing bias in active learning. In an embodiment, a data point is selected from a training dataset for a current training iteration while monitoring for data bias at each addition of data to a virtual training dataset. In addition, a machine learning model is examined for bias after adding the selected data point to the virtual training dataset. When data bias and/or model bias is detected, the data point is considered for potential label modification. The selected data point is modified and, if the raw value of the modified data point is within a predefined tolerance and within a bin of a desired class, the modified data point having a label of the target class is retained. Otherwise, it can be discarded.

Abstract: Approaches presented herein enable optimization of a developing application to a user base. More specifically, application-centric data is gathered during a cultivation phase of the developing application. Substantially concurrently with the cultivation phase of the developing application, the application-centric data is analyzed according to static code of the developing application, a testing of the developing application, or a user experience (UX) design of the developing application. A machine learning model is applied to the analyzed application-centric data. This machine learning model is trained on historic application feedback data from applications available to the user base. Based on the machine learning model, a recommended change to optimize the developing application to the user base is generated.

Abstract: In an approach for automatically ranking and routing data quality remediation tasks, a processor analyzes a data set ingested by a repository to produce a set of data quality problems. A processor computes a score for each data quality problem of the set of data quality problems. A processor identifies a route to send each data quality problem of the set of data quality problems. A processor exports each data quality problem according to the score and the route.

Abstract: A method, computer system, and a computer program product for utilizing provenance data to improve machine learning is provided. Embodiments of the present invention may include collecting provenance data. Embodiments of the present invention may include identifying model quality improvements based on the collected provenance data. Embodiments of the present invention may include identifying related models based on the collected provenance data. Embodiments of the present invention may include recommending model quality improvements to a user.

Abstract: A processor may receive a record. The record may include one or more segments of text. The processor may automatically generate a first summary of the record. The processor may determine an overall bias of the first summary. The overall bias of the first summary may be identified from one or more instances of bias in the first summary. The processor may generate a second summary of the record. The second summary of the record may include an indicator of the overall bias of the first summary. The indicator may include a description of a type of overall bias of the first summary and a numerical value of the overall bias of the first summary. The processor may determine an overall bias of the second summary. The processor may display the second summary of the record to a user.

Abstract: A processor may receive a record. The record may include one or more segments of text. The processor may tag each segment of text with an indicator. The indicator may denote a specific instance of bias in each of a respective segment of text. The processor may automatically generate a summary of the record. The summary of the record may include a set of segments of text. The set of segments of text may have a different overall bias than the record. The processor may display the summary of the record to a user.

Abstract: Relationship discovery can include receiving at a first mobile device a pair of ultrasonic signals conveyed at different frequencies from a second mobile device. The pair of ultrasonic signals can convey, respectively, a second user's contact information in an encrypted form and a key indicator. A contact number can be selected from a first user's contact list electronically stored on the first mobile device. The contact number can be selected based on the key indicator. A mutual contact can be identified in response to decrypting the second user's contact information using the contact number as a decryption key.

Abstract: An article is automatically augmented. The article and one or more comments are received. Comment elements are extracted from the one or more comments, and article elements are extracted from the article. Alignment scores are generated for comment-article pairs based on the extracted comment and article elements. Further, it is determined that at least one comment-article pair has an alignment score at or above a threshold alignment score. At least one augmentation feature is then generated.

Abstract: An article is automatically augmented. The article and one or more comments are received. Comment elements are extracted from the one or more comments, and article elements are extracted from the article. Alignment scores are generated for comment-article pairs based on the extracted comment and article elements. Further, it is determined that at least one comment-article pair has an alignment score at or above a threshold alignment score. At least one augmentation feature is then generated.

Abstract: A method, computer program product, and system for analyzing performance of a computer system where at a plurality of time intervals, a set of KPIs of a database and database performance outputs are monitored. A database performance value is calculated based on the monitored KPIs and one or more database performance outputs. In response to determining the database performance value is below a first threshold, a KPI correlation coefficient is calculated. The calculated KPI correlation coefficient with the greatest value is selected as a target KPI. In response to determining that a count of functions of the target KPI meets a second threshold, a dynamic tabular representation of the determined target KPI is generated. A correlation matrix is generated based on the generated tabular representation and the plurality of time intervals and a first set of target functions are identified based on the generated correlation matrix.

Abstract: A method for analyzing performance of a computer system where at a plurality of time intervals, a set of KPIs of a database and database performance outputs are monitored. A database performance value is calculated based on the monitored KPIs and one or more database performance outputs. In response to determining the database performance value is below a first threshold, a KPI correlation coefficient is calculated. The calculated KPI correlation coefficient with the greatest value is selected as a target KPI. In response to determining that a count of functions of the target KPI meets a second threshold, a dynamic tabular representation of the determined target KPI is generated. A correlation matrix is generated based on the generated tabular representation and the plurality of time intervals and a first set of target functions are identified based on the generated correlation matrix.

Abstract: A method for analyzing performance of a computer system where at a plurality of time intervals, a set of KPIs of a database and database performance outputs are monitored. A database performance value is calculated based on the monitored KPIs and one or more database performance outputs. In response to determining the database performance value is below a first threshold, a KPI correlation coefficient is calculated. The calculated KPI correlation coefficient with the greatest value is selected as a target KPI. In response to determining that a count of functions of the target KPI meets a second threshold, a dynamic tabular representation of the determined target KPI is generated. A correlation matrix is generated based on the generated tabular representation and the plurality of time intervals and a first set of target functions are identified based on the generated correlation matrix.

Abstract: A method, computer program product, and system for analyzing performance of a computer system where at a plurality of time intervals, a set of KPIs of a database and database performance outputs are monitored. A database performance value is calculated based on the monitored KPIs and one or more database performance outputs. In response to determining the database performance value is below a first threshold, a KPI correlation coefficient is calculated. The calculated KPI correlation coefficient with the greatest value is selected as a target KPI. In response to determining that a count of functions of the target KPI meets a second threshold, a dynamic tabular representation of the determined target KPI is generated. A correlation matrix is generated based on the generated tabular representation and the plurality of time intervals and a first set of target functions are identified based on the generated correlation matrix.