Abstract: A method includes receiving, by a computing device, concepts of a domain; determining, by the computing device, objects relevant to the concepts; generating, by the computing device, a new image by stitching the relevant objects together; determining, by the computing device, whether the new image is accurate or inaccurate; and in response to determining the new image is inaccurate, propagating, by the computing device, the inaccurate new image back to a convolutional neural network (CNN).

Abstract: Information associated with a user and a presentation made by the user is received. A presentation readiness of the user is dynamically detected. One or more of a presentation mode of a presentation software and a recording mode of the presentation software is initiated based on the received information and the dynamically detected presentation readiness.

Abstract: A profile associated with a user that has unintentionally disconnected from an electronic conference is determined. Content missed by the user is determined. A determination is made whether the content missed by the user is relevant to the user based on the determined profile associated with the user and an indicated preference of the user. In response to determining that the content missed by the user is relevant to the user, the content missed by the user is transmitted to a computing device of the user.

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 source of bias identification (SoBI) tool is provided that identifies sources of bias in a dataset. A bias detection operation is performed on results of a computer model, based on an input dataset, to generate groupings of values for a protected attribute corresponding to a detected bias in the operation of the computer model. The SoBI tool generates a plurality of sub-groups for each grouping of values. Each sub-group comprises an individual value, or a sub-range, for the protected attribute. The SoBI tool analyzes each of the sub-groups in the plurality of sub-groups, based on at least one source of bias identification criterion, to identify one or more sources of bias in the input dataset. The SoBI tool outputs a bias notification to an authorized computing device specifying the one or more sources of bias in the input dataset.

Abstract: An approach is provided in which the approach receives scored records that include a selected scored record comprising a first fairness group attribute, a first prediction, and a first confidence value corresponding to the first prediction. The approach perturbs the selected scored record to a second fairness group attribute in response to determining that the first confidence value is below a confidence threshold. The approach scores the perturbed record to generate a second prediction and a second confidence value corresponding to the second prediction. The approach modifies the selected scored record by changing the first prediction to the second prediction in response to determining that the second prediction is different from the first prediction and that the second confidence value is higher than the confidence threshold.

Abstract: An embodiment includes activating, responsive to receiving an update notification, an update mode of a mobile device, wherein the activating of the update mode includes disabling a primary communication interface and enabling a secondary communication interface, and wherein the update notification includes notification of a software update available for the mobile device. The embodiment also includes initiating execution of the software update on the mobile device while the mobile device remains in the update mode. The embodiment also includes deactivating, responsive to completing the software update, the update mode of the mobile device, wherein the deactivating of the update mode includes enabling the primary communication interface and disabling the secondary communication interface.

Abstract: A profile associated with a user that has unintentionally disconnected from an electronic conference is determined. Content missed by the user is determined. A determination is made whether the content missed by the user is relevant to the user based on the determined profile associated with the user and an indicated preference of the user. In response to determining that the content missed by the user is relevant to the user, the content missed by the user is transmitted to a computing device of the user.

Abstract: One or more computer processors determine a storage strategy for each chunked data block in a training dataset based on a respective computed usefulness score and a series of usefulness thresholds, wherein the storage strategy comprises RAID strategies that include striping, mirroring, parity, and double parity. The one or more computer processors distribute each data block in the training dataset according to the respective determined storage strategy.

Abstract: Application-specific prioritization of streaming data replication. Data streamed from connected devices is selectively replicated to data storage clusters based on needs of the applications being served by the data. Data characterization supports prioritized replication processing. Statistical metrics compare streaming data with estimated values to characterize the data for prioritization.

Abstract: Aspects of the present invention disclose a method for performing playback functions of a multimedia feed based on a task of a request and an activity of a user. The method includes one or more processors identifying a task of multimedia of a video sharing application of a computing device. The method further includes determining an activity of a user utilizing a live video feed of a camera of the computing device. The method further includes determining whether the activity of the user correlates to the task of the multimedia. The method further includes in response to determining that the activity of the user correlates to the task of the multimedia, initiating a playback function of the video sharing application of the computing device based at least in part on the activity of the user and the task of the multimedia.

Abstract: A marking onto a collaboration board, located in a first location, is detected. The marking includes the creation of one or more content elements on the collaboration board. Responsive to detecting the markings one or more content elements on the collaboration board are analyzed. A first content element of the one or more content elements is classified based on analyzing the one or more content elements. The first content element is classified as a first privileged content element. The first location of the collaboration board is scanned. The scanning is based on the classifying of the first privileged content element. An unauthorized person is identified based on scanning the first location. The unauthorized person is not permitted to view the collaboration board. A security response is performed on the collaboration board regarding the one or more content elements. The security response is performed responsive to identifying the unauthorized person.

Abstract: A system and related method score a fairness of an outcome model. The method comprises receiving a set of original transaction records (OTRs), and selecting an OTR subset of the OTRs according to a subset selection criteria in order to reduce a number of OTRs to send to outcome model. For each OTR in the subset a perturbed transaction record (PTR) is created based on the OTR that includes changing at least one attribute in the PTR from the OTR, sending the OTR and the PTR to the outcome model, receiving an OTR outcome and a PTR outcome from the outcome model, and determining a record bias score for the OTR outcome and the PTR outcome respectively that indicates bias in the respective outcome. The OTR and the PTR bias score are stored in a bias determination system (BDS) database.

Abstract: Aspects of the present invention disclose a method for dynamic password inducing techniques for security configuration and validation of a user. The method includes one or more processors identifying textual data of a user. The method further includes generating a set of password parameters for a password. The method further includes identifying one or more password dynamisms of the user from one or more sources, where the one or more password dynamisms are dynamic inputs, from the user, to an authentication device. The method further includes modifying the textual data to include a text placeholder, wherein the text placeholder includes a replacement parameter that induces the user to provide a password dynamism. The method further includes configuring the set of password parameters for the password of the user based at least in part on the one or more password dynamisms of the user from the one or more sources.

Abstract: A machine learning model data quality improvement detection tool is provided for identifying an accurate reference group and an accurate monitored group of a machine learning model. The tool monitors a behavior of the machine learning model for a predetermined time frame. The tool compares a determined fairness metric a pre-defined fairness threshold. Responsive to the fairness metric failing to meet the pre-defined fairness threshold, the tool modifies the monitored group to include a first portion of the reference group. The tool compares a newly determined fairness metric to the pre-defined fairness threshold. Responsive to the newly determined fairness metric meeting the pre-defined fairness threshold, the tool identifies the modified monitored group including the first portion of the user-defined reference group as a new monitored group and the modified reference group without the first portion of the user-defined reference group as a new reference group.

Abstract: Embodiments relate to a system, computer program product, and method for determining shelter areas for two-wheeler vehicles, and, more specifically, for dynamically distinguishing the behavior of two-wheeler vehicles and non-two-wheeler vehicles as an indicator of shelter areas from inclement weather. The behavior of the vehicles is distinguished through a plurality of two-wheeler vehicles slowing down and congregating at a particular location as a shelter against inclement weather, while non-two-wheeler vehicles may slow down, however, not stop proximate this location.

Abstract: A computer device receives historical prediction data, where the historical prediction data includes historical data and corresponding predictions generated for the historical data by a regression machine learning model. The computing device identifies undesired predictions in the historical prediction data based, at least in part, on a perturbation analysis, where the perturbation analysis includes modifying an attribute of the historical data and using the regression machine learning model to generate predictions for the historical data with the modified attribute. The computing device trains a binary classification model to classify predictions as undesired, using the historical prediction data and the identified undesired predictions as training data. The computing device generates a prediction for a new data entry utilizing the regression machine learning model and the binary classification model.

Abstract: One example of a method comprises identifying a model to be validated that is stored in a repository; automatically computing and recording one or more model metrics for the model to be validated in a tamper-proof manner; comparing the computed tamper-proof metrics with one or more encoded rules and policies to determine if the model to be validated complies with the one or more encoded rules and policies; and outputting a notification to a device indicating a validation status of the model to be validated based on the comparison of the computed tamper-proof metrics with the one or more encoded rules and policies.

Abstract: A method, computer system, and computer program product for explaining time series machine learning model are provided. The embodiment may include determining a first order difference in time series input data and historical training data. The embodiment may also include performing perturbation of time series input data based on the determined first order difference and the determined historical training data. The embodiment may further include computing closeness of the determined first order difference in the historical training data to the determined first order difference in the time series input data. The embodiment may also include generating a uniform random sample of first value input to a time series machine learning model. The embodiment may further include determining values of other inputs to the time series machine learning model based on the generated random sample and a random sample from the historical training data first order differences.

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.