Abstract: A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.

Abstract: According to one embodiment, a method, computer system, and computer program product for provisioning a tag schema. The embodiment may include determining data types for tag-keys within an existing ecosystem of tags. Each tag of the existing ecosystem comprises a tag-key and a tag-value. The embodiment may include performing pairwise clustering of the tags within the ecosystem. The embodiment may include identifying a main tag-key within each cluster of tags. The embodiment may include grouping the clusters into a broader category. The embodiment may include providing a tag schema recommendation for the ecosystem based on the grouped clusters.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: According to one embodiment, a method, computer system, and computer program product for provisioning a tag schema. The embodiment may include determining data types for tag-keys within an existing ecosystem of tags. Each tag of the existing ecosystem comprises a tag-key and a tag-value. The embodiment may include performing pairwise clustering of the tags within the ecosystem. The embodiment may include identifying a main tag-key within each cluster of tags. The embodiment may include grouping the clusters into a broader category. The embodiment may include providing a tag schema recommendation for the ecosystem based on the grouped clusters.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: An embodiment includes determining if a new incident report of a new incident matches any resolved incident reports associated with resolved incidents. The embodiment performs a first classification operation on the new incident report to determine if the new incident report is likely to be similar to any resolved incident reports associated with resolved incidents. The embodiment also performs a second classification operation on the new incident report to generate a ranked list of changes that are likely to be similar to the new incident report. The embodiment outputs the ranked list of changes to an incident manager for evaluation, then receives an input representative of a selected change from among the ranked list of changes responsible for causing the new incident. The embodiment revises the new incident report to include a reference to the selected change.

Abstract: An embodiment includes generating an input document that includes a plurality of text fields of attribute data. The embodiment also includes extracting a set of candidate features from the attribute data using a feature extraction module that evaluates the attribute data using first and second machine learning models, where the first machine learning model scores terms in the input document and the second machine learning model includes a deep learning model. The embodiment also includes calculating feature-selection values for respective features of the set of candidate features and generating tag information for a remote computing asset using a machine learning classifier that predicts the tag information based on the feature-selection values.

Abstract: A classification server perform a method for classifying an entity and identifying reason codes for the classification. The classification server can use a gradient boosting machine to build a classification model using training data. The classification model can be an ensemble of decision trees where each terminal node in the decision tree is associated with a response. The responses from each decision tree can be aggregated by the classification server in order to determine a classification for a new entity. The classification server can determine feature contribution values based on expected feature values. These feature contribution values can be associated with each of the responses in the classification model. These feature contribution values can be used to determine reason codes for the classification of the entity. As such, the classification server can perform a single traversal of the classification model to classify the entity and identify reason codes.

Abstract: A classification server perform a method for classifying an entity and identifying reason codes for the classification. The classification server can use a gradient boosting machine to build a classification model using training data. The classification model can be an ensemble of decision trees where each terminal node in the decision tree is associated with a response. The responses from each decision tree can be aggregated by the classification server in order to determine a classification for a new entity. The classification server can determine feature contribution values based on expected feature values. These feature contribution values can be associated with each of the responses in the classification model. These feature contribution values can be used to determine reason codes for the classification of the entity. As such, the classification server can perform a single traversal of the classification model to classify the entity and identify reason codes.