Abstract: Digital image text editing techniques as implemented by an image processing system are described that support increased user interaction in the creation and editing of digital images through understanding a content creator's intent as expressed using text. In one example, a text user input is received by a text input module. The text user input describes a visual object and a visual attribute, in which the visual object specifies a visual context of the visual attribute. A feature representation generated by a text-to-feature system using a machine-learning module based on the text user input. The feature representation is passed to an image editing system to edit a digital object in a digital image, e.g., by applying a texture to an outline of the digital object within the digital image.

Abstract: Systems and methods for color representation are described. Embodiments of the inventive concept are configured to receive an attribute-object pair including a first term comprising an attribute label and a second term comprising an object label, encode the attribute-object pair to produce encoded features using a neural network that orders the first term and the second term based on the attribute label and the object label, and generate a color profile for the attribute-object pair based on the encoded features, wherein the color profile is based on a compositional relationship between the first term and the second term.

Abstract: In some embodiments, a version control system receives a first version of a digital object and a second version of the digital object. The version control system extracts data from the first version of the digital object, and data from the second version of the digital object. The version control system utilizes a categorization machine learning model to generate structured representations of the first digital object and a second digital object. The version control system identifies differences between the two digital objects and utilizes a natural language technique to produce pseudo-instructions representing an update request. The version control system validates differences between the two digital objects and sends an auto-validation report to a client computing device when the differences are compliant with the pseudo-instructions.

Abstract: Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.

Abstract: The disclosed embodiments illustrate method and system for medical data processing for generating personalized advisory information. The method includes receiving a request comprising a generic clinical care pathway report and medical data of a patient from a user-computing device. Further, the medical data comprises one or more factors associated with a patient profile and a population segment of the patient. The method further includes extracting one or more influence factors and one or more population segments to generate one or more knowledge bases. The method further includes comparing the one or more factors with the extracted one or more influence factors and the one or more population profiles. The method further includes generating a personalized advisory information. The method further includes rendering the generated personalized advisory information on an interactive user interface of the user-computing device over the communication network for selection by a medical practitioner.

Abstract: Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.