Abstract: Techniques for personalized digital content generation are described and are implementable to generate digital content based on content that depicts a scene from one or more locations and content that depicts one or more individuals. The described implementations, for instance, enable generation of a personalized photo album for a user depicting the user at the one or more locations. The described implementations further enable generation of an itinerary including personalized digital content. In an example, an input including a location is received. Environment content is generated that depicts a scene of the location. User content is generated that includes a representation of one or more individuals. Personalized content is then generated by incorporating the representation of the one or more individuals into the scene, and the personalized content is output for display. The personalized content is further usable to generate a personalized itinerary for a user.

Abstract: Techniques for digital content capture recommendations are described and are implementable to generate a recommendation for a digital content capture at a particular location based on capture data stored in a database. The described implementations enable generation of a recommendation including a suggested location, time of day, date, and/or various device configuration settings to capture an instance of digital content such as a photograph. For instance, a request for a recommendation for a digital content capture at a particular location is received. A database including capture data associated with digital content captured at the particular location is accessed, and the recommendation is generated based on the capture data. The recommendation is then displayed in a user interface of a client device.

Abstract: Techniques for personalized product recommendations based on an itinerary are described and are implementable to generate a recommendation for a good and/or service based on product data associated with a destination. The described implementations, for instance, enable generation of a recommendation for a user to acquire the good and/or service prior to visiting the destination based on digital content associated with the destination. The described implementations further enable generation of synthetic digital content that depicts the user at the destination with the good and/or service. Additionally, the techniques described herein include a monitoring system that is operable to detect a development related to the destination, and generate an updated recommendation based on the development.

Abstract: Techniques for personalized product recommendations based on an itinerary are described and are implementable to generate a recommendation for a good and/or service based on product data associated with a destination. The described implementations, for instance, enable generation of a recommendation for a user to acquire the good and/or service prior to visiting the destination based on digital content associated with the destination. The described implementations further enable generation of synthetic digital content that depicts the user at the destination with the good and/or service. Additionally, the techniques described herein include a monitoring system that is operable to detect a development related to the destination, and generate an updated recommendation based on the development.

Abstract: In aspects of display photo update recommendations, a catalyst is identified for updating a display photo of a user on a social media website. Further, a first collection of photos that are associated with the catalyst are retrieved from a photo gallery maintained in memory of a computing device. Using a machine learning model trained on a second collection of display photos utilized by connections of the user on the social media website, one or criteria are determined for display photos. Moreover, candidate display photos that satisfy the one or more criteria are identified from the first collection of photos and output to the photo gallery.

Abstract: A device may receive, in near-real time, unstructured data associated with an application or a system, and may extract textual data from the unstructured data. The device may parse the textual data to generate parsed textual data, and may perform natural language processing on the parsed textual data to generate processed textual data. The device may process the processed textual data, with a clustering model, to identify topical data associated with the processed textual data, and may process the topical data, with a classification model, to group the topical data into categories. The device may generate a knowledge graph based on the categories, and may store the knowledge graph in a data structure. The knowledge graph may enable the device to provide answers to questions associated with the application or the system.

Abstract: In some implementations, a device may receive project management data including information related to project elements associated with one or more projects and associations among the project elements associated with the one or more projects. The device may generate one or more data structures representing the project elements associated with the one or more projects and the associations among the project elements associated with the one or more projects. The device may receive a request to visualize project data, wherein the request may indicate a view to use in order to visualize the project data and/or specify criteria related to the project data to be visualized in the view. The device may search the one or more data structures to obtain the project data to be visualized based on the criteria specified in the request. The device may render the project data using the view indicated in the request.

Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: A device may receive, in near-real time, unstructured data associated with an application or a system, and may extract textual data from the unstructured data. The device may parse the textual data to generate parsed textual data, and may perform natural language processing on the parsed textual data to generate processed textual data. The device may process the processed textual data, with a clustering model, to identify topical data associated with the processed textual data, and may process the topical data, with a classification model, to group the topical data into categories. The device may generate a knowledge graph based on the categories, and may store the knowledge graph in a data structure. The knowledge graph may enable the device to provide answers to questions associated with the application or the system.

Abstract: A device may obtain first data relating to a machine learning model. The device may pre-process the first data to alter the first data to generate second data. The device may process the second data to select a set of features from the second data. The device may analyze the set of features to evaluate a plurality of types of machine learning models with respect to the set of features. The device may select a particular type of machine learning model for the set of features based on analyzing the set of features to evaluate the plurality of types of machine learning models. The device may tune a set of parameters of the particular type of machine learning model to train the machine learning model. The device may receive third data for prediction. The device may provide a prediction using the particular type of machine learning model.

Abstract: A device receive data regarding a project. The device may automatically evaluate the data using a set of analytics tools. The device may communicate with a client device to provide a virtual agent user interface component based on evaluating the data using the set of analytics tools. The virtual agent user interface component may include information identifying a set of alerts relating to the project and a set of recommendations relating to the set of alerts. The device may receive information indicating that a particular recommendation, of the set of recommendations is to be implemented. The device may communicate with one or more other devices to implement the particular recommendation.

Abstract: A device receive data regarding a project. The device may automatically evaluate the data using a set of analytics tools. The device may communicate with a client device to provide a virtual agent user interface component based on evaluating the data using the set of analytics tools. The virtual agent user interface component may include information identifying a set of alerts relating to the project and a set of recommendations relating to the set of alerts. The device may receive information indicating that a particular recommendation, of the set of recommendations is to be implemented. The device may communicate with one or more other devices to implement the particular recommendation.

Abstract: Laminates of improved wear resistant are made using decor sheets prepared by treating printed décor paper with a mordant so that decorative ink is bonded to the paper. The decor sheets are then suitable for use in high pressure or low pressure laminates.

Abstract: Laminates of improved wear resistant are made using decor sheets prepared by treating printed décor paper with a mordant so that decorative ink is bonded to the paper. The decor sheets are then suitable for use in high pressure or low pressure laminates.

Abstract: Laminates of improved wear resistant are made using decor sheets prepared by treating printed decor paper with a mordant so that decorative ink is bonded to the paper. The decor sheets are then suitable for use in high pressure or low pressure laminates.