Abstract: Disclosed embodiments provide techniques for analyzing a semantic priority of an activity utilizing computing resources, and provides mitigation actions to resolve resource shortages in real-time. The computing resources can include network bandwidth usage, as well as processing cycles, memory usage, and/or other shared computing resources. Disclosed embodiments perform a semantic priority analysis of user activities. The semantic priority analysis can include utilizing natural language processing (NLP), analysis of a user calendar, and/or additional application data to infer a semantic priority. When computing resources such as network bandwidth exceed a predetermined level, then a mitigation action is executed, enabling the computing resources to be reduced while still allowing the higher priority activities (e.g., work and school) to continue.

Abstract: Embodiments of technologies for machine learning-assisted content moderation include receiving a first electronic communication from a user device, where the first electronic communication identifies a content item. At least one trained machine learning model is selected from a set of trained machine learning models based on the content item and the first electronic communication. The selected trained machine learning model(s) are applied to the identified content item. The selected trained machine learning model(s) identify sub-items of the content item, corresponding content labels, and associated confidence metrics. Using the confidence metrics and one or more additional electronic communications received from the user device, a content moderation process is applied to the content item and/or the sub-items.

Abstract: Technologies for language agnostic OCR extraction include identifying a word region of an image using optical character recognition, applying a language agnostic machine learning model to the word region, where the language agnostic machine learning model is trained on training data including a set of image-text pairs and a set of multilingual text translation pairs, receiving, from the language agnostic machine learning model, a word region embedding that is associated with the word region, searching a multilingual index for a text embedding that matches the word region embedding, receiving, from the multilingual index, text associated with the text embedding; and outputting at least one of the text or the text embedding to at least one downstream process, application, system, component, or network.

Abstract: Targeted acquisition of data for model training includes identifying attributes of classified samples of a collection of samples classified by a classification model, and generating at least one query based on the identified attributes, the at least one query tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Abstract: One embodiment provides a method, including: receiving a plurality of consumer feedback comments regarding one of a plurality of agricultural food products, wherein each of the plurality of consumer feedback comments comprises information regarding a characteristic of a given agricultural food product, wherein each of the plurality of agricultural food products corresponds to an agricultural source producing an agricultural food product category; updating a rating of each of the plurality of agricultural food products based upon consumer feedback comments corresponding to a given agricultural food product, wherein the updating comprises aggregating the received consumer feedback comments with previously supplied consumer feedback comments for agricultural food products within the agricultural food product category of a given agricultural source; ranking the plurality of agricultural food products based upon the ratings of the plurality of agricultural food products, wherein the ranking comprises ranking the p

Abstract: Techniques are described with respect to a system, method, and computer product for improving product demand. An associated method includes generating a food profile based on a food inventory of a user the food inventory available to a computing device in a computer-accessible form; and identifying at least one food recipe based on the food profile, the food recipe available in a computer-accessible form. The method further includes determining a plurality of food items associated with the food inventory; identifying one or more absent food items associated with the at least one food recipe from the plurality of food items, the one or more absent food items being absent from the food inventory; and communicating to the user that the one or more absent food items are absent from the food inventory.

Abstract: Provided are a computer program product, system, and method for determining an assignment of virtual objects to positions in a user field of view to render in a mixed reality display. A reward score for a virtual object is calculated based on an easiness score indicating an easiness to view the virtual object in previous interactions with the virtual object and frequency of interactions with the virtual object. The reward scores and the viewability scores for the positions are inputted into a virtual object placement program to output an assignment of the virtual objects to the positions. The assignment of the virtual objects to the positions are transmitted to the mixed reality display to cause the mixed reality display to render the virtual objects at the positions in the field of view of the user indicated in the assignment.

Abstract: An embodiment includes analyzing text content of a user query to identify via natural language processing (NLP) a query topic. The embodiment maps the query topic to a topic cluster at a node of a hierarchical model of a text database. The embodiment generates query demand data indicative of demand for the topic cluster based on user queries. The embodiment identifies the topic cluster as a topic-cache candidate based on the query demand data. The embodiment compares an amount of memory required for storing text associated with the first topic cluster to available cache memory. The embodiment caches the text of the topic cluster candidate upon determining that there is sufficient available cache memory space.

Abstract: Classification and visualization of user interactions with an interactive computing platform is provided by building and providing a graphical user interface (GUI) of graphical elements for display on a display device. The graphical elements present visualizations of user interactions between users and an interactive computing platform in progression of the users through tasks based on the user interactions. The graphical elements also present identified features of the user interactions relative to the tasks and progression of the users therethrough. The building and providing includes building and providing, for a task of the tasks, a task element with first features corresponding to subtasks of the task and second features providing relations between the first features.

Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: Targeted acquisition of data for model training includes automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded. The samples described by the automatically generated metadata are classified by a classification model, and include both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods. Additionally, attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples are identified, and queries are generated based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Abstract: An approach for modifying in real-time by removing or reinforcing stroboscopic effect from images associated with a viewing experience is disclosed. The approach includes identifying video clips, detecting environmental parameters and calculating display setting. The approach also analyzes display setting using recommendation from GAN, output displaying setting on an AR display and receiving feedback from user.

Abstract: A method includes determining a presence of one or more people around a user and determining, based on the presence of the one or more people, an exposure level. The method also includes receiving a message for the user, the message comprising a first portion and a second portion and removing, based on the exposure level, the first and second portions from the message to produce a first message. The method further includes removing, based on the exposure level, the first portion from the message to produce a second message and presenting, based on the exposure level, the first message to the user. The method also includes receiving, from the user, feedback about the first message and presenting the second message to the user in response to the feedback.

Abstract: Methods, systems and computer program products for automated learning are provided herein. A computer-implemented method includes authenticating a plurality of users for an automated learning session, wherein the plurality of users correspond to at least one device, and providing the automated learning session for the plurality of users. Providing the automated learning session comprises analyzing a plurality of learning models corresponding to one or more of the plurality of users, determining, based on the analysis, one or more activities to be performed by the plurality of users during the automated learning session, and executing the one or more activities on at least one device.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of code datasets prior to a data migration; analyzing the identified code datasets for a plurality of parameters; dynamically predicting a carbon footprint associated with the analyzed code datasets based on the plurality of parameters for each analyzed code dataset; and automatically optimizing the analyzed code datasets based on the predicted carbon footprint for data migration.

Abstract: Classification and visualization of user interactions with an interactive computing platform is provided by building and providing a graphical user interface (GUI) of graphical elements for display on a display device. The graphical elements present visualizations of user interactions between users and an interactive computing platform in progression of the users through tasks based on the user interactions. The graphical elements also present identified features of the user interactions relative to the tasks and progression of the users therethrough. The building and providing includes building and providing, for a task of the tasks, a task element with first features corresponding to subtasks of the task and second features providing relations between the first features.

Abstract: Classification and visualization of user interactions with an interactive computing platform is provided by building and providing a graphical user interface (GUI) of graphical elements for display on a display device. The graphical elements present (i) visualizations of user interactions between users and an interactive computing platform in progression of the users through tasks based on the user interactions and (ii) identified features of the user interactions relative to the tasks and progression of the users therethrough. The building and providing includes, for each of the tasks, building and providing a respective task element that reflects identified features of a set of user interactions of one or more users in progressing through that task. Building a task element for a task includes building the task element as a polygon having vertices corresponding to subtasks of the task and edges between the vertices.

Abstract: User interactions with a supply chain system are monitored based on a tracked ontology enrichment process, an explainable reasoning graph is constructed based on the monitored user interactions and domain specific reasoning information; and an explainable insight of the monitored user interactions is learned, as is a user interaction embedding for an embedding space, based on the constructed explainable reasoning graph and the explainable insight. External data is incorporated into the embedding space, a joint embedding is learned based on the user interaction embedding, and missing entities and relationships are identified for incorporation into an ontology based on the user interactions and joint embedding. The ontology is revised to incorporate the missing entities and relationships into the ontology to create a revised ontology, and a supply chain is controlled based on the revised ontology.

Abstract: Spatio-temporal climate forecasts are analyzed and one or more resiliency policies for a supply chain are dynamically generated. The resiliency policy is embedded in a resiliency reasoning graph and a temporal feedback loop is performed based on user feedback regarding the generated resiliency policy and user interaction with the resiliency reasoning graph. One or more machine learning models are updated based on the user feedback and a joint optimization of the machine learning models is re-solved based on the user feedback. The resiliency policy is updated based on the updated machine learning models based on the user feedback and an operation of a supply chain is adjusted based on the updated resiliency policy.

Abstract: A method and system generate news headlines from user input parameters. The user input parameters include a specified geographic region of interest and an industry of interest. Climate data and carbon emissions data for the specified geographic region of interest is retrieved. Supply chain dependencies are determined. A machine learning model is generated using the specified geographic region of interest, the industry, the climate data, the carbon emissions data, and the supply chain dependencies. The machine learning model performs an impact analysis on a supply chain based on the climate data and the carbon emissions data. The machine learning model predicts a supply chain performance for the industry based on the impact analysis. A news headline is automatically generated describing the predicted supply chain performance. The news headline includes an underlying basis for the predicted supply chain performance.