Glyph accessibility techniques are described as implemented by a digital content processing system involving accessing glyphs and glyph alternatives. These techniques include preprocessing techniques in which a base font is used to determine similarity of glyphs within the base font to each other. Glyph metadata that describes this similarity is cached in a storage device and used during runtime to increase efficiency in locating similar glyphs in other fonts.

Systems and methods for training an artificial intelligence (AI) classifier of scanned items. The items may include a training set of sample raw scans. The set may include in-class objects and not-in-class raw scans. An AI classifier may be configured to sample raw scans in the training set, measure errors in the results, update classifier parameters based on the errors, and detect completion of training.

A processor-implemented method with data recognition includes: extracting input feature data from input data; calculating a matching score between the extracted input feature data and enrolled feature data of an enrolled user, based on the extracted input feature data, common component data of a plurality of enrolled feature data corresponding to the enrolled user, and distribution component data of the plurality of enrolled feature data corresponding to the enrolled user; and recognizing the input data based on the matching score.

Disclosed herein are methods for training and deploying a predictive model for generating a prediction, e.g., patient eligibility for a CAR-T therapy. Datasets, such as open healthcare claims datasets, may be missing data. Missing data may hamper the ability to generate sufficient information needed for training a predictive model. Methods include leveraging comprehensive datasets, such as closed claims datasets, to create training examples for input into a machine learning algorithm. In various embodiments, the comprehensive dataset is modified to simulate the data missingness in the target dataset; then, the modified dataset is paired with the ground truth label derived from the comprehensive dataset to create training examples. In various embodiments, a comprehensive dataset is paired with a target dataset to create training examples. After training a predictive model on such examples, the model can be deployed to make predictions in the target dataset even in light of missing data.

Systems and methods are provided for sharing a screen from a mobile device. For example, a method includes receiving, at a second mobile device, an image of a screen captured from a first mobile device and determining whether to trigger an automated action. The method may also include displaying, responsive to not triggering the automated action, annotation data generated for the image with the image on a display of the second mobile device, the annotation data including at least one visual cue corresponding to content in the image relevant to a user of the second mobile device. The method may further include, responsive to triggering the automated action, determining that a mobile application associated with the image is installed on the second mobile device and replaying user input actions received with the image on the second mobile device starting from a reference screen associated with the mobile application.

Authentication is performed based on a user narrative. A narrative, such as a personal story, can be requested during a setup process. Content, voice signature, and emotion can be determined or inferred from analyzing the narrative. Subsequently, a user can provide vocal input associated with the narrative, such as by retelling the narrative or answering questions regarding the narrative. The vocal input can be analyzed for content, voice signature and emotion, and compared with the initial narrative. An authentication score can then generated based on the comparison.

Even if an object to be detected is not remarkable in images, and the input includes images including regions that are not the object to be detected and have a common appearance on the images, a region indicating the object to be detected is accurately detected. A local feature extraction unit 20 extracts a local feature of a feature point from each image included in an input image set. An image-pair common pattern extraction unit 30 extracts, from each image pair selected from images included in the image set, a common pattern constituted by a set of feature point pairs that have similar local features extracted by the local feature extraction unit 20 in images constituting the image pair, the set of feature point pairs being geometrically similar to each other. A region detection unit 50 detects, as a region indicating an object to be detected in each image included in the image set, a region that is based on a common pattern that is omnipresent in the image set, of common patterns extracted by the image-pair common pattern extraction unit 30.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

Systems and methods change a user interface for the purpose of guiding a user in supplementing a product listing with an image to evidence the product's authenticity. Example embodiments include a machine-implemented method for accessing at least one database to retrieve an authenticity criterion mapped to a product and at least one reference image that depicts adequate detail of a product specimen to fulfill the authenticity criterion. The machine can further cause a user device to display the reference image to the user along with a suggestion that the user submit a candidate image depicting similar detail of the product. In some example embodiments, the method further includes retrieving the candidate image, confirming receipt of the candidate image, and displaying the candidate image, as well as adjusting a rank for a candidate specimen based on various factors.

This disclosure provides methods and systems for predicting missing links and previously unknown numerals in a knowledge graph. A jointly trained multi-task machine learning model is disclosed for integrating a symbolic pipeline for predicting missing links and a regression numerical pipeline for predicting numerals with prediction uncertainty. The two prediction pipelines share a jointly trained embedding space of entities and relationships of the knowledge graph. The numerical pipeline additionally includes a second-layer multi-task regression neural network containing multiple regression neural networks for parallel numerical prediction tasks with a cross stich network allowing for information/model parameter sharing between the various parallel numerical prediction tasks.