An information processing apparatus (10) includes a controller (11) that acquires an image containing a figure and a character string and generates association information indicating an association between the figure and the character string based on a positional relationship between the figure and the character string in the image.

In one embodiment, a system includes a tangible token comprising a transparent gemstone, wherein: the transparent gemstone is internally etched with information pertaining to a blockchain, and the information comprises at least a private key, a public key, and an address, and the information is represented as a quick response code. The system includes a computing device configured to execute instructions that cause the computing device to: read the information, and validate, via a network and the address, the public key and the private key are associated with at least one block on the blockchain.

In one embodiment, a system includes a tangible token comprising a transparent gemstone, wherein: the transparent gemstone is internally etched with information pertaining to a blockchain, and the information comprises at least a private key, a public key, and an address, and the information is represented as a quick response code. The system includes a computing device configured to execute instructions that cause the computing device to: read the information, and validate, via a network and the address, the public key and the private key are associated with at least one block on the blockchain.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting a target neural network using automatically generated test cases before deployment of the target neural network in a deployment environment. One of the methods may include generating a plurality of test inputs by using a test case generation neural network; processing the plurality of test inputs using a target neural network to generate one or more test outputs for each test input; and identifying, from the one or more test outputs generated by the target neural network for each test input, failing test inputs that result in generation of test outputs by the target neural network that fail one or more criteria.

The present disclosure provides an information extraction method and apparatus, an electronic device and a readable storage medium, and relates to the field of natural language processing technologies. The information extraction method includes: acquiring a to-be-extracted text; acquiring a sample set, the sample set including a plurality of sample texts and labels of sample characters in the plurality of sample texts; determining a prediction label of each character in the to-be-extracted text according to a semantic feature vector of each character in the to-be-extracted text and a semantic feature vector of each sample character in the sample set; and extracting, according to the prediction label of each character, a character meeting a preset requirement from the to-be-extracted text as an extraction result of the to-be-extracted text. The present disclosure can simplify steps of information extraction, reduce costs of information extraction and improve flexibility and accuracy of information extraction.

A computer-implemented method is provided. The method includes: obtaining a sample text and a sample image corresponding to the sample text; labeling a true semantic tag for the sample text according to a first preset rule; obtaining a text feature representation of the sample text and a predicted semantic tag output by a text coding sub-model; obtaining an image feature representation of the sample image output by an image coding sub-model; calculating a first loss based on the true semantic tag and the predicted semantic tag; calculating a contrast loss based on the text feature representation of the sample text and the image feature representation of the sample image; adjusting parameters of the text coding sub-model based on the first loss and the contrast loss; and adjusting parameters of the image coding sub-model based on the contrast loss.

Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

A method for generating training data for hand-printed text recognition includes obtaining a structured document, obtaining a set of hand-printed character images and database metadata from a database, generating a modified document page image, and outputting a training file. The structured document includes a document page image that includes text characters and document metadata that associates each of the text characters to a document character label. The database metadata associates each of the set of hand-printed character images to a database character label. The modified document page image is generated by iteratively processing each of the text characters. The iterative processing includes determining whether an individual text character should be replaced, selecting a replacement hand-printed character image from the set of hand-printed character images, scaling the replacement hand-printed character image, and inserting the replacement hand-printed character image into the modified document page image.

An apparatus (1) for providing image quality feedback during a medical imaging examination includes at least one electronic processor (20) programmed to: receive a live video feed (17) of a display (6) of an imaging device controller (4) of an imaging device (2) performing the medical imaging examination; extract a preview image (12) from the live video feed; perform an image analysis (38) on the extracted preview image to determine whether the extracted preview image satisfies an alert criterion; and output an alert (30) when the extracted preview image satisfies the alert criterion as determined by the image analysis.

Techniques for dynamically developing a contextual set of prompts based on relevant aspects extracted from s set of training data. One technique includes obtaining training data comprising text examples and associated labels, extracting aspects from the training data, generating prompting templates based on the training data and the extracted aspects, concatenating each of the text examples with the respective generated prompting template to create prompting functions, training a machine learning language model on the prompting functions to predict a solution for a task, where the training is formulated as a masked language modeling problem with blanks of the prompting templates being set as text labels and expected output for the task being set as specified solution labels, and the training learns or updates model parameters of the machine learning language model for performing the task. The machine learning language model is provided with the learned or updated model parameters.