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44results about How to "Reduced legibility" patented technology

System, plug-in, and method for improving text composition by modifying character prominence according to assigned character information measures

A computer implemented system, plug-in application and method for composing a formatted text input to improve legibility, readability and / or print economy while preserving the format of the text input and satisfying any user selected aesthetic constraints. This is accomplished by reading in blocks of text input having defined characters including letters and punctuation in a given input format. A language unit such as a lexical or sub-lexical unit, a subset of punctuation or another defined unit for a particular language is examined and an information measure (IM) is assigned to each character in the language unit indicating the predictability of that character to differentiate the language unit from other language units. Typically, multiple different IMs are assigned to each character and combined to form a combined IM (CIM). The process is repeated for at least a plurality of language units and typically until all the text input in the block has been analyzed and information measures assigned to all of the characters. An adjustment to a physical feature is determined for each character in the plurality of units to modify the visual prominence of that character according to the values of the assigned information measures and a permitted range of physical variation for the block. The adjustments are applied to each character to compose the text input consistent with the input format.
Owner:LANGUAGE TECH

Small adversarial patch generation method and device

The invention discloses a small adversarial patch generation method and device, and the method comprises the steps: carrying out the random initialization of an adversarial patch image, adding the initialized adversarial patch image to a selected pasting region on a target object in training data, and manufacturing an adversarial sample; transmitting the adversarial samples into a deep learning model for adversarial feature extraction, and transmitting benign samples without adversarial patch images into the deep learning model for benign feature extraction; jointly inputting the adversarial features and the benign features into a feature enhancement loss function for loss calculation to obtain a loss result; adding a loss result into a model loss function, and updating a pixel value of the adversarial patch through an optimizer after back propagation; and after preset times of iteration, enabling the adversarial patch to enable the deep learning model to output an error result, and ending the adversarial patch processing process. According to the method, the size of the anti-patch in the physical world can be smaller, the manufacturing cost is reduced, the identifiability of the anti-patch is reduced, and a defense method based on detection is broken through more easily.
Owner:BEIJING REALAI TECH CO LTD
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