5 results about "Distance space" patented technology

A multi-machine collaborative vulnerability detection system based on vulnerability clustering and distance space partitioning is proposed. The system extracts potential vulnerabilities from the source code of the program under test through a vulnerability extraction and clustering module. An improved clustering algorithm is used to aggregate representative vulnerability points as baseline reference points. A fuzzing module performs fuzzing tests on the program. During the seed mutation phase, based on distance judgment and pre-calculated basic block-level distances, the currently executed seed (i.e., the comprehensive distance between the test input in the fuzzing test and the baseline reference point of that instance) is calculated, and appropriate energy is allocated based on the comprehensive distance. An information synchronization module performs asynchronous and transparent information synchronization among multiple fuzzing instances, collecting seeds from the seed queues of each instance, calculating the affinity of each seed to each instance, and assigning the seed to its actual fuzzing instance. This invention utilizes additional CPU resources, using some sensitive functions in the program as baseline reference points, and divides the program into multiple segments according to the distance space, allocating them to multiple fuzzing instances for multi-machine collaborative fuzzing, thereby improving the performance of fuzzing by utilizing computing resources.

The invention provides an image classification method based on prompt incremental aggregation of an energy field and a distance space, and the method comprises the steps: obtaining a data set of a field reinforcement learning scene, and the data set comprises the data of at least one field; training is carried out through a data set, and during training, the data of the current field and the frozen CLIP model are utilized to optimize the personalized prompt model of the field through minimizing a loss function; during reasoning, a to-be-classified image is coded through the CLIP model to obtain a feature vector, the distance between the feature vector and each field is calculated, for each field, the personalized prompt model of the field is fused with the CLIP model to generate energy and prediction probability of the to-be-classified image, a distance factor and an energy factor are determined based on the distance and the energy, and the prediction probability of the to-be-classified image is calculated. And combining the distance factor and the energy factor to obtain a domain weight, calculating a weighted sum of prediction probabilities by using the domain weight, and generating a classification mixing probability. By adopting the scheme, the cross-domain increment adaptation capability of the model is improved.

A computer-implemented method for computing a surface of a multiple-layer object is disclosed. The method includes obtaining reference data describing a reference surface, obtaining layer data describing a respective thickness and a respective boundary of each layer of the multiple layers defined on the reference surface, and computing, based on the reference data and the layer data, a spatial field that encloses a spatial region located between the reference surface and a region corresponding to a sum of the thicknesses of the multiple layers stacked on the reference surface. The spatial field describes, for each of its constituent field points, a distance of the respective field point to the boundary of the respective layer and to the reference surface. The computing of the spatial field includes computing the spatial field to describe the field to a precision that is based on a distance to the boundary of the respective layer. The computer-implemented method includes computing the surface based on the spatial field.

In a non-contact position and / or distance sensor for determining the distance, spatial orientation, material properties or the like of a target object, and in a method for its operation, it is particularly provided that at least two sensor elements forming a sensor module (100) are provided and signals (103) supplied by the at least two sensor elements are jointly evaluated by means of at least one artificial neural network (110).