Abstract: A method for joint optimization of resource allocation includes: obtaining network data volumes of two services; obtaining queue statuses at a time t; computing sub-channel slices; computing a local CPU speed scaling, a user association, a sub-carrier assignment, and a power allocation of service 1; computing a user association, a video quality decision, and a sub-carrier assignment of service 2; obtaining an initial sub-carrier assignment and an initial power allocation; obtaining the user association; obtaining the power allocation and the sub-carrier assignment of service 1; obtaining the video quality decision; obtaining the sub-carrier assignment of service 2; obtaining an optimal data transmission rate and the user association to obtain a data rate allocation; and obtaining an optimal CPU speed scaling, an optimal user association, an optimal sub-carrier assignment, an optimal power allocation, an optimal video quality decision and an optimal sub-channel allocation.

Abstract: A method for defending against an adversarial sample in image classification includes: denoising, by an adversarial denoising network, an input image to acquire a reconstructed image; acquiring, by a target classification model, a predicted category probability distribution of the reconstructed image; acquiring, by the target classification model, a predicted category probability distribution of the original input image; calculating an adversarial score of the input image, and determining the input image as an adversarial sample or a benign sample according to a threshold; outputting a category prediction result of the reconstructed image if the input image is determined as the adversarial sample; and outputting a category prediction result of the original input image if the input image is determined as the benign sample. A system for defending against an adversarial sample in image classification, and a data processing terminal are further provided.

Abstract: A distributed support vector machine privacy-preserving method includes: dividing a secret through secret sharing among all participating entities, iteratively exchanging a part of the information divided by the participating entities, and solving sub-problems locally; performing an iteration until a convergence is reached to find a global optimal solution; and in consideration of the generality of the privacy-preserving method, adopting a privacy-preserving method based on a vertical data distribution and a privacy-preserving method based on a horizontal data distribution, respectively; wherein the participating entities do not trust each other, and interact through a multi-party computation for local training. The method is applied to an honest-but-curious scenario, and uses the idea of data division to perform local computation through the interaction of part of the data among users to finally reconstruct the secret to preserve data privacy.

Abstract: A method for joint optimization of resource allocation includes: obtaining network data volumes of two services; obtaining queue statuses at a time t; computing sub-channel slices; computing a local CPU speed scaling, a user association, a sub-carrier assignment, and a power allocation of service 1; computing a user association, a video quality decision, and a sub-carrier assignment of service 2; obtaining an initial sub-carrier assignment and an initial power allocation; obtaining the user association; obtaining the power allocation and the sub-carrier assignment of service 1; obtaining the video quality decision; obtaining the sub-carrier assignment of service 2; obtaining an optimal data transmission rate and the user association to obtain a data rate allocation; and obtaining an optimal CPU speed scaling, an optimal user association, an optimal sub-carrier assignment, an optimal power allocation, an optimal video quality decision and an optimal sub-channel allocation.

Abstract: A trusted graph data node classification method includes: (1) inputting a topological graph and node features, and calculating a discrete Ricci curvature of the discrete topological graph; (2) preprocessing the curvature and the node features; (3) mapping the curvature, reconstructing original features, and performing a semi-supervised training on graph data containing adversarial examples; and (4) performing a classification on unlabeled nodes. The new method uses a discrete curvature to extract topological information, and uses a residual network to reconstruct node feature vectors without knowing the technical details of the adversarial examples, and without using a large number of adversarial examples for adversarial training. Hence, the system effectively defends against attacks from adversarial examples on the graph data, outperforms the existing mainstream models in terms of accuracy when used in data without adversarial examples, and is thus a trusted node classification system.