Research and Implementation of Time Synchronous Dynamic Password Based on SM3 Hash Algorithm

With the rapid development of information technology, demand of network & information security has increased. People enjoy many benefits by virtue of information technology. At the same time network security has become the important challenge, but network information security has become a top priority. In the field of authentication, dynamic password technology has gained users’ trust and favor because of its safety and ease of operation. Dynamic password, SHA (Secure Hash Algorithm) is widely used globally and acts as information security mechanism against potential threat. The cryptographic algorithm is an open research area, and development of these state-owned technology products helps secure encryption product and provides safeguard against threats. Dynamic password authentication technology is based on time synchronization, using the state-owned password algorithm. SM3 hash algorithm can meet the security needs of a variety of cryptographic applications for commercial cryptographic applications and verification of digital signatures, generation and verification of message authentication code. Dynamic password basically generates an unpredictable random numbers based on a combination of specialized algorithms. Each password can only be used once, and help provide high safety. Therefore, the dynamic password technology for network information security issues is of great significance. In our proposed algorithm, dynamic password is generated by SM3 Hash Algorithm using current time and the identity ID and it varies with time and changes randomly. Coupled with the SM3 hash algorithm security, dynamic password security properties can be further improved, thus it effectively improves network authentication security.

CurveNet:Curvature-Based Multitask Learning Deep Networks for 3D Object Recognition

In computer vision fields,3D object recognition is one of the most important tasks for many real-world applications.Three-dimensional convolutional neural networks(CNNs)have demonstrated their advantages in 3D object recognition.In this paper,we propose to use the principal curvature directions of 3D objects(using a CAD model)to represent the geometric features as inputs for the 3D CNN.Our framework,namely CurveNet,learns perceptually relevant salient features and predicts object class labels.Curvature directions incorporate complex surface information of a 3D object,which helps our framework to produce more precise and discriminative features for object recognition.Multitask learning is inspired by sharing features between two related tasks,where we consider pose classification as an auxiliary task to enable our CurveNet to better generalize object label classification.Experimental results show that our proposed framework using curvature vectors performs better than voxels as an input for 3D object classification.We further improved the performance of CurveNet by combining two networks with both curvature direction and voxels of a 3D object as the inputs.A Cross-Stitch module was adopted to learn effective shared features across multiple representations.We evaluated our methods using three publicly available datasets and achieved competitive performance in the 3D object recognition task.

A perceptual quality metric for 3D triangle meshes based on spatial pooling

In computer graphics, various processing operations are applied to 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In this context, perceptual quality assessment of 3D triangle meshes has become a crucial issue. In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh. Our analysis indicates that the overall quality of a distorted mesh is sensitive to the distortion distribution. The proposed metric is based on a spatial pooling strategy and statistical descriptors of the dis- tortion distribution. We generate a perceptual distortion map for vertices in the reference mesh while taking into account the visual masking effect of the human visual system. The proposed metric extracts statistical descriptors from the dis- tortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt a support vector regression model to predict the mesh quality score. We validate the performance of our method with three publicly available databases, and the comparison with state-of-the-art metrics demonstrates the superiority of our method. Experimental results show that our proposed method achieves a high correlation between objective assessment and subjective scores.