Abstract The fusion of a human prior with neural rendering techniques has recently emerged as one of the most promising approaches to processing dynamic human‐body scenes with sparse inputs. However, learning geometric details and appearance in dynamic human‐body scenes based solely on a human prior model represents a severely under‐constrained problem. A new human‐body representation method to solve this problem: a neural radiance field with vertex‐texture latent codes (VT‐NeRF) is proposed. VT‐NeRF uses joint latent code to improve access to detailed information, combining vertex latent codes with 2D texture latent codes for the body surface. Referencing a 3D human skeleton for accurate guidance, the human model can quickly match poses and learn information about the body in different frames. VT‐NeRF can integrate body information from different frames and different poses quickly because it uses an information‐rich human prior: a 3D human skeleton and parametric models. A 3D human scene is then presented as an implied field of density and colour. Experiments with the ZJU‐MoCap dataset show that our method outperforms previous methods in terms of both novel‐view synthesis and 3D human reconstruction quality. It is twice as fast as Neural Body, and its average accuracy reaches 95.9%.

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

Summary Co‐training algorithm is one of the main methods of semi‐supervised learning in machine learning, which explores the effective information in unlabeled data by multi‐learner collaboration. Based on the development of co‐training algorithm, the research work in recent years was further summarized in this article. In particular, three main steps of relevant co‐training algorithms are introduced: view acquisition, learners' differentiation, and label confidence estimation. Finally, we summarized the problems existing in the current co‐training methods, gave some suggestions for improvement, and looked forward to the future development direction of the co‐training algorithm.

Despite the demonstrated effectiveness of dynamic taint analysis (DTA) in a variety of security applications, the poor performance achieved by available DTA prototypes prevents their widespread adoption in production systems, especially the Android system with limited computation and storage resources. To overcome DTA’s overhead bottlenecks, recent research efforts aim to decouple taint tracking logic from program execution. Continuing this line of research, this work proposes FSAFlow, a novel hybrid taint tracking and control system, to reduce DTA overhead significantly while ensuring sound Android privacy protection. FSAFlow further separates the path tracking logic from the corresponding taint tracking logic and the control of the information flow path is optimized. Specifically, a classic static analysis algorithm is first modified to search target paths and their key branch information. Then, the potential paths that violate the user’s predefined privacy protection policy are chosen and encoded with a Finite State Automaton (FSA). A small amount of FSA-based state management code is inserted into the corresponding position in the program. Finally, it monitors the program’s state of path execution and prevents information leakage during runtime. The efficiency and correctness of FSAFlow are proved by theoretical analysis. The experimental results show that FSAFlow incurs lower overhead than several representative DTA optimization approaches, 2.06% for popular applications, and 5.41% on CaffeineMark 3.0. FSAFlow has fewer false negatives in implicit flow tracking than the Android DTA platform, TaintDroid, and achieves higher precision than the static analysis tool, FlowDroid, by verifying the paths that never occur and tracking in the complete execution stage of the loop body at runtime.

Mobile operating systems such as Android are facing serious security risk. First, they have a large number of users and store a large number of users’ private data, which have become major targets of network attack; second, their openness leads to high security risks; third, their coarse-grained static permission control mechanism leads to a large number of privacy leaks. Recent decentralized information flow control (DIFC) operating systems such as Asbestos, HiStar, and Flume dynamically adjust the label of each process. Asbestos contains inherent covert channels due to this implicit label adjustment. The others close these covert channels through the use of explicit label change, but this impedes communication and increases performance overhead. We present an enhanced implicit label change model (EILCM) for mobile operating systems that can close the known covert channel in these models with implicit label change and supports dynamic constraints on tags for separation of duty. We also formally analyze the reasons why EILCM can close the known covert channels and prove that abstract EILCM systems have the security property of noninterference with declassification by virtue of the model checker tool FDR. We also prove that the problem of EILCM policy verification is NP-complete and propose a backtrack-based search algorithm to solve the problem. Experiments are presented to show that the algorithm is effective.

"Draw portraits by music", an interactive work of art. Compared with music visualization and image style conversion, it's AI's imitation of human synaesthetic. New portraits gradually appear on the screen and are synchronized with music in real-time. Users select music and images as the main interactive contents, the parameters of the music are used as the dynamic expression of human emotions, and the new pixel generation process of the image is regarded as the result of emotions affecting humans.