In the context of the unmanned aerial vehicle (UAV)-assisted vehicular networking system, more network factors need to be considered to ensure the safe operation of connected vehicles. A large volume of delay-sensitive and computationally demanding tasks necessitate offloading to UAVs or roadside units for processing. And the efficient allocation of various network resources of vehicles, UAVs, and roadside units under constrained conditions determines the efficiency of task offloading. Deep reinforcement learning (DRL) has demonstrated its efficacy as an experienced approach for solving such problems. In this paper, we delve into the utilization of deep reinforcement learning to design an efficient UAV-assisted vehicular edge computing task offloading strategy. Under the constraints of limited network bandwidth and limited UAV power, the trajectory and the task offloading strategy of the UAV are jointly optimized. The primary objective of our proposed strategy is to achieve a notable reduction in the system delay of the edge computing network. Given the dynamic variability of tasks arrival, we employ a long short-term memory (LSTM) network with the attention mechanism and a deep deterministic policy gradient (DDPG) algorithm to effectively model the optimization problem as a Markov decision process. This approach can obtain the optimal policy through interactive learning from the UAV and the vehicle environment. The experiment results illustrate that this strategy outperforms other baseline strategies in terms of convergence speed, network delay, and task offloading ratio.

The production of traditional Peking opera facial masks often relies on hand painting by experienced painters, which restricts the inheritance and development of this intangible cultural heritage. Current research mainly focuses on the digital reconstruction and storage of existing Peking opera facial masks, while high-quality facial mask generation technology is still in an infancy stage. In this paper, different deep learning frameworks are improved for learning features of Peking opera facial masks and generating new masks, which can effectively promote the creative application of Peking opera facial masks. First, using different data enhancement methods, an improved Style Generative Adversarial Network-2 (StyleGAN2) can learn implicit and explicit features of Peking opera facial masks and automatically generate new facial masks. In addition, an image translation framework for joint cross-domain communication under weak supervision is used to translate face sketches and color reference maps to an intermediate feature domain, and then synthesize new facial masks through an image generation network. The experimental results show that the generated Peking opera facial masks have good local randomness and excellent visual quality.

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.

Polar code has the characteristics of simple coding and high reliability, and it has been used as the control channel coding scheme of 5G wireless communication. However, its decoding algorithm always encounters problems of large decoding delay and high iteration complexity when dealing with channel noise. To address the above challenges, this paper proposes a channel noise optimized decoding scheme based on a convolutional neural network (CNN). Firstly, a CNN is adopted to extract and train the colored channel noise to get more accurate estimation noise, and then, the belief propagation (BP) decoding algorithm is used to decode the polar codes based on the output of the CNN. To analyze and verify the performance of the proposed channel noise optimized decoding scheme, we simulate the decoding of polar codes with different correlation coefficients, different loss function parameters, and different code lengths. The experimental results show that the CNN‐BP concatenated decoding can better suppress the colored channel noise and significantly improve the decoding gain compared with the traditional BP decoding algorithm.

The development of network technology has created various platforms and methods for information dissemination. When rumors spread in social networks, they will rapidly spread and may cause social harm. Also, there are groups in social networks that create and spread rumors for the purpose of profit, thus expanding the scope of rumors. Therefore, based on the theory of complex network propagation dynamics, the study of the propagation law of rumors and the design of effective prevention and control strategies is of practical importance and theoretical significance for understanding the propagation laws of rumors and controlling the outbreak of rumors. The spreading process of rumors on social network platforms is focused here. The intentional spreader based on the classic rumor‐spreading model is introduced. First, 2SIR rumor‐spreading models on homogeneous and heterogeneous networks are established, respectively. Second, the steady‐state analysis was separately carried out, and the corresponding propagation critical value was obtained: in the homogeneous network, the condition for the large‐scale spread of rumors is ; in the heterogeneous network, the condition for the large‐scale spread of rumors is . Finally, the simulation calculation and model feasibility verification were carried out on the model. The results show that the theoretical propagation threshold corresponds with the simulation results. According to the simulation results, the final influence of rumors has significantly decreased with decreasing values of β (intentional spreading rate) instead of α (unintentional spreading rate). It can be concluded that in the real‐life rumor control process, more resources need to be invested in reducing the rate of intentional transmission instead of being indiscriminately put on controlling all spreaders of rumors.