With the advancements in AI-generated content (AIGC) technologies and edge cloud networks, the demand for AIGC services is increasing. The diffusion model stands out for its ability to generate images from text. However, its high computational requirements challenge user devices, leading to ongoing efforts to improve inferring speed while preserving image quality. In this study, we propose a novel edge-user collaborative inferring framework. By capitalizing on collaboration among the devices in edge and user, the proposed framework optimizes the service delay, network resource consumption, and user device computing resource consumption of the user’s AIGC service, and improves the user’s QoE. To demonstrate the efficiency of our proposed framework, we conduct comparative experiments and ablation experiments on a variety of datasets. Experimental results show that the proposed framework achieves better-generated image quality and reduces a large number of computing resources and network consumption, improving user QoE.

Deep learning (DL) models have been widely studied in the field of micro-expression recognition (MER). However, micro-expressions (MEs) suffer from small number of samples and difficulty in extracting subtle and transient features, resulting in limited improvement in the recognition performance. In addition, DL models are prone to overfitting problems and are difficult to extract discriminative features of facial actions from ME images or sequences. To address these issues, we propose a MER method that combines a nonlinear deep subspace network and optical flow features. Firstly, facial motion features are captured using optical flow computation, and then the optical flow features are input into a kernel principal component analysis network (KPCANet) to further learn deeper spatio-temporal features. Finally, a linear support vector machine (SVM) is used for ME classification. Experiments conducted on four public spontaneous ME datasets including SMIC, CASME, CASME II and SAMM validate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method achieves even better recognition performance compared to existing state-of-the-art MER methods.

Salient object detection (SOD) is a fundamental task in computer vision, which attempts to mimic human visual systems that rapidly respond to visual stimuli and locate visually salient objects in various scenes. Perceptual studies have revealed that visual contrast is the most important factor in bottom-up visual attention process. Many of the proposed models predict saliency maps based on the computation of visual contrast between salient regions and backgrounds. In this paper, we design an end-to-end multi-scale global contrast convolutional neural network (CNN) that explicitly learns hierarchical contrast information among global and local features of an image to infer its salient object regions. In contrast to many previous CNN based saliency methods that apply super-pixel segmentation to obtain homogeneous regions and then extract their CNN features before producing saliency maps region-wise, our network is pre-processing free without any additional stages, yet it predicts accurate pixel-wise saliency maps. Extensive experiments demonstrate that the proposed network generates high quality saliency maps that are comparable or even superior to those of state-of-the-art salient object detection architectures.

Edge caching presents a promising avenue for mitigating backbone network congestion by strategically caching frequently accessed content at the network periphery. As most current edge caching solutions are designed for single-modal content requests, they cannot deal with the increasing volume of multi-modal content requests. In this study, we investigate the issue of multimodal content caching in edge-cloud networks. Firstly, we establish a heterogeneous edge-cloud network adept at caching multimodal content proximate to end-users to facilitate expeditious content delivery. By leveraging latent representations of multimodal content, we identify distinct user request modalities for multimodal content. Subsequently, we formulate caching replacement operations as a Markov Decision Process (MDP) aimed at minimizing user-content access latency. Moreover, we propose a decentralized multimodal content caching framework at the network edge based on federated deep reinforcement learning. This framework affords distributed decision-making and learning capabilities, thereby alleviating the strain on centralized resources and augmenting caching efficacy. To demonstrate the efficacy of our proposed framework, we conduct comprehensive experiments utilizing the Noah-Wukong dataset. Experimental results provide evidence that our framework reduces average latency by up to 10% compared to traditional methods, highlighting its proficiency in enhancing cache performance in edge-cloud networks.

Traffic signal failures can lead to severe accidents and citywide traffic disruptions, making their reliable operation essential. This study collected real-world data from intersection traffic signals and constructed a dataset. Based on the current data from traffic signals, the formatted pass-dot product attention-LSTM (FD-LSTM) model was proposed. The model utilizes one-dimensional convolution and max pooling for feature extraction, employs dual LSTM units to process forward and backward information, incorporates Dropout to prevent overfitting, and uses the dot-product attention mechanism to enhance the importance of key features. The model is trained using the Adam optimization algorithm. Comparative experiments demonstrate that the FD-LSTM model outperforms other models.

Ultra-dense and highly heterogeneous network (HetNet) deployments make the allocation of limited wireless resources among ubiquitous Internet of Things (IoT) devices an unprecedented challenge in 5G and beyond (B5G) networks. The interactions among mobile users and HetNets remain to be analyzed, where mobile users choose optimal networks to access and the HetNets adopt proper methods for allocating their own network resource. Existing works always need complete information among mobile users and HetNets. However, it is not practical in a realistic situation where important individual information is protected and will not be public to others. This paper proposes a distributed pricing and resource allocation scheme based on a Stackelberg game with incomplete information. The proposed model proves to be more practical by solving the problem that important information of either mobile users or HetNets is difficult to acquire during the resource allocation process. Considering the unknowability of channel gain information, the follower game among users is modeled as an incomplete information game, and channel gain is regarded as the type of each player. Given the pricing strategies of networks, users will adjust their bandwidth requesting strategies to maximize their expected utility. While based on the sub-equilibrium obtained in the follower game, networks will correspondingly update their pricing strategies to be optimal. The existence and uniqueness of Bayesian Nash equilibrium is proved. A probabilistic prediction method realizes the feasibility of the incomplete information game, and a reverse deduction method is utilized to obtain the game equilibrium. Simulation results show the superior performance of the proposed method.