GNSS/INS integrated navigation positioning methods are widely used in vehicle positioning services. However, high-accuracy and reliable vehicle localization remains a challenge under GNSS signal degradation or INS drift. We propose PUKF, a novel fusion algorithm combining Nonlinear Predictive Filtering (NPF) with Unscented Kalman Filtering (UKF), to address model error-induced degradation in GNSS/INS integrated navigation. PUKF introduces real-time model error estimation into the prediction phase of UKF to dynamically adapt to complex environments. Preliminary results from simulation and KITTI dataset-based experiments show that PUKF outperforms the traditional UKF in high-dynamic environments, especially when model errors are large, significantly enhancing positioning accuracy and system stability under abrupt dynamics and low-cost sensors. The proposed method has promising applications in realtime web-based vehicle services.

Gait-based WiFi user identification enables devicefree recognition by analyzing signal variations caused by human motion. However, existing methods face performance drops in new environments and cannot detect unauthorized users. This paper proposes GIWiD (Gait-based user Identification with WiFi Device-free sensing), which employs adversarial learning to extract domain-invariant features and enhance cross-domain generalization. A two-stage training strategy is adopted: the first stage performs identity recognition, and the second detects unauthorized users via reconstruction errors. GIWiD uses passive WiFi sensing to preserve privacy and ensure accuracy. Experiments on data from 13 volunteers across diverse indoor settings show that GIWiD outperforms existing methods in both crossdomain identification and unauthorized user detection.

The Internet of Everything (IoE) has recently become a hot topic. With the development of Internet of Things (IoT) technology, people can connect to networks in increasingly diverse ways. The surge in users, devices, and requests poses significant challenges to network capacity and backhaul links. Content caching technology has long been considered a promising approach to improving network performance. However, existing methods still have room for improvement in terms of content transmission efficiency and user access latency. To address these issues, this paper proposes an Influence-Driven Content Caching (IDCC) method. Specifically, based on a caching strategy of “caching content that is likely to have the greatest future influence on the most influential edge devices", this paper designs a comprehensive framework encompassing content selection, updating, and placement to optimize content caching efficiency, enhance network spectral efficiency, and improve user’s quality of experience (QoE). First, a content selection strategy based on the popularity dynamics prediction method is developed by utilizing graph neural networks and contrastive learning to model heterogeneous data. Second, a content update mechanism for cached content and key caching information is designed based on the popularity of content and Near-Field Communications (NFC) between users. Furthermore, interconnected network devices are represented as a graph, and the communication influence of key network nodes is predicted using autoencoders and graph neural networks to identify the optimal caching nodes for maximizing benefits. Finally, extensive experiments show that the proposed IDCC method offers significant advantages in reducing network latency and improving network utilization.

Aspect-based sentiment classification aims to predict the sentiment polarity of an aspect term in a sentence. It has been verified that syntactic dependent trees, especially integrating with graph convolutional networks (GCN) can provide crucial syntactic features for sentiment classification. However, it can not consider the dependency label information between the aspect and context in the sentence, which contains rich semantic information. In this paper, we propose a bidirectional edge-enhanced graph convolutional networks (BE-GCN), which combines the syntactic structure and the dependency label information effectively. Specifically, we design an edge-enhanced module to dynamically update the dependency edge according to dependency relation and contextual information. Then the dependency edge can update the word representation reversely, thereby selectively outputting sentiment features according to the given aspect. Comparison experiments demonstrate the effectiveness of our model using dependency label information and syntactic structure.