Mesh simplification of 3D models can accelerate rendering, reduce storage space, and improve performance. However, for high-poly 3D models, there are ongoing concerns about potentially compromising the Quality of Experience (QoE), the need to set simplification ratios or parameters, and the time-consuming nature of the simplification process. To address these issues, we proposed a new mesh simplification for the preprocessing step. Based on the Quadratic Error Metric (QEM) simplification algorithm, we conducted human-centered 3D model comparison experiments to determine the optimal simplification ratio for high-poly 3D models in full body shots. From experimental data, we proposed and implemented ASimp, an automatic 3D mesh simplification scheme. In evaluation experiments, ASimp demonstrated rapid preprocessing speeds while ensuring QoE and the effectiveness of its simplification products. We hope that ASimp will contribute to the optimization of 3D models and find applications in fields such as cultural heritage, archaeology, visual effects, video games, medicine, metaverse, and beyond.

V2X technology facilitates real-time communication between vehicles, enabling collision avoidance systems, proactive hazard warnings, and cooperative maneuvers to prevent potential accidents. Due to the inherent openness of wireless communication channels, vehicular networks are highly susceptible to various security threats. Digital signatures have been widely adopted as an effective verification mechanism to ensure message integrity and authenticity. However, in high-density traffic environments, the sheer volume of messages imposes a significant computational burden on the verification process, leading to excessive delays and potential packet loss which compromises the timeliness and reliability of safety-critical applications. To address this issue, we propose a priority-aware signature verification scheme DPBV that dynamically prioritizes V2X messages based on their urgency and relevance. By leveraging clustering-based classification and batch verification techniques, the proposed approach optimizes the processing efficiency of safety messages while maintaining stringent security guarantees. Simulation results demonstrate that our scheme significantly reduces verification latency and improves message authentication throughput, making it well-suited for real-time V2X communication in high-density vehicular networks.

Multi-view clustering (MvC) utilizes information from multiple views to uncover the underlying structures of data. Despite significant advancements in MvC, mitigating the impact of missing samples in specific views on the integration of knowledge from different views remains a critical challenge. This paper proposes a novel Mask-informed Deep Contrastive Incomplete Multi-view Clustering (Mask-IMvC) method, which elegantly identifies a view-common representation for clustering. Specifically, we introduce a mask-informed fusion network that aggregates incomplete multi-view information while considering the observation status of samples across various views as a mask, thereby reducing the adverse effects of missing values. Additionally, we design a prior knowledge-assisted contrastive learning loss that boosts the representation capability of the aggregated view-common representation by injecting neighborhood information of samples from different views. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed Mask-IMvC method over state-of-the-art approaches across multiple MvC datasets, both in complete and incomplete scenarios. The demo code for our work will be publicly available at https://github.com/guanyuezhen/Mask-IMvC.

The majority of the current mispronunciation detection and diagnosis (MD&D) methods rely on manually annotated data for model training. However, annotating mispronunciations produced by second language (L2) learners is costly. Consequently, data scarcity emerges as a significant challenge in MD&D tasks. In this paper, we employ model-agnostic meta-learning (MAML) to train a phoneme recognition model for MD&D. We conduct experiments using varied meta-learning task partitioning and training strategies to endow the model’s ability to rapidly adapt to unfamiliar speakers. Our best-performing method achieves an F-measure of 61.45%, surpassing both the method using fine-tuned pre-trained model wav2vec2.0 and the approach of incorporating reference text during training. These related works also aim to address the challenge of data scarcity in MD&D. Notably, with few-shot fine-tuning, our model still yielded some remarkable results on F-measure, which suggest that in MD&D tasks, meta-learning is indeed effective.