Multi-View Clustering (MVC) aims to enhance clustering performance by integrating multi-source complementary information. However, existing deep MVC methods face inherent challenges in balancing the learning of shared consensus representations with the preservation of view-specific information: independent encoders hinder effective cross-view collaboration, while a single shared encoder tends to sacrifice representation diversity. Although the recently introduced Mixture-of-Experts (MoE) model offers a novel approach to facilitating view collaboration, its flattened expert pool design often leads to entanglement between shared and specific information, and its routing mechanism limits collaboration potential by neglecting cross-view context. To address these challenges, this paper proposes a novel deep multi-view clustering framework—Decoupled Mixture-of-Experts with Context-Aware Routing for Multi-View Clustering (DMCAR-MVC). At its core is an innovative Decoupled MoE (D-MoE) architecture. We establish a public expert pool to learn cross-view shared representations while equipping each view with an independent private expert pool to capture its unique information, thereby structurally enforcing the decoupling of shared and specific representations. Building on this, we further design a Context-Aware Hierarchical Routing (CAHR) mechanism. When routing for the public expert pool, this mechanism introduces a global context vector to guide expert selection, enabling more efficient and globally informed cross-view collaboration. Finally, to optimize the model, we adopt a multi-level contrastive learning paradigm: on one hand, a cross-view alignment loss ensures semantic consistency in shared representations; on the other, an orthogonality constraint is imposed to further enhance separability between shared and specific representations. Extensive experiments on multiple benchmark datasets demonstrate that DMCAR-MVC significantly outperforms state-of-the-art methods across key clustering metrics. Additionally, comprehensive ablation studies thoroughly validate the effectiveness and necessity of each proposed component.

On top of the Resource Public Key Infrastructure (RPKI), the Route Origin Authorization (ROA) creates a cryptographically verifiable binding of an autonomous system to a set of IP prefixes it is authorized to originate. By their design, ROAs can protect the inter-domain routing system against prefix and sub-prefix hijacks. However, inappropriate configurations bring in vulnerabilities to other types of routing security attacks. As such, the state-of-the-art approach implements the minimal-ROA principle, eliminating the risk of using ROAs at the cost of system scalability. This paper proposes the hanging ROA, a novel bitmap-based encoding scheme for ROAs, that not only ensures strong security, but also significantly improves system scalability. According to the performance evaluation with real-world data sets, the hanging ROA outperforms the state-of-the-art approach 2.4 times in terms of the compression ratio, and it can reduce the cost of a router to synchronize all validated ROA payloads by 44.5% ~ 64.7%.

An end-to-end deep learning (DL) control model is proposed to solve autonomous landing problem of the quadrotor in way of supervised learning. Traditional methods mainly focus on getting the relative position of the quadrotor through GPS signal which is not always reliable or position-based vision servo (PBVS) methods. In this paper, we have constructed a deep neural network based on convolutional neural network(CNN) whose input is raw image. A monocular camera is used as only sensor to capture down-looking image which contains landing area. To train our deep neural network, we have used our self-built image dataset. After training phase, the well-trained control model is tested and the results perform well. Light changes and background interferences have little influence on the model`s performance, which shows the robustness and adaptation of our deep learning model.