Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanisms of target HGNNs, overlooking the common vulnerability caused by the significant differences in hyperedge pivotality along aggregation paths in most HGNNs, thereby limiting the transferability and effectiveness of attacks. In this paper, we present a novel framework, i.e., Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges (TH-Attack), to address these limitations. Specifically, we design a hyperedge recognizer via pivotality assessment to obtain pivotal hyperedges within the aggregation paths of HGNNs. Furthermore, we introduce a feature inverter based on pivotal hyperedges, which generates malicious nodes by maximizing the semantic divergence between the generated features and the pivotal hyperedges features. Lastly, by injecting these malicious nodes into the pivotal hyperedges, TH-Attack improves the transferability and effectiveness of attacks. Extensive experiments are conducted on six authentic datasets to validate the effectiveness of TH-Attack and the corresponding superiority to state-of-the-art methods.

Modern vision networks achieve high accuracy but depend on millions of weights and offer limited insight into prediction confidence. Fuzzy reasoning produces graded rule strengths that quantify uncertainty, and variational quantum circuits add compact expressive power, yet quantum neural networks and fuzzy reasoning rarely appear together in a unified model. We present the Intensively Quantized Integrated Fuzzy Neural Network (IQI-FNN), a quantized architecture that replaces the membership and defuzzification blocks of a classical fuzzy neural network with a shallow parameterized quantum circuit while retaining only minimal classical layers. Its quantum-fuzzy module merges single-qubit membership encoding, angle reuploading, a lightweight rule layer and a clustered-CNOT defuzzifier, which keeps gate count linear in feature dimension. Across standard image classification benchmarks IQI-FNN surpasses relevant state-of-the-art models and attains peak accuracy with modest hyperparameter tuning. Complexity analysis shows gate depth and parameter size grow gently with input dimension, the network stays lightweight, and fidelity remains high under representative gate-level noise in simulation. IQI-FNN therefore offers a compact, interpretable and noise-resilient alternative to conventional vision backbones and advances quantum-native fuzzy learning.

Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group identity of hyperedges, thereby resulting in limited attack performance and detectable attacks. In this manuscript, we present a novel framework, i.e., Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges (IE-Attack), to tackle these challenges. Initially, utilizing the node spanning in the hypergraph, we propose the elite hyperedges sampler to identify hyperedges to be injected. Subsequently, a node generator utilizing Kernel Density Estimation (KDE) is proposed to generate the homogeneous node with the group identity of hyperedges. Finally, by injecting the homogeneous node into elite hyperedges, IE-Attack improves the attack performance and enhances the imperceptibility of attacks. Extensive experiments are conducted on five authentic datasets to validate the effectiveness of IE-Attack and the corresponding superiority to state-of-the-art methods.

Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. Specifically, we first employ hypergraphs to model high-order interactions within social networks. Subsequently, temporal network snapshots generated during the propagation process are sequentially fed in reverse order into Mamba to infer underlying propagation dynamics. Finally, to empower the sequential model to effectively capture propagation patterns while integrating structural information, we propose a novel graph-aware state update mechanism, wherein the state of each node is propagated and refined by both temporal dependencies and topological context. Extensive evaluations on eight datasets demonstrate that SourceDetMamba consistently outperforms state-of-the-art approaches.

Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Existing approaches in rumor source detection predominantly focus on dyadic interactions, which inadequately address the complexity of more intricate relational structures. In this study, we present a novel approach for Source Detection in Hypergraphs (HyperDet) via Interactive Relationship Construction and Feature-rich Attention Fusion. Specifically, our methodology employs an Interactive Relationship Construction module to accurately model both the static topology and dynamic interactions among users, followed by the Feature-rich Attention Fusion module, which autonomously learns node features and discriminates between nodes using a self-attention mechanism, thereby effectively learning node representations under the framework of accurately modeled higher-order relationships. Extensive experimental validation confirms the efficacy of our HyperDet approach, showcasing its superiority relative to current state-of-the-art methods.

In recent years, the rampant spread of misinformation on social media has made accurate detection of multimodal fake news a critical research focus. However, previous research has not adequately understood the semantics of images, and models struggle to discern news authenticity with limited textual information. Meanwhile, treating all emotional types of news uniformly without tailored approaches further leads to performance degradation. Therefore, we propose a novel Knowledge Augmentation and Emotion Guidance Network (KEN). On the one hand, we effectively leverage LVLM's powerful semantic understanding and extensive world knowledge. For images, the generated captions provide a comprehensive understanding of image content and scenes, while for text, the retrieved evidence helps break the information silos caused by the closed and limited text and context. On the other hand, we consider inter-class differences between different emotional types of news through balanced learning, achieving fine-grained modeling of the relationship between emotional types and authenticity. Extensive experiments on two real-world datasets demonstrate the superiority of our KEN.

This article investigates the leaderless output consensus control problem for a class of nonlinear multiagent systems with heterogenous system orders and unmatched unknown parameters via output-feedback control. The interaction topology among the agents is undirected and jointly connected. Due to the heterogenous system orders and switching topology among the agents, the classical distributed adaptive backstepping-based control technique cannot be applied to solve the problem considered in this article. To solve this issue, a novel distributed reference system is first proposed for each agent, by using only relative outputs of the neighboring agents. Subsequently, a fully distributed reference system-based adaptive leaderless output consensus control scheme is designed via output-feedback control. A remarkable merit of the proposed control scheme lies in that precisely known nonlinear dynamics, system states, distributed parameter estimates, and the states of virtual reference system are no longer needed to be shared with neighbors. This implies that the communication burden can be effectively alleviated, and even the communication network can be replaced by some perception sensors. Finally, two illustrative examples are provided to verify the effectiveness of the proposed control scheme.

Recognizing and understanding various objects in visual information is paramount for ensuring safe and efficient autonomous navigation, especially in challenging environmental conditions. However, relying solely on single-modal data to perceive information, such as visible images, can compromise the quality and reliability of the extracted information, posing potential risks to autonomous driving systems. To address this challenge, we present a novel fusion method based on mutual information theory in semantic segmentation tasks for secure and efficient autonomous vehicles. The proposed method involves two primary components, i.e., multispectral fusion representation module (FRM) and semantic segmentation module (SSM). To optimize the FRM, we establish a unified quality representation for feature fusion by incorporating an image restoration mechanism, enhancing autonomous driving systems' overall performance and adaptability in complex environments. Meanwhile, we employ mutual information maximization to capture the interimage relations among pixels from the same semantic content, which is achieved by leveraging the semantic representation learned by the SSM in a high-dimensional feature space. Experimental results and comparisons with famous fusion approaches and segmentation models on four public datasets validate our method's effectiveness, robustness, and overall superiority.

Remote-controlled vascular interventional robots have great potential for use in minimally invasive vascular surgeries in recent years due to their ability to reduce the occupational risk of surgeons and improve the stability and accuracy of surgical procedures. However, blood vessels will suffer from the damage caused by collision with medical instruments to some extent even though the surgeries are very successful. Moreover, when surgeons perform unsafe operations, the unsafe operations will not only seriously affect surgical safety (or even cause serious complications) but also restrict the continuity of operation. In this article, a multilevel concept for operating force is first introduced into surgical procedures as a reference for the choice and design of operation strategies. Based on this concept, a novel multilevel operation strategy is first proposed to reduce blood vessel damage, ensure surgical safety, and allow for continuous operation. This strategy can remind surgeons about the operative conditions in real-time, reduce collision to blood vessels, and eliminate unsafe operations online. A prototype was fabricated and calibrated through calibration experiments and the performance of the multilevel operation strategy was validated throughin vitroandex vivoexperiments. Experimental results demonstrate the engineering effectiveness of the proposed method and motivate the need for furtherin vivostudies to evaluate improvement on surgical safety.

The cost of data acquisition is relatively high in airborne LiDAR platform, not suitable for the entire industry promotion. Point-Cloud extraction technique based on image, a method based on low-altitude drone image and flight control data for 3D reconstruction is adopted. Do not depend on prior knowledge of camera calibration or other premise, multi-angle, high-overlap image sequence acquired by visible light camera, to restore the three-dimensional reconstruction information. Aiming at the problem of insufficient memory when dealing with high-resolution unmanned aerial vehicle (UAV) images by SIFT (Scale-Invariant Feature Transform) feature extraction operator, an improved SFIT algorithm for image segmentation is proposed. The point cloud filtering is carried out by the method of orthogonal polynomial banding filtering, the ground point and the non-ground point are separated. The non-ground point is used to extract the roof set of the building. This experiment has obtained good test results based on the UAV sequence image in the test area, which has certain application value for low-cost building 3D modeling, 3D modeling of digital city, and damage building damage identification.

Redundant systems, which usually consist of a number of same/similar components (or modules), have been used in various critical infrastructures to ensure the system's normal function. Usually, system reliability can be improved with the adoption of additional components or redundancies. Typically, two types are mainly included, i.e., majority voters and standby redundancies (referred to as SPARE gates for simplicity). Nevertheless, with the increment of redundancies, the consumed cost or space requirement also grows. This study considers a tradeoff between cost and reliability in order to pursue the cost-effective optimal design. The relationships between the total cost and corresponding parameters are discussed thoroughly. Besides, in order to determine the cost-effective design at the expense of a unit of cost, a revised evaluation standard is proposed (referred to as R per Cost). In this paper, we perform a cost-effective analysis of majority voters with different implementations; and we also perform the analyses of SPARE gates (here, a warm spare gate and a cold spare gate are mainly focused). For deriving corresponding total cost, algorithms are presented to predict the necessary failure time of components. In this line, cost-effective analyses of several case studies are performed.

Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems, and online prediction is always necessary in many real applications. To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time, we propose a new adaptive online method based on least squares support vector regression (LS-SVR). This method adopts two approaches. One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model. This approach can control the loss of useful information in sample data, improve the generalization capability of the prediction model, and reduce the prediction time. The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model. This approach can reduce the calculation time in the process of adaptive online training. Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.