The industrial internet of things tightly integrates heterogeneous sensing and communication devices, but it remains difficult to obtain lightweight authentication while simultaneously protecting the post-authentication data plane. This paper addresses both gaps with a unifieddual-securityframework. First, we proposechannel-assisted hyperelliptic curve cryptography, which binds channel state information to hyperelliptic-curve primitives to derive one-time key (OTK) for mutual authentication with low overhead. Second, we introduce anOTK-based non-orthogonal artificial noisemechanism that synthesizes artificial noise from the negotiated OTK and optimizes its power allocation to jam eavesdroppers while remaining transparent to legitimate receivers. Security and cost analyses highlight the robustness and efficiency of channel-assisted hyperelliptic curve cryptography; an NS-3 implementation confirms practicality. Simulations show that OTK-based non-orthogonal artificial noise increases secrecy capacity by 61.11% and 167.74% relative to traditional artificial noise and OTK-based non-orthogonal artificial noise, respectively. We integrate channel-assisted hyperelliptic curve cryptography with OTK-based non-orthogonal artificial noise. Together, they provide end-to-end protection for industrial internet of things authentication and communication.

As time-series data from the Internet of Medical Things (IoMT) increasingly permeates various aspects of medical research, public governance, and clinical treatment, its sensitivity raises significant privacy concerns, hindering the potential of deep learning applications for cross-institutional data integration. Previous practices focused on deep learning methods based on centralized data storage and processing, which are often unsuitable for decentralized and privacy-sensitive IoMT data scenarios. Most existing methods rely on mechanisms such as trusted coordinators, which face challenges in addressing potential passive data leakage and side-channel attacks, failing to effectively protect the privacy of sensitive data during collaborative training. To address these issues, we propose a privacy-preserving collaborative training model, Secure Long Sequence Time-Series Forecasting (SecLSTF), for IoMT time-series data and design a mapping strategy between model components and Multi-Party Computation (MPC) protocols. Building on this foundation, we propose a novel secret sharing protocol, Pleione, which focuses on optimizing the computational efficiency of the low-level secret-sharing protocol. The protocol centers on a hyper-invertible matrix and adopts a paired double random expansion mechanism, significantly reducing the communication rounds required for random number generation. This optimization enhances the overall training speed of SecLSTF. Subsequently, we replace the original computational support protocol with Pleione. Experimental results show that SecLSTF-Pleione significantly reduces computational time while maintaining computational accuracy, outperforming other protocols in component efficiency. This study offers a potential pathway for cross-institutional IoMT data sharing.

Lane line detection in complex scenarios is a key part of the intelligent driving environment sensing technology, essential for ensuring traffic safety and improving transportation system efficiency. However, existing models ignore the data imbalance of the far-end, near-end, foreground object and background image, it leads to the inconsistency between the detected curve lane line and reality, and the poor complementarity of different scales of information. To solve the above problems, a high-precision and intelligent lane line detection algorithm based on bow height feature points (BHFP) and cross-scale feature correlation network (CFCNet) is proposed, the sub-module functions are shown below. A key point regression method based on BHFP extraction and box intersection over the union loss function correction was used to enhance the automatic modeling ability of the curve lane line. The CFCNet is used to mine the similarity and different characteristics of backbone network output data at different scales. A global information reflow enhancement model is used to highlight the advantages of deep and shallow features in correcting elongated target locations and sharpening structural details. The experimental results show that this algorithm improves the robustness and positioning efficiency of the lane line detection in complex road scenarios, and reduces the missing detection phenomenon of the model. It has significant advantages in the regression localization of lane line far-end targets.

The intelligent fault diagnosis has achieved notable success in identifying known mechanical failures; however, reliably detecting out-of-distribution (OOD) faults remains a key challenge to achieve the diagnostic robustness. In industrial applications, vibration signals are typically collected as time-series data whose dynamic characteristics vary with load, speed, and environmental interference, with weak early fault patterns that blur class boundaries. As a result, models trained under limited laboratory conditions inevitably encounter unseen OOD inputs after deployment, requiring the ability to recognize and reject them reliably. Existing representation- and similarity-based OOD methods have shown promise but typically rely on single-granularity prototypes, capturing only coarse similarity structures and overlooking latent subclass relations—thus limiting the generalization under complex degradation modes. To address these limitations, we propose a multigranularity fuzzy inference (MgFI) framework for enhanced uncertainty quantification in fault diagnosis. MgFI models fine-grained subclass memberships on a hyperspherical manifold, aggregates them into class-level fuzzy sets, and infers coarse-grained In-distribution (ID) confidence through the hierarchical fuzzy reasoning. Extensive experiments demonstrate that MgFI substantially improves the OOD detection accuracy and provides a principled, interpretable framework for trustworthy open-set industrial diagnostics.

Classifiers fusion can be seen as a kind of multisource information fusion (MSIF), and classifiers fusion based on Dempster–Shafer (DS) evidence theory is an effective approach to improve the accuracy of classification tasks. However, different classifiers usually exhibit varying performances, making it challenging to achieve enhanced classification accuracy through direct fusion. Simultaneously, when the frame of discernment (FoD) of the target class expands, the number of focal elements involved in the fusion increases, resulting in a rapid growth in computational complexity. To enhance the classification performance while reducing the time cost of fusion, a novel weighted fusion of classifiers method based on approximate reasoning and reliability evaluation (WFC-AR-RE) is proposed in this article. Specifically, at first, the key focal elements are determined based on the outputs of classifiers, and an approximate basic belief assignment (BBA) is generated. Subsequently, the validation set is utilized to evaluate the performance of each classifier, thus obtaining the self-reliability of each BBA. Afterward, a novel divergence measure is introduced to quantify the discrepancy between BBAs, determining the relative reliability of each BBA. Finally, the fusion weight of each BBA is derived from its self-reliability and relative reliability, and Dempster’s rule is applied to combine the weighted BBA. The proposed WFC-AR-RE algorithm is applied to the MSIF system, and its effectiveness is demonstrated on 12 public datasets.

Model partitioning techniques, which decompose and collaboratively execute subtasks of deep neural networks (DNNs), have emerged as a critical strategy for enhancing distributed inference efficiency. However, in mobile edge computing (MEC), dynamic load fluctuations at edge nodes and the complexity of cross-node task dependencies make the delay minimization problem extremely challenging. Existing studies predominantly adopt a decoupled optimization framework that separately addresses partitioning-offloading and pipeline scheduling, neglecting their inherent cyclic state-dependent coupling. This oversight leads to suboptimal solutions, such as pipeline stagnation caused by mismatched computation and communication timestamps. To address these challenges, we propose a multi-endpoint directed acyclic graph (DAG)-driven cooperative optimization approach, enabling partitioning-offloading and pipeline scheduling in MEC. Specifically, the approach involves two core steps: 1)Dynamic pre-scheduling: We propose an improved DNN scheduling algorithm for constrained subtasks, which simulates node-level queuing delays and pipeline stalls under real-world constraints, translating runtime states into latency objectives. 2)Partitioning and offloading solution retrieval: Based on latency objectives, we introduce a novel multi-endpoint DAG structure and design a multi-node collaborative optimization retrieval algorithm, enabling adaptive partitioning-offloading remapping of subtasks. Experiments demonstrate the superiority of the proposed method over other advanced methods, reducing the time overhead by an average of 24% and 75% in two different scenarios, respectively. The resource code can be found at: https://github.com/aiheiheiheii/Partition_Scheduling.git.

The latest remote sensing image saliency detectors primarily rely on RGB information alone. However, spatial and geometric information embedded in depth images is robust to variations in lighting and color. Integrating depth information with RGB images can enhance the spatial structure of objects. In light of this, we innovatively propose a remote sensing image saliency detection model that fuses RGB and depth information, named the multimodal guided transformer architecture (MGTA). Specifically, we first introduce the strong correlated complementary fusion (SCCF) module to explore cross-modal consistency and similarity, maintaining consistency across different modalities while uncovering multidimensional common information. Additionally, the global-local context information interaction (GLCII) module is designed to extract global semantic information and local detail information, effectively utilizing contextual information while reducing the number of parameters. Finally, a cascaded feature-guided decoder (CFGD) is employed to gradually fuse hierarchical decoding features, effectively integrating multi-level data and accurately locating target positions. Extensive experiments demonstrate that our proposed model outperforms 14 state-of-the-art methods. The code and results of our method are available at https://github.com/Zackisliuzao/MGTANet.

Moving object detection (MOD) in satellite video sequences faces persistent challenges including low contrast against complex backgrounds, limited motion modeling, and severe scale variation. To effectively address these, we propose STFDNet, a novel combined model-driven and data-driven framework. STFDNet comprises three core components: the hybrid temporal motion module (HTMM), dynamic frequency learning (DFL), and a progressive cascaded learning strategy (PCLS). Initially, HTMM leverages both explicit and implicit strategies to model multi-frame temporal differences, effectively capturing both short- and long-term motion. Then, DFL integrates a frequency-domain dynamic filter to learn global frequency characteristics, enhancing feature representation beyond the spatial domain and improving distinction from background noise. Finally, PCLS progressively refines detection results by fusing multi-domain features in a step-by-step manner, transitioning from coarse-grained to fine-grained representations. Experiments on the Jilin-1 satellite video dataset demonstrate that the proposed method effectively mitigates background noise, limited motion modeling, and object scale variations, significantly improving detection accuracy and robustness, achieving an F1-score of 85.4%.

Achieving accurate 3-D environment perception is a key task in the field of remote sensing. Multispectral point cloud has rich integrated 3-D spatial–spectral information, which provides a data basis for realizing more detailed scene understanding and perception. However, the diversity of land covers and the complexity of its features pose challenges to classification. In addition, the current methods mechanically pool and fuse local features to obtain global information, which has limited the utility for multispectral point cloud classification. In this article, we propose a masking graph cross-convolution network (MGC2N), which aims to address these problems by utilizing spectral features to construct point-to-point relationships independent of spatial distance. A self-attention masking (SAM) module and a spatial–spectral cross-convolution (S2C2) module are innovatively designed into the proposed MGC2N. The former is used to adaptively adjust the nodes and edges of the adjacency matrix to dynamically extract effective features for different land covers; the latter is used to extract spatial distribution features and local spectral features of the land covers in the scene to enhance the discriminative ability of the learned features. Our method achieves the best-in-class performance on two real multispectral point cloud datasets, demonstrating its effectiveness in improving classification accuracy and robustness.

Recently, the hypergraph convolutional network (HGCN) has attracted increasing attention in hyperspectral image (HSI) classification. Compared to graph convolutional networks, HGCN has a stronger ability to mine nonlinear high-order correlations. However, the problems of intraclass variability and interclass similarity exist due to the effects of light, environment, and sensor bias, resulting in insufficient reliability of hypergraphs constructed by directly utilizing the original spectral features. Motivated by the observation that the land cover in HSI contains the spatial distribution semantic information of community structures, which can be used to extract deeper contextual semantic features, we propose a novel community structure guided network (CSGNet) for HSI classification. Specifically, CSGNet adopts a dual-branch architecture: the HGCN branch focuses on superpixel-level high-order feature extraction, while the convolutional neural network (CNN) branch enhances pixel-level local features. In HGCN branch, a novel reliable hypergraph construction approach is introduced, which strikes a balance between depth-first search (DFS) and breadth-first search (BFS), effectively representing different community structure features and improving the ability of edge detection. Meanwhile, kernel function mapping is used to achieve more accurate node connections and enhances classification within classes. Finally, to achieve balanced training of the HGCN and CNN branches, we add their cross-entropy loss as an auxiliary component in the backpropagation process. Experimental results demonstrate that CSGNet outperforms the state-of-the-art methods. The code will be released athttps://github.com/KustTeamWQW/CSGNet.

Multispectral LiDAR point clouds are valuable in remote sensing for their spatial-spectral consistency, yet their high acquisition and annotation costs pose significant challenges. To mitigate this, self-supervised learning has emerged as a promising solution, reducing reliance on annotated data while improving model generalization. However, existing self-supervised frameworks for point clouds often overlook the complexity of ground object distribution in large-scale remote sensing scenarios and fail to leverage the spectral information inherent in multispectral point clouds. In this paper, we introduce the Self-Supervised Spatial-Spectral Distance Reconstruction Network (S4DR-Net), a novel self-supervised pre-training network designed for multispectral point cloud classification. Serving as the key component of the network, the Spatial-Spectral Distance Prediction module (S-SDP) effectively addresses these limitations by reconstructing the distance relationships between voxel blocks in three-dimensional Euclidean as well as spectral spaces. By jointly considering spatial and spectral distances, S-SDP enables the network to learn a unified representation that captures the intrinsic spatial-spectral consistency of multispectral point clouds. This design allows S4DR-Net to generate low-dimensional feature representations in a self-supervised manner, without reliance on manual annotations. We conducted experiments and evaluated on two real-world multispectral point cloud datasets. The results demonstrate that S4DR-Net consistently outperforms existing self-supervised pre-training methods, achieving superior accuracy and generalization compared with current state-of-the-art approaches. The code will be released at https://github.com/KustTeamWQW/S4DR-Net.

The heart, responsible for circulating blood throughout our body, contains four chambers. Existing analysis methods primarily focus on one single ventricle. Transthoracic echocardiography provides real-time estimations of cardiac function and enables comprehensive observations of the entire heart, especially through the apical 4-chamber view. However, no current clinical indices evaluate cardiac function considering all four chambers simultaneously. Manual estimation of the four chambers is laborious, inefficient, and complicated by anatomical complexity and variable image quality, including motion artifacts and unclear borders. There is a significant need for a high-performance segmentation tool that can assess all four chambers concurrently. To address this, we collected a clinically representative dataset of 2D apical 4-chamber view echocardiograms, with annotated 4-chamber regions serving as the basis for automatic 4-chamber synergy analysis. We then proposed a boundary-enhanced network, denoted as $BeU^{2}$-Net, tailored for transthoracic echocardiography 4-chamber segmentation using our private dataset. Specifically, our network employs a two-level nested encoder-decoder architecture, utilizing a segmentation-specific residual U-block with a mixture of receptive fields at each stage to capture multi-level and multi-scale features. A dedicated boundary prediction branch, incorporating edge details, is integrated to enhance boundary segmentation performance. Experiments on both private and public datasets demonstrate that our $BeU^{2}$-Net possesses superior boundary detection capabilities and achieves high segmentation performance for echocardiographic images.

Knowledge graph construction is aimed at storing and representing the knowledge of the objective world in a structured form. Existing methods for automatic construction of knowledge graphs have problems such as difficulty in understanding potential semantics and low precision. The emergence of Large Language Models (LLMs) provides an effective way for automatic knowledge graph construction. However, using LLMs as automatic knowledge graph construction engines relies on the embedding of schema layers, which brings challenges to the input length of LLMs. In this paper, we present a framework for Adaptive Construction of Knowledge Graph by leveraging the exceptional generation capabilities of LLMs and the latent relational semantic information of triples, named ACKG-LLM. Our proposed framework divides the knowledge graph construction task into three subtasks within a unified pipeline: triple extraction of open information, additional relational semantic information embedding and knowledge graph normalization based on schema-level embedding. The framework can construct knowledge graphs in different domains, making up for the defects of existing frameworks that need to retrain and fine-tune the internal model. Extensive experiments demonstrate that our proposed ACKG-LLM performs favorably against representative methods on the REBEL and WiKi-NRE datasets. The code is available athttps://github.com/KustTeamWQW/ACKG-LLM

Due to the inherent visual similarity between the camouflaged object and background, camouflaged object detection (COD) is widely recognized as a challenging task in the field of computer vision, and traditional object detection networks often struggle to extract features and accurately identify camouflaged objects. In this paper, we propose an edge-guided pixel level connected component assisted network for COD. Specifically, the edge prior is used to guide object feature extraction and the pixel level connected component obtained from the extracted feature is used to refine the bounding box of the object. We selectively employ a gray-polarization COD dataset to showcase the ability of feature extraction from backgrounds where camouflaged objects may blend in or be occluded. Numerous experiments demonstrate the superiority of our method compared to state-of-the-arts in the case of limited information.

In recent years, salient object detection (SOD) has attracted more and more attention. However, the SOD in remote sensing images (RSI-SOD) faces various issues, including large scene span, cluttered background and changeable object scale. To address these challenges, an edge complementary multi-scale aggregation network (ECMANet) is proposed in this paper. Specifically, a multi-scale feature aggregation module (MFAM) is designed to extract hierarchical multi-scale information and reduce the noise interference of different scale information. In addition, foreground edge guidance module (FEGM) is designed to cross-refine foreground information and edge information. Finally, the foreground, edge, and background are generated by background-foreground fusion module (BFFM) to complement the overall network information. Extensive experiments are conducted on two popular datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

Multispectral LiDAR contributes to the rapid acquisition of 3D spatial and spectral information of land covers, providing more comprehensive features for classification. Despite the impressive performance of existing Graph Neural Networks (GNNs) in point cloud classification, extracting local features with discriminative ability remains challenging in multispectral LiDAR scenes due to the uneven distribution of geometric and spectral information. To enhance the local representation of spectral features, we propose a novel Graph Convolutional Network with Local Topology and Spectral Feature Representation (GCN-LTSFR). The network constructs optimal local topological graphs of corresponding scales based on the feature distribution density of the point cloud to enhance local spectral features. Experimental results demonstrate that the proposed GCN-LTSFR outperforms several state-of-the-art methods on a real multispectral point cloud.

With the development of remote sensing technology for earth observation, the collaborative utilization of Hyperspectral images (HSI) and multispectral images (MSI) has received increasing attention in terrestrial observation. HSI and MSI represent two typical types of optical remote sensing data and can provide rich complementary information. However, the paradoxical problem of high spatial resolution and high spectral resolution leads to difficulties in extracting complementary information. In this paper, we propose an Adaptive Feature Exchange network with Complementary Advantages (AFECAnet). Specifically, we enhance the discriminative feature extraction of HSI-MSI by introducing a spectral-spatial feature enhancement module based on a dual attention mechanism. Subsequently, in order to reduce redundant information, we design an adaptive feature interaction strategy based on batch normalization privatization factors. This strategy helps to accurately replace redundant information and reduces the computational burden on the network. Experimental results demonstrate that the proposed AFECAnet has a significant improvement in HSI-MSI collaborative classification.

Recently, some hybrid networks, combining graph convolutional network (GCN) and convolutional neural network (CNN) into a unified framework, have drawn increasing attention in hyperspectral image (HSI) classification. Compared with the single CNN or GCN architecture, hybrid networks can simultaneously perform feature learning on pixel-level and superpixel-level regions and generate complementary spectral-spatial features. However, existing methods primarily employ simple fusion strategies such as linear combination or concatenation, resulting in the extracted complementary features not being fully exploited and utilized. In this work, we propose a differential feature-enhanced fusion network (DFEFN) for HSI classification. Specifically, DFEFN consists of two different convolutional network architectures, (i.e. GCN and CNN), and a differential feature enhancement fusion (DFEF) module. The features extracted by CNN and GCN can be enhanced and fused through the DFEF module. Experiments results on two benchmark HSI datasets demonstrate that DFEFN achieves better classification performance compared with state-of-the-art methods.

In accomplishing the task of semantic segmentation of RGB-T remote sensing images, there is a great challenge due to severe occlusion, long-tailed data distribution, and insufficient pixel representation of certain objects. The effective use of high-level semantic contexts among various ground object categories is crucial, yet presents considerable diffi-culties. Traditional RGB-T remote sensing image semantic segmentation methods often fail to capture and utilize complex relationships and interdependencies among these categories, leading to reduced semantic segmentation accuracy. This paper proposes a novel, knowledge-enhancement module, empowering the model to utilize human-like commonsense knowledge. Specifically, we firstly employ the self-attention and cross-attention mechanisms to fuse RGB and Thermal features. Subsequently, we collect the weights of the classification layer to get a high-level semantic pool based on all the categories. Alongside this, a prior knowledge graph is developed to enable information propagation among all categories. We applied this knowledge-enhancement module to enhance the Mask R-CNN, named KEMask R-CNN. Experiments results on the RS RGB-T dataset demonstrate the progressiveness of the proposed method.

Recent research on unmanned aerial vehicle-based (UAV) computational offloading algorithms has employed energy harvesting mechanisms to improve the efficiency of edge computing. However, these approaches treat UAVs as relays or power providers which are rarely regarded as the computational nodes in the energy harvesting condition. The most challenging issue is not only the computational efficiency, but also the energy stability of individual computing devices. Stable energy state represents a stable computing service capability, which is very important for edge systems. It depends heavily on proper computational offloading algorithms. In this study, we construct a novel model that uses a cluster of UAVs with energy harvesting capability as a computational core. It is capable of providing long-term computational services for various scenarios. Then, we construct a Lyapunov function through a designed virtual battery energy queue and prove the existence of an upper bound for the Lyapunov drift-plus-penalty function through mathematical transformations. Therefore, we obtain a theoretically stable battery energy queue and design a Lyapunov-chain offloading algorithm based on it. Simulation results show that the proposed Lyapunov-chain offloading algorithm is able to maintain the strong energy stability of each node. It also provides robustness for edge UAV clusters while minimizing the execution delay compared to the baseline offloading scheme.

Recently, the hypergraph neural network (HGNN) has drawn increasing attention in modeling complex high-order correlations. Compared to simple graph neural networks, HGNNs exhibit more powerful representational ability. There are two limitations in the application of hypergraph theory to hyperspectral image (HSI) classification. One is the inadequate explicit representation of semantic information contained in HSI. Another is the loss of pixel-level spectral-spatial information. Thus, an enhanced hypergraph neural network (EHGNN) is proposed to promote the application of hypergraph theory to HSI classification. Specifically, two important enhancements are introduced: 1) the concept of key hypergraph, providing more rich semantic information and improving the interpretability for complex distribution structures, and 2) the integration of convolutional neural network (CNN) and HGNN architectures into an end-to-end framework, the loss of spectral-spatial information at the pixel-level is effectively reduced. Through these two enhancements, EHGNN exhibits a 4% improvement in overall accuracy (OA) on the Pavia University dataset and a 2% improvement in OA on the Xuzhou dataset compared to HGNN. Furthermore, the test results on two HSI datasets demonstrate that our EHGNN achieves competitive performance compared to other state-of-the-art methods.

In recent years, numerous outstanding technologies have been proposed for salient object detection (SOD) in remote sensing images (RSIs), but most of them focus solely on improving performance while disregarding computational, thereby lacking portability and mobility. This article introduces a novel lightweight progressive multilevel feature collaborative network, termed LPMFCNet. This framework constructs progressive feature information through multilevel image content extraction and designs a multichannel interactive deep neural network with information fusion and filtering functions. First, a spatial detail enhancement module (SDEM) is devised to acquire distant feature information through intermediate branch expansion of receptive fields while preserving multiscale information extraction. Second, an advanced semantic interaction module (ASIM) is proposed to model distant dependency relationships between deep semantic features to better identify the positional information of salient objects. Finally, a multilevel feature collaboration module (MFCM) is designed to collaboratively utilize target features from a multilevel perspective, which fully mining deep-level semantic positional information while retaining target detail information. Extensive experimental comparisons are conducted on two remote sensing datasets with 17 advanced methods. Results demonstrate that the proposed method exhibits superior detection performance while maintaining lightweightness. The LPMFCNet only contains 3.26M parameters and runs 0.5G FLOPs for a$256\times 256$image.

In this article, we propose a novel mutual prediction self-supervised learning (MPS2L) method for remote sensing (RS) image change detection (CD). Compared with the previous self-supervised CD methods based on contrastive learning (CL), MPS2L employing a pixel-level training strategy based on masked image modeling (MIM) can effectively train the model to interpret the local scene of RS images. Utilizing global and local scenes and temporal change features extracted from masked bitemporal images to achieve cross-temporal mutual prediction makes the model have the ability to understand the overall observation scene and capture the change information. The training of the two abilities is carried out simultaneously, avoiding the problem of multiobjective conflict or mutual inhibition. To better focus on the changing regions in RS scenes, we further introduce a change feature interaction module (CFIM), comprising spatial and channel feature interaction. The channel interaction module (CIM) can facilitate the cross-temporal transmission of global scene information by channel attention, and the spatial interaction module (SIM) can promote the network to capture information on changing regions by spatial attention. The experimental results on three benchmark RS CD datasets demonstrate the effectiveness and priority of our proposed MPS2L compared to some existing state-of-the-art (SOTA) methods. The source code of the proposed MPS2L will be made available publicly athttps://github.com/KustTeamWQW/MPS2L.

Remote sensing cross-scene classification has always been an important research field, especially in the field of 3-D classification, which is of great significance. Considering the diversity of collection conditions, seasons, and regional styles, deep learning networks well-trained on one source domain dataset tend to suffer from severe performance degradation when applied to other target domain datasets. To tackle the issue, in this article, we propose a new cross-scene classification method, which combines pre-alignment and Shannon entropy constraint to accomplish unsupervised domain adaptive classification (PS-UDA). On the one hand, the pre-alignment employs$L_{2}$-paradigm constraint and Laplace matrix to pre-align the features. With the$L_{2}$-paradigm constraint, the originally distant features of the source and target domain are constrained to the same sphere surface, and it is easier to make the distribution alignment on the sphere surface. Further, the Laplace matrix is used to map the source and target domain. In this way, similar features of the source and target domain are further aligned, and dissimilar features become discrete from each other. On the other hand, this article employs the Shannon entropy constraint to motivate the network to obtain more high-confidence target domain pseudo-labels. In addition, to fully utilize the unlabeled target domain information, the target domain features are augmented using the adjacency matrix. Experimental results of two cross-scene multispectral point cloud classifications demonstrate that the proposed PS-UDA can effectively mitigate the spectral shift issue in cross-scene multispectral point clouds, achieving state-of-the-art performance.

To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and secret sharing algorithm without the invisible trust between nodes required by the algorithm. Meanwhile, the VSSB-Raft consensus algorithm uses the SM2 signature algorithm to realize the characteristics of zero trust requiring authentication before data use. In addition, by introducing the NDN network, we redesign the communication between nodes and guarantee the communication quality among nodes. The VSSB-Raft consensus algorithm proposed in this paper can make the algorithm Byzantine fault tolerant by setting a threshold for secret sharing while maintaining the algorithm’s complexity to be O(n). Experiments show that the VSSB-Raft consensus algorithm is secure and efficient with high throughput and low consensus latency.

Internet of things (IoT) development tends to reduce the reliance on centralized servers. The zero-trust distributed system combined with blockchain technology has become a hot topic in IoT research. However, distribution data storage services and different blockchain protocols make network interoperability and cross-platform more complex. Relay chain is a promising cross-chain technology that solves the complexity and compatibility issues associated with blockchain cross-chain transactions by utilizing relay blockchains as cross-chain connectors. Yet relay chain cross-chain transactions need to collect asset information and implement asset transactions via two-way peg. Due to the release of user transaction information, there is the issue of privacy leakage. In this article, we propose a cross-chain privacy protection protocol based on the Groth16 zero-knowledge proof algorithm and coin-mixing technology, which changes the authentication mechanism and uses a combination of generating functions to map virtual external addresses in transactions. It allows fast cross-chain anonymous transactions while hiding the genuine user’s address. The experiment shows that, in a zero-trust IoT context, our scheme can effectively protect user privacy information, accomplish controlled transaction traceability operations, and guarantee cross-chain transaction security.

Although 5G and beyond communication technology empower a large number of edge heterogeneous devices and applications, the stringent security remains a major concern when dealing with the millions of edge computing tasks in the highly dynamic heterogeneous networks (HDHNs). Blockchains contribute significantly to addressing security challenges by guaranteeing the reliability of data and information. Since the node’s mobility, there are risks of exiting the network and leaving the remaining tasks noncomputed. Therefore, we model the cost function of offloaded computing tasks as a dynamic stochastic game. To reduce the computational complexity, the Time-Variant Mean-Field term (TVMF) is adopted to solve the cost-optimized problem. What’s more, we design an Adaptivity-Aware Practical byzantine fault tolerance consensus Protocol (AAPP) to dynamically formulate domains, execute leader node selection with regard to task completion and quickly verify computational results. In addition, a Dynamic Multi-domain Fractional Repetition uncoded repair storage (DMFR) scheme with variant redundancy is proposed to reduce the storage pressure and repair overhead. The simulation is implemented to demonstrate our scheme outperforms the benchmarks in terms of cost and time overhead.

Cross-scene multispectral point cloud classification aims to transfer knowledge of labeled source scenes to improve the discriminability of the model on the unlabeled target scenes. From a novel perspective, we argue that the information transfer between the source and target scenes can be used to solve cross-scene multispectral point cloud classification task. Specifically, we propose a Coupled Graph Convolutional Network (Coupled-GCN) to achieve joint alignment of node- and class-level structures within scenes by passing information between different scenes. To reduce the effect of spectral shift between the source and target scenes and seek scene-invariant intrinsic features, we propose a scene adaptive learning module by optimizing three different loss functions, namely, source classifier loss, domain classifier loss, and target classifier loss as a whole. In the cross-scene multispectral point cloud classification task, the proposed Coupled-GCN can alleviate the spectral shift problem compared to the traditional GCN and achieves an overall F_score of 65.04%.

Multispectral point clouds provide the data basis for finer land cover classification due to the simultaneous spatial and spectral information. How to jointly utilize spatial-spectral information becomes a hot research direction. Benefiting from the excellent performance of graph neural networks (GNNs) on non-Euclidean data, it is well suited to modelling multispectral point clouds to achieve higher classification accuracy. This paper proposes a novel graph convolutional networks with multi-kernel learning (GCN-MKL) for adaptively constructing a graph of multispectral point cloud for finer classification. Specifically, we use multiple base kernels to map the multispectral point cloud into a high-dimensional feature space and learn a linear combination of base kernels through a multi-kernel learning mechanism embedded in the network. The learned multi-kernel graph can effectively measure the high-dimensional similarity between multispectral points. Experimental results demonstrate that the proposed GCN-MKL outperforms several state-of-the-art methods on a real multispectral point cloud.

With the rapid deployment of Internet of Things (IoT) devices in various industries and fields, the massive amount of data produced by these devices can yield greater value through sharing. A critical challenge in the data-sharing process is ensuring that the data are high quality. However, the quality of data provided by a large number of IoT devices is impacted by the variability of factors contributing to the data quality (DQ). Effective and safe sharing of perception data by the limited resources of IoT devices is a problem worth investigating. In this article, we propose a smart contract-based and DQ-driven incentive mechanism. First, a smart contract is proposed to realize security in the data-sharing process, while the proposed DQ evaluation mechanism ensures the quality of the shared data. Second, a two-layer Stackelberg game of nested coalitional (TLSNC) scheme is designed to obtain the maximum overall social welfare according to the trust score obtained during DQ evaluation while satisfying the limitation of loose and insufficient computing resources. Moreover, we designed a smart contract for automatic execution of the data-sharing transaction and used a trusted execution environment (TEE) to complete the security calculation of shared data. Finally, the numerical results reveal the effectiveness of the DQ evaluation mechanism and the security of our TEE-based model. Based on the proposed scheme, sustainable incentives for user participation and high-quality data sharing can be achieved. In addition, our system can significantly improve the overall social welfare compared to traditional solutions.

In real-world scenarios, the information quality provided by RGB and thermal (RGB-T) sensors often varies across samples. This variation will negatively impact the performance of semantic segmentation models in utilizing complementary information from RGB-T modalities, resulting in a decrease in accuracy and fusion credibility. Dynamically estimating the uncertainty of each modality for different samples could help the model perceive such information quality variation and then provide guidance for a reliable fusion. With this in mind, we propose a novel uncertainty-guided trustworthy fusion network (UTFNet) for RGB-T semantic segmentation. Specifically, we design an uncertainty estimation and evidential fusion (UEEF) module to quantify the uncertainty of each modality and then utilize the uncertainty to guide the information fusion. In the UEEF module, we introduce the Dirichlet distribution to model the distribution of the predicted probabilities, parameterized with evidence from each modality and then integrate them with the Dempster-Shafer theory (DST). Moreover, illumination evidence gathering (IEG) and multi-scale evidence gathering (MEG) modules by considering illumination and target multi-scale information respectively are designed to gather more reliable evidence. In the IEG module, we calculate the illumination probability and model it as the illumination evidence. The MEG module can collect evidence for each modality across multiple scales. Both qualitative and quantitative results demonstrate the effectiveness of our proposed model in accuracy, robustness and trustworthiness. The code will be accessible at https://github.com/KustTeamWQW/UTFNet.

Existing RGB-Infrared detection models do not explicitly encourage RGB and infrared to achieve effective multimodal learning. We find that when fusing RGB and infrared images, cross-modal redundant information weakens the degree of complementary information fusion. Inspired by this observation, we propose Redundant Information Suppression Network (RISNet) which suppresses cross-modal redundant information and facilitates the fusion of RGB-Infrared complementary information. Specifically, we design a novel mutual information minimization module to reduce the redundancy between appearance features from RGB images and infrared radiation features from infrared images, which enables the network to take full advantage of the complementary advantages of multimodality and improve the detection performance. Experimental results demonstrate that RISNet outperforms the best competitive algorithm for RGB-Infrared pedestrian detection.

Owing to the technology of 5G and beyond, collaborative edge computing-as-a-service has enabled trillions of interconnected edge applications. It has also become a prospective paradigm for providing computing services by offloading computationally intensive assignments to mobile-edge servers or fog nodes due to terminals constrained computing and caching resources. Nevertheless, in this process, trust of computing-as-a-service scheduling and edge data sharing in heterogeneous systems is an unavoidable challenge of paramount importance. As a powerful tool that addresses security issues, blockchains can ensure the trustworthiness and irreversibility of computing data by consensus mechanisms. However, in the Industrial Internet of Energy (IIoE), the storage burden of a single blockchain has increased. Therefore, from the perspective of a stable real-time operation, we propose a multiedgechain structure that accommodates thousands of edge data and promotes on-chain data efficiency to achieve cross-chain edge data sharing for heterogeneous blockchain systems. Moreover, aiming at the profits of computing resource scheduling in the IIoE, a two-stage Stackelberg game strategy with an optimal scheduling demand and reward is provided considering the edge user’s preferences and risk factors. Finally, the simulation results verify the superiority of the proposed scheme, regarding the game equilibrium, utility optimization, and data sharing efficiency of cloud–edge collaboration.

Recently deep learning-based depth estimation has shown the promising result, especially with the help of sparse depth reference samples. Existing works focus on directly inferring the depth information from sparse samples with high confidence. In this paper, we propose a Heuristic Depth Estimation Network (HDEN) with progressive depth reconstruction and confidence-aware loss. The HDEN leverages the reference samples with low confidence to distill the spatial geometric and local semantic information for dense depth prediction. Specifically, we first train a U-NET network to generate a coarse-level dense reference map. Second, the progressive depth reconstruction module successively reconstructs the fine-level dense depth map from different scales, where a multi-level upsampling block is designed to recover the local structure of object. Finally, the confidence-aware loss is proposed to trigger the reference samples with low confidence, which enforces the model focusing on estimating the depth of the tiny structure. Extensive experiments on the NYU-Depth-v2 and KITTI-Odometry dataset show the effectiveness of our method. Visualization results demonstrate that the dense depth maps generated by HDEN have better consistency at the entity edge with RGB image.