Spatial domain identification is a central task in spatial transcriptomics (ST) data analysis. We present GDST, a novel graph contrastive learning framework that leverages graph diffusion to enhance spatial domain delineation in ST data. In contrast to prior approaches based on random perturbations or masking strategies, GDST introduces biologically inspired diffusion augmentation to simulate intercellular signal propagation while preserving the intrinsic topological structure of the spatial graph. To further reduce noise from irrelevant neighbors, a graph attention mechanism is incorporated to enable adaptive neighborhood aggregation. Extensive evaluations on five benchmark ST datasets spanning three experimental platforms demonstrate that GDST consistently outperforms several state-of-the-art deep learning models in spatial domain identification.

Utilizing 2D images for place recognition within 3D point cloud maps presents significant challenges in autonomous driving applications, primarily due to the inherent cross-modal disparity between visual and LiDAR data. In this study, we propose a novel cross-modal visual-LiDAR place recognition method based on Bird’s Eye View (BEV) feature distillation. Our framework is the first end-to-end solution designed to achieve cross-modal place recognition between surround-view images and LiDAR point clouds. By encoding features into a unified BEV representation, our approach effectively bridges the modality gap between 3D and 2D data. Additionally, we introduce a teacher-student distillation training strategy to further enhance the network’s cross-modal generalization capabilities. Extensive experiments on benchmark datasets, including nuScenes and Argoverse, demonstrate that our method achieves state-of-the-art (SOTA) performance in cross-modal place recognition tasks. Furthermore, validation on the SJTU-Sanya dataset confirms the robustness and adaptability of our approach in real-world scenarios. We publicly release our network model and implementation details at https://github.com/IRMVLab/CrossBEV-PR.

ABSTRACT Self‐sovereign identity management systems operate in open network environments and face security threats from semi‐trusted or malicious adversary models. In such environments, verifiable credentials are susceptible to attacks such as theft and forgery. In response to the privacy risks associated with verifiable credentials during issuance and revocation, this article proposes a privacy protection scheme for user information during the issuance and revocation processes of verifiable credentials in self‐sovereign identity management based on blockchain technology. First, a privacy‐preserving method that does not rely on a single identity provider and resists Sybil attacks has been designed using secure multi‐party computation cryptographic techniques. Second, the consortium blockchain committee nodes act as the issuer of verifiable credentials. By combining attribute commitments and zero‐knowledge proof techniques, the user's identity information is hidden, achieving the privacy protection goal during the issuance of verifiable credentials. Furthermore, in order to protect user privacy during the revocation of verifiable credentials (VCs), we employ a cryptographic accumulator technique to implement the revocation operation. This approach ensures the security of user privacy while effectively managing the revocation of credentials. Finally, this paper conducts a security analysis and performance evaluation of the proposed scheme. The results show that our scheme strikes a balance between security needs and time efficiency.

Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To solve these problems, a novel pipeline termed LHMap-loc is proposed, which achieves accurate and efficient monocular localization in LiDAR maps. Firstly, feature encoding is carried out on the original LiDAR point cloud map by generating offline heat point clouds, by which the size of the original LiDAR map is compressed. Then, an end-to-end online pose regression network is designed based on optical flow estimation and spatial attention to achieve real-time monocular visual localization in a pre-built map. In addition, a series of experiments have been conducted to prove the effectiveness of the proposed method. Our code is available at: https://github.com/IRMVLab/LHMap-loc.

The issue of data privacy protection must be considered in distributed federated learning (FL) so as to ensure that sensitive information is not leaked. In this article, we propose a two-stage differential privacy (DP) framework for FL based on edge intelligence. Various levels of privacy preservation can be provided according to the degree of data sensitivity. In the first stage, the randomized response mechanism is used to perturb the original feature data by the user terminal for data desensitization, and the user can self-regulate the level of privacy preservation. In the second stage, noise is added to the local models by the edge server to further guarantee the privacy of the models. Finally, the model updates are aggregated in the cloud. In order to evaluate the performance of the proposed end-edge-cloud FL framework in terms of training accuracy and convergence, extensive experiments are conducted on a real electrocardiogram (ECG) signal dataset. Bi-directional long-short-term memory (BiLSTM) neural network is adopted to training classification model. The effect of different combinations of feature perturbation and noise addition on the model accuracy is analyzed depending on different privacy budgets and parameters. The experimental results demonstrate that the proposed privacy-preserving framework provides good accuracy and convergence while ensuring privacy.

The application of vehicle ad-hoc networks (VANETs) has brought huge potential to the development of intelligent transportation systems. By traffic event reporting, traffic efficiency can be significantly improved. However, people usually lack the motivation for reporting traffic events or driving violations, because once the identity of the reporter is leaked, he or she may be tracked by malicious attackers or be retaliated by violators. However, if the reporter is anonymous, it is difficult to guarantee the accuracy and authenticity of the reports. In order to solve this contradiction, a mechanism that combines certificateless message authentication and blockchain incentives is proposed in this paper. A certificateless message signature algorithm is designed to provide the anonymity and non-repudiation for traffic-related message reporters. Simultaneously, an adaptive t-threshold multi-signature mechanism is introduced in our scheme. Similar to crowdsourcing, it requires multiple participants to witness the authenticity of the message together. Aggregation signature verification improves message authentication efficiency. A trust currency called TCoin is introduced to motivate reporters' enthusiasm for participation in intelligent transportation. The security analysis and simulation results show that our scheme is secure and effective in the intelligent transportation system.

Abstract With the advancement of information technology and the reduction of costs, the application of unmanned aerial vehicle (UAV) has gradually expanded from the military field to the industrial field and civilian field. It brings great convenience to people in surveillance, detection, transportation, emergency rescue etc. However, UAVs usually work in harsh natural environments, and their communication security confronts various challenges. Due to UAVs' limited resources, such as computing capability, storage space, and energy, traditional security protection schemes based on complex cryptographic algorithms are not suitable for UAV systems directly. Therefore, a two‐stage lightweight identity authentication and key agreement protocol for UAV is proposed in this paper. The entire process only uses hash and XOR operations, which significantly improves the authentication efficiency. Simultaneously, the physical unclonable function (PUF) is introduced and embedded into the UAV hardware to ensure UAV network communication security when a UAV suffers a physical capture attack. In the paper, the security of the proposed protocol is proved with Burrows–Abadi–Needham (BAN) logic, Real‐or‐Random (ROR) model, and AVISPA simulation tools. An informal security analysis is also provided to illustrate that the protocol satisfies the security requirements of UAV networks. Finally, the protocol is compared with other existing protocols regarding function properties, computation cost, and communication cost, which shows that the proposed protocol has effectiveness and practicality.

Cyber–physical systems (CPSs) is a security real-time embedded system. CPS integrates the information sensed by the current physical sensors, through high-speed real-time transmission, and then carries out powerful information processing to effectively interact and integrate the physical and the information worlds. With the aim to improve the quality of service, optimize the existing physical space, and increase security, collaborative filtering algorithms have also been widely used in various recommendation models for Internet of Things (IoT) services. However, general collaborative filtering algorithms cannot capture complex interactive information in the sparse Mashup–Web service call matrix, which leads to lower recommendation performance. Based on the artificial intelligence technology, this study proposes a recommendation algorithm for a security collaborative filtering service that integrates content similarity. A security collaborative filtering module is used to capture the complex interaction information between Mashup and Web services. By applying the content similarity module to extract the semantic similarity information between the Mashup and Web services, the two modules are seamlessly integrated into a deep neural network to accurately and quickly predict the rating information of Mashup for the Web services. Real data set on the intelligent CPS is captured and then compared with mainstream service recommendation algorithms. Experimental results show that the proposed algorithm not only efficiently completes the Web service recommendation task under the premise of sparse data but also shows better accuracy, effectivity, and privacy. Thus, the proposed method is highly suitable for the application of intelligence CPS.

In Industry 5.0, the massive number of Internet of Things devices have increasing demands for group communication with a high communication efficiency and low energy consumption. However, group communication meets continuously increasing security risk challenges. Existing authentication and group key agreement schemes have encountered many problems, such as lack of anonymity and untraceability. In this article, we propose an anonymous authentication and dynamic group key agreement scheme based on the Blockchain and token mechanism, where each group member can apply for a time-sensitive token during the first authentication and only needs to check the validity of the token in the subsequent authentication, reducing the computational and transmission costs considerably. The verification on the security of the proposed scheme is tackled through mathematical analysis and validated using ProVerif, and comparisons with existing schemes demonstrate that the proposed scheme reduces the security risks and each group member’s energy consumption.

With the rapid advancement of video and image processing technologies in the Internet of Things, it is urgent to address the issues in real-time performance, clarity, and reliability of image recognition technology for a monitoring system in foggy weather conditions. In this work, a fast defogging image recognition algorithm is proposed based on bilateral hybrid filtering. First, the mathematical model based on bilateral hybrid filtering is established. The dark channel is used for filtering and denoising the defogging image. Next, a bilateral hybrid filtering method is proposed by using a combination of guided filtering and median filtering, as it can effectively improve the robustness and transmittance of defogging images. On this basis, the proposed algorithm dramatically decreases the computation complexity of defogging image recognition and reduces the image execution time. Experimental results show that the defogging effect and speed are promising, with the image recognition rate reaching to 98.8% after defogging.

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description.We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework.The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.

With the coming of digital newspaper, user-oriented special topic generation becomes extremely urgent to satisfy the users’ requirements both functionally and emotionally. We propose an applicable automatic special topic generation system for digital newspapers based on users’ interests. Firstly, extract subject heading vector of the topic of interest by filtering out function words, localizing Latent Dirichlet Allocation (LDA) and training the LDA model. Secondly, remove semantically repetitive vector component by constructing a synonymy word map. Lastly, organize and refine the special topic according to the similarity between the candidate news and the topic, and the density of topic-related terms. The experimental results show that the system has both simple operation and high accuracy, and it is stable enough to be applied for user-oriented special topic generation in practical applications.