The rapid growth of intelligent manufacturing within the Industrial Internet of Things (IIoT) has highlighted the importance of quality of service (QoS) as a key metric for system performance and reliability. Due to its dynamic and heterogeneous nature, accurate QoS prediction is essential but remains difficult under data silos, privacy constraints, and limited trust among industrial entities. To overcome these limitations, this article proposes a robust and secure multiparty collaborative (RSMC) QoS prediction scheme for IIoT service recommendation. RSMC leverages a federated learning architecture with a secure aggregation strategy, facilitating joint model training across factories without exposing raw data. Specifically, a hybrid local time-aware prediction model integrating a temporal convolutional network with a bidirectional simple recurrent unit is employed to capture long-term dependencies and nonlinear dynamics in QoS time series. To ensure the security of collaborative training, a robust functional encryption mechanism is introduced to achieve secure parameter aggregation and effectively resist inference and collusion attacks. Experiments on real QoS datasets show that the proposed scheme achieves superior prediction accuracy compared with mainstream approaches, while preserving privacy and enabling large-scale collaboration.

The 6G Computing Power Network (CPN) is envisioned to orchestrate vast, distributed computing resources for future intelligent applications. However, achieving efficient, trusted, and privacy-preserving computing resource sharing in this decentralized environment poses significant challenges. To address these intertwined issues, this article proposes a holistic blockchain and evolutionary algorithm-based computing resource sharing (BECS) mechanism. BECS is designed to dynamically and adaptively balance task offloading among computing resources within the 6G CPN, thereby enhancing resource utilization. We model computing resource sharing as a multi-objective optimization problem, aiming to navigate these trade-offs. To tackle this NP-hard problem, we devise a kernel-distance-based dominance relation and incorporate it into the Non-dominated Sorting Genetic Algorithm III (NSGA-III), thereby significantly enhancing population diversity. In addition, we propose a pseudonym scheme based on zero-knowledge proofs to protect user privacy during computing resource sharing. Finally, security analysis and simulation results demonstrate that BECS can effectively leverage all computing resources in the 6G CPN, thereby significantly improving resource utilization while preserving user privacy.

With the rapid development of earth observation technology, the joint classification of panchromatic (PAN) and multispectral (MS) images has gained significant research value. However, despite the acquisition of large amounts of data, the few-shot problem often arises due to insufficient labeled data, and the function space of the network degrades the generalization performance. In this paper, we propose a recurrent progressive few-shot network (RPF-Net) for the classification of dual-source remote sensing images. It mainly consists of two parts: solving for the optimized fusion direction and adaptive feature-trusted decision-level fusion. In the first part, considering the problem of insufficient function space constraints under few-shot conditions, we propose representative-reinforcement learning, which performs the next fusion step by analyzing the state of the current moment and selecting the optimal action. This recurrent progressive propagation process dynamically adjusts the fusion features, guiding them toward the optimal fusion direction within a larger function space under few-shot conditions. In the second part, considering that the importance of different source features in multiple fusions is different, we focus on uncertainty theory and perform focused decision-level fusion by analyzing the characteristics of different source features. This network can dynamically adjust the fusion direction and fusion method of features, solving the problem of too large function space under few-shot conditions. The results on multiple datasets have verified the effectiveness and stability of the proposed algorithm. Our code is available at: https://github.com/cominclip/RPF-Net.

The ongoing evolution of network technology and increasing service requirements have positioned spectrum resource management and exploration as a central focus in current and future network research. To accommodate the diverse services anticipated in future 6G networks, dynamic spectrum sharing (DSS) must be implemented across multiple factors to optimize the utilization of existing resources, in addition to exploring new frequency bands. This article proposes BEE, a two-stage, sharding blockchain-based DSS mechanism designed for service-centric 6G networks operating across various frequency bands. In the first stage, BEE utilizes an improved evolutionary algorithm to establish a fine-grained spectrum allocation scheme, facilitating dynamic spectrum management between providers and requesters. In the second stage, BEE offers price-guided spectrum trading for operators and users, utilizing evolutionary game theory to maximize the number of served users. The security analysis demonstrates that BEE provides a secure and reliable platform for DSS. Furthermore, simulation results demonstrate that BEE effectively improves spectrum utilization across various factors and offers operators effective guidance for price adjustments, thereby meeting the personalized spectrum management needs of 6G networks.

Maximum Distance Separable (MDS) self-dual codes are of significant theoretical and practical importance. Generalized Reed-Solomon (GRS) codes are the most prominent MDS codes. Correspondingly there have been many research on constructions of Euclidean self-dual MDS codes by using GRS codes. However, the study on Hermitian self-dual GRS codes is relatively limited. Since Hermitian self-dual GRS codes do not exist for n>q+1, this paper is devoted to an investigation of GRS codes in the case where n≤ q+1. First, we prove that when n≤ q+1, there are only two classes of Hermitian self-dual GRS codes, confirming the conjecture in [13] and providing its proof simultaneously. Second, we present two explicit construction methods. Thus, the existence and construction of Hermitian self-dual GRS codes are fully solved.

The rapid development of intelligent transportation systems raises stricter requirements for communication reliability, latency, and security. These growing demands are compounded by dynamic vehicular topology and high mobility, which make efficient resource allocation increasingly challenging. We propose SHIELD-VecNet, a blockchain-enabled and reconfigurable intelligent surface (RIS)-assisted vehicular edge computing (VEC) framework designed for secure and efficient resource allocation. The system is modeled as a Markov decision process (MDP) to achieve joint optimization across four dimensions: the age of information (AoI) of vehicle-to-infrastructure links, energy consumption, blockchain latency, and the reliability of vehicle-to-vehicle links. To tackle the high-dimensional hybrid action space and conflicting optimization objectives, we develop the Pareto-aware off-policy soft actor-critic (PAO-SAC) algorithm. This algorithm integrates a dynamic Pareto archive of non-dominated solutions into the replay buffer and incorporates a feature-crossing layer to capture latent correlations and trade-offs among the state space. Furthermore, a vehicular reputation-based byzantine fault tolerance (VRBFT) consensus protocol that utilizes an adaptive dual-threshold triggering mechanism and reputation-driven node selection is designed to reduce blockchain-induced overhead. Simulation results show that SHIELD-VecNet achieves balanced improvements in timeliness, energy efficiency, and reliability, providing an efficient and secure solution for vehicular communication networks.

In today's service-oriented digital environment, ensuring the quality of service (QoS) is crucial, which makes QoS prediction a prominent topic in current research on Web service recommendation. Recently, some existing works have made significant advancements in modeling both users and services. However, several key issues have not been well studied in existing research, including issues related to bilateral trust, user preferences, and privacy protection. To effectively resolve these concerns, we put forward TEPP, a robust trust-enhanced privacy-preserving QoS prediction method for Web service recommendation. First, we evaluate user reputation values through the Dirichlet distribution and integrate user similarity to jointly compute trust values between users, thereby identifying a group of trustworthy and similar users. At the same time, we utilize an exponential mechanism to protect the privacy of user information. Secondly, we calculate the preference similarity between users, taking into account their preferences. Finally, we determine a set of trustworthy similar services by combining the reputation value and similarity of the service providers, and predict missing QoS by a fusion model that integrates the above three methods. To make TEPP more practical and robust in Web service recommendation, we embed a bilateral trust model in TEPP based on evolutionary game theory to constrain and guide users and service providers to honestly participate in the Web service recommendation. Experimental simulation results demonstrate that the proposed scheme not only outperforms existing schemes in prediction accuracy but also can fully motivate both users and service providers to choose trusted strategic behaviors in the Web service recommendation.

Human pose generation is a complex task due to the non-rigid and highly variable nature of human body structures and appearances. However, existing methods often overlook the fundamental differences between spatial transformations of poses and texture generation for appearance, which makes them prone to overfitting. To address this issue, we propose a multi-pose generation framework driven by disentangled pose and appearance guidance. Our approach includes a Global-aware Pose Generation module that iteratively generates pose embeddings, enabling effective control over non-rigid body deformations. Additionally, we introduce the Global-aware Transformer Decoder, which leverages similarity queries and attention mechanisms to achieve spatial transformations and enhance pose consistency through a Global-aware block. In the appearance generation phase, we condition a diffusion model on pose embeddings produced in the initial stage and introduce an Appearance Adapter that extracts high-level contextual semantic information from multi-scale features, enabling further refinement of pose appearance textures and providing appearance guidance. Extensive experiments on the UBC Fashion and TikTok datasets demonstrate that our framework achieves state-of-the-art results in both quality and fidelity, establishing it as a powerful approach for complex pose generation tasks.

Lindell17 is a widely used two-party threshold ECDSA protocol that offers security under concurrent executions and supports global abort upon failure. In this paper, we extend the attack model of Makriyannis, Yomtov, and Galansky by proposing a more general and covert class of attacks, which we term Digit-by-Digit Extraction (DBDE). Our attacks allow the adversary to choose an arbitrary base b and craft nonce values k_2 = b^ℓ , enabling the gradual leakage of the honest party’s key share through signature outcomes. We demonstrate DBDE attacks on Lindell17 and its three main variants: (1) Lindell17 (+), (2) Lindell17 ( · ) in 2-of-2 setting, (3) the actively refreshed variant Refreshed-Lindell17 (+), and (4) Patched HD Lindell17. To mitigate these attacks, we propose a practical countermeasure that prevents adversarial control over nonce selection by introducing a jointly generated k_2 . Our patch neutralizes all known attacks that exploit nonce selection strategies. We also provide proof-of-concept code to validate our attacks and demonstrate their real-world feasibility.

Self-dual maximum distance separable (MDS) codes over finite fields are linear codes with significant combinatorial and cryptographic applications. Twisted generalized Reed-Solomon (TGRS) codes can be both MDS and self-dual. In this paper, we study a general class of TGRS codes (A-TGRS), which encompasses all previously known special cases. First, we establish a sufficient and necessary condition for an A-TGRS code to be Hermitian self-dual. Furthermore, we present four constructions of self-dual TGRS codes, which, to the best of our knowledge, nearly cover all the related results previously reported in the literature. More importantly, we also obtain several new classes of Hermitian self-dual TGRS codes with flexible parameters. Based on this framework, we derive a sufficient and necessary condition for an A-TGRS code to be Hermitian self-dual and MDS. In addition, we construct a class of MDS Hermitian self-dual TGRS code by appropriately selecting the evaluation points. This work investigates the Hermitian self-duality of TGRS codes from the perspective of matrix representation, leading to more concise and transparent analysis. More generally, the Euclidean self-dual TGRS codes and the Hermitian self-dual GRS codes can also be understood easily from this point.

Unlike on Fe and Co catalysts, the CO conversion effect on Ru catalyst performance is little reported. This study is undertaken to explore the issue using a series of Ru/NaY catalysts under 200–230 °C, 2.0 MPa, H2/CO = 2, and 10–60% CO conversion in a 1 L continuous stirred tank reactor (CSTR). The results are comparatively studied with those of Fe and Co catalysts reported previously. The NaY support and four 1.0%, 2.5%, 5.0%, and 7.5% Ru/NaY catalysts were characterized by BET, H2 chemisorption, H2O-TPD, XRD, HRTEM, and XANES/EXAFS techniques. The BET and XRD results suggest a high surface area (730 m2/g), high degree of crystallinity of the NaY support, and high dispersion of Ru, while an hcp Ru structure and well-reduced Ru were reflected in the HR-TEM FFT and XANES/EXAFS results. The reaction results indicate that the CO conversion effect on CH4 and C5+ selectivities on the Ru is the same as that on the Fe and Co catalysts, with CH4 selectivity decreasing and C5+ selectivity increasing with increasing CO conversion. However, the CO conversion effect on olefin formation for the Ru catalyst was found to be opposite to that of the Fe and Co; increasing CO conversion enhanced olefin formation but suppressed secondary reactions of 1-olefins. The H2O cofeeding experiments showed that H2O impacted olefin formation by suppressing hydrogen adsorption and hydrogenation. The H2O-TPD experiment evidenced a much stronger H2O adsorption capacity (6.8 mmol/g-cat) on Ru followed by Co (1 mmol/g-cat), and then Fe (0.2 mmol/g-cat)., which showed only a very low H2O adsorption capacity.This finding may explain the opposite CO conversion effect on olefin formation observed on the Ru catalyst, and may also explain why low CH4 selectivity (i.e., 3%) occurred on the Ru catalyst and high CH4 selectivity (i.e., 6–8%) occurred on the Co catalyst, both of which possess low water gas shift (WGS) activity.

The widespread deployment of 5G networks has accelerated the development of Internet of Vehicles (IoV), laying the foundation for the development of intelligent transportation systems (ITS) in future 6G networks. The unprecedented intelligence of 6G ITS is expected to enable the automation of vehicles, seamless collaboration, and intelligent management. High-volume information sharing plays a crucial role in this case, and its security and reliability becoming important cornerstones for 6G ITS. Without adequate security protection and trusted environments may result in unreliable and inefficient network services. In this article, we propose a reputation management-based blockchain, Info-Chain, for secure, trustworthy, and privacy-preserving information sharing in 6G ITS. Furthermore, to accommodate the distributed traffic environment in 6G ITS, we construct a novel consensus mechanism for Info-Chain, denoted as Proof of Reputation and sum (PoRs), which combines reputation with traffic environmental factors as competitive conditions. Finally, we propose a dynamic incentives and punishments mechanism that utilizes evolutionary game theory to guide vehicles to actively and honestly participate in information sharing. The security analysis shows that the proposed mechanism is capable of resisting common attacks, and the simulation result demonstrates that the proposed scheme not only enables efficient and secure information sharing in 6G ITS, but also encourages vehicles to actively participate in information sharing.

Linear complementary dual (LCD) codes introduced by Massey are the codes whose intersections with their dual codes are trivial. It can help to improve the security of the information processed by sensitive devices, especially against side-channel attacks (SCA) and fault invasive attacks. In this paper, By construction of puncturing, extending, shortening and combination codes, many good ternary LCD codes are presented. We give a Table 1 with the values of dLCD(n, k) for length n ≤ 20. In addition, Many of these ternary LCD codes given in this paper are optimal which are saturating the lower or upper bound of Grassl’s codetable in [5] and some of them are nearly optimal.

The precise forecasting of photovoltaic (PV) power is important for efficient grid management. To enhance the analysis and processing capability of PV characteristics, address the feature extraction challenges for long sequences, and improve forecasting accuracy, this study presents a robust hybrid deep learning model for PV power forecasting. First, a dynamic mean pre-processing algorithm is applied for data cleaning. Subsequently, an improved whale variational mode decomposition (IWVMD) algorithm is proposed for data decomposition in multichannel multi-scale modeling. Furthermore, a novel context-embedded causal convolutional Transformer (CCTrans) structure is used to predict each subsequence, and an optimal strategy is formulated for both input and output under the combined dynamic contextual information and single target variable forecasting (CDCTF) pattern. Finally, the forecasting results are reconstructed. Experiments are conducted to evaluate the performance of the model across different seasons, using publicly available datasets from the Desert Knowledge Australia Solar Center (DKASC). Ablation studies, validation with diverse datasets, and comparisons with other models confirm the effectiveness, accuracy, robustness, and generalizability of the model. In addition, recommendations for optimal forecasting ranges for different seasons are provided.

To realize unprecedented applications and individualized services, dynamic and efficient communications and networks resources (CNRs) sharing is crucial in the 6G era. While exploring novel CNRs undoubtedly enhances comprehensive performance in 6G networks, improving the effective utilization of existing resources can balance the burden on communication networks and increase the quality of service. This article systematically investigates the challenges associated with future CNRs sharing in 6G networks, and clarifies the potential of blockchain and evolutionary algorithms in CNRs sharing. Meanwhile, we propose a generalized architecture for CNRs sharing in 6G networks, which can support various types of CNRs sharing, single or hybrid. Specifically, a blockchain embedded with an adjustable consensus mechanism is employed to ensure the security and efficiency of resource trading. Furthermore, evolutionary algorithms are utilized to achieve customizable and flexible resource matching. The simulation result indicates that the proposed architecture not only facilitates efficient resource trading but also effectively improves resource utilization, thereby demonstrating the feasibility of the proposed architecture. Finally, we outline open issues for future work related to the proposed architecture.

5G networks provide secure and reliable information transmission services for the Internet of Everything, thus paving the way for 6G networks, which is anticipated to be an AI-based network, supporting unprecedented intelligence across applications. Abundant computing resources will establish the 6G Computing Power Network (CPN) to facilitate ubiquitous intelligent services. In this article, we propose BECS, a computing sharing mechanism based on evolutionary algorithm and blockchain, designed to balance task offloading among user devices, edge devices, and cloud resources within 6G CPN, thereby enhancing the computing resource utilization. We model computing sharing as a multi-objective optimization problem, aiming to improve resource utilization while balancing other issues. To tackle this NP-hard problem, we devise a kernel distance-based dominance relation and incorporated it into the Non-dominated Sorting Genetic Algorithm III, significantly enhancing the diversity of the evolutionary population. In addition, we propose a pseudonym scheme based on zero-knowledge proof to protect the privacy of users participating in computing sharing. Finally, the security analysis and simulation results demonstrate that BECS can fully and effectively utilize all computing resources in 6G CPN, significantly improving the computing resource utilization while protecting user privacy.

Three-dimensional point cloud registration is an important field in computer vision. Recently, due to the increasingly complex scenes and incomplete observations, many partial-overlap registration methods based on overlap estimation have been proposed. These methods heavily rely on the extracted overlapping regions with their performances greatly degraded when the overlapping region extraction underperforms. To solve this problem, we propose a partial-to-partial registration network (RORNet) to find reliable overlapping representations from the partially overlapping point clouds and use these representations for registration. The idea is to select a small number of key points called reliable overlapping representations from the estimated overlapping points, reducing the side effect of overlap estimation errors on registration. Although it may filter out some inliers, the inclusion of outliers has a much bigger influence than the omission of inliers on the registration task. The RORNet is composed of overlapping points' estimation module and representations' generation module. Different from the previous methods of direct registration after extraction of overlapping areas, RORNet adds the step of extracting reliable representations before registration, where the proposed similarity matrix downsampling method is used to filter out the points with low similarity and retain reliable representations, and thus reduce the side effects of overlap estimation errors on the registration. Besides, compared with previous similarity-based and score-based overlap estimation methods, we use the dual-branch structure to combine the benefits of both, which is less sensitive to noise. We perform overlap estimation experiments and registration experiments on the ModelNet40 dataset, outdoor large scene dataset KITTI, and natural data Stanford Bunny dataset. The experimental results demonstrate that our method is superior to other partial registration methods. Our code is available at https://github.com/superYuezhang/RORNet.

Pancreatic β-cells death mediated by apoptosis plays a key role in the development of diabetes. Pro-inflammatory cytokines activate the apoptotic program in pancreatic β-cells. Therefore, inhibiting inflammatory cytokines induced apoptosis could preserve pancreatic β-cells. The fruit of Lycium is widely used as an antioxidant food and its pharmacological action is partially mediated by Lycium barbarum polysaccharides (LBP). However, whether LBP can inhibit inflammatory cytokines-induced β-cells apoptosis has not been reported. In this study, we showed that LBP could alleviate the apoptosis of pancreatic β-cells through the inhibition of interferon-γ (IFN-γ) pathway. In healthy mice, LBP administration could improve glucose tolerance. In streptozotocin-induced β-cells injury mice, preventive and continuous administration of LBP could alleviate hyperglycemia and protect pancreatic insulin content. Our study identified a novel function of LBP in prevention and protection of pancreatic β-cells apoptosis, which was partially through the inhibition of IFN-γ pathway.

Machine Learning as a Service (MLaaS) provides clients with well-trained neural networks for predicting private data. Conventional prediction processes of MLaaS require clients to send sensitive inputs to the server, or proprietary models must be stored on the client-side device. The former reveals client privacy, while the latter harms the interests of model providers. Existing works on privacy-preserving MLaaS introduce cryptographic primitives to allow two parties to perform neural network inference without revealing either party's data. However, nonlinear activation functions bring high computational overhead and response delays to the inference process of these schemes.In this paper, we analyze the mechanism by which activation functions enhance model expressivity, and design an activation function S -cos that is friendly to secure neural network inference. Our proposed S -cos can be re-parameterized into a linear layer during the inference phase. Further, we propose an inference-time linear model called Beyond Linear Neural Network (B-LNN) equipped with S -cos, which exhibits promising performance on several benchmark datasets.

Low latency and low energy consumption are prerequisites for Internet of Vehicles (IoV) research; nevertheless, consensus mechanisms of legacy blockchain applied to IoV typically incur high delay and serious energy consumption due to the computational puzzle. In this paper, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to implement a novel reputation consensus mechanism for IoV based on vehicle reputation values and traffic environment, which can not only reduce the computational pressure but also ensure the fairness of consensus. Firstly, the information transmission vehicles (ITVs) would be picked following the reputation value and its associated environmental data that makes the choice more comprehensive and objective than other mechanisms. Secondly, the authenticity of sharing information is determined with the help of Bayesian inference, aided by the reputation value of message reporting vehicles (MRVs). Finally, simulation results show that the proposed scheme can improve consensus efficiency by picking ITVs in a short time and avoid the spreading of false information effectively in the IoV system.