Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it vulnerable to server failures. While existing solutions utilize blockchain technology to implement Decentralized Federated Learning (DFL), the statistical heterogeneity of data distributions among clients severely degrades the performance of DFL. Driven by this issue, this paper proposes a decentralized federated prototype learning framework, named DFPL, which significantly improves the performance of DFL under heterogeneous data distributions. Specifically, DFPL introduces prototype learning into DFL to mitigate the impact of statistical heterogeneity and reduces the amount of parameters exchanged between clients. Additionally, blockchain is embedded into our framework, enabling the training and mining processes to be executed locally on each client. From a theoretical perspective, we analyze the convergence of DFPL by modeling the required computational resources during both training and mining. The experiment results highlight the superiority of DFPL in both model performance and communication efficiency across four benchmark datasets with heterogeneous data distributions.

Federated learning (FL), a distributed computing paradigm, is vulnerable to poisoning attacks that impair model performance and privacy attacks that leak participant information. Existing FL defense schemes struggle to counter poisoning attacks under data heterogeneity and high privacy computation overhead, limiting the practicality of federated learning. To address these issues, this paper proposes a model-contrastive federated learning framework with lightweight privacy preservation and poisoning attack detection, named MCFL. Specifically, we design a novel model-contrastive term by aligning intermediate-layer representations of models in the local optimization function to promote consistency of model updates among benign participants. Additionally, we design a secure aggregation protocol that adopts two-server aggregation instead of the single server to resist poisoning attacks with lightweight privacy protection. The proposed MCFL is theoretically proven in terms of convergence, robustness, and privacy. Extensive experiments demonstrate the superiority of MCFL compared to existing FL defense schemes.

Ensuring safe and reliable data sharing is crucial for the development of Internet of Vehicles (IoV) technology. To provide a trusted data environment for IoV and enable traditional consensus algorithms to meet the high dynamic requirements of the IoV. In this article, we propose a blockchain-based data sharing scheme for IoV (BLDTS) to achieve secure and trusted sharing. First, we design a false information identification strategy that utilizes a bayesian inference model to determine the authenticity of shared data with the assistance of reputation value. Second, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to construct a novel lightweight consensus mechanism based on vehicle reputation values and traffic environment factors, and the nodes with high scores were selected as participants in the consensus, which can reduce the computational overhead. Finally, experimental results show that our scheme has advantages in improving the accuracy of false message identification and consensus efficiency.

Cross-chain payment, serving as critical infrastructure for multi-chain ecosystem interoperability, confronts the fundamental challenge of simultaneously ensuring privacy preservation, regulatory compliance, and quantum-resistant security—objectives that are inherently difficult to reconcile. This paper proposes a Lattice-based Dynamic Privacy-preserving Cross-chain Payment Scheme (LDPCPS) that innovatively integrates advanced cryptographic primitives. Specifically, LDPCPS employs a privacy-preserving scalar product (PPSP) protocol enabling ciphertext-domain aggregation and verification, constructs a dynamic regulatory framework using signatures of knowledge (SoK) for zero-knowledge compliance proofs and risk-triggered traceability, and implements proxy re-encryption to facilitate seamless quantum-resistant key migration. Experimental results demonstrate that LDPCPS has significant superiority over state-of-the-art alternatives in quantum resistance, computational efficiency, and regulatory adaptability, thereby establishing a robust foundation for secure and compliant cross-chain transactions.

With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.

This article proposes return-on-equity (ROE) networks for portfolio optimization, which integrate the DuPont analysis and graph theory. Portfolio diversification is interpreted as follows: An intercluster relationship of the network structure diversifies business models, whereas an innercluster relationship variegates different industries. The proposed approach is applied to the Chinese stock market. It shows that, in terms of the annualized return, the ROE network optimized portfolio reached$13.20\%$compared with$6.02\%$of the Shanghai Stock Exchange (SSE) Composite Index. It also shows that portfolios with 100–200 stocks, which are composed of the top 10%–20% ROE stocks, reached the highest return-risk efficiency.

The emergence of mobile head-mounted displays with robust "inside-out" markerless tracking and video-passthrough permits the creation of novel mixed reality (MR) experiences in which architectural spaces of arbitrary size can be transformed into immersive multi-user visualisation arenas. Here we outline ViewR, an open-source framework for rapidly constructing and deploying architectural-scale multi-user MR experiences. ViewR includes tools for rapid alignment of real and virtual worlds, tracking loss detection and recovery, user trajectory visualisation and world state synchronisation between users with persistence across sessions. ViewR also provides control over the blending of the real and the virtual, specification of site-specific blending zones, and video-passthrough avatars, allowing users to see and interact with one another directly. Using ViewR, we explore the transformation of large architectural structures into immersive arenas by creating a range of experiences in various locations, with a particular focus on architectural affordances such as mezzanines, stairs, gangways and elevators. Our tests reveal that ViewR allows for experiences that would not be possible with pure virtual reality, and indicate that, with certain strategies for recovering from tracking errors, it is possible to construct large scale multi-user MR experiences using contemporary consumer virtual reality head-mounted displays.

This paper presents Pearl, a mixed-reality approach for the analysis of human movement data in situ. As the physical environment shapes human motion and behavior, the analysis of such motion can benefit from the direct inclusion of the environment in the analytical process. We present methods for exploring movement data in relation to surrounding regions of interest, such as objects, furniture, and architectural elements. We introduce concepts for selecting and filtering data through direct interaction with the environment, and a suite of visualizations for revealing aggregated and emergent spatial and temporal relations. More sophisticated analysis is supported through complex queries comprising multiple regions of interest. To illustrate the potential of Pearl, we developed an Augmented Reality-based prototype and conducted expert review sessions and scenario walkthroughs in a simulated exhibition. Our contribution lays the foundation for leveraging the physical environment in the in-situ analysis of movement data.

In this paper, we present Dynascape, an immersive approach to the composition and playback of dynamic real-world scenes in mixed and virtual reality. We use spatially tracked RGB-D cameras to capture point cloud representations of arbitrary dynamic real-world scenes. Dynascape provides a suite of tools for spatial and temporal editing and composition of such scenes, as well as fine control over their visual appearance. We also explore strategies for spatiotemporal navigation and different tools for the in situ authoring and viewing of mixed and virtual reality scenes. Dynascape is intended as a research platform for exploring the creative potential of dynamic point clouds captured with mobile, tracked RGB-D cameras. We believe our work represents a first attempt to author and playback spatially tracked RGB-D video in an immersive environment, and opens up new possibilities for involving dynamic 3D scenes in virtual space.

This study investigates how interactive dashboards influence decision making by exploring how specific dashboard features impact design task performance, efficiency, understanding, and confidence. An experiment was conducted in which undergraduate student participants were given a design activity and randomly assigned to one of five dashboards, each using the same underlying functions but varying in the visualization features employed. These features include different graphical representations of the design decision inputs and performance outputs. Participants were first asked to use their assigned dashboard to design a catapult system that maximizes launch distance while meeting requirements related to height, weight, and cost. Following the design task, they were asked a series of questions about their experiences with the dashboard and their understanding of the catapult model. A between-subjects analysis then evaluated how the dashboard design influenced various outcomes of interest. The results show that students who used the most feature-rich dashboard did not perform objectively better than those with the most feature-sparse dashboard, though their self-reported performance was higher. The performance of female versus male participants was also compared, with no significant differences found. The findings support the notion that dashboards should be designed with minimal features to convey the necessary information, and they also point out the disconnect between objective performance and user-assessed performance with interactive dashboards.

In this paper, a utility maximization-based resource scheduling and sharing (UM-RSS) scheme is proposed for downlink multiuser multiple-input-multiple-output orthogonal frequency-division multiple access (MU-MIMO-OFDMA) systems. Before performing the UM-RSS scheduling scheme, we first allocate the best antenna sequence for every served user by a suboptimal multiuser antenna selection (MAS) algorithm according to the channel state information (CSI). To balance efficiency and fairness, the integrated scheduling algorithm of UM-RSS is responsible for assigning subcarriers to different users, as well as distributing the assigned subcarriers among multiple services for the same user. For a MU-MIMO-OFDMA system, the joint spatial and frequency scheduling may improve the system spectrum efficiency by exploiting the multiuser diversity gain in both frequency and spatial domain. Finally, numeric result simulated show that the UM-RSS scheduling scheme outperforms traditional scheduling schemes in terms of system throughput, system spectral efficiency, and fairness criterion.

In this paper, the utility-based resource allocation algorithms for the secondary users (SU) supporting heterogeneous services in orthogonal frequency division multiplexing (OFDM)-based cognitive radio cellular networks (CogCells) are studied. Two kinds of users are considered: best-effort-only service users and multiple services users. According to the convex optimization theory, the Lagrangian dual method is proposed, in which the joint subcarrier assignment and power allocation are performed to achieve the optimal solution. To simplify the computation complexity, a low complexity dynamic subcarrier allocation algorithm, named Max Utility for Multiple Services on Cognitive Radio (CR-MUMS), is formulated to extend the non-linear integer optimization to a continuous convex optimization. Final simulation results illustrate that the proposed algorithm with low computational complexity provides better optimal performance than Modified Largest Weighted Delay First (M-LWDF) and Proportional Fair (PF) algorithms.