Federated graph-level clustering (FGC) provides an effective solution for analyzing decentralized graph data with privacy protection. Existing methods typically assume that all clients have the same number of clusters. This assumption simplifies the learning task and has achieved preliminary success. However, this assumption rarely holds in practice, as clients often exhibit substantial heterogeneity in both data distributions and semantic granularity. As a result, cluster-specific knowledge becomes misaligned during server-side aggregation, which ultimately degrades the overall clustering performance. To address this challenge, we propose a novel Federated Graph Clustering under Inter-Client Cluster Number Discrepancy (FedCND) framework, which aligns inter-client heterogeneous distributions by decoupling graph data into public and private patterns. Specifically, after initial local training and clustering on each client, we design a public learner and a private learner to model public and private graph data, respectively. Only anonymized, cluster-level public information is uploaded to the server, while private information remains local. On the server, cluster-level public prototypes are aggregated based on affinities between reconstructed cluster-level graphs, enabling privacy-preserving prototype alignment across clients with heterogeneous cluster numbers and mitigating interference from misaligned information during global aggregation. Finally, private subgraphs derive client-specific prototypes through local relearning, which are subsequently fused with globally oriented public prototypes for better clustering. Extensive experiments demonstrate that the proposed FedCND achieves an average of 4.9% accuracy improvement against current state-of-the-art methods.

In modern decentralized information systems, establishing verifiable trust in remote computing environments has emerged as a critical challenge for secure cross-domain collaboration. Hardware-based Trusted Execution Environments (TEEs) such as Intel SGX and ARM TrustZone offer a promising foundation for addressing this challenge through cryptographically verifiable execution guarantees, but their incompatible Remote Attestation (RA) mechanisms create fundamental barriers to cross-platform trust establishment. Current solutions either focus on single-vendor ecosystems or introduce prohibitive architectural complexity, failing to address the critical need for lightweight, decentralized interoperability. This paper presents Pontis, a decentralized blockchain-based framework that solves unified RA and cross-platform communication challenges for heterogeneous TEEs through three key innovations: (1) a distributed off-chain Coordinator that normalizes vendor-specific attestation protocols, combined with blockchain-anchored decentralized identifiers that provide immutable distributed identities for TEE instances; (2) blockchain-based smart contracts implementing Registration, Attestation, and Management functions for immutable trust status propagation; and (3) secure cross-TEE communication channels built on the Noise protocol framework. Pontis reduces attestation complexity from O ( n 2 ) → O ( n ), achieving up to 99% reduction in required attestations for large-scale deployments. Comprehensive evaluations on heterogeneous TEE platforms, including Intel SGX and ARM TrustZone, demonstrate that Pontis maintains an attestation latency of 60 ms with over 10,000 TEE instances, while establishing a trusted channel between heterogeneous TEEs requires only 5 ms. These results demonstrate the robustness and feasibility of Pontis, establishing a robust foundation for secure, scalable, and flexible cross-platform collaboration in demanding heterogeneous computing environments.

With its wide range of applications, the Internet shows a future trend towards abundant and diverse data resources with multiple types of identifiers (multi-identifiers). However, the legacy Domain Name System (DNS) in the current TCP/IP network architecture has failed to manage these identifiers due to the centralized security issue. While some decentralized DNS alternatives have been proposed, they also face scalability issues. In this paper, we propose a blockchain-based Hierarchical Multi-Identifier System, named H-MIS, as a DNS alternative. Specially, it realizes optimal decentralization and scalability by introducing the Zero-Knowledge rollup (ZK-rollup) solution to synchronize the upper and lower on-chain identifier data, as well as off-chain associated resource data. Finally, we implement H-MIS on Ethereum and evaluate its performance. The experimental results indicate that compared to the original MIS and Ethereum Name Service (ENS), H-MIS has advantages in such aspects as efficiency, data consumption, and Gas fees.

Unsupervised domain adaptation aims to train a classification model from the labeled source domain for the unlabeled target domain. Since the data distribution of the two domains are different, the model often performs poorly on the target domain. The existing methods align the global features of the source domain and the target domain, and learn the domain invariant features to improve the performance of the model, which ignores the difference between the foreground features and the background features, and does not consider the structural information in the image foreground object. Therefore we proposed a method called foreground object structure transfer (FOST), it avoids the problem of ignoring differences in the structure information of foreground features and background features, exploits foreground feature enhancement from source-to-target transfer during adaptation and structural contrast loss to drive the domain alignment process. FOST relies on prior knowledge to distinguish foreground and background features, and considers the structural information of the object, which makes the intra-class spatial distribution more compact, the interclass spatial distribution more separated, improves the transferability and improves the classification efficiency. Extensive experimental results on various benchmarks under different domain adaptation settings illustrated that our FOST compares favorably against the state-of-the-art domain adaptation methods, we achieved the accuracies of 95.3%, 91.3%, 76.6%, and 87.55% on the ImageCLEF-DA, Office-31, Office-Home, and Visda-2017 data sets, respectively.

Semantic segmentation technique plays a crucial role in Internet of Things applications, such as industrial robotics and self-driving. Recently deep learning approaches have boosted semantic segmentation accuracy greatly. However, their comprehensive performance in terms of accuracy and efficiency is still far from satisfactory. We observe that (1) accuracy-oriented methods rely on numerous convolution layers and sophisticated architectures, which result in heavy computational complexity and usually take a long time for inference; (2) efficiency-oriented methods fail to capture the multiscale context information for discriminative representations during the feature fusion process, thus leading to suboptimal performance. Previous semantic segmentation approaches fail to address these two challenges simultaneously. To tackle the dilemma of precise segmentation and efficient inference, we propose a novel lightweight Multiscale Information Fusion Network (MIFNet). Specifically, the proposed MIFNet mainly consists of two core components, that is, Pyramid Refinement Connection Module (PRCM) and Lightweight Information Fusion Module (LIFM). The PRCM exploits skip learning to establish dependency between different stages. Meanwhile, the pyramid attention mechanism (PAM) in PRCM, which adjusts the weight of hybrid pyramid attention vector to refine spatial features of low-level, is developed to alleviate the semantic gap. Moreover, the LIFM is designed to detect objects at multiple scales from the global-local perspective. In LIFM, the proposed multiscale dense concatenation (MDC) adopts various dilated convolution to extract multiscale local context information. Extensive experimental results on benchmarks data sets demonstrate the significantly better performance of the proposed MIFNet compared with most existing state-of-the-art methods.

Malicious code is an ever-growing security threats to computer systems and networks, while malware detection provides effective defense against malicious codes. In this paper, a brief overview is presented on currently prevalent methods to detect malicious codes, including signature-based methods, behavioral-based detection and machine learning (ML) based ones. More specifically, the potentially effective malicious features are summarized and the novel methods using ML are deeply discussed. Furthermore, an ensemble interpretable framework is explored for automatic and efficient malicious code detection. Based on the knowledge graph of malware, the novel framework inclines to achieve robust malware detection even confronted with unseen malicious codes. Finally, both advantages and disadvantages are discussed and experimental results are outlined to verify the effectiveness of the novel methods.