The proliferation of internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial rol...
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Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these adva...
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Infrared small target detection (ISTD) is vital for long-range surveillance systems, particularly in military defense, maritime monitoring, and early warning applications. Despite its strategic importance, ISTD remain...
Infrared small target detection (ISTD) is vital for long-range surveillance systems, particularly in military defense, maritime monitoring, and early warning applications. Despite its strategic importance, ISTD remains challenging due to two fundamental limitations: targets typically occupy less than 0.15% of the image area and exhibit low distinguishability from complex backgrounds. While recent advances in deep learning have shown promise, existing methods struggle with information loss during downsampling and inefficient modeling of global context. This paper presents SAMamba, a novel framework that synergistically integrates SAM2’s hierarchical feature learning with Mamba’s selective sequence modeling to address these challenges. Our key innovations include: (1) Feature Selection Adapter (FS-Adapter) that enables efficient domain adaptation from natural to infrared imagery by employing a dual-stage selection mechanism, which includes token-level selection via a learnable task embedding and channel-wise refinement through adaptive transformations; (2) Cross-Channel State-Space Interaction (CSI) module that achieves efficient global context modeling through selective state space modeling with linear complexity; and (3) Detail-Preserving Contextual Fusion (DPCF) module that adaptively combines multi-scale features through learnable fusion strategies, utilizing a gating mechanism to balance contributions from high-resolution and low-resolution features. SAMamba effectively addresses the core challenges of ISTD by bridging the domain gap, maintaining fine-grained target details, and efficiently modeling long-range dependencies. Extensive experiments on NUAA-SIRST, IRSTD-1k and NUDT-SIRST datasets demonstrate that SAMamba significantly outperforms state-of-the-art methods, particularly in challenging scenarios with heterogeneous backgrounds and varying target scales. Code is available at https://***/zhengshuchen/SAMamba .
internet of Things (IoT) devices generate large amounts of data every day that can be combined with intelligent platforms for predictive analytics and scientific research. However, concerns about privacy and security ...
internet of Things (IoT) devices generate large amounts of data every day that can be combined with intelligent platforms for predictive analytics and scientific research. However, concerns about privacy and security hinder the willingness of individuals to share data. Blockchain emerged as a promising infrastructure for facilitating secure data sharing due to its decentralized, immutability, and auditable benefits. In this paper, we propose a blockchain-based cloud–edge collaborative privacy protection data sharing scheme (BCE-PPDS), which is decentralized and enables data requesters (DRs) to search data resources using smart contracts to efficiently obtain target data. To protect the identity privacy of data owners (DOs), we propose a novel certificateless linkable ring signature algorithm with efficient performance. This algorithm is not only suitable for deployment on resource-limited IoT devices, so that DOs can realize anonymous identity authentication, but also can aggregate the generated ring signatures for batch verification, so as to improve the efficiency of signature verification. In addition, we designed a key distribution algorithm using the Asmuth–Bloom secret sharing scheme to ensure the security of the key. Under the random oracle model, BCE-PPDS is provably secure. The experimental results verify that BCE-PPDS is efficient and practical.
The lymphatic system hosts a large number of therapeutic targets that can be used to modulate a wide range of diseases including cancers, autoimmune and inflammatory disorders, infectious diseases and metabolic syndro...
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The lymphatic system hosts a large number of therapeutic targets that can be used to modulate a wide range of diseases including cancers, autoimmune and inflammatory disorders, infectious diseases and metabolic syndrome;however, drug access to the lymphatic system is often challenging. Over the past decades significant efforts have been made to promote drug transport to the lymphatics through medicinal chemistry approaches, and a number of promising progresses are emerging. Nevertheless, so far it remains difficult to clearly delineate the mechanism of lymphatic drug transport and to map the design criteria for lymphotropic drug molecules, and the attempts to synthesize lymph-directing drug candidates or drug derivatives are largely in an experience-driven, trial and error basis. Furthermore, complex experimental procedures required for the study of lymphatic drug transport have limited data accumulation in the field, and this in turn hampers mechanistic studies and understanding of drug design criteria. Our current study aims to 1) review and summarize published work that assessed lymphatic drug transport by both direct measurement (e.g. determination of drug concentrations in lymph fluid) or indirect measurement (e.g. imaging methods or by comparing the changes of pharmacokinetics profile in the absence and presence of lymphatic transport blocker);2) to analyze lymphatic drug transport data of 185 drugs according to experimental models and conditions, followed by dataset regrouping according to the extent of lymphatic transport;3) to establish different Artificial Intelligence (AI) models including Graph Convolutional Network (GCN), Graph Attention Network (GAT) and Graph Transformer (GT) to predict the potential of drug transport via the lymphatics following oral administration, during which process data augmentation approaches were employed to compensate for the limited data. The results demonstrated that our model can enhance data and lymphatic drug transport p
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial...
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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.
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