Multimodal Large Language Models (MLLMs) extend the capacity of LLMs to understand multimodal information comprehensively, achieving remarkable performance in many vision-centric tasks. Despite that, recent studies ha...
With the wide application of the Internet of Things(IoT),storing large amounts of IoT data and protecting data privacy has become a meaningful *** general,the access control mechanism is used to prevent illegal users ...
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With the wide application of the Internet of Things(IoT),storing large amounts of IoT data and protecting data privacy has become a meaningful *** general,the access control mechanism is used to prevent illegal users from accessing private ***,traditional data access control schemes face some non-ignorable problems,such as only supporting coarse-grained access control,the risk of centralization,and high trust *** this paper,an attribute-based data access control scheme using blockchain technology is *** address these problems,attribute-based encryption(ABE)has become a promising solution for encrypted data access ***,we utilize blockchain technology to construct a decentralized access control scheme,which can grant data access with transparency and ***,our scheme also guarantees the privacy of policies and attributes on the blockchain ***,we optimize an ABE scheme,which makes the size of system parameters smaller and improves the efficiency of *** optimizations enable our proposed scheme supports large attribute universe requirements in IoT ***,to prohibit attribute impersonation and attribute replay attacks,we design a challenge-response mechanism to verify the ownership of ***,we evaluate the security and performance of the *** comparisons with other related schemes show the advantages of our proposed *** to existing schemes,our scheme has more comprehensive advantages,such as supporting a large universe,full security,expressive policy,and policy hiding.
Currently, protocol fuzzing techniques mainly employ two approaches: greybox fuzzing based on mutation and blackbox fuzzing based on generation. Greybox fuzzing techniques use message exchanges between the protocol se...
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Deep learning-based autonomous driving systems have been extensively researched due to their superior performance compared to traditional methods. Specifically, end-To-end deep learning systems have been developed, wh...
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With the proposal of a“smart battery,”real-time sensing by rechargeable batteries has become progressively more important in both fundamental research and practical ***,many traditional sensing technologies suffer f...
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With the proposal of a“smart battery,”real-time sensing by rechargeable batteries has become progressively more important in both fundamental research and practical ***,many traditional sensing technologies suffer from low sensitivity,large size,and electromagnetic interference problems,rendering them unusable in the harsh and complicated electrochemical environments of *** optical sensor is an alternative approach to realize multiple-parameter,multiple-point measurements ***,it has garnered significant *** analyzing these measured parameters,the state of interest can be decoded to monitor a battery's *** review summarizes current progress in optical sensing techniques for batteries with respect to various sensing parameters,discussing the current limitations of optical fiber sensors as well as directions for their future development.
Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenar...
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Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).
Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compressio...
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Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.
security vulnerabilities are a major threat to network security, and fuzzing is one of the most widely used vulnerability mining techniques. Traditional fuzzing consists of three phases, where security experts perform...
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In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working *** the systems are attacked,timely identification of outliers in time series is critical to ensure *** many anomaly ...
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In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working *** the systems are attacked,timely identification of outliers in time series is critical to ensure *** many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these *** to the superior capability of Transformer in learning time series *** paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved ***,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are ***,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each *** scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each *** interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory *** introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph *** on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.
The rapid and steady development of machine learning, especially deep learning, has promoted significant progress in the field of image classification. However, Machine learning models are demonstrated to be vulnerabl...
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