Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data r...
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Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.
The traditional methods for analyzing environmental impact factors in urban road traffic accidents suffer from issues such as low recall, poor precision, and time-consuming processes. To address these challenges, a no...
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Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation ...
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Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN's node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.
Therapeutic peptides contribute significantly to human health and have the potential for personalized medicine. The prediction for the therapeutic peptides is beneficial and emerging for the discovery of drugs. Althou...
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Therapeutic peptides contribute significantly to human health and have the potential for personalized medicine. The prediction for the therapeutic peptides is beneficial and emerging for the discovery of drugs. Although several computational approaches have emerged to discern the functions of therapeutic peptides, predicting multi-functional therapeutic peptide types is challenging. In this research, a novel approach termed TPpred-SC has been introduced. This method leverages a pretrained protein language model alongside multi-label supervised contrastive learning to predict multi-functional therapeutic *** framework incorporates sequential semantic information directly from large-scale protein sequences in TAPE. Then, TPpred-SC exploits multi-label supervised contrastive learning to enhance the representation of peptide sequences for imbalanced multi-label therapeutic peptide prediction. The experimental findings demonstrate that TPpred-SC achieves superior performance compared to existing related methods. To serve our work more efficiently, the web server of TPpred-SC can be accessed at http://***/TPpred-SC.
The virtual private cloud service currently lacks a real-time end-to-end consistency validation mechanism, which prevents tenants from receiving immediate feedback on their requests. Existing solutions consume excessi...
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The virtual private cloud service currently lacks a real-time end-to-end consistency validation mechanism, which prevents tenants from receiving immediate feedback on their requests. Existing solutions consume excessive communication and computational resources in such large-scale cloud environments, and suffer from poor timeliness. To address these issues, we propose a lightweight consistency validation mechanism that includes real-time incremental validation and periodic full-scale validation. The former leverages message layer aggregation to enable tenants to swiftly determine the success of their requests on hosts with minimal communication overhead. The latter utilizes lightweight validation checksums to compare the expected and actual states of hosts locally, while efficiently managing the checksums of various host entries using inverted indexing. This approach enables us to efficiently validate the complete local configurations within the limited memory of hosts. In summary, our proposed mechanism achieves closed-loop implementation for new requests and ensures their long-term effectiveness.
Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined ...
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Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.
Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadv...
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To insight into the B-site ordering in RFe_(0.5)Cr_(0.5)O_(3)ceramics,a series of RFe_(0.5)Cr_(0.5)O_(3)ceramics(R=La,Y,Lu)were synthesized by the sol-gel method,and the structural and magnetic properties were systemi...
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To insight into the B-site ordering in RFe_(0.5)Cr_(0.5)O_(3)ceramics,a series of RFe_(0.5)Cr_(0.5)O_(3)ceramics(R=La,Y,Lu)were synthesized by the sol-gel method,and the structural and magnetic properties were systemically *** using the Rietveld refinement of all samples,it is found that the structural distortion is increased as the R ionic radius decreases,leading to the weakened interactions between Fe/Cr ***,the Fe and Cr are arranged in disorder in LaFe_(0.5)Cr_(0.5)O_(3),but partially ordered in YFe_(0.5)Cr_(0.5)O_(3)and LuFe_(0.5)Cr_(0.5)O_(3),showing an increasing trend of the proportion of ordered domains with the decrease of R ionic *** fitting the temperature-dependent magnetizations,it is identified that the magnetization reversal(MR)in disorder LaFe_(0.5)Cr_(0.5)O_(3)is resulted from the competition between the moments of Cr and Fe *** the partially ordered YFe_(0.5)Cr_(0.5)O_(3)and LuFe_(0.5)Cr_(0.5)O_(3)ceramics,because of the presence of Fe-O-Cr networks in the ordered domains whose moment is antiparallel to that of Fe-O-Fe and Cr-O-Cr in the disordered domains,the compensation temperature T_(comp)of MR is increased by nearly 50 *** results suggest that the changing of R-site ions could be used very effectively to modify the Fe-O-Cr ordering,apart from the structural distortion,which has a direct effect on the magnetic exchange interactions in RFe_(0.5)Cr_(0.5)O_(3)*** at values of composition where ordered domains are expected to be larger in number as compared to disordered domains and with a weaker structural distortion,one can expect a higher transition temperature Tcomp,providing a different view for adjustment of the magnetic properties of RFe_(0.5)Cr_(0.5)O_(3)ceramics for practical applications.
Visible and infrared image fusion(VIF)aims to combine information from visible and infrared images into a single fused *** VIF methods usually employ a color space transformation to keep the hue and saturation from th...
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Visible and infrared image fusion(VIF)aims to combine information from visible and infrared images into a single fused *** VIF methods usually employ a color space transformation to keep the hue and saturation from the original visible ***,for fast VIF methods,this operation accounts for the majority of the calculation and is the bottleneck preventing faster *** this paper,we propose a fast fusion method,FCDFusion,with little color *** preserves color information without color space transformations,by directly operating in RGB color *** incorporates gamma correction at little extra cost,allowing color and contrast to be rapidly *** regard the fusion process as a scaling operation on 3D color vectors,greatly simplifying the calculations.A theoretical analysis and experiments show that our method can achieve satisfactory results in only 7 FLOPs per *** to state-of-theart fast,color-preserving methods using HSV color space,our method provides higher contrast at only half of the computational *** further propose a new metric,color deviation,to measure the ability of a VIF method to preserve *** is specifically designed for VIF tasks with color visible-light images,and overcomes deficiencies of existing VIF metrics used for this *** code is available at https://***/HeasonLee/FCDFusion.
Internal polyhedral structures of a granular system can be investigated using the Voronoi *** technique has gained increasing recognition in research of kinetic properties of granular *** systems with mono-sized spher...
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Internal polyhedral structures of a granular system can be investigated using the Voronoi *** technique has gained increasing recognition in research of kinetic properties of granular *** systems with mono-sized spherical particles,Voronoi tessellations can be utilized,while radial Voronoi tessellations are necessary for analyzing systems with multi-sized spherical ***,research about polyhedral structures of non-spherical particle systems is *** utilize the discrete element method to simulate a system of ellipsoidal particles,defined by the equation(x/a)^(2)+(y/1)^(2)+(z/1/a)^(2)=1,where a ranges from 1.1 to *** system is then dissected by using tangent planes at the contact points,and the geometric quantities of the resulting polyhedra in different shaped systems,such as surface area,volume,number of vertices,number of edges,and number of faces,are ***,the longitudinal and transverse wave velocities within the system are calculated with the time-of-flight *** results demonstrate a strong correlation between the sound velocity of the system and the geometry of the dissected *** sound velocity of the system increases with the increase in a,peaking at a=1.3,and then decreases as a continues to *** average volume,surface area,number of vertices,number of edges,and number of faces of the polyhedra decrease with the increase in sound *** is,these quantities initially decrease with the increase in a,reaching minima at a=1.3,and then increase with further increase of *** relationship between sound velocity and the geometric quantities of the dissected polyhedra can serve as a reference for acoustic material design.
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