With the diversification of space-based information network task requirements and the dramatic increase in demand, the efficient scheduling of various tasks in space-based information network becomes a new challenge. ...
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With the diversification of space-based information network task requirements and the dramatic increase in demand, the efficient scheduling of various tasks in space-based information network becomes a new challenge. To address the problems of a limited number of resources and resource heterogeneity in the space-based information network, we propose a bilateral pre-processing model for tasks and resources in the scheduling pre-processing stage. We use an improved fuzzy clustering method to cluster tasks and resources and design coding rules and matching methods to match similar categories to improve the clustering effect. We propose a space-based information network task scheduling strategy based on an ant colony simulated annealing algorithm for the problems of high latency of space-based information network communication and high resource dynamics. The strategy can efficiently complete the task and resource matching and improve the task scheduling performance. The experimental results show that our proposed task scheduling strategy has less task execution time and higher resource utilization than other algorithms under the same experimental conditions. It has significantly improved scheduling performance.
Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inher...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
Mobile edge computing(MEC) provides edge services to users in a distributed and on-demand *** to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resourceconstrained dev...
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Mobile edge computing(MEC) provides edge services to users in a distributed and on-demand *** to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resourceconstrained devices is a key challenge for service providers. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network(GON) model for predicting resource failure and a deep deterministic policy gradient(DDPG) model for yielding preemptive migration *** show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service, in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time,and energy consumption than other existing methods.
The modern university computer lab and kindergarden through 12th grade classrooms require a centralized solution to efficiently manage a large number of desktops. The existing solutions either bring virtualization ove...
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The modern university computer lab and kindergarden through 12th grade classrooms require a centralized solution to efficiently manage a large number of desktops. The existing solutions either bring virtualization overhead in runtime or requires loading a large image over 30 GB leading to an unacceptable network latency. In this work, we propose Troy which takes advantage of the differencing virtual hard disk techniques in Windows *** such, Troy only loads the modifications made on one machine to all other machines. Troy consists of two modules that are responsible to generate an initial image and merge a differencing image with its parent image, respectively. Specifically, we identify the key fields in the virtual hard disk image that links the differencing image and the parent image and find the modified blocks in the differencing images that should be used to replace the blocks in the parent image. We further design a lazy copy solution to reduce the I/O burden in image merging. We have implemented Troy on bare metal machines. The evaluation results show that the performance of Troy is comparable to the native implementation in Windows, without requiring the Windows environment.
Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders（VAEs） is a popular strategy for generalized zero-shot learning（GZSL）. However, due to the lac...
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Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders（VAEs） is a popular strategy for generalized zero-shot learning（GZSL）. However, due to the lack of fine-grained instance-wise annotations, existing VAE methods can easily suffer from the posterior collapse problem. In this paper, we propose an innovative asymmetric VAE network by aligning enhanced feature representation（AEFR） for GZSL. Distinguished from general VAE structures, we designed two asymmetric encoders for visual and semantic observations and one decoder for visual reconstruction. Specifically, we propose a simple yet effective gated attention mechanism（GAM） in the visual encoder for enhancing the information interaction between observations and latent variables, alleviating the possible posterior collapse problem effectively. In addition, we propose a novel distributional decoupling-based contrastive learning（D2-CL） to guide learning classification-relevant information while aligning the representations at the taxonomy level in the latent representation space. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. The source code is available at https://***/seeyourmind/AEFR.
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 pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps(CAMs)to generate pseudo masks as ***,existing methods often incorporate trainable modules to expand the immature cl...
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The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps(CAMs)to generate pseudo masks as ***,existing methods often incorporate trainable modules to expand the immature class activation maps,which can result in significant computational overhead and complicate the training *** this work,we investigate the semantic structure information concealed within the CNN network,and propose a semantic structure aware inference(SSA)method that utilizes this information to obtain high-quality CAM without any additional training ***,the semantic structure modeling module(SSM)is first proposed to generate the classagnostic semantic correlation representation,where each item denotes the affinity degree between one category of objects and all the ***,the immature CAM are refined through a dot product operation that utilizes semantic structure ***,the polished CAMs from different backbone stages are fused as the *** advantage of SSA lies in its parameter-free nature and the absence of additional training costs,which makes it suitable for various weakly supervised pixel-dense prediction *** conducted extensive experiments on weakly supervised object localization and weakly supervised semantic segmentation,and the results confirm the effectiveness of SSA.
Implementing quantum wireless multi-hop network communication is essential to improve the global quantum network system. In this paper, we employ eight-level GHZ states as quantum channels to realize multi-hop quantum...
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Implementing quantum wireless multi-hop network communication is essential to improve the global quantum network system. In this paper, we employ eight-level GHZ states as quantum channels to realize multi-hop quantum communication, and utilize the logical relationship between the measurements of each node to derive the unitary operation performed by the end node. The hierarchical simultaneous entanglement switching(HSES) method is adopted, resulting in a significant reduction in the consumption of classical information compared to multi-hop quantum teleportation(QT)based on general simultaneous entanglement switching(SES). In addition, the proposed protocol is simulated on the IBM Quantum Experiment platform(IBM QE). Then, the data obtained from the experiment are analyzed using quantum state tomography, which verifies the protocol's good fidelity and accuracy. Finally, by calculating fidelity, we analyze the impact of four different types of noise(phase-damping, amplitude-damping, phase-flip and bit-flip) in this protocol.
Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general ***,most e-auction schemes involve a trusted auctioneer,which is not always credible in **...
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Electronic auctions(e-auctions)remove the physical limitations of traditional auctions and bring this mechanism to the general ***,most e-auction schemes involve a trusted auctioneer,which is not always credible in *** studies have applied cryptography tools to solve this problem by distributing trust,but they ignore the existence of *** this paper,a blockchain-based Privacy-Preserving and Collusion-Resistant scheme(PPCR)for double auctions is proposed by employing both cryptography and blockchain technology,which is the first decentralized and collusion-resistant double auction scheme that guarantees bidder anonymity and bid privacy.A two-server-based auction framework is designed to support off-chain allocation with privacy preservation and on-chain dispute resolution for collusion resistance.A Dispute Resolution agreement(DR)is provided to the auctioneer to prove that they have conducted the auction correctly and the result is fair and *** addition,a Concise Dispute Resolution protocol(CDR)is designed to handle situations where the number of accused winners is small,significantly reducing the computation cost of dispute *** experimental results confirm that PPCR can indeed achieve efficient collusion resistance and verifiability of auction results with low on-chain and off-chain computational overhead.
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