The attack events (such as the Stuxnet and BlackEnergy) that targeted the industrial control system (ICS) have validated its vulnerability to cyber intrusions. The prevention of ICS from cyberattacks is undoubtedly im...
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In recent years, with the advent of the era of bigdata and the rise of data mining technology, people used to waste a lot of time looking for advantageous information, which was very inefficient. Music is an indispen...
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This paper aims to investigate the problem of gaze object prediction in single images. We propose an application-friendly network based on CLIP for gaze object prediction. To avoid domain bias, we utilize a shallow fe...
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Defining the structure characteristics of amorphous materials is one of the fundamental problems that need to be solved urgently in complex materials because of their complex structure and long-range *** this study,we...
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Defining the structure characteristics of amorphous materials is one of the fundamental problems that need to be solved urgently in complex materials because of their complex structure and long-range *** this study,we develop an interpretable deep learning model capable of accurately classifying amorphous configurations and characterizing their structural *** results demonstrate that the multi-dimensional hybrid convolutional neural network can classify the two-dimensional(2D)liquids and amorphous solids of molecular dynamics *** classification process does not make a priori assumptions on the amorphous particle environment,and the accuracy is 92.75%,which is better than other convolutional neural ***,our model utilizes the gradient-weighted activation-like mapping method,which generates activation-like heat maps that can precisely identify important structures in the amorphous configuration *** obtain an order parameter from the heatmap and conduct finite scale analysis of this *** findings demonstrate that the order parameter effectively captures the amorphous phase transition process across various *** results hold significant scientific implications for the study of amorphous structural characteristics via deep learning.
Transmission line(TL)Parameter Identification(PI)method plays an essential role in the transmission *** existing PI methods usually have two limitations:(1)These methods only model for single TL,and can not consider t...
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Transmission line(TL)Parameter Identification(PI)method plays an essential role in the transmission *** existing PI methods usually have two limitations:(1)These methods only model for single TL,and can not consider the topology connection of multiple branches for simultaneous identification.(2)Transient bad data is ignored by methods,and the random selection of terminal section data may cause the distortion of PI and have serious ***,a multi-task PI model considering multiple TLs’spatial constraints and massive electrical section data is proposed in this *** Graph Attention Network module is used to draw a single TL into a node and calculate its influence coefficient in the transmission ***-Task strategy of Hard Parameter Sharing is used to identify the conductance ofmultiple branches *** show that themethod has good accuracy and *** to the consideration of spatial constraints,the method can also obtain more accurate conductance values under different training and testing conditions.
In this paper,{z_(n)}_(n=1)^(∞)acts as an interpolating sequence for Q_(p)∩H^(∞).An analytic function f is constructed,and f(z_(n))=∑_(j)λ_(j)f_(z_(j))(z_(n))=λ_(n),n=1,2,…for any{λ_(n)}∈l~∞,wheref and{λn}...
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In this paper,{z_(n)}_(n=1)^(∞)acts as an interpolating sequence for Q_(p)∩H^(∞).An analytic function f is constructed,and f(z_(n))=∑_(j)λ_(j)f_(z_(j))(z_(n))=λ_(n),n=1,2,…for any{λ_(n)}∈l~∞,wheref and{λn}∈l^(∞),where f and f_(zj)belong to Q_(p)∩H^(∞).As a result,the study achieves a comparable outcome for F(p,p-2,s)∩H^(∞).
Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features i...
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Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.
Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause signi...
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Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.
In this paper, we mainly study the structure of bi-Jordan n-derivations in triangular rings under conditions of maximal quotient rings and faithful bimodules, respectively. It is shown that every bi-Jordan n-derivatio...
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In this paper, we mainly study the structure of bi-Jordan n-derivations in triangular rings under conditions of maximal quotient rings and faithful bimodules, respectively. It is shown that every bi-Jordan n-derivation can be decomposed into the sum of an inner biderivation and an extremal biderivation in two different conditions. As by-products, the structures of bi-Jordan n-derivation over upper triangular matrix rings and nest algebras are characterized, respectively,and generalize the known results.
Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of servi...
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Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.
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