This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation...
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The robustness of graph neural networks（GNNs） is a critical research topic in deep *** researchers have designed regularization methods to enhance the robustness of neural networks,but there is a lack of theoretical...
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The robustness of graph neural networks（GNNs） is a critical research topic in deep *** researchers have designed regularization methods to enhance the robustness of neural networks,but there is a lack of theoretical analysis on the principle of *** order to tackle the weakness of current robustness designing methods,this paper gives new insights into how to guarantee the robustness of GNNs.A novel regularization strategy named Lya-Reg is designed to guarantee the robustness of GNNs by Lyapunov *** results give new insights into how regularization can mitigate the various adversarial effects on different graph *** experiments on various public datasets demonstrate that the proposed regularization method is more robust than the state-of-theart methods such as L1-norm,L2-norm,L2-norm,Pro-GNN,PA-GNN and GARNET against various types of graph adversarial attacks.
In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge ...
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In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...
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Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of *** experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
This study addresses the resilient sliding mode control(SMC) problem for two-dimensional cyber-physical systems(2D CPSs) characterized by the Roesser model under denial-of-service attack(DoSA), which can interfere wit...
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This study addresses the resilient sliding mode control(SMC) problem for two-dimensional cyber-physical systems(2D CPSs) characterized by the Roesser model under denial-of-service attack(DoSA), which can interfere with signal transmission over the communication network. First, the DoS-A model is established by introducing constraints on the Do S frequency and duration. Then, based on active or silent attack situations, the considered system is described as a switched mode. Furthermore, together with Lyapunov theory, the average dwell time technique is employed to deduce sufficient criteria that assure the existence of the desired sliding mode controller. Finally, verification examples are provided to show the validity of the established SMC scheme.
Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging ...
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To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches a...
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Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control ...
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Graphitic carbon nitride（g-C3N4） is a promising material for photocatalytic hydrogen production owing to its tunable band structure and high charge carrier density, however, it displays a high carrier recombination ...
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Graphitic carbon nitride（g-C3N4） is a promising material for photocatalytic hydrogen production owing to its tunable band structure and high charge carrier density, however, it displays a high carrier recombination *** engineering can solve this problem by providing active sites within g-C3N4to accelerate the separation of photogenerated electrons and holes and enhance its solar light absorption. Previous studies have implied that there exists a defect concentration threshold for obtaining the maximum photocatalytic hydrogen production performance, but the underlying mechanism remains unclear. In this study, the relationship between charge carrier density and exciton recombination was investigated to address this issue. A series of g-C3N4photocatalysts with different tri-coordinate nitrogen（N3C） vacancy concentrations were synthesized using in-situ coprecipitation polymerization. The catalytic performance is related to the vacancy concentration, higher vacancy concentration will lead to higher carrier separation efficiency and carrier density of g-C3N4, as well as better photocatalytic hydrogen production performance. However, when excessive vacancies act as recombination centers, the charge carrier recombination rate is increased, which will also adversely affect the photocatalytic hydrogen production performance.
As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that...
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