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A Deep Deterministic Policy Gradient-Based Method for Enforcing Service Fault-Tolerance in MEC

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Chinese Journal of Electronics 2024年第4期33卷 899-909页

作者： Tingyan LONG Peng CHEN Yunni XIA Yong MA Xiaoning SUN Jiale ZHAO Yifei LYU College of computer {1. Chongqing University school of {1. and Software engineering Xihua University school of {1. and Information engineering Jiangxi Normal University school of {1. and Information science Chongqing Normal University

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.

关键词： Fault tolerance Multi-access edge computing Processor scheduling Fault detection Image edge detection Fault tolerant systems Quality of service

Active self-training for weakly supervised 3D scene semantic segmentation

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Computational Visual Media 2024年第3期10卷 425-438页

作者： Gengxin Liu Oliver van Kaick Hui Huang Ruizhen Hu College of computer {1.&Software engineering Shenzhen UniversityShenzhen 518060China school of {1. science Carleton UniversityOttawa K1S 5B6Canada

Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data. These methods aretypically based on learning with contrastive losses whileautomatically deriving per-point pseudo-labels from asparse set of user-annotated labels. In this paper, ourkey observation is that the selection of which samplesto annotate is as important as how these samplesare used for training. Thus, we introduce a methodfor weakly supervised segmentation of 3D scenes thatcombines self-training with active learning. Activelearning selects points for annotation that are likelyto result in improvements to the trained model, whileself-training makes efficient use of the user-providedlabels for learning the model. We demonstrate thatour approach leads to an effective method that providesimprovements in scene segmentation over previouswork and baselines, while requiring only a few userannotations.

关键词： semantic segmentation weakly supervised self-training active learning

BioKG-CMI: a multi-source feature fusion model based on biological knowledge graph for predicting circRNA-miRNA interactions

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science China(Information sciences) 2024年第8期67卷 343-344页

作者： Mengmeng WEI Lei WANG Yang LI Zhengwei LI Bowei ZHAO Xiaorui SU Yu WEI Zhuhong YOU school of {1. science and Technology China University of Mining and Technology school of Information {1. and engineering Zaozhuang University school of {1. science and Information engineering Hefei University of Technology Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences school of Information {1. Xijing University school of {1. science Northwestern Polytechnical University

Increasing experimental evidence has shown that circRNA has the potential to serve as a biomarker for disease diagnosis and prognosis, especially in cancer [1.. circ RNA actively participates in numerous pathological processes by serving as an miRNA sponge. Consequently, the precise prediction of circRNA-miRNA interactions(CMIs) is crucial for narrowing down the scope of biological experiments and expediting the research and development of disease treatments.

关键词： Knowledge graph

BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

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computers, Materials & Continua 2024年第3期78卷 3929-3951页

作者： Hong Huang Xingxing Zhang Ye Lu Ze Li Shaohua Zhou school of {1. science and engineering Sichuan University of Science&EngineeringYibin644002China school of Mathematics and Statistics Sichuan University of Science&EngineeringYibin644002China

While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious *** address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)*** the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial *** fusion of features from both granularities serves as the ultimate multidimensional representation of malicious *** approach achieves accuracy and F1.score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC201. dataset,and effectively reduces sample confusion within the Neris and Virut *** experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

关键词： Encrypted malicious traffic classification bidirectional encoder representations from transformers text convolutional neural network bidirectional gated recurrent unit

Malware Evasion Attacks Against IoT and Other Devices: An Empirical Study

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Tsinghua science and Technology 2024年第1期29卷 127-142页

作者： Yan Xu Deqiang Li Qianmu Li Shouhuai Xu school of {1. science and engineering Nanjing University of Science and TechnologyNanjing 210094China school of {1. science Nanjing University of Posts and TelecommunicationsNanjing 210023China Department of computer {1. University of Colorado Colorado SpringsColorado SpringsCO 80918USA

The Internet of Things(loT)has grown rapidly due to artificial intelligence driven edge *** enabling many new functions,edge computing devices expand the vulnerability surface and have become the target of malware ***,attackers have used advanced techniques to evade defenses by transforming their malware into functionality-preserving *** systematically analyze such evasion attacks and conduct a large-scale empirical study in this paper to evaluate their impact on *** specifically,we focus on two forms of evasion attacks:obfuscation and adversarial *** the best of our knowledge,this paper is the first to investigate and contrast the two families of evasion attacks *** apply 1. obfuscation attacks and 9 adversarial attacks to 2870 malware *** obtained findings are as follows.(1.Commercial Off-The-Shelf(COTS)malware detectors are vulnerable to evasion attacks.(2)Adversarial attacks affect COTS malware detectors slightly more effectively than obfuscated malware examples.(3)Code similarity detection approaches can be affected by obfuscated examples and are barely affected by adversarial attacks.(4)These attacks can preserve the functionality of original malware examples.

关键词： Android malware obfuscation adversarial examples

CoDetect: cooperative anomaly detection with privacy protection towards UAV swarm

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science China(Information sciences) 2024年第5期67卷 325-326页

作者： Teng LI Weiguo LIN Ruichen MA Zhuo MA Yulong SHEN Jianfeng MA school of Cyber {1. Xidian University school of {1. science and Technology Xidian University

As mission scenarios grow more complex, the role of a single unmanned aerial vehicle(UAV) is limited. The UAV swarm,surpassing the single UAV, dynamically establishes temporary communication networks, ensuring high transmission rates and expansive communication coverage [1.. Unlike a single UAV, a swarm faces increased security risks, requiring specific capabilities.(1. Anomalies detection.

关键词： Unmanned aerial vehicles (UAV)

Numerical simulation for the initial state of avalanche in polydisperse particle systems

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Chinese Physics B 2024年第2期33卷 405-412页

作者：韩韧李亭迟志鹏杨晖李然 school of Optical-Electrical and {1. engineering University of Shanghai for Science and TechnologyShanghai 200093China

Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse *** examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1.nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse *** also consider the distribution characteristics of the average coordination number and average velocity for the moving *** results support that the polydisperse particle systems are more stable in the T2 stage.

关键词： avalanche initial state polydisperse particle systems propagation

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Recommendation Method for Contrastive Enhancement of Neighborhood Information

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computers, Materials & Continua 2024年第1期78卷 453-472页

作者： Hairong Wang Beijing Zhou Lisi Zhang He Ma school of {1. science and engineering North Minzu UniversityNingxia750021China

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation ***,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge *** tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge ***,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and ***,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view *** paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge ***,this paper introduces multi-task learning to mitigate the problem of weak supervisory *** validate the effectiveness of our method,experiments are conducted on theMovieLens-1.,MovieLens-20M,Book-Crossing,and Last-FM *** results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

关键词： Contrastive learning knowledge graph recommendation method

Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge Computing

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Tsinghua science and Technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland school of Computing Ulster UniversityBelfastBT151EDUK school of {1. and Communication engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic engineering University of CagliariSardinia 09124Italy school of {1. science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing