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Collaborative Offloading Method for Digital Twin Empowered Cloud Edge Computing on Internet of Vehicles

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Tsinghua Science and Technology 2023年第3期28卷 433-451页

作者： Linjie Gu Mengmeng Cui Linkun xu Xiaolong Xu Changwang School of Honors Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21044China School of Artificial Intelligence Nanjing University of Information and TechnologyNanjing 210044China

Digital twinning and edge computing are attractive solutions to support computing-intensive and servicesensitive Internet of Vehicles applications.Most of the existing Internet of Vehicles service offloading solutions only consider edge–cloud collaboration,but the collaboration between small cell eNodeB(SCeNB)should not be ignored.Service delays far lower than offloading tasks to the cloud can be obtained through reasonable collaborative computing between nodes.The proposed framework realizes and maintains the simulation of collaboration between SCeNB nodes by constructing a digital twin that maintains SCeNB nodes in the central controller,thereby realizing user task offloading positions,sub-channel allocation,and computing resource allocation.Then an algorithm named AUC-AC is proposed,based on the dominant actor–critic network and the auction mechanism.In order to obtain a better command of global information,the convolutional block attention mechanism(CBAM)is used in the digital twin of each SCeNB node to observe its environment and learn strategies.Numerical results show that our experimental scheme is better than several baseline algorithms in terms of service delay.

关键词： digital twin cloud-edge computing reinforcement learning actor-critic network

Dynamic defenses in cyber security:Techniques,methods and challenges Author links open overlay panel

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Digital Communications and Networks 2022年第4期8卷 422-435页

作者： Yu Zheng Zheng Li Xiaolong Xu Qingzhan Zhao School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044JiangsuChina Key Laboratory for Novel Software Technology Nanjing UniversityNanjing210023JiangsuChina College of Information Science and Technology Shihezi University Geospatial Information Engineering Research CenterChina

Driven by the rapid development of the Internet of Things,cloud computing and other emerging technologies,the connotation of cyberspace is constantly expanding and becoming the fifth dimension of human activities.However,security problems in cyberspace are becoming serious,and traditional defense measures(e.g.,firewall,intrusion detection systems,and security audits)often fall into a passive situation of being prone to attacks and difficult to take effect when responding to new types of network attacks with a higher and higher degree of coordination and intelligence.By constructing and implementing the diverse strategy of dynamic transformation,the configuration characteristics of systems are constantly changing,and the probability of vulnerability exposure is increasing.Therefore,the difficulty and cost of attack are increasing,which provides new ideas for reversing the asymmetric situation of defense and attack in cyberspace.Nonetheless,few related works systematically introduce dynamic defense mechanisms for cyber security.The related concepts and development strategies of dynamic defense are rarely analyzed and summarized.To bridge this gap,we conduct a comprehensive and concrete survey of recent research efforts on dynamic defense in cyber security.Specifically,we firstly introduce basic concepts and define dynamic defense in cyber security.Next,we review the architectures,enabling techniques and methods for moving target defense and mimic defense.This is followed by taxonomically summarizing the implementation and evaluation of dynamic defense.Finally,we discuss some open challenges and opportunities for dynamic defense in cyber security.

关键词： Cyber security Dynamic defense Moving target defense Mimic defense

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

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Computers, Materials & Continua 2023年第6期75卷 5573-5595页

作者： Zhanyang xu Jianchun Cheng Luofei Cheng Xiaolong Xu Muhammad Bilal School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory Novel Software Technology Nanjing UniversityNanjing210023China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siGyeonggi-do17035Korea

Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

关键词： Federated learning feature selection credit risk assessment MSEs

Microstructure evolution and grain refinement mechanism of rapidly solidified single-phase copper based alloys

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Journal of Materials Science & Technology 2022年第33期128卷 160-179页

作者： Xiaolong Xu Cheng Tang Hongfu Wang Yukang An Yuhong Zhao College of Materials Science and Engineering School of Mechanical EngineeringNorth University of ChinaTaiyuanShanxi 030051China

The Cu65Ni35,Cu60Ni40 and Cu55Ni45 alloys were undercooled by fluxing method,and the rapid solidification structure with different undercoolings were also obtained.At the same time,the interface migration process during rapid solidification was photographed by high-speed photography,and the relationship between the morphological characteristics of solidification front and undercooling was analyzed.The microstructures of the three alloys were observed by metallographic microscope,and the microstructure characteristics and evolution law were systematically studied.It was found that two grain refinement events occurred in the low undercooling range and high undercooling range,respectively.The EBSD test of grain refined microstructures showed that the microstructure in the low undercooling range has a high proportion of low-angle grain boundaries and high strength textures.However,there were a large proportion of high-angle grain boundaries and a high proportion of twin grain boundaries and more randomly oriented grains in the microstructure in the high undercooling range.The TEM test of the Cu55Ni45 alloy with the maximum undercooling of 284 K showed that there were high-density dislocation networks and stacking faults in the grains.Finally,the evolution relationship between microstructure hardness and undercooling was systematically studied.It was found that the microhardness of the three alloys decreased sharply near the critical undercooling.Combined with EBSD,TEM and microhardness analysis,it was confirmed that the grain refinement under low undercooling was caused by dendrite remelting,while the grain refinement under high undercooling was caused by stress-induced recrystallization.

关键词： Rapid solidification structure Grain refinement Dendrite remelting Recrystallization

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

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Computers, Materials & Continua 2024年第4期79卷 1105-1135页

作者： Lin Wang Juan Zhao Hu Song Xiaolong Xu Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210042China School of Network Security Jinling Institute of TechnologyNanjing211169China State Grid Jiangsu Electric Power Company Limited Nanjing210000China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210042China

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation

关键词： Multi-face expression recognition smart classroom end-to-end detection group emotion assessment

Artificial Intelligence for Edge Service Optimization in Internet of Vehicles:A Survey

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Tsinghua Science and Technology 2022年第2期27卷 270-287页

作者： Xiaolong Xu Haoyuan Li Weijie xu Zhongjian Liu Liang Yao Fei Dai the School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China the Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology and EngineeringNanjing 210044China the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China the School of Big Data and Intelligence Engineering Southwest Forestry UniversityKunming 650233China

The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information.Generally,collected information is transmitted to a centralized resourceintensive cloud platform for service implementation.Edge Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users.Additionally,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time requests.Artificial Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically.Although extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite few.To address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV.Firstly,we establish the general condition and relative concepts about IoV,EC,and AI.Secondly,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading.Finally,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

An Adaptive Many-objective Robust Optimization Model of Dynamic Reactive Power Sources for Voltage Stability Enhancement

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1756-1769页

作者： Yuan Chi Anqi Tao Xiaolong Xu Qianggang Wang Niancheng Zhou IEEE State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqing 400044China

The deployment of dynamic reactive power sourcecan effectively improve the voltage performance after a disturbance for a power system with increasing wind power penetration level and ubiquitous induction loads.To improve the voltage stability of the power system,this paper proposes an adaptive many-objective robust optimization model to deal with thedeployment issue of dynamic reactive power sources.Firstly,two metrics are adopted to assess the voltage stability of the system at two different stages,and one metric is proposed to assess the tie-line reactive power flow.Then,a robustness index isdeveloped to assess the sensitivity of a solution when subjectedto operational uncertainties,using the estimation of acceptablesensitivity region(ASR)and D-vine Copula.Five objectives areoptimized simultaneously:①total equipment investment;②adaptive short-term voltage stability evaluation;③tie-line power flow evaluation;④prioritized steady-state voltage stabilityevaluation;and⑤robustness evaluation.Finally,an anglebased adaptive many-objective evolutionary algorithm(MaOEA)is developed with two improvements designed for the application in a practical engineering problem:①adaptive mutationrate;and②elimination procedure without a requirement for athreshold value.The proposed model is verified on a modifiedNordic 74-bus system and a real-world power system.Numerical results demonstrate the effectiveness and efficiency of theproposed model.

关键词： Voltage stability reactive power planning robust many-objective optimization tie-line correlated uncertainty

Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

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Journal of Systems Engineering and Electronics 2016年第2期27卷 457-469页

作者： Xiaolong Xu Lingling Cao Xinheng Wang College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China School-of Computing University of the West of Scotland Paisley PA12BE UK

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

关键词： green cloud computing power consumption prediction resource allocation probabilistic matching simulated annealing

Recent advances in carbon-based electrocatalysts for oxygen reduction reaction

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Chinese Chemical Letters 2020年第3期31卷 626-634页

作者： Lulu Chen Xiaolong Xu Wenxiu Yang Jianbo Jia State Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China University of Science and Technology of China Hefei 230026China Department of Materials Science&Engineering&Department of Energy and Resources Engineering College of EngineeringPeking UniversityBeijing 100871China School of Chemical and Environmental Engineering Wuyi UniversityJiangmen 529020China

Fuel cells are one of the most promising clean energy devices to substitute for fossil fuel in the future to alleviate energy crisis and environmental pollution.As the key reaction on the cathode in the fuel cells,oxygen reduction reaction(ORR)still requires efficient noble metal catalysts such as the comme rcial Pt/C to boost the reaction for its sluggish kinetics.Therefore,it is critical to design earth-abundant carbonbased catalysts with high efficiency and long-term stability to replace the noble metal-based catalysts.This review focuses on the recent progress about carbon-based ORR catalysts including non-metal doped carbon materials,transition metal-nitrogen-carbon species,transition metal carbides/carbon,single atom catalysts,and other carbon hybrids.And we further infer that the excellent ORR performances can be achieved by the balance of geometric and electronic structures of catalysts such as conductivity,surface area,hierarchical porous structure,defect and doping effect.Additionally,the perspective development trend is also proposed to guide the rational designation of carbon-based catalysts for ORR and even extend to other energy storage and conversion fields in the future.

关键词： Oxygen reduction reaction Carbon materials Transition metal catalysts Single atom catalysts Fuel cells