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A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing 认领 引用
1
作者 Kaili Shao Hui Fu +1 位作者 Ying Song Bo Wang 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期2259-2274,共16页
To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of... To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of edge and cloud computing.But nowadays,E2C still suffers from low service quality and resource efficiency,due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility.To address these issues,this paper focuses on task offloading,which makes decisions that which resources are allocated to tasks for their processing.This paper first formulates the problem into binary non-linear programming and then proposes a particle swarm optimization(PSO)-based algorithm to solve the problem.The proposed algorithm exploits an imbalance mutation operator and a task rescheduling approach to improve the performance of PSO.The proposed algorithm concerns the resource heterogeneity by correlating the probability that a computing node is decided to process a task with its capacity,by the imbalance mutation.The task rescheduling approach improves the acceptance ratio for a task offloading solution,by reassigning rejected tasks to computing nodes with available resources.Extensive simulated experiments are conducted.And the results show that the proposed offloading algorithm has an 8.93%–37.0%higher acceptance ratio than ten of the classical and up-to-date algorithms,and verify the effectiveness of the imbalanced mutation and the task rescheduling. 展开更多
关键词 Cloud computing edge computing edge cloud task scheduling task offloading particle swarm optimization
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A Method for Time-Series Location Data Publication Based on Differential Privacy 认领 引用 被引量：4
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作者 KANG Haiyan ZHANG Shuxuan JIA Qianqian 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2019年第2期107-115,共9页
In the age of information sharing, logistics information sharing also faces the risk of privacy leakage. In regard to the privacy leakage of time-series location information in the field of logistics, this paper propo... In the age of information sharing, logistics information sharing also faces the risk of privacy leakage. In regard to the privacy leakage of time-series location information in the field of logistics, this paper proposes a method based on differential privacy for time-series location data publication. Firstly, it constructs public region of interest(PROI) related to time by using clustering optimal algorithm. And it adopts the method of the centroid point to ensure the public interest point(PIP) representing the location of the public interest zone. Secondly, according to the PIP, we can construct location search tree(LST) that is a commonly used index structure of spatial data, in order to ensure the inherent relation among location data. Thirdly, we add Laplace noise to the node of LST, which means fewer times to add Laplace noise on the original data set and ensures the data availability. Finally, experiments show that this method not only ensures the security of sequential location data publishing, but also has better data availability than the general differential privacy method, which achieves a good balance between the security and availability of data. 展开更多
关键词 sequential location data publishing region of interest location search tree differential privacy
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A wireless geophone based on STM32 认领 引用
3
作者 陈雷 CAO Peilin +1 位作者 BAI Jinghao ZHANG Ting 《High Technology Letters》 EI CAS 2023年第3期318-324,共7页
Seismic geophone is the key instrument for seismic data acquisition in the fields of oil and gas exploration and other relevant fields.It plays an important role in data quality,subsequent processing and interpretatio... Seismic geophone is the key instrument for seismic data acquisition in the fields of oil and gas exploration and other relevant fields.It plays an important role in data quality,subsequent processing and interpretation.Aiming at the shortcomings of the existing geophones,such as large volume,heavy weight,unable to monitor in real-time,and heavy workload of data transmission and storage,this paper designs and implements a wireless geophone based on STM32 embedded microcontroller.It is mainly composed of STM32 microcontroller,acceleration sensor,global positioning system(GPS)module,Wi-Fi module and real-time seismic data receiving and processing module.Firstly,the acceleration of vibration in three directions is sensed by the acceleration sensor;secondly,the sampling and analog-to-digital conversion of the vibration data is controlled by the microcontroller;thirdly,the vibration and GPS data are sent to the personal computer(PC)through Wi-Fi,and the personal coomputer PC-side software module completes the reception,display,monitoring and storage of the seismic data;finally,the feasibility and effectiveness of the designed wireless geophone are verified through actual seismic data acquisition experiments. 展开更多
关键词 STM32 microcontroller geophone sensor global positioning system(GPS)
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A fault recognition method based on clustering linear regression 认领 引用
4
作者 陈雷 SHI Jiaqi ZHANG Ting 《High Technology Letters》 EI CAS 2023年第4期406-415,共10页
Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clus... Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clustering linear regression is proposed.Firstly,Hough transform is used to detect the line segment of the enhanced image obtained by the coherence cube algorithm.Secondly,the endpoint of the line segment detected by Hough transform is taken as the key point,and the adaptive clustering linear regression algorithm is used to cluster the key points adaptively according to the lin-ear relationship between them.Finally,a fault is generated from each category of key points based on least squares curve fitting method to realize fault identification.To verify the feasibility and pro-gressiveness of the proposed method,it is compared with the traditional method and the latest meth-od on the actual seismic data through experiments,and the effectiveness of the proposed method is verified by the experimental results on the actual seismic data. 展开更多
关键词 fault recognition clustering linear regression curve fitting seismic interpreta-tion
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A Fast Detection Method of Network Crime Based on User Portrait 认领 引用
5
作者 Yabin Xu Meishu Zhang Xiaowei Xu 《Journal of Information Hiding and Privacy Protection》 2021年第1期17-28,共12页
In order to quickly and accurately find the implementer of the network crime,based on the user portrait technology,a rapid detection method for users with abnormal behaviors is proposed.This method needs to construct ... In order to quickly and accurately find the implementer of the network crime,based on the user portrait technology,a rapid detection method for users with abnormal behaviors is proposed.This method needs to construct the abnormal behavior rule base on various kinds of abnormal behaviors in advance,and construct the user portrait including basic attribute tags,behavior attribute tags and abnormal behavior similarity tags for network users who have abnormal behaviors.When a network crime occurs,firstly get the corresponding tag values in all user portraits according to the category of the network crime.Then,use the Naive Bayesian method matching each user portrait,to quickly locate the most likely network criminal suspects.In the case that no suspect is found,all users are audited comprehensively through matching abnormal behavior rule base.The experimental results show that,the accuracy rate of using this method for fast detection of network crimes is 95.9%,and the audit time is shortened to 1/35 of that of the conventional behavior audit method. 展开更多
关键词 User portrait abnormal behavior audit network crime abnormal behavior rule base
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Hybrid Neural Network Model for MOOC Dropout Prediction 认领 引用
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作者 Ke Niu Jiuyun Cai +2 位作者 Yuhang Zhou Wenjuan Tai Xue Feng 《Complex System Modeling and Simulation》 EI 2026年第1期88-98,共11页
Within the context of Massive Open Online Courses(MOoCs),the application of Human-in-the-loop systems is becoming increasingly prevalent.Given the issue of high dropout rates in MOoCs,developing accurate dropout predi... Within the context of Massive Open Online Courses(MOoCs),the application of Human-in-the-loop systems is becoming increasingly prevalent.Given the issue of high dropout rates in MOoCs,developing accurate dropout prediction models is essential to improve educational outcomes and ensure the sustainability of the platform.Researchers usually use interactive information between students and platforms to build predictive models.However,existing methods have the problem that the single construction of behavioral characteristics leads to misclassification of special student groups.Therefore,after in-depth analysis of the behavioral data of MOOC learners,this paper constructs the characteristics of learning activity based on the two dimensions of learning time and number of clicks to reduce the misclassification of special groups.On this basis,we propose a hybrid neural network model(CNN-LSTMAE)to model and predict dropout behavior.CNNLSTMAE uses the Convolutional Neural Network(CNN)to extract local features of students'behaviors,and uses the Long-Short-Term Memory Autoencoder(LSTMAE)module to perform a high-level abstract representation of local features.Experimental results on the KDD Cup 2015 dataset show that the proposed model has better predictive performance than the baseline methods. 展开更多
关键词 massive open online courses dropout prediction data mining hybrid neural network model
DeltaFuzz: Historical Version Information Guided Fuzz Testing 认领 引用 被引量：3
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作者 Jia-Ming Zhang Zhan-Qi Cui +3 位作者 Xiang Chen Huan-Huan Wu Li-Wei Zheng Jian-Bin Liu 《Journal of Computer Science & Technology》 SCIE EI CSCD 2022年第1期29-49,共21页
With the widespread use of agile software development methods,such as agile and scrum,software is iteratively updated more frequently.To ensure the quality of the software,regression testing is conducted before new ve... With the widespread use of agile software development methods,such as agile and scrum,software is iteratively updated more frequently.To ensure the quality of the software,regression testing is conducted before new versions are released.Moreover,to improve the efficiency of regression testing,testing efforts should be concentrated on the modified and impacted parts of a program.However,the costs of manually constructing new test cases for the modified and impacted parts are relatively expensive.Fuzz testing is an effective method for generating test data automatically,but it is usually devoted to achieving higher code coverage,which makes fuzz testing unsuitable for direct regression testing scenarios.For this reason,we propose a fuzz testing method based on the guidance of historical version information.First,the differences between the program being tested and the last version are analyzed,and the results of the analysis are used to locate change points.Second,change impact analysis is performed to find the corresponding impacted basic blocks.Finally,the fitness values of test cases are calculated according to the execution traces,and new test cases are generated iteratively by the genetic algorithm.Based on the proposed method,we implement a prototype tool DeltaFuzz and conduct experiments on six open-source projects.Compared with the fuzzing tool AFLGo,AFLFast and AFL,DeltaFuzz can reach the target faster,and the time taken by DeltaFuzz was reduced by 20.59%,30.05%and 32.61%,respectively. 展开更多
关键词 fuzz testing regression testing change impact analysis fitness function
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Structural recognition of ancient Chinese ideographic characters 认领 引用
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作者 Li Ning Chen Dan 《仪器仪表学报》 EI CAS CSCD 北大核心 2014年第S2期233-237,共5页
Ancient Chinese characters, typically the ideographic characters on bones and bronze before Shang Dynasty(16th—11th century B.C.), are valuable culture legacy of history. However the recognition of Ancient Chinese ch... Ancient Chinese characters, typically the ideographic characters on bones and bronze before Shang Dynasty(16th—11th century B.C.), are valuable culture legacy of history. However the recognition of Ancient Chinese characters has been the task of paleography experts for long. With the help of modern computer technique, everyone can expect to be able to recognize the characters and understand the ancient inscriptions. This research is aimed to help people recognize and understand those ancient Chinese characters by combining Chinese paleography theory and computer information processing technology. Based on the analysis of ancient character features, a method for structural character recognition is proposed. The important characteristics of strokes and basic components or radicals used in recognition are introduced in detail. A system was implemented based on above method to show the effectiveness of the method. 展开更多
关键词 ideographic character recognition structural recognition Chinese information processing
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PedFed:A performance evaluation-driven federated learning framework for efficient communication 认领 引用
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作者 Ke Niu Wenjuan Tai +3 位作者 Xueping Peng Zhongmin Guo Can Zhang Heng Li 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2025年第3期1-18,共18页
Protecting healthcare data privacy and security is crucial in advanced manufacturing,which involves medical devices.It encompasses patient records and clinical trial data.Federated learning emerges as a solution that ... Protecting healthcare data privacy and security is crucial in advanced manufacturing,which involves medical devices.It encompasses patient records and clinical trial data.Federated learning emerges as a solution that enables model training across different institutions without compromising data privacy and security.However,existing frameworks often exhibit a bias towards clients with larger data volumes,neglecting the connection between global and local model performance.This can result in suboptimal aggregation of the global model,thereby affecting the effectiveness and efficiency of the overall process.To address these limitations,we propose a performance evaluation-driven federated learning framework(PedFed).The primary objective of PedFed is to enhance global model aggregation and improve communication efficiency.Our approach involves a client selection strategy based on performance evaluation of local and global models.Specifically,we introduce the concept of local model improvement(LMI)using Intersection over Union(IoU)for client selection in medical image segmentation scenarios.Moreover,we introduce a dynamic aggregation framework incorporating validation IoU as a weighting factor to mitigate model divergence caused by not independent and identically distributed(non-IID)data.We focus on performing image segmentation tasks to simulate the analysis of sensitive data in the healthcare domain.Experimental results conducted on brain tumor and heart segmentation datasets demonstrate the superiority of the PedFed framework over the baseline framework,confirming its benefits in communication efficiency. 展开更多
关键词 Federated learning data privacy non-IID data medical image segmentation communication efficiency
Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply 认领 引用 被引量：15
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作者 Ying Chen Fengjun Zhao +1 位作者 Yangguang Lu Xin Chen 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2023年第3期421-432,共12页
Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive services.The task offloading of MEC has become an important re... Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive services.The task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)devices.This study investigates task offloading scheduling in a dynamic MEC system.By integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply model.We jointly optimize local computing,offloading duration,and edge computing decisions to minimize system cost.On the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME.DTOME can make task offloading decisions by weighing system cost and queue stability.We quote dynamic programming theory to obtain the optimal task offloading strategy.Simulation results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies. 展开更多
关键词 mobile edge computing Internet of Things dynamic offloading hybrid energy supply stochastic optimization
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Electricity Services Based Dependability Model of Power Grid Communication Networking 认领 引用 被引量：4
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作者 Jiye Wang Kun Meng +3 位作者 Junwei Cao Zhen Chen Lingchao Gao Chuang Lin 《Tsinghua Science and Technology》 SCIE EI CAS 2014年第2期121-132,共12页
The technology of Ultra-High Voltage （UHV） transmission requires higher dependability for electric power grid. Power Grid Communication Networking （PGCN）, the fundamental information infrastructure, severs data tr... The technology of Ultra-High Voltage （UHV） transmission requires higher dependability for electric power grid. Power Grid Communication Networking （PGCN）, the fundamental information infrastructure, severs data transmission including control signal, protection signal, and common data services. Dependability is the necessary requirement to ensure services timely and accurately. Dependability analysis aims to predicate operation status and provide suitable strategies getting rid of the potential dangers. Due to the dependability of PGCN may be affected by external environment, devices quality, implementation strategies, and so on, the scale explosion and the structure complexity make the PGCN＇s dependability much challenging. In this paper, with the observation of interdependency between power grid and PGCN, we propose an electricity services based dependability analysis model of PGCN. The model includes methods of analyzing its dependability and procedures of designing the dependable strategies. We respectively discuss the deterministic analysis method based on matrix analysis and stochastic analysis model based on stochastic Petri nets. 展开更多
关键词 power grid communication networking dependability stochastic Petri nets strategy design
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DP-Share: Privacy-Preserving Software Defect Prediction Model Sharing Through Differential Privacy 认领 引用 被引量：2
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作者 Xiang Chen Dun Zhang +2 位作者 Zhan-Qi Cui Qing Gu Xiao-Lin Ju 《Journal of Computer Science & Technology》 SCIE EI CSCD 2019年第5期1020-1038,共19页
In current software defect prediction (SDP) research, most previous empirical studies only use datasets provided by PROMISE repository and this may cause a threat to the external validity of previous empirical results... In current software defect prediction (SDP) research, most previous empirical studies only use datasets provided by PROMISE repository and this may cause a threat to the external validity of previous empirical results. Instead of SDP dataset sharing, SDP model sharing is a potential solution to alleviate this problem and can encourage researchers in the research community and practitioners in the industrial community to share more models. However, directly sharing models may result in privacy disclosure, such as model inversion attack. To the best of our knowledge, we are the first to apply differential privacy (DP) to privacy-preserving SDP model sharing and then propose a novel method DP-Share, since DP mechanisms can prevent this attack when the privacy budget is carefully selected. In particular, DP-Share first performs data preprocessing for the dataset, such as over-sampling for minority instances (i.e., defective modules) and conducting discretization for continuous features to optimize privacy budget allocation. Then, it uses a novel sampling strategy to create a set of training sets. Finally it constructs decision trees based on these training sets and these decision trees can form a random forest (i.e., model). The last phase of DP-Share uses Laplace and exponential mechanisms to satisfy the requirements of DP. In our empirical studies, we choose nine experimental subjects from real software projects. Then, we use AUC (area under ROC curve) as the performance measure and holdout as our model validation technique. After privacy and utility analysis, we find that DP-Share can achieve better performance than a baseline method DF-Enhance in most cases when using the same privacy budget. Moreover, we also provide guidelines to effectively use our proposed method. Our work attempts to fill the research gap in terms of differential privacy for SDP, which can encourage researchers and practitioners to share more SDP models and then effectively advance the state of the art of SDP. 展开更多
关键词 software defect prediction model sharing differential privacy cross project defect prediction empirical study
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GenMuNN: A mutation-based approach to repair deep neural network models 认领 引用 被引量：1
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作者 Huanhuan Wu Zheng Li +1 位作者 Zhanqi Cui Jianbin Liu 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2022年第2期250-266,共17页
Deep neural network(DNN)models have been widely used in e-commerce,games,auto-mobiles,manufacturing,and so on.Improper structure,parameters,activation function,or incorrect loss function of the DNN models may cause de... Deep neural network(DNN)models have been widely used in e-commerce,games,auto-mobiles,manufacturing,and so on.Improper structure,parameters,activation function,or incorrect loss function of the DNN models may cause defects in performance or secu-rity.As a result,there are some researches that focus on repairing DNN such as MODE and Apricot.However,the cost of repairing is high or the repair may lead to overfit-ting.In order to solve this problem,we propose GenMuNN,which is a Mutation-Based Approach to Repair Deep Neural Network Models.First,it analyzes the importance of the weights of the neurons in each layer of the DNN model to the correctness of the final prediction results,and ranks the weights according to the influence on the prediction results of the DNN model.Second,mutation is performed to generate mutants based on the rank of weights,and genetic algorithms are used to select mutants for the next round of mutation until the stop condition is touched.Experiments are carried on a set of DNN models which are trained with the MNIST dataset.The experimental results show that GenMuNN can improve the accuracy of the DNN models. 展开更多
关键词 DNN model defect repair mutation genetic algorithm
Fuzzy control decision-making framework adapted to the uncertainty environment of complex software system 认领 引用 被引量：1
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作者 Jingjing He Liwei Zheng Zhanqi Cui 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2022年第2期168-186,共19页
As the system becomes more intelligent and embedded,the operating environment is gradually changed from closed,static,and controllable to more open,dynamic,and difficult to control.As a result,the design of complex so... As the system becomes more intelligent and embedded,the operating environment is gradually changed from closed,static,and controllable to more open,dynamic,and difficult to control.As a result,the design of complex software systems is faced with many challenges arising from the uncertainty of the environment(UoE).On the one hand,ignoring the UoE to manually describe requirements is not only a tough job,but it can also hinder the discovery of potential requirements;on the other hand,it is a challenge to integrate the representation of and reasoning of UoE into the process of modeling complex systems.Based on the analysis of the characteristics of complex systems engineering,this paper takes solving the UoE caused by stakeholders prefer-ences and complex environment context as the entry point and designs a fuzzy con-trol decision-making framework.Specifically,the framework contributes to the spiral of complex systems while solving the UoE by constructing a closed-loop intelligent sys-tem based on automatic data flow between information space and physical space for environment sensing,uncertainty analysis,requirements mining,fuzzy reasoning,deci-sion execution as well as feedback optimization.Finally,the framework is validated with a concrete example of an adaptive treadmill system based on the support tools developed. 展开更多
关键词 UoE decision making fuzzy control
A modeling method of algorithm-hardware based on SysML 认领 引用
15
作者 Liu Yue Zhao Chun Zhang Lin 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2022年第3期74-97,共24页
In the process of complex product design,modeling in different fields and different disciplines is often involved.Designers often face many different development kits,platforms,and theories,among which significant dif... In the process of complex product design,modeling in different fields and different disciplines is often involved.Designers often face many different development kits,platforms,and theories,among which significant differences exist.Especially in the process of algorithm-hardware implementation,it is necessary to have mastery of the knowledge including algorithm,hardware,circuit,and system engineering.In this paper,a modeling method of algorithm-hardware based on SysML is proposed to reduce the difficulty of algorithm-hardware modeling.By using the method,the designers who do not know the knowledge of hardware can also easily build the algorithm-hardware model.In this method,a method of graphical system modeling based on SysML is used,where the elements of the algorithm-hardware model are described by SysML graphical models.Then,the SysML graphical models are converted to Very-High-Speed Integrated Circuit Hardware Description Language.At last,a detecting algorithm of random number is complemented by the modeling method in this paper and the simulation results are presented at the conclusion. 展开更多
关键词 SysML VHDL FPGA MDA
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