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10th International Conference on Big Data Computing and Communications, BIGCOM 2024

作者： Liu, Tong Zeng, Fanping Xia, Pengcheng School of Computer Science and Technology University of Science and Technology of China Anhui China University of Science and Technology of China Chinese Academy of Sciences Key Laboratory of Wireless-Optical Communications Hefei China

ISBN: (纸本)9798331509538

Kubernetes, as a mainstream container orchestration platform, is widely used to manage microservice-based applications in cloud environments. However, native scheduler is relatively single in resource scheduling, and only considers CPU and memory, which cannot meet the needs of collaborative edge computing for refined resource scheduling. In addition, it only relies on the resource requests of containers for scheduling, and does not fully consider the actual node load, which may lead to load imbalance problem. To solve these problems, a flexible and dynamic task scheduler, FlexiTask scheduler, is designed. Based on the real-time load and historical resource utilization data of nodes, the scheduler constructs a multi-index model that adapts to the task requirements of collaborative edge computing. Its core goal is not only to achieve cluster load balancing, but also to improve system stability and overall resource utilization efficiency in the environment of node load fluctuation and resource change. The experimental results show that compared with the native scheduler and Tencent Crane scheduler, FlexiTask scheduler improves the task deployment efficiency by 30.5% and 11.2% respectively, reduces the cluster resource imbalance by 44.1% and 34.0%, and in specific application scenarios, the task scale is increased by 96.0% and 22.5%, which fully demonstrates its excellent performance. © 2024 IEEE.

关键词： Cloud platforms

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Comparison of different valent iron on anaerobic sludge digestion:Focusing on oxidation reduction potential,dissolved organic nitrogen and microbial community

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Frontiers of Environmental science & Engineering 2022年第6期16卷 159-170页

作者： Zecong Yu Keke Xiao Yuwei Zhu Mei Sun Sha Liang Jingping Hu Huijie Hou Bingchuan Liu Jiakuan Yang School of Environmental Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Provincial Engineering Laboratory of Solid Waste Treatment Disposal and RecyclingWuhan 430074China State Key Laboratory of Coal Combustion Huazhong University of Science and TechnologyWuhan 430074China

This study compared effects of three different valent iron(Fe^(0),Fe(II)and Fe(III))on enhanced anaerobic sludge digestion,focusing on the changes of oxidation reduction potential(ORP),dissolved organic nitrogen(DON),and microbial *** the same iron dose in range of 0−160 mg/L after an incubation period of 30 days(d),the maximum methane production rate of sludge samples dosed with respective Fe^(0),Fe(II)and Fe(III)at the same concentration showed indiscernible differences at each iron dose,regardless of the different iron ***,their behavior in changes of ORP,DON and microbial community was different:(1)the addition of Fe^(0) made the ORP of sludge more negative,and the addition of Fe(II)and Fe(III)made the ORP of sludge less ***,whether being more or less negative,the changes of ORP may show unobservable effects on methane yield when it ranged from−278.71 to−379.80 mV;(2)the degradation of dissolved organic nitrogen,particularly proteins,was less efficient in sludge samples dosed with Fe^(0) compared with those dosed with Fe(II)and Fe(III)after an incubation period of 30 *** the same dose of 160 mg/L iron,more cysteine was noted in sludge samples dosed with Fe(II)(30.74 mg/L)and Fe(III)(27.92 mg/L)compared with that dosed with Fe^(0)(21.75 mg/L);(3)Fe^(0) particularly promoted the enrichment of Geobacter,and it was 6 times higher than those in sludge samples dosed with Fe(II)and Fe(III)at the same dose of 160 mg/L iron.

关键词： Enhanced anaerobic sludge digestion Different iron valence Oxidation reduction potential Dissolved organic nitrogen Microbial community

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Panoramic image depth estimation based on deformable convolution and attention mechanism 2

Panoramic image depth estimation based on deformable convolu...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Shao, Fangfang Xu, Chao Han, Cheng Wang, Shiwen Changchun University of Science and Technology School of Computer and Science Changchun China

ISBN: (纸本)9798350331417

Currently, mainstream panoramic depth estimation methods primarily focus on correcting distortion effects. For Convolutional Neural Networks (CNN) based on standard convolution, it is difficult to fully perceive the complexity of the panoramic structure due to the fixation of its receptive fields. In this research, we employ deformable convolutions as a replacement for traditional standard convolutions, allowing sample points to adapt more flexibly to changes in object shapes, thereby extending the effective receptive field. Furthermore, this study incorporates the Bottleneck Attention Module (BAM) attention model in the feature fusion module to enhance focus on key *** a series of experiments, the effectiveness of deformable convolution and BAM is verified. © 2023 IEEE.

关键词： BAM deformable convolutions depth estimation

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Deep Learning Methods for Vibration-Based Structural Health Monitoring: A Review

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Iranian Journal of science and technology - Transactions of Civil Engineering 2024年第4期48卷 1837-1859页

作者： Wang, Hao Wang, Baoli Cui, Caixia School of Computer Science and Technology Taiyuan Normal University Jinzhong 030619 China School of Mathematics and Information Technology Yuncheng University Yuncheng 044000 China

The vibration signal is an effective diagnostic tool in structural health monitoring (SHM) fields that is closely related to abnormal states. Deep learning methods have got remarkable success in utilizing vibration signals for damage detection. This paper presents a systematic review of deep learning methods for SHM, focusing on the utilization of vibration signal data from different model perspectives. In recent years, there has been a significant increase in research on deep learning for vibration-based SHM. The accuracy of such works is equivalent to that of traditional machine learning approaches, and better results could be achieved by integrating multiple approaches. Furthermore, we found that transfer learning methods yield promising results when limited data are available to train the model. This paper aims to comprehensively review deep learning research on health monitoring using vibration signal data from multiple perspectives, with a particular emphasis on transfer learning methods for SHM. It fills the gap that existing reviews lack in the discussion of transfer learning for SHM. Finally, we analyze the challenges faced by current research and provide recommendations for future work. © 2023, The Author(s), under exclusive licence to Shiraz university.

关键词： Deep learning Domain adaptation Population-based structural health monitoring Structural health monitoring Transfer learning

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GrCol-PPFL:User-Based Group Collaborative Federated Learning Privacy Protection Framework

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computers, Materials & Continua 2023年第1期74卷 1923-1939页

作者： Jieren Cheng Zhenhao Liu Yiming Shi Ping Luo Victor S.Sheng School of Compute Science and Technology Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Hainan UniversityHaikou570228China Department of Computer Science Texas Tech University TX79409USA

With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more *** on the premise of protecting users’personal privacy data,federated learning(FL)uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original ***,since FL can still leak the user’s original data by exchanging gradient *** existing privacy protection strategy will increase the uplink time due to encryption *** is a huge challenge in terms of *** there are a large number of devices,the privacy protection cost of the system is *** on these issues,we propose a privacy-preserving scheme of user-based group collaborative federated learning(GrCol-PPFL).Our scheme primarily divides participants into several groups and each group communicates in a chained transmission *** groups work in parallel at the same *** server distributes a random parameter with the same dimension as the model parameter for each participant as a mask for the model *** use the public datasets of modified national institute of standards and technology database(MNIST)to test the model *** experimental results show that GrCol-PPFL not only ensures the accuracy of themodel,but also ensures the security of the user’s original data when users collude with each ***,through numerical experiments,we show that by changing the number of groups,we can find the optimal number of groups that reduces the uplink consumption time.

关键词： Federated learning privacy protection uplink consumption time

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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Analysis of the Correlation Between Student Listening Behavior and Academic Performance 4

Analysis of the Correlation Between Student Listening Behavi...

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4th International Conference on Educational technology, ICET 2024

作者： Chen, Shiying Cai, Shipeng Xu, Wei Sun, Jun Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Huazhong University of Science and Technology School of Basic Medicine Tongji Medical College Department of Biochemisty and Molecular Biology Wuhan China

ISBN: (纸本)9798350376944

With the popularization and application of artificial intelligence technology, classroom behavior research has once again become a research hotspot in the field of educational information technology. Many researchers have attempted to use computer vision technology to recognize the behavior of teachers and students in the classroom, among which the head posture classification method with object detection as the core has received more attention. It has been effectively used to calculate classroom head up rate and characterize the overall attention level of the classroom. However, unlike previous studies, this article focuses for the first time on the calculation of individual student head up rate and head posture switching, and analyzes the correlation between these listening behavior data and academic performance. The results showed that student seating and head posture switching were significantly correlated with academic performance, while individual head up rates were not significantly correlated with academic performance. © 2024 IEEE.

关键词： Students

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A clustering federated learning algorithm based on three-layer structure 3

A clustering federated learning algorithm based on three-lay...

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2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Zhang, Jianfei Li, Hongyue Lv, Shuaishuai School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130022 China

ISBN: (纸本)9798400716478

Federated learning is a new distributed machine learning method. Decentralized clients can train data locally, and multi-party machine learning can be implemented efficiently by aggregating clients to update models through a central server. Therefore, federated learning has become an effective tool to solve the problem of data privacy leakage and data silos. However, there are huge differences in the distribution of data between clients in the real world. These data are usually Non-IID. Using such data can lead to problems such as reduced model accuracy, performance degradation, and slow convergence. To solve the problems, we propose a clustering federated learning algorithm based on three-layer structure, TCFL. In TCFL, the clients in each cluster are trained in a pre-arranged order. The central server collects the in-cluster model uploaded by the local server to obtain the optimal target. The experimental results show that the test accuracy of TCFL is improved by 3.42% ∼9.49% compared with the federal average algorithm on Non-IID data classification training of different degrees. Compared with the baseline algorithm, TCFL has achieved excellent results on tests of time rate, accuracy and training loss, and reduced the communication overhead between the central server and the client. Robustness in solving Non-IID data had been verified. © 2023 ACM.

关键词： Learning algorithms

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On modelling strain gradient viscoelasticity of polymer nanocomposites 3

On modelling strain gradient viscoelasticity of polymer nano...

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2024 3rd International Conference on Applied Mechanics and Advanced Materials, ICAMAM 2024

作者： Jiang, Yiyuan Li, Li Hu, Yujin State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

Theories of generalized continuum mechanics have found great success in the analysis of nanostructures. However, there exists no work on analysing composites whose constituents are generalized continua. The present work fills this gap and studies the strain gradient viscoelasticity of polymeric nanocomposites. The key problem is to assign the nonclassical boundary condition of the representative volume element (RVE). To resolve it, a perturbation field is superposed on the homogeneous displacement boundary condition. The wavelength of perturbation is comparable to the strain gradient characteristic length. Simulations to obtain the macroscopic effective mechanical properties are performed, which agree well with the experimental data. The frequency dependence of the perturbation field is revealed, and it has a clear physical interpretation in terms of the segmental motions of polymer chains. © Published under licence by IOP Publishing Ltd.

关键词： Viscoelasticity

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A Membership Inference and Adversarial Attack Defense Framework for Network Traffic Classifiers

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 317-332页

作者： Liu, Guangrui Zhang, Weizhe Wang, Xurun King, Stephen Yu, Shui Harbin Institute of Technology School of Cyberspace Science Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Leland High school San JoseCA95120 United States University of Technology Sydney School of Computer Science UltimoNSW2007 Australia

Malicious traffic identification methods in intrusion detection systems have evolved from rule-based matching to machine learning. However, security risks such as membership inference and adversarial attacks hinder the practical deployment of machine learning-based network intrusion detection systems (ML-NIDSs). In this work, we design a defense framework called hierarchical differential privacy (HierarchicalDP) to safeguard ML-NIDS against membership inference and adversarial attacks. First, we analyze the principles of membership inference and adversarial attacks to find their correlation. Based on this, we propose the feature distribution security metric (FDSM) to measure the risk of membership inference and adversarial attacks on ML-NIDS. Then, we design the HierarchicalDP framework, which partitions network traffic sample features according to security levels and introduces distinct noise on each security level feature to satisfy FDSM, thus defensing against membership inference and adversarial attacks. Finally, we evaluate the defensive performance of the HierarchicalDP framework on two network traffic datasets and four machine-learning models. The HierarchicalDP defense framework, based on Laplace noise, reduces the success rate of membership inference from 64.9% to 54.4% (ineffective binary classification), the evasion rate of adversarial samples from 86.1% to 23.2%, and maintains model accuracy (ACC) fluctuations within 4.2%. Furthermore, the HierarchicalDP framework adjusts sample features without modifying the model, thereby not affecting the inference speed. HierarchicalDP offers efficient and convenient defenses for ML-NIDS deployed in a network. © 2020 IEEE.

关键词： Machine learning

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