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Multi-user reinforcement learning based task migration in mobile edge computing

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Frontiers of Computer Science 2024年第4期18卷 161-173页

作者： Yuya CUI Degan ZHANG Jie ZHANG Ting ZHANG Lixiang CAO Lu CHEN tianjin key lab of intelligent computing and novel software technology Tianjin University of TechnologyTianjin 300384China School of Internet of Things Engineering Jiangsu Vocational College of Information TechnologyWuxi 214153China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Sports Economics and Management Tianjin University of SportTianjin 301617China

Mobile Edge computing(MEC)is a promising *** service migration is a key technology in *** order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real *** to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service ***,it is very challenging to design an optimal migration *** this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration *** order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)*** distributed execution and centralized training are adopted to solve the high-dimensional *** show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.

关键词： mobile edge computing mobility service migration deep reinforcement learning deep deterministic policy gradient

On-line Detection and Analysis of Alloy Steel Elements Based on the LIBS technology and Random Forest Regression

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On-line Detection and Analysis of Alloy Steel Elements Based...

Joint Deep Matching Model of OCT Retinal Layer Segmentation

第37届中国控制会议

作者： Yang Jixuan Zhang Qin Yang Peng Sun Junqing tianjin key laboratory of intelligent computing and novel software technology Tianjin University of Technology

The Laser Induced Breakdown Spectroscopy（LIBS） technology can be used to detect the elements in the alloy steel in real time. Quantitative analysis method of the traditional LIBS technology mainly has the calibration method and calibration free method, but there are two shortcomings: low prediction accuracy and over fitting. Random Forest Regression（RFR） algorithm can be used for classification and regression, can effectively avoid "overfitting" phenomenon. Therefore, in this paper, we combine the random forest regression algorithm with laser induced breakdown spectroscopy applied to the detection of the concentration of alloy steel elements in the metallurgy industry. At the same time, compared with partial least squares method based on the LIBS, the results show that the random forest algorithm combined with the LIBS technology has the higher prediction accuracy, lower root mean square error and better robustness.

关键词： LIBS quantitative analysis RFR PLS root mean square error

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Computers, Materials & Continua 2020年第6期63卷 1485-1498页

作者： Mei Yang Yuanjie Zheng Weikuan Jia Yunlong He Tongtong Che Jinyu Cong School of Information Science and Engineering at Shandong Normal University Jinan250358China key lab of intelligent computing and Information Security in Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software TechnologyInstitute of Biomedical SciencesShandong Normal UniversityJinan250358China INSA Lyon University of LyonCNRSInsermVilleurbanne69621 CedexFrance

Optical Coherence Tomography(OCT)is very important in medicine and provide useful diagnostic *** retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular *** the existing retinal layer segmentation approaches,learning or deep learning-based methods belong to the ***,most of these techniques rely on manual-marked layers and the performances are limited due to the image *** order to overcome this limitation,we build a framework based on gray value curve matching,which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from *** depth convolution network learns the column correspondence in the OCT image *** whole OCT image participates in the depth convolution neural network operation,compares the gray value of each column,and matches the gray value sequence of the transformation column and the next *** this algorithm,when a boundary point is manually specified,we can accurately segment the boundary between retinal *** experimental results obtained from a 54-subjects database of both normal healthy eyes and affected eyes demonstrate the superior performances of our approach.

关键词： OCT retinal segmentation deep learning 1D convolution

Automatic architecture design for distributed quantum computing

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Chinese Physics B 2024年第12期33卷 45-59页

作者： Ting-Yu Luo Yu-Zhen Zheng Xiang Fu Yu-Xin Deng Shanghai key laboratory of Trustworthy computing East China Normal UniversityShanghai 200062China Institute for Quantum Information&State key laboratory of High Performance computing College of ComputerNational University of Defense TechnologyChangsha 410073China tianjin Institute of Advanced technology Tianjin 300459China

In distributed quantum computing(DQC),quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip ***,quantum software tries its best to reduce the required number of remote quantum gates between ***,this“hardware first,software follows”methodology may not fully exploit the potential of *** by classical software-hardware co-design,this paper explores the design space of application-specific DQC *** specifically,we propose Auto Arch,an automated quantum chip network(QCN)structure design *** qubits grouping followed by a customized QCN design,AutoArch can generate a near-optimal DQC architecture suitable for target quantum *** results show that the DQC architecture generated by Auto Arch can outperform other general QCN architectures when executing target quantum algorithms.

关键词： distributed quantum computing quantum architecture quantum circuit partitioning

A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA key lab of intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of computing National University of SingaporeSingapore 119077Singapore laboratory of Parallel software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

An Effective Network Repair Strategy Against Both Random and Malicious Edge Attacks

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An Effective Network Repair Strategy Against Both Random and...

Algebraic analysis and control of networked opacity with bounded communication delays

第40届中国控制会议

作者： Jianda Zhao Shiwen Sun Hao Shen Chengyi Xia School of Computer Science and Engineering Tianjin University of Technology key laboratory of Intelligence computing and novel software technology Tianjin University of Technology Engineering Research Center of Learning-Based intelligent System Ministry of Education

Attack vulnerability and protection of large-scale complex networks are of great significance in academic and engineering *** this study,the problem of structural vulnerability of complex networks induced by attacks on edges is *** order to restore network’s performance after attacks,a simple two-stage attack-recovery model based on activating backup edges is ***,with the consideration of two different edge attack strategies,the joint influence of repair cost（*** extent of edge redundancy） and repair targets are extensively *** simulations are implemented on artificially generated random networks and real-world networks,which demonstrate the effectiveness of proposed repair *** research results can provide some useful suggestion on constructing robust infrastructure systems,also help to better understand the structural complexity of large-scale networks.

关键词： Complex network attack structural vulnerability repair cost redundant edges

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Algebraic analysis and control of networked opacity with bou...

Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

第40届中国控制会议

作者： Qian Xu Zhipeng Zhang Chengyi Xia Zengqiang Chen tianjin key laboratory of Intelligence computing and novel software technology Tianjin University of Technology Engineering Research Center of Learning-Based intelligent System Ministry of Education College of Artificial Intelligence Nankai University

Networked finite state machine takes the delay factor into consideration, and its security has become an significant research topic. In this paper, we explore the impact of bounded communication delays on networked opacity of finite state machine from an algebraic point. Firstly, by making use of Boolean semi-tensor product（Boolean STP）, we propose an algebraic equation to characterize the current dynamics with bounded observation delays. Subsequently, we give the verification criterion for networked opacity, and calculate the delayed dynamics by introducing the state reachability matrix. Meanwhile, we investigate the effect of bound number on the opacity-enforcement problem, and compute the minimum bound of communication delay to ensure the state opacity property. Finally, a numerical example is provided to demonstrate the results.

关键词： Semi-tensor product(STP) of matrices finite state machine state opacity observation delays

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial key lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and intelligent software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

Pre-Locator Incorporating Swin-Transformer Refined Classifier for Traffic Sign Recognition

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intelligent Automation & Soft computing 2023年第8期37卷 2227-2246页

作者： Qiang Luo Wenbin Zheng School of software Engineering Chengdu University of Information TechnologyChengdu610225China V.C.&V.R.key lab of Sichuan Province Sichuan Normal UniversityChengdu610068China

In the field of traffic sign recognition,traffic signs usually occupy very small areas in the input *** object detection algorithms directly reduce the original image to a specific size for the input model during the detection process,which leads to the loss of small object ***-tionally,classification tasks are more sensitive to information loss than local-ization *** paper proposes a novel traffic sign recognition approach,in which a lightweight pre-locator network and a refined classification network are *** pre-locator network locates the sub-regions of the traffic signs from the original image,and the refined classification network performs the refinement recognition task in the ***,an innovative module(named SPP-ST)is proposed,which combines the Spatial Pyramid Pool module(SPP)and the Swin-Transformer module as a new feature extractor to learn the special spatial information of traffic sign *** results show that the proposed method is superior to the state-of-the-art methods(82.1 mAP achieved on 218 categories in the TT100k dataset,an improvement of 19.7 percentage points compared to the previous method).Moreover,both the result analysis and the output visualizations further demonstrate the effectiveness of our proposed *** source code and datasets of this work are available at https://***/DijiesitelaQ/TSOD.

关键词： Traffic sign recognition swin-transformer YOLOX small object