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

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 ***,collected information is transmitted to a centralized resourceintensive cloud platform for service *** Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular ***,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time *** Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources *** extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite *** address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in ***,we establish the general condition and relative concepts about IoV,EC,and ***,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service ***,we discuss a number of open issues in optimizing edge services with AI.

关键词： edge service internet of vehicles artificial intel igence

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HVIS: A Human-like Vision and Inference System for Human Motion Prediction 39

HVIS: A Human-like Vision and Inference System for Human Mot...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lyu, Kedi Chen, Haipeng Liu, Zhenguang Yin, Yifang Lin, Yukang Jiao, Yingying College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China A*STAR Singapore Tsinghua Shenzhen International Graduate School Nanshan District Shenzhen China

ISBN: (纸本)157735897X

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8% on Human3.6M, 15.7% on CMU Mocap, and 11.1% on G3D. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Adversarial machine learning

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Consistency-aware unsupervised label learning for cross-domain person re-identification

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Multimedia Tools and Applications 2024年 1-20页

作者： Geng, Yanbing Lian, Yongjian Cui, Fangshu Zhang, Xiaowei Zhou, Mingliang Zhang, Geao School of Data Science and Technology North University of China Shanxi Taiyuan030051 China Shandong Key Laboratory of Intelligent Information Processing School of Computer Science and Technology Qingdao University Qingdao266071 China School of Computer Science Chongqing University 174 Shazheng Street Shapingba District Chongqing400044 China College of Information Science and Engineering Northeastern University 11 Lane 3 Wenhua Road Heping District Liaoning Shenyang110819 China

Un-refined pseudo labels always disturb the cross-domain Re-ID performance in unsupervised clustering methods. In this paper, we propose a consistency-aware unsupervised label learning network to refine noisy labels for promising domain adaptation. Specifically, we use a hetero-generated label consistency constraint to mine possible label information, keep a reference consistency of the target pairs on the relative comparative characteristics. We then use a cross-granularity attention consistency constraint to refine the pseudo labels meanwhile learning multi-granularity discriminative representation, keeping a self-similarity consistency of the corresponding parts on the discriminative regions. Our model is fine-tuned by the refined pseudo-labels to reduce the domain gap while coping with the intra-domain variation. Experimental results demonstrate that the proposed method can achieve superior performance on several benchmark datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Benchmarking

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RailPC: A large-scale railway point cloud semantic segmentation dataset

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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Towards Exploratory Query Optimization for Template-Based SQL Workloads 40

Towards Exploratory Query Optimization for Template-Based SQ...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Feng, Jieming Li, Zhanhuai Chen, Qun School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shaanxi Xi'an China

ISBN: (纸本)9798350317152

SQL query optimization aims to choose an optimal Query Execution Plan (QEP) for a query. The existing optimizers usually choose the plan with the minimal execution cost. However, in some real scenarios (e.g., cloud OLAP), it is a business imperative to minimize the cost of an entire workload. Unfortunately, the greedy optimizers are prone to generating execution plans that are only suboptimal from the workload perspective. In this paper, we propose a novel QEP optimization approach for template-based SQL that aims to minimize the execution cost of an entire workload instead of individual queries. While choosing an execution plan for a query, the proposed approach considers a plan's impact on future query execution as well as its own execution cost. We first define an exploratory metric, Plan Exploration Value (PEV), to measure a plan's potential benefit to future query execution. Then, we model exploratory plan selection as an optimization problem with cost constraints, and present an efficient algorithm based on dynamic programming for its solution. Finally, we evaluate the performance of the proposed approach by a comparative study on benchmark workloads. Our extensive experiments have shown that it generates more efficient workload execution plans than the existing alternatives. © 2024 IEEE.

关键词： Dynamic programming

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Application of a neural network model with multimodal fusion for fluorescence spectroscopy

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Nuclear Science and Techniques 2024年第10期35卷 135-148页

作者： Lin Tang Shuang Zhou Kai-Bo Shi Hong-Tao Shen Lei You College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Vanadium and Titanium Resource Comprehensive Utilization Key Laboratory of Sichuan Province Panzhihua UniversityPanzhihua 617000China Key Laboratory of Interior Layout optimization and Security Institutions of Higher Education of Sichuan ProvinceChengdu Normal UniversityChengdu 611130China

In energy-dispersive X-ray fluorescence spectroscopy,the estimation of the pulse amplitude determines the accuracy of the spectrum *** error generated by the amplitude estimation of the pulse output distorted by the measurement system leads to false peaks in the measured *** eliminate these false peaks and achieve an accurate estimation of the distorted pulse amplitude,a composite neural network model is proposed,which embeds long and short-term memory(LSTM)into the UNet *** UNet network realizes the fusion of pulse sequence features and the LSTM model realizes pulse amplitude *** model is trained using simulated pulse datasets with different amplitudes and distortion *** the pulse height estimation,the average relative error of the trained model on the test set was approximately 0.64%,which is 27.37% lower than that of the traditional trapezoidal shaping *** processing of a standard iron source further validated the pulse height estimation performance of the UNet-LSTM *** estimating the amplitude of the distorted pulses using the model,the false peak area was reduced by approximately 91% over the full spectrum and was corrected to the characteristic peak region of interest(ROI).The corrected peak area accounted for approximately 1.32%of the characteristic peak ROI *** results indicate that the model can accurately estimate the height of distorted pulses and has substantial corrective effects on false peaks.

关键词： UNet Long-and short-term memory Pulse distortion Pulse height estimation Fluorescent spectroscopy

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An Efficient Customized Blockchain System for Inter-Organizational Processes

An Efficient Customized Blockchain System for Inter-Organiza...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Puwei Sun, Zhouxing Li, Rui Chen, Jinchuan Gong, Ping Du, Xiaoyong Renmin University of China School of Information China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China Renmin University of China School of Mathematics China Fujian Normal University College of Computer and Cyber Security China

ISBN: (纸本)9798350304855

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently. © 2023 IEEE.

关键词： Blockchain

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Fabric4show:real-time vision system for fabric defect detection and post-processing

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Visual Intelligence 2024年第1期2卷 146-158页

作者： Huaizhou Lin Dan Cai Zengmin Xu Jinsong Wu Lixian Sun Haibin Jia School of Materials Science and Engineering Guangxi Key Laboratory of InformationGuilin University of Electronic TechnologyGuilinChina School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic TechnologyCenter for Applied Mathematics of Guangxi(GUET)GuilinChina Anview.ai GuilinChina School of Artificial Intelligence Guilin University of Electronic TechnologyGuilinChina Department of Electrical Engineering University of ChileSantiagoChile

The exploration of computer vision applications for fabric defect detection has immense potential ***,current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency,while neglecting the practical industrial production ***,we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network(CNN)features(i.e.,Faster R-CNN)for defect detection,defect localization and detection model *** addition,the proposed intelligent system incorporates novel approaches,such as a rearranged fabric dataset,anomaly detection,recommended clipping region division,and a replenishment *** study illustrates an example of artificial intelligence(AI)-driven automated technology in fabric *** accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related *** results demonstrate that the proposed approach achieves comparable performance to other models,while significantly reducing computational resource *** potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted,which is likely to have relevant implications for the textile industry.

关键词： Computer vision Fabric manufacturing Automation Defect detection Faster R-CNN

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Balancing the piezoelectric coefficient and carrier concentration of Bi_(2)WO_(6−x) for ultrahigh piezocatalysis

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Journal of Advanced Ceramics 2024年第11期13卷 1737-1747页

作者： Ying Wang Xiaoli Xu Lingbo Xiao Lutao Li Qiuhua Xu Zhenhai Wen Laishun Qin Yanmin Jia Dong-Liang Peng Wanping Chen Da Chen College of Materials and Chemistry China Jiliang UniversityHangzhou 310018China Key Laboratory of Jiangxi Province for Persistent Pollutants Control National-Local Joint Engineering Research Center of Heavy Metals Pollutants Control and Resource Utilization and Resources RecycleNanchang Hangkong UniversityNanchang 330063China Department of Physics Zhejiang University of Science and TechnologyHangzhou 310008China Jiangsu Key Laboratory for Science and Applications of Molecular Ferroelectrics Southeast UniversityNanjing 211189China Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province College of EnergySoochow UniversitySuzhou 215006China State Key Laboratory of Structural Chemistry and Fujian Provincial Key Laboratory of Materials and Techniques toward Hydrogen EnergyFujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China School of Physics and Information Technology Shaanxi Normal UniversityXi’an 710121China State Key Laboratory of Physical Chemistry of Solid Surface Fujian Key Laboratory of Surface and Interface Engineering for High Performance MaterialsCollege of MaterialsXiamen UniversityXiamen 361005China School of Physics and Technology Wuhan UniversityWuhan 430072China

Balancing the piezoelectric coefficient and carrier concentration of materials is key in the field of *** this work,Bi_(2)WO_(6) material with both piezoelectric and semiconductor properties was chosen as a model material.A one-step ethylene glycol(EG)-assisted solvothermal method was used to synthesize Bi2WO6 with oxygen *** controlling the solvothermal time and temperature,the oxygen vacancy concentration(COV)was *** COV increases,the piezoelectric coefficient decreases,the carrier concentration increases,and the hydrogen production rate first increases but then *** COV reaches 1.45×10^(12) spins·mg^(−1),the corresponding piezoelectric coefficient and carrier concentration are 13.9 pm·V^(−1) and 2.90×10^(20) cm^(−3),*** optimal hydrogen production rate per power of 2.21μmol·g^(−1)·h^(−1)·W^(−1) is equivalent to or even better than that of most reported *** piezoelectric coefficient and carrier concentration,as two factors,jointly determine the piezocatalytic *** findings of this research can provide important and deep-seated insights for better piezocatalysts in the future.

关键词： piezocatalysis oxygen vacancy carrier concentration piezoelectric coefficient hydrogen evolution

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Content Prediction for Proactive Tile-Based VR Video Streaming in Mobile Edge Caching System 13th

Content Prediction for Proactive Tile-Based VR Video Stream...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Liu, Qiuming Chen, Hao Zhou, Yang Wu, Dong Li, Zihui Bai, Yaxin School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications Nanchang330013 China Information and Communication Branch State Grid Jiangxi Electric Power Co. Nanchang330095 China

ISBN: (纸本)9783031554704

Content prediction can avoid VR video streaming delay in mobile edge caching system. To reduce request delay, popular content should be cached on edge server. Existing work either focuses on content prediction or on caching algorithms. However, in the end-edge-cloud system, prediction and caching should be considered together. In this paper, we jointly optimize the four stages of prediction, caching, computing and transmission in mobile edge caching system, aimed to maximize the user’s quality of experience. We propose a progressive policy to optimize the four steps of VR video streaming. Since the user’s QoE is determined by the performance of the resource allocation and caching algorithm, we design a caching algorithm with unknown future request content, which can efficiently improve the content hit rate, as well as the durations for prediction, computing and transmission. We optimize the four stages under arbitrary resource allocation and simulate the proposed algorithm according to the degree of overlap, as well as completion rate. Finally, under the real scenario, the proposed algorithm is verified by comparing with several other caching algorithms, simulation results show that the user’s QoE is improved under the progressive policy and the proposed algorithm. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Forecasting

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