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VPI:Vehicle Programming Interface for Vehicle Computing

Journal of computer science & Technology 2024年第1期39卷 22-44页

作者：吴宝福仲任王昱心万健张纪林施巍松 School of computer science and Technology Hangzhou Dianzi UniversityHangzhou 310018China Department of computer science Wayne State UniversityDetroitMI 48202U.S.A Department of computer and Information sciences University of DelawareNewarkDE 19716U.S.A

The emergence of software-defined vehicles(SDVs),combined with autonomous driving technologies,has en-abled a new era of vehicle computing(VC),where vehicles serve as a mobile computing ***,the interdisci-plinary complexities of automotive systems and diverse technological requirements make developing applications for au-tonomous vehicles *** simplify the development of applications running on SDVs,we propose a comprehen-sive suite of vehicle programming interfaces(VPIs).In this study,we rigorously explore the nuanced requirements for ap-plication development within the realm of VC,centering our analysis on the architectural intricacies of the Open Vehicu-lar Data Analytics Platform(OpenVDAP).We then detail our creation of a comprehensive suite of standardized VPIs,spanning five critical categories:Hardware,Data,Computation,Service,and Management,to address these evolving pro-gramming *** validate the design of VPIs,we conduct experiments using the indoor autonomous vehicle,Ze-bra,and develop the OpenVDAP prototype *** comparing it with the industry-influential AUTOSAR interface,our VPIs demonstrate significant enhancements in programming efficiency,marking an important advancement in the field of SDV application *** also show a case study and evaluate its *** work highlights that VPIs significantly enhance the efficiency of developing applications on *** meet both current and future technologi-cal demands and propel the software-defined automotive industry toward a more interconnected and intelligent future.

关键词： software-defined vehicle(SDV) vehicle computing(VC) vehicle programming interface(VPI) au-tonomous system

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Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

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computers, Materials & Continua 2024年第3期78卷 4091-4107页

作者： Qinyue Wu Hui Xu Mengran Liu School of computer science Hubei University of TechnologyWuhan430068China

Network traffic identification is critical for maintaining network security and further meeting various demands of network ***,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data *** the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic ***,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal ***,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal ***,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate *** the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)*** simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO *** experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,***,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identificati

关键词： Network security network traffic identification data analytics feature selection dung beetle optimizer

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A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

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computers, Materials & Continua 2024年第1期78卷 653-676页

作者： Haibo Li Yongbo Yu Zhenbo Zhao Xiaokang Tang College of computer science and Technology Huaqiao UniversityXiamen361021China

Accurate forecasting of time series is crucial across various *** prediction tasks rely on effectively segmenting,matching,and time series data *** instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction ***,these events of varying granularity frequently intersect with each other,which may possess unequal *** minor differences can result in significant errors when matching time series with future ***,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction ***,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation *** framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on *** data from a nationwide online car-hailing service in China ensures the method’s *** average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)*** other experiment is conducted on stock data froma public data *** proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained usin

关键词： Time series short-term prediction multi-granularity event alignment event matching

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Facial Expression Recognition with High Response-Based Local Directional Pattern (HR-LDP) Network

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computers, Materials & Continua 2024年第2期78卷 2067-2086页

作者： Sherly Alphonse Harshit Verma School of computer science and Engineering Vellore Institute of TechnologyChennaiIndia

Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled *** use of Local Directional Patterns(LDP),which has good characteristics for emotion detection has yielded encouraging *** innova-tive end-to-end learnable High Response-based Local Directional Pattern(HR-LDP)network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed *** combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions,this network considerably minimizes the number of network *** cost of the parameters in our fully linked layers is up to 64 times lesser than those in currently used deep learning-based detection *** seven well-known databases,including JAFFE,CK+,MMI,SFEW,OULU-CASIA and MUG,the recognition rates for seven-class facial expression recognition are 99.36%,99.2%,97.8%,60.4%,91.1%and 90.1%,*** results demonstrate the advantage of the proposed work over cutting-edge techniques.

关键词： Emotion classification CNN network HR-LDP

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YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

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computers, Materials & Continua 2024年第5期79卷 2547-2563页

作者： Zhongyuan Zhang Wenqiu Zhu School of computer science Hunan University of TechnologyZhuzhou412007China

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate *** inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing ***,these complexities contribute to inaccuracies in target localization and hinder precise target *** paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery ***,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex *** resolve these issues,we introduce a novel ***,we propose the implementation of a lightweight multi-scale module called *** module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature *** effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing ***,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone *** allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed ***,a dynamic head attentionmechanism is *** allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different ***,the precision of object detection is significantly *** trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 a

关键词： Object detection YOLOv8 multi-scale attention mechanism dynamic detection head

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Endogenous Security Formal Definition,Innovation Mechanisms,and Experiment Research in Industrial Internet

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Tsinghua science and Technology 2024年第2期29卷 492-505页

作者： Hongsong Chen Xintong Han Yiying Zhang School of computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

With the rapid development of information technologies,industrial Internet has become more open,and security issues have become more *** endogenous security mechanism can achieve the autonomous immune mechanism without prior ***,endogenous security lacks a scientific and formal definition in industrial ***,firstly we give a formal definition of endogenous security in industrial Internet and propose a new industrial Internet endogenous security architecture with cost ***,the endogenous security innovation mechanism is clearly ***,an improved clone selection algorithm based on federated learning is ***,we analyze the threat model of the industrial Internet identity authentication scenario,and propose cross-domain authentication mechanism based on endogenous key and zero-knowledge *** conduct identity authentication experiments based on two types of blockchains and compare their experimental *** on the experimental analysis,Ethereum alliance blockchain can be used to provide the identity resolution services on the industrial *** of Things Application(IOTA)public blockchain can be used for data aggregation analysis of Internet of Things(IoT)edge ***,we propose three core challenges and solutions of endogenous security in industrial Internet and give future development directions.

关键词： industrial Internet endogenous security architecture federated learning blockchain

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Jigsaw-Sketch: a fast and accurate algorithm for finding top-k elephant flows in high-speed networks

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science China(Information sciences) 2024年第4期67卷 113-130页

作者： Boyu ZHANG He HUANG Yu-E SUN Yang DU Dan WANG School of computer science and Technology Soochow University School of Rail Transportation Soochow University

Finding top-k elephant flows in high-speed networks is one of the most fundamental network measurement tasks. It is more challenging than per-flow size estimation since the IDs and sizes of top-k flows must be tracked simultaneously. Most existing studies only record the IDs of a small number of elephant flows to fit their estimators in the extremely limited high-speed on-chip memory. However, these solutions need too many memory accesses when a packet arrives to track the elephant flows with high accuracy, which limits their practicability. Therefore, this paper proposes Jigsaw-Sketch, a new algorithm to find the top-k elephant flows with much fewer memory accesses while achieving high memory efficiency and accuracy. In this design, we propose a novel two-stage jigsaw storage scheme, which can capture the candidate top-k flows from massive network steams efficiently, and further find the top-k elephant flows with high memory efficiency and only a few memory accesses for each packet. Extensive experimental results based on real network traces show that Jigsaw-Sketch improves the packet processing throughput by at least 86%, while achieving smaller memory footprints and higher accuracy compared to the SOTA.

关键词： sketch top-k network measurement elephant flow high-speed networks

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Towards Effective Author Name Disambiguation by Hybrid Attention

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Journal of computer science & Technology 2024年第4期39卷 929-950页

作者： Qian Zhou Wei Chen Peng-Peng Zhao An Liu Jia-Jie Xu Jian-Feng Qu Lei Zhao School of computer science and Technology Soochow UniversitySuzhou 215006China

Author name disambiguation(AND)is a central task in academic search,which has received more attention recently accompanied by the increase of authors and academic *** tackle the AND problem,existing studies have proposed various approaches based on different types of information,such as raw document features(e.g.,co-authors,titles,and keywords),the fusion feature(e.g.,a hybrid publication embedding based on multiple raw document features),the local structural information(e.g.,a publication's neighborhood information on a graph),and the global structural information(e.g.,interactive information between a node and others on a graph).However,there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so *** fill the gap,we propose a novel framework named EAND(Towards Effective Author Name Disambiguation by Hybrid Attention).Specifically,we design a novel feature extraction model,which consists of three hybrid attention mechanism layers,to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients,raw document features,and the fusion *** hybrid attention mechanism layer contains three key modules:a local structural perception,a global structural perception,and a feature ***,the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector *** results on two real-world datasets demonstrate that EAND achieves superior performance,outperforming state-of-the-art methods by at least+2.74%in terms of the micro-F1 score and+3.31%in terms of the macro-F1 score.

关键词： author name disambiguation multiple-feature information hybrid attention pruning strategy structural information loss of vector space

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4D-MAP:Multipath Adaptive Packet Scheduling for Live Streaming over QUIC

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Journal of computer science & Technology 2024年第1期39卷 159-176页

作者：宋丛溪韩彪苏金树 College of computer science and Technology National University of Defense TechnologyChangsha 410073China

In recent years,live streaming has become a popular application,which uses TCP as its primary transport *** UDP Internet Connections(QUIC)protocol opens up new opportunities for live ***,how to leverage QUIC to transmit live videos has not been studied *** paper first investigates the achievable quality of experience(QoE)of streaming live videos over TCP,QUIC,and their multipath extensions Multipath TCP(MPTCP)and Multipath QUIC(MPQUIC).We observe that MPQUIC achieves the best performance with bandwidth aggregation and transmission ***,network fluctuations may cause heterogeneous paths,high path loss,and band-width degradation,resulting in significant QoE *** by the above observations,we investigate the multipath packet scheduling problem in live streaming and design 4D-MAP,a multipath adaptive packet scheduling scheme over ***,a linear upper confidence bound(LinUCB)-based online learning algorithm,along with four novel scheduling mechanisms,i.e.,Dispatch,Duplicate,Discard,and Decompensate,is proposed to conquer the above problems.4D-MAP has been evaluated in both controlled emulation and real-world networks to make comparison with the state-of-the-art multipath transmission *** results reveal that 4D-MAP outperforms others in terms of improving the QoE of live streaming.

关键词： multipath transmission live streaming Quick UDP Internet Connections(QUIC) quality of experience(QoE) packet scheduling

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Deep Learning Based Signal Detection for Quadrature Spatial Modulation System

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China Communications 2024年第10期21卷 78-85页

作者： Shu Dingyun Peng Yuyang Yue Ming Fawaz AL-Hazemi Mohammad Meraj Mirza School of computer science and Engineering Macao University of Science and TechnologyMacaoChina Department of computer and Network Engineering University of JeddahSaudi Arabia Department of computer science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

With the development of communication systems, modulation methods are becoming more and more diverse. Among them, quadrature spatial modulation(QSM) is considered as one method with less capacity and high efficiency. In QSM, the traditional signal detection methods sometimes are unable to meet the actual requirement of low complexity of the system. Therefore, this paper proposes a signal detection scheme for QSM systems using deep learning to solve the complexity problem. Results from the simulations show that the bit error rate performance of the proposed deep learning-based detector is better than that of the zero-forcing(ZF) and minimum mean square error(MMSE) detectors, and similar to the maximum likelihood(ML) detector. Moreover, the proposed method requires less processing time than ZF, MMSE,and ML.

关键词： bit error rate complexity deep learning quadrature spatial modulation

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