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Individualized Driving Intention Prediction With Inverse Reinforcement Learning

IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 8125-8139页

作者： Liu, Siqi Li, Xinyang Chen, Jiansheng Guo, Chenghao Wu, Jiehui Luo, Qifeng Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Advanced Driver Assistance Systems (ADAS) are designed to prevent collisions, identify the condition of drivers while operating vehicles, and provide additional information to enhance drivers' awareness of potential hazards on the road. Today, ADAS are capable of predicting drivers' actions several seconds in advance, preparing for potential future hazards to prevent accidents or reduce injuries to occupants. Most previous works have achieved prediction results by analyzing and processing a vast amount of driving data from multiple drivers, based on the macro intention preferences of multiple drivers and external environmental features, collectively referred to as the generalized intention prediction network. This network utilizes extensive driving data to predict the common driving intentions of the overall driving population, without considering individualized driving styles. However, according to our research, different drivers exhibit distinct latent preferences in real-world driving scenarios. The generalized intention prediction network is influenced by these latent preferences, resulting in poor generalization capabilities and inaccurate predictions across different drivers. In this study, we propose a individualized driver intention prediction network. Based on Inverse Reinforcement Learning (IRL), it extracts individualized driving intention feature preferences that influence driving intentions from the driver's historical behavior to improve generalized prediction results and achieve individualized driving intention prediction. We demonstrate that preferences vary among different drivers in the driving domain, leading to biases in model predictions. Upon experimental validation, the method we have proposed demonstrates remarkable efficacy on both the Brain4Cars and IESDD datasets, thereby showcasing its enhanced applicability in real-world scenarios. © 2000-2011 IEEE.

关键词： Advanced driver assistance systems

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QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

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Chinese Journal of Electronics 2024年第3期33卷 645-656页

作者： Zequn NIU Jingfeng XUE Yong WANG Tianwei LEI Weijie HAN Xianwei GAO School of Computer Science and Technology Beijing Institute of Technology School of Space Information Space Engineering University

In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.

关键词： Active learning Online learning Malicious traffic Concept drift Data imbalance

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Adversarial Attack on Object Detection via Object Feature-Wise Attention and Perturbation Extraction

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1174-1189页

作者： Wei Xue Xiaoyan Xia Pengcheng Wan Ping Zhong Xiao Zheng School of Computer Science and Technology Anhui University of TechnologyMaanshan 243032China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense TechnologyChangsha 410073China

Deep neural networks are commonly used in computer vision tasks,but they are vulnerable to adversarial samples,resulting in poor recognition *** traditional algorithms that craft adversarial samples have been effective in attacking classification models,the attacking performance degrades when facing object detection models with more complex *** address this issue better,in this paper we first analyze the mechanism of multi-scale feature extraction of object detection models,and then by constructing the object feature-wise attention module and the perturbation extraction module,a novel adversarial sample generation algorithm for attacking detection models is ***,in the first module,based on the multi-scale feature map,we reduce the range of perturbation and improve the stealthiness of adversarial samples by computing the noise distribution in the object *** in the second module,we feed the noise distribution into the generative adversarial networks to generate adversarial perturbation with strong attack *** doing so,the proposed approach possesses the ability to better confuse the judgment of detection *** carried out on the DroneVehicle dataset show that our method is computationally efficient and works well in attacking detection models measured by qualitative analysis and quantitative analysis.

关键词： adversarial attack transfer attack object detection generative adversarial networks multi-scale feature map

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DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model

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computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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Jamming-Resilient Consensus for Wireless Blockchain Networks

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Tsinghua science and technology 2025年第1期30卷 262-278页

作者： Yifei Zou Meng Hou Li Yang Minghui Xu Libing Wu Dongxiao Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China

As the device complexity keeps increasing,the blockchain networks have been celebrated as the cornerstone of numerous prominent platforms owing to their ability to provide distributed and immutable ledgers and data-driven autonomous *** distributed consensus algorithm is the core component that directly dictates the performance and properties of blockchain ***,the inherent characteristics of the shared wireless medium,such as fading,interference,and openness,pose significant challenges to achieving consensus within these networks,especially in the presence of malicious jamming *** cope with the severe consensus problem,in this paper,we present a distributed jamming-resilient consensus algorithm for blockchain networks in wireless environments,where the adversary can jam the communication channel by injecting jamming *** on a non-binary slight jamming model,we propose a distributed four-stage algorithm to achieve consensus in the wireless blockchain network,including leader election,leader broadcast,leader aggregation,and leader announcement *** high probability,we prove that our jamming-resilient algorithm can ensure the validity,agreement,termination,and total order properties of consensus with the time complexity of O(n).Both theoretical analyses and empirical simulations are conducted to verify the consistency and efficiency of our algorithm.

关键词： consensus in blockchain jamming attacks distributed algorithm

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Knowledge-Empowered,Collaborative,and Co-Evolving AI Models:The Post-LLM Roadmap

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工程（英文） 2025年第1期44卷 87-100页

作者： Fei Wu Tao Shen Thomas Bäck Jingyuan Chen Gang Huang Yaochu Jin Kun Kuang Mengze Li Cewu Lu Jiaxu Miao Yongwei Wang Ying Wei Fan Wu Junchi Yan Hongxia Yang Yi Yang Shengyu Zhang Zhou Zhao Yueting Zhuang Yunhe Pan College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Leiden Institute of Advanced Computer Science Leiden UniversityLeiden 2333 CCNetherlands College of Electrical Engineering Zhejiang UniversityHangzhou 310027China School of Engineering Westlake UniversityHangzhou 310024China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Cyber Science and Technology Sun Yat-Sen UniversityShenzhen 518107China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.

关键词： Artificial intelligence Large language models Knowledge empowerment Model collaboration Model co-evolution

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A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

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Journal of computer science & technology 2023年第6期38卷 1203-1222页

作者：刘嘉聿汪飞马海平黄振亚刘淇陈恩红苏喻 School of Data Science University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Cognitive Intelligence Hefei 230088China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230026China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Computer Science and Artificial Intelligence Hefei Normal UniversityHefei 230061China

Cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge *** existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning *** this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis ***,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness ***,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model *** UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.

关键词： cognitive diagnosis probabilistic graphical model item response theory(IRT) stochastic process expectation maximization(EM)algorithm

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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

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Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi-directional optimization iot devices swarm deep learning

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A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

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Digital Communications and Networks 2024年第2期10卷 481-497页

作者： Sheng Hao Huyin Zhang Fei Yang Chenghao Li Jing Wang School of Computer Science Central China Normal UniversityWuhan430079PR China Hubei Key Laboratory of intelligent Robot(Wuhan Institute of Technology) Wuhan430079PR China School of Computer Science Wuhan UniversityWuhan430079China School of Information Science and Engineering Shandong Normal UniversityJinan250000China School of Computer Science Hubei University of TechnologyWuhan430079China

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware *** research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)*** designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network *** solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 ***,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,*** on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC *** the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into ***,we prove the proposed analytical model has the ***,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

关键词： Hybrid power-line/Visible light communication (HPVC)networks MAC protocol CSMA/CA IEEE 802.15.7 IEEE 1901 Performance analysis

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