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A Decision-Making Model for Autonomous Vehicles Considering Pedestrian’s Time Pressure Based on Game Theory and Reinforcement Learning

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IEEE Access 2025年 13卷 87730-87739页

作者： Lin, Qing-Feng Xue, Heng-Yu Lyu, Yang Li, Qing-Kun Ou, Ju-Shang Beihang University School of Transportation Science and Engineering Beijing100191 China Intelligent Policing Key Laboratory of Sichuan Province Luzhou646000 China Chinese Academy of Sciences Institute of Software Beijing100190 China Automotive Software Innovation Center Chongqing400015 China

Due to the obvious randomness, pedestrian crossing behavior is hard to predict, which challenges the decision-making of autonomous vehicles (AVs). Recent solutions have been able to adapt to structured road scenes with crossing signals or markings. However, there is still a gap in extending the pedestrian-vehicle interaction (PVI) performance in structured road scenes to unstructured road scenes. This paper proposed a vehicle decision-making model considering pedestrian intention based on game theory and reinforcement learning (RL). We designed and conducted a simulation experiment based on a virtual reality platform. Then, leveraging game theory, we established a pedestrian crossing decision-making model considering pedestrian heterogeneity evoked by time pressure (TP). A reward function was developed to enhance driving performance by combining safety, efficiency, and comfort. The RL agent of AVs learns to control the vehicle speed in a pattern that maximizes cumulative rewards through trials and errors by interacting with pedestrians in the simulation environment. The results show that AVs can effectively and safely interact with heterogeneous pedestrians on unstructured roads based on the proposed model. This study contributes to developing AVs that interact better with pedestrians and improve traffic safety, efficiency, and user acceptance of autonomous vehicles. © 2013 IEEE.

关键词： Reinforcement learning

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Fix the Tests: Augmenting LLMs to Repair Test Cases with Static Collector and Neural Reranker 35

Fix the Tests: Augmenting LLMs to Repair Test Cases with Sta...

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35th IEEE International Symposium on software Reliability engineering, ISSRE 2024

作者： Liu, Jun Yan, Jiwei Xie, Yuanyuan Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software China Chinese Academy of Sciences Technology Center of Software Engineering Institute of Software China China UCAS School of Intelligent Science and Technology Hangzhou Institute for Advanced Study China

ISBN: (纸本)9798350353884

During software evolution, it is advocated that test code should co-evolve with production code. In real development scenarios, test updating may lag behind production code changing, which may cause compilation failure or bring other troubles. Existing techniques based on pre-trained language models can be directly adopted to repair obsolete tests caused by such unsynchronized code changes, especially syntactic-related ones. However, the lack of task-oriented contextual information affects the repair accuracy on large-scale projects. Starting from an obsolete test, the key challenging task is precisely identifying and constructing Test-Repair-Oriented Contexts (TROCtxs) from the whole repository within a limited token *** this paper, we propose Synter (SYNtactic-breaking- changes-induced TEst Repair), a novel approach based on LLMs to automatically repair obsolete test cases via precise and concise TROCtxs construction. Inspired by developers' programming practices, we design three types of TROCtx: class context, usage context, and environment context. Given an obsolete test case to repair, Synter firstly collects the related code information for each type of TROCtx through static analysis techniques automatically. Then, it generates reranking queries to identify the most relevant TROCtxs, which will be taken as the repair-required key contexts and be input to the large language model for the final test *** evaluate the effectiveness of Synter, we construct a benchmark dataset that contains a set of obsolete tests caused by syntactic breaking changes. The experimental results show that Synter outperforms baseline approaches both on textual- and intent-matching metrics. With the augmentation of constructed TROCtxs, hallucinations are reduced by 57.1%. © 2024 IEEE.

关键词： Syntactics

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A Color Image Encryption Algorithm Based on Complementary Map and Iterative Convolutional Code 24

A Color Image Encryption Algorithm Based on Complementary Ma...

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8th International Conference on Machine Learning and Soft Computing, ICMLSC 2024

作者： Xie, Yanlu Zhou, Shihua Lv, Hui Wang, Bin Che, Chao Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9798400716546

Encryption is a valid means to safeguard the safety of images, and for color images, encryption should be performed considering the intrinsic correlation between R, G, and B components. In this paper, we propose an image encryption algorithm based on a complementary map and an iterative convolutional code. Firstly, the plain image is input into the convolutional encoders for iteration to generate the correctional secret key. Secondly, we design a new complementary map. From the test data, the new chaotic map has passed the NIST testing, exhibits a good chaotic characteristic, and has a wider range of chaotic parameters. Thirdly, global scrambling is performed on the color image to disrupt the distribution between R, G, and B. Then, a row-layer and a column-layer are randomly selected to form a set of elements to be encrypted. Finally, performing global diffusion on the image further increases the safety of our scheme. Experimental results show that our algorithm has a preferable encryption effect and elevated safety. © 2024 ACM.

关键词： Convolutional codes

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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

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Frontiers of Computer Science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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Lightweight Anonymous Authentication Frameworks for Wireless Sensor Networks 3

Lightweight Anonymous Authentication Frameworks for Wireless...

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3rd International Conference on Computer Technologies, ICCTech 2024

作者： Fu, Qin Xiong, Ling He, Mingxing Yang, Xingchun Xihua University School of Computer and Software Engineering Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9798350395419

Due to the limited resources, authentication schemes for low-cost wireless sensors networks (WSNs) environment prefer to use lightweight cryptographic primitives, like symmetric key encryption/decryption or hash functions. It makes the designing of a lightweight anonymous authentication scheme with forward secrecy to be a challenge. In the past decade, researchers have proposed a large number of lightweight authentication schemes to achieve user anonymity and forward secrecy. However, to the best of our knowledge, most of these schemes suffers from a variety of security issues or design flaws, such as the problem of asynchronization, impractical gateway node search operation, etc. In this work, two general frameworks are presented for both security features from existing authentication schemes in WSNs. Then, we compare the security features and performances of representative schemes, and an assessment result is provided for lightweight anonymous authentication with forward secrecy. © 2024 IEEE.

关键词： Wireless sensor networks

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Based on the Conformer End-to-End Chinese Speech Recognition Method 4

Based on the Conformer End-to-End Chinese Speech Recognition...

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4th International Conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Feng, Haiwen Shi, Yunxiao School of Software Engineering Shenyang University of Technology Shenyang Key Laboratory of Intelligent Manufacturing of High-end Industrial Equipment Liaoning Shenyang China School of Software Engineering Shenyang University of Technology ShenganpLiaoning China

ISBN: (纸本)9798331531409

Aiming at the problem that the acoustic input network of Conformer encoder is insufficient for Fbank speech feature extraction, an end-to-end speech recognition modeling method of DM-Conformer is proposed. Firstly, the Dilated convolution is utilized in the downsampling module to increase the receptive field, which captures more speech feature information without adding extra model parameters, while ensuring that the height and width of the original input feature map remain unchanged. Then, the Mish activation function is introduced to alleviate the problem of slow convergence of the ReLU activation function due to its negative value. Finally, experiments are carried out on the public dataset AISHELL-1, and the speech recognition model with the improved downsampling module proposed in this paper reduces the word error rate by 5.6% compared with the Conformer baseline model on the test set, and the model finally achieves a word error rate of 6.15%. © 2024 IEEE.

关键词： Speech enhancement

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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Exploring implicit influence for social recommendation based on GNN

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Soft Computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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Expression Recognition and intelligent Classroom State Mining in Teaching Videos Based on Semi-Supervised Learning Generative Adversarial Network 5

Expression Recognition and Intelligent Classroom State Minin...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Fan, Ruoyu Liang, Meiyu Yin, Mengran Du, Junping Multimedia School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software Beijing China

ISBN: (纸本)9798350393521

We constructed a student classroom teaching video dataset, constructed a student classroom expression dataset through video frame extraction, face detection, face alignment, and facial extraction, and constructed a student classroom behavior dataset through object detection algorithm. Aiming at the characteristics that the annotation information of the students' classroom expression dataset constructed in this paper is limited and the data of some categories are less, an expression recognition method based on semi-supervised learning generative adversarial network (SSLGAN) is proposed. The problem of limited annotation information is solved through semi-supervised learning, and the problem of less data of some categories is solved through data enhancement of generative adversarial network. An intelligent classroom state mining method that integrates student expressions and behaviors has been proposed, achieving intelligent classroom state mining of teaching videos and automatic evaluation of classroom listening status for individual and overall students in different types of classrooms, such as interactive classes, discussion classes, and self-study classes. Compared with CNN, RMN, Temporal Ensembling and Mean Teacher, the proposed SSLGAN has achieved the highest accuracy in the benchmark dataset FER2013, JAFFE, RaFD and self-built student classroom expression dataset. In the case where there are few labels in the self-built student classroom expression dataset, the accuracy of student expression recognition results still reaches 76%, and the proposed intelligent classroom state mining method that integrates student expressions and behaviors for different types of classrooms also has high accuracy on the self-built student classroom teaching video dataset. © 2023 IEEE.

关键词： Students

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Oil Well Acoustic Dynamic Liquid Level Detection Based on EEMD 3

Oil Well Acoustic Dynamic Liquid Level Detection Based on EE...

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3rd International Conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Yang, Ming Sha, Zhiqiang Sun, Yanqing School of Information Engineering Harbin University Harbin150086 China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin University Harbin150086 China Daqing Software Technology Research Institute Co. Ltd. Daqing163711 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection of oil well dynamic liquid level using acoustic methods requires digital signal processing techniques to reduce environmental noises. Most denoising techniques consist of filter-based approaches and spectral methods with time or frequency transformations. In this study, the Ensemble Empirical Mode Decomposition (EEMD) algorithm is implemented at the filtering step within the standard procedures for oil industrial use to detect dynamic liquid level. Five datasets from oil well production are tested using EEMD compared to standard filtering procedures in industrial use. The EEMD algorithm achieves results in good agreement with the reference results in general with worst case relative error reaching 0.51%. In particular, EEMD processed signal visibly displays more salient echo wave feature compared to reference for one case with unknown submerging noises or disturbances. Under such circumstances, the combination of a denoising filter and EEMD furthermore stabilizes the results, the maximum relative error falling to 0.39%. Importantly, the ensemble averaged Intrinsic Mode Functions (IMFs) of frequencies linked to reflected infra-sound could provide good knowledge helping locate the reflection of infra-sound wave. EEMD is a promising method for filtering signals of echo wave based dynamic liquid level detection. Further in-depth investigations are required to better interpret signals mixed with unknown noises or disturbances. © 2024 SPIE.

关键词： Empirical mode decomposition

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