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6th International Conference on High Performance Compilation, Computing and Communications, HP3C 2022

作者： Zhu, Yu Zhao, Haixing Huang, Jianqiang Wang, Xiaoying Qinghai University Department of Computer Technology and Applications Xining810000 China Qinghai Normal University State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810000 China

ISBN: (纸本)9781450396295

In real life, there are many cases that cannot be described by the network abstracted as the graph, but can be described perfectly by the hypernetwork abstracted as the hypergraph. Different from the network, the hypernetwork structure is more complex and poses a great challenge to the existing network representation learning methods. Therefore, in order to overcome the challenge of the hypernetwork structure, a hypernetwork representation learning method with the transformation strategy is proposed. Firstly, as three types of transformation strategies from the hypergraph to the graph, line graph, incidence graph and 2-section graph are combined into three types of integral graphs with the hyperedge information, namely incidence graph + 2-section graph, line graph + incidence graph and line graph + incidence graph + 2-section graph. Secondly, a shallow neural network algorithm is trained respectively on five types of networks abstracted as incidence graph, 2-section graph, incidence graph + 2-section graph, line graph + incidence graph and line graph +incidence graph + 2-section graph to obtain node representation vectors. Finally, the evaluation experiment is conducted on four different types of hypernetwork datasets. The experimental results demonstrate that the node classification performance of 2-section graph is better than that of other graphs, and the link prediction performance of incidence graph + 2-section graph is better than that of other graphs. © 2022 ACM.

关键词： Graph theory

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Deep Learning Image Steganalysis Method Fused with CBAM 12th

Deep Learning Image Steganalysis Method Fused with CBAM

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Chen, Huasuo Jiao, Ge College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421002 China

ISBN: (纸本)9789811969003

Steganography is a critical technical tool for preventing the disclosure of sensitive information. The detection performance of picture steganography algorithms based on deep learning has to be enhanced in tandem with the ongoing improvement of adaptive steganography algorithm performance. In this paper, a new model SRNet-CBAM based on SRNet fusion channel attention module and spatial attention module was presented to address the challenge of adaptive steganography analysis and the difficulty of model focused analysis for picture favorable regions. In three different embedding rates of WOW, S-UNIWARD, and HUGO algorithms, the experimental results reveal that the SRNet-CBAM model increases the accuracy of the SRNet model by 1.36% on average. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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Optimizing label correlation in deep learning-based side-channel analysis

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Microelectronics Journal 2025年 162卷

作者： Xia, Shengcheng Li, Lang Ou, Yu Xiang, Jiahao College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang421002 China

Label distribution learning techniques can significantly enhance the effectiveness of side-channel analysis. However, this method relies on using probability density functions to estimate the relationships between labels. The settings of parameters play a crucial role in the impact of the attacks. This study introduces a non-parametric statistical method to calculate the distribution between labels, specifically employing smoothing with the Gaussian kernel function and adjusting bandwidth. Then, the aggregation of the results from each label processed by the Gaussian kernel facilitates a hypothesis-free estimation of the label distribution. This method accurately represents the actual leakage distribution, speeding up guess entropy convergence. Secondly, we exploit similarities between profiling traces, proposing an analysis scheme for sample correlation locally of label distribution learning. Furthermore, Signal to-Noise Ratio (SNR) is employed to re-extract and reduce dataset dimensions to 500 power consumption points, resulting in noise reduction. Our results showcase the successful training of 800 profiling traces using our method for sample correlation locally of label distribution learning, with the findings indicating its exceptional performance. © 2025

关键词： Gaussian distribution

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Research on Fast Adaptive Transmission Models for International Inland Port Based on Edge Intelligence 10

Research on Fast Adaptive Transmission Models for Internatio...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yiwen, Liu Zhirong, Zhu Tangyan Wenkan, Wen Xiaoning, Peng Yuanquan, Shi Huaihua University School of Computer and Artificial Intelligence Huaihua Hunan418000 China Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Huaihua Hunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9798350312461

In this paper, we propose a logistics International Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. The proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua International Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations. © 2023 IEEE.

关键词： Resource allocation

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MAKT: Multichannel Attention Networks based Knowledge Tracing with Representation Learning 11

MAKT: Multichannel Attention Networks based Knowledge Tracin...

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11th IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022

作者： Jiang, Xuyang Ouyang, Yuanxin Liu, Zhuang Rong, Wenge Xiong, Zhang Beihang University State Key Laboratory of Software Development Environment Beijing China Beihang University Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beijing China

ISBN: (纸本)9781665491174

As an effective and emerging component of intelligent education, Knowledge Tracing(KT) achieves the combination of artificial intelligence and individualized learning, whose aim is to assess students' mastery of knowledge concepts and assist in developing learning plans. Several existing KT models either use concepts sequence as input and evaluate students' knowledge state or treat exercise as input to predict students' future performance. In this paper, we introduce a constraint factor to extract concepts' and exercises' relation matrix, design three methods in representation learning, and propose a Multichannel Attention Networks based KT model(MAKT). Specifically, we restrict the co-occurrence relationship within a time window to extract the relation matrix and then train their representations via graph generative learning, graph contrastive learning, and matrix decomposition, respectively. In MAKT, a sliding window is implemented by multichannel where input sequence is sequentially lagged in turn by one position and attention mechanism is applied. We conduct experiments on several benchmark datasets and demonstrate that MAKT with concepts' and exercises' representation trained by matrix decomposition outperforms state-of-the-art models. © 2022 IEEE.

关键词： Students

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Adaptive Image Steganographic Analysis System Based on Deep Convolutional Neural Network 11th

Adaptive Image Steganographic Analysis System Based on Deep ...

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11th International Conference on computer Engineering and Networks, CENet2021

作者： Jiao, Ge College of Computer Science and Technology Hengyang Normal University HengyangHunan421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangHunan421002 China

ISBN: (纸本)9789811665530

Adaptive steganography emplaces the message into the hard-to-detect noise area or the complex texture area of the image, so the steganography analysis method based on artificial design features needs to design a very complex feature extraction algorithm to detect the steganography image. In view of the advantages of deep learning steganography in automatic extraction of image features and high detection accuracy, an image steganography analysis algorithm is designed by using deep convolutional neural network, and an image steganography analysis system based on deep learning is developed. The system realizes the functions of neural network structure analysis, image steganography and verification, steganography analysis and so on. The system simplifies the operation process of algorithm comparison, reduces the complexity of algorithm performance evaluation, and verifies the feasibility of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Traffic Sign Recognition System Based on YOLOv5 12th

Traffic Sign Recognition System Based on YOLOv5

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Zhang, Lin Sun, Yaqi Chen, Wenli Liang, Xiaoman College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421008 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421008 China

ISBN: (纸本)9789811969003

Traffic sign recognition is an important part of intelligent driving and an important content of intelligent transportation system. However, the current traffic sign detection algorithm has some problems, such as high network complexity, low recognition efficiency, poor recognition accuracy, and difficult edge deployment. Aiming at the above problems, this paper proposes to use YOLOv5 single-stage target detection algorithm to identify and detect traffic signs. The input terminal of YOLOv5 enhances and improves adaptive image scaling through Mosaic data, thus reducing information redundancy, reducing computation and improving target detection speed. The CSP structure of Backbone solves the problem of large amount of calculation in reasoning design from the Angle of network structure design. By adopting FPN + CPN structure, the fusion ability of network features is strengthened. Through hardware and software integration design, YOLOv5s model can be deployed to edge equipment, thus forming a set of traffic sign target detection system with fast inference speed, strong anti-interference ability and high identification accuracy. The results show that the overall identification accuracy of traffic signs is up to 98% by adjusting the parameters and improving the algorithm. Through testing 676 images, the recognition accuracy is 96.15% and the average detection speed is 19 frames/s, which meets the requirements of real-time traffic sign detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic signs

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Research on Model Predictive Control-Based Feedback Strategies for Autonomous Driving

Research on Model Predictive Control-Based Feedback Strategi...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Ting Li Xianxia Xu Xuejuan Liu Jie Liu School of Computer and Information Engineering Guangxi Vocational Normal University Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

To address the issues of control stability and safety in autonomous vehicles, this study focuses on research related to Model Predictive Control (MPC) algorithms and algorithm optimization. By leveraging MPC control algorithms and system dynamics techniques, we have achieved stable autonomous driving. Simulation experiments based on Model Predictive Control have been conducted to validate the feasibility and effectiveness of MPC algorithms for feedback control in autonomous driving. Two distinct simulation scenarios have been designed to verify the MPC algorithm’s performance under different road conditions. The results indicate that the MPC algorithm effectively enhances the control stability and safety of autonomous driving vehicles.

关键词： System dynamics Heuristic algorithms Roads Predictive models Prediction algorithms Stability analysis Safety

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RaDIO: Real-Time Hallucination Detection with Contextual Index Optimized Query Formulation for Dynamic Retrieval Augmented Generation 39

RaDIO: Real-Time Hallucination Detection with Contextual Ind...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Jia Guo, Hanghui Shi, Weijie Chen, Zhangze De Meo, Pasquale The Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology China Department of Computer Science University of Messina Italy

ISBN: (纸本)157735897X

The Dynamic Retrieval Augmented Generation (RAG) paradigm actively decides when and what to retrieve during the text generation process of Large Language Models (LLMs). However, current dynamic RAG methods fall short in both aspects: identifying the optimal moment to activate the retrieval module and crafting the appropriate query once retrieval is triggered. To overcome these limitations, we introduce an approach, namely, RaDIO, Real-Time Hallucination Detection with Contextual Index Optimized query formulation for dynamic RAG. The approach is specifically designed to make decisions on when and what to retrieve based on the LLM’s real-time information needs during the text generation process. We evaluate RaDIO along with existing methods comprehensively over several knowledge-intensive generation datasets. Experimental results show that RaDIO achieves superior performance on all tasks, demonstrating the effectiveness of our work. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Research on Environmental Perception Based on YOLOv8 and LiDAR Data Fusion

Research on Environmental Perception Based on YOLOv8 and LiD...

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Information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： ZhenHong Luo HuiLing Li Ting Li Jie Liu School of Computer and Information Engineering Guangxi Vocational Normal University Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

To enhance the sensing capability of driverless cars in complex dynamic scenarios, a fusion method of radar and depth camera is emphasized and a technical framework for integrating multi-source sensors is proposed. A YOLOv8 neural network detection model combined with a self-attention network structure is first used to achieve correct recognition and depth estimation of vehicles and pedestrians, then further distance data of dynamic environment is acquired by M10P LiDAR, and finally information from D435i camera and radar information are fused by matrix projection. In the experiments, the conventional YOLOv8 model was compared to the YOLOv8 model enhanced with self-attention, the camera and LIDAR measurements were evaluated against the fusion results. The experimental results demonstrate that the new model significantly improves the confidence and stability of target detection, while the fused distance measurements are more accurate than those from individual sensors.

关键词： Laser radar Pedestrians Radar detection Object detection Sensor fusion Cameras Data models Stability analysis Vehicle dynamics Intelligent sensors

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