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intelligent Robotics and Systems (ISoIRS), International Symposium on

作者： Qiankun Yan Xiujuan Du College of Computer Qinghai Normal University Qinghai Provincial Key Laboratory of IoT Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (数字)9798350364798

ISBN: (纸本)9798350364804

Classification of underwater fish images is of great scientific research significance for assessing the ocean's health. However, compared with land-based optical images, underwater optical images are characterized by color distortion, low contrast, and high noise intensity, making it challenging to perform effective feature extraction and train a highly accurate recognition model for underwater images. This paper proposes a supervised contrastive learning-based classification method for underwater fish images. First, the residual idea is integrated to improve the representation of the last four stages of the residual network to obtain the stage residual network, which more fully utilizes the shallow features and reduces the risk of network overfitting at the same time; second, the channel attention mechanism is incorporated at the end of the last four stages of the backbone network, which prompts the model to pay more attention to the feature channels with high contribution; finally, the contrastive learning loss function is improved by the introduction of labels, and realizes the supervised contrastive learning. Dividing the backbone network and the classifier into two stages of training effectively closes the intra-class distance and distances the inter-class distance and improves the model's performance. The experimental results show that the accuracy, precision, recall, and F1 value of this paper's model on the public benchmark dataset Fish4Knowledge are 99.28%, 98.17%, 94.15%, and 95.71%, and it can realize the classification of underwater fish images under extreme noise conditions more efficiently.

关键词： Training Accuracy Oceans Contrastive learning Optical distortion Fish Optical imaging

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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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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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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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Variational Graph Autoencoders Method Based on Attentional Mechanisms for Overlapping Community Detection 12th

Variational Graph Autoencoders Method Based on Attentional M...

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

作者： Wen, Kunhui Lin, Mugang Zhu, Xuanying Zhao, Huihuang 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

Detecting overlapping communities of an attribute network is an important and difficult issue in network science. Traditional methods for overlapping community detection generally considered only its topological structure and neglected node attribute so that the detected communities could be inaccurate. Graph neural networks (GNN) have recently gained attention because of their learning representation ability for node attributes and the topology of a graph and demonstrating superior performance in natural language processing, data mining and pattern recognition, etc. However, GNN methods are still less for overlapping community detection. In this paper, we present an overlapping community detection method based on GNN for attribute networks. It applies a variational graph autoencoder based on attentional mechanisms to learn the representation of nodes in the graph and enhances the representation learning capability of GNN through semi-supervised learning. Various real-world networks of publicly available datasets are used to verify the effectiveness of the proposed approach. The experimental results show that the proposed method outperforms many other methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graph neural networks

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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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Computing Flow Experience Based on Physiological Signals: A Systematic Literature Review

Computing Flow Experience Based on Physiological Signals: A ...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Hong, Yichen Zhou, Chao Liu, Zelu Zifeng, Ni Liu, Juan Li, Xiangxian Yang, Chenglei Bian, Yulong Shandong University Weihai China Human Computer Interaction Technology and Intelligent Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China Shandong University School of Mechanical Electrical & Information Engineering Weihai China Shandong University Jinan China School of Software Shandong University Jinan China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Shandong Weihai China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application China

ISBN: (纸本)9798400713958

Flow experience is a key user experience in human-computer interaction (HCI). Compared with traditional subjective measurements, physiological signal-based methods (i.e. physiological computation of flow experience) can measure flow experience in a more objective and real-time way without interrupting the ongoing activity of the user. Despite scattered studies, a systematic review of research in this field is absent. Synthesizing key aspects of this field, including the conceptualization of flow experience, prevalent theoretical models, foundational research steps for experiment, computerized tasks for inducing flow, commonly used physiological signals, methods for analyzing flow state, and application in HCI. Finally, we discuss the potential of this field to propel HCI and user experience evaluation technologies, while projecting future research avenues. This paper is expected to help researchers grasp existing work in physiological signal-based flow computing and inspire subsequent explorations. © 2025 Copyright held by the owner/author(s).

关键词： Deep learning Flow experience Flow model Machine learning Physiological signals

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Research on Security Trust Model of P2P Network Based on Improved Search Algorithm 11th

Research on Security Trust Model of P2P Network Based on Imp...

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11th International Conference on Computer Engineering and Networks, CENet2021

作者： Zheng, Guangyong Xu, Yuming Hengyang Normal University HengyangHunan421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China Changsha Normal University ChangshaHunan410199 China

ISBN: (纸本)9789811665530

With the rapid development and popularization of the Internet, Peer-to-Peer (P2P) network technology has been developing rapidly, and the security trust mechanism of P2P network has become a prominent problem. This paper presents an improved node resource search algorithm. The algorithm improves the reliability of the system and the success rate of the search by setting the parameter value whether the node is trusted or not and the maximum value of the valid reply message received by the node. At the same time, in view of the shortcomings of the existing credibility calculation methods, an improved credibility update calculation method is proposed. This method can effectively prevent malicious nodes from attacking the trusted nodes and normal nodes and also prevent malicious nodes from improving the credibility of each node by means of small group cooperation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network security

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Leveraging Static Relationships for Intra-Type and Inter-Type Message Passing in Video Question Answering

arXiv

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arXiv 2025年

作者： Liang, Lili Sun, Guanglu School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Intelligent Information Processing and Application Harbin150080 China

Video Question Answering (VideoQA) is an important research direction in the field of artificial intelligence, enabling machines to understand video content and perform reasoning and answering based on natural language questions. Although methods based on static relationship reasoning have made certain progress, there are still deficiencies in the accuracy of static relationship recognition and representation, and they have not fully utilized the static relationship information in videos for in-depth reasoning and analysis. Therefore, this paper proposes a reasoning method for intra-type and inter-type message passing based on static relationships. This method constructs a dual graph for intra-type message passing reasoning and builds a heterogeneous graph based on static relationships for inter-type message passing reasoning. The intra-type message passing reasoning model captures the neighborhood information of targets and relationships related to the question in the dual graph, updating the dual graph to obtain intra-type clues for answering the question. The inter-type message passing reasoning model captures the neighborhood information of targets and relationships from different categories related to the question in the heterogeneous graph, updating the heterogeneous graph to obtain inter-type clues for answering the question. Finally, the answers are inferred by combining the intra-type and inter-type clues based on static relationships. Experimental results on the ANetQA and Next-QA datasets demonstrate the effectiveness of this method. © 2025, CC BY.

关键词： Question answering

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Development prospects of metal-based two-dimensional nanomaterials in lithium-sulfur batteries

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Chinese Chemical Letters 2023年第1期34卷 102-113页

作者： Yuxue Mo Liling Liao Dongyang Li Rongwu Pan Yanhong Deng Yanliang Tan Haiqing Zhou College of Physics and Electronic Engineering Hengyang Normal UniversityHengyang 421002China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan ProvinceDepartment of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal UniversityChangsha 410081China Hunan Joint International Laboratory of Advanced Materials and Technology for Clean Energy Changsha 410082China Hunan Shengli High-tech Energy Technology Co. Ltd.Yongzhou 425000China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang 421002China

A lithium-sulfur(Li-S)system is an important candidate for future lithium-ion system due to its low cost and high specific theoretical capacity(1675 m Ah/g,2600 Wh/kg),which is greatly hindered by the poor conductivity of sulfur,large volume change and dissolution of lithium ***-dimensional(2D)materials with monolayers or few-layers usually have peculiar structures and physical/chemical properties,which can resolve the critical issues in Li-S ***,the metal-based 2D nanomaterials,including ferrum,cobalt or other metal-based composites with various anions,can provide high conductivity,large surface area and abundant reaction sites for restraining the diffusion for lithium *** this mini-review,we will present an overview of recent developments on metal-based 2D nanomaterials with various anions as the electrode materials for Li-S *** the main bottleneck for the Li-S system is the shuttle of polysulfides,emphasis is placed on the structure and components,physical/chemical interaction and interaction mechanisms of the 2D ***,the challenges and prospects of metal-based 2D nanomaterials for Li-S batteries are discussed and proposed.

关键词： Lithium-sulfur battery Two-dimensional materials Cathode materials Anode materials Anode protection

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