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NRPP: A Learning Graph Representation Approach for Network Robustness Prediction

NRPP: A Learning Graph Representation Approach for Network R...

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2024 ieee International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Wenli Chen, Liang Zhang, Shuai Li, Junli School of Electronics and Communication Engineering Tianjin Normal University Tianjin300382 China College of Computer Science Sichuan Normal University Chengdu610066 China Chengdu610068 China

ISBN: (纸本)9781665410205

In the field of modern network science, robustness is a key factor in evaluating the characteristics of complex networks. Connectivity robustness and controllability robustness are two important measures. They refer to a network's ability to maintain connectivity and controllability during malicious attacks or random failures. Traditional methods for assessing network robustness, which typically involve time-consuming attack simulations, often suffer from limited accuracy and computational inefficiency. Thus, this paper proposes a simple yet effective method, NRPP, for predicting network robustness using a learning graph representation. This method transforms local nodal information into a graph representation and extracts multi-scale features. Extensive experiments on undirected synthetic networks show: 1) NRPP effectively combines node sorting (NR) with pyramid pooling (PP) to obtain graph-level vector representations, improving network robustness predictions. Ablation experiments validate the necessity of node sorting and pyramid pooling. 2) Experimental results demonstrate that NRPP outperforms three state-of-the-art CNN-based models in predicting network robustness. © 2024 ieee.

关键词： Undirected graphs

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Green Coffee Bean Defect Detection Using Shift-Invariant Features and Non-local Block 6

Green Coffee Bean Defect Detection Using Shift-Invariant Fea...

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6th ieee International conference on Knowledge Innovation and Invention, ICKII 2023

作者： Ke, Liang-Ying Chen, Evan Hsia, Chih-Hsien National Llan University Department of Computer Science and Information Engineering Yilan Taiwan Taipei American School Taipei City Taiwan

ISBN: (纸本)9798350323535

In recent years, markets targeting the production of coffee have been flourishing. In order to achieve and preserve higher-quality coffee beans, it is essential to detect defects before roasting them. However, the manual process often results in human errors due to fatigue, not to mention additional labor costs in production. In view of this, we propose the deep convolutional neural networks (DCNNs) for capturing coffee bean images, while applying non-local block and shift invariant features to enhance the degree of discrimination. The results indicate that the method shows approximately 98% of accuracy based on the F1 score for detecting coffee bean defects. Furthermore, the lightweight model, which only consists of about two hundred thousand parameters is practical in the coffee beans market due a low cost. © 2023 ieee.

关键词： Convolutional neural networks

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Forest Monitoring and Protection using Motion Sensors and Metal Detection 2

Forest Monitoring and Protection using Motion Sensors and Me...

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Manikandan, N. K. Muktha, Chaturya Abhinai, Yegi Manivannan, D. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & CSE Dept Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

This research explores the implementation of advanced monitoring and protection systems to safeguard forests from illegal activities. By leveraging devices, remote sensing, and drones equipped with high-resolution cameras, these systems enable real-time surveillance and data analysis. Artificial intelligence is employed to detect suspicious activities, triggering alerts to forest rangers and conservation authorities. Community engagement and education programs are integrated to foster sustainable practices among local populations. This combined approach aims to preserve biodiversity, combat climate change, and ensure the long-term health of forest habitats. The proposed system, combining motion and metal detection, provides a comprehensive solution for monitoring high- risk areas. By detecting suspicious activities and sending immediate alerts, this system enhances vigilance and discourages illegal activities, ultimately contributing to the preservation of forests.

关键词： wireless sensor networks (WSNs) Light Detection and Ranging (LiDAR) conference on computer Vision and Pattern Recognition (CVPR)

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Motion Diffusion Model for Long Motion Generation 5

Motion Diffusion Model for Long Motion Generation

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5th International conference on Machine Learning and computer Application, ICMLCA 2024

作者： Han, Xiaoxu Xu, Hegen Sun, Huilling College of Electronics and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798331530334

Long-term human movement prediction has become a key research focus, driven by its numerous applications in domain such as robotics, virtual reality, video analysis. Traditional methodologies, primarily based on artificial modeling, often unable capture the complicated dynamics of human movement. Recent developments have explored recurrent neural networks and generative models for sequence prediction, but challenges persist in achieving accurate and realistic long-term predictions. This study introduces a deep generative motion infiller leveraging a diffusion model, capable of seamlessly generating motion sequences of arbitrary length from historical or sparsely available key-frames. This model adeptly addresses the complexities of filling in missing or occluded body movements by utilizing observed motion data and predicting new sequences. The proposed method incorporates a Transformer-based structure optimized for lightweight operation, which is employed within a sliding window framework to establish a recurrent generation system. Additionally, a novel method is proposed for style transfer, applicable during inference, enhancing the diversity and the resulting sense of motion. The proposed model also demonstrates effectiveness in generating short-term and long predictions, comprehensive evaluations indicate that the proposed approach yields more diverse and realistic motion predictions compared to existing methods, we evaluated the model in indicate of global and local diversity, which have the better result. © 2024 ieee.

关键词： Recurrent neural networks

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 ieee International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 ieee.

关键词： Image segmentation

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Clothing Detection Action Recognition Based on Hierarchical Transformer networks 16

Clothing Detection Action Recognition Based on Hierarchical ...

引用

16th International conference on Communication Software and networks, ICCSN 2024

作者： Ma, Yuancheng Wang, Xiaodong Yuan, Jian Zhang, Lianjun Chen, Jimeng Fen, Keshu Yu, Linyun Ningbo University College of Electrical Engineering and Computer Sciences China Zhejiang Risun Intelligent Technology Co. Ltd Ningbo China

ISBN: (纸本)9798331506322

In the application of industrial production operation action analysis based on computer vision, Transformer networks have the problem of large computational overhead and insufficient real-time performance. To address this issue and enable the network to be applied to clothing detection in industrial production operation recognition, a hierarchical Transformer network was constructed using network pooling and unit attention designs, which improved the accuracy of action recognition and focused on computing local information. A cosine re-weighting method was designed to replace the softmax operator in the self-attention mechanism of the Transformer network to reduce network overhead. The robustness of this network was verified on the HMDB51 dataset, achieving an accuracy of 91.7%, with a FLOPs of 42.8 G and 29 M parameters. Compared with other Transformer models, it significantly reduced network overhead while achieving high accuracy. For the analysis of clothing detection actions on a self-built dataset, an edge detection algorithm was added to the network to improve recognition accuracy. The improved network achieved a FLOPs of 22.9 G and 10 M parameters on the self-built clothing detection action dataset, with an accuracy of 9 7. 4 %. © 2024 ieee.

关键词： Hosiery manufacture

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Security Vulnerabilities in IPv6 Link-local networks: An Examination of DAD Mechanism

Security Vulnerabilities in IPv6 Link-Local Networks: An Exa...

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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Pragya Shukla, Kartikey Gupta, Preeti Tomar, Gourav Chandan, Radha Raman Dev Shivahare, Basu Galgotias University Scse Department of Computer Science & Engineering Greater Noida India Apolis El SegundoCA90245 United States Jain Deemed-to-be University Department of Computer Science and Engineering Bangalore India Amity University Department of Civil Engineering Greater Noida India School of Management Sciences Department of Cs Uttar Pradesh Varanasi India

ISBN: (纸本)9798331508685

computer networking is understanding a network of connected computers that may communicate and share resources, by following a set of principles referred to as communications protocols. The Internet Protocol (IP) lies in the network layer protocol that forms a part of the Internet protocol suite, enabling the transmission of data packets across different network boundaries. To join a network, a device requires an IPv6 address, which must be different and unique. The DAD process is a helpful method for finding duplicate addresses in a network. When a machine enters the network, it must generate a distinct IPv6 address to connect with other nodes Before assigning an IPv6 home address to the physical interface, Duplicate address detection (DAD), an essential technique, assures that it is the only IPv6 home address on the local-area-network (LAN). This paper digs into significance of DAD, its potential vulnerabilities, and various solutions proposed by the authors. Additionally, this paper provides several literary perspectives and recommends potential areas for future research on DAD. © 2025 ieee.

关键词： Internet protocols

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Rotation-Invariant LiDAR-based localization Method Based on Feature Pyramid and Vector Neural Network 4

Rotation-Invariant LiDAR-based Localization Method Based on ...

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4th International conference on Neural networks, Information and Communication Engineering, NNICE 2024

作者： Tian, Gengxuan Zhao, Junqiao Tongji University School of Electronics and Information Engineering Department of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350394375

LiDAR-based localization for unmanned vehicle has recently become a highly prominent research area. The LiDAR-based localization task typically involves two crucial tasks: place recognition and pose estimation. However, current research often fails to effectively address issues of place recognition retrieval failures caused by rotations. Some existing methods employ a strategy of coupling the pose estimation task with location recognition, but they often separate these two tasks using two distinct sets of networks to achieve robustness against rotations. Unfortunately, such methods frequently exhibit poor generalization. In this paper, we propose an innovative rotation-invariant LiDAR-based localization method based on feature pyramids and vector neural networks. This approach extracts rotation-invariant global features to accomplish the location recognition task and utilizes rotation-invariant local features for matching corresponding points in point clouds. Employing a coarse-to-fine point matching filtering method to eliminate outliers, and ultimately implementing pose estimation through the RANSAC algorithm, we successfully achieve high-precision and rotation-robust LiDAR-based localization. The experiments indicate that on the Oxford dataset, our method consistently achieves a remarkable 94.7% AR@1% regardless of whether the point cloud undergoes rotation, a performance significantly superior to other methods © 2024 ieee.

关键词： Rotation

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Application of Artificial Intelligence using Mammograms to Identify Breast Cancer 6

Application of Artificial Intelligence using Mammograms to I...

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6th International conference on Computational Intelligence and networks, CINE 2024

作者： Rath, Sudeshna Giri, Kalyanbrata Dash, Satya Ranjan Barra, Paola Abdullah, Azian Azamimi KIIT University School of Computer Engineering India University of Naples 'Parthenope' Department of Science and Technology Italy Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Malaysia

ISBN: (纸本)9798331516796

An affordable and precise way to identify breast cancer is through Mammography. It has significantly decreased mortality by detecting malignancies early and is essential for early detection. It has also increased survival rates. However, disparities in mammography access are still mostly driven by socioeconomic factors. Ongoing research aims to address these problems, improve screening methods, and raise the precision and usability of mammography. Additionally, radiologists are being assisted by computer-aided detection (CAD) and artificial intelligence (AI), which are being used to improve diagnostic accuracy. The goal of this research is to identify the best AI algorithms for breast cancer detection. We employ a range of preprocessing methods in this study, first resizing the images to see which method cleans up our data the best. After segmenting our images to locate the region of interest, we extract features using a local binary pattern. Numerous classification models have been employed, such as SVM, KNN, Random Forest, and Decision Tree. Moreover, CNN and RCN have been used for the same objective. Furthermore, it was shown that, generally speaking, neural networks outperformed classification. © 2024 ieee.

关键词： Lung cancer

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Research on Style Transfer Method from Human Facial Images to Peking Opera Facial Images 5

Research on Style Transfer Method from Human Facial Images t...

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5th International conference on Intelligent Computing and Human-computer Interaction, ICHCI 2024

作者： Zhang, Kaixin Shi, Lin Kunming University of Science and Technology Kunming China

ISBN: (纸本)9798350368284

Face makeup transfer is designed to transfer the makeup features of one reference image to another face image to achieve the transformation of different makeup styles. Ordinary makeup has the characteristics of uniform color in a single area and simple color in the overall area, while opera facial makeup is mostly hand-painted images, rich in colors and textures. It is difficult for non-professional actors to draw. At present, there is no deep learning-based method to study makeup transfer between face and Peking Opera facial makeup images. In this paper, we proposes a facial makeup transfer method based on generative adversarial networks and affine transformation. We create a facial makeup dataset from Peking Opera facial makeup images, and obtains the corresponding mask dataset through a semantic segmentation feature extraction model for facial parsing. A local histogram matching strategy is used to calculate the pixel-level histogram loss, which can better preserve the color characteristics of the image. An auxiliary discriminant network is introduced to synthesize reference labels by performing affine transformation on Peking Opera facial makeup images and face images, which helps the generator network to better reduce the information loss caused by image transformation. Through the research in this paper, it is expected to contribute to the research and application of this field, and provide new possibilities for the protection and inheritance of traditional culture and art. © 2024 ieee.

关键词： Generative adversarial networks

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