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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Bo Wang, Qirui Li, Longjiao Yang, Jinqiao Lin, Hongfei Ding, Feng Dalian University of Technology School of Software Engineering Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350337488

The liver is an internal organ that is most commonly affected by cancer metastasis. Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. Identifying the histological category of this disease is a crucial step in determining the most appropriate treatment plan. Currently, the classification of HCC primarily relies on the observation of pathologists through a visual microscope, which is undoubtedly subjective. To address this issue, the present study conducted pathological examination of liver tumors based on few-shot learning. In order to achieve automatic identification of the three categories in HCC tissue, we propose a few-shot learning based HCC histological image classification method that combines contrastive learning (CL) using different backbone architectures with several well-known machine learning methods. We evaluate 12 backbone-classifier combinations, and the best performance is achieved by combining CL using DeiT-TinyR as the backbone architecture with support vector machine (SVM), with an accuracy of 74.42% and an F1 score of 74.58% in the 3-way-10-shot setting. Experimental results demonstrate the effectiveness of this method in NB histological image classification. © 2023 IEEE.

关键词： classification contrastive learning few-shot hepatocellular carcinoma hyperspectral image

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Deep Learning Emotion Recognition Method 10

Deep Learning Emotion Recognition Method

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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： Xiao, Weidong Tan, Wenjin Xiong, Naixue Yang, Ce Chen, Lin Xie, Rui University of Technology Schoole of Software Engineering Xiamen Xiamen361024 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

Emotion recognition refers to the process of actively analyzing human emotions through computer technology, and it has become an important part of modern society. Traditional emotion recognition is mainly based on a single information source, such as text, speech, video, etc., from which emotional features are extracted for classification or regression to recognize human emotions. With the development of artificial intelligence technology, multimodal emotion recognition is gradually becoming widely used. It combines two or more types of information, such as text, speech, and visual information, in different ways to analyze emotions. Multimodal emotion recognition is far superior to a single modality in understanding emotions. This article mainly analyzes the technology of emotion analysis. Firstly, we introduce the basic concepts and research status of emotion recognition. Then, we introduce the main types of emotion recognition and describe various methods used in the process in detail. Finally, we discuss the challenges and future developments of emotion recognition. © 2023 IEEE.

关键词： Emotion Recognition

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Building LOD representation for 3D urban scenes

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 226卷 16-32页

作者： Pan, Shanshan Zhang, Runze Liu, Yilin Gong, Minglun Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Guelph Guelph Canada

The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models often contain a large number of geometry primitives, making interactive manipulation and rendering challenging, especially on resource-constrained devices like virtual reality platforms. Therefore, the generation of appropriate levels-of-detail (LOD) representations for these models is crucial. Additionally, automatically reconstructed 3D models tend to suffer from noise and lack semantic information. Dealing with these issues and creating LOD representations that are robust against noise while capturing semantic meaning present significant challenges. In this paper, we propose a novel algorithm to address these challenges. We begin by analyzing the properties of planar primitives detected from the input and group these primitives into multiple level sets by forming meaningful 3D structures. These level sets form the nodes of our innovative LOD-Tree. By selecting nodes at appropriate depths within the LOD-Tree, different LOD representations can be generated. Experimental results on real and complex urban scenes demonstrate the merits of our approach in generating clean, accurate, and semantically meaningful LOD representations. © 2025

关键词： Three dimensional computer graphics

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STTG-net:a Spatio-temporal network for human motion prediction based on transformer and graph convolution network

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 224-238页

作者： Lujing Chen Rui Liu Xin Yang Dongsheng Zhou Qiang Zhang Xiaopeng Wei tional and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622China School of Computer Science and Technology Dalian University of TechnologyDalian 116024China

In recent years,human motion prediction has become an active research topic in computer ***,owing to the complexity and stochastic nature of human motion,it remains a challenging *** previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive ***,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in ***,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each *** overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous *** experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction *** effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.

关键词： Human motion prediction Transformer Gragh convolutional network

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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Local and Global Feature Interaction Network for Endoscope Image Classification 12th

Local and Global Feature Interaction Network for Endoscope I...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Dong, Zhengqi Xu, Benzhu Shi, Jun Zheng, Liping School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China

ISBN: (纸本)9783031463136

Convolutional Neural Network (CNN) shows great performance in the field of endoscopic image classification in past few years. It can capture local features of endoscopic images, but it fails to exploit global semantic information. Recently transformer is proposed and successfully applied in computer vision, which can model global long-range dependencies based on the self-attention mechanism. Considering the advantages of CNN and transformer, we propose a novel local and global feature interaction network for endoscope image classification. Different from the existed methods fusing CNN and transformer features, our method achieves these two kinds of features interaction during feature learning and further enhances the feature discrimination ability of CNN and transformer features. Specifically, our backbone consists of multiple CNN-FIB-Transformer (CFT) modules which includes CNN block, feature interaction block (FIB) and transformer encoder. Features extracted from CNN block and transformer encoder are fed into our proposed FIB in parallel, which can interact local and global features of endoscopic images through cross-attention mechanism and further improve their own feature representation capability. Furthermore, we also introduce the loss function of prediction alignment to guarantee the prediction consistency of CNN and transformer features, in addition of conventional cross-entropy loss functions for their own features. Experiments on two public endoscope image datasets (Kvasir2 and HyperKvasir) have demonstrated the feasibility and effectiveness of the proposed method for endoscope image classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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SA-ResNet:An Intrusion Detection Method Based on Spatial Attention Mechanism and Residual Neural Network Fusion

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computers, Materials & Continua 2025年第5期83卷 3335-3350页

作者： Zengyu Cai Yuming Dai Jianwei Zhang Yuan Feng School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450066China College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450066China Faculty of Information Engineering Xuchang Vocational Technical CollegeXuchang461000China School of Electronic Information Zhengzhou University of Light IndustryZhengzhou450066China

The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic,highlighting the growing importance of network *** Detection Systems(IDS)are essential for safeguarding network *** address the low accuracy of existing intrusion detection models in identifying network attacks,this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network(SA-ResNet).Utilizing residual connections can effectively capture local features in the data;by introducing a spatial attention mechanism,the global dependency relationships of intrusion features can be extracted,enhancing the intrusion recognition model’s focus on the global features of intrusions,and effectively improving the accuracy of intrusion *** proposed model in this paper was experimentally verified on theNSL-KDD *** experimental results showthat the intrusion recognition accuracy of the intrusion detection method based on SA-ResNet has reached 99.86%,and its overall accuracy is 0.41% higher than that of traditional Convolutional Neural Network(CNN)models.

关键词： Intrusion detection deep learning residual neural network spatial attention mechanism

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Education and Teaching Research of IoT Embedded Subject under the National Vocational and Technical Skills Standards 6

Education and Teaching Research of IoT Embedded Subject unde...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Yanglu Wang, Xingchen Yadav, Amit Shah, Abhishek Pratap College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University Darwin Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to teach), faculty team (who teaches), and teaching evaluation (how well the teaching is) under the current status of embedded teaching on the Internet of Things (IoT) engineering discipline. Guided by the latest National Occupational Technical Skill Standards for IoT engineering professionals and in accordance with the educational philosophy of Chengdu Neusoft University, here we propose a design for teaching system for IoT embedded talent courses, clarified the key elements and formulated new ideas for the current development of talents in this direction to assist improvement and perfection of related curriculum systems, and to promote the establishment and operation of practical and technological activity platforms. It aims to provide practical references for the construction of professionalism in education and teaching standards of IoT. © 2025 IEEE.

关键词： Curricula

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BFT Consensus Algorithms 10

BFT Consensus Algorithms

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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： Zhang, Xiaojun Zhong, Weiyu Yang, Ce Chen, Lin Liao, Jing Xiong, Naixue QuanZhou University of Information Engineering Software College QuanZhou362000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

With the advent of Bitcoin, the blockchain as the underlying technology of Bitcoin has entered people's field of vision. In recent years, the blockchain and its related technologies have developed rapidly. The consensus algorithm is the key to the realization of blockchain technology. There will be Byzantine nodes in the process of reaching consensus among the nodes in the blockchain. Byzantine nodes are tolerated in the blockchain to achieve consensus. Consensus is an essential performance of consensus algorithms. This paper first introduces blockchain technology and the application of consensus algorithms in it;secondly, introduces the classic PBFT algorithm;then, introduces some existing consensus algorithms;finally summarizes the research status and challenges of consensus algorithms, and analyzes its Prospects. © 2023 IEEE.

关键词： Blockchain

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