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Thermal Infrared Image Semantic Segmentation with Lightweight Edge Assisted Context Guided Network 3

Thermal Infrared Image Semantic Segmentation with Lightweigh...

引用

3rd International Conference on Machine Learning and computer Application, ICMLCA 2022

作者： Wang, Futian Zheng, Yu Lv, Weijie Tang, Jin Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

ISBN: (纸本)9781510664814

Thermal power image segmentation is the key step of power equipment infrared diagnosis. Power equipment infrared diagnosis can timely and accurately diagnose the potential accidents and fault precursors of operating power equipment, which is an important part of power inspection. With the increasing popularity of mobile intelligent terminals, modern power inspection requires deploying semantic segmentation models on mobile devices. Considering the small memory capacity of mobile devices, we propose a new lightweight network architecture, called Edge-Assisted Context Guided Network (ECGNet), for semantic segmentation of thermal infrared electrical equipment images. ECGNet has been carefully designed to learn the context information of thermal infrared images and improve the problem of edge blur on the premise of small parameters and small memory consumption. Under the same number of parameters, a large number of experiments on LS-ETS dataset show that ECGNet can obtain better results than the most advanced method. © 2023 SPIE.

关键词： Semantic Segmentation

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Multimodal Multi-objective Particle Swarm Optimization Algorithm Based on Multi-directional Guidance 4

Multimodal Multi-objective Particle Swarm Optimization Algor...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Li, Wei Gao, Yetong Wang, Lei School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

In multimodal multi-objective optimization, the key issue is to find as many Pareto optimal solutions as possible and select promising solutions in the environmental selection. This paper proposes a multimodal multi-objective particle swarm optimization algorithm based on multi-directional guidance (MM-PSO-MG) to solve these problems. In the proposed algorithm, multi-directional guidance strategy is introduced to avoid premature convergence and find more Pareto optimal solutions. Moreover, the rank-based special crowding distance strategy is used to select promising solutions. 11 multimodal multi-objective test problems are used to verify the performance of the proposed algorithm. The results show that the proposed algorithm is competitive. © 2022 IEEE.

关键词： Multiobjective optimization

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Vulnerability Detection Based on Pre-trained Code Language Model and Convolutional Neural Network 36

Vulnerability Detection Based on Pre-trained Code Language M...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Liao, Tingfeng Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

Software vulnerabilities damage the reliability of software systems. Recently, many methods based on deep learning have been proposed for vulnerability detection by learning features from code sequences or various property graphs. However, sequence-based methods frequently fail to consider the structural information of the code, whereas graph-based methods often encounter difficulties in capturing long-distance contextual semantic information. To overcome these limitations, we propose PTCN, a novel vulnerability detection method that integrates Pre-Trained code language model and Convolutional Neural Network (CNN). First, the code tokens sequence and the propagation chains are constructed from the source code in preparation for the subsequent feature extraction. Next, GraphCodeBERT, a graph-based pre-trained code language model, is introduced as a means of learning both the semantic and structural information contained in the code tokens sequence and the propagation chains. Then, a CNN architecture is designed to effectively extract the crucial vulnerability features from the hidden states of the last Transformer layer of GraphCodeBERT for vulnerability detection. The experiments on the public benchmark dataset REVEAL indicate that PTCN outperforms the state-of-the-art methods with respect to the Accuracy and F1-score metrics by 3.41%-15.80% and 22.68%-104.27%, respectively. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Convolutional neural networks

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Multimodal Aspect-Based Sentiment Analysis under Conditional Relation 31

Multimodal Aspect-Based Sentiment Analysis under Conditional...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Xinjing Li, Ruifan Ye, Shuqin Zhang, Guangwei Wang, Xiaojie School of Artificial Intelligence Beijing University of Posts and Telecommunications China School of Computer Science Beijing University of Posts and Telecommunications China Engineering Research Center of Information Networks Ministry of Education China Key Laboratory of Interactive Technology and Experience System Ministry of Culture and Tourism China

ISBN: (纸本)9798891761964

Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract aspect terms from text-image pairs and identify their sentiments. Previous methods are based on the premise that the image contains the objects referred by the aspects within the text. However, this condition cannot always be met, resulting in a suboptimal performance. In this paper, we propose COnditional Relation based Sentiment Analysis framework (CORSA). Specifically, we design a conditional relation detector (CRD) to mitigate the impact of the unmet conditional image. Moreover, we design a visual object localizer (VOL) to locate the exact condition-related visual regions associated with the aspects. With CRD and VOL, our CORSA framework takes a multi-task form. In addition, to effectively learn CORSA we conduct two types of annotations. One is the conditional relation using a pretrained referring expression comprehension model;the other is the bounding boxes of visual objects by a pretrained object detection model. Experiments on our built C-MABSA dataset show that CORSA consistently outperforms existing methods. The code and data are available at https://***/Liuxj-Anya/CORSA. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Defending Adversarial Examples by Negative Correlation Ensemble 7th

Defending Adversarial Examples by Negative Correlation Ense...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Luo, Wenjian Zhang, Hongwei Kong, Linghao Chen, Zhijian Tang, Ke School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation School of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

ISBN: (纸本)9789811989902

The security issues in DNNs, such as adversarial examples, have attracted much attention. Adversarial examples refer to the examples which are capable to induce the DNNs return incorrect predictions by introducing carefully designed perturbations. Obviously, adversarial examples bring great security risks to the real-world applications of deep learning. Recently, some defence approaches against adversarial examples have been proposed. However, the performance of these approaches are still limited. In this paper, we propose a new ensemble defence approach named the Negative Correlation Ensemble (NCEn), which achieves competitive results by making each member of the ensemble negatively correlated in gradient direction and gradient magnitude. NCEn can reduce the transferability of the adversarial samples among the members in ensemble. Extensive experiments have been conducted, and the results demonstrate that NCEn could improve the adversarial robustness of ensembles effectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Risk Region-based Prediction Model for the Epidemic Spreading 26

Risk Region-based Prediction Model for the Epidemic Spreadin...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Li, Fuwei Lu, Dianjie An, Baoxu Zhang, Guijuan Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

Studying the spread process of epidemics in the crowd is a significant topic, which can help to take measures in advance to avoid epidemic spread and reduce the losses of life and property of the general public. However, this is a challenging problem that how to capture the spatio-temporal characteristics of the epidemic spreading. In this paper, we propose a risk region-based prediction model to characterize the spread feature. First, we propose a risk region-based spread model to describe the spatial relationship between individuals by dividing risk regions and assigning different spread characteristics to different risk regions. Second, we develop a spatio-temporal point process-based infection intensity quantification method to describe the variation of epidemic spread characteristics over time in different risk regions in the form of intensity. Then, we propose a long short-term memory (LSTM) based intensity function prediction method to solve the corresponding intensity and predict the spread process by the spatio-temporal point process (STPP). Finally, we implement an epidemic spreading simulation platform to verify our method and visualize the experimental results. The proposed model is expected to provide guidance for predicting the spread of epidemics. © 2023 IEEE.

关键词： Long short-term memory

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A multi-scale attention network for grading of invasive ductal cancer 3

A multi-scale attention network for grading of invasive duct...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Xinxin Lu, Cheng Bai, Jiayang Chen, Yuli School of Computer Science Shaanxi Normal University Xi'an710119 China Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Southern Medical University Guangzhou510080 China

ISBN: (纸本)9781510681279

Invasive ductal carcinoma (IDC) represents the most prevalent form of breast cancer. Automatic and precise IDC grading is essential for the clinical assessment of tumor status. However, current methodologies struggle with accurate grading due to the intricate spatial structure within IDC tumor regions, coupled with significant similarities between different classes of images and high variability within the same class, posing a substantial challenge for IDC grading. To tackle these challenges, we propose a novel multi-scale attention model named MS-HiFuse. This model integrates the multi-scale convolution and the multi-head attention mechanism into the local feature block of the HiFuse network, creating an enhanced multi-scale local feature block that more effectively captures the nuanced features of the tumor area and facilitates the learning of fine-grained feature representations. Furthermore, to overcome the limited number of original data and to ensure data quality, we augment the dataset through operations such as maximum rectangle cropping, subdivision of plots, and image flipping for each sample. Comparison experiments demonstrate that our proposed MSHiFuse network attains an AUC of 80.84%, outperforming both the current competing networks and the original model. Hence, the application of the MS-HiFuse network proposed herein holds significant promise for the grading of IDC pathology images. © 2024 SPIE.

关键词： Grading

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Deep reinforcement learning-based panic crowd evacuation simulation 26

Deep reinforcement learning-based panic crowd evacuation sim...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： An, Baoxu Zhang, Guijuan Li, Fuwei Song, Nianyun Liu, Hong Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

Crowd evacuation simulation can provide guidance for public emergencies or mass casualties which is easy to cause crowding and stampede. Deep reinforcement learning is an effective method for the path planning of crowd evacuation simulation which can reduce the dependence on data and has strong generalization. However, current methods of deep reinforcement learning ignore the consideration of panic emotion which could result in weak authenticity since the panic emotion has a significant impact on crowd evacuation. To address this problem, we propose a novel deep reinforcement learning for the panic crowd evacuation. First, a quadrant based crowd network is constructed to guide the movement of agents according to the change of panic degree in different quadrants. Second, a method of quantifying panic degree is proposed. Through the mean field equation, the dynamic evolution of agent states is realized, and the panic degree in different regions is quantified. Finally, a panic crowd evacuation model based on multi-agent deep deterministic policy gradient (MADDPG) was established. The computated panic degree is introduced into the reward function to recommend a path for multiagent to avoid panic. Experimental results show the effectiveness of our method. © 2023 IEEE.

关键词： Reinforcement learning

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Singular Value Decomposition Based Pedestrian Trajectory Prediction 26

Singular Value Decomposition Based Pedestrian Trajectory Pre...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Heng Zhang, Guijuan Liu, Hong Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

Numerous cameras deployed in venues can collect video data, which can be analyzed to help evacuate people in emergency situations. Most surveillance videos can provide data support for pedestrian trajectory prediction. However, data-driven prediction method do not consider the impact of personalization on pedestrian trajectories, which results in ignoring individual personalization. How to accurately analyze the differences among pedestrians and yet accurately predict pedestrian trajectories is a challenging problem. To solve the above problems, we propose an singular value decomposition (SVD) based pedestrian trajectory prediction method, which introduces matrix decomposition to pedestrian trajectory prediction for the first time. Thus, This method analyzes the impact of pedestrian personalization on motion trajectories by mining the interaction patterns between pedestrians and environment. Firstly, we propose an information collection method based on environmental semantics, which can extract scene information from historical video data to construct an environmental information matrix. Secondly, we propose an SVD-based individual environmental feature preference method, which uses singular value decomposition methods to mine the data and analyze personalized pedestrian motion patterns to construct a personalized individual preference matrix. Finally, we built an personalized trajectory prediction method to predict pedestrian movement trajectories. The experimental results show that the method can not only analyze the effect of personalization on pedestrian movement but also accurately predict the pedestrian movement trajectory. © 2023 IEEE.

关键词： Singular value decomposition

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Analysis of vocational education dynamics evolution based on topic modeling 14

Analysis of vocational education dynamics evolution based on...

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14th International Conference on Information technology in Medicine and Education, ITME 2024

作者： Wang, Xinhua Yu, Xiaomei Jiang, Yunmeng Zheng, Xiangwei Li, Wei Shandong Normal University School of Information Science and Engineering Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China Shandong Normal University Library Shandong Normal University Jinan China

ISBN: (纸本)9798350356670

Clarifying the focus of vocational education dynamics is crucial for understanding the current situation and future development trends. However, comprehensive exploration on vocational education dynamics is inadequate presently. In this paper, textual information on vocational education is analyzed with Latent Dirichlet Allocation, BERTopic, and the Dynamic Topic Model. Two key issues are thoroughly investigated as follows: (1) What are the main aspects of the current development of vocational education;(2) How to learn the embeddings of vocational education text and further to promote the formulation of policy. The experiments are conducted and the experimental results show that LDA and BERTopic model achieve effective performance in clustering the texts of vocational education dynamics, and the DTM captures the topics trend of vocational education, which facilitate the government to guide the formulation and implementation of policies. © 2024 IEEE.

关键词： Apprentices

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