2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)最新文献_第3页

Graph Regularized Low-Rank Representation for Hyperspectral Anomaly Detection 高光谱异常检测的图正则化低秩表示

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213066

Qing Yu, Guang Yan, Xiangdong Li, Jinhuan Xu, Xiuwei Yang

引用次数: 0

APT Attack Investigation via Fine-grained Sequence Construction and Learning 基于细粒度序列构建和学习的APT攻击调查

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213187

Tianqi Wu, Zhuo Lv, Daojuan Zhang, Kexiang Qian, Ming Wang

引用次数: 0

MCUNeXt: An Efficient U-Shaped Network for Pathology Image Segmentation MCUNeXt:用于病理图像分割的高效u型网络

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213207

Haojun Yuan, Xi Gong, ShiFan Fan, Xiaofeng He

{"title":"MCUNeXt: An Efficient U-Shaped Network for Pathology Image Segmentation","authors":"Haojun Yuan, Xi Gong, ShiFan Fan, Xiaofeng He","doi":"10.1109/ISCTIS58954.2023.10213207","DOIUrl":"https://doi.org/10.1109/ISCTIS58954.2023.10213207","url":null,"abstract":"Accurate pathology image segmentation is a process that assists physicians in developing medical plans and evaluating the effectiveness of treatment. Due to the complex background of pathology images and many cell nuclei, manual segmentation is time-consuming and laborious, so it is important to design a model for automatic segmentation of pathology images. In this paper, we proposed a U-shaped network that can effectively improve the accuracy and reduce the model complexity. We use depth-separable convolution and convolution kernels of different sizes to admit the convolutional blocks of UNet with the aim of reducing the number of parameters, obtaining multi-scale capabilities, and being able to effectively combine global and local information. Also, the inverted bottleneck structure is proposed to be able to increase the accuracy while reducing the number of parameters. We replace pooling with convolution for downsampling and are able to retain more information. Meanwhile, our proposed method has been extensively experimented on CRAG dataset, and compared with the standard UNet, our parameter quantity is reduced by 36%, Jaccard is 3.61% higher, and our method has less hole phenomenon and sticking phenomenon, better boundary accuracy compared with other excellent methods, and the results show that our method is competitive.","PeriodicalId":334790,"journal":{"name":"2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134498956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on network security situation awareness method based on topology analysis 基于拓扑分析的网络安全态势感知方法研究

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213111

Huichao Liang, Shuai Li, Yifan Song, Han Liu

引用次数: 0

Prediction of Time Series Using Generative Adversarial Networks 基于生成对抗网络的时间序列预测

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213145

Ao Di Ding

{"title":"Prediction of Time Series Using Generative Adversarial Networks","authors":"Ao Di Ding","doi":"10.1109/ISCTIS58954.2023.10213145","DOIUrl":"https://doi.org/10.1109/ISCTIS58954.2023.10213145","url":null,"abstract":"The GAN model consists of an LSTM as the time series generator and an ANN as the discriminator, using the simple moving average and exponentially weighted moving average results as input features for the GAN network, followed by the Fourier transform, ARIMA to create the input features, and finally XGBoost to filter the final prediction data. The GAN network model is generally used for adversarial image generation, and the GAN adversarial network is usually trained as two separate and alternating networks: the recognition network is trained first, then the generation network, then the recognition network, and so on, until a Nash equilibrium is reached. The power of GAN is that it can automatically define the potential loss function. The discriminatory network can automatically learn a good discriminant, which is equivalently understood as learning a good loss function to compare good or bad discriminant results. Although the overall loss function is still artificially defined, the discriminant network potentially learns the loss function hidden in the network, which varies from problem to problem, so that the potential loss function can be learned automatically. Using this particular property of the week to predict time series would be a new approach.","PeriodicalId":334790,"journal":{"name":"2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124507783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research of Application on Deblurring model for Guqin Performance 古琴演奏去模糊模型的应用研究

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213120

Lanlan Lyu, Chen Xu

引用次数: 0

Blind Image Deblurring with Extreme Gradient and Dark Channel Priors 盲图像去模糊与极端梯度和暗通道先验

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10212998

Chao Yang, Qing Li, Chun Xing Li, Yu Zheng

引用次数: 0

Dynamic Scheduling Optimization for Multi-Satellite Mission Measurement and Control Planning Using Evolutionary Algorithms and Model Predictive Control 基于进化算法和模型预测控制的多卫星测控规划动态调度优化

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213062

X. Yao, Wenhua Li, Xiaogang Pan, Yaoyu Li, Y. Jiao

{"title":"Dynamic Scheduling Optimization for Multi-Satellite Mission Measurement and Control Planning Using Evolutionary Algorithms and Model Predictive Control","authors":"X. Yao, Wenhua Li, Xiaogang Pan, Yaoyu Li, Y. Jiao","doi":"10.1109/ISCTIS58954.2023.10213062","DOIUrl":"https://doi.org/10.1109/ISCTIS58954.2023.10213062","url":null,"abstract":"This paper presents a dynamic scheduling optimization method for the multi-satellite mission measurement and control planning problem, which is a complex and uncertain optimization problem that involves rationalizing the missions of satellites and allocating limited resources to the measurement and control missions of satellites. The proposed method combines an evolutionary algorithm and a model predictive control framework, which can effectively deal with the complexity and uncertainty of the problem and achieve optimal observation and communication of multiple targets by multiple satellites. The paper develops an information-guided evolutionary algorithm that uses expert knowledge and conflict reduction rules to generate high-quality solution sets, applies a model predictive control framework that performs online adjustment and update based on actual data and feedback mechanism and designs a series of experimental scenarios to compare the performance of different methods in different situations.","PeriodicalId":334790,"journal":{"name":"2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116450506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of Machine Learning to Predict Mental Health Disorders and Interpret Feature Importance 机器学习在心理健康障碍预测和特征重要性解释中的应用

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213032

Yifan Li

{"title":"Application of Machine Learning to Predict Mental Health Disorders and Interpret Feature Importance","authors":"Yifan Li","doi":"10.1109/ISCTIS58954.2023.10213032","DOIUrl":"https://doi.org/10.1109/ISCTIS58954.2023.10213032","url":null,"abstract":"The mental health and mental illness crisis has become increasingly acute in recent years, and many digital solutions with artificial intelligence at their core offer hope for reversing the deterioration of our mental health. Machine deep learning techniques can be used to analyse big data to build predictive models for psycho-education, assessment and screening to assess the mental health status of subjects, and can help the clinical community discover information that is not available to many traditional psychological research tools. This paper presents an in-depth analysis of a mental health survey and examines how it can be applied to the Al/ML domain of mental health research and how machine learning models can be used in this domain for fitting and prediction. Based on this, the importance of the presence or absence of current mental health disorders on other characteristics of respondents is assessed and visualised. It was found that the Cross Gradient Booster (Random Forest) model gave the best prediction fit among the various types of machine learning models, and the Grid Search algorithm was used to confirm that the final model had the highest accuracy of 0.79784 at a learning rate of 0.1. The Permutation Importance analysis revealed that the most important characteristic is whether or not the person has suffered from a mental health disorder in the past.","PeriodicalId":334790,"journal":{"name":"2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125947441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study on the Adversarial Sample Generation Algorithm Based on Adversarial Quantum Generation Adversarial Network 基于对抗性量子生成的对抗性样本生成算法研究

2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2023-07-07 DOI: 10.1109/ISCTIS58954.2023.10213103

Wen Cheng, Shibin Zhang, Yusheng Lin

{"title":"Study on the Adversarial Sample Generation Algorithm Based on Adversarial Quantum Generation Adversarial Network","authors":"Wen Cheng, Shibin Zhang, Yusheng Lin","doi":"10.1109/ISCTIS58954.2023.10213103","DOIUrl":"https://doi.org/10.1109/ISCTIS58954.2023.10213103","url":null,"abstract":"In response to the inefficiency of the existing advGAN method, which requires calculating the differences between the adversarial samples and the normal samples one by one when generating adversarial perturbations, this paper proposes a quantum adversarial sample generation algorithm (QASGA) based on adversarial quantum generative adversarial networks. First, the real samples are encoded into quantum states, then the generator G of QGAN is used to generate adversarial perturbations, which are superimposed with normal samples to obtain adversarial samples. At the same time, the SWAP-TEST method is used to calculate the similarity between all real samples and adversarial samples at once, thereby accelerating adversarial attacks. Experimental results show that the QASGA algorithm proposed in this paper can generate high-fidelity adversarial samples with a one-time similarity calculation on the IRIS dataset, and can reduce the classification accuracy of the BP neural network from 96.67% to 26.67%, verifying the effectiveness of the proposed QASGA algorithm.","PeriodicalId":334790,"journal":{"name":"2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129502743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0