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共 155 条记录，以下是121-130 订阅

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Tabular GAN-Based Oversampling of Imbalanced Time-to-Event data for Survival Prediction

Tabular GAN-Based Oversampling of Imbalanced Time-to-Event D...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Huaning Tan Renxing Chen Meng Qin Lining Tang Zhibing Wu Qianlin Luo Yujuan Quan International School Jinan University Guangzhou China School of Intelligent Systems Science and Engineering Jinan University Zhuhai China School of Economics Jinan University Guangzhou China Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization College of Information Science and Technology Jinan University Guangzhou China

Class imbalance causes an underestimation (overestimation) of the hazard of minority class in survival prediction. A common strategy to handle class imbalance is to oversample the minority class by generating synthetic samples. this paper explores the potential of tabular Generative Adversarial Networks (GANs) for oversampling based on real world survival datasets and simulated imbalanced datasets. We compare GAN-based oversampling methods with traditional methods on generation of minority instances and balanced survival prediction. Experimental results show that balanced survival prediction after GAN-based oversampling can outperforms baseline in some situations, and also demonstrate that traditional oversampling methods perform better than GAN-based methods on both minority samples generation and balanced survival prediction.

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Research on the Construction of Secondary Psychological Counseling Station in Colleges and Universities Under the “Internet+” Environment by data Analysis

Research on the Construction of Secondary Psychological Coun...

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ieee international conference on Smart cloud (Smartcloud)

作者： Haolun Xu College of Computer Science and Technology Wuhan University Of Science And Technology WuHan China

With the rapid development of Internet technology, psychological education in colleges and universities is facing unprecedented challenges. the characteristics of the “Internet +” era have affected the mental health of college students to a certain extent. At the same time, teachers' understanding and application of “Internet +” is far from enough. therefore, under this new situation, how to use the power of cloud computing and data analysis to optimize the construction of secondary psychological counseling stations in colleges and universities and play their unique role in mental health education has become a new topic. Overall, the “Internet +” era has brought challenges as well as opportunities. the secondary psychological counseling stations in colleges and universities should actively apply new technologies such as cloud computing and data analysis to optimize services, improve work efficiency, and better serve college students' mental health. Based on the mental health data of college students obtained from big data, this paper adopts mean clustering and C4.5 algorithm to find out the factors leading to the mental health problems of college students through data analysis, and propose feasible solutions to the problems.

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Analysis of the Impact of big data Reform on Experimental Teaching in Colleges

Analysis of the Impact of Big Data Reform on Experimental Te...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Chun-Li Wang College of Educational Science and Technology Northwest Minzu University Lanzhou China

With the progress of science and technology and the development of economic and social information technology, the era of big data (BD) has become one of the most important signs of this period. BD technology has very broad applications in all aspects of human economy and society, especially the realization and application of BD analysis in higher education, which will greatly promote the progress and development of higher education. Higher education has a long history. In the process of sustainable development, it has benefited from major technological changes and economic and social development many times. Now, if the arrow of the times is pointed to the era of BD analysis, then the development of higher education will be affected by BD. the far-reaching impact of analysis. this article aims to study the impact of BD changes on experimental teaching in colleges. On the basis of analyzing the characteristics of BD in education, the problems in experimental teaching and the principles of integrating BD technology into experimental teaching in colleges, in order to understand the BD technology in teaching. the current application status of the application and the current difficulties in teaching, this article uses a questionnaire survey to investigate the current status of experimental teaching in a university. the survey results show that the university uses BD technology more in experimental teaching, but the popularity of virtual reality and other technologies is still relatively low.

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Unsupervised Domain Adaptation via Variational Autoencoder with Explicit and Implicit Feature Alignment for Cardiac Segmentation

Unsupervised Domain Adaptation via Variational Autoencoder w...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Yan Li Yifan Wang Fan Zheng Linkai Cheng Di Xu Hengfei Cui School of Computer Science Northwestern Polytechnical University Xi’an China Media Innovation Lab Huawei Cloud Xi’an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of vital importance in the diagnosis of myocardial infarction (MI). However, the LGE image annotations are limited, it is urgently needed to transfer the learned knowledge in other modalities. We propose an unsupervised domain adaptation (UDA) algorithm based on variational autoencoder (VAE) with a Combination of Explicit and Implicit Alignment of feature distributions (VCEIA). First, we re-derive the Kullback-Leibler (KL) divergence of the features extracted in the source and target domains as a measure of distribution discrepancy. Secondly, for explicit category-level distribution alignment, we use contrast learning to reduce the distance between prototypes of the same category in the source and target domains. Finally, we add a domain discriminator to the output space to implicitly align the feature distributions. Extensive experiments were conducted to validate the effectiveness of VCEIA on the MS-CMRSeg 2019 dataset, which showed competitive results.

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Visualization Analytics of Mao Zedong Research in 2022 Based on Mapping Knowledge Domain

Visualization Analytics of Mao Zedong Research in 2022 Based...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Miao Li Jinhua Yang School of Marxism Huazhong University of Science and Technology Wuhan China

In recent years, Mao Zedong’s research has received much attention by lots of researchers of multidisciplinary and publications. Particularly, the bibliometric analyses of Mao Zedong Research could induce the trend and hotspots for the inheritance and development of Mao Zedong thought with the centenary of the founding the Communist Party of China (CPC). According to the visual and cluster analysis technology, this study analyses the development status, research distribution and future trends of Mao Zedong Research in the 2022. Firstly, a total number of 1204 published paper indexed by CNKI in 2022 are investigated. then, according to the Mapping Knowledge Domain (MKD) approaches, the visualization of keywords, research organization and co-authorship analyses are presented based on the VOSviewer, which stimulates insightful findings. these cluster analysis results show that the Spiritual pedigree of the CPC, Party history education, fighting spirit, and national security thought of Mao Zedong are research hotspots, which could devote to the Party history education and the Partys theoretical innovation.

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UT-MT: A Semi-Supervised Model of Fusion Transformer for 3D Medical Image Segmentation

UT-MT: A Semi-Supervised Model of Fusion Transformer for 3D ...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Xianchang Liu Peishun Liu Jinyu Wang Qinshuo Wang Qing Guo Ruichun Tang Faculty of Information Science and Engineering Ocean University of China Qingdao China

the training of 3D medical image segmentation model requires a large amount of labeled data, but the availability of labeled data is difficult, and the scarcity of labeled data makes the prediction quality of unlabeled data cannot be effectively guaranteed. To solve the above problems, the 3D medical image segmentation model UT-MT proposed in this paper combines ViT and CNN, and consists of a student model and a teacher model, and the student model learns the teacher model by minimizing the segmentation loss and consistency loss. By combining the feature learning advantages of CNN and ViT, this method enables the model to enhance the learning ability of taking into account both local and global aspects in feature extraction, and further improves the model accuracy and performance. the evaluation was performed on a public left atrial benchmark dataset, and the results at 10% of the labeled images show that the proposed method improves the Dice coefficient by 6.69% over the fully supervised method and has better segmentation of the boundaries, while our method outperforms five advanced semi-supervised segmentation methods.

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Self-Supervised Augmented Vision Transformers for Remote Physiological Measurement

Self-Supervised Augmented Vision Transformers for Remote Phy...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Liyu Pang Xiaoou Li Zhen Wang Xueyi Lei Yulong Pei School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China College of Medical Instruments Shanghai University of Medicine & Health Sciences Shanghai China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China

Video based heart rate (HR) measurement conducting remote photoplethysmography (rPPG) exhibits immense prospects and achieves creditable results in constrained scenarios. (1) Background: Non-contact facial video-based HR estimation is a constantly evolving direction, in which deep learning models achieved state-of-the-art performance. However, these methods usually have higher computational burden, which are difficult to deploy on mobile or edge devices directly; (2) Methods: To address this problem, we propose a lightweight model for HR estimation based on rPPG. this model uses vision transformer to serves as the backbone, and the contraction factor is adopted to reduce the model complexity. (3) Results: the proposed model is tested on the challenging VIPL-HR-V2 dataset. the results show that the proposed model can greatly reduce the model complexity while attaining reasonable accuracy.

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Multimodal Brain Tumor Image Segmentation Based on 3D CTrans U-Net: Replacing Skip Connection with Channel-Wise Attention Module

Multimodal Brain Tumor Image Segmentation Based on 3D CTrans...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Xiao Sheng Yifeng Zhang School of Information Science and Engineering Southeast University Nanjing China

Automatic segmentation of brain tumors can be used as a powerful auxiliary means for clinical diagnosis, bringing better treatment. In recent years, medical image segmentation technology has mainly developed into two genres: convolution-based and Transformer-based. However, both of them more focus on the design of feature extraction, while less on the role of the skip-connection in the network structure. Based on the analysis of the contributions and disadvantages of skip-connection, the 3D CTrans U-Net is proposed to apply channel-wise cross fusion Transformer (CCT) and channel-wise cross attention (CCA) module to replace the skip-connection in 3D U-Net, which can better integrate multi-scale encoder features and bridge the semantic gap between encoder and decoder. the proposed method has achieved more accurate segmentation results on the BraTS2019 dataset, which has been improved to a certain extent compared with other state-of-the-art methods.

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Multi-scale Parallel Fusion Convolution Network for Crop Disease Identification

Multi-scale Parallel Fusion Convolution Network for Crop Dis...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Huyue Ren Wanhui Chen Peishun Liu Ruichun Tang Jinyu Wang Chang Ge School of Computer Science and Technology Ocean University of China Qingdao China

In order to identify crop disease accurately and efficiently, we propose a multi-scale parallel fusion convolution network called MSPFNet for the automatic crop disease identification. It combines multi-scale convolution and attention mechanism. By mixing convolution kernels of different sizes in the same convolution unit, multi-scale convolution can identify crop disease features at different scales better. In this paper, Convolutional Block Attention Module is introduced to improve the representation ability of the model. By analyzing the information of spatial attention and channel attention, model can pay more attention to important features. In addition, we design a feature fusion module to realize the fusion of features at different depths. And feature fusion module can solve the problem of the disappearance of disease features in the process of image feature extraction. We validate the proposed model on the publicly available apple leaf pest data set. Experimental results show that the proposed MSPFNet can achieve higher recognition accuracy.

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A Pipelined Multi-level Checkpoint Storage System for Virtual Cluster Checkpointing

A Pipelined Multi-level Checkpoint Storage System for Virtua...

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ieee international conference on cloud computing and big data Analysis (ICCCBDA)

作者： Jumpol Yaothanee Kasidit Chanchio Department of Computer Science Thammasat University Patum Thanee Thailand

Checkpoint-Restart (CR) is an important mechanism to provide fault tolerance for long-running applications in the cloud. Virtual Cluster (VC) checkpointing is the mechanism to perform CR operations on a cluster of Virtual Machines (VMs). However, saving of state of every VM in a VC directly to a shared storage, such as a SAN storage, simultaneously can cause high checkpoint overheads due to I/O contentions. this paper presents a novel Pipedlined Multi-Level Checkpoint Storage (PMLCS) system that allows users to schedule the saving of VM checkpoints on multiple storage devices to avoid I/O contention. We propose a novel conceptual diagram called the Backup Chain (BC) diagram to describe the scheduled saving and backing up of VM checkpoint files on various storage devices. We have implemented the PMLCS prototype and integrated it with two hypervisor-level VC checkpointing systems. We conducted experiments by checkpointing a VC running an MPI program from NAS parallel benchmark. Experimental results show that the PMLCS system using the BC diagram that represents a combination of SSD and SAN storage is an efficient and scalable checkpoint storage solution.

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