2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第3页

CoBABO: A Hyperparameter Search Method with Cost Budget Awareness CoBABO:具有成本预算意识的超参数搜索方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754655

Wenyuan Qian, Zhenying He, Linwei Li, Xiaoqing Liu, Feng Gao

引用次数: 0

A Semi-Supervised Deception Jamming Discrimination Method 一种半监督欺骗干扰判别方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754679

Hongliang Luo, Jieyi Liu, Siyuan Wu, Zhao Nie, Hao Li, Jie Wu

{"title":"A Semi-Supervised Deception Jamming Discrimination Method","authors":"Hongliang Luo, Jieyi Liu, Siyuan Wu, Zhao Nie, Hao Li, Jie Wu","doi":"10.1109/CCIS53392.2021.9754679","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754679","url":null,"abstract":"For anti-deception jamming discrimination on multistatic radar system, the existing anti-jamming methods based on artificial intelligence require enough training samples with a large amount of labeled data, but it is difficult to obtain lots of labeled radar echo data in reality combat environment. This paper proposes a convolutional deep belief network-based deception jamming discrimination method for the insufficient labeled data. The constructed anti-jamming network is trained with a large number of unlabeled radar echo data, and enhances the discriminating capability of the network with a small number of labeled echo data. It realizes a more accurate deception jamming discrimination network, which achieves full information utilization and broadens the limitation condition of jamming discrimination. Simulation results show that compared with the existing artificial intelligence-based jamming discrimination method utilizing tens of thousands of labeled data, the proposed method satisfies the same performance utilizing 2000 labeled data. It reduces data requirements and enhances operational capabilities.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123763002","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

Optimization of Classification Rules and Voting Strategies for Random Forest 随机森林分类规则和投票策略的优化

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754599

Shishi Huang, Wanrong Gu, Shixin Chen

{"title":"Optimization of Classification Rules and Voting Strategies for Random Forest","authors":"Shishi Huang, Wanrong Gu, Shixin Chen","doi":"10.1109/CCIS53392.2021.9754599","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754599","url":null,"abstract":"As an efficient learning method, random forest is widely used in data mining, machine learning, artificial intelligence and other fields. It has excellent capabilities in specific practice. However, the decision tree model used in the classification process for random forest traverses all attribute values to find the split points, which leads to over-fitting and reduction of algorithm efficiency. In addition, the meta-base models of random forests vote with the same weight, which may result in decreasing algorithm accuracy. In this paper we accomplish the following two optimization tasks. Firstly, the continuous attributes are discretized based on the boundary theorem of Fayyad and Irani. Secondly, Gaussian mixture model is used to adjust the weight of the meta-base models in optimized random forest according to the similarity between the subsets and the training sets. Finally, the optimized algorithm is applied to the student information data set and the terrain types data set. The experiment results show that the optimized algorithm can effectively improve the classification efficiency and prediction accuracy.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124995172","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

Federated Graph Neural Network for Cross-graph Node Classification 跨图节点分类的联邦图神经网络

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754598

Zeli Guan, Yawen Li, Zhe Xue, Yuxin Liu, Hongrui Gao, Yingxia Shao

{"title":"Federated Graph Neural Network for Cross-graph Node Classification","authors":"Zeli Guan, Yawen Li, Zhe Xue, Yuxin Liu, Hongrui Gao, Yingxia Shao","doi":"10.1109/CCIS53392.2021.9754598","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754598","url":null,"abstract":"In this paper, we propose a novel distributed scalable federated graph neural network (FGNN) to solve the cross-graph node classification problem. In the existing cross-graph node classification methods, the source graph and target graph need to share their graph data and label, for the nodes in the source graph and target graph are in the same semantic space. However, source graphs cannot share graph data and label without encryption due to regulations and interests. In order to satisfy the privacy of all parties, the universal classification rules of cross-graph nodes are learned. We add PATE mechanism into the domain adversarial neural network (DANN) to construct a cross-network node classification model, and extract effective information from node features of source and target graphs for encryption and spatial alignment. Moreover, we use a one-to-one approach to construct cross-graph node classification models for multiple source graphs and the target graph. Federated learning is used to train the model jointly through multi-party cooperation to complete the target graph node classification task. Finally, we carry out extensive experiments on five datasets to demonstrate the effectiveness of the proposed method.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122448982","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: 21

Incorporating Option and Out-of-domain Knowledge for Multi-choice Machine Reading Comprehension 多选题机器阅读理解的选择与领域外知识融合

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754687

Yuan Xu, Shumin Shi, Heyan Huang

引用次数: 0

A Heart Sound Classification Method Based on Time Series Analysis 基于时间序列分析的心音分类方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754612

Zhuo Chen, Qiao Pan, Chen Hua

{"title":"A Heart Sound Classification Method Based on Time Series Analysis","authors":"Zhuo Chen, Qiao Pan, Chen Hua","doi":"10.1109/CCIS53392.2021.9754612","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754612","url":null,"abstract":"Auscultation of heart sound is the main diagnostic method of cardiovascular and cerebrovascular diseases. However, the traditional heart auscultation relies too much on the sensitivity of human ear and the subjective experience of doctors, which makes it difficult to make a correct judgment of heart sound. This paper proposes a heart sound signal classification method based on time series. The use of advanced signal processing methods and deep learning methods can effectively alleviate this problem. The method first uses the biorthogonal wavelet base to denoise, and uses band-pass filtering to filter out the unqualified frequency band signals. By calculating the wavelet entropy range of all heart sound data, it is used to filter out the fuzzy heart sound data that is not within the threshold range; Then, according to the contribution of each feature's SHAPLEY value to the model, the MFCC feature combination that is most suitable for the model is selected; Finally, a TCN-LSTM model is designed to process timing information. Experiments show that this method can accurately detect the benign and malignant of audio data.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116159711","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

Subgraph Convolutional Network for Recommendation 推荐的子图卷积网络

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754683

Yan Zhao, Li Zhou, Liwei Deng, Vincent W. Zheng, Hongzhi Yin, Kai Zheng

{"title":"Subgraph Convolutional Network for Recommendation","authors":"Yan Zhao, Li Zhou, Liwei Deng, Vincent W. Zheng, Hongzhi Yin, Kai Zheng","doi":"10.1109/CCIS53392.2021.9754683","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754683","url":null,"abstract":"Nowadays recommendation systems play an important role in our lives, which help users to quickly identify their desirable items. The networking trend for world (i.e., every sector of our world can be networked) has made the recommendation systems one of the intensively studied research areas in the last decades. In this paper, we formulate a graph-based recommendation problem, which aims to find the most relevant nodes for a given set of query nodes in the graph. For graph embedding, Graph Convolutional Network (GCN), which aggregates neighbor information via convolution layers, is an effective model. However, a convolution layer in a GCN only considers unstructured information, i.e., it takes single nodes as input and only leverages the first-order neighbor information, so only limited local information can be learned. To overcome the mentioned limitations, we develop a Subgraph Convolutional Network (SCN) model which aggregates both neighbor node information and structural information via convolution layers. Moreover, the fully connected layer based link prediction is integrated for effective recommendations. The experimental results on real-world datasets verify the effectiveness of our solution.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115421700","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: 1

Multi-scale Features Fusion Network for Unsupervised Change Detection in Heterogeneous Optical and SAR Images 非均匀光学和SAR图像无监督变化检测的多尺度特征融合网络

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754667

Jiao Shi, Zeping Zhang, Tancheng Wu, Xiaoyang Li, Deyun Zhou, Yu Lei

引用次数: 0

Construction of Data Quality Evaluation Index for Manufacturing Multi-value Chain Collaborative Data Space Based on the Whole Life Cycle of Data 基于数据全生命周期的制造业多价值链协同数据空间数据质量评价指标构建

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754682

Shan Peng, Zhuxiao Tian, Zhuoya Siqin, Xiaomin Xu

{"title":"Construction of Data Quality Evaluation Index for Manufacturing Multi-value Chain Collaborative Data Space Based on the Whole Life Cycle of Data","authors":"Shan Peng, Zhuxiao Tian, Zhuoya Siqin, Xiaomin Xu","doi":"10.1109/CCIS53392.2021.9754682","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754682","url":null,"abstract":"As a new data management model, data space can effectively manage a large amount of multi-heterogeneous dynamic data, but the construction of data space often needs to be based on accurate and scientific original data and to obtain valuable information in data, which poses a challenge to the data quality control of the whole life cycle of data, so it is especially important to evaluate the data quality. By analyzing the synergistic effect of multi-value chain in manufacturing industry and combining the dynamic system of the whole life cycle of data, the data quality evaluation index system is proposed from three aspects of data provider, data space construction and data user, combining four levels of data itself, technology, data flow layer and data management. Through the construction of AHP-TOPSIS data quality evaluation model, AHP is used to determine the index weight, TOPSIS is used to calculate the ideal solution and relative closeness degree, and the evaluation results are obtained. Through the application analysis of examples, quantitative evaluation of data quality, the construction, access and mining of multi-value chain collaborative data space can provide practical experience.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125878078","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: 1

Stable and Diverse: A Unified Approach for Computerized Adaptive Testing 稳定与多样化:计算机化自适应测试的统一方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754532

Yuting Ning, Ye Liu, Zhenya Huang, Haoyang Bi, Qi Liu, Enhong Chen, Dan Zhang

{"title":"Stable and Diverse: A Unified Approach for Computerized Adaptive Testing","authors":"Yuting Ning, Ye Liu, Zhenya Huang, Haoyang Bi, Qi Liu, Enhong Chen, Dan Zhang","doi":"10.1109/CCIS53392.2021.9754532","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754532","url":null,"abstract":"Computerized Adaptive Testing (CAT), aiming to provide personalized tests for each examinee, is an emerging task in the intelligent education field. A CAT system selects questions step by step according to the knowledge states of each examinee, which are estimated by Cognitive Diagnosis Models (CDM). Most existing methods depend on the performance of a single CDM, which is often unstable. Besides, they may select similar questions to generate a test, which to some extent ignores the diversity of selected questions. To this end, in this paper, we propose a novel framework, namely Ensembled Computerized Adaptive Testing (EnCAT). Specifically, EnCAT comprises two components, ensemble part and explore part. In the ensemble part, we ensemble multiple CDMs to determine whether a question is informative, which ensures the stability of CAT process. Then, in the explore part, we learn the question representation from the question content and design a mechanism to quantify the similarity of different questions, which avoids the selection of similar questions and is free of expensive human labeling. Finally, extensive experiments are conducted on a real-world dataset, where the experimental results demonstrate the effectiveness of our proposed EnCAT framework with good performance.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125891793","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