2019 IEEE International Conference on Big Knowledge (ICBK)最新文献

Sparse Tensor Decomposition for Multi-task Interaction Selection 稀疏张量分解用于多任务交互选择

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/ICBK.2019.00022

Jun-Yong Jeong, C. Jun

引用次数: 1

A Performance Comparison of Cloud-Based Container Orchestration Tools 基于云的容器编排工具的性能比较

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/ICBK.2019.00033

Yao Pan, Ian Chen, F. Brasileiro, G. Jayaputera, R. Sinnott

{"title":"A Performance Comparison of Cloud-Based Container Orchestration Tools","authors":"Yao Pan, Ian Chen, F. Brasileiro, G. Jayaputera, R. Sinnott","doi":"10.1109/ICBK.2019.00033","DOIUrl":"https://doi.org/10.1109/ICBK.2019.00033","url":null,"abstract":"Compared to the traditional approach of using virtual machines as the basis for the development and deployment of applications running in Cloud-based infrastructures, container technology provides developers with a higher degree of portability and availability, allowing developers to build and deploy their applications in a much more efficient and flexible manner. A number of tools have been proposed to orchestrate complex applications comprising multiple containers requiring continuous monitoring and management actions to meet application-oriented and non-functional requirements. Different container orchestration tools provide different features that incur different overheads. As such, it is not always easy for developers to choose the orchestration tool that will best suit their needs. In this paper we compare the benefits and overheads incurred by the most popular open source container orchestration tools currently available, namely: Kubernetes and Docker in Swarm mode. We undertake a number of benchmarking exercises from well-known benchmarking tools to evaluate the performance overheads of container orchestration tools and identify their pros and cons more generally. The results show that the overall performance of Kubernetes is slightly worse than that of Docker in Swarm mode. However, Docker in Swarm mode is not as flexible or powerful as Kubernetes in more complex situations.","PeriodicalId":383917,"journal":{"name":"2019 IEEE International Conference on Big Knowledge (ICBK)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121135955","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

Edge Sign Prediction Based on Orthogonal Graph Regularized Nonnegative Matrix Factorization for Transfer Learning 基于正交图正则化非负矩阵分解的迁移学习边号预测

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/ICBK.2019.00050

Junwu Yu, Shuyin Xia, Guoyin Wang

引用次数: 1

Co-training Based on Semi-Supervised Ensemble Classification Approach for Multi-label Data Stream 基于半监督集成分类方法的多标签数据流协同训练

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/ICBK.2019.00016

Zhe Chu, Peipei Li, Xuegang Hu

{"title":"Co-training Based on Semi-Supervised Ensemble Classification Approach for Multi-label Data Stream","authors":"Zhe Chu, Peipei Li, Xuegang Hu","doi":"10.1109/ICBK.2019.00016","DOIUrl":"https://doi.org/10.1109/ICBK.2019.00016","url":null,"abstract":"A large amount of data streams in the form of texts and images has been emerging in many real-world applications. These data streams often present the characteristics such as multi-labels, label missing and new class emerging, which makes the existing data stream classification algorithm face the challenges in precision space and time performance. This is because, on the one hand, it is known that data stream classification algorithms are mostly trained on all labeled single-class data, while there are a large amount of unlabeled data and few labeled data due to it is difficult to obtain labels in the real world. On the other hand, many of existing multi-label data stream classification algorithms mostly focused on the classification with all labeled data and without emerging new classes, and there are few semi-supervised methods. Therefore, this paper proposes a semi-supervised ensemble classification algorithm for multi-label data streams based on co-training. Firstly, the algorithm uses the sliding window mechanism to partition the data stream into data chunks. On the former w data chucks, the multi-label semi-supervised classification algorithm COINS based on co-training is used to training a base classifier on each chunk, and then an ensemble model with w COINS classifiers is generated ensemble model to adapt to the environment of data stream with a large number of unlabeled data. Meanwhile, a new class emerging detection mechanism is introduced, and the w+1 data chunk is predicted by the ensemble model to detect whether there is a new class emerging. When a new label is detected, the classifier is retrained on the current data chunk, and the ensemble model is updated. Finally, experimental results on five real data sets show that: as compared with the classical algorithms, the proposed approach can improve the classification accuracy of multi-label data streams with a large number of missing labels and new labels emerging.","PeriodicalId":383917,"journal":{"name":"2019 IEEE International Conference on Big Knowledge (ICBK)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123579648","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: 10

A Multi-granularity Genetic Algorithm 一种多粒度遗传算法

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/ICBK.2019.00027

Caoxiao Li, Shuyin Xia, Zizhong Chen, Guoyin Wang

引用次数: 2

Approximate Query Answering in Complex Gaussian Mixture Models 复杂高斯混合模型的近似查询应答

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/ICBK.2019.00019

Mattis Hartwig, M. Gehrke, R. Möller

引用次数: 0

U-Net Based Defects Inspection in Photovoltaic Electroluminecscence Images 基于U-Net的光伏电致发光图像缺陷检测

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/ICBK.2019.00036

Muhammad Rameez Ur Rahman, Haiyong Chen, Wen Xi

{"title":"U-Net Based Defects Inspection in Photovoltaic Electroluminecscence Images","authors":"Muhammad Rameez Ur Rahman, Haiyong Chen, Wen Xi","doi":"10.1109/ICBK.2019.00036","DOIUrl":"https://doi.org/10.1109/ICBK.2019.00036","url":null,"abstract":"Efficient defects segmentation from photovoltaic (PV) electroluminescence (EL) images is a crucial process due to the random inhomogeneous background and unbalanced crack non-crack pixel distribution. The automatic defect inspection of solar cells greatly influences the quality of photovoltaic cells, so it is necessary to examine defects efficiently and accurately. In this paper we propose a novel end to end deep learning-based architecture for defects segmentation. In the proposed architecture we introduce a novel global attention to extract rich context information. Further, we modified the U-net by adding dilated convolution at both encoder and decoder side with skip connections from early layers to later layers at encoder side. Then the proposed global attention is incorporated into the modified U-net. The model is trained and tested on Photovoltaic electroluminescence 512x512 images dataset and the results are recorded using mean Intersection over union (IOU). In experiments, we reported the results and made comparison between the proposed model and other state of the art methods. The mean IOU of proposed method is 0.6477 with pixel accuracy 0.9738 which is better than the state-of-the-art methods. We demonstrate that the proposed method can give effective results with smaller dataset and is computationally efficient.","PeriodicalId":383917,"journal":{"name":"2019 IEEE International Conference on Big Knowledge (ICBK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121712107","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: 2

[Copyright notice] (版权)

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/icbk.2019.00003

引用次数: 0

Ensemble Classification Method Based on Truth Discovery 基于真相发现的集成分类方法

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/ICBK.2019.00024

Yuxin Jin, Ze Yang, Ying He, Xianyu Bao, Gongqing Wu

引用次数: 0

ICDM/ICBK 2019 Panel: Marketing Intelligence – Let Marketing Drive Efficiency and Innovation ICDM/ICBK 2019专题讨论:营销智能——让营销驱动效率和创新

2019 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2019-11-01 DOI: 10.1109/icbk.2019.00008

Xindong Wu

引用次数: 0