2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)最新文献_第9页

Improving Web Browsing Experience with Personalized Edge Computing 利用个性化边缘计算改善网页浏览体验

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00149

Zhaoxin Wu, Yasushi Shinjo

{"title":"Improving Web Browsing Experience with Personalized Edge Computing","authors":"Zhaoxin Wu, Yasushi Shinjo","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00149","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00149","url":null,"abstract":"In recent years, webpages are becoming complex rapidly and their loading times are also becoming longer. This paper tackles this problem with personalized edge computing. In typical edge computing, an edge server collaborates with cloud web servers. In personalized edge computing, on the other hand, an edge server called an Edge Server in the Middle (ESM) collaborates with users' mobile devices. Based on personalized edge computing, this paper focuses on two techniques: edge aided caching and edge aided reprioritizing. Edge aided caching reduces the page loading time on mobile devices because an ESM automatically keeps the cached components up to date. Edge aided reprioritizing forces a web browser to show visual components earlier and reduces the white screen time. The ESM also uses HTTP/2 instead of HTTP/1.1. This reduces the number of interactions between a mobile device and the ESM, and makes it possible to use advanced features such as server push and priority. Edge aided caching has been implemented in a PC for the web browser Google Chrome for Android. An experimental result shows that edge aided caching reduced the page loading time of a popular webpage by 59% in a crowded network condition. Another experimental result shows that edge aided reprioritizing reduced the white screen time of a webpage with many photo images by 21%.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132563942","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

Message from the SmartCNS 2019 General Chairs 2019年SmartCNS大会主席致辞

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/iucc/dsci/smartcns.2019.00009

引用次数: 0

M^2 S^2 F^2 : Multiscale Multistage Spectral-Spatial Features Fusion Framework for Hyperspectral Image Classification M^2 S^2 F^2:用于高光谱图像分类的多尺度多阶段光谱-空间特征融合框架

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00116

Xiangbin Shi, Kuo Song, Zhaokui Li, Jing Bi, Deyuan Zhang

{"title":"M^2 S^2 F^2 : Multiscale Multistage Spectral-Spatial Features Fusion Framework for Hyperspectral Image Classification","authors":"Xiangbin Shi, Kuo Song, Zhaokui Li, Jing Bi, Deyuan Zhang","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00116","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00116","url":null,"abstract":"Hyperspectral image classification has been widely applied in many fields, but it also faces challenges because of small number of labeled samples. In this paper, we propose the Multiscale Multistage Spectral-Spatial Feature Fusion Framework (M^2 S^2 F^2 ) for hyperspectral image classification using small training samples. The Framework is the combination of two deep convolutional neural networks, which can extract more representative and discriminative features by combining the following operations. Firstly, two different scale 3-D cubes are the inputs for the spectral and spatial feature extraction respectively. Secondly, by fusing strong complementary information between different layers, we form multistage spectral and spatial features by fusion primary, intermediate and advanced features of the spectral and spatial features respectively. Spectral and spatial features are extracted by spectral and spatial skipped residual blocks, which can effectively alleviate the problems of gradient degradation. Thirdly, the fusion of complementary multistage spectral and spatial features can improve the classification accuracy. Experimental results on the IN, UP and KSC datasets show the effectiveness of the proposed method using small training samples.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301282","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

Fruit Tree Disease Recognition Based on Convolutional Neural Networks 基于卷积神经网络的果树病害识别

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00048

Zechen Zheng, Shaowei Pan, Yichi Zhang

{"title":"Fruit Tree Disease Recognition Based on Convolutional Neural Networks","authors":"Zechen Zheng, Shaowei Pan, Yichi Zhang","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00048","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00048","url":null,"abstract":"In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution Neural Network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution Neural Network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution Neural Network models are used to predict test sets, and the results show that deeper Convolution Neural Networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121557516","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: 6

A Chinese Character Generation Model for Cloud Information Security 面向云信息安全的汉字生成模型

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00114

Zhang Li, Qingsheng Li, Yunqing Guan

引用次数: 0

Message from the DSCI 2019 General Chairs 2019年DSCI大会主席致辞

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/iucc/dsci/smartcns.2019.00007

引用次数: 0

An Object Detection Network for Embedded System 嵌入式系统目标检测网络

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00110

Yanpeng Sun, Chenlu Wang, L. Qu

引用次数: 3

Optimal Guidance Method for UCAV in Close Free Air Combat 无人机近距离自由空战最优制导方法

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00086

Yaofei Chen, Xiaoping Sun, Dejian Liu, S. Li

引用次数: 0

Graph Convolutional Networks with Objects for Skeleton-Based Action Recognition 基于骨架的动作识别的对象图卷积网络

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00074

Xiangbin Shi, Haowen Li, Fang Liu, Deyuan Zhang, Jing Bi, Zhaokui Li

{"title":"Graph Convolutional Networks with Objects for Skeleton-Based Action Recognition","authors":"Xiangbin Shi, Haowen Li, Fang Liu, Deyuan Zhang, Jing Bi, Zhaokui Li","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00074","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00074","url":null,"abstract":"Recently, graph convolutional neural networks has become a research hotspot for skeleton-based action recognition because of its excellent performance on graph structure data. Compared to traditional methods, it can explicitly exploit the natural connectivity among the joints and improve greater expressive power. In this paper, we propose a two-stream graph convolutional networks with objects for skeleton-based action recognition. An algorithm is designed for matching similar skeleton in adjacent frames, so that we can get the right skeletons which belong to the same person. It performs well when there are other irrelevant persons in the scene. In addition, other features are less employed except for the human joint in skeleton-based action recognition. We introduce limbs orientation information and related objects information. The related objects are treated as joint points which link with hands. The two-stream networks are built to model coordinate features and orientation features respectively, the results of two streams are fused to one. We get good results on the Kinetics dataset with our methods.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129950507","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

Research and Application of Decision Tree Algorithm in QoS-Aware Service for Fault Diagnosis 决策树算法在qos感知服务故障诊断中的研究与应用

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00142

Jin Ge, Lexi Xu, Lei Tong, Yuanbing Tian, Xuan Chen, Xiqing Liu, Shiyu Zhou, Shiyu Hu

引用次数: 3