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

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Saliency Region Detection in Complex Scenes Based on Multi-scale Cascaded Attention 基于多尺度级联注意的复杂场景显著区域检测
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754628
Yifeng Wang, Zhen Huo, Aoli Liu, Lin Zhao, Di Wang
{"title":"Saliency Region Detection in Complex Scenes Based on Multi-scale Cascaded Attention","authors":"Yifeng Wang, Zhen Huo, Aoli Liu, Lin Zhao, Di Wang","doi":"10.1109/CCIS53392.2021.9754628","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754628","url":null,"abstract":"This paper proposes a saliency detection method based on multi-scale cascade attention mechanism. It utilizes both channel and spatial weight attention mechanism to effectively learn the salient regions. By generating multi-scale intermediate feature maps, the shallow features are divided into categories of foreground and background. Then, the channel weights are calculated by using the foreground and background feature distribution, and the spatial weights are computed by using the predicted feature map, so that the network is more focused on salient regions and suppresses the interference of background regions. Experimental results show that the model can reliably and accurately detect salient targets and delivers better performance.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"77 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":"134099198","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 China’s Primary Industry: Evidence From Regional Analysis Based on SVM and Moran’s Index 中国第一产业研究:基于支持向量机和莫兰指数的区域分析证据
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754653
Shiyu Jiang, Junjie Jia, Yi Yuan, Yuxiong Wu, Tianqi Wang
{"title":"Research on China’s Primary Industry: Evidence From Regional Analysis Based on SVM and Moran’s Index","authors":"Shiyu Jiang, Junjie Jia, Yi Yuan, Yuxiong Wu, Tianqi Wang","doi":"10.1109/CCIS53392.2021.9754653","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754653","url":null,"abstract":"With advanced technology and efficient policy management in China’s primary industry, productivity has increased significantly. This article aims to use machine learning and Moran’s I to analyze the current situation of China’s primary industry from a regional perspective. Principal component analysis and Lagrange polynomial interpolation are used for data pre-processing. Classification result from the support vector machine reveals that there exist boundaries between each region based on the features of the primary industry. Our results show that fishery and forestry show positive spatial correlations in the Moran’s I scatter diagram, while animal husbandry and farming show negative spatial correlations, and regional agriculture development can improve China’s primary industry in the long run.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"8 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":"134031181","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
An Enhanced Method for Entity Trigger Named Entity Recognition Based on POS Tag Embedding 一种基于POS标签嵌入的实体触发器命名实体识别方法
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754614
Liwen Ma, Weifeng Liu
{"title":"An Enhanced Method for Entity Trigger Named Entity Recognition Based on POS Tag Embedding","authors":"Liwen Ma, Weifeng Liu","doi":"10.1109/CCIS53392.2021.9754614","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754614","url":null,"abstract":"In the task of Named Entity Recognition, plenty of human annotations are required in deed. However, a large number of annotations in articles are time-consuming and labor-intensive. In order to solve these problems above, an enhanced method for entity trigger named entity recognition based on POS tag embedding is proposed in this paper. Firstly, by employing lexical annotation tool, it can not only obtain the POS tag of the word, but also connect the word embedding with the POS tag embedding. Secondly, train the attention representation of sentences and triggers, and learn the semantic relationship between entity triggers and sentences based on the attention model. Lastly, the model is instructed with a new sentence attention representation as the input of the CRF (Conditional Random Fields) network. The simulation experiments explicate that the proposed can expand the semantic information of words, so as to improve the recognition ability of entities in a relatively small amount of labeled training data.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"54 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":"127739172","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
Urban Fatigue Driving Prediction With Federated Learning 基于联邦学习的城市疲劳驾驶预测
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754649
Yongqiang Ma, Yingxia Shao, Zhe Xue, Ziqiang Yu
{"title":"Urban Fatigue Driving Prediction With Federated Learning","authors":"Yongqiang Ma, Yingxia Shao, Zhe Xue, Ziqiang Yu","doi":"10.1109/CCIS53392.2021.9754649","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754649","url":null,"abstract":"Fatigue driving results in a great damage to road safety. Therefore, monitoring the fatigue driving is essential to protect the traffic participants. In reality, fatigue driving behavior on highways is simply defined by driving time, while the measurement of fatigue driving in cities is not clear. It is difficult to monitor fatigue driving in urban areas in real time. In this paper, we propose a clear criterion for determining urban fatigue driving behavior. The criterion integrates the driver’s current driving status and objective factors on the road. To process a large number of continuous vehicle trajectories in real time, we propose a distributed paradigm based on a cluster of servers. In addition, we use federal learning in our experiments for fatigue driving prediction while protecting user privacy. Finally, we confirm the performance of our proposal in real data published by DiDi.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"17 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":"127887900","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
Generalized Zero-Shot Text Classification via Inter-Class Relationship 基于类间关系的广义零射击文本分类
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754674
Yiwen Zhang, Caixia Yuan, Xiaojie Wang
{"title":"Generalized Zero-Shot Text Classification via Inter-Class Relationship","authors":"Yiwen Zhang, Caixia Yuan, Xiaojie Wang","doi":"10.1109/CCIS53392.2021.9754674","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754674","url":null,"abstract":"Generalized zero-shot text classification (GZSTC) aims to classify textual instances from both previously seen classes and novel classes which are totally unseen during training. However, previous supervised metric learning methods cause severe domain bias problem. To tackle this problem, we propose a GZSTC method to reduce the gap from the fully trained seen domain and unaware unseen domain using relationship. Concretely, the proposed model gains beneficial experiences through multiple mimic GZSTC tasks during training. In every mimic GZSTC task, the model explicitly takes advantage of the relationship between the mimetic seen classes and unseen classes, which generalizes well on the real testing unseen classes. We extensively evaluate the performance on two GZSTC datasets. The results show that our method can alleviate the domain bias problem and outperform the state-of-the-arts by a large margin.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 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":"128995310","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
Dense Crowd Counting Based on ResNet 基于ResNet的密集人群计数
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754656
Yang Wang, Shouqiang Liu, Mingyue Jiang, Liming Chen, Jianming Zeng, Wanggan Yang
{"title":"Dense Crowd Counting Based on ResNet","authors":"Yang Wang, Shouqiang Liu, Mingyue Jiang, Liming Chen, Jianming Zeng, Wanggan Yang","doi":"10.1109/CCIS53392.2021.9754656","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754656","url":null,"abstract":"High-density crowd gathering is very prone to various accidents, so real-time monitoring and analysis of dense crowds to prevent accidents is of great practical significance. In this paper, the density crowd detection counting is implemented based on the fine-tuned optimization of the ResNet model, and the evaluation and warning function is added. The average absolute error of the comprehensive performance index obtained after the final training model test reaches 7.9, that is, each prediction result is controlled within $pm {mathrm {7.9}}$ of the correct value, which proves that the model can effectively count high-density crowds and give evaluation and warning results.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"335 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":"116908506","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
A Multiobjective Multitask Evolutionary Algorithm Based on Decomposition and Multivariate Gaussian Distribution 基于分解和多元高斯分布的多目标多任务进化算法
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754601
Zhongjian Wu, Wu Lin, Huimei Tang, Qiuzhen Lin
{"title":"A Multiobjective Multitask Evolutionary Algorithm Based on Decomposition and Multivariate Gaussian Distribution","authors":"Zhongjian Wu, Wu Lin, Huimei Tang, Qiuzhen Lin","doi":"10.1109/CCIS53392.2021.9754601","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754601","url":null,"abstract":"Evolutionary multiobjective multitask optimization (EMTO) has attracted widespread attention in recent years, which solves multiple tasks simultaneously in a single population. How to extract effective knowledge and recognize valuable transferred solutions is the key to enhance the performance of EMTO. However, few research studies consider these two issues at the same time. To fill this research gap, we propose a novel multiobjective multitask evolutionary algorithm based on decomposition and multivariate Gaussian distribution, called MTEA-DMG, in which a candidate transferred solution set is generated using resource allocation according to the priority of subproblems to multiple tasks. Then, one most valuable knowledge transfer carrier is selected by online statistical estimation of distribution density. The experimental results on a set of benchmark problems with different degrees of similarity show that MTEA-DMG is superior to other state-of-the-art EMTO algorithms.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"125 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":"117339667","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
An Extraction Model Based on RoBERTa-BiLSTM-CRF for Chinese Financial Event 基于RoBERTa-BiLSTM-CRF的中国金融事件提取模型
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754636
Dagao Duan, Wenwen Liu, Zhongming Han
{"title":"An Extraction Model Based on RoBERTa-BiLSTM-CRF for Chinese Financial Event","authors":"Dagao Duan, Wenwen Liu, Zhongming Han","doi":"10.1109/CCIS53392.2021.9754636","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754636","url":null,"abstract":"Event Extraction is one of the important research tasks of Information Extraction in Natural Language Processing. It tries to extract information from a large amount of chaotic data and presents information in a structural form. The existing Chinese event extraction methods have the inaccuracies of Chinese word segmentation, which will directly lead to incorrect identification of Chinese financial entities, affecting the accuracy of event element extraction. This paper takes Chinese financial event extraction as a sequence labeling task. It proposes an event extraction model based on PreTraining Model, Bidirectional Long-Short Term Memory Network, and Conditional Random Field. Additionally, this paper constructs the Chinese financial event dataset FinEE. At the same time, financial events are filtered from public dataset DuEE to construct dataset DuEE_Fin. As the experimental results show that the proposed Chinese financial event extraction model Roberta-BilSTM-CRF has improved accuracy, recall rate, and F1 score compared with existing models on FinEE and DuEE_Fin datasets.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"35 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":"127462831","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
PowerDet: Efficient and Lightweight Object Detection for Electric Power Open Scenes PowerDet:用于电力开放场景的高效轻量级目标检测
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754678
Shigeng Wang, Zhonghong Ou, Meina Song
{"title":"PowerDet: Efficient and Lightweight Object Detection for Electric Power Open Scenes","authors":"Shigeng Wang, Zhonghong Ou, Meina Song","doi":"10.1109/CCIS53392.2021.9754678","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754678","url":null,"abstract":"In recent years, with the expansion of the electric network, maintenance cost of electric power transmission and transformer substation equipment has become increasingly greater. Moreover, it requires a lot of manpower as well. At present, there are certain schemes which leverage artificial intelligence techniques to detect equipment flaws or errors automatically. Nevertheless, there are problems with the schemes in open electric power scenes, e.g., only able to detect single category, low detection accuracy of multi-scale objects, and difficulty in deploying models on mobile devices. To address the challenges mentioned above, we propose an object detection model, named PowerDet. It is able to detect 9 different types of power facilities efficiently with low cost. To verify the effectiveness of PowerDet, we collect an open scene facility entity dataset and conduct a series of experiments. Experimental results demonstrate that PowerDet achieves 86.8% AP50 on the dataset, which outperforms the state-of-the-art. The lightweight version of PowerDet, i.e., PowerDet-Lite, can achieve real-time inference on mainstream mobile devices.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"64 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":"121549375","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
Construction of Intelligent Management System Model Based on Multi-value Chain Collaboration Data Space 基于多价值链协同数据空间的智能管理系统模型构建
2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754626
Jingdong Wang, Qi Mu, Xiaolong Yang, Jieping Han
{"title":"Construction of Intelligent Management System Model Based on Multi-value Chain Collaboration Data Space","authors":"Jingdong Wang, Qi Mu, Xiaolong Yang, Jieping Han","doi":"10.1109/CCIS53392.2021.9754626","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754626","url":null,"abstract":"To solve the problems of data collaboration, application-driven, intelligent data center construction and high latitude data aggregation in data space based on multi-value chain collaborative, so as to realize the functions of internal program monitoring, underlying service application interaction, independent and coordinated management, complete transaction processing, concurrency control, data recovery, engine data integration functions like data integration, data update, data monitoring, as well as the functions of management engine center and data object administration, data set management, data intelligent service within the data space management engine. In this paper, by studying the intelligent screening model of data center which integrates blockchain technology and data mapping theory, we adopt the design method of distributed multi-agent intelligent system build the data space management system model.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 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":"131852401","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
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