2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第2页

A Novel Topic Extraction Model for Science and Technology Demand Data 一种新的科技需求数据主题抽取模型

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754535

Haiyan Cui, Zhe Xue, Junping Du, Xin Xu, Junqing Xi

引用次数: 0

A Key-phrase Extraction Method Based on Multi-size Convolution Windows for Scientific Literatures 基于多尺度卷积窗的科学文献关键词提取方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754645

Yuhong Zhang, Yuxin Xie, Peipei Li, Xuegang Hu

引用次数: 0

ReCUS: Reconvolution and Upsampling Network for Object Detection ReCUS:用于目标检测的卷积和上采样网络

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754606

Fudong Li, Dongyang Gao, Yuequan Yang, Zhiqiang Cao, Wei Wang

引用次数: 0

A Review on Indicator-based Multi-objective Evolutionary Algorithms 基于指标的多目标进化算法研究进展

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754639

Zilu Huang, Feng Wang

引用次数: 1

CESMP: Chinese-English Segment-aligned Multi-field Patent Data CESMP:中英文分段对齐的多领域专利数据

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754662

Wuying Liu, Lin Wang, Fumao Hu

{"title":"CESMP: Chinese-English Segment-aligned Multi-field Patent Data","authors":"Wuying Liu, Lin Wang, Fumao Hu","doi":"10.1109/CCIS53392.2021.9754662","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754662","url":null,"abstract":"Patent data from various countries in the world implies the essence of scientific discovery and technological innovation of all human beings, but language differences have become a huge obstacle to patent data retrieval and communication. We hope to build a bridge from Chinese to English in the patent domain, so that English speakers can make better use of Chinese patent data. With the help of natural language processing technologies such as optical character recognition, Chinese text processing, machine translation and English text processing, we construct digital Chinese-English segment-aligned multi-field patent (CESMP) data from scanned Chinese patents. The current CESMP data consists of 610,310 patent documents in XML format. Each patent document contains six required fields (date, publication, ipc, title, abstract, and claim) and four optional fields (cpc, wipo, originalapplicant, and currentowner), among which the wipo, title, abstract, and claim fields are aligned with Chinese and English segments. Supported by well-structured bilingual patent data, on the one hand, the resource construction algorithms can efficiently build a bilingual patent dictionary and a parallel patent segment bank; on the other hand, the deep natural language processing algorithms can be effectively implemented into many practical intelligent applications such as cross-language patent retrieval, patent spam filtering, patent network analysis, patent machine translation, etc.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"24 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120854910","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

Manufacturing Multi-value Chain Product Demand Forecasting Based on KPCA-PSO-SVM : -Taking the Demand of the Ring Network Cabinet as an Example 基于KPCA-PSO-SVM的制造业多价值链产品需求预测——以环网柜需求为例

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754531

D. Niu, Min Yu, Ruoyun Du, Lijie Sun, Xiaomin Xu, Huanfen Zhang

{"title":"Manufacturing Multi-value Chain Product Demand Forecasting Based on KPCA-PSO-SVM : -Taking the Demand of the Ring Network Cabinet as an Example","authors":"D. Niu, Min Yu, Ruoyun Du, Lijie Sun, Xiaomin Xu, Huanfen Zhang","doi":"10.1109/CCIS53392.2021.9754531","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754531","url":null,"abstract":"With the growth of social demand and economy, the ring network cabinet (RNC) has become the key link of the last kilometer of distribution. Forecasting the demand of RNC from the perspective of enterprises can provide suggestions for the production plan and business decisions of enterprises. Starting from the manufacturing multi-value chain, this paper fully taps the effective information of supply chain, production chain and marketing chain as the input variables of demand forecasting. Considering too many factors involved, this paper uses KPCA to reduce the data dimension, and uses the support vector machine optimized by particle swarm optimization (PSO) to predict the demand of A ring-main unit manufacturing enterprises. In order to verify the validity of the model, ARMA, SVM and PSO-SVM are selected to compare the models. The results show that the KPCA-PSO-SVM adopted in this paper has higher prediction accuracy and efficiency. According to the prediction results, this paper gives corresponding decision-making suggestions.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"15 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":"125945762","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

Multiple Adaptive Strategies-based Rat Swarm Optimizer 基于多自适应策略的鼠群优化算法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754632

Ziyue Xu, Xiaodan Liang, Maowei He, Hanning Chen

引用次数: 3

Hyper-parameter Tuning of Federated Learning Based on Particle Swarm Optimization 基于粒子群优化的联邦学习超参数整定

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754676

Zhiyuan Li, Hao Li, Mingyang Zhang

{"title":"Hyper-parameter Tuning of Federated Learning Based on Particle Swarm Optimization","authors":"Zhiyuan Li, Hao Li, Mingyang Zhang","doi":"10.1109/CCIS53392.2021.9754676","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754676","url":null,"abstract":"The learning task of federated learning (FL) is solved by a federation of a center server and individual clients. Contrary to traditional deep learning models, federated learning consists of two parts, the global model and individual models. However, since the process is carried out by two parts of equal importance, it is ideal to deal with both parts simultaneously. To achieve superior performance, federated learning requires carefully selected hyper-parameters, which has more hyper-parameters than those of traditional deep learning models. To solve this tuning problem, we propose a method using particle swarm optimization (PSO) algorithm to tune the hyperparameters of federated learning. PSO algorithm is a gradient-free, stochastic optimization method which is better than grid search method when it comes to large search space. It helps locate the optimal combination of multiple parameters. In this article, we focus on applying PSO method on tuning the hyper-parameters of FL models, and prove that it is an efficient way to acquire satisfactory results. Experiments on MNIST dataset with convolution neural networks have proved the superiority of the proposed method.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"45 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":"127429367","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: 3

Target-based Sentiment Analysis in Finance with Domain Knowledge 基于领域知识的金融目标情感分析

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754611

Caihua Yang, Jianzhu Bao, Xiaoqi Yu, Ruifeng Xu

引用次数: 0

Improvement of YOLOV5 Model Based on the Structure of Multiscale Domain Adaptive Network for Crowdscape 基于多尺度域自适应网络结构的人群景观YOLOV5模型改进

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754600

Xiangping Zhang, H. Fan, Hongjin Zhu, Xianzhen Huang, Tao Wu, Hong-bin Zhou

引用次数: 6