2020 16th International Conference on Computational Intelligence and Security (CIS)最新文献_第2页

Research on the Dimensions and Influencing Factors of Enterprise Humanism Management — An Empirical Study Based on the Questionnaire of Dongguan Enterprises 企业人本管理的维度及影响因素研究——基于东莞企业问卷调查的实证研究

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00044

Cheng-Jun Wang, Hanlei Xu, Mingkun Jiang

{"title":"Research on the Dimensions and Influencing Factors of Enterprise Humanism Management — An Empirical Study Based on the Questionnaire of Dongguan Enterprises","authors":"Cheng-Jun Wang, Hanlei Xu, Mingkun Jiang","doi":"10.1109/CIS52066.2020.00044","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00044","url":null,"abstract":"Humanism management is manifested as a multi-dimensional structural system, which embodies the comprehensive attributes of humanism management. Through the development of enterprise humanism management questionnaires, 1,530 corporate survey data in Dongguan were collected. The result of exploratory factor analysis shows that enterprise humanism management is composed of five dimensions: management responsibility, employee motivation, staff promotion, interpersonal relationship and corporate culture. The result of confirmatory factor analysis shows the fitting effect of the five-factor model of humanism management of Dongguan enterprises better. The results of the analysis of variance show that different types of enterprises have different influences on the four dimensions of humanism management: employee motivation, staff promotion, interpersonal relationships and corporate culture. The result of the mean comparison shows that the employee's identity in the enterprise has a differential impact on the various dimensions of humanistic management.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121972810","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

Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance 停车场引导布局优化算法的设计与实现

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00039

Zhendong Liu, Yurong Yang, Dongyan Li, Xiaofeng Li, Xinrong Lv, Xi Chen

引用次数: 4

The security and privacy of blockchain-enabled EMR storage management scheme 支持区块链的EMR存储管理方案的安全性和隐私性

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00067

Guangfu Wu, Yingjun Wang

引用次数: 2

A Novel Hybrid Tabu Search Algorithm With Binary Differential Operator for Knapsack Problems 背包问题的二元微分算子混合禁忌搜索算法

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00062

Jun Hu, Qingfu Zhang, Yongkang Jiao

引用次数: 0

The 5G NOMA networks planning based on the multi-objective evolutionary algorithm 基于多目标进化算法的5G NOMA网络规划

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00021

Jinli Zhang, Jie Li, Mingjiao Cai, Dining Li, Qiang Wang

引用次数: 3

Improved Spatio-Temporal Convolutional Neural Networks for Traffic Police Gestures Recognition 基于改进时空卷积神经网络的交警手势识别

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00032

Zhixuan Wu, Nan Ma, Y. Cheung, Jiahong Li, Qin He, Yongqiang Yao, Guoping Zhang

{"title":"Improved Spatio-Temporal Convolutional Neural Networks for Traffic Police Gestures Recognition","authors":"Zhixuan Wu, Nan Ma, Y. Cheung, Jiahong Li, Qin He, Yongqiang Yao, Guoping Zhang","doi":"10.1109/CIS52066.2020.00032","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00032","url":null,"abstract":"In the era of artificial intelligence, human action recognition is a hot spot in the field of vision research, which makes the interaction between human and machine possible. Many intelligent applications benefit from human action recognition. Traditional traffic police gesture recognition methods often ignore the spatial and temporal information, so its timeliness in human computer interaction is limited. We propose a method that is Spatio-Temporal Convolutional Neural Networks (ST-CNN) which can detect and identify traffic police gestures. The method can identify traffic police gestures by using the correlation between spatial and temporal. Specifically, we use the convolutional neural network for feature extraction by taking into account both the spatial and temporal characteristics of the human actions. After the extraction of spatial and temporal features, the improved LSTM network can be used to effectively fuse, classify and recognize various features, so as to achieve the goal of human action recognition. We can make full use of the spatial and temporal information of the video and select effective features to reduce the computational load of the network. A large number of experiments on the Chinese traffic police gesture dataset show that our method is superior.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130526391","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

Reverse logistics network design model for used power battery under the third-party recovery mode 第三方回收模式下废旧动力电池逆向物流网络设计模型

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00069

Qian Guan, Yuxiang Yang

引用次数: 1

SLFB-CNN: An interpretable neural network privacy protection framework SLFB-CNN:一个可解释的神经网络隐私保护框架

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00070

De Li, Yuhang Hu, Jinyan Wang

引用次数: 1

Review of Image Classification Method Based on Deep Transfer Learning 基于深度迁移学习的图像分类方法综述

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00031

Chuanzi Li, Jining Feng, Li Hu, Junhong Li, Haibin Ma

{"title":"Review of Image Classification Method Based on Deep Transfer Learning","authors":"Chuanzi Li, Jining Feng, Li Hu, Junhong Li, Haibin Ma","doi":"10.1109/CIS52066.2020.00031","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00031","url":null,"abstract":"With the continuous development of deep learning technology, neural networks such as convolutional neural network (CNN) have shown good performance in many fields, such as image processing. Meanwhile, the relevant algorithm has made great progress. But the experiment results show that the deeper the network layers, the more the number of parameters that need to be trained in neural network, and the massive computing resources will be consumed to reconstruct and train the deep convolutional neural network (DCNN) model. These parameters often need to be trained in large dataset. But in many practical applications, the effective sample dataset that can be collected are usually small and lack of annotated samples. It is a pity that models that perform well on large datasets often have overfitting problems when applied to small datasets. And transfer learning can recognize and apply knowledge and skills learned in previous domains/tasks to novel domains/tasks. Combining deep convolutional neural network learning with transfer learning can make full use of existing models with good performance to solve problems in new fields, so has received considerable attentions due to its high research value and wide application prospect. This paper focuses on the combination of CNN and transfer learning, analyzes their characteristics, summarizes the relevant models, methods and applications, so as to promote their effective fusion in image classification.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131782748","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

Connected Domain Algorithm Based on Asymmetric Square NAM 基于非对称方形NAM的连通域算法

2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00085

Cen Caichun, He Jie, Guo Hui

引用次数: 0