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

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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
{"title":"Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance","authors":"Zhendong Liu, Yurong Yang, Dongyan Li, Xiaofeng Li, Xinrong Lv, Xi Chen","doi":"10.1109/CIS52066.2020.00039","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00039","url":null,"abstract":"It is not perfect in view of the fact that the information guidance system of parking spaces in large and medium-sized parking lots at present, it is difficult to find a empty parking spaces in parking lots. One of the problems is large amount of calculation in traditional Dijkstra algorithm. In this paper, the improved Dijkstra algorithm is presented and optimized to find the best parking path with the purpose of looking for the nearest free parking space based on the layout model in parking lot parking guidance. The experiments show that it can find the optimal parking space and the optimal parking path by the improved Dijkstra algorithm, and improve the parking efficiency.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":"37 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":"129895816","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: 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
{"title":"The security and privacy of blockchain-enabled EMR storage management scheme","authors":"Guangfu Wu, Yingjun Wang","doi":"10.1109/CIS52066.2020.00067","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00067","url":null,"abstract":"The Electronic Medical Record (EMR) management system currently used by healthcare institutions has security concerns for data sharing and access control. To address these issues, we have proposed a security and privacy management scheme for blockchain-based EMR called GAC-PSPR (Granular Access Control supporting Privilege Separation and Property Revocation). The GAC-PSPR scheme uses a dual chain structure of private blockchain and consortium blockchain. The technology of permission separation and property revocation ensures that data cannot be arbitrarily modified, thus solving the security problem of access control. Protect data privacy by protecting against quantum attacks with Number Theory Research Unit (NTRU) encryption. Besides, we have proposed a ring signature solution based on the lattice to address the issue of too large signatures and the key lengths that secures the sharing of EMR data. We provide extensive security analysis and performance analysis to demonstrate that the GAC-PSPR scheme is effective and secure.","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":"129535969","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
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
{"title":"A Novel Hybrid Tabu Search Algorithm With Binary Differential Operator for Knapsack Problems","authors":"Jun Hu, Qingfu Zhang, Yongkang Jiao","doi":"10.1109/CIS52066.2020.00062","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00062","url":null,"abstract":"The knapsack problem (KP) is a typical NP- complete problem. In this paper, a novel tabu search (TS) algorithm with the elite set is presented to solve the KPs. The algorithm is a hybrid TS with a binary differential operator (BDO), which is denoted by TSBDO. The binary differential operator is employed to mutate individuals which are generated randomly from the list EliteSol. To validate the proposed TSBDO algorithm, 15 high-dimensional KP examples are randomly generated. Simulated results indicate that the TSBDO algorithm can yield better solutions than some existing algorithms such as GMBO and BABC-DE.","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":"129556052","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
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
{"title":"The 5G NOMA networks planning based on the multi-objective evolutionary algorithm","authors":"Jinli Zhang, Jie Li, Mingjiao Cai, Dining Li, Qiang Wang","doi":"10.1109/CIS52066.2020.00021","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00021","url":null,"abstract":"This paper studies the base station (BS) planning problem of 5G non-orthogonal multiple access (NOMA) heterogeneous network, and considers the goal of maximizing network throughput and minimizing the construction cost. Due to the conflict between the two objectives, the BS planning problem of 5G NOMA heterogeneous network is modeled as a multiobjective integer optimization problem. It is very difficult to solve the multi-objective integer optimization problem by using traditional optimization methods. Therefore, we adopt the multiobjective evolutionary algorithm to solve the problem. Simulation results show that the proposed multi-objective optimization scheme for 5G heterogeneous network planning can effectively improve the network transmission performance and reduce the network construction cost.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":"41 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":"130008237","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
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
{"title":"Reverse logistics network design model for used power battery under the third-party recovery mode","authors":"Qian Guan, Yuxiang Yang","doi":"10.1109/CIS52066.2020.00069","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00069","url":null,"abstract":"In order to effectively solve the problem of location optimization of power battery recycling enterprises, and promote the long-term and effective development of the industry, in this paper, the power battery is taken as the research object, and considering the differences in the use of new energy vehicles, there are two ways to recycle used power batteries, namely “vehicle scrap” and “old-for-new”. Based on the third-party recycling mode of power batteries, a reverse logistics network consisting of consumers, third-party recovery centers and third-party processing points is constructed. Considering the impact of power batteries on residents and environment in the process of recovery, a dual objective mixed integer linear programming model is established. We use genetic algorithm to solve the model, and analyze the impact of the adjustment coefficient of social negative effect on the facility locations and the total cost.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":"224 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":"121095930","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
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
{"title":"SLFB-CNN: An interpretable neural network privacy protection framework","authors":"De Li, Yuhang Hu, Jinyan Wang","doi":"10.1109/CIS52066.2020.00070","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00070","url":null,"abstract":"The feedforward-designed convolutional neural network (FF-CNN) method was recently proposed by Kuo et al. It has strong interpretability and low training complexity. In this paper, we have proposed two improvements (1) We merge two algorithms Layer-wise Relevance Propagation (LRP) and FF-CNN to build an interpretable neural network framework called LFB-CNN. The back-propagation (BP) algorithm is used to train the fully connected layer of FF-CNN. Meanwhile, the LRP algorithm is used to decompose and calculate the correlation between the input and output of the fully connected layer, and further improve the model performance without reducing the interpretability. (2) We conducted a privacy analysis on the LFB-CNN framework. Once the parameters of the framework are disclosed, the privacy of the data provider will be leaked. Therefore, we use differential privacy to propose a secure LFB-CNN (SLFB-CNN) algorithm. At last, we verified the effectiveness of our proposed method on the MNIST, Fashion-MNIST and CIFAR-10 datasets.","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":"130187041","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
Chinese Coreference Resolution via Bidirectional LSTMs using Word and Token Level Representations 基于词和令牌级表示的双向lstm中文互指解析
2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00024
Kun Ming
{"title":"Chinese Coreference Resolution via Bidirectional LSTMs using Word and Token Level Representations","authors":"Kun Ming","doi":"10.1109/CIS52066.2020.00024","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00024","url":null,"abstract":"Coreference resolution is an important task in the field of natural language processing. Most existing methods usually utilize word-level representations, ignoring massive information from the texts. To address this issue, we investigate how to improve Chinese coreference resolution by using span-level semantic representations. Specifically, we propose a model which acquires word and character representations through pre-trained Skip-Gram embeddings and pre-trained BERT, then explicitly leverages span-level information by performing bidirectional LSTMs among above representations. Experiments on CoNLL-2012 shared task have demonstrated that the proposed model achieves 62.95% F1-score, outperforming our baseline methods.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":"120 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":"123238837","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
Domain-Specific Chinese Transformer-XL Language Model with Part-of-Speech Information 具有词性信息的领域特定中文转换- xl语言模型
2020 16th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2020-11-01 DOI: 10.1109/CIS52066.2020.00026
Huaichang Qu, Haifeng Zhao, Xin Wang
{"title":"Domain-Specific Chinese Transformer-XL Language Model with Part-of-Speech Information","authors":"Huaichang Qu, Haifeng Zhao, Xin Wang","doi":"10.1109/CIS52066.2020.00026","DOIUrl":"https://doi.org/10.1109/CIS52066.2020.00026","url":null,"abstract":"Language models hope to use more context to predict the next word. However, not all words in the context are related to the next word and are effective for prediction. The language model based on the attention mechanism can select more useful word representations from the context and efficiently use long-term historical information. In this paper, we will apply Transformer-XL language model to Chinese automatic speech recognition in a specific domain. We add part-of-speech information for domain adaptation. First, we construct a Chinese corpus dataset in a specific domain. And by collecting common vocabulary and extracting new words in the domain, we also construct a domain vocabulary. Then, the Chinese word boundary information is added to the Transformer-XL language model to make the model can better adapt to the characteristics of the domain. Finally, our experimental results show that the method is effective on the dataset we provided. It can further reduce the Character Error Rate (CER) in speech recognition.","PeriodicalId":106959,"journal":{"name":"2020 16th International Conference on Computational Intelligence and Security (CIS)","volume":"698 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":"122969803","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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