Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258761
Yi Jiang, Hailiang Zhao, Junxuan He
Fuzzy set can be divided into single parameter or multi-parameter's ones with which to depict their fuzzy attributes. In references, it is usual defined in a single parameter universe and yet multi-parameter's ones often appears in so-called fuzzy relation. Fuzzy soft set can be used to describe a fuzzy object by aggregating fuzzy parameters in different attributes. Fuzzy set can be employed to do the same things. It can be concluded that there must be a certain relation between the two concepts. And to give the relation, integrative fuzzy set is proposed in this paper. And which can be decomposed into a fuzzy soft set under certain condition. Fuzzy soft set also can be translated into an integrative fuzzy set by some operator. Both of them can be used for decision-making. Theoretical analysis and examples show the optimal decision-making results are equivalent.
{"title":"The relation between fuzzy soft set and integrative fuzzy set","authors":"Yi Jiang, Hailiang Zhao, Junxuan He","doi":"10.1109/ISKE.2017.8258761","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258761","url":null,"abstract":"Fuzzy set can be divided into single parameter or multi-parameter's ones with which to depict their fuzzy attributes. In references, it is usual defined in a single parameter universe and yet multi-parameter's ones often appears in so-called fuzzy relation. Fuzzy soft set can be used to describe a fuzzy object by aggregating fuzzy parameters in different attributes. Fuzzy set can be employed to do the same things. It can be concluded that there must be a certain relation between the two concepts. And to give the relation, integrative fuzzy set is proposed in this paper. And which can be decomposed into a fuzzy soft set under certain condition. Fuzzy soft set also can be translated into an integrative fuzzy set by some operator. Both of them can be used for decision-making. Theoretical analysis and examples show the optimal decision-making results are equivalent.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129548047","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258796
Caijuan Zhang, Ming Jiang, Xi Luo
The system management theory is one of the many theories of management science and engineering discipline. It has important guiding significance for management practice and intelligent assessment activity. In china, the personnel examination is a kind of selective intelligence evaluation work of human resource. According to the theory of system management, the intelligent evaluation of personnel examination should follow the several principles: the content of the evaluation should be holistic; evaluation management should pay attention to the system; the evaluation process should have a dynamic controllable service system; the evaluation results should pay attention to social feedback.
{"title":"A study on the long-term mechanism of personnel testing based on the system management theory","authors":"Caijuan Zhang, Ming Jiang, Xi Luo","doi":"10.1109/ISKE.2017.8258796","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258796","url":null,"abstract":"The system management theory is one of the many theories of management science and engineering discipline. It has important guiding significance for management practice and intelligent assessment activity. In china, the personnel examination is a kind of selective intelligence evaluation work of human resource. According to the theory of system management, the intelligent evaluation of personnel examination should follow the several principles: the content of the evaluation should be holistic; evaluation management should pay attention to the system; the evaluation process should have a dynamic controllable service system; the evaluation results should pay attention to social feedback.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127390027","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258745
Wiem Chebil, L. Soualmia, Mohamed Nazih Omri, S. Darmoni
We proposed a new approach denoted SDIBN (Semantic Documents Indexing using Bayesian Networks) for indexing biomedical documents with terminologies. The main contribution of SDIBN is to use Bayesian Networks (BN) and the probability inference to perform a partial match between documents and biomedical concepts. The biomedical terminologies exploited are MeSH (Medical Subject Headings) thesaurus and SNOMED CT (Systematized Nomenclature of Medicine-Clinical Terms). Our approach exploits also UMLS (Unified Medical Language System) to filter the extracted concepts which allows to keep only relevant concepts. Our contribution also is to use DCG(Discount Cumulative Gain) measure for the first time to evaluate the indexing approaches. The experiments of SDIBN which are performed on subsets of OHSUMED and Cismef collections showed encouraging results.
{"title":"Indexing biomedical documents with Bayesian networks and terminologies","authors":"Wiem Chebil, L. Soualmia, Mohamed Nazih Omri, S. Darmoni","doi":"10.1109/ISKE.2017.8258745","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258745","url":null,"abstract":"We proposed a new approach denoted SDIBN (Semantic Documents Indexing using Bayesian Networks) for indexing biomedical documents with terminologies. The main contribution of SDIBN is to use Bayesian Networks (BN) and the probability inference to perform a partial match between documents and biomedical concepts. The biomedical terminologies exploited are MeSH (Medical Subject Headings) thesaurus and SNOMED CT (Systematized Nomenclature of Medicine-Clinical Terms). Our approach exploits also UMLS (Unified Medical Language System) to filter the extracted concepts which allows to keep only relevant concepts. Our contribution also is to use DCG(Discount Cumulative Gain) measure for the first time to evaluate the indexing approaches. The experiments of SDIBN which are performed on subsets of OHSUMED and Cismef collections showed encouraging results.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127459602","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258815
Qinzheng Zhuo, Qianmu Li, Han Yan, Yong Qi
This paper proposes a model of neural network which can be used to combine Long Short Term Memory networks (LSTM) with Deep Neural Networks (DNN). Autocorrelation coefficient is added to model to improve the accuracy of prediction model. It can provide better than the other traditional precision of the model. And after considering the autocorrelation features, the neural network of LSTM and DNN has certain advantages in the accuracy of the large granularity data sets. Several experiments were held using real-world data to show effectivity of LSTM model and accuracy were improve with autocorrelation considered.
{"title":"Long short-term memory neural network for network traffic prediction","authors":"Qinzheng Zhuo, Qianmu Li, Han Yan, Yong Qi","doi":"10.1109/ISKE.2017.8258815","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258815","url":null,"abstract":"This paper proposes a model of neural network which can be used to combine Long Short Term Memory networks (LSTM) with Deep Neural Networks (DNN). Autocorrelation coefficient is added to model to improve the accuracy of prediction model. It can provide better than the other traditional precision of the model. And after considering the autocorrelation features, the neural network of LSTM and DNN has certain advantages in the accuracy of the large granularity data sets. Several experiments were held using real-world data to show effectivity of LSTM model and accuracy were improve with autocorrelation considered.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125881382","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258777
Qingshan Chen, Yang Xu, Guanfeng Wu, Xingxing He
The modern SAT solvers usually update score of corresponding variables by increasing a bump value on each conflict. Those values are usually constant, but are independent of decision levels and conflicts. Sometimes, it is more efficient for local conflict optimization but weak in global searching. In this paper, we propose a CRB method (Conflicting Rate Branching), which is a variant of VSIDS but different implementation. The CRB updates the score which integrated with the decision level and conflicts whenever a variable is used in conflict analysis. We integrated CRB with MiniSat solvers and evaluated CRB on instances from the SAT Race 2015. Experimental results show that the proposed strategy can solve more instances than EVSIDS and improve performance for both SAT and UNSAT instances.
{"title":"Conflicting rate based branching heuristic for CDCL SAT solvers","authors":"Qingshan Chen, Yang Xu, Guanfeng Wu, Xingxing He","doi":"10.1109/ISKE.2017.8258777","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258777","url":null,"abstract":"The modern SAT solvers usually update score of corresponding variables by increasing a bump value on each conflict. Those values are usually constant, but are independent of decision levels and conflicts. Sometimes, it is more efficient for local conflict optimization but weak in global searching. In this paper, we propose a CRB method (Conflicting Rate Branching), which is a variant of VSIDS but different implementation. The CRB updates the score which integrated with the decision level and conflicts whenever a variable is used in conflict analysis. We integrated CRB with MiniSat solvers and evaluated CRB on instances from the SAT Race 2015. Experimental results show that the proposed strategy can solve more instances than EVSIDS and improve performance for both SAT and UNSAT instances.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114705639","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258768
Bin Wang, Tianrui Li, Yanyong Huang, Huaishao Luo, Dongming Guo, S. Horng
We introduce the concept of diverse activation functions, and apply them into Convolutional Auto-Encoder (CAE) to develop diverse activation CAE (DaCAE), which considerably reduces the reconstruction loss. In contrast to vanilla CAE only with activation functions of the same types, DaCAE incorporates diverse activations by considering their cooperation and location. In terms of the reconstruction capability, DaCAE significantly outperforms vanilla CAE and full connected Auto-Encoder, and we conclude rules of thumb on designing diverse activations networks. Based on the high quality of the latent bottleneck features extracted from DaCAE, we demonstrate a satisfying advantage that fuzzy rules classifier performs better than softmax layer in supervised learning. These results could be seen as new research points in the attempts at using diverse activations to train deep neural networks and combining fuzzy inference systems with deep learning.
{"title":"Diverse activation functions in deep learning","authors":"Bin Wang, Tianrui Li, Yanyong Huang, Huaishao Luo, Dongming Guo, S. Horng","doi":"10.1109/ISKE.2017.8258768","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258768","url":null,"abstract":"We introduce the concept of diverse activation functions, and apply them into Convolutional Auto-Encoder (CAE) to develop diverse activation CAE (DaCAE), which considerably reduces the reconstruction loss. In contrast to vanilla CAE only with activation functions of the same types, DaCAE incorporates diverse activations by considering their cooperation and location. In terms of the reconstruction capability, DaCAE significantly outperforms vanilla CAE and full connected Auto-Encoder, and we conclude rules of thumb on designing diverse activations networks. Based on the high quality of the latent bottleneck features extracted from DaCAE, we demonstrate a satisfying advantage that fuzzy rules classifier performs better than softmax layer in supervised learning. These results could be seen as new research points in the attempts at using diverse activations to train deep neural networks and combining fuzzy inference systems with deep learning.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114294332","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258813
Shengdong Du, Tianrui Li, Xun Gong, Yan Yang, S. Horng
Traffic flow forecasting is a key problem in the field of intelligent traffic management. In this work, we propose a hybrid deep learning framework for short-term traffic flow forecasting. It is built by the multi-layer integration deep learning architecture and jointly learns the spatial-temporal features. According to the highly nonlinear and non-stationary characteristics of traffic flow data, the framework consists of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). The former is to capture long temporal dependencies by using Long Short-Term Memory (LSTM) units and the latter is to capture the local trend features. The proposed framework is compared with other traditional shallow and deep learning models for traffic flow forecasting on PeMS datasets. The experimental results indicate that the hybrid framework is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness.
{"title":"Traffic flow forecasting based on hybrid deep learning framework","authors":"Shengdong Du, Tianrui Li, Xun Gong, Yan Yang, S. Horng","doi":"10.1109/ISKE.2017.8258813","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258813","url":null,"abstract":"Traffic flow forecasting is a key problem in the field of intelligent traffic management. In this work, we propose a hybrid deep learning framework for short-term traffic flow forecasting. It is built by the multi-layer integration deep learning architecture and jointly learns the spatial-temporal features. According to the highly nonlinear and non-stationary characteristics of traffic flow data, the framework consists of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). The former is to capture long temporal dependencies by using Long Short-Term Memory (LSTM) units and the latter is to capture the local trend features. The proposed framework is compared with other traditional shallow and deep learning models for traffic flow forecasting on PeMS datasets. The experimental results indicate that the hybrid framework is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127087474","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258807
Xinchun Ming, Fangyu Hu
Network representation learning aims at learning low-dimensional representation for each vertex in a network, which plays an important role in network analysis. Conventional shallow models often achieve sub-optimal network representation results for non-linear network characteristics. Most network representation methods merely concentrate on structure but ignore text information related to each node. In the paper, we propose a novel semi-supervised deep model for network representation learning. We adopt a random surfing model to capture the global structure and incorporate text features of vertices based on the PV-DBOW model. The joint similarity between vertices achieved by combining network structure and text information is applied as the unsupervised component. While the first-order proximity in a network is used as the supervised component. By jointly optimizing them, our method can obtain reliable low-dimensional vector representations. The experiments on two real-world networks show that our method outperforms other baselines in the task of multi-class classification of vertices.
{"title":"Semi-supervised deep network representation with text information","authors":"Xinchun Ming, Fangyu Hu","doi":"10.1109/ISKE.2017.8258807","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258807","url":null,"abstract":"Network representation learning aims at learning low-dimensional representation for each vertex in a network, which plays an important role in network analysis. Conventional shallow models often achieve sub-optimal network representation results for non-linear network characteristics. Most network representation methods merely concentrate on structure but ignore text information related to each node. In the paper, we propose a novel semi-supervised deep model for network representation learning. We adopt a random surfing model to capture the global structure and incorporate text features of vertices based on the PV-DBOW model. The joint similarity between vertices achieved by combining network structure and text information is applied as the unsupervised component. While the first-order proximity in a network is used as the supervised component. By jointly optimizing them, our method can obtain reliable low-dimensional vector representations. The experiments on two real-world networks show that our method outperforms other baselines in the task of multi-class classification of vertices.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129122592","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258754
A. Padmanabha, Abhishek M. Appaji, M. Prasad, H. Lu, Sudhanshu Joshi
Early, diagnosis is essential for diabetic patients to avoid partial or complete blindness. This work presents a new analysis method of texture features for classification of Diabetic Retinopathy (DR). The proposed method masks the blood vessels and optic disk segmented and directly extracts the textural features from the remaining retinal region. The proposed method is much simpler with comparison of the other methods that detect the defective regions first and then extract the required features for classification. The Haralick texture measures calculated are used for classification of DR. The proposed method is evaluated through a classification of DR using both Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results of SVM have a better accuracy (87.5%) over ANN (79%). The performance of the proposed method is presented also in terms of sensitivity and specificity.
{"title":"Classification of diabetic retinopathy using textural features in retinal color fundus image","authors":"A. Padmanabha, Abhishek M. Appaji, M. Prasad, H. Lu, Sudhanshu Joshi","doi":"10.1109/ISKE.2017.8258754","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258754","url":null,"abstract":"Early, diagnosis is essential for diabetic patients to avoid partial or complete blindness. This work presents a new analysis method of texture features for classification of Diabetic Retinopathy (DR). The proposed method masks the blood vessels and optic disk segmented and directly extracts the textural features from the remaining retinal region. The proposed method is much simpler with comparison of the other methods that detect the defective regions first and then extract the required features for classification. The Haralick texture measures calculated are used for classification of DR. The proposed method is evaluated through a classification of DR using both Support Vector Machine (SVM) and Artificial Neural Network (ANN). The results of SVM have a better accuracy (87.5%) over ANN (79%). The performance of the proposed method is presented also in terms of sensitivity and specificity.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132491726","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}
Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258789
Xinwen Hu, Zhuang Yi, Zining Cao, Tong Ye, Mi Li
With the rapid development of embedded software, embedded software has a highly security demand, such as confidentiality and integrity. UML provides the foundation for the construction and analysis of embedded software, but it cannot provide accurate semantics for the validation of embedded software security properties. Using the formal method based on Z language to model the security properties of embedded software, can provide the rigorous semantics for the security properties of embedded software, which can help to discover its early design errors and reduce the cost of testing and maintenance. Developing the model transformation tool of UML model to Z model, which can avoid repetitive modeling of the manual establishment of Z model, reduce the possibility of introducing artificial logic error in the model. Verifying the correctness of the confidentiality and integrity model by using the formal verification tool Z/EVES, which can make the embedded software satisfy the user's security requirement. This paper construct the static structure model and dynamic behavior model of embedded software confidentiality and integrity modeling based on Z at first; and then establish the model transformation rules of UML modeling elements to Z modeling elements, which is designed and implemented based on the XSLT technology; finally, the formal model is validated by using the verification tool Z/EVES through the example of a bicycle parking embedded software, and the correctness of the embedded software security model presented in this paper is explained.
{"title":"Modeling and validation for embedded software confidentiality and integrity","authors":"Xinwen Hu, Zhuang Yi, Zining Cao, Tong Ye, Mi Li","doi":"10.1109/ISKE.2017.8258789","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258789","url":null,"abstract":"With the rapid development of embedded software, embedded software has a highly security demand, such as confidentiality and integrity. UML provides the foundation for the construction and analysis of embedded software, but it cannot provide accurate semantics for the validation of embedded software security properties. Using the formal method based on Z language to model the security properties of embedded software, can provide the rigorous semantics for the security properties of embedded software, which can help to discover its early design errors and reduce the cost of testing and maintenance. Developing the model transformation tool of UML model to Z model, which can avoid repetitive modeling of the manual establishment of Z model, reduce the possibility of introducing artificial logic error in the model. Verifying the correctness of the confidentiality and integrity model by using the formal verification tool Z/EVES, which can make the embedded software satisfy the user's security requirement. This paper construct the static structure model and dynamic behavior model of embedded software confidentiality and integrity modeling based on Z at first; and then establish the model transformation rules of UML modeling elements to Z modeling elements, which is designed and implemented based on the XSLT technology; finally, the formal model is validated by using the verification tool Z/EVES through the example of a bicycle parking embedded software, and the correctness of the embedded software security model presented in this paper is explained.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130459501","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}
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