Pub Date : 2017-11-01DOI: 10.1109/ICSESS.2017.8343056
Weidong Ma, Zhiying Wang
This paper introduces a requirement description language called CosRDL for modeling and analyzing of the time series embedded control systems. The behavior of control system is composed with a set of operations for process the specific events. Designers extract the features of environment and system state to build the CosRDL model. The requirement model and system model of the CosRDL are proposed in the paper. The requirement model of CosRDL is built by directed acyclic graph (DAG) to describe the system behavior. The system model is translated from the requirement model by five tuple elements. Meanwhile, a case study is presented to illustrate our approach to requirement modeling in the development of event-driven control systems.
{"title":"A control-oriented system requirement description language based on directed acyclic graph","authors":"Weidong Ma, Zhiying Wang","doi":"10.1109/ICSESS.2017.8343056","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8343056","url":null,"abstract":"This paper introduces a requirement description language called CosRDL for modeling and analyzing of the time series embedded control systems. The behavior of control system is composed with a set of operations for process the specific events. Designers extract the features of environment and system state to build the CosRDL model. The requirement model and system model of the CosRDL are proposed in the paper. The requirement model of CosRDL is built by directed acyclic graph (DAG) to describe the system behavior. The system model is translated from the requirement model by five tuple elements. Meanwhile, a case study is presented to illustrate our approach to requirement modeling in the development of event-driven control systems.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"371 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":"131331536","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/ICSESS.2017.8342896
Juan Fang, Mengxuan Wang, Mingxia Gao, Jianhua Wei
Heterogeneous multi-core platforms are increasingly prevalent due to perceived superior performance over homogeneous systems. In order to maximize performance, each task needs to be mapped to the most appropriate processor. This paper implements a task allocation method based on genetic algorithm. The genetic algorithm is used to sample the application load feature in the task scheduling time slice, and its complicated iterative process is distributed to the following multiple scheduling sampling periods to select the core which complies with its calculation characteristic for each task. Experimental results demonstrate that the algorithm can effectively improve the system performance, compared with the built-in task scheduling mechanism of Linux 2.6 kernel.
{"title":"A task allocation method for heterogeneous multi-core system based on genetic algorithm","authors":"Juan Fang, Mengxuan Wang, Mingxia Gao, Jianhua Wei","doi":"10.1109/ICSESS.2017.8342896","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342896","url":null,"abstract":"Heterogeneous multi-core platforms are increasingly prevalent due to perceived superior performance over homogeneous systems. In order to maximize performance, each task needs to be mapped to the most appropriate processor. This paper implements a task allocation method based on genetic algorithm. The genetic algorithm is used to sample the application load feature in the task scheduling time slice, and its complicated iterative process is distributed to the following multiple scheduling sampling periods to select the core which complies with its calculation characteristic for each task. Experimental results demonstrate that the algorithm can effectively improve the system performance, compared with the built-in task scheduling mechanism of Linux 2.6 kernel.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"20 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":"115032363","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/ICSESS.2017.8342946
Yao Zhong-hua, Wu Lingda
With the increment of the scale of the network, the task of large-scale network data becomes more difficult. In the process of network drawing, it is difficult to fully reflect its internal structure. This study proposes to model the whole network as a multi-particle simulation system, experiment results show that the proposed method can effectively reduce the time complexity of the network layout algorithm and efficiently show the network structure characteristics.
{"title":"Accelerated layout for large-scale network based on quadtree","authors":"Yao Zhong-hua, Wu Lingda","doi":"10.1109/ICSESS.2017.8342946","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342946","url":null,"abstract":"With the increment of the scale of the network, the task of large-scale network data becomes more difficult. In the process of network drawing, it is difficult to fully reflect its internal structure. This study proposes to model the whole network as a multi-particle simulation system, experiment results show that the proposed method can effectively reduce the time complexity of the network layout algorithm and efficiently show the network structure characteristics.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"35 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":"121901190","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/ICSESS.2017.8342884
Kitti Klinbua, W. Vatanawood
In recent years, Docker is rapidly a powerful and better efficient deployment environment in software as a service (SaaS) development. Using Docker in a software project could solve many former DevOps problems and increase quality of the project. The Docker-Compose is an official tool that helps manage Docker containers by defining the appropriate SaaS configuration in a docker-compose YAML file. As it is not easy for newbies of Docker to write a docker-compose YAML file due to the complicated parameters of a large compounded services' configurations, the TOCSA diagramming should alternatively provide several service templates which are more practical and adequately descriptive to the common SaaS designers. In this paper, we encourage and provide the SaaS designer a set of predefined TOSCA service templates to configure the target SaaS project. Then, we propose a mean to translate the configured TOSCA diagram into docker-compose YAML file using ANTLR. The resulting docker-compose YAML file could be readily exploited in Docker environment.
近年来，Docker在SaaS (software as a service)开发中迅速成为一个功能强大、效率更高的部署环境。在软件项目中使用Docker可以解决许多以前的DevOps问题，并提高项目质量。Docker- compose是一个官方工具，它通过在Docker- compose YAML文件中定义适当的SaaS配置来帮助管理Docker容器。由于大型复合服务配置的参数复杂，对于Docker新手来说，编写一个由Docker组成的YAML文件并不容易，因此TOCSA图应该提供一些对普通SaaS设计人员更实用和充分描述的服务模板。在本文中，我们鼓励并为SaaS设计人员提供一组预定义的TOSCA服务模板来配置目标SaaS项目。然后，我们提出了一种使用ANTLR将配置的TOSCA图转换为由docker组成的YAML文件的方法。生成的Docker -compose YAML文件可以很容易地在Docker环境中使用。
{"title":"Translating TOSCA into docker-compose YAML file using ANTLR","authors":"Kitti Klinbua, W. Vatanawood","doi":"10.1109/ICSESS.2017.8342884","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342884","url":null,"abstract":"In recent years, Docker is rapidly a powerful and better efficient deployment environment in software as a service (SaaS) development. Using Docker in a software project could solve many former DevOps problems and increase quality of the project. The Docker-Compose is an official tool that helps manage Docker containers by defining the appropriate SaaS configuration in a docker-compose YAML file. As it is not easy for newbies of Docker to write a docker-compose YAML file due to the complicated parameters of a large compounded services' configurations, the TOCSA diagramming should alternatively provide several service templates which are more practical and adequately descriptive to the common SaaS designers. In this paper, we encourage and provide the SaaS designer a set of predefined TOSCA service templates to configure the target SaaS project. Then, we propose a mean to translate the configured TOSCA diagram into docker-compose YAML file using ANTLR. The resulting docker-compose YAML file could be readily exploited in Docker environment.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","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":"122130823","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/ICSESS.2017.8342879
M. Kabir, Omar A. M. Salem, M. U. Rehman
The usages of mobile application have increased rapidly in recent days. It is also becoming more popular in recent business applications where multiple users are connected through a mobile application to complete the business circle. In this aspect, the demand of quality mobile application is increasing. Usability is the main quality factor for enhancing the quality of application. For this reason, the usability improvement is getting more priority for this kind of application. So, discovering the experiences of the users can lead to improving the usability of mobile application. For this, we introduce Fuzzy Association Rule algorithm (FAR) based on fuzzy association rule mining to discover the experience from the mobile application's users. To validate our approach, we consider a supply change management system where multiple users are linked through the mobile application. In this paper, we examine twelve usability factors that are extracted from ten usability evaluation models to improve the usability. After conducting our experiment, we get knowledge from the users of the mobile application that can be used for the improvement of usability. We get several experiment outcomes and knowledge that can be implemented in practices.
{"title":"Discovering knowledge from mobile application users for usability improvement: A fuzzy association rule mining approach","authors":"M. Kabir, Omar A. M. Salem, M. U. Rehman","doi":"10.1109/ICSESS.2017.8342879","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342879","url":null,"abstract":"The usages of mobile application have increased rapidly in recent days. It is also becoming more popular in recent business applications where multiple users are connected through a mobile application to complete the business circle. In this aspect, the demand of quality mobile application is increasing. Usability is the main quality factor for enhancing the quality of application. For this reason, the usability improvement is getting more priority for this kind of application. So, discovering the experiences of the users can lead to improving the usability of mobile application. For this, we introduce Fuzzy Association Rule algorithm (FAR) based on fuzzy association rule mining to discover the experience from the mobile application's users. To validate our approach, we consider a supply change management system where multiple users are linked through the mobile application. In this paper, we examine twelve usability factors that are extracted from ten usability evaluation models to improve the usability. After conducting our experiment, we get knowledge from the users of the mobile application that can be used for the improvement of usability. We get several experiment outcomes and knowledge that can be implemented in practices.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","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":"116999361","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}
This research compares retrieval performance between two frequency based feature against texture image retrieval. The aim is that to study the retrieval behavior by using two well-known frequency based features, which has a tiny differences of decomposition basis between DCT and DFT, this work come up with the assumption that different decomposing method might give different retrieval result. In this experiment, feature extraction performs straightforwardly by transforming grayscale global textural of each image into frequency domain without any pre-processing, then similarity measurement performs by Euclidean distance method. The result shows that DFT outperforms DCT for overall precision and recall.
{"title":"A performance comparison of two versatile frequency transformation approach in texture image retrieval","authors":"Sawet Somnugpong, Khumphicha Tantisantisom, Phrommate Verapan, Jindaporn Ongate, Kanokwan Khiewwan","doi":"10.1109/ICSESS.2017.8342860","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342860","url":null,"abstract":"This research compares retrieval performance between two frequency based feature against texture image retrieval. The aim is that to study the retrieval behavior by using two well-known frequency based features, which has a tiny differences of decomposition basis between DCT and DFT, this work come up with the assumption that different decomposing method might give different retrieval result. In this experiment, feature extraction performs straightforwardly by transforming grayscale global textural of each image into frequency domain without any pre-processing, then similarity measurement performs by Euclidean distance method. The result shows that DFT outperforms DCT for overall precision and recall.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"6 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":"123955833","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/ICSESS.2017.8343002
Qiang Li, Hui Li, Zhongling Wen, Pengfei Yuan
P2P is a full distributed network and Kad is the most popular P2P file-share system. Each node could join and leave dynamically in Kad. The S ybil attack means that any attacker could disguise as a normal node and join in the Kad network arbitrarily. By analyzing Kad protocol and its source codes, we found that Kad has several Sybil vulnerabilities. Based on this analysis, this paper designed a Sybil attack detection algorithm, DetectSybil which could performed well in Kad. It uses cluster algorithm to distinguish Sybil nodes from the whole p2p network. The experiments show that DetectSybil could detect Sybil attacks in Kad network effectively.
{"title":"Research on the P2P Sybil attack and the detection mechanism","authors":"Qiang Li, Hui Li, Zhongling Wen, Pengfei Yuan","doi":"10.1109/ICSESS.2017.8343002","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8343002","url":null,"abstract":"P2P is a full distributed network and Kad is the most popular P2P file-share system. Each node could join and leave dynamically in Kad. The S ybil attack means that any attacker could disguise as a normal node and join in the Kad network arbitrarily. By analyzing Kad protocol and its source codes, we found that Kad has several Sybil vulnerabilities. Based on this analysis, this paper designed a Sybil attack detection algorithm, DetectSybil which could performed well in Kad. It uses cluster algorithm to distinguish Sybil nodes from the whole p2p network. The experiments show that DetectSybil could detect Sybil attacks in Kad network effectively.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","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":"125809897","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/ICSESS.2017.8342903
Xiaona Fu, Kaifan Ji, Yunfei Yang, W. Duan, H. Deng, Xiaoli Zhang
Traditional correction methods can not be used to correct effectively the solar images with uneven illumination, such as the bilinear interpolation method. We adopt an improved algorithm that combine the background fitting method and the mask method. The algorithm consists of the following main steps: segmenting the image, selecting the sampling points, interpolating the sampling points, calculating the mask, correcting the image. By comparing four evaluation indicators of the corrected image, including the information entropy of the image, the mean brightness, the mean variance and the peak signal-to-noise ratio (PSNR), this improved algorithm is proved to be effectively in the solar images with uneven illumination.
{"title":"A method for correcting illumination unevenness of solar image","authors":"Xiaona Fu, Kaifan Ji, Yunfei Yang, W. Duan, H. Deng, Xiaoli Zhang","doi":"10.1109/ICSESS.2017.8342903","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342903","url":null,"abstract":"Traditional correction methods can not be used to correct effectively the solar images with uneven illumination, such as the bilinear interpolation method. We adopt an improved algorithm that combine the background fitting method and the mask method. The algorithm consists of the following main steps: segmenting the image, selecting the sampling points, interpolating the sampling points, calculating the mask, correcting the image. By comparing four evaluation indicators of the corrected image, including the information entropy of the image, the mean brightness, the mean variance and the peak signal-to-noise ratio (PSNR), this improved algorithm is proved to be effectively in the solar images with uneven illumination.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","volume":"73 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":"124727419","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/ICSESS.2017.8343016
W. Qun, Yingbin Zhang, Xinying Zhu, Youming Qiu, Wang Yize, Zhisheng Zhang
In this paper, the short-term load forecasting model based on ridgelet neural network optimized by the particle swarm optimization algorithm is proposed. The ridgelet neural network is simulated based on the visual cortex of the human brain. Compared with the traditional neural network, the neurons of the ridgelet neural network have directional characteristics, which can receive more dimensional information and have the ability to process higher dimensional data, and can better approximate nonlinear high dimensional functions. The particle swarm optimization algorithm is used to train the ridgelet neural network in this paper. The learning algorithm can not only speed up the convergence of the network, but also greatly reduce the probability of getting into the local minimum in the learning process. Through the simulation using the actual load data of power grid, simulation results show that the proposed model can effectively realize load forecasting and achieve the engineering accuracy requirements.
{"title":"Short-term load forecasting model based on ridgelet neural network optimized by particle swarm optimization algorithm","authors":"W. Qun, Yingbin Zhang, Xinying Zhu, Youming Qiu, Wang Yize, Zhisheng Zhang","doi":"10.1109/ICSESS.2017.8343016","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8343016","url":null,"abstract":"In this paper, the short-term load forecasting model based on ridgelet neural network optimized by the particle swarm optimization algorithm is proposed. The ridgelet neural network is simulated based on the visual cortex of the human brain. Compared with the traditional neural network, the neurons of the ridgelet neural network have directional characteristics, which can receive more dimensional information and have the ability to process higher dimensional data, and can better approximate nonlinear high dimensional functions. The particle swarm optimization algorithm is used to train the ridgelet neural network in this paper. The learning algorithm can not only speed up the convergence of the network, but also greatly reduce the probability of getting into the local minimum in the learning process. Through the simulation using the actual load data of power grid, simulation results show that the proposed model can effectively realize load forecasting and achieve the engineering accuracy requirements.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","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":"125083908","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/ICSESS.2017.8342906
Wang Xiao, Liu Guoqi, L. Bin
Aiming at the problem of data integration about heterogeneous and large amount of data in big data 4V features, the method of data integration based on Karma modeling is explored, and the data set of literature area is used as an example to verify the method. First of all, analyze specifically part of the literature data sets that are obtained. And then using Protégé ontology modeling tool to build the related domain ontology. Through the Karma modeling tool, the literature data set is mapped to the literature domain ontology and uniformly published as RDF data so that the semantic mapping is achieved, which effectively solve the important problem of multi-source and heterogeneous data. The Karma model that is built and published will be applied to complete big data set for big data integration. Finally, we sum up the results of the practice and address our future works.
{"title":"Research on big data integration based on Karma modeling","authors":"Wang Xiao, Liu Guoqi, L. Bin","doi":"10.1109/ICSESS.2017.8342906","DOIUrl":"https://doi.org/10.1109/ICSESS.2017.8342906","url":null,"abstract":"Aiming at the problem of data integration about heterogeneous and large amount of data in big data 4V features, the method of data integration based on Karma modeling is explored, and the data set of literature area is used as an example to verify the method. First of all, analyze specifically part of the literature data sets that are obtained. And then using Protégé ontology modeling tool to build the related domain ontology. Through the Karma modeling tool, the literature data set is mapped to the literature domain ontology and uniformly published as RDF data so that the semantic mapping is achieved, which effectively solve the important problem of multi-source and heterogeneous data. The Karma model that is built and published will be applied to complete big data set for big data integration. Finally, we sum up the results of the practice and address our future works.","PeriodicalId":179815,"journal":{"name":"2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)","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":"128542717","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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