The paper reports internet media user behavior characteristics measurement modeling and a preliminary investigation into interpersonal communication in the social media. It addresses the potential need to reformulate current thinking about what influences the internet media has brought to social communication. We look particularly at some of the most potent forms of today's interpersonal communication-We-chat, Weixin, QQ, Facebook, Twitter etc. The media users behavior characteristics is measured and modeling to improve communications level. We explored motivation driving behavior in social media. The serial random event chain model is adapted to analysis the event correlation. The conclusion summarizes the highlight measure point that we still need the demand for original way of interpersonal communication. The study of user behavior characteristics in the social media is great significance for the public opinions, network marketing promotion and improving user experience.
{"title":"The Measurement and Modeling Analysis for Internet Users Behavior Properties","authors":"Yijing Ren, Ren R, Wang Wr","doi":"10.1109/CIIS.2017.28","DOIUrl":"https://doi.org/10.1109/CIIS.2017.28","url":null,"abstract":"The paper reports internet media user behavior characteristics measurement modeling and a preliminary investigation into interpersonal communication in the social media. It addresses the potential need to reformulate current thinking about what influences the internet media has brought to social communication. We look particularly at some of the most potent forms of today's interpersonal communication-We-chat, Weixin, QQ, Facebook, Twitter etc. The media users behavior characteristics is measured and modeling to improve communications level. We explored motivation driving behavior in social media. The serial random event chain model is adapted to analysis the event correlation. The conclusion summarizes the highlight measure point that we still need the demand for original way of interpersonal communication. The study of user behavior characteristics in the social media is great significance for the public opinions, network marketing promotion and improving user experience.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126952845","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}
Jipu Gao, Changbao Xu, Li Zhang, Shuaiwei Liu, Weigang Feng, S. Xiong, Shan Tan
An infrared image change detection method based on Markov Random Field (MRF) was proposed to estimate the status of substation equipment in the power system. The method classified changed and unchanged regions between bitemporal images using MRF with k-means clustering initializing the label of all pixels of the sample image. The proposed method used the target pixel and its neighborhood information to realize the determination of the category of the target pixel. In our method, the original bi-temporal infrared images were converted to two gray-level images, and one difference image was obtained by subtracting one gray-level image from another, pixel by pixel. Change areas were then detected on the gray-level difference image using inference techniques on MRF. To demonstrate the excellent performance of our method, comparative experiments were made using the other four classical approaches, including Image Differencing, Image Ratioing, Change vector analysis (CVA) and Principal Component Analysis (PCA). In order to quantify the performance of different algorithms for a quantitative comparison, six performance indexes, i.e. Kappa value, Probability of False detection (PF), Probability of Omission detection (PO), Card Similarity Index (CSI), Classification Error (CE) and Area Error (AE) were adopted in this paper. The experimental results showed that compared with the four classical methods, the proposed method can effectively reduce PO and PF, and improve the overall detection accuracy.
{"title":"Infrared Image Change Detection of Substation Equipment in Power System Using Markov Random Field","authors":"Jipu Gao, Changbao Xu, Li Zhang, Shuaiwei Liu, Weigang Feng, S. Xiong, Shan Tan","doi":"10.1109/CIIS.2017.54","DOIUrl":"https://doi.org/10.1109/CIIS.2017.54","url":null,"abstract":"An infrared image change detection method based on Markov Random Field (MRF) was proposed to estimate the status of substation equipment in the power system. The method classified changed and unchanged regions between bitemporal images using MRF with k-means clustering initializing the label of all pixels of the sample image. The proposed method used the target pixel and its neighborhood information to realize the determination of the category of the target pixel. In our method, the original bi-temporal infrared images were converted to two gray-level images, and one difference image was obtained by subtracting one gray-level image from another, pixel by pixel. Change areas were then detected on the gray-level difference image using inference techniques on MRF. To demonstrate the excellent performance of our method, comparative experiments were made using the other four classical approaches, including Image Differencing, Image Ratioing, Change vector analysis (CVA) and Principal Component Analysis (PCA). In order to quantify the performance of different algorithms for a quantitative comparison, six performance indexes, i.e. Kappa value, Probability of False detection (PF), Probability of Omission detection (PO), Card Similarity Index (CSI), Classification Error (CE) and Area Error (AE) were adopted in this paper. The experimental results showed that compared with the four classical methods, the proposed method can effectively reduce PO and PF, and improve the overall detection accuracy.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115194137","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}
In cloud services, trust management is more important than ever before in the use of information and communica-tion technologies. Obviously, the cloud service business relies on the fact the service users trust cloud service providers. A user who trusts the cloud service believes that utilizing the service is a positive thing and helps in achieving his/her goals. In general, the question of trust measurement proved to be a difficult one due to the dynamic nature of cloud resources. This paper presents a QoS-based trust computing system for SLA guarantee in cloud computing system. By embedding our trust system into the SLA (Service Level Agreement) architecture, trust management system can prepare the best trustworthy resources for each service request in advance, and allocate the best resources to users. Experimental results show that our trust system converges more rapidly and accurately than do existing approaches, thereby verifying that it can effectively take on trust measurement tasks in cloud computing.
{"title":"QoS-based Trust Computing Scheme for SLA Guarantee in Cloud Computing System","authors":"Lihong Bao","doi":"10.1109/CIIS.2017.42","DOIUrl":"https://doi.org/10.1109/CIIS.2017.42","url":null,"abstract":"In cloud services, trust management is more important than ever before in the use of information and communica-tion technologies. Obviously, the cloud service business relies on the fact the service users trust cloud service providers. A user who trusts the cloud service believes that utilizing the service is a positive thing and helps in achieving his/her goals. In general, the question of trust measurement proved to be a difficult one due to the dynamic nature of cloud resources. This paper presents a QoS-based trust computing system for SLA guarantee in cloud computing system. By embedding our trust system into the SLA (Service Level Agreement) architecture, trust management system can prepare the best trustworthy resources for each service request in advance, and allocate the best resources to users. Experimental results show that our trust system converges more rapidly and accurately than do existing approaches, thereby verifying that it can effectively take on trust measurement tasks in cloud computing.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132829635","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 : 2013-02-28DOI: 10.4156/JCIT.VOL8.ISSUE4.63
Jin Wang
Particle swarm optimization (PSO) is an intelligent algorithm inspired by swarm intelligence. It has been shown that PSO is a good optimizer on various optimization problems. Due to the inherent randomness of PSO, it easily falls into local minima when dealing with multimodal optimization problems. In order to enhance the performance of PSO on multimodal problems, this paper proposes a novel PSO algorithm by employing adaptive parameter control and example-based learning. Conducted experiments on nine well-known multimodal problems show that our approach outperforms the standard PSO, unified PSO (UPSO), fully informed PSO (FIPS), fitness-distance-ratio based PSO (FDR-PSO), cooperative PSO (CPSO-H) and comprehensive learning PSO (CLPSO) in terms of the solution accuracy.
{"title":"Enhancing Particle Swarm Algorithm for Multimodal Optimization Problems","authors":"Jin Wang","doi":"10.4156/JCIT.VOL8.ISSUE4.63","DOIUrl":"https://doi.org/10.4156/JCIT.VOL8.ISSUE4.63","url":null,"abstract":"Particle swarm optimization (PSO) is an intelligent algorithm inspired by swarm intelligence. It has been shown that PSO is a good optimizer on various optimization problems. Due to the inherent randomness of PSO, it easily falls into local minima when dealing with multimodal optimization problems. In order to enhance the performance of PSO on multimodal problems, this paper proposes a novel PSO algorithm by employing adaptive parameter control and example-based learning. Conducted experiments on nine well-known multimodal problems show that our approach outperforms the standard PSO, unified PSO (UPSO), fully informed PSO (FIPS), fitness-distance-ratio based PSO (FDR-PSO), cooperative PSO (CPSO-H) and comprehensive learning PSO (CLPSO) in terms of the solution accuracy.","PeriodicalId":254342,"journal":{"name":"2017 International Conference on Computing Intelligence and Information System (CIIS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128385501","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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