In this paper, a novel blind digital image watermarking scheme in contour let domain with singular value decomposition is proposed. In contrast to traditional methods where the watermark bits are embedded directly on the contourlet transform coefficients, the proposed scheme is based on watermark bits embedding on the singular value of the selected blocks within lowpass subband of the original gray image contourlet transform. Experimental results demonstrate that the quality of watermarked image is robust against attacks such as JPEG compression, low pass filtering, noise addition, scaling and cropping. Watermark extraction is efficient and blind in the sense only quantization strategies but not the original image is required.
{"title":"A Novel Blind Watermarking Scheme in Contourlet Domain Based on Singular Value Decomposition","authors":"Shao-min Zhu, Jian-ming Liu","doi":"10.1109/WKDD.2009.162","DOIUrl":"https://doi.org/10.1109/WKDD.2009.162","url":null,"abstract":"In this paper, a novel blind digital image watermarking scheme in contour let domain with singular value decomposition is proposed. In contrast to traditional methods where the watermark bits are embedded directly on the contourlet transform coefficients, the proposed scheme is based on watermark bits embedding on the singular value of the selected blocks within lowpass subband of the original gray image contourlet transform. Experimental results demonstrate that the quality of watermarked image is robust against attacks such as JPEG compression, low pass filtering, noise addition, scaling and cropping. Watermark extraction is efficient and blind in the sense only quantization strategies but not the original image is required.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123113428","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}
It was developed that the method of spectral analysis was used to quantitatively analyze the rape moisture content. The method of region stepwise regression (RSR) was proposed to select the characteristic wavelengths for rape leaf moisture content prediction. The spectrum curve was segmented into several regions by the middle points of adjacent zeros of derivative spectrum data. Each region included a spectral absorption peak or an absorption valley. Stepwise regression was applied to each region, where the correlation coefficient and root mean square error (RMSE) was taken as the evaluation standard to select the spectral characteristic wavelength regions for the model in each region. In order to avoid wrongly choosing characteristic wavelengths or neglecting the necessary information, applied further choice to the selected characteristic wavelengths according to the former research findings of our team and regularities of molecular spectrum absorption band distribution. The method of principal component regression analysis¿PCA¿was used to establish the model between the moisture content and the characteristic wavelengths of rape leaf. The method could diminish runtime and overcome the effect of multiple co-linearity while enhance model prediction precision. From the spectral date of rape leaves under different water stress conditions, it was found that the rape leaf moisture content had a significant correlation with the spectral reflectance at 460nm, 510nm, 1450nm, 1650nm, 1900nm and derivative of spectral reflectance at 702nm. The correlation coefficient between the estimated value and the real value is 0.92; the root mean square error is 0.37
{"title":"Establishment of Rape Leaf Moisture Content Spectral Character Models Based on RSR-PCA Method","authors":"Xiaodong Zhang, H. Mao","doi":"10.1109/WKDD.2009.70","DOIUrl":"https://doi.org/10.1109/WKDD.2009.70","url":null,"abstract":"It was developed that the method of spectral analysis was used to quantitatively analyze the rape moisture content. The method of region stepwise regression (RSR) was proposed to select the characteristic wavelengths for rape leaf moisture content prediction. The spectrum curve was segmented into several regions by the middle points of adjacent zeros of derivative spectrum data. Each region included a spectral absorption peak or an absorption valley. Stepwise regression was applied to each region, where the correlation coefficient and root mean square error (RMSE) was taken as the evaluation standard to select the spectral characteristic wavelength regions for the model in each region. In order to avoid wrongly choosing characteristic wavelengths or neglecting the necessary information, applied further choice to the selected characteristic wavelengths according to the former research findings of our team and regularities of molecular spectrum absorption band distribution. The method of principal component regression analysis¿PCA¿was used to establish the model between the moisture content and the characteristic wavelengths of rape leaf. The method could diminish runtime and overcome the effect of multiple co-linearity while enhance model prediction precision. From the spectral date of rape leaves under different water stress conditions, it was found that the rape leaf moisture content had a significant correlation with the spectral reflectance at 460nm, 510nm, 1450nm, 1650nm, 1900nm and derivative of spectral reflectance at 702nm. The correlation coefficient between the estimated value and the real value is 0.92; the root mean square error is 0.37","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123132977","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 this paper, we introduce a robust watermark scheme against shearing attack for digital images based on video series wavelet transform. Based on the characteristics of the image theory and human visual system, the proposed method make use of the there-dimensional video wavelet transform to split the image to a sense of image videos and the watermark is adaptively weighed to the different positions of the middle frequency region. The method makes use of the characteristics of image 2D wavelet transform and the person’s sense of vision and shows excellent advantage against shearing attack. The method could show the watermark clearly when more than half of the image has been cut. Experimental results show this method excellent robustness for image shearing. The goal of this paper is to achieve robust digital image watermark against shearing. The algorithm performs well in StirMark test and is robust to geometrical attacks.
{"title":"Image Watermarking Based on Video Series Against Shearing","authors":"Minghui Deng","doi":"10.1109/WKDD.2009.66","DOIUrl":"https://doi.org/10.1109/WKDD.2009.66","url":null,"abstract":"In this paper, we introduce a robust watermark scheme against shearing attack for digital images based on video series wavelet transform. Based on the characteristics of the image theory and human visual system, the proposed method make use of the there-dimensional video wavelet transform to split the image to a sense of image videos and the watermark is adaptively weighed to the different positions of the middle frequency region. The method makes use of the characteristics of image 2D wavelet transform and the person’s sense of vision and shows excellent advantage against shearing attack. The method could show the watermark clearly when more than half of the image has been cut. Experimental results show this method excellent robustness for image shearing. The goal of this paper is to achieve robust digital image watermark against shearing. The algorithm performs well in StirMark test and is robust to geometrical attacks.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125963157","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}
Web has already become the richest information resource of the world. Web sources are open, dynamic, and autonomous. Evaluating and selecting high quality Web source is a key for the success of Web-based applications. Currently, most of the web source evaluation and selection are performed by web experts. However, there are too many Web sources in any field, this process has proved to be very time consuming and costly. In this paper, we propose a novel approach which evaluates web source quality by mass collaboration. The approach shifts the enormous endeavor from the expert to the consumers and promotes web source quality to be evaluated quickly and effectively.
{"title":"Web Source Evaluation and Selection by Mass Collaboration","authors":"Yanhui Ding, Qingzhong Li, Yongquan Dong","doi":"10.1109/WKDD.2009.71","DOIUrl":"https://doi.org/10.1109/WKDD.2009.71","url":null,"abstract":"Web has already become the richest information resource of the world. Web sources are open, dynamic, and autonomous. Evaluating and selecting high quality Web source is a key for the success of Web-based applications. Currently, most of the web source evaluation and selection are performed by web experts. However, there are too many Web sources in any field, this process has proved to be very time consuming and costly. In this paper, we propose a novel approach which evaluates web source quality by mass collaboration. The approach shifts the enormous endeavor from the expert to the consumers and promotes web source quality to be evaluated quickly and effectively.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123406475","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 complex process of industrial production, it need deal with a large number of data, multiple dimensions, and generate complex data. If the neural network control indirect used, it is easy that lead to some shortcomings, such as inaccurate results and training stage of neural network lack convergence and so forth. In response to these circumstances, the integration model of data optimize processing algorithms is put forward, which is the survival of the fittest each other of dynamic K-means improve cluster algorithm and fuzzy c mean value clustering. Through two clusters to process complex data, in order that obtain accurate cluster quantity and membership. Finally through the simulation of the coal mining product data, the results proof the validity of the model.
{"title":"The Study of Optimizing Model Based on Data Cluster of Information Fusion of Multiple Parameters","authors":"Jie Sun, Tiejun Zhang","doi":"10.1109/WKDD.2009.192","DOIUrl":"https://doi.org/10.1109/WKDD.2009.192","url":null,"abstract":"In complex process of industrial production, it need deal with a large number of data, multiple dimensions, and generate complex data. If the neural network control indirect used, it is easy that lead to some shortcomings, such as inaccurate results and training stage of neural network lack convergence and so forth. In response to these circumstances, the integration model of data optimize processing algorithms is put forward, which is the survival of the fittest each other of dynamic K-means improve cluster algorithm and fuzzy c mean value clustering. Through two clusters to process complex data, in order that obtain accurate cluster quantity and membership. Finally through the simulation of the coal mining product data, the results proof the validity of the model.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123584475","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 paper deals with the study of a water quality prediction model through application of LS-SVM in Liuxi River in Guangzhou. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome some shortcoming in the Multilayer Perceptron (MLP) and the PSO is used to tune the LS-SVM parameters automatically. It enhances the efficiency and the capability of prediction. Through simulation testing the model shows high efficiency in forecasting the water quality of the Liuxi River.
{"title":"Water Quality Prediction Using LS-SVM and Particle Swarm Optimization","authors":"Yunrong Xiang, Liang-zhong Jiang","doi":"10.1109/WKDD.2009.217","DOIUrl":"https://doi.org/10.1109/WKDD.2009.217","url":null,"abstract":"This paper deals with the study of a water quality prediction model through application of LS-SVM in Liuxi River in Guangzhou. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome some shortcoming in the Multilayer Perceptron (MLP) and the PSO is used to tune the LS-SVM parameters automatically. It enhances the efficiency and the capability of prediction. Through simulation testing the model shows high efficiency in forecasting the water quality of the Liuxi River.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115076333","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}
Evaluation of water environment quality plays an important role in environment science. Because of many factors affecting environment quality, it is a basic task to select rational pattern and make full use of limited information from monitors so as to describe environment quality objectively. In view of the deficiency of the traditional methods, based on the grey theory, a grey clustering model is established to evaluate water quality. The proposed model was applied to assess the water quality of 20 sections in Suzhou River. The evaluation result was compared with that of the traditional method and the reported results in the Suzhou River. It is indicated that the performance of the proposed model is practically feasible in the application of water quality assessment and its application is simple.
{"title":"Evaluation of Water Quality Using Grey Clustering","authors":"Chang-jun Zhu, Qinghua Liu","doi":"10.1109/WKDD.2009.28","DOIUrl":"https://doi.org/10.1109/WKDD.2009.28","url":null,"abstract":"Evaluation of water environment quality plays an important role in environment science. Because of many factors affecting environment quality, it is a basic task to select rational pattern and make full use of limited information from monitors so as to describe environment quality objectively. In view of the deficiency of the traditional methods, based on the grey theory, a grey clustering model is established to evaluate water quality. The proposed model was applied to assess the water quality of 20 sections in Suzhou River. The evaluation result was compared with that of the traditional method and the reported results in the Suzhou River. It is indicated that the performance of the proposed model is practically feasible in the application of water quality assessment and its application is simple.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116079308","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}
The major weakness of associative classification is examined. A novel approach for classifying imbalanced dataset is proposed. It is an associative classification. Rules which are un-frequent are used to build the classifier rule set. Besides the confidence of pattern “X→Y”, the confidence of pattern “Y→X” is used in the approach. Further more, only features of rare classes are preserved while training. The good performance of the approach is shown by the experiments.
{"title":"A Novel Approach to Classify Imbalanced Dataset Based on Rare Attributes and Double Confidences","authors":"Yingjie Li, Yixin Yin","doi":"10.1109/WKDD.2009.20","DOIUrl":"https://doi.org/10.1109/WKDD.2009.20","url":null,"abstract":"The major weakness of associative classification is examined. A novel approach for classifying imbalanced dataset is proposed. It is an associative classification. Rules which are un-frequent are used to build the classifier rule set. Besides the confidence of pattern “X→Y”, the confidence of pattern “Y→X” is used in the approach. Further more, only features of rare classes are preserved while training. The good performance of the approach is shown by the experiments.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122189097","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}
With the deepening development of globalization, knowledge economy and information technology gradually, many large corporations actively explore distributed innovation to keep the competitive power and expand their firms. Basing on the empiric analysis of Midea Group of China, this paper puts forward the concept of distributed innovation and its characters, and then reveals the driving factors of enterprise distributed innovation and this would be a reference for national strategic decision-making.
{"title":"Study on Dynamic Mechanism of Enterprise Distributed Innovation","authors":"Guoxin Liu, Xia Li, Xiaoqin Gao","doi":"10.1109/WKDD.2009.139","DOIUrl":"https://doi.org/10.1109/WKDD.2009.139","url":null,"abstract":"With the deepening development of globalization, knowledge economy and information technology gradually, many large corporations actively explore distributed innovation to keep the competitive power and expand their firms. Basing on the empiric analysis of Midea Group of China, this paper puts forward the concept of distributed innovation and its characters, and then reveals the driving factors of enterprise distributed innovation and this would be a reference for national strategic decision-making.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116604306","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}
Modern network computing is in the process of changingfrom manual management to autonomic computing. As thecore of network computing, database management systemwith autonomic computing must be able to participate in theprocess to meet the performance requirements of thenetwork applications. This paper studies on workloadadaptation technology. In the Workload AdaptationFramework, the performance forecasting component is thebase of developing workload control scheme. Layeredqueuing network modeling techniques was used to establishthe performance model of DBMS. A test platform based onthis architecture has been built and been tested. Test resultshows that this framework has played an effective role inworkload control, and improved client satisfaction rate andthe application performance of the network.
{"title":"The Workload Adaptation in Autonomic DBMSs Based on Layered Queuing Network Model","authors":"Yan Qiang, Yi Li, Junjie Chen","doi":"10.1109/WKDD.2009.191","DOIUrl":"https://doi.org/10.1109/WKDD.2009.191","url":null,"abstract":"Modern network computing is in the process of changingfrom manual management to autonomic computing. As thecore of network computing, database management systemwith autonomic computing must be able to participate in theprocess to meet the performance requirements of thenetwork applications. This paper studies on workloadadaptation technology. In the Workload AdaptationFramework, the performance forecasting component is thebase of developing workload control scheme. Layeredqueuing network modeling techniques was used to establishthe performance model of DBMS. A test platform based onthis architecture has been built and been tested. Test resultshows that this framework has played an effective role inworkload control, and improved client satisfaction rate andthe application performance of the network.","PeriodicalId":143250,"journal":{"name":"2009 Second International Workshop on Knowledge Discovery and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128457045","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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