Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258804
Shicheng Cui, Bin Xia, Tao Li, Ming Wu, Deqiang Li, Qianmu Li, Hong Zhang
In this paper, we present a novel method, namely SimWalk, to learn latent representations of networks. SimWalk maps nodes to a continuous vector space which maximizes the likelihood of node sequences. We design a probability-guided random walk procedure based on relation similarity, which encourages node sequences to preserve context-related neighborhoods. Different with previous work which generates rigid node sequences, we believe that relations in social networks, especially similarity, can guide the walk to generate a more linguistic sequence. In this perspective, our model learns more meaningful representations. We demonstrate SimWalk on several multi-label real-world network classification tasks over state-of-the-art methods. Our results show that SimWalk outperforms the popular methods in complex networks.
{"title":"SimWalk: Learning network latent representations with social relation similarity","authors":"Shicheng Cui, Bin Xia, Tao Li, Ming Wu, Deqiang Li, Qianmu Li, Hong Zhang","doi":"10.1109/ISKE.2017.8258804","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258804","url":null,"abstract":"In this paper, we present a novel method, namely SimWalk, to learn latent representations of networks. SimWalk maps nodes to a continuous vector space which maximizes the likelihood of node sequences. We design a probability-guided random walk procedure based on relation similarity, which encourages node sequences to preserve context-related neighborhoods. Different with previous work which generates rigid node sequences, we believe that relations in social networks, especially similarity, can guide the walk to generate a more linguistic sequence. In this perspective, our model learns more meaningful representations. We demonstrate SimWalk on several multi-label real-world network classification tasks over state-of-the-art methods. Our results show that SimWalk outperforms the popular methods in complex networks.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"70 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113987240","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.2017.8258794
Bin Luo, Xiaohong Liu
China is the largest developing country in the world. In the west of China, there are some less developed areas. In order to build a moderately prosperous society in all respects in 2020, these less developed areas need to accelerate economic development and improve people's livelihood and undertake the manufacturing industry of developed countries and Chinese eastern developed area, which have negative impacts on sustainable development in the short term. This paper focuses on the goal of sustainable development and analyzes the short-term challenges faced by the strategy of sustainable development in some less developed areas in the west of China. The paper puts forward model of sustainable development, evaluation methods and suggestions in some less developed areas in the west of China based on the connotation of sustainable development.
{"title":"An evaluation of sustainable development in less developed areas of Western China","authors":"Bin Luo, Xiaohong Liu","doi":"10.1109/ISKE.2017.8258794","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258794","url":null,"abstract":"China is the largest developing country in the world. In the west of China, there are some less developed areas. In order to build a moderately prosperous society in all respects in 2020, these less developed areas need to accelerate economic development and improve people's livelihood and undertake the manufacturing industry of developed countries and Chinese eastern developed area, which have negative impacts on sustainable development in the short term. This paper focuses on the goal of sustainable development and analyzes the short-term challenges faced by the strategy of sustainable development in some less developed areas in the west of China. The paper puts forward model of sustainable development, evaluation methods and suggestions in some less developed areas in the west of China based on the connotation of sustainable development.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"180 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114041599","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.2017.8258729
Xingguang Pan, Xiongtao Zhang, Zhibin Jiang, Shitong Wang
Maximum Entropy Clustering (MEC) is an algorithm based on fuzzy c means by embedding an entropy generalization term in it. However, MEC is not robust to both noise and outliers, which leads to poor accuracy in clustering processes. In this paper, a novel clustering algorithm based on Shannon entropy is proposed, the new algorithm named Anti-noise Possibilistic Maximum Entropy Clustering (A-PMEC) is verified much more robustness in noisy dataset. We introduce the detailed formulation of A-PMEC and as well as experimental study to demonstrate the merits of the proposed method.
{"title":"Anti-noise possibilistic clustering based on maximum entropy","authors":"Xingguang Pan, Xiongtao Zhang, Zhibin Jiang, Shitong Wang","doi":"10.1109/ISKE.2017.8258729","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258729","url":null,"abstract":"Maximum Entropy Clustering (MEC) is an algorithm based on fuzzy c means by embedding an entropy generalization term in it. However, MEC is not robust to both noise and outliers, which leads to poor accuracy in clustering processes. In this paper, a novel clustering algorithm based on Shannon entropy is proposed, the new algorithm named Anti-noise Possibilistic Maximum Entropy Clustering (A-PMEC) is verified much more robustness in noisy dataset. We introduce the detailed formulation of A-PMEC and as well as experimental study to demonstrate the merits of the proposed method.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"29 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":"114614851","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.2017.8258827
Bin Wu, Han Yu, Xiangdong Chen
Image recognition is a feature extraction and pattern matching technique to classify various objectives in computer science. In theory, it mainly relies on the images with distinguishable features as a good starting point. Each image has its own unique characteristics, and image features can be categorized into color features, texture features, and shape features etc. Therefore, using modern recognition techniques, we extract the image features through various algorithms to search for images with high similarities. There are many image recognition algorithms, and some of them are with high recognition rate while others are robust. But not all algorithms can be unconditionally implemented without adjusting to the real situation. In this paper, we introduce the classification algorithm based on Support Vector Machine (SVM) and the feature extraction method based on Principal Components Analysis (PCA). We employ the feature extraction algorithm to characterize facial features and recognize faces by comparing them to those stored in the training data set. Finally, we show the applications of feature extraction and classification algorithms in character recognition. The real characters printed on a cord are preprocessed by PCA, and then classified and identified by SVM with a good recognition rate if properly processed.
{"title":"New applications of image classification in character recognition","authors":"Bin Wu, Han Yu, Xiangdong Chen","doi":"10.1109/ISKE.2017.8258827","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258827","url":null,"abstract":"Image recognition is a feature extraction and pattern matching technique to classify various objectives in computer science. In theory, it mainly relies on the images with distinguishable features as a good starting point. Each image has its own unique characteristics, and image features can be categorized into color features, texture features, and shape features etc. Therefore, using modern recognition techniques, we extract the image features through various algorithms to search for images with high similarities. There are many image recognition algorithms, and some of them are with high recognition rate while others are robust. But not all algorithms can be unconditionally implemented without adjusting to the real situation. In this paper, we introduce the classification algorithm based on Support Vector Machine (SVM) and the feature extraction method based on Principal Components Analysis (PCA). We employ the feature extraction algorithm to characterize facial features and recognize faces by comparing them to those stored in the training data set. Finally, we show the applications of feature extraction and classification algorithms in character recognition. The real characters printed on a cord are preprocessed by PCA, and then classified and identified by SVM with a good recognition rate if properly processed.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"481 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":"117056029","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.2017.8258759
Linna Wang, Ling Liu, Xin Yang, Pan Zhuo
In practical decision-making, we prefer to characterize the uncertain problems with the hybrid data, which consists of various types of data, e.g., categorical, numerical, set-valued and interval-valued. The extended rough sets can deal with single types of data based on specific binary relation, including the equivalence relation, neighborhood relation, partial order relation, tolerance relation, etc. However, the fusion of these relations is a significant challenge task in such composite information table. To tackle this issue, this paper proposes the intersection and union composite relation, and further introduces a quantitative composite decision-theoretic rough set model. Moreover, we present a novel matrix-based approach to compute the upper and lower approximations in proposed model. Finally, an numerical example is conducted to illustrate the efficiency of proposed method.
{"title":"Quantitative composite decision-theoretic rough set","authors":"Linna Wang, Ling Liu, Xin Yang, Pan Zhuo","doi":"10.1109/ISKE.2017.8258759","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258759","url":null,"abstract":"In practical decision-making, we prefer to characterize the uncertain problems with the hybrid data, which consists of various types of data, e.g., categorical, numerical, set-valued and interval-valued. The extended rough sets can deal with single types of data based on specific binary relation, including the equivalence relation, neighborhood relation, partial order relation, tolerance relation, etc. However, the fusion of these relations is a significant challenge task in such composite information table. To tackle this issue, this paper proposes the intersection and union composite relation, and further introduces a quantitative composite decision-theoretic rough set model. Moreover, we present a novel matrix-based approach to compute the upper and lower approximations in proposed model. Finally, an numerical example is conducted to illustrate the efficiency of proposed method.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"108 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":"124732698","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.2017.8258770
Pulin Li, P. Jiang
Knowledge-based Innovative Methods offer a novel approach to achieve collaborative manufacturing in quality control for high-end equipment outsourcing chain. This paper introduces the new problems that modern quality control faces firstly. Then give a definition on Knowledge-based Innovative Method and its system's architecture. After that, the operating logics were described and a software platform was established. In the end, a case study was done and some conclusions were coming up.
{"title":"Knowledge-based innovative methods for collaborative quality control in equipment outsourcing chain","authors":"Pulin Li, P. Jiang","doi":"10.1109/ISKE.2017.8258770","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258770","url":null,"abstract":"Knowledge-based Innovative Methods offer a novel approach to achieve collaborative manufacturing in quality control for high-end equipment outsourcing chain. This paper introduces the new problems that modern quality control faces firstly. Then give a definition on Knowledge-based Innovative Method and its system's architecture. After that, the operating logics were described and a software platform was established. In the end, a case study was done and some conclusions were coming up.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"196 S565","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113972707","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.2017.8258721
Yang Liu, Ningning Zhou, Guofang Huang
In order to overcome the Excessive time complexity of bad quality when the disposing the picture whose foreground is similar to background of grabcut method, a grabcut method based on superpixel is proposed in this paper. This method, firstly, extracting the superpixel block of the picture. And then, split the picture which is extracted. The experimental results show that this method is effective to improve the speed of segmentation. What's more, this method can solve the problem of bad quality when the disposing the image whose foreground is similar to background in a certain extent. This paper has certain advantage in disposing those image whose size are too big and foreground is similar to background.
{"title":"Research on the grabcut image segmentation method based on superpixel","authors":"Yang Liu, Ningning Zhou, Guofang Huang","doi":"10.1109/ISKE.2017.8258721","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258721","url":null,"abstract":"In order to overcome the Excessive time complexity of bad quality when the disposing the picture whose foreground is similar to background of grabcut method, a grabcut method based on superpixel is proposed in this paper. This method, firstly, extracting the superpixel block of the picture. And then, split the picture which is extracted. The experimental results show that this method is effective to improve the speed of segmentation. What's more, this method can solve the problem of bad quality when the disposing the image whose foreground is similar to background in a certain extent. This paper has certain advantage in disposing those image whose size are too big and foreground is similar to background.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"13 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":"123856872","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.2017.8258822
Yanping Li, Yutao Zuo, Zhen Yang, Xi Shao
Two main challenges introduced in current voice conversion are the dependence on parallel training data and the trade-off between speaker similarity and speech quality. To tackle the latter problem, this paper proposes a novel conversion method based on Iterative Self-organizing DATA Analysis Techniques Algorithm (ISODATA) clustering algorithm. Specially, we use ISODATA during the training of Gaussian mixture model, the optimized mixture number can guarantee the validity and accuracy of the GMM model, which can acquire speaker's identity effectively related to speaker similarity between original target speech and converted speech, Next, we combine improved GMM and bilinear frequency warping for the conversion stage, which can get a good balance between speaker similarity and speech quality. Theory analysis and experimental results demonstrate that the proposed algorithm can achieve higher quality and similarity compared with other two methods.
{"title":"High quality voice conversion based on ISODATA clustering algorithm","authors":"Yanping Li, Yutao Zuo, Zhen Yang, Xi Shao","doi":"10.1109/ISKE.2017.8258822","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258822","url":null,"abstract":"Two main challenges introduced in current voice conversion are the dependence on parallel training data and the trade-off between speaker similarity and speech quality. To tackle the latter problem, this paper proposes a novel conversion method based on Iterative Self-organizing DATA Analysis Techniques Algorithm (ISODATA) clustering algorithm. Specially, we use ISODATA during the training of Gaussian mixture model, the optimized mixture number can guarantee the validity and accuracy of the GMM model, which can acquire speaker's identity effectively related to speaker similarity between original target speech and converted speech, Next, we combine improved GMM and bilinear frequency warping for the conversion stage, which can get a good balance between speaker similarity and speech quality. Theory analysis and experimental results demonstrate that the proposed algorithm can achieve higher quality and similarity compared with other two methods.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"38 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":"125914441","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.2017.8258820
Lisha Xiao, Qin Yan, Shuyu Deng
Scene classification is an important research branch of image comprehension, which gains information from images and interprets them using computer system by imitating the biological systems of human beings. AlexNet model is limited in image classification because of the large convolution kernel and stride in the first convolutional layer leading to over rapid decline of feature maps resolution and excessive compression of spatial information. This paper proposed an improved AlexNet model according to the design principle of convolutional neural networks (CNNs). The large convolution kernel is decomposed into a structure cascaded by two small convolution kernels with reduced stride. Another convolutional layer is added after the first one to enhance the integration process of the low-level features or the spatial information. The asymmetric convolution kernel is applied in the last three convolutional layers. The experiments on two datasets show that the classification accuracy of the improved AlexNet model is higher than those of AlexNet model and ZFNet model for 23 categories of scene classification.
{"title":"Scene classification with improved AlexNet model","authors":"Lisha Xiao, Qin Yan, Shuyu Deng","doi":"10.1109/ISKE.2017.8258820","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258820","url":null,"abstract":"Scene classification is an important research branch of image comprehension, which gains information from images and interprets them using computer system by imitating the biological systems of human beings. AlexNet model is limited in image classification because of the large convolution kernel and stride in the first convolutional layer leading to over rapid decline of feature maps resolution and excessive compression of spatial information. This paper proposed an improved AlexNet model according to the design principle of convolutional neural networks (CNNs). The large convolution kernel is decomposed into a structure cascaded by two small convolution kernels with reduced stride. Another convolutional layer is added after the first one to enhance the integration process of the low-level features or the spatial information. The asymmetric convolution kernel is applied in the last three convolutional layers. The experiments on two datasets show that the classification accuracy of the improved AlexNet model is higher than those of AlexNet model and ZFNet model for 23 categories of scene classification.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"39 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":"133550115","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.2017.8258806
Xuehuai Shi, Qianmu Li, Yong Qi, Tiantian Huang, Jianmei Li
As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.
{"title":"An accident prediction approach based on XGBoost","authors":"Xuehuai Shi, Qianmu Li, Yong Qi, Tiantian Huang, Jianmei Li","doi":"10.1109/ISKE.2017.8258806","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258806","url":null,"abstract":"As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"16 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":"131087525","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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