Pub Date : 2014-11-20DOI: 10.1109/ICOT.2014.6956614
Yongsheng Pan, Yong Xia
Traveling salesman Problem (TSP) is a classical NP-hard problem and has been extensively studied in literature. Eliminating the cross paths, which commonly exist in approximate solutions to large scale TSP, can effectively improve the quality of the solutions. Through studying the impact of cross paths on the cost of a loop, in this paper we develop a method to detect and dismantle cross paths, and thus propose a novel greedy algorithm-based approach to the TSP. This approach has been evaluated on ten TSP data sets and compared to three classical optimization techniques, including the elastic network, ant colony algorithm and genetic algorithm. Our results show that the proposed approach can get approximate solution of high quality with far less computational cost and has an excellent performance in solving large-scale TSP.
{"title":"Solving TSP by dismantling cross paths","authors":"Yongsheng Pan, Yong Xia","doi":"10.1109/ICOT.2014.6956614","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956614","url":null,"abstract":"Traveling salesman Problem (TSP) is a classical NP-hard problem and has been extensively studied in literature. Eliminating the cross paths, which commonly exist in approximate solutions to large scale TSP, can effectively improve the quality of the solutions. Through studying the impact of cross paths on the cost of a loop, in this paper we develop a method to detect and dismantle cross paths, and thus propose a novel greedy algorithm-based approach to the TSP. This approach has been evaluated on ten TSP data sets and compared to three classical optimization techniques, including the elastic network, ant colony algorithm and genetic algorithm. Our results show that the proposed approach can get approximate solution of high quality with far less computational cost and has an excellent performance in solving large-scale TSP.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134172853","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6956611
Dengwang Li, Li Liu, Jinhu Chen, Hongsheng Li, Yong Yin
A multi step liver segmentation method is proposed by combining improved level set based method with texture analysis technique for computed tomography (CT) images in this work. The aiming of proposed algorithm is to overcome the segmentation problem which is caused by similar intensities between liver region and its neighboring tissues, also robust to the variations of shape and size within liver region. Firstly, the total variation with the L1 norm (TV-L1) was used for obtaining the initial liver region, which can make the algorithm more efficient and robust. Secondly, both of global and local energy functions with the level set based method are used for extracting the liver region. Finally, the texture analysis method which is based on gray level co-occurrence matrix (GLCM) was used for refining the liver region boundary. The experimental results on 16 clinical planning CT for radiation therapy were used for demonstrating the efficiency of the proposed method both quantitatively and qualitatively.
{"title":"A multistep liver segmentation strategy by combining level set based method with texture analysis for CT images","authors":"Dengwang Li, Li Liu, Jinhu Chen, Hongsheng Li, Yong Yin","doi":"10.1109/ICOT.2014.6956611","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956611","url":null,"abstract":"A multi step liver segmentation method is proposed by combining improved level set based method with texture analysis technique for computed tomography (CT) images in this work. The aiming of proposed algorithm is to overcome the segmentation problem which is caused by similar intensities between liver region and its neighboring tissues, also robust to the variations of shape and size within liver region. Firstly, the total variation with the L1 norm (TV-L1) was used for obtaining the initial liver region, which can make the algorithm more efficient and robust. Secondly, both of global and local energy functions with the level set based method are used for extracting the liver region. Finally, the texture analysis method which is based on gray level co-occurrence matrix (GLCM) was used for refining the liver region boundary. The experimental results on 16 clinical planning CT for radiation therapy were used for demonstrating the efficiency of the proposed method both quantitatively and qualitatively.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114597381","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6956626
Hsien-Shun Kuo, Po-Hsun Sung, Sheng-Chieh Lee, Ta-Wen Kuan, Jhing-Fa Wang
An auditory-based feature extraction algorithm is proposed for enhancing the robustness of automatic speech recognition. In the proposed approach, the speech signal is characterized using a new feature referred to as the Basilar-membrane Frequency-band Cepstral Coefficient (BFCC). In contrast to the conventional Mel-Frequency Cepstral Coefficient (MFCC) method based on a Fourier spectrogram, the proposed BFCC method uses an auditory spectrogram based on a gammachirp wavelet transform in order to more accurately mimic the auditory response of the human ear and improve the noise immunity. In addition, a Hidden Markov Model (HMM) is used for both training and testing purposes. The evaluation results obtained using the AURORA 2 noisy speech database show that compared to the MFCC method, the proposed scheme improves the speech recognition rate by 15% on average given speech samples with Siganl-to-Noise Ratios (SNRs) ranging from 0 to 20 dB. Thus, the proposed method has significant potential for the development of robust speech recognition systems for ambient assisted living.
{"title":"Auditory-based robust speech recognition system for ambient assisted living in smart home","authors":"Hsien-Shun Kuo, Po-Hsun Sung, Sheng-Chieh Lee, Ta-Wen Kuan, Jhing-Fa Wang","doi":"10.1109/ICOT.2014.6956626","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956626","url":null,"abstract":"An auditory-based feature extraction algorithm is proposed for enhancing the robustness of automatic speech recognition. In the proposed approach, the speech signal is characterized using a new feature referred to as the Basilar-membrane Frequency-band Cepstral Coefficient (BFCC). In contrast to the conventional Mel-Frequency Cepstral Coefficient (MFCC) method based on a Fourier spectrogram, the proposed BFCC method uses an auditory spectrogram based on a gammachirp wavelet transform in order to more accurately mimic the auditory response of the human ear and improve the noise immunity. In addition, a Hidden Markov Model (HMM) is used for both training and testing purposes. The evaluation results obtained using the AURORA 2 noisy speech database show that compared to the MFCC method, the proposed scheme improves the speech recognition rate by 15% on average given speech samples with Siganl-to-Noise Ratios (SNRs) ranging from 0 to 20 dB. Thus, the proposed method has significant potential for the development of robust speech recognition systems for ambient assisted living.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121412624","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6956615
Chen Chen, Hou Chunyan, Yu Xiaojie
Recommendation systems have been widely used in social media. One of recommendation tasks in social media is to provide relevant messages for users. Although many models have been proposed, how to make accurate recommendation for new users with little historical information still remains a big challenge, which is called the cold start problem. In order to address this problem, many models have been proposed, which use information of social media to improve the recommendation performance. However, lack of such versatility limits the successful application of these models. In this study, we propose an effective facet-based trend model to describe the trend interests of the entire user community in social media. Trend facet is the probability distribution of all users' preference to an attribute. In contrast to the general feature, the facet stems from the users' history and captures the interests to the attribute in social media. We evaluate our models in the context of personalized ranking of microblogs. Experiments on real-world data show that trend modeling can alleviate the cold start problem significantly. In addition, we compare the performance of user modeling and trend modeling, and find that user modeling outperforms trending model and the impact is slightly negative when combining trend modeling with user modeling.
{"title":"Facet-based trend modeling for cold start of recommendation in social media","authors":"Chen Chen, Hou Chunyan, Yu Xiaojie","doi":"10.1109/ICOT.2014.6956615","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956615","url":null,"abstract":"Recommendation systems have been widely used in social media. One of recommendation tasks in social media is to provide relevant messages for users. Although many models have been proposed, how to make accurate recommendation for new users with little historical information still remains a big challenge, which is called the cold start problem. In order to address this problem, many models have been proposed, which use information of social media to improve the recommendation performance. However, lack of such versatility limits the successful application of these models. In this study, we propose an effective facet-based trend model to describe the trend interests of the entire user community in social media. Trend facet is the probability distribution of all users' preference to an attribute. In contrast to the general feature, the facet stems from the users' history and captures the interests to the attribute in social media. We evaluate our models in the context of personalized ranking of microblogs. Experiments on real-world data show that trend modeling can alleviate the cold start problem significantly. In addition, we compare the performance of user modeling and trend modeling, and find that user modeling outperforms trending model and the impact is slightly negative when combining trend modeling with user modeling.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131475503","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6956610
Zexuan Ji, Jinyao Liu, Guannan Li
Fuzzy clustering has been extensively used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation. A novel spatial factor is proposed by incorporating the spatial information with a simple metric, which is fast and easy to implement. By taking the spatial direction into account based on the posterior and prior probabilities, the proposed method can preserve more details and overcome the over-smoothing disadvantage. Finally, the fuzzy objective function is integrated with the bias field estimation model to overcome intensity inhomogeneity in the image. Experimental results demonstrate that the proposed algorithm can substantially improve the accuracy of brain MR image segmentation.
{"title":"A fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation","authors":"Zexuan Ji, Jinyao Liu, Guannan Li","doi":"10.1109/ICOT.2014.6956610","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956610","url":null,"abstract":"Fuzzy clustering has been extensively used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation. A novel spatial factor is proposed by incorporating the spatial information with a simple metric, which is fast and easy to implement. By taking the spatial direction into account based on the posterior and prior probabilities, the proposed method can preserve more details and overcome the over-smoothing disadvantage. Finally, the fuzzy objective function is integrated with the bias field estimation model to overcome intensity inhomogeneity in the image. Experimental results demonstrate that the proposed algorithm can substantially improve the accuracy of brain MR image segmentation.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114396056","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6954693
Wei Wei, Yanning Zhang, Lei Zhang, Hangqi Yan, Bobo Wang
Bridge plays an important role in people's life. The automatic bridge detection has great value especially in disaster situation. In this paper, we propose an automatic bridge detection method in multispectral image to detect bridges over water. First, we extract the water region using NDWI index method by taking the advantage of the spectrum properties of water. Second, the water region is extended to include the bridges over water and the extended water region is adaptively segmented to locate the bridges roughly. Then, we separate the suspects into two kinds: Suspects along the bounder of the extended water region or the ones inside in this region, which are processed with different strategy. Partial interior suspects of the extended water region are extracted by the proposed RX detector. Finally, the context information of bridges is introduced to get the final decision. The experimental results on the TM images demonstrate the effectiveness of the proposed method.
{"title":"Multispectral images based bridge detection method with RX detector","authors":"Wei Wei, Yanning Zhang, Lei Zhang, Hangqi Yan, Bobo Wang","doi":"10.1109/ICOT.2014.6954693","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6954693","url":null,"abstract":"Bridge plays an important role in people's life. The automatic bridge detection has great value especially in disaster situation. In this paper, we propose an automatic bridge detection method in multispectral image to detect bridges over water. First, we extract the water region using NDWI index method by taking the advantage of the spectrum properties of water. Second, the water region is extended to include the bridges over water and the extended water region is adaptively segmented to locate the bridges roughly. Then, we separate the suspects into two kinds: Suspects along the bounder of the extended water region or the ones inside in this region, which are processed with different strategy. Partial interior suspects of the extended water region are extracted by the proposed RX detector. Finally, the context information of bridges is introduced to get the final decision. The experimental results on the TM images demonstrate the effectiveness of the proposed method.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123795190","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6956638
Xingyu Wei, T. Zhou, Huiling Lu
This article proposed a new fusion algorithm of PET/CT based on dual-tree complex wavelet transform and self-adaption Gaussian membership function. Firstly, preprocessed and registered PET and CT image of non-small cell lung cancer. Secondly, used dual-tree complex wavelet transform to decompose PET and CT image in order to get the low-frequency and high-frequency components. Thirdly, using self-adaption Gaussian membership function to fuse low-frequency components. Finally, using two experiments to verify validity and feasibility of the proposed algorithm. The experiments results shown that the algorithm is efficiency.
{"title":"A fusion algorithm of PET-CT based on dual-tree complex wavelet transform and self-adaption Gaussian membership function","authors":"Xingyu Wei, T. Zhou, Huiling Lu","doi":"10.1109/ICOT.2014.6956638","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956638","url":null,"abstract":"This article proposed a new fusion algorithm of PET/CT based on dual-tree complex wavelet transform and self-adaption Gaussian membership function. Firstly, preprocessed and registered PET and CT image of non-small cell lung cancer. Secondly, used dual-tree complex wavelet transform to decompose PET and CT image in order to get the low-frequency and high-frequency components. Thirdly, using self-adaption Gaussian membership function to fuse low-frequency components. Finally, using two experiments to verify validity and feasibility of the proposed algorithm. The experiments results shown that the algorithm is efficiency.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121339745","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6956627
Shu-Chiang Chung, S. Barma, Ta-Wen Kuan, Ting-Wei Lin
This paper proposes a novel method to improve happiness status by detection negative emotional status based on frowning lines on face and a new term called facial expression factor (FEF). The FEF correlates the frowning and with emotional status. The frowning lines are detected using SOBEL filter and FEF factors are calculated from selected frowning lines to know the actual emotional status. Thus the negative emotional state are detected which could help to promote the happiness further. The experiment is conducted on 10 participants. In total 40 images (including 20 neutral and 20 frowning expression) are considered for experiment. The results show that the emotional status of 8 persons out of 10 participants is recognized correctly. Further, the wrong recognition results are corrected by tuning the threshold. Hence, the results depict the recognition accuracy up to 80%. The proposed work is based on simple training which also reduces the training time cost effectively. Furthermore, the proposed method is able to detect more complex facial expression (e.g., forced smile) using FEF. The tuning of threshold makes the method more effective. Therefore, such results show its effectiveness by detecting negative emotional state to promote the happiness.
{"title":"Frowning expression detection based on SOBEL filter for negative emotion recognition","authors":"Shu-Chiang Chung, S. Barma, Ta-Wen Kuan, Ting-Wei Lin","doi":"10.1109/ICOT.2014.6956627","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956627","url":null,"abstract":"This paper proposes a novel method to improve happiness status by detection negative emotional status based on frowning lines on face and a new term called facial expression factor (FEF). The FEF correlates the frowning and with emotional status. The frowning lines are detected using SOBEL filter and FEF factors are calculated from selected frowning lines to know the actual emotional status. Thus the negative emotional state are detected which could help to promote the happiness further. The experiment is conducted on 10 participants. In total 40 images (including 20 neutral and 20 frowning expression) are considered for experiment. The results show that the emotional status of 8 persons out of 10 participants is recognized correctly. Further, the wrong recognition results are corrected by tuning the threshold. Hence, the results depict the recognition accuracy up to 80%. The proposed work is based on simple training which also reduces the training time cost effectively. Furthermore, the proposed method is able to detect more complex facial expression (e.g., forced smile) using FEF. The tuning of threshold makes the method more effective. Therefore, such results show its effectiveness by detecting negative emotional state to promote the happiness.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129388518","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6956599
Tao Zhuo, Peng Zhang, Yanning Zhang, Wei Huang
Orange technologies focus on individual behavior analysis, and the core of which is object tracking, especially arbitrary object tracking. One of the popular solution for arbitrary object tracking is tracking by detection. These approaches regard the tracking problem as a detection task, and use the online learning methods to adapt the classifier to various object appearance changes. However, due to lack of prior knowledge and unpredictable appearance changes, it is always hard to get accurate target location during the whole tracking process. In this paper, we incorporate a motion model into the tracking by detection framework. Besides object prediction, the motion model also guides the model updating process to guarantee the performance of the classifier. Experimentally, we show that our algorithm is able to outperform state of art trackers on benchmark data sets.
{"title":"Online object tracking based on L1-loss SVMs with motion constraints","authors":"Tao Zhuo, Peng Zhang, Yanning Zhang, Wei Huang","doi":"10.1109/ICOT.2014.6956599","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956599","url":null,"abstract":"Orange technologies focus on individual behavior analysis, and the core of which is object tracking, especially arbitrary object tracking. One of the popular solution for arbitrary object tracking is tracking by detection. These approaches regard the tracking problem as a detection task, and use the online learning methods to adapt the classifier to various object appearance changes. However, due to lack of prior knowledge and unpredictable appearance changes, it is always hard to get accurate target location during the whole tracking process. In this paper, we incorporate a motion model into the tracking by detection framework. Besides object prediction, the motion model also guides the model updating process to guarantee the performance of the classifier. Experimentally, we show that our algorithm is able to outperform state of art trackers on benchmark data sets.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126326000","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 : 2014-11-20DOI: 10.1109/ICOT.2014.6956639
H. Huang, Tsung-Han Hsieh
Users can use social network sites automatically, it comes from user's habit, it's a no-conscious action (Wohn et al., 2012). Therefore, users have interactions in social networks sites, it makes life well-being. At the same time, it creates postive effects. For the reason, there are more and more people who like to participate in social networks sites. In the research, the study try to use the concept of social capital theoy to build a Moble App system that is conform the good design principle and possesses the capabitly to transmiting social capital throgh internet interactions.
用户可以自动使用社交网站，它来自于用户的习惯，是一种无意识的行为(Wohn et al.， 2012)。因此，用户在社交网站上进行互动，使生活幸福。同时，它也产生了积极的影响。因此，越来越多的人喜欢参加社交网站。在研究中，本研究试图运用社会资本理论的概念，构建一个符合良好设计原则，并具有通过互联网互动传递社会资本能力的移动App系统。
{"title":"Builing the Moble App system of postive social coummity by using social capital theory","authors":"H. Huang, Tsung-Han Hsieh","doi":"10.1109/ICOT.2014.6956639","DOIUrl":"https://doi.org/10.1109/ICOT.2014.6956639","url":null,"abstract":"Users can use social network sites automatically, it comes from user's habit, it's a no-conscious action (Wohn et al., 2012). Therefore, users have interactions in social networks sites, it makes life well-being. At the same time, it creates postive effects. For the reason, there are more and more people who like to participate in social networks sites. In the research, the study try to use the concept of social capital theoy to build a Moble App system that is conform the good design principle and possesses the capabitly to transmiting social capital throgh internet interactions.","PeriodicalId":343641,"journal":{"name":"2014 International Conference on Orange Technologies","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128617596","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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