Pub Date : 2009-12-18DOI: 10.1109/ICDIM.2009.5356780
A. Chua, D. Goh
The reduction in news cycle time coupled with high Internet penetration have resulted in a phenomenon known as ‘citizen journalism’ where ordinary people who are non-journalists collect, analyze and disseminate news pieces. This paper leverages tags drawn from iReport, an active citizen journalism Website to elicit breaking news. The goal is to examine the coverage and effectiveness of news elicitation in iReport vis-à-vis those reported in the mainstream media. The data collection procedure involved manually culling major news events reported in mainstream news sources between April 8 and June 6 2008. In parallel, tags from iReport postings were extracted during the same study period. Tags were analyzed using correlational analysis and relative frequencies. Results show that out of the 10 major news events reported in mainstream sources, five could be elicited from tags in iReport. Implications and suggestions for future research are also discussed
{"title":"Using tags for breaking news elicitation","authors":"A. Chua, D. Goh","doi":"10.1109/ICDIM.2009.5356780","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356780","url":null,"abstract":"The reduction in news cycle time coupled with high Internet penetration have resulted in a phenomenon known as ‘citizen journalism’ where ordinary people who are non-journalists collect, analyze and disseminate news pieces. This paper leverages tags drawn from iReport, an active citizen journalism Website to elicit breaking news. The goal is to examine the coverage and effectiveness of news elicitation in iReport vis-à-vis those reported in the mainstream media. The data collection procedure involved manually culling major news events reported in mainstream news sources between April 8 and June 6 2008. In parallel, tags from iReport postings were extracted during the same study period. Tags were analyzed using correlational analysis and relative frequencies. Results show that out of the 10 major news events reported in mainstream sources, five could be elicited from tags in iReport. Implications and suggestions for future research are also discussed","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130041964","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 : 2009-12-18DOI: 10.1109/ICDIM.2009.5356776
Shin Ishida, Qiang Ma, Masatoshi Yoshikawa
In some sense, news is probably never free from the agencies ' subjective valuation and external forces such as owners and advertisers. As a result, the perspective of news content may be biased. To clarify such a bias, we propose a novel method to extract characteristic descriptions on a certain entity (person, location, organization, etc.) in articles of a news agency. For a given entity, a description is one tuple (called SVO tuple) that consists ofthat entity and the other words or phrases appearing in the same sentence on the basis of their SVO (Subject(S), Verb(V) and Object(O)) roles. By computing the frequency and inverse agency frequency of each description, we extract the characteristic description on a certain entity. Intuitively, a SVO tuple, which is often used by the news agency but not commonly used by the others, has high probability of being of a characteristic description. To validate our method, we carried out an experiment to extract characteristic descriptions on persons by using articles from three well-known Japanese newspaper agencies. The experimental results show that our method can elucidate the different features of each agency's writing style. We discuss the useful application using our method and further work.
{"title":"Analysis of news agencies' descriptive feature by using SVO structure","authors":"Shin Ishida, Qiang Ma, Masatoshi Yoshikawa","doi":"10.1109/ICDIM.2009.5356776","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356776","url":null,"abstract":"In some sense, news is probably never free from the agencies ' subjective valuation and external forces such as owners and advertisers. As a result, the perspective of news content may be biased. To clarify such a bias, we propose a novel method to extract characteristic descriptions on a certain entity (person, location, organization, etc.) in articles of a news agency. For a given entity, a description is one tuple (called SVO tuple) that consists ofthat entity and the other words or phrases appearing in the same sentence on the basis of their SVO (Subject(S), Verb(V) and Object(O)) roles. By computing the frequency and inverse agency frequency of each description, we extract the characteristic description on a certain entity. Intuitively, a SVO tuple, which is often used by the news agency but not commonly used by the others, has high probability of being of a characteristic description. To validate our method, we carried out an experiment to extract characteristic descriptions on persons by using articles from three well-known Japanese newspaper agencies. The experimental results show that our method can elucidate the different features of each agency's writing style. We discuss the useful application using our method and further work.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125587524","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 : 2009-12-18DOI: 10.1109/ICDIM.2009.5356768
Yehia El Rakaiby, F. Cuppens, N. Cuppens-Boulahia
In this paper, we present a formal contextual security model for pervasive computing applications. Main features of the model are: support of authorization and obligation policies, monitoring and dynamic revocation of access rights, support of personalized security rule contexts, and support of collaborative applications. The model is also logic-based. Therefore, it enables the use of formal policy conflict and dynamic system analysis techniques.
{"title":"From state-based to event-based contextual security policies","authors":"Yehia El Rakaiby, F. Cuppens, N. Cuppens-Boulahia","doi":"10.1109/ICDIM.2009.5356768","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356768","url":null,"abstract":"In this paper, we present a formal contextual security model for pervasive computing applications. Main features of the model are: support of authorization and obligation policies, monitoring and dynamic revocation of access rights, support of personalized security rule contexts, and support of collaborative applications. The model is also logic-based. Therefore, it enables the use of formal policy conflict and dynamic system analysis techniques.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116635063","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 : 2009-12-18DOI: 10.1109/ICDIM.2009.5356791
Chendong Li
Association rule mining is a popular technique in data mining and it has an extremely wide application area. In this paper, we study the association rule mining problem and propose a cascaded approach to extract the interesting healthy nutritional dietary patterns. Our approach is mainly based on the Apri-ori algorithm and rule deduction techniques. To test the feasibility and effectiveness of the new approach, we conduct series of experiments with the data obtained from the U. S. Department of Agriculture Food and Nutrient Database for Dietary Studies 3.0. Our experimental results demonstrate that the proposed approach can successfully extract many interesting healthy nutritional dietary patterns. Also some important patterns are unknown before.
{"title":"Towards the healthy nutritional dietary patterns","authors":"Chendong Li","doi":"10.1109/ICDIM.2009.5356791","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356791","url":null,"abstract":"Association rule mining is a popular technique in data mining and it has an extremely wide application area. In this paper, we study the association rule mining problem and propose a cascaded approach to extract the interesting healthy nutritional dietary patterns. Our approach is mainly based on the Apri-ori algorithm and rule deduction techniques. To test the feasibility and effectiveness of the new approach, we conduct series of experiments with the data obtained from the U. S. Department of Agriculture Food and Nutrient Database for Dietary Studies 3.0. Our experimental results demonstrate that the proposed approach can successfully extract many interesting healthy nutritional dietary patterns. Also some important patterns are unknown before.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134098921","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 : 2009-12-18DOI: 10.1109/ICDIM.2009.5356798
Peter Bergström, Darren C. Atkinson
Web-based digital libraries have sped up the process that scholars use to find new, important research papers. Unfortunately, current digital libraries are limited by their inadequate webpage-based paradigm, and it is easy for even the most experienced scholar to get lost. A paper and its immediate references are shown on a webpage, but it is not obvious where that paper belongs in the larger context of a field of research. The goal for our research was to develop and test the effectiveness of a web-based application, PaperCube, that was designed to augment a scholar's interaction with a digital library and explore bibliographic meta data using a defined set of visualizations. These visualizations needed to provide different levels of visibility into a paper's citation network without losing focus of the currently viewed paper. PaperCube was validated through a user study which showed that it was very useful when it comes to augmenting digital library search by reducing the ¿cognitive load¿ put on a scholar and aiding the ¿discoverability¿ of new research material.
{"title":"Augmenting the exploration of digital libraries with web-based visualizations","authors":"Peter Bergström, Darren C. Atkinson","doi":"10.1109/ICDIM.2009.5356798","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356798","url":null,"abstract":"Web-based digital libraries have sped up the process that scholars use to find new, important research papers. Unfortunately, current digital libraries are limited by their inadequate webpage-based paradigm, and it is easy for even the most experienced scholar to get lost. A paper and its immediate references are shown on a webpage, but it is not obvious where that paper belongs in the larger context of a field of research. The goal for our research was to develop and test the effectiveness of a web-based application, PaperCube, that was designed to augment a scholar's interaction with a digital library and explore bibliographic meta data using a defined set of visualizations. These visualizations needed to provide different levels of visibility into a paper's citation network without losing focus of the currently viewed paper. PaperCube was validated through a user study which showed that it was very useful when it comes to augmenting digital library search by reducing the ¿cognitive load¿ put on a scholar and aiding the ¿discoverability¿ of new research material.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128223980","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 : 2009-12-18DOI: 10.1109/ICDIM.2009.5356792
Huajing Yao, Imran Ahmad
In this paper, we propose a simple and novel method for background modeling and foreground segmentation for visual surveillance applications. This method employs histogram based median method using HSV color space and a fuzzy k-means clustering. A histogram for each pixel among the training frames is constructed first, then the highest bin of the histogram is chosen and the median value among this bin is selected as the estimated value of background model for this pixel. A background model is established after the above procedure is applied to all the pixels. Fuzzy k-means clustering is used to classify each pixel in current frame either as the background pixel or the foreground pixel. Experimental results on a set of indoor videos show the effectiveness of the proposed method. Compared with other two contemporary methods — k-means clustering and Mixture of Gaussians (MoG) — the proposed method is not only time efficient but also provides better segmentation results.
{"title":"Adaptive foreground segmentation using fuzzy approach","authors":"Huajing Yao, Imran Ahmad","doi":"10.1109/ICDIM.2009.5356792","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356792","url":null,"abstract":"In this paper, we propose a simple and novel method for background modeling and foreground segmentation for visual surveillance applications. This method employs histogram based median method using HSV color space and a fuzzy k-means clustering. A histogram for each pixel among the training frames is constructed first, then the highest bin of the histogram is chosen and the median value among this bin is selected as the estimated value of background model for this pixel. A background model is established after the above procedure is applied to all the pixels. Fuzzy k-means clustering is used to classify each pixel in current frame either as the background pixel or the foreground pixel. Experimental results on a set of indoor videos show the effectiveness of the proposed method. Compared with other two contemporary methods — k-means clustering and Mixture of Gaussians (MoG) — the proposed method is not only time efficient but also provides better segmentation results.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133867573","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 : 2009-12-18DOI: 10.1109/ICDIM.2009.5356774
Marc Moreau, W. Osborn, B. Anderson
We propose the mqr-tree, a two-dimensional spatial access method that improves upon the 2DR-tree. The 2DR-tree uses two-dimensional nodes so that the relationships between all objects are maintained. The existing structure of the 2DR-tree has many advantages. However, limitations include higher tree height, overcoverage and overlap than is necessary. The mqr-tree improves utilizes a different node organization, set of validity rules and insertion strategy. A comparison versus the R-tree shows significant improvements in overlap and overcoverage, with comparable height and space utilization. In addition, zero overlap is achieved when the mqr-tree is used to index point data.
{"title":"The mqr-tree: Improving upon a 2-dimensional spatial access method","authors":"Marc Moreau, W. Osborn, B. Anderson","doi":"10.1109/ICDIM.2009.5356774","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356774","url":null,"abstract":"We propose the mqr-tree, a two-dimensional spatial access method that improves upon the 2DR-tree. The 2DR-tree uses two-dimensional nodes so that the relationships between all objects are maintained. The existing structure of the 2DR-tree has many advantages. However, limitations include higher tree height, overcoverage and overlap than is necessary. The mqr-tree improves utilizes a different node organization, set of validity rules and insertion strategy. A comparison versus the R-tree shows significant improvements in overlap and overcoverage, with comparable height and space utilization. In addition, zero overlap is achieved when the mqr-tree is used to index point data.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830230","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 : 2009-12-18DOI: 10.1109/ICDIM.2009.5356767
K. Denecke, T. Risse, Thomas Baehr
In this paper, we introduce a method for categorizing digital items according to their topic, only relying on the document's metadata, such as author name and title information. The proposed approach is based on a set of lexical resources constructed for our purposes (e.g., journal titles, conference names) and on a traditional machine-learning classifier that assigns one category to each document based on identified core features. The system is evaluated on a real-world data set and the influence of different feature combinations and settings is studied. Although the available information is limited, the results show that the approach is capable to efficiently classify data items representing documents.
{"title":"Text classification based on limited bibliographic metadata","authors":"K. Denecke, T. Risse, Thomas Baehr","doi":"10.1109/ICDIM.2009.5356767","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356767","url":null,"abstract":"In this paper, we introduce a method for categorizing digital items according to their topic, only relying on the document's metadata, such as author name and title information. The proposed approach is based on a set of lexical resources constructed for our purposes (e.g., journal titles, conference names) and on a traditional machine-learning classifier that assigns one category to each document based on identified core features. The system is evaluated on a real-world data set and the influence of different feature combinations and settings is studied. Although the available information is limited, the results show that the approach is capable to efficiently classify data items representing documents.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126289162","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 : 2009-12-18DOI: 10.1109/ICDIM.2009.5356781
Xin Ying Qiu
The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general research question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive models with annual reports. More specifically, we focus on these two questions: 1) can the advantages of the ranking algorithm help achieve better predictive performance with annual reports? and 2) can we integrate meta semantic features to help support our prediction? We compare models built with different ranking algorithms and document models. We evaluate our models with a simulated investment portfolio. Our results show significantly positive average returns over 5 years with a power law trend as we increase the ranking threshold. Adding meta features to document model has shown to improve ranking performance. The SVR & Meta-augemented model outperforms the others and provides potential for explaining the textual factors behind the prediction.
{"title":"Learning to rank firms with annual reports","authors":"Xin Ying Qiu","doi":"10.1109/ICDIM.2009.5356781","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356781","url":null,"abstract":"The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general research question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive models with annual reports. More specifically, we focus on these two questions: 1) can the advantages of the ranking algorithm help achieve better predictive performance with annual reports? and 2) can we integrate meta semantic features to help support our prediction? We compare models built with different ranking algorithms and document models. We evaluate our models with a simulated investment portfolio. Our results show significantly positive average returns over 5 years with a power law trend as we increase the ranking threshold. Adding meta features to document model has shown to improve ranking performance. The SVR & Meta-augemented model outperforms the others and provides potential for explaining the textual factors behind the prediction.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125029267","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 : 2009-11-01DOI: 10.1109/ICDIM.2009.5356771
Yazid Benazzouz, P. Beaune, F. Ramparany, Laure Chotard
Context data-driven approach refers to the process of collecting and storing context data from a wide range of context sources as sensors and web services. This approach differs from existing context-aware applications, where context models and applications are closely related and ignore how the context is derived from sources and interpreted. In this paper, we propose a context data model based on semantic web technologies for ubiquitous computing applications. This model facilitates the specification of context data to be easy interpreted by applications and services. In addition, this model is supported by existing communications protocols. Therefore, our context data model is applied to promote mining of context data towards service adaptation in ubiquitous computing systems.
{"title":"Context data-driven approach for ubiquitous computing applications","authors":"Yazid Benazzouz, P. Beaune, F. Ramparany, Laure Chotard","doi":"10.1109/ICDIM.2009.5356771","DOIUrl":"https://doi.org/10.1109/ICDIM.2009.5356771","url":null,"abstract":"Context data-driven approach refers to the process of collecting and storing context data from a wide range of context sources as sensors and web services. This approach differs from existing context-aware applications, where context models and applications are closely related and ignore how the context is derived from sources and interpreted. In this paper, we propose a context data model based on semantic web technologies for ubiquitous computing applications. This model facilitates the specification of context data to be easy interpreted by applications and services. In addition, this model is supported by existing communications protocols. Therefore, our context data model is applied to promote mining of context data towards service adaptation in ubiquitous computing systems.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116417172","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：