Pub Date : 2015-03-02DOI: 10.1109/ICOSC.2015.7050821
H. Yamaguchi, M. Gotaishi, S. Tsujii
The ultimate objective of the problem solving system is to provide an interrelated framework for prospective users to facilitate their work, such as biological and biomedical knowledge retrieval, discovery, capture. In addition to this objective, there is demand for secure and practical computing algorithms which address the challenge to safely outsource data processing onto remote computing resources. This allows users to confidently outsource computation over sensitive information from the security level of the remote delegate. In this paper, we present the computing algorithms which preserve privacy of users and confidentiality of service providers in the cloud environment.
{"title":"Secure problems solving scheme","authors":"H. Yamaguchi, M. Gotaishi, S. Tsujii","doi":"10.1109/ICOSC.2015.7050821","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050821","url":null,"abstract":"The ultimate objective of the problem solving system is to provide an interrelated framework for prospective users to facilitate their work, such as biological and biomedical knowledge retrieval, discovery, capture. In addition to this objective, there is demand for secure and practical computing algorithms which address the challenge to safely outsource data processing onto remote computing resources. This allows users to confidently outsource computation over sensitive information from the security level of the remote delegate. In this paper, we present the computing algorithms which preserve privacy of users and confidentiality of service providers in the cloud environment.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125664452","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050848
M. Alruily, Mohammad A. Alghamdi
Most Arabic systems developed for newspaper information extraction relate to events that occurred in the past. This paper presents a proposal for developing a web-based system that will be able to regularly collect news reports from Arabic newspaper websites, and then be able to extract information relating to future events, e.g. event type, date and location. Also, the system will be able to deposit the extracted data in an online database in order to enable users to access them. The proposed approach is based on Arabic grammar and on dependency relationships.
{"title":"Extracting information of future events from Arabic newspapers: an overview","authors":"M. Alruily, Mohammad A. Alghamdi","doi":"10.1109/ICOSC.2015.7050848","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050848","url":null,"abstract":"Most Arabic systems developed for newspaper information extraction relate to events that occurred in the past. This paper presents a proposal for developing a web-based system that will be able to regularly collect news reports from Arabic newspaper websites, and then be able to extract information relating to future events, e.g. event type, date and location. Also, the system will be able to deposit the extracted data in an online database in order to enable users to access them. The proposed approach is based on Arabic grammar and on dependency relationships.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126538245","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050858
D. Ostrowski
Due to its predictive nature, Social Media has proved to be an important resource in support of the identification of trends. In Customer Relationship Management there is a need beyond trend identification which includes understanding the topics propagated through Social Networks. In this paper, we explore topic modeling by considering the techniques of Latent Dirichlet Allocation which is a generative probabilistic model for a collection of discrete data. We evaluate this technique from the perspective of classification as well as identification of noteworthy topics as it is applied to a filtered collection of Twitter messages. Experiments show that these methods are effective for the identification of sub-topics as well as to support classification within large-scale corpora.
{"title":"Using latent dirichlet allocation for topic modelling in twitter","authors":"D. Ostrowski","doi":"10.1109/ICOSC.2015.7050858","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050858","url":null,"abstract":"Due to its predictive nature, Social Media has proved to be an important resource in support of the identification of trends. In Customer Relationship Management there is a need beyond trend identification which includes understanding the topics propagated through Social Networks. In this paper, we explore topic modeling by considering the techniques of Latent Dirichlet Allocation which is a generative probabilistic model for a collection of discrete data. We evaluate this technique from the perspective of classification as well as identification of noteworthy topics as it is applied to a filtered collection of Twitter messages. Experiments show that these methods are effective for the identification of sub-topics as well as to support classification within large-scale corpora.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126450927","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050781
Talal Bonny, B. Soudan
Sequence comparison is one of the important database computing applications used in computer science, computational linguistics, social science, biology, etc. This kind of applications processes large database sequences and considered to be high consumers of computation time. Traditional methods apply comparing algorithms on the whole database to find the most matched sequences. We introduce novel and efficient technique to accelerate the sequence comparison by filtering the database to reduce the scope of searching. This will exclude a large number of the database sequences from the searching and will provide the results in reasonable time. Using our filtering technique, we explicitly accelerate the database sequence comparison by 50% in comparison to the traditional known methods.
{"title":"Filtering technique for high speed database sequence comparison","authors":"Talal Bonny, B. Soudan","doi":"10.1109/ICOSC.2015.7050781","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050781","url":null,"abstract":"Sequence comparison is one of the important database computing applications used in computer science, computational linguistics, social science, biology, etc. This kind of applications processes large database sequences and considered to be high consumers of computation time. Traditional methods apply comparing algorithms on the whole database to find the most matched sequences. We introduce novel and efficient technique to accelerate the sequence comparison by filtering the database to reduce the scope of searching. This will exclude a large number of the database sequences from the searching and will provide the results in reasonable time. Using our filtering technique, we explicitly accelerate the database sequence comparison by 50% in comparison to the traditional known methods.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128476409","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050809
Lydia Odilinye, F. Popowich, Evan Zhang, J. Nesbit, P. Winne
Automatic question generation from text has been used and adapted to online and self-directed learning platforms. We incorporate methods into the automatic question generation process that are designed to improve question quality by aligning them to the specified pedagogical goals and to a learner's model. This is achieved by extracting, ranking and filtering relevant sentences in the given learning document as well as the questions automatically generated by their semantic associations to the learner model and instructor goals. We propose evaluation techniques for assessing the quality of the questions generated using both human and automatic evaluation.
{"title":"Aligning automatically generated questions to instructor goals and learner behaviour","authors":"Lydia Odilinye, F. Popowich, Evan Zhang, J. Nesbit, P. Winne","doi":"10.1109/ICOSC.2015.7050809","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050809","url":null,"abstract":"Automatic question generation from text has been used and adapted to online and self-directed learning platforms. We incorporate methods into the automatic question generation process that are designed to improve question quality by aligning them to the specified pedagogical goals and to a learner's model. This is achieved by extracting, ranking and filtering relevant sentences in the given learning document as well as the questions automatically generated by their semantic associations to the learner model and instructor goals. We propose evaluation techniques for assessing the quality of the questions generated using both human and automatic evaluation.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132352711","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050853
L. Tuovinen, Jari Kahelin, J. Röning
Semantic metadata has many uses in online virtual environments, but the technology for acquiring and managing metadata is not yet fully developed. This paper presents a partial solution that combines and extends traditional top-down and bottom-up approaches to semantic annotation. The paper proposes a metadata architecture composed of three layers, each of which has a metadata store and an interface for importing metadata from a specific group of providers. Under certain conditions, metadata can be propagated between layers, enabling it to be shared with other users of the same application or other applications based on the same virtual reality model. A proof-of-concept implementation demonstrates the feasibility of implementing the architecture using realXtend, an open platform for online multiuser virtual reality applications.
{"title":"A conceptual framework for middle-up-down semantic annotation of online 3D scenes","authors":"L. Tuovinen, Jari Kahelin, J. Röning","doi":"10.1109/ICOSC.2015.7050853","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050853","url":null,"abstract":"Semantic metadata has many uses in online virtual environments, but the technology for acquiring and managing metadata is not yet fully developed. This paper presents a partial solution that combines and extends traditional top-down and bottom-up approaches to semantic annotation. The paper proposes a metadata architecture composed of three layers, each of which has a metadata store and an interface for importing metadata from a specific group of providers. Under certain conditions, metadata can be propagated between layers, enabling it to be shared with other users of the same application or other applications based on the same virtual reality model. A proof-of-concept implementation demonstrates the feasibility of implementing the architecture using realXtend, an open platform for online multiuser virtual reality applications.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132945564","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050782
S. Manna, Xing Hu, Robert Correa
Due to the rapid advancement of the internet technology, there is proliferation of textual data, as a result of which automatic summarization has become one of the useful means of coping with the problem of information overload. These textual data are not just in English; thus researchers started focusing on multilingual summarization platforms, so that single framework can be used to cope with different languages. Chinese being another widely spoken language, in this paper, we present an extension of Subjective Logic summarization framework (SubSum) [1], for Chinese. SubSum extracts significant sentences from documents to form extractive summaries. Quantifying uncertainty is the key advantage of SubSum over commonly used summarization methods. The main aim of this work is to show how well SubSum can be adapted to a completely different language, without making changes to the core framework. Moreover, extensive experiments on benchmark datasets demonstrate the effectiveness of SubSum applied for Chinese summarization.
{"title":"Towards multi-lingual adaptability of subjective logic based document summarization: A case study with Chinese documents","authors":"S. Manna, Xing Hu, Robert Correa","doi":"10.1109/ICOSC.2015.7050782","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050782","url":null,"abstract":"Due to the rapid advancement of the internet technology, there is proliferation of textual data, as a result of which automatic summarization has become one of the useful means of coping with the problem of information overload. These textual data are not just in English; thus researchers started focusing on multilingual summarization platforms, so that single framework can be used to cope with different languages. Chinese being another widely spoken language, in this paper, we present an extension of Subjective Logic summarization framework (SubSum) [1], for Chinese. SubSum extracts significant sentences from documents to form extractive summaries. Quantifying uncertainty is the key advantage of SubSum over commonly used summarization methods. The main aim of this work is to show how well SubSum can be adapted to a completely different language, without making changes to the core framework. Moreover, extensive experiments on benchmark datasets demonstrate the effectiveness of SubSum applied for Chinese summarization.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"898 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132317436","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050783
M. Sakurada, T. Yairi, Y. Nakajima, N. Nishimura, Devi Parikh
In this paper, we introduce a novel approach where the system involves human knowledge in the classification task using decision trees. Machine learning techniques are now applied to a variety of tasks in real-world problems. The computer performs complex computations better than humans. However, in many real-world applications, humans have background domain knowledge about the problem that the computer often does not have. For instance, in a spacecraft status classification task, humans have a sense for which factors are likely to correlate with the classes of interest. Without this knowledge, machines may overfit to training data. We propose to combine two models: one based on human reasoning, common sense, or heuristics, and the other learned by a machine learning algorithm in a data-driven manner. In our experiments, we use decision trees and categorical features so that the model consists of rules which are semantic and interpretable for humans. Our proposed approach results in an improvement in classification performance over either models alone. Our work illustrates the possibility of integrating human knowledge and artificial intelligence.
{"title":"Semantic classification of spacecraft's status: integrating system intelligence and human knowledge","authors":"M. Sakurada, T. Yairi, Y. Nakajima, N. Nishimura, Devi Parikh","doi":"10.1109/ICOSC.2015.7050783","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050783","url":null,"abstract":"In this paper, we introduce a novel approach where the system involves human knowledge in the classification task using decision trees. Machine learning techniques are now applied to a variety of tasks in real-world problems. The computer performs complex computations better than humans. However, in many real-world applications, humans have background domain knowledge about the problem that the computer often does not have. For instance, in a spacecraft status classification task, humans have a sense for which factors are likely to correlate with the classes of interest. Without this knowledge, machines may overfit to training data. We propose to combine two models: one based on human reasoning, common sense, or heuristics, and the other learned by a machine learning algorithm in a data-driven manner. In our experiments, we use decision trees and categorical features so that the model consists of rules which are semantic and interpretable for humans. Our proposed approach results in an improvement in classification performance over either models alone. Our work illustrates the possibility of integrating human knowledge and artificial intelligence.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128174627","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050823
Mariia Gavriushenko, M. Kankaanranta, P. Neittaanmäki
This article focuses on proposed semantically enhanced model of decision support system for learning management system (LMS). The model is based on a survey of LMSs and various plugins used in these to improve educational process. Systems based on semantic technologies are capable of integrating heterogeneous data, flexibly changing data schemas, semantic search (using ontologies), and joint knowledge development. The knowledge base that was developed for the proposed system model is presented in an ontological form. Ontology-based applications limit the "fragility" of the software and increase the likelihood of its reuse. In addition, they profitably redirect the efforts previously focused on software development and maintenance of creation and modification of knowledge structures. In the proposed knowledge base, we developed the necessary rules for further recommendations of specialization and courses for users. These recommendations are based on users' data extracted from profiles and user preferences.
{"title":"Semantically enhanced decision support for learning management systems","authors":"Mariia Gavriushenko, M. Kankaanranta, P. Neittaanmäki","doi":"10.1109/ICOSC.2015.7050823","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050823","url":null,"abstract":"This article focuses on proposed semantically enhanced model of decision support system for learning management system (LMS). The model is based on a survey of LMSs and various plugins used in these to improve educational process. Systems based on semantic technologies are capable of integrating heterogeneous data, flexibly changing data schemas, semantic search (using ontologies), and joint knowledge development. The knowledge base that was developed for the proposed system model is presented in an ontological form. Ontology-based applications limit the \"fragility\" of the software and increase the likelihood of its reuse. In addition, they profitably redirect the efforts previously focused on software development and maintenance of creation and modification of knowledge structures. In the proposed knowledge base, we developed the necessary rules for further recommendations of specialization and courses for users. These recommendations are based on users' data extracted from profiles and user preferences.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128268256","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 : 2015-03-02DOI: 10.1109/ICOSC.2015.7050817
R. Saripalle, A. D. L. R. Algarin, Timoteus B. Ziminski
Information privacy and security plays a major role in domains where sensitive information is handled, such as case studies of rare diseases. Currently, security for accessing any sensitive information is provided by various mechanisms at the user/system level by employing access control models such as Role Based Access Control. However, these approaches leave security at the knowledge level unattended, which can be inadequate. For example, in healthcare, ontology-based information extraction is employed for extracting medical knowledge from sensitive structured/unstructured data sources. These information extraction systems act on sensitive data sources which are protected against unauthorized access at the system level based on the user, context and permissions, but the knowledge that can be extracted from these sources is not. In this paper we tackle the security or access control at the knowledge level by presenting a model, to enforce knowledge security/access by leveraging knowledge sources (currently focused on RDF) with the RBAC model. The developed model filters out knowledge by means of binary permissions on the knowledge source, providing each user with a different view of the knowledge source.
{"title":"Towards knowledge level privacy and security using RDF/RDFS and RBAC","authors":"R. Saripalle, A. D. L. R. Algarin, Timoteus B. Ziminski","doi":"10.1109/ICOSC.2015.7050817","DOIUrl":"https://doi.org/10.1109/ICOSC.2015.7050817","url":null,"abstract":"Information privacy and security plays a major role in domains where sensitive information is handled, such as case studies of rare diseases. Currently, security for accessing any sensitive information is provided by various mechanisms at the user/system level by employing access control models such as Role Based Access Control. However, these approaches leave security at the knowledge level unattended, which can be inadequate. For example, in healthcare, ontology-based information extraction is employed for extracting medical knowledge from sensitive structured/unstructured data sources. These information extraction systems act on sensitive data sources which are protected against unauthorized access at the system level based on the user, context and permissions, but the knowledge that can be extracted from these sources is not. In this paper we tackle the security or access control at the knowledge level by presenting a model, to enforce knowledge security/access by leveraging knowledge sources (currently focused on RDF) with the RBAC model. The developed model filters out knowledge by means of binary permissions on the knowledge source, providing each user with a different view of the knowledge source.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126547342","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：