• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.119-140
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• 2007

Recommender systems enable a user to decide which information is interesting and valuable in our world of information overload. As the recent studies of distributed computing environment have been progressing actively, recommender systems, most of which were centralized, have changed toward a peer-to-peer approach. Collaborative Filtering (CF), one of the most successful technologies in recommender systems, presents several limitations, namely sparsity, scalability, cold start, and the shilling problem, in spite of its popularity. The move from centralized systems to distributed approaches can partially improve the issues; distrust of recommendation and abuses of personal information. However, distributed systems can be vulnerable to attackers, who may inject biased profiles to force systems to adapt their objectives. In this paper, we consider both effective CF in P2P environment in order to improve overall performance of system and efficient solution of the problems related to abuses of personal data and attacks of malicious users. To deal with these issues, we propose a multi-agent framework for a distributed CF focusing on the trust relationships between individuals, i.e. web of trust. We employ an agent-based approach to improve the efficiency of distributed computing and propagate trust information among users with effect. The experimental evaluation shows that the proposed method brings significant improvement in terms of the distributed computing of similarity model building and the robustness of system against malicious attacks. Finally, we are planning to study trust propagation mechanisms by taking trust decay problem into consideration.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.608-615
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• 2008

In Knowledge Q&A services where information is created by unspecified users, document quality is an important factor of user satisfaction with search results. Previous work on quality prediction of Knowledge Q&A documents evaluate the quality of documents by using non-textual information, such as click counts and recommendation counts, and focus on enhancing retrieval performance by incorporating the quality measure into retrieval model. Although the non-textual information used in previous work was proven to be useful by experiments, data sparseness problem may occur when predicting the quality of newly created documents with such information. To solve data sparseness problem of non-textual features, this paper proposes new features for document quality prediction, namely text-confidence features, which indicate how trustworthy the content of a document is. The proposed features, extracted directly from the document content, are stable against data sparseness problem, compared to non-textual features that indirectly require participation of service users in order to be collected. Experiments conducted on real world Knowledge Q&A documents suggests that text-confidence features show performance comparable to the non-textual features. We believe the proposed features can be utilized as effective features for document quality prediction and improve the performance of Knowledge Q&A services in the future.

• Journal of Korean Society of Steel Construction
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• v.15 no.1
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• pp.1-8
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• 2003

In locations where the cost of concrete is relatively high or in situations where the weight of concrete members has to be kept to a minimum, it may be more economical to use hollow reinforced concrete vertic al members. Hollow reinforced concrete colun-ms with a low axial load, a moderate longitudinal steel percentage and a reasonably thick wall were found to perform in a ductile manner at the flexural strength, similar to solid columns. Hollow reinforced concrete columns with a high axial load, a high longitudinal steel percentage, and a thin wall were found, however, to behave in a brittle manner at the flexural strength, since the neutral axis is forced to occur away from the inside face of the tube towards the section centroid and, as a result, crushing of concrete occurs near the unconfined inside face of the section. If, however, a steel tube is placed near the inside face of a circular hollow column, the column can be expected not to fail in a brittle manner through the disintegration of the concrete in the compression zone. A design recommendation and example through the moment-curvature analysis program for curvature ductility are herein presented. A theoretical moment-curvature analysis for reinforced concrete columns, indicating the available flexural strength and ductility, can be conducted, providing that the stress-strain relation for the concrete and steel are known. In this paper, a unified stress-stain model for confined concrete by Mander is developed foi members with circular sections.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.343-350
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• 2010

We can get answers we want to know via questioning in Knowledge Search Service (KSS) based on Q&A Community. However, it is getting more difficult to find credible documents in enormous documents, since many anonymous users regardless of credibility are participate in answering on the question. In previous works in KSS, researchers evaluated the quality of documents based on textual information, e.g. recommendation count, click count and non-textual information, e.g. answer length, attached data, conjunction count. Then, the evaluation results are used for enhancing search performance. However, the non-textual information has a problem that it is difficult to get enough information by users in the early stage of Q&A. The textual information also has a limitation for evaluating quality because of judgement by partial factors such as answer length, conjunction counts. In this paper, we propose the QualityRank algorithm to improve the problem by textual and non-textual information. This algorithm ranks the relevant and credible answers by considering textual/non-textual information and user centrality based on Social Network Analysis(SNA). Based on experimental validation we can confirm that the results by our algorithm is improved than those of textual/non-textual in terms of ranking performance.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.183-187
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• 2017

Smart phone users prefer fast reading and texting. Hence, users frequently use abbreviated sequences of words and phrases. Nowadays, abbreviations are widely used from chat terms to technical terms. Therefore, gathering abbreviations would be helpful to many services, including information retrieval, recommendation system, and so on. However, manually gathering abbreviations needs to much effort and cost. This is because new abbreviations are continuously generated whenever a new material such as a TV program or a phenomenon is made. Thus it is required to generate of abbreviations automatically. To generate Korean abbreviations, the existing methods use the rule-based approach. The rule-based approach has limitations, in that it is unable to generate irregular abbreviations. Another problem is to decide the correct abbreviation among candidate abbreviations generated rules. To address the limitations, we propose a method of generating Korean abbreviations automatically using sequence-to-sequence learning in this paper. The sequence-to-sequence learning can generate irregular abbreviation and does not lead to the problem of deciding correct abbreviation among candidate abbreviations. Accordingly, it is suitable for generating Korean abbreviations. To evaluate the proposed method, we use dataset of two type. As experimental results, we prove that our method is effective for irregular abbreviations.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.1-23
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• 2011

The contextual subjective well-being (SWB) of context-aware system users can be very helpful in recommending relevant mental health services, especially for those who struggle with mental illness due to a metabolic syndrome or melancholia. Self-surveying measuring or auto-sensing methods have been suggested to monitor users' SWB. However, self-surveying measuring method is not inappropriate for a context-aware service due to requesting personal data in a manual and hence obtrusive manner. Moreover, auto-sensing methods still suffer from accuracy problem to be applied in mental health services. Hence, the purpose of this paper is to propose a contextual SWB estimation method to estimate the user's mental health in unobtrusive and accurate manners. This method is timely in that it acquires context data from the user's literal responses, which expose their temporal feeling. In particular, we developed a measuring method based on exposed feeling verbs and degree adverbs in chat and other text-based communications which show anger or negative feelings. Based on the proposed contextual SWB degree estimation method, we developed an idea of well-being life care recommendation. From the experiment with actual drivers, we demonstrated that the proposed method accurately estimate the user's degree of negative feelings even though it does not require a self-survey.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.764-773
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• 2008

The web ontology language(OWL) has become a W3C recommendation to publish and share ontologies on the semantic web. In order to check the satisfiablity of concepts in OWL ontology, OWL reasoners have been introduced. But most reasoners simply report check results without providing a justification for any arbitrary entailment of unsatisfiable concept in OWL ontologies. In this paper, we propose dependency label based causing inconsistency axiom (CIA) detection for debugging unsatisfiable concepts in ontology. CIA is a set of axioms to occur unsatisfiable concepts. In order to detect CIA, we need to find axiom to cause inconsistency in ontology. If precise CIA is gave to ontology building tools, these ontology tools display CIA to debug unsatisfiable concepts as suitable presentation format. Our work focuses on two key aspects. First, when a inconsistency ontology is given, it detect axioms to occur unsatisfiable and identify the root of them. Second, when particular unsatisfiable concepts in an ontology are detected, it extracts them and presents to ontology designers. Therefore we introduce a tableau-based decision procedure and propose an improved method which is dependency label based causing inconsistency axiom detection. Our results are applicable to the very expressive logic SHOIN that is the basis of the Web Ontology Language.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup1
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• pp.1-9
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• 2016

In Vietnam, the WHO Framework Convention on Tobacco Control (WHO FCTC) took effect in March 2005 while MPOWER has been implemented since 2008. This paper describes the progress and challenges of implementation of the MPOWER package in Vietnam. We can report that, in term of monitoring, Vietnam is very active in the Global Tobacco Surveillance System, completing two rounds of the Global Adult Tobacco Survey (GATS) and three rounds of the Global Youth Tobacco Survey (GYTS). To protect people from tobacco smoke, Vietnam has issued and enforced a law requiring comprehensive smoking bans at workplaces and public places since 2013. Tobacco advertising and promotion are also prohibited with the exception of points of sale displays of tobacco products. Violations come in the form of promotion girls, corporate social responsibility activities from tobacco manufacturers and packages displayed by retail vendors. Vietnam is one of the 77 countries that require pictorial health warnings to be printed on cigarette packages to warn about the danger of tobacco and the warnings have been implemented effectively. Cigarette tax is 70% of factory price which is equal to less than 45% of retail price and much lower than the recommendation of WHO. However, Vietnam is one of the very few countries that require manufacturers and importers to make "compulsory contributions" at 1-2% of the factory price of cigarettes sold in Vietnam for the establishment of a Tobacco Control Fund (TCF). The TCF is being operated well. In 2015, 67 units of 63 provinces/cities, 22 ministries and political-social organizations and 6 hospitals received funding from TCF to implement a wide range of tobacco control activities. Cessation services have been starting with a a toll-free quit-line but need to be further strengthened. In conclusion, Vietnam has constantly put efforts into the tobacco control field with high commitment from the government, scientists and activists. Though several remarkable achievements have been gained, many challenges remain. To overcome those challenges, implementation strategies that take into account the contextual factors and social determinants of tobacco use in Vietnam are needed.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.5
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• pp.11-24
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• 2011

A real-time hybrid test of a 3 story-3 bay reinforced concrete frame which is divided into numerical and physical substructure models under uniaxial earthquake excitation was run using a fixed iteration implicit HHT time integration method. The first story inner non-ductile column was selected as the physical substructure model, and uniaxial earthquake excitation was applied to the numerical model until the specimen failed due to severe damage. A finite-element analysis program, Mercury, was newly developed and optimized for a real-time hybrid test. The drift ratio based on the top horizontal displacement of the physical substructure model was compared with the result of a numerical simulation by OpenSees and the result of a shaking table test. The experiment in this paper is one of the most complex real-time hybrid tests, and the description of the hardware, algorithm and models is presented in detail. If there is an improvement in the numerical model, the evaluation of the tangent stiffness matrix of the physical substructure model in the finite element analysis program and better software to reduce the computational time of the element state determination for the force-based beam-column element, then the comparison with the results of the real-time hybrid test and the shaking table test deserves to make a recommendation. In addition, for the goal of a "Numerical simulation of the complex structures under dynamic loading", the real time hybrid test has enough merit as an alternative to dynamic experiments of large and complex structures.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.83-96
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• 2018

Extracting keywords representing documents is very important because it can be used for automated services such as document search, classification, recommendation system as well as quickly transmitting document information. However, when extracting keywords based on the frequency of words appearing in a web site documents and graph algorithms based on the co-occurrence of words, the problem of containing various words that are not related to the topic potentially in the web page structure, There is a difficulty in extracting the semantic keyword due to the limit of the performance of the Korean tokenizer. In this paper, we propose a method to select candidate keywords based on semantic similarity, and solve the problem that semantic keyword can not be extracted and the accuracy of Korean tokenizer analysis is poor. Finally, we use the technique of extracting final semantic keywords through filtering process to remove inconsistent keywords. Experimental results through real web pages of small business show that the performance of the proposed method is improved by 34.52% over the statistical similarity based keyword selection technique. Therefore, it is confirmed that the performance of extracting keywords from documents is improved by considering semantic similarity between words and removing inconsistent keywords.