• Journal of Digital Convergence
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• v.18 no.11
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• pp.443-448
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• 2020

The purpose of research is identifying visual factors affecting purchase of convenient store packed-meals through mobile applications. Previous studies have not covered mobile applications on purchase of convenient store packed-meals and this inspired the research topic. Document analysis and online survey are mainly implemented and 4 visual factors; typography, product image, main color, brand logo have been set as a research variables. It is revealed that consumers recognize product image prior to the rest and their purchase intentions are most significantly affected by product image. In conclusion, the product image should encourage consumers to have expectation on packed-meal and need to deliver credibility at the same time. Hence, the application should be designed to solely spotlight product image to lead consumer's concentration on it. The research can be further expanded by including non-visual factors as its variables or increasing scale of survey samples.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.45-51
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• 2011

In this paper, we propose an unsupervised learning method for modeling motion trajectory patterns effectively. In our approach, observations of an object on a trajectory are treated as words in a document for latent dirichlet allocation algorithm which is used for clustering words on the topic in natural language process. This allows clustering topics (e.g. go straight, turn left, turn right) effectively in complex scenes, such as crossroads. After this procedure, we learn patterns of word sequences in each cluster using Baum-Welch algorithm used to find the unknown parameters in a hidden markov model. Evaluation of abnormality can be done using forward algorithm by comparing learned sequence and input sequence. Results of experiments show that modeling of semantic region is robust against noise in various scene.

• Archives of Plastic Surgery
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• v.40 no.2
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• pp.109-115
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• 2013

Background Breast reconstruction refers to the rebuilding of a woman's breast using autologous tissue or prosthetic material to form a natural-looking breast. It is increasingly offered to women undergoing mastectomy for breast cancer. However, there is no systematic analysis available for the expanding area of research on breast reconstruction. Methods A bibliometric method was used to obtain a view of the scientific production about breast reconstruction by data extracted from the Institute for Scientific Information (ISI). Specific parameters were retrieved from the ISI. Articles about breast reconstruction were analyzed to obtain a view of the topic's structure, history, and document relationships using HistCite software. Trends in the most influential publications and authors were analyzed. Results The number of articles was constantly increasing. Most highly cited articles described the methods of flap construction in the surgery. Other highly cited articles discussed the psychological or emotional aspects of breast reconstruction, skin sparing mastectomy, and breast reconstruction in the irradiated breast. Conclusions This was the first breast reconstruction scientometric analysis, representing the characteristics of papers and the trends of scientific production. A constant increase in the number of breast reconstruction papers and also the increasing number of citations shows that there is an increasing interest in this area of medical science. It seems that most of the research in this field is focused on the technical aspects of surgery.

• Journal of Educational Research in Mathematics
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• v.11 no.2
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• pp.273-289
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• 2001

Given that the culture of the mathematics classroom has been perceived as an important topic in mathematics education research, this paper deals with the construct of sociomathematical norms which can be used as an analytical tool in understanding classroom mathematical culture. This paper first reviews the theoretical foundations of the construct such as symbolic interactionism and ethnomethodology, and describes the actual classroom contexts in which social and sociomathematical norms were originally identified. This paper then provides a critical analysis of the previous studies with regard to sociomathematical norms. Whereas such studies analyze how sociomathematical norms become constituted and stabilized in the specific classroom contexts, they tend to briefly document sociomathematical norms mainly as a precursor to the detailed analysis of classroom mathematical practice. This paper reveals that the trend stems from the following two facts. First, the construct of sociomathematical norms evolved out of a classroom teaching experiment in which Cobb and his colleagues attempted to account for students' conceptual loaming as it occurred in the social context of an inquiry mathematics classroom. Second, the researchers' main role was to design instructional devices and sequences of specific mathematical content and to support the classroom teacher to foster students' mathematical learning using those sequences Given the limitations in terms of the utility of sociomathematical norms, this paper suggests the possibility of positioning the sociomathematical norms construct as more centrally reflecting the quality of students' mathematical engagement in collective classroom processes and predicting their conceptual teaming opportunities. This notion reflects the fact that the construct of sociomathematical norms is intended to capture the essence of the mathematical microculture established in a classroom community rather than its general social structure. The notion also allows us to see a teacher as promoting sociomathematical norms to the extent that she or he attends to concordance between the social processes of the classroom, and the characteristically mathematical ways of engaging. In this way, the construct of sociomathematical norms include, but in no ways needs to be limited to, teacher's mediation of mathematics discussions.

• Journal of Korean Medical classics
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• v.31 no.4
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• pp.27-46
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• 2018

Objectives : In this paper, we investigated the applicability of text mining to Korean Medical Classics and suggest that researchers of Medical Classics utilize this methodology. Methods : We applied text mining to the Huangdi's internal classic, a seminal text of Korean Medicine, and visualized networks which represent connectivity of terms and documents based on vector similarity. Then we compared this outcome to the prior knowledge generated through conventional qualitative analysis and examined whether our methodology could accurately reflect the keyword of documents, clusters of terms, and relationships between documents. Results : In the term network, we confirmed that Qi played a key role in the term network and that the theory development based on relativity between Yin and Yang was reflected. In the document network, Suwen and Lingshu are quite distinct from each other due to their differences in description form and topic. Also, Suwen showed high similarity between adjacent chapters. Conclusions : This study revealed that text mining method could yield a significant discovery which corresponds to prior knowledge about Huangdi's internal classic. Text mining can be used in a variety of research fields covering medical classics, literatures, and medical records. In addition, visualization tools can also be utilized for educational purposes.

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.227-243
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• 2008

This study investigates an event detection method with the aim of generating an event-focused news summary from a set of news articles on a certain event using a multi-document summarization technique. The event detection method first classifies news articles into the event related topic categories by employing a SVM classifier and then creates event clusters containing news articles on an event by a modified single pass clustering algorithm. The clustering algorithm applies a time penalty function as well as cluster partitioning to enhance the clustering performance. It was found that the event detection method proposed in this study showed a satisfactory performance in terms of both the F-measure and the detection cost.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.684-695
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• 2001

Recent recommender system uses a method of combining collaborative filtering system and content based filtering system in order to solve sparsity and first rater problem in collaborative filtering system. Collaborative filtering systems use a database about user preferences to predict additional topics. Content based filtering systems provide recommendations by matching user interests with topic attributes. In this paper, we describe a method for discovery of user preference through combining two techniques for recommendation that allows the application of machine learning algorithm. The proposed collaborative filtering method clusters user using genetic algorithm based on items categorized by Naive Bayes classifier and the content based filtering method builds user profile through extracting user interest using relevance feedback. We evaluate our method on a large database of user ratings for web document and it significantly outperforms previously proposed methods.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.445-455
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• 2015

According as internet and computer technology develops, the amount of information has increased exponentially, arising from a variety of web authoring tools and is a new web standard of appearance and a wide variety of web content accessibility as more convenient for the web are produced very quickly. However, web documents are put out on a variety of topics divided into some blocks where each of the blocks are dealing with a topic unrelated to one another as well as you can not see with contents such as many navigations, simple decorations, advertisements, copyright. Extract only the exact area of the web document body to solve this problem and to meet user requirements, and to study the effective information. Later on, as the reconstruction method, we propose a web search system can be optimized systematically manage documents.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.251-258
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• 2007

With the increase of a web service technology, a new access control mechanism has developed for XML documents. As a result, as legacy access control systems, access control systems has become an active research topic. In this paper, we propose a methodology to translate access control policies for XML documents into formal specification language CSP. To do this, first, we introduce a method to translate a hierarchical access to XML documents using XPath language into CSP process algebra. Second, we explain a method to represent a XML schema as a formal model like automata. Third, we present a method for representing the semantics of access control policies such as the scope of rules and confliction resolution into a process algebra language. Finally, a CSP specification example of an XML schema and path expressions aye shown to illustrate the validity of our approach.

• Journal of health informatics and statistics
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• v.43 no.4
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• pp.329-335
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• 2018

Objectives: In recent years, there has been an increased need for a way to extract desired information from multiple medical literatures at once. This study was conducted to confirm the usefulness of unstructured data analysis using previously published medical literatures to search for new indications. Methods: The new indications were searched through text mining, network analysis, and topic modeling analysis using 5,057 articles of atorvastatin, a treatment for hyperlipidemia, from 1990 to 2017. Results: The extracted keywords was 273. In the frequency of text mining and network analysis, the existing indications of atorvastatin were extracted in top level. The novel indications by Term Frequency-Inverse Document Frequency (TF-IDF) were atrial fibrillation, heart failure, breast cancer, rheumatoid arthritis, combined hyperlipidemia, arrhythmias, multiple sclerosis, non-alcoholic fatty liver disease, contrast-induced acute kidney injury and prostate cancer. Conclusions: Unstructured data analysis for discovering new indications from massive medical literature is expected to be used in drug repositioning industries.