• 건축역사연구
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• 제8권2호
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• pp.87-108
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• 1999

Since 1960, the change of architectural trend was dominated by two factors ; the one, the introduction of theory of language (including semantic, syntactic, pragmatic, linguistic, semiotic, structuralism, post-structurism) in design concept, the other, the adaption of high technology in building construction. In particular, the theory of language played an important role in the emergence of new tendency, which could be the alternative of modern architecture. Post-modernism and Typology in the 1960-70s, Deconstructivism in the 1980s and 'Folding' architecture in the 1990s, have continually borrowed a theoretical base from the thee of language. Placing the focus on the relation of contemporary architecture and theory of language with the interdisciplinary view, this study comes to the conclusion that the diverse architectural tendencies since 1960 depend on the 'champ d'enonce', which Michel Foucault, French philosopher, defined in his . The writings of many architects, like Robert Venturi, Micheal Graves, Aldo Rossi, Peter Eisenman, Rem Koolhaas, Bernard Tschumi, Gerg Lynn demonstrate our conclusion. This is an important finding which make possible consistent understanding about contemporary architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• 한국문헌정보학회지
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• 제51권3호
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• pp.109-132
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• 2017

본 연구는 집단독서치료가 중년여성들의 자존감 향상에 초래하는 변화를 파악하기 위해 현상학적 방법인 Giorgi 연구방법으로 분석되었다. 중년 후기 여성 6명을 대상으로 12회에 걸친 독서치료를 실시하고, 녹음파일 속의 참여자의 진술을 221개의 의미단위, 16개의 하위상황/주제, 6개 상황/주제, 3개의 그룹으로 분석 통합하였다. 연구결과, 참여자들은 가족 관계의 어려움으로 신경증적 증상을 경험하였으나 독서치료 경험 후 자존감, 가족, 인생관에 대한 태도가 변화되었다. 참여자간의 상호작용과 독서치료의 매개체인 그림책은 참여자들의 경험을 표현하는데 긍정적인 정서적, 심리적 반응을 불러일으켰다. 또한, 중년여성들의 종교생활 참여는 마음의 상처 치유에 큰 역할을 했음을 발견하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.2963-2978
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• 2017

The Top-K query is currently played a key role in a wide range of road network, decision making and quantitative financial research. In this paper, a Top-K recommendation algorithm is proposed to solve the cold-start problem and a tag generating method is put forward to enhance the semantic understanding of the OLAP session. In addition, a recommendation system for OLAP sessions called "OLAP4R" is designed using collaborative filtering technique aiming at guiding the user to find the ultimate goals by interactive queries. OLAP4R utilizes a mixed system architecture consisting of multiple functional modules, which have a high extension capability to support additional functions. This system structure allows the user to configure multi-dimensional hierarchies and desirable measures to analyze the specific requirement and gives recommendations with forthright responses. Experimental results show that our method has raised 20% recall of the recommendations comparing the traditional collaborative filtering and a visualization tag of the recommended sessions will be provided with modified changes for the user to understand.

• 한국수학교육학회지시리즈A:수학교육
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• 제48권4호
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• pp.419-442
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• 2009

This study was to find the difficulties students faced in their mathematical learning and to identify the instructional dimensions a teacher provided for the students from multi-cultural background. Since the study was focused on the process of students' learning, the qualitative method was chosen through clinical interviews with 2 students in a total of 11 units which played a role of compensating their learning of mathematics as an extra curriculum. The students solved the computational problems relying on formal procedure without understanding of concepts and principles and solved the word problems based on own interpretation of certain words without semantic comprehension out of math sentences. As the instructional dimensions of teaching mathematics, tasks, a tool and classroom norm were found in the activities they performed. For the tasks, situated tasks, challenging tasks, tasks with lack of conditions, and open-ended exploratory tasks were used. As the tool, pictorial representations were very useful to describe their ideas. Finally, as the classroom norm, consider equity for everyone, and cooperate and encourage each other were found.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.1150-1155
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• 2009

정보화 사회에서 정보의 양은 기술의 발달로 급격하게 증가하고 있다. 그로 인해 정보의 다양화와 복잡성 또한 증가하여 빠른 정보처리에 어려움을 주고 있다. 정보의 복잡성 속에 정보의 구조화, 범주화는 사용자가 쉽게 정보에 접근할 수 있게 만들며 처리 속도도 빠르게 해 준다. 본 연구는 정보의 범주화에서 색을 통한 범주화가 정보처리 속도 향상에 어떠한 영향을 주는지를 실험적으로 확인해 보려 한다. 실험 1은 복잡한 정보를 가진 노선도에서 역을 찾는 과제를 시행 하였을 때, target 역 이름의 색과 노선의 색이 동일 할 때 그렇지 않는 경우 보다 탐색시간을 빠름을 보여주고자 한다. 그리고 실험2는 단어 분류 과제에서 색의 범주화가 단어의미 범주화 보다 빨리 처리되며, 색의 대비가 클 때 더 효과적임을 보여 주고자 한다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.3-3
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• 2003

With biomedical literature expanding so rapidly, there is an urgent need to discover and organize knowledge extracted from texts. Although factual databases contain crucial information the overwhelming amount of new knowledge remains in textual form (e.g. MEDLINE). In addition, new terms are constantly coined as the relationships linking new genes, drugs, proteins etc. As the size of biomedical literature is expanding, more systems are applying a variety of methods to automate the process of knowledge acquisition and management. In my talk, I focus on the project, GENIA, of our group at the University of Tokyo, the objective of which is to construct an information extraction system of protein - protein interaction from abstracts of MEDLINE. The talk includes (1) Techniques we use fDr named entity recognition (1-a) SOHMM (Self-organized HMM) (1-b) Maximum Entropy Model (1-c) Lexicon-based Recognizer (2) Treatment of term variants and acronym finders (3) Event extraction using a full parser (4) Linguistic resources for text mining (GENIA corpus) (4-a) Semantic Tags (4-b) Structural Annotations (4-c) Co-reference tags (4-d) GENIA ontology I will also talk about possible extension of our work that links the findings of molecular biology with clinical findings, and claim that textual based or conceptual based biology would be a viable alternative to system biology that tends to emphasize the role of simulation models in bioinformatics.

• Journal of Computing Science and Engineering
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• 제6권3호
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• pp.193-206
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• 2012

Keys play a fundamental role in all data models. They allow database systems to uniquely identify data items, and therefore, promote efficient data processing in many applications. Due to this, support is required to discover keys. These include keys that are semantically meaningful for the application domain, or are satisfied by a given database. We study the discovery of keys from SQL tables. We investigate the structural and computational properties of Armstrong tables for sets of SQL keys. Inspections of Armstrong tables enable data engineers to consolidate their understanding of semantically meaningful keys, and to communicate this understanding to other stake-holders. The stake-holders may want to make changes to the tables or provide entirely different tables to communicate their views to the data engineers. For such a purpose, we propose data mining algorithms that discover keys from a given SQL table. We combine the key mining algorithms with Armstrong table computations to generate informative Armstrong tables, that is, key-preserving semantic samples of existing SQL tables. Finally, we define formal measures to assess the distance between sets of SQL keys. The measures can be applied to validate the usefulness of Armstrong tables, and to automate the marking and feedback of non-multiple choice questions in database courses.

• International Journal of Internet, Broadcasting and Communication
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• 제9권3호
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• pp.59-69
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• 2017

The best 'hub' to communicate with the citizen is using social media to marketing the business. However, there has several issued and the most common issue that face in critical is a capital issue. This issue is always highlight because most of automatic sentiment detection tool for Facebook or any other social media price is expensive and they lack of technical skills in order to control the tool. Therefore, in directly they have some obstacle to get faster product's feedback from customers. Thus, the personal online retailing need to struggle to stay in market because they need to compete with successful online company such as G-market. Sentiment analysis also known as opinion mining. Aim of this research is develop the tool that allow user to automatic detect the sentiment comment on social media account. RAD model methodology is chosen since its have several phases could produce more activities and output. Soppy tool will be develop using Microsoft Visual. In order to generate an accurate sentiment detection, the functionality testing will be use to find the effectiveness of this Soppy tool. This proposed automated Soppy Tool would be able to provide a platform to measure the impact of the customer sentiment over the postings on their social media site. The results and findings from the impact measurement could then be use as a recommendation in the developing or reviewing to enhance the capability and the profit to their personal online retailing company.

• Journal of Computing Science and Engineering
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• 제2권4호
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• pp.375-393
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• 2008

The dynamic cast operation allows flexibility in the design and use of data management facilities in object-oriented programs. Dynamic cast has an important role in the implementation of the Data Management Services (DMS) of the Mission Data System Project (MDS), the Jet Propulsion Laboratory's experimental work for providing a state-based and goal-oriented unified architecture for testing and development of mission software. DMS is responsible for the storage and transport of control and scientific data in a remote autonomous spacecraft. Like similar operators in other languages, the C++ dynamic cast operator does not provide the timing guarantees needed for hard real-time embedded systems. In a recent study, Gibbs and Stroustrup (G&S) devised a dynamic cast implementation strategy that guarantees fast constant-time performance. This paper presents the definition and application of a cosimulation framework to formally verify and evaluate the G&S fast dynamic casting scheme and its applicability in the Mission Data System DMS application. We describe the systematic process of model-based simulation and analysis that has led to performance improvement of the G&S algorithm's heuristics by about a factor of 2. In this work we introduce and apply a library for extracting semantic information from C++ source code that helps us deliver a practical and verifiable implementation of the fast dynamic casting algorithm.