• 한국콘텐츠학회논문지
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• 제15권5호
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• pp.132-141
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• 2015

스마트TV는 일반TV를 대체할 차세대 TV로 사용자경험(user experience: UX)의 중요성이 높아지고 있다. UX는 사용자의 감정 상태인 몰입, 기쁨, 흥미 정도를 평가하는 것으로 사용성과 함께 스마트TV 설계에서 고려되어야 할 중요한 원칙이며 사용만족도를 증진시켜 지속적인 구매를 유도하게 된다. 그러나 UX는 사용성보다 측정이 어렵고 생리적 또는 심리적 평가방법은 실험 비용과 실험환경의 제약이 많다는 것이 단점이 있다. 본 연구에서는 기존의 스마트TV의 UX 평가방법을 고찰한 후 새로운 UX 측정방법으로 텍스트로부터 감정을 평가하는 방법을 제안한다. 텍스트는 인터넷 쇼핑몰에서 스마트TV를 구매한 사람들이 남긴 상품후기를 사용한다. 이 방법은 설문조사 방법보다 적은 비용으로 감정을 파악할 수 있다는 것이 장점이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.721-739
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• 2023

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machinesfor rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. This model uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.624-626
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• 2023

Credit scoring is a technique used by financial institutions to assess the creditworthiness of potential borrowers. This involves evaluating a borrower's credit history to predict the likelihood of defaulting on a loan. This paper presents an ensemble of two Transformer based models within a framework for discriminating the default risk of loan applications in the field of credit scoring. The first model is FinBERT, a pretrained NLP model to analyze sentiment of financial text. The second model is FT-Transformer, a simple adaptation of the Transformer architecture for the tabular domain. Both models are trained on the same underlying data set, with the only difference being the representation of the data. This multi-modal approach allows us to leverage the unique capabilities of each model and potentially uncover insights that may not be apparent when using a single model alone. We compare our model with two famous ensemble-based models, Random Forest and Extreme Gradient Boosting.

• 정보처리학회논문지D
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• 제13D권2호
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• pp.235-240
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• 2006

소프트웨어 아키텍처는 텍스트 기반 아키텍처 기술 언어(ADL)를 사용하면서 기술하게 된다. ADL의 중요한 목적은 다른 이해관계자 사이에서 대체 디자인을 통신하고, 재사용할 수 있는 구조를 찾아내고, 그리고 디자인 결정을 기록하는 것이다. 본 논문은 구조적인 관점의 3차원 표현을 위한 도구를 만듦으로써 표현 문제에 대한 해법을 제공한다. 도구는 첫째 소프트웨어 아키텍처와 아키텍처에서 관점을 기술하는 아키텍처 기술 언어(VTADL)로 구성되었고, 각 관점을 분리된 가상현실 세계로 번역하는 VTADL-to-VRML 변환기로 구성되었다. 본 논문에서는 ADL을 요구된 관점에 의거하여 효과적인 VRML 표현으로 변환하기 위한 알고리즘을 고안했다. VRML 표현은 그 전체적인 디자인에 이해를 강화하고 다양한 이해관계자 사이에 통신을 개선할 것이다.

• 패션비즈니스
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• 제25권1호
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• pp.96-107
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• 2021

The purpose of this study was to determine how to visualize classic makeup through Korean visual language in old literature. It provides consumers with creativity to understand and communicate and proposes a new conceptual visualization model. The research method was carried out by drawing from old literature studies, previous reproduction records, examples, and international standard diagram text language expression. First, the visualization work expressed in visual language in old literature was an objective and efficient method of information delivery as a characteristic of information design. Second, visual language expressed in old documents could be divided into makeup materials and actions. Also, the diagrams were appropriate for visualizing materials and materials for storytelling. Third, in the visualization of Korean classic makeup in old literature, images were more appropriate than diagrams in the case of action. The researcher proposed a method of visualizing historical knowledge that went one step beyond the existing simple event timing method. Timeline, correlation diagram, image, and text were combined in various ways to find the most effective historical knowledge visualization method. The representation of Korean classic makeup goes beyond the meaning of language or text and is the cultural content of re-creation, which requires systematic globalization.

• 동의생리병리학회지
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• 제25권5호
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• pp.765-772
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• 2011

Knowledge which is represented by formal logic are widely used in many domains such like artificial intelligence, information retrieval, e-commerce and so on. And for medical field, medical documentary records retrieval, information systems in hospitals, medical data sharing, remote treatment and expert systems need knowledge representation technology. To retrieve information intellectually and provide advanced information services, systematically controlled mechanism is needed to represent and share knowledge. Importantly, medical expert's knowledge should be represented in a form that is understandable to computers and also to humans to be applied to the medical information system supporting decision making. And it should have a suitable and efficient structure for its own purposes including reasoning, extendability of knowledge, management of data, accuracy of expressions, diversity, and so on. we call it ontology which can be processed with machines. We can use the ontology to represent traditional medicine knowledge in structured and systematic way with visualization, then also it can also be used education materials. Hence, the authors developed an Shanghanlun ontology by way of showing an example, so that we suggested a methodology for ontology development and also a model to structure the traditional medical knowledge. And this result can be used for student to learn Shanghanlun by graphical representation of it's knowledge. We analyzed the text of Shanghanlun to construct relational database including it's original text, symptoms and herb formulars. And then we classified the terms following some criterion, confirmed the structure of the ontology to describe semantic relations between the terms, especially we developed the ontology considering visual representation. The ontology developed in this study provides database showing fomulas, herbs, symptoms, the name of diseases and the text written in Shanghanlun. It's easy to retrieve contents by their semantic relations so that it is convenient to search knowledge of Shanghanlun and to learn it. It can display the related concepts by searching terms and provides expanded information with a simple click. It has some limitations such as standardization problems, short coverage of pattern(證), and error in chinese characters input. But we believe this research can be used for basic foundation to make traditional medicine more structural and systematic, to develop application softwares, and also to applied it in Shanghanlun educations.

• 한국전자거래학회지
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• 제21권4호
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• pp.1-14
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• 2016

시멘틱 검색은 검색 사용자의 인지적 노력을 최소화하면서 사용자 질의의 문맥을 이해하여 의미에 맞는 문서를 정확히 찾아주는 기술이다. 아직 시멘틱 검색 기술은 온톨로지 또는 시멘틱 메타데이터 구축의 난제를 갖고 있으며 상용화 사례도 매우 미흡한 실정이다. 본 논문은 기존 시멘틱 검색 엔진의 한계를 극복하기 위하여 이전 연구에서 고안한 위키피디아 기반의 시멘틱 텐서공간모델을 활용하여 새로운 시멘틱 검색 기법을 제안한다. 제안하는 시멘틱 기법은 문서 집합에 출현하는 '단어'가 텐서공간모델에서 '문서-개념'의 2차 텐서(행렬), '개념'은 '문서-단어'의 2차 텐서로 표현된다는 성질을 이용하여 시멘틱 검색을 위해 요구되는 온톨로지 구축의 필요성을 없앤다. 그럼에도 불구하고, OHSUMED, SCOPUS 데이터셋을 이용한 성능평가를 통해 제안 기법이 벡터공간모델에서의 기존 검색 기법보다 우수함을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1973-1993
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• 2022

Model-driven engineering (MDE) and aspect-oriented software development (AOSD) contribute to the common goal of development of high-quality code in reduced time. To complement each approach with the benefits of the other, various methods of integration of the two approaches were proposed in the past. Aspect-oriented code generation, which targets obtaining aspect-oriented code directly from aspect models, offers some unique advantages over the other integration approaches. However, the existing aspect-oriented code generation approaches do not comprehensively address all aspects of a model-driven code generation system, such as a textual representation of graphical models, conceptual mapping, and incorporation of behavioral diagrams. These problems limit the worth of generated code, especially in practical use. Here, we propose AJFCode, an approach for aspect-oriented model-driven code generation, which comprehensively addresses the various aspects including the graphical models and their text-based representation, mapping between visual model elements and code, and the behavioral code generation. Experiments are conducted to compare the maintainability and reusability characteristics of the aspect-oriented code generated using the AJFCode with the most comprehensive object-oriented code generation approach. AJFCode performs well in terms of all metrics related to maintainability and reusability of code. However, the most significant improvement is noticed in the separation of concerns, coupling, and cohesion. For instance, AJFCode yields significant improvement in concern diffusion over operations (19 vs 51), coupling between components (0 vs 6), and lack of cohesion in operations (5 vs 9) for one of the experimented concerns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4275-4291
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• 2021

With the unprecedented growth of textual information on the Internet, an efficient automatic summarization system has become an urgent need. Recently, the neural network models based on the encoder-decoder with an attention mechanism have demonstrated powerful capabilities in the sentence summarization task. However, for paragraphs or longer document summarization, these models fail to mine the core information in the input text, which leads to information loss and repetitions. In this paper, we propose an abstractive document summarization method by applying guidance signals of key sentences to the encoder based on the hierarchical encoder-decoder architecture, denoted as KI-HABS. Specifically, we first train an extractor to extract key sentences in the input document by the hierarchical bidirectional GRU. Then, we encode the key sentences to the key information representation in the sentence level. Finally, we adopt key information representation guided selective encoding strategies to filter source information, which establishes a connection between the key sentences and the document. We use the CNN/Daily Mail and Gigaword datasets to evaluate our model. The experimental results demonstrate that our method generates more informative and concise summaries, achieving better performance than the competitive models.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권8호
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• pp.301-310
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• 2021

TATI 모델이란 Traffic Accident Text to RGB Image 모델로, 교통사고 심각 정도 예측을 위한 본 논문에서 제안하는 방법론이다. 교통사고 치사율은 매년 감소하는 추세이나 OECD 회원국 중 하위권에 속해있다. 교통사고 치사율 감소를 위해 많은 연구들이 진행되었고, 그 중에서 교통사고 심각 정도를 예측하여 발생 및 치사율을 줄이기 위한 연구가 꾸준하게 진행되고 있다. 이와 관련하여 최근에는 통계 모델과 딥러닝 모델을 활용하여 교통사고 심각 정도 예측을 하는 연구가 활발하다. 본 논문에서는 교통사고 심각 정도를 예측하기 위해서 교통사고 데이터를 컬러 이미지로 변환하고, CNN 모델을 통해 이를 수행한다. 성능 비교를 위해 제안하는 모델과 다른 모델들을 같은 데이터로 학습시키고, 예측결과를 비교하는 실험을 진행했다. 10번의 실험을 통해 4개의 딥러닝 모델의 정확도와 오차 범위를 비교하였다. 실험 결과에 따르면 제안하는 TATI 모델의 정확도가 0.85로 가장 높은 정확도를 보였고, 0.03으로 두 번째로 낮은 오차 범위를 보여 성능의 우수성을 확인하였다.