• 한국전자거래학회지
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• 제7권1호
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• pp.141-152
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• 2002

Data dictionary is a collection of meta-data, which describes data produced and consumed while performing business processes. Data dictionary is an essential element for business process standardization and automation, and has a fundamental role in ERP application management and customization. Also, data dictionary facilitates B2B processes by enabling painless integration of business processes between various enterprises. We implemented data dictionary support in SEA＋, a component- based scalable ERP system developed in ETRI, and found out that it's a plausible feature of business information system. We discovered that data dictionary promotes semantic, not syntactic, data management, which can make it possible to leverage viability of the tool in the coming age of more meta-data oriented computing world. We envision that business data dictionary is a firm foundation of adapting business knowledge, applications and processes into the semantic web based enterprise infra-structure.

• ETRI Journal
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• 제32권4호
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• pp.596-602
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• 2010

This paper proposes a computationally efficient learning-based super-resolution algorithm using k-means clustering. Conventional learning-based super-resolution requires a huge dictionary for reliable performance, which brings about a tremendous memory cost as well as a burdensome matching computation. In order to overcome this problem, the proposed algorithm significantly reduces the size of the trained dictionary by properly clustering similar patches at the learning phase. Experimental results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.

• 정보처리학회논문지D
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• 제9D권3호
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• pp.457-466
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• 2002

본 논문에서는 인터넷을 통해 실시간으로 항상 최신의 컴퓨터 용어 검색 서비스를 제공할 수 있는 웹을 기반으로 한 컴퓨터 용어 사전을 설계하고 구현하였다. 본 용어사전은 FOLDOC(Free On-Line Dictionary Of Computing)의 사전을 기본으로 영문 해설을 제공하고 각 용어에 대해 한 명 이상의 번역자가 번역할 수 있도록 함으로써 기존 컴퓨터 사전에서 제공하지 않는 기능을 추가하였다. 그리고 SQL Server DBMS와 SQL을 이용한 다양한 검색 인터페이스(입력 문자로 시작하는 용어 검색, 입력 문자가 해설에 들어간 용어 검색 등)를 제공함으로써 적은 정보만으로도 원하는 용어를 검색할 수 있게 하였다. 본 컴퓨터 용어 사전의 성능 평가를 위해서 FOLDOC Mirror Site의 로그를 분석하여 CPU 부하율을 측정하였다. 실험 결과 본 컴퓨터 용어 사전은 최대 1780여명 이상의 동시 사용자를 수용할 수 있다는 결론을 얻었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3244-3260
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• 2013

A three-party password-based authenticated key exchange (PAKE) protocol allows two clients registered with a trusted server to generate a common cryptographic key from their individual passwords shared only with the server. A key requirement for three-party PAKE protocols is to prevent an adversary from mounting a dictionary attack. This requirement must be met even when the adversary is a malicious (registered) client who can set up normal protocol sessions with other clients. This work revisits three existing three-party PAKE protocols, namely, Guo et al.'s (2008) protocol, Huang's (2009) protocol, and Lee and Hwang's (2010) protocol, and demonstrates that these protocols are not secure against offline and/or (undetectable) online dictionary attacks in the presence of a malicious client. The offline dictionary attack we present against Guo et al.'s protocol also applies to other similar protocols including Lee and Hwang's protocol. We conclude with some suggestions on how to design a three-party PAKE protocol that is resistant against dictionary attacks.

• 말소리와 음성과학
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• 제1권3호
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• pp.103-108
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• 2009

This paper proposes a method for building a transliteration dictionary, which is based on pronouncing information extracted from two kinds of existing dictionaries. Also, it proposes a method for transforming the pronouncing information into Korean translitered words. To express the pronouncing information, we define Phoman code system. In order to avoid phonetic estimation process of English words which is the most important problem, the proposed method uses the pronouncing information extracted from the existing dictionaries. Therefore, unlike previous approaches, the proposed method does not need any incomplete phonetic estimation process so that it can produce accurate transliteration results. The proposed method has been fully implemented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권1호
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• pp.15-19
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• 2012

A Domain Dictionary generation algorithm based on pseudo feedback model is presented in this paper. This algorithm can increase the precision of domain dictionary generation algorithm. The generation of Domain Dictionary is regarded as a domain term retrieval process: Assume that top N strings in the original retrieval result set are relevant to C, append these strings into the dictionary, retrieval again. Iterate the process until a predefined number of domain terms have been generated. Experiments upon corpus show that the precision of pseudo feedback model based algorithm is much higher than existing algorithms.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2001년도 International Conference CALS/EC KOREA
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• pp.483-491
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• 2001

Data dictionary is a collection of metadata about data defined, produced and consumed while performing business processes. Data dictionary is an essential element for business process standardization and automation. Data dictionary also has a fundamental role in ERP application management and customization. Finally, data dictionary helps B2B by gracefully integrating intra-enterprise business processes and inter-enterprise business processes. This paper gives some clues about the importance of data dictionary in ERP and B2B, and introduces data dictionary support of SEA+, a component-based scalable ERP package system.

• International Journal of Contents
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• 제17권4호
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• 한국전자거래학회지
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• 제8권3호
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• pp.37-52
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• 2003

PLIB으로 알려진 ISO 13584 부품 라이브러리 국제 표준은 상품의 분류와 각 상품 분류별 특성을 묘사하는 기준으로서 전자 상거래 분야로 그 응용영 역을 넓혀 나가고 있다. PLIB 표준은 여러 권으로 구성된 표준인데, 그 중 파트 42는 전자 카탈로그 또는 부품 라이브러리의 데이터 사전 (Data Dictionary)를 작성하는데 정보모델 (Information Model)과 설계원칙 (Design Principles)을 제공한다. PLIB 파트42의 정보모델을 기반으로 작성된 데이터 사전을 이용하여 전자 카탈로그 시스템을 구축하면, 향후 산업별, 부품대상별로 구축될 다양한 전자 카탈로그 시스템간의 통합 (Integration)과 상호운용 (Interoperation)을 쉽게 달성할 수 있다. 본 연구는 우선, 전자 카탈로그 또는 부품 라이브러리에서 데이터 사전의 역할과 요구 사항을 정리하고, PLIB 파트 42의 내용을 분석한다. 그리고 분석 결과를 바탕으로 금형부품 데이터 사전을 작성하고 이를 이용하여 기업간 전자 상거래 (B2B e-Commerce)용 전자 카탈로그 시스템을 구축한 결과를 정리한다.