• The Journal of Society for e-Business Studies
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• v.7 no.1
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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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• v.32 no.4
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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.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.457-466
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• 2002

In this paper, we designed and implemented a web-based dictionary of computing which keeps the data up-to-date. This dictionary shows the English information based on the FOLDOC (Free On-Line Dictionary Of Computing) dictionary file at the beginning of searching, and then one or more users can translate the information into Korean. This function is the new one only this dictionary has. Also, we can easily find any words we want to took up, even if we don't know the spelling completely, because the dictionary has various searching interfaces (searching for the words starting with inputted characters, searching for the words including inputted characters in the description, etc.) using a SQL Server DBMS and SQL. The performance test for CPU load factor shows that the server can support at least 1780 users at the same time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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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.

• Phonetics and Speech Sciences
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• v.1 no.3
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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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• v.12 no.1
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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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• v.17 no.2
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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%.

• Proceedings of the CALSEC Conference
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• 2001.08a
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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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• v.17 no.4
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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.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.37-52
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• 2003

ISO 13584 Parts Library (PLIB) standard is making its way into e-business as a norm for classifying products and their characteristics. PLIB is a multi-parts standard, and the Part 42: Methodology for structuring Parts families Provides the information model and design Principles for the data dictionary of parts library or e-catalog. If e-catalog systems are built using a data dictionary that is constructed based on PLIB dictionary data model, many different e-catalog systems can be easily integrated and interoperated. This paper studies the roles and requirements of the data dictionary in e-catalog, and applies the data model and design principles of PLIB Part 42 to construct a data dictionary from the viewpoint of ontology Based on the analysis results, we propose a data dictionary of die and mold parts, and implementat the B2B e-catalog system.