• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1843-1848
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• 2010

The Key exchange protocols are very crucial tools to provide the secure communication in the broadband satellite access network. They should be required to satisfy various requirements such as security, Key confirmation, and Key freshness. In this paper, we propose Two authenticated key exchange protocols Two Pass EKE-E(Encrypted Key Exchange-Efficient) and Two Pass EKE-S(Encrypted Key Exchange-Secure) are introduced. A basic idea of the protocols is that a password can be represented by modular addition N, and the number of possible modular addition N representing the password is $2^N$ The Two Pass EKE-E is secure against the attacks including main-in-the-middle attack and off-line dictionary attack, and the performance is excellent so as beyond to comparison with other authenticated key exchange protocols. The Two Pass EKE-S is a slight modification of the Two Pass EKE-E. The Two Pass EKE-S provides computational in feasibility for learning the password without having performed off line dictionary attack while preserving the performance of the Two Pass EKE-E.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.179-186
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• 2021

We have been developed the smart tourism app and the Instagram and YouTube contents to provide personalized tourism information and travel product information to individual tourists. In this paper, we develop a rule-based smart tourism chatbot with the khaiii (Kakao Hangul Analyzer III) morphological analyzer and Neo4J graph database. In the proposed chatbot system, we use a morpheme analyzer, a proper noun dictionary including tourist destination names, and a general noun dictionary including containing frequently used words in tourist information search to understand the intention of the user's question. The tourism knowledge base built using the Neo4J graph database provides adequate answers to tourists' questions. In this paper, the nodes of Neo4J are Area based on tourist destination address, Contents with property of tourist information, and Service including service attribute data frequently used for search. A Neo4J query is created based on the result of analyzing the intention of a tourist's question with the property of nodes and relationships in Neo4J database. An answer to the question is made by searching in the tourism knowledge base. In this paper, we create the tourism knowledge base using more than 1300 Jeju tourism information used in the smart tourism app. We plan to develop a multilingual smart tour chatbot using the named entity recognition (NER), intention classification using conditional random field(CRF), and transfer learning using the pretrained language models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.1-8
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• 2019

Among the methods for extracting the most appropriate information from a large amount of log data, there is a method using inductive inference. In this paper, we use SVM (Support Vector Machine), which is an excellent classification method for inductive inference, in order to determine the ranking of intrusion logs in digital forensic analysis. For this purpose, the logs of the training log set are classified into intrusion logs and normal logs. The associated words are extracted from each classified set to generate a related word dictionary, and each log is expressed as a vector based on the generated dictionary. Next, the logs are learned using the SVM. We classify test logs into normal logs and intrusion logs by using the log set extracted through learning. Finally, the recommendation orders of intrusion logs are determined to recommend intrusion logs to the forensic analyst.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.519-521
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• 2003

개체명 인식은 효과적인 정보추출 시스템을 구축하기 위해 반드시 선행되어야 하는 작업이다. 지금까지의 개체명 인식에 관한 연구는 인명이나 조직, 장소와 같은 일반적인 개체명 인식 작업이 대부분이었다. 그러나, 효과적인 정보추출을 위해서는 이런 일반적인 개체명들을 더욱 세분화할 필요가 있다. 본 논문에서는 SVM기반 기계학습법과 기구축된 사전과의 편집거리 비교법을 이용하여 개체명을 세분화하는 방법을 제시한다. 실험은 개체명과 세분화된 범주가 부착된 공연 관련 문서 100개 중 80개는 학습집합, 20개는 실험집합으로 사용하였고 성능 평가 척도는 정확도(accuracy)를 이용해 개별적으로 평가하였다. 실험 결과 기계학습법과 사전을 이용한 방법을 결합한 모델이 가장 좋은 성능(정확도 72.91%)을 보였다.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.960-968
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• 2018

The mobile visual search service uses a query image to acquire linkage information through pre-constructed DB search. From the standpoint of this purpose, it would be more useful if you could perform a search on a web-based keyword search system instead of a pre-built DB search. In this paper, we propose a representative query extraction algorithm to be used as a keyword on a web-based search system. To do this, we use image classification labels generated by the CNN (Convolutional Neural Network) algorithm based on Deep Learning, which has a remarkable performance in image recognition. In the query extraction algorithm, dictionary meaningful words are extracted using Wikipedia, and hierarchical categories are constructed using WordNet. The performance of the proposed algorithm is evaluated by measuring the system response time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2109-2123
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• 2017

This paper presents a super-resolution reconstruction method based on sparse representation for two-dimensional bar code images. Considering the features of two-dimensional bar code images, Kirsch and LBP (local binary pattern) operators are used to extract the edge gradient and texture features. Feature extraction is constituted based on these two features and additional two second-order derivatives. By joint dictionary learning of the low-resolution and high-resolution image patch pairs, the sparse representation of corresponding patches is the same. In addition, the global constraint is exerted on the initial estimation of high-resolution image which makes the reconstructed result closer to the real one. The experimental results demonstrate the effectiveness of the proposed algorithm for two-dimensional bar code images by comparing with other reconstruction algorithms.

• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.179-182
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• 2014

본 논문에서는 외국인을 대상으로 보다 쉽고 재미있게 우리말을 배울 수 있도록 도와주는 교육용 앱 소프트웨어를 제안한다. 이 앱에서는 사용자가 입력한 문장을 형태소 분석하여 용언 및 서술어를 중심으로 어형 및 문형의 올바른 사용법을 제시함으로써, 우리말의 용법을 쉽게 이해할 수 있도록 한다. 또한 제안한 방법을 음성인식을 활용한 스마트폰 앱으로 개발함으로써 사용자의 접근성 및 편의성을 높였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.293-296
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• 2019

개체명 인식은 입력 문장에서 인명, 지명, 기관명, 날짜, 시간과 같이 고유한 의미를 갖는 단어들을 찾아 개체명을 부착하는 기술이다. 최근 개체명 인식기는 형태소 단위나 음절 단위의 입력을 사용하는 연구가 주로 진행되고 있다. 그러나 형태소 단위 개체명 인식은 미등록어를 처리하지 못하는 문제점이 존재하고 음절 단위 개체명 인식은 단어의 의미를 제대로 반영하지 못하는 문제점이 존재한다. 본 논문에서는 이 문제점을 보완하기 위해 품사 정보를 활용한 음절 단위 개체명 인식기를 제안한다. 또한 개체명 인식 성능에 큰 영향을 미치는 개체명 사전 자질을 더 효과적으로 사용할 수 있는 방법을 제안하며 이 방법을 사용했을 때 기존의 방법보다 향상된 개체명 인식 성능(F1-score 0.8576)을 보였다.