• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1191-1199
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• 2016

To ensure the fairness, journal reviewers use blind-review system which hides the author information of the journal. Even though the author information is blinded, we could identify the author by looking at the field of the journal or containing words and phrases in the text. In this paper, we collected 315 journals of 20 authors and extracted text data. Bag-of-words were generated after preprocessing and used as an input of artificial neural network. The experiment shows the possibility of circumventing the blind review through identifying the author of the journal. By the experiment, we demonstrate the limitation of the current blind-review system and emphasize the necessity of robust blind-review system.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.345-350
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• 2020

Based on the analysis of physical and learning characteristics and requirements of the silver generation, a video English dictionary was developed and evaluated as English learning contents. The video English dictionary was developed using OCR as an input method and video as an output method, and 17 silver generations were evaluated for academic achievement, learning satisfaction, and ease of use. As a result of the analysis, both the text English dictionary and the video English dictionary showed high learning satisfaction, but the video English dictionary showed higher results than the text English dictionary in an academic achievement and ease of use.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of Broadcast Engineering
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• v.15 no.3
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• pp.346-354
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• 2010

T-DMB is using MPEG-4 BIFS standard format for broadcasting interactive data service. BIFS enables us to represent contents as a scene which consists of various objects such as AV, image, graphic, and text. It also enables us to control objects by using user interaction. BIFS was designed to be adapted to multimedia systems with various input devices. Today, however, we are in lack of considering about mobile device with restricted input unit. The problem is that a consistent user control of interactive data contents is not possible due to the limitations of input units in T-DMB terminals. To solve the problem, we defined KeyNavigator node that provides a means to select or navigate objects (like menu) in BIFS contents by arrow keys and enter key of mobile terminal. By using KeyNavigater node, not only BIFS contents providers can make BIFS contents as they want, but also users can get a way to control BIFS contents consistently and easily.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.295-302
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• 2010

Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.

• Proceedings of the Korea Association of Information Systems Conference
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• 2001.12a
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• pp.413-426
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• 2001

본 연구는 첫째, 정보통신기술의 발달이 시각장애인 복지 증진에 미칠 수 있는 가능성에 대한 이론적인 부분을 고찰하였다. 둘째, 우리나라 시각장애인 정보화의 문제점과 해결책을 도출하였고 셋째, 시각장애 사용자를 위한 전자상거래 인터페이스 디자인의 분석 및 설계를 통해 전자상거래에서 시각장애 사용자들이 큰 제약없이 사용할 수 있는 방안을 제시하고자 한다. 현재 시각장애인들의 웹 사용을 보면 시각장애 전용 S/W의 보조 하에 사용을 하고 있다. 그러한 보조 도구의 실정에 맞도록 텍스트 버전 및 Non-Frame버전, Alt-Text 옵션, 캡션 등을 넣어 접근성을 확보하고 인터넷을 큰 제약을 받지않고 이용할 수 있도록 웹 페이지의 설계가 필요한 실정이다. 이를 위하여 먼저 시각장애에 대한 개념과 원인 및 종류 그리고 특성을 통해 시각장애인에 대한 이론적 배경을 파악하였다. 그리고 시각장애인의 정보화 환경과 이용 현황과 시각장애인의 정보 접근을 제도적, 기기 및 소프트웨어 개발 측면에서 분석을 하였고, 장애인을 위한 정보통신기술 중 대표적인 사례를 검토해 보았다. 다음으로 국내외의 대표적인 전자상거래 사이트에서의 인터페이스를 화면구성(Layout), 텍스트(Text), 그래픽(Graphic), 멀티미디어(MultiMedia) 측면에서 분석을 하였다. 분석한 내용을 바탕으로 시각장애 사용자의 입력(User Input) 부분을 고려한 인터페이스 방향을 제시하고 프로토타입을 개발하여 시험 대상 사이트와의 만족도를 시각장애 사용자를 통해 비교 ·분석하였다. 결론부분에서는 정보불평등을 해소하고, 정보통신기술이 장애인의 복지향상에 기여하도록 하기 위해 전자상거래 싸이트에서의 시각 장애인들을 위한 방향을 제시하고자 한다.박의 표현, 등록 및 색인방법 (c) 공급 선박의 분류와 표현 방법 (d) 에이전트의 정보 수집을 위한 메시지 표현 방법 (e) 수집된 선박정보의 데이터베이스 저장 표현방법 (f) 요구 선박을 찾아주는 정보제공 서비스가 요구된다.동을 보여 조사대상 5호분, 6호분, 7호분, 중 가장 심한 거동을 보이고 있다. 이는 고분 벽돌의 깨짐이 6호분이 가장 심하다는 사실과 무관하지 않은 것으로 판단된다. 봉분내부의 토양층구조에 대한 지오레이다 영상단면을 분석한 결과 무령왕릉 연도상부의 누수지방지층이 심하게 균열되어 있음을 발견하였다. 이 곳은 고분내부로 직접누수가 발생하는 곳이다. 직접누수와 지하수 형태로 유입된 침투수는 고분군 주위의 지반의 함수비를 증가시켜 지반의 지지력을 약화시키고 또한 고분내로 서서히 유입되어 고분내부의 습도를 100%로 유지시키는 주된 원인이다. 이러한 높은 습도는 고분내의 남조류의 번식을 가져왔으며 남조류의 번식은 현재 6호분이 가장 심각하고 7호분이 우려되는 수준이며 5호분은 문제가 없는 것으로 판단된다. 이와 같이 고분군의 발굴후 인위적인 환경변화와 지속적인 강우침투 및 배수 불량의 영향은 고분군의 안정성에 상당한 위험을 초래하였으며, 현 상태는 각 고분에 대한 보강이 불가피한 것으로 판단된다. 고분 벽돌의 깨짐, 고분 벽체의 거동, 조류의 서식등을 포함하여 송산리 고분군에서 발생되고 있는 보존상의 제반 문제점들을 일차적으로 누수 및 침투수에 의한 결과이다. 그러므로 무엇보다도 고분군 내부 및 고분 주변으로의 강우 및 지하수 침투를 막는 차수 대책이 시급한 것으로 판단된다. 또한 이미 발생한 변위가 더 이상 진행되지 않도록 하중을 경감하고 토압의 균형을 이루는 보강대책이 시급한 실정이다. 고분군

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.57-64
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• 2017

Short message service (SMS) is one of the most important communication methods for people who use mobile phones. However, illegal advertising spam messages exploit people because they can be used without the need for friend registration. Recently, spam message filtering systems that use machine learning have been developed, but they have some disadvantages such as requiring many calculations. In this paper, we implemented a spam message filtering system using the set-based POI search algorithm and sentence similarity without servers. This algorithm can judge whether the input query is a spam message or not using only letter composition without any server computing. Therefore, we can filter the spam message although the input text message has been intentionally modified. We added a specific preprocessing option which aims to enable spam filtering. Based on the experimental results, we observe that our spam message filtering system shows better performance than the original set-based POI search algorithm. We evaluate the proposed system through extensive simulation. According to the simulation results, the proposed system can filter the text message and show high accuracy performance against the text message which cannot be filtered by the 3 major telecom companies.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.175-183
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• 2015

The proposed algorithm consists of two stages: the learning and synthesis stages. At the learning stage, we first collect various high-resolution (HR)-low-resolution (LR) text image pairs, and quantize the LR images, and extract HR-LR block pairs. Based on quantized LR blocks, the LR-HR block pairs are clustered into a pre-determined number of classes. For each class, an optimal 2D-FIR filter is computed, and it is stored into a dictionary with the corresponding LR block for indexing. At the synthesis stage, each quantized LR block in an input LR image is compared with every LR block in the dictionary, and the FIR filter of the best-matched LR block is selected. Finally, a HR block is synthesized with the chosen filter, and a final HR image is produced. Also, in order to cope with noisy environment, we generate multiple dictionaries according to noise level at the learning stage. So, the dictionary corresponding to the noise level of the input image is chosen, and a final HR image is produced using the selected dictionary. Experimental results show that the proposed algorithm outperforms the previous works for noisy images as well as noise-free images.

• Database Research
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• v.34 no.3
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• pp.148-158
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• 2018

The optical character recognition (OCR) is a technique to extract and recognize texts from images. It is an important preprocessing step in data analysis since most actual text information is embedded in images. Many OCR engines have high recognition accuracy for images where texts are clearly separable from background, such as white background and black lettering. However, they have low recognition accuracy for images where texts are not easily separable from complex background. To improve this low accuracy problem with complex images, it is necessary to transform the input image to make texts more noticeable. In this paper, we propose a method to segment an input image into text lines to enable OCR engines to recognize each line more efficiently, and to determine the final output by comparing the recognition rates of CLAHE module and Two-step module which distinguish texts from background regions based on image processing techniques. Through thorough experiments comparing with well-known OCR engines, Tesseract and Abbyy, we show that our proposed method have the best recognition accuracy with complex background images.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.481-492
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• 2023

This study explores how to build a Korean dataset to extract information from text using generative large language models. In modern society, mixed information circulates rapidly, and effectively categorizing and extracting it is crucial to the decision-making process. However, there is still a lack of Korean datasets for training. To overcome this, this study attempts to extract information using text-based zero-shot learning using a generative large language model to build a purposeful Korean dataset. In this study, the language model is instructed to output the desired result through prompt engineering in the form of "system"-"instruction"-"source input"-"output format", and the dataset is built by utilizing the in-context learning characteristics of the language model through input sentences. We validate our approach by comparing the generated dataset with the existing benchmark dataset, and achieve 25.47% higher performance compared to the KLUE-RoBERTa-large model for the relation information extraction task. The results of this study are expected to contribute to AI research by showing the feasibility of extracting knowledge elements from Korean text. Furthermore, this methodology can be utilized for various fields and purposes, and has potential for building various Korean datasets.