• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.269-273
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• 2006

In this paper, a remote screen control by mobile networks on embedded system is implemented. For this system a server program is ported on the embedded system connected with internet. And on the side of a mobile phone, a client program is ported using GVM. The embedded system can display the text and image from the mobile phone on its LCD. In the implemented embedded system the text and image data from GVM emulator is sent to the system for display on its LCD. The realization of the proposed embedded system can display the text :md image from a working mobile phone.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1790-1806
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• 2016

Digital image encryption is widely used for image data security. JPEG standard compresses image with great performance on reducing file size. Thus, to encrypt an image in JPEG format we should keep the quality of original image and reduced size. This paper proposes a JPEG image encryption scheme based on quantized DC and non-zero AC coefficients inner category scrambling. Instead of coefficient value encryption, the address of coefficient is encrypted to get the address of cipher text. Then 8*8 blocks are shuffled. Chaotic iteration is employed to generate chaotic sequences for address scrambling and block shuffling. Analysis of simulation shows the proposed scheme is resistant to common attacks. Moreover, the proposed method keeps the file size of the encrypted image in an acceptable range compared with the plain text. To enlarge the cipher text possible space and improve the resistance to sophisticated attacks, several additional procedures are further developed. Contrast experiments verify these procedures can refine the proposed scheme and achieve significant improvements.

• 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.

• Asia Pacific Journal of Business Review
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• v.1 no.2
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• pp.63-88
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• 2017

With the advent of new frontiers in robotics, the spectrum of robot research area has widened in many fields and applications. Other than conventional robot research, many technologies such as smart devices, drones, healthcare robots, and soft robots are emerging as promising applications. Due to the research complexity of this topic, this research requires international collaboration and should be fertilized by R&D policies. This paper aims to propose a method to perform a cross-national analysis of robot research with unstructured data such as papers in the proceedings of an international conference. Text analytics are applied to extract research issues and applications in an automatic manner.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.99-107
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• 2018

This study started from the public interest purpose to help accessibility for the information acquisition of the vulnerable groups due to visual difficulties such as the elderly and the visually impaired. In this study, the server resources are minimized and implemented in most of the user smart phones. In addition, we implement a method to gather necessary information by collecting only pattern information by utilizing crawl & clipping without having to visit the site of the information of the various sites having the data necessary for the user, and to have it in the server. Especially, we applied the TTS(Text-To-Speech) service composed of smart phone apps and tried to develop a unified customized information collection service based on voice-based information collection method.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.820-831
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• 2020

Extractive document summarization aims to select a few sentences while preserving its main information on a given document, but the current extractive methods do not consider the sentence-information repeat problem especially for news document summarization. In view of the importance and redundancy of news text information, in this paper, we propose a neural extractive summarization approach with joint sentence semantic clipping and selection, which can effectively solve the problem of news text summary sentence repetition. Specifically, a hierarchical selective encoding network is constructed for both sentence-level and document-level document representations, and data containing important information is extracted on news text; a sentence extractor strategy is then adopted for joint scoring and redundant information clipping. This way, our model strikes a balance between important information extraction and redundant information filtering. Experimental results on both CNN/Daily Mail dataset and Court Public Opinion News dataset we built are presented to show the effectiveness of our proposed approach in terms of ROUGE metrics, especially for redundant information filtering.

• English & American cultural studies
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• v.17 no.1
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• pp.81-106
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• 2017

Using recently emerging quantitative methods, this article provides a comparative study of the diachronic changes in the narrations of novel, history, and science from the early 18th-century to the 20th-century. To trace the narrative changes in different genres, this article discusses how text-mining methodology can be introduced in literary studies. We compared the traces of narrative in three genres—novel, history, and science—as a pilot study, with the three major grammatical elements of narrative: pronoun, subordinating conjunction, and action verbs in past tense. The results of data-mining show that the use of pronoun and action verb has increased in the genre of novel toward the $20^{th}$ century, while history and science has developed less story-like writing styles.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.36-41
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• 2021

Text-based image retrieval is not only cumbersome as it requires the manual input of keywords by the user, but is also limited in the semantic approach of keywords. However, content-based image retrieval enables visual processing by a computer to solve the problems of text retrieval more fundamentally. Vision applications such as extraction and mapping of image characteristics, require the processing of a large amount of data in a mobile environment, rendering efficient power consumption difficult. Hence, an effective image retrieval method on mobile platforms is proposed herein. To provide the visual meaning of keywords to be inserted into images, the efficiency of image retrieval is improved by extracting keywords of exchangeable image file format metadata from images retrieved through a content-based similar image retrieval method and then adding automatic keywords to images captured on mobile devices. Additionally, users can manually add or modify keywords to the image metadata.

• Educational Technology International
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• v.23 no.1
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• pp.97-127
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• 2022

With the recent advancement of technology, there has been an increase in professional development activities, including teachers using blogs to share practical knowledge and reflect on teaching and learning. This study was conducted to identify the contents of practical knowledge shared through the K-12 teachers' blogs. To achieve the research objective, 70,571 blog posts were collected from 329 blogs of K-12 teachers in Korean and analyzed using text mining techniques. The results of the study are as follows. First, practical knowledge sharing activities using teacher blogs have increased. Teachers posted a lot of blogs during the semester. Second, primary school teachers share various curriculum activities, reflections on project classes, class management, opinions related to education, and personal. Third, secondary school teachers share summaries and reviews of curriculum, materials related to college entrance exams, various instructional materials, opinions related to education, and personal experiences on their blogs. This study suggested that blogs are widely used as a venue for sharing practical knowledge of teachers, and that blogs can be a useful way to develop professionalism.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.43-50
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• 2022

The students' feedback is important to measure and improve teaching performance. Many teacher performance evaluation systems are based on responses to closed question, but the free text answers can contain useful information which had to be explored. In this paper we present a lexicon-based sentiment analysis to explore students' text feedback. The data was collected from a system for the evaluation of teachers by students developed and used in our university. The students comments are in Romanian language so we built a Romanian sentiment word lexicon. We used this to categorize the feeback text as positive, negative or neutral. In addition, we added a new polarity - indifferent - in order to categorize blank and "I don't answer" responses.