• The Journal of Bigdata
• /
• v.1 no.1
• /
• pp.1-7
• /
• 2016

Social media such as twitter has been popular by the diffusion of internet, and thanks to the radical improvement of computational ability of computers big data analysis became possible. This research is regarding about smart watch which is receiving attention as post-smartphone technology. Among various types of smart watch, this research focuses on the recently released Samsung Galaxy Gear S2. The main purpose of the research is to analyze customer's actual twitter data that was produced before and after the release of the smart watch to the market. Through the analysis, this research provides practical marketing strategy guideline, and also the analysis framework used in this research can be a research framework for other area and product researches.

• Journal of The Korean Association For Science Education
• /
• v.16 no.4
• /
• pp.351-364
• /
• 1996

This study aimed at identifying the organization of contents, the level and scope, the time of study and experiment acivities in science text books by comparing and analizing the characteristics of the Elementary School Educational Curriculum in Korea, China and Japan. First, the objectives of science subject are focussed on understanding nature exactly, learning inquiry methods and developping scientific attitudes. This is very desirable in the lights of teaching students' characteristics. Second, three countries, Korea, China and japan treat the natural phenomena impartially in the formation of the contents of natural science. Especially, china threats scientic contents related to the real life themes importantly. Third, the number of concepts and pages of the natural science textbook are put in Korea. China and Japan in order. Time of study and the level scope of contents in natural science should be composed of desirable national situations. Forth, the time of experiment activities is put in Korea, Japan and china also in order.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.14 no.2
• /
• pp.61-67
• /
• 2018

This media conversion in which webtoons are remediated to movies and dramas has taken off since the mid-2000s. Webtoons may be favorable for media conversion into movies and dramas as the story as finished and has proven to be fun with a fixed readership: however, only a small number of webtoon were successful box office hits or received high viewer ratings. Then in 2017, the movie based on the webtoon, succeed in attracting more more then 10 million viewers. In this regard, this study derived the success factors by comparing and analyzing the narrative structure and visual elements of , which was the biggest hit movie, with the original webtoon. The case analysis showed that there are two necessary elements for success: a text configuration of strategy optimized for media conversion, which is based on understanding the different media characteristics of webtoon and movie; and a configuration strategy that exaggerates the personalities of the characters and compresses the story of a webtoon which features various events and many characters in a long series, in consideration of the characteristics of movie which needs to give a big impact in 2 hours.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2588-2609
• /
• 2019

Speech homomorphic encryption has become one of the key components in secure speech storing in the public cloud computing. The major problem of speech homomorphic encryption is the huge data expansion of speech cipher-text. To address the issue, this paper presents a speech homomorphic encryption scheme with less data expansion, which is a probabilistic statistics and addition homomorphic cryptosystem. In the proposed scheme, the original digital speech with some random numbers selected is firstly grouped to form a series of speech matrix. Then, a proposed matrix encryption method is employed to encrypt that speech matrix. After that, mutual information in sample speech cipher-texts is reduced to limit the data expansion. Performance analysis and experimental results show that the proposed scheme is addition homomorphic, and it not only resists statistical analysis attacks but also eliminates some signal characteristics of original speech. In addition, comparing with Paillier homomorphic cryptosystem, the proposed scheme has less data expansion and lower computational complexity. Furthermore, the time consumption of the proposed scheme is almost the same on the smartphone and the PC. Thus, the proposed scheme is extremely suitable for secure speech storing in public cloud computing.

• International Journal of Contents
• /
• v.15 no.3
• /
• pp.32-38
• /
• 2019

We usually suffer from difficulties in treating or managing Big Data generated from various digital media and/or sensors using traditional mining techniques. Additionally, there are many problems relative to the lack of memory and the burden of the learning curve, etc. in an increasing capacity of large volumes of text when new data are continuously accumulated because we ineffectively analyze total data including data previously analyzed and collected. In this paper, we propose a general-purpose classifier and its structure to solve these problems. We depart from the current feature-reduction methods and introduce a new scheme that only adopts changed elements when new features are partially accumulated in this free-style learning environment. The incremental learning module built from a gradually progressive formation learns only changed parts of data without any re-processing of current accumulations while traditional methods re-learn total data for every adding or changing of data. Additionally, users can freely merge new data with previous data throughout the resource management procedure whenever re-learning is needed. At the end of this paper, we confirm a good performance of this method in data processing based on the Big Data environment throughout an analysis because of its learning efficiency. Also, comparing this algorithm with those of NB and SVM, we can achieve an accuracy of approximately 95% in all three models. We expect that our method will be a viable substitute for high performance and accuracy relative to large computing systems for Big Data analysis using a PC cluster environment.

• Journal of the Korean Society of Safety
• /
• v.36 no.5
• /
• pp.52-60
• /
• 2021

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

• Journal of Information Processing Systems
• /
• v.18 no.1
• /
• pp.1-11
• /
• 2022

Online video telephony systems have been increasingly used in several industrial areas because of coronavirus disease 2019 (COVID-19) spread. The existing session initiation protocol (SIP)-based video call system is being usefully utilized, however, there is a limitation that it is very inconvenient for users to transmit additional information during conversation to the other party in real time. To overcome this problem, an enhanced scheme is presented based on augmented real-time reality (AR). In this scheme, augmented information is automatically searched from the Internet and displayed on the user's device during video telephony. The proposed approach was qualitatively evaluated by comparing it with other conferencing systems. Furthermore, to evaluate the feasibility of the approach, we implemented a simple network application that can generate SIP call requests and answer with AR object pre-fetching. Using this application, the call setup time was measured and compared between the original SIP and pre-fetching schemes. The advantage of this approach is that it can increase the convenience of a user's mobile phone by providing a way to automatically deliver the required text or images to the receiving side.

• SUVANNABHUMI
• /
• v.14 no.1
• /
• pp.133-160
• /
• 2022

This paper examines narratives of women's marginal position in Bao Ninh's Short Stories and Svetlana Alexievich's Unwomanly Face of War from a feminist narratological approach. In analyzing voices of marginalized women, direct and indirect descriptions of women's beauty and pain, and private-public narratives of women's love stories, this paper aims to identify presentations of women's real authority in the text written by a male author, Bao Ninh, and in the one by a female author. The paper argues that juxtaposing these texts reveals an overturn of the traditional conception of sexual and gender differences. Specifically, distinguishing between male/female discourse does not show powerful /nonpowerful language, but recognizes the real authority of each type of discourse based on sexual differences. The writing also illustrates that masculine language becomes powerless and deficient in the women's world; meanwhile, in writing about herself, woman establishes a type of a powerful feminine discourse, which blends both emotional, enthusiastic, and gossipy characteristics of female language and direct, rational, and strong ones of male language. Thus, the feminists' radical segregation on male/female discourses to overturn masculine authority and create a language for women at par with men has been clearly shifted when comparing the two writers' texts based on the juxtapositional model of the comparative literature.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.4
• /
• pp.693-700
• /
• 2023

Sentiment analysis, a branch of natural language processing that classifies and identifies subjective opinions and emotions in text documents as positive or negative, can be used for various promotions and services through customer preference analysis. To this end, recent research has been conducted utilizing various techniques in machine learning and deep learning. In this study, we propose an optimal language model by comparing the accuracy of sentiment analysis for movie, product, and game reviews using existing RNN-based models and recent Transformer-based language models. In our experiments, LMKorBERT and GPT3 showed relatively good accuracy among the models pre-trained on the Korean corpus.

• Educational Technology International
• /
• v.19 no.2
• /
• pp.175-197
• /
• 2018

Flipped classroom (FC) has gained attention as an active learning approach. Designing effective out-of-class activities to help prepare students for in-class activities is fundamental for successful implementation of FC. This study investigated the effectiveness of Goal-Based Scenarios (GBS) for out-of-class learning in FC. Four out of twelve units in a medical humanities course for Year 2 medical students was redesigned into a FC format, where e-learning modules were designed using a GBS approach for out-of-class activities and classroom debates were implemented for in-class activities. The other eight units were delivered in a conventional classroom debate format, which included reading text materials as pre-class assignments. A formative evaluation study was conducted using questionnaires and interview methods and students' academic achievements were evaluated by comparing their pre- and post-test scores between FC and conventional units. Students had positive perceptions of the e-learning modules in GBS approach and preferred the structure of learning in the FC format. Students' pre-test scores were slightly higher in the FC units, yet their post-test scores were comparable with conventional units. This study illustrates students' perceptions that the learning was bettered structured in FC and that the out-of-class learning using the GBS approach helped them better prepared for in-class activities.