• Journal of Digital Convergence
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• v.19 no.3
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• pp.279-285
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• 2021

This paper criticized that online classes during the Covid-19 period were centered on knowledge and information education, and sought ways to improve empathy as a way to improve students' sociality. The teaching-learning process was designed around the movie <(500) Days of Summer> which has the theme and story of parting and growth. On this paper the stage of empathy was divided into three stages, recognize-into, feeling-into, emotional-transaction stage. In particular, considering the process of transitioning from emotional empathy to behavioral empathy as the key to communication education, the class was designed in five stages, with an expression stage between the feeling-into stage and the emotional-transaction stage. This course is possible when learners sympathize with the work itself and reflect on their own narrative, so literary therapeutic was used, and students's response statements were collected to prove that this process is meaningful for improving empathy. In this article, the class was designed for the movie <(500) Days of Summer>, but this teaching-learning model can be applied to other contemporary film texts.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.665-675
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• 2022

After the COVID-19 pandemic, there has been growing interests in metaverse technology and social use of virtual reality platforms in non-face-to-face environments, but social issues and ethical concerns raised by metaverse have not been sufficiently discussed. In this paper, we discuss the social functions of the metaverse by examining the movie 'Ready Player One', and investigate the ethical problems that may arise in various implementation of metaverse. We identify the potential ethical problems that could occur in the context of metaverse including identity fragmentation, metaverse violence and crime, and mismanagement of personal information. We also propose some promising approaches to tackle these ethical problems ranging over descriptive ethics, normative ethics, and analytical (meta) ethics.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.909-917
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• 2023

Semiotic theory plays a vital role in the cognition of realistic films. Realistic films aim to reflect events and situations in real life, and arouse the audience's thinking about real problems through realistic depiction. In this process, the theory of semiotics helps to reveal the symbolic elements in the film and the profound meaning behind them. This study focused on the realism movie 도 가 니 "(Silenced), on the basis of the theory of saussure's semiotics, combined with its proposed the concept of "signifier" and "mean", through the study of arbitrariness and secondary film symbol sign system, designed to dig deeper into the symbolic meanings in the movie, and their cultural significance and social evaluation. By analyzing the implied narrative structure, meaning structure and ideology in the film, this paper probes into the influence and effect of mass culture on society and reveals the potential information conveyed by symbols in the film. This study aims to provide creative insights and explanations to provide useful references for research in related fields.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• Journal of Korea Game Society
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• v.20 no.2
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• pp.113-122
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• 2020

This study is the research tendency(2016~2019) on the content and the intelligent information technology. After the IIT emerged as a social topic, related research increased, and interest in VR and AR was the highest. In games, more research has been done on VR and AR. In the case of big data technology, it was a tendency to pay attention to the study of movie contents. Many studies have attempted a technological approach to IIT. With regard to artificial intelligence technology, there were differences by technology and content area, mainly viewed from a legal and institutional perspective.

• Journal of The Korean Association of Information Education
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• v.12 no.1
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• pp.65-75
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• 2008

With the advent of convergence environment of broadcasting and communication, IPTV has been widely used. It provides services such as information, movie contents and broadcasting with TV through super-highway information networks. The purpose of this study is to suggest its educational applications to overcome the limits existing in the education. For this goal, we sought its concept and characteristic and drew educational implications by analyzing its contents. We also suggested what-if scenarios for its educational applications, considered its possibilities in educational applications and suggested the kinds of teaching-learning utilizing it and its educational applications for the various subjects. Finally, we examined problems which were considered before its applications in the education with the classification of legal, technical and educational problems.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.409-412
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• 2003

The 3-D shape use to effect of movie, animation, industrial design, medical treatment service, education, engineering etc... But it is not easy to make 3-D shape from the information of 2-D image. There are two methods in restoring 3-D video image through 2-D image; First the method of using a laser; Second, the method of acquiring 3-D image through stereo vision. Instead of doing two methods with many difficulties, I study the method of simple 3-D image in this research paper. We present here a simple and efficient method, called direct calibration, which does not require any equations at all. The direct calibration procedure builds a lookup table(LUT) linking image and 3-D coordinates by a real 3-D triangulation system. The LUT is built by measuring the image coordinates of a grid of known 3-D points, and recording both image and world coordinates for each point; the depth values of all other visible points are obtained by interpolation.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.569-572
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• 2018

본 연구는 영화 포스터를 대상으로 OpenCV를 활용하여 k-means clustering 기반의 색감을 분석하는 기법을 제안한다. 또한 이를 활용하여 영화 포스터 간의 유사도를 구하고 특정 영화와 대표색을 가지는 영화를 추천하는 시스템을 제안한다. 이를 위해 본 연구에서 다음과 같은 가정을 기반으로 한다. 첫 번째, 포스터는 해당 영화를 가장 잘 나타내는 이미지로, 포스터의 색감은 영화의 전반적인 분위기를 가진다. 두 번째, 영화 사이에 유사한 색감을 가진다면, 해당 영화들은 유사한 분위기를 가진다. 본 연구에서는 2단계로 나누어 연구를 진행한다. 우선 k-means clustering 기법을 통하여 데이터를 전처리 하여 영화별 대표색을 선정한다. 이 때, 선정된 대표색을 이용하여 각 영화간 색감 유사도를 분석한 결과를 통해, 같은 장르의 영화도는 유사도가 높음을 확인할 수 있었다. 다음으로 앞의 색감 유사도 분석을 통하여 특정 영화와 높은 유사도를 가지는 영화를 추천한다. 본 연구에서 추천된 영화는 기존의 영화 선택 기준에 비하여 사용자 본인의 취향을 반영한다. 본 연구 내용이 영화를 추천하는 과정에서 반영된다면 추천 시스템의 정확도와 사용자 만족도 향상에 기여할 것으로 기대된다.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.86-90
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• 2021

Sentiment refers to a person's thoughts, opinions, and feelings toward an object. Sentiment analysis is a process of collecting opinions on a specific target and classifying them according to their emotions, and applies to opinion mining that analyzes product reviews and reviews on the web. Companies and users can grasp the opinions of public opinion and come up with a way to do so. Recently, natural language processing models using the Transformer structure have appeared, and Google's BERT is a representative example. Afterwards, various models came out by remodeling the BERT. Among them, the Facebook AI team unveiled the XLM-R (XLM-RoBERTa), an upgraded XLM model. XLM-R solved the data limitation and the curse of multilinguality by training XLM with 2TB or more refined CC (CommonCrawl), not Wikipedia data. This model showed that the multilingual model has similar performance to the single language model when it is trained by adjusting the size of the model and the data required for training. Therefore, in this paper, we study the improvement of Korean sentiment analysis performed using a pre-trained XLM-R model that solved curse of multilinguality and improved performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.