• Cross-Cultural Studies
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• v.40
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• pp.111-131
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• 2015

An unusual success engendering loyalty among cult fans in the United States, Mamoru Oshii's 1995 cyberpunk anime, Ghost in the Shell (GITS) revolves around a female cyborg assassin named Motoko Kusanagi, a.k.a. "the Major." When the news came out last year that Scarlett Johansson was offered 10 million dollars for the role of the Major in the live action remake of GITS, the frustrated fans accused DreamWorks of "whitewashing" the classic Japanimation and turning it into a PG-13 film. While it would be premature to judge a film yet to be released, it appears timely to revisit the core achievement of Oshii's film untranslatable into the Hollywood formula. That is, unlike ultimately heteronormative and humanist sci-fi films produced in Hollywood, such as the Matrix trilogy or Cloud Atlas, GITS defies a Hollywoodization by evoking much bafflement in relation to its queer, posthuman characters and settings. This essay homes in on Major Kusanagi's body in order to update prior criticism from the perspectives of posthumanism and queer theory. If the Major's voluptuous cyborg body has been read as a liberating or as a commodified feminine body, latest critical work of posthumanism and queer theory causes us to move beyond the moralistic binaries of human/non-human and male/female. This deconstruction of binaries leads to a radical rethinking of "reality" and "identity" in an image-saturated, hypermediated age. Viewed from this perspective, Major Kusanagi's body can be better understood less as a reflection of "real" women than as an embodiment of our anxieties on the loss of self and interiority in the SNS-dominated society. As is warned by many posthumanist and queer critics, queer and posthuman components are too often used to reinforce the human. I argue that the Major's hybrid body is neither a mere amalgam of human and machine nor a superficial postmodern blurring of boundaries. Rather, the compelling combination of individuality, animality, and technology embodied in the Major redefines the human as always, already posthuman. This ethical act of revision-its shifting focus from oppressive humanism to a queer coexistence-evinces the lasting power of GITS.

• Journal of Popular Narrative
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• v.27 no.2
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• pp.143-177
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• 2021

In the history of Korean literature, evaluations on 『Jirisan』 (Lee Byeong-ju) are bisected. Some evaluate it as a novel of authentic records which reproduces the history before and after the emancipation objectively while others say it takes advantage of anti-communistic ideology. This study analyzes that difference is resulted not from the distinction of perspectives but from cracks in the text. This is associated with the process of 『Jirisan』's publication. 『Jirisan』 was published serially in 『Sedae』 from 1972, and then, part of the manuscript was published in 1978 and the whole edition published in a series came to be republished in 1981. After that, in 1981 and 1985, part of the follow-up story was printed on the magazine, and then, with the memoirs of those two years as materials, the sixth and seventh volumes were again published through 'revision'. In other words, the publication of 『Jirisan』 is divided into that of the edition published in a series and that of the edition published in 1985 including the contents of revision. The theme of the work, 『Jirisan』 differs according to the point of its completion you may think of. This researcher pays attention to the difference of perspectives between the contents up to the fifth volume and those of the sixth and seventh volumes. Particularly, his evaluation on 'partisans' seems to have changed. In the edition published in a series, he extended 'partisans' into the independence movement in the Japanese colonial era under the Revitalizing Reforms system and adopted the representation of 'partisans' three-dimensionally whereas in the sixth and seventh volumes, he reproduced 'partisans' as beings that were the 'doctrinaire' and 'vicious' 'Reds' and had to be punished. In brief, with 『Jirisan』, he represented 'partisans' in the background of history before and after the emancipation and segmented the discourse, representation and ideology of the Cold War system, but in the process of revision, he stitched up 'partisans' as beings that were evil and losers. Consequently, with 『Jirisan』, he revealed the process of division and contention that proceeded around anti-communism/capitalism within the abyss of the 1970's to 80's and reproduced 'partisans' as beings that were either 'hostile (the Reds)' or 'unknown (losers)

• Journal of Popular Narrative
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• v.27 no.2
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• pp.215-262
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• 2021

This thesis focuses on Wandeuki and Elegant Lies - novels written by Kim Ryeo-reong and adapted into the film by Director Lee Han; this thesis analyzes the process of storytelling being transformed as the media is converted. Also, this thesis discusses cultural-political implications of transmedia storytelling where different narrative responses coexist concerning post-IMF family disorganization and "individualization." First of all, this thesis critically reviews existing discourses on the concept of transmedia storytelling and refers to 'transfictionality' the narratological concept of Marie-Laure Ryan in order to look into media conversion storytelling that starts from original novels. The novels Wandeuki and Elegant Lies show two aspects of "individualization" that adopts existential conditions of family disorganization. Wandeuki deviates from patriarchal family romance through self-discovery and exhibits loose family bond, which is something similar to companionship of close individuals. Elegant Lies shows individualization of pain by portraying a teenager who found herself completely isolated, while showing that it is impossible for the people left behind to mourn. On the other hand, director Lee Han's films and show stories in which family members, who are confronting family dissolution, rediscover and restore their families against family dissolution. The film promotes the expansion of family community through multicultural identity, and the film completes condolence of the people left behind by having the remaining families survive as survivors of suicide. The storyworld of the novels puts emphasis on 'self-discovery' of individual adolescents, while the storyworld of the movies puts emphasis on 'rediscovery of family'. Through transformation of storytelling - especially the redesigning of narrative structures called "modification" - transmedia storytelling shows that the relationship between media-converted texts is far from "faithful representation," but rather, shows conflicting themes and perspectives. With a reference point of 'the emergence of character' transmedia storytelling, which is predicated on the original work but aims to free itself from the original work by transforming storytelling through media conversion, opens up polyphonic storyworld by creating heterogeneous voices. In the post IMF-era, where uncertainty mounts over family dissolution and individualization, polyphonic storyworld created by transmedia storytelling provides an opportunity to experience disparate desires over individual freedom/risk and complacency toward community. We can call this the cultural-political implication of transmedia storytelling based on transferring, transcednding, and transforming.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.457-473
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• 2023

Asbestos has been produced, imported and used in various industries in Korea over the past decades. Since asbestos causes fatal diseases such as malignant mesothelioma and lung cancer, the use of asbestos has been generally banned in Korea since 2009. However, there are still many asbestos-containing materials around us, and safe management is urgently needed. This study aims to examine asbestos-related trend changes using major asbestos-related keywords based on the asbestos trend analysis using big data for the past 32 years (1991 to 2022) in Korea. In addition, we reviewed both domestic trends related to the production, import, and use of asbestos before 1990 and asbestos-related policies from 2023 to 2027. From 1991 to 2000, main keywords related to asbestos were research, workers, carcinogens, and the environment because the carcinogenicity of asbestos was highlighted due to domestic production, import, and use of asbestos. From 2001 to 2010, the main keywords related to asbestos were lung cancer, litigation, carcinogens, exposure, and companies because lawsuits were initiated in the US and Japan in relation to carcinogenicity due to asbestos. From 2011 to 2020, the high ranking keywords related to asbestos were carcinogen, baseball field, school, slate, building, and abandoned asbestos mine due to the seriousness of the asbestos problem in Korea. From 2021 to present (2023), the main search keywords related to asbestos such as school, slate (asbestos cement), buildings, landscape stone, environmental impact assessment, apartment, and cement appeared.

• (The) Research of the performance art and culture
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• no.38
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• pp.143-191
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• 2019

This research argues that Pansori had patrons in its development. Patrons are commonly discussed aspect of history of any art form. Pansori is no exception. While Pansori originally began as the art of the common people, Yangban class became the primary audience. This paper examines the role of royal family of Choson dynasty in development of Pansori. Heungseon Daewongun (흥선대원군) in particular was a Pansori aficionado. The record around Daewongun's involvement to Pansori proves that heavy monetary investment was made. He hosted Pansori competitions and sponsored creation of Pansori tradition, Boseong Sori (보성소리) and Gangsanje (강산제). Also the aspect of Pansori patronage lies not just in Yangban class, but also in Jung'in class, which is roughly analoguous to European bourgeois in that they were not of Yangban class, but had gained monetary status, and had aesthetics of both Yangban and commoner class. I argue that Heungseon Daewongun's ties to the Jung'in class is reflected in his actions towards Pansori artists. The traditions he had sponsored have important characteristics, including sophisticated lyrics heavily utilizing Classical Chinese poetry, highly artistic musical composition, and conservative Confucian ethics. Those characteristics indicate that the Pansori traditions sponsored by the royal patrons have changed to cater to their artistic taste and philosophy. This paper conducts a textual comparative analysis between Gangsanje Pansori Jeokbyeokga (강산제 판소리 적벽가), Dongpyeonje's Pansori Jeokbyeokga (동편제 판소리 적벽가), and Seopyeonje Pansori Jeokbyeokga, who share the same plot yet offers a stark differences in tone, philosophy, and sense of humor. Daewongun was a primary sponsor of Pansori, which proves that Yangban class and the royal family have played important role as patrons of Pansori.

• Journal of The Korean Association For Science Education
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• v.44 no.5
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• pp.391-404
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• 2024

The development of science and technology brings abundance and convenience to human life, but it also brings risks. The risks caused by science and technology are universal and far-reaching, affecting the lives of humans, and they are living in an uncertain VUCA era where humans cannot predict when and where they will encounter risks. In order to respond to these risks, it is necessary to increase the level of citizens' risk awareness through risk education. It is necessary to discuss the role of science education in helping citizens to judge and respond to risks scientifically and objectively. On the other hand, in the process of judging and assessing risks, citizens are affected by the frames and ways in which risk information is expressed, a phenomenon known as the "Framing Effect". In this study, we categorized the factors that cause the framing effect, and based on the categorization, we compared and analyzed the frames of risk expression presented in the 2015 revised curriculum science textbooks. For this purpose, we categorized the factors that cause the framing effect by looking at papers published in KCI and SSCI journals with keywords "Framing Effect", and extracted the risk expression texts in textbooks and analyzed them according to the categories. We were able to derive eight factors causing framing effect and categorize the relationship between the factors in a 5x5 matrix. The differences in the frequency of risk expressions by subject in the 2015 revised science curriculum were related to the nature of the subject and the achievement standards, and the differences in the frequency of risk expressions could be identified by the categories of framing and presentation methods. This study is significant in that it examines the way risk is expressed by science subjects based on the factors that cause the framing effect and suggests the importance of the framing effect in risk education.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.