• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.35 no.1
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• pp.10-24
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• 2017

The research takes note of the formation process and settling period of Gwandong-Palkyung(關東八景, Eight Sites of Eastern Korea), the representative palgyeong(prominent eight sites) and jipgyeong(集景, landscape collection of scenic beauty), and investigates the time of formation regarding the palkyung and jipgeyong of Gwandong's scenic beauty through the analysis and interpretation of bibliographic data, and reference data. The result of the study is as follows. As the first document that records the terminology of "Gwandong-Palkyung" is "Daphongeunggil(答洪應吉)" of Yi, Hwang(李滉), Gwandong-Palkyung is inferred to be settled within the recognition of the people even before the 16th century. The geographic analysis result including "Sinjeung Donggukyeojiseungram(新增東國輿地勝覽)", Gwandong-Palkyung expanded as Gwandong-Sipkyung in early to middle of the 16th century. The first confirmed landscape collection regarding Gwandong-Palkyung in this study is confirmed in Shin Zup(申楫)'s "Yeonggwandong-Palkyung(詠關東八景)", thus, the terminology of Gwandong-Palkyung existed before 16th century at the latest. The settlement time of current "Palkyung" collection is estimated to be early 17th century at the latest. Poetries regarding Gwandong-Palkyung, and the frequency on the appearance of Gwandong scenic beauties are analyzed as making clear of the concentrated phenomenon on the sceneries of Gwandong-Palkyung. On the other hand, the collection of Gwandong-Palkyung in the domain of arts is confirmed initially in the ${\ll}$Gwandongpalkyungdobyeong(關東八景圖屛)${\gg}$ of Heo, Pil(許泌). Gwandong-Palkyung, expressed as the actual scene landscape painting shows similar tendencies of the conditions in the jipgyeong from the poetry, but the appearance rate of the painting subject was more prominent in visual solidarity and cohesion due to the reflection of the importance on icon(圖像) of the art works produced with particular meaning in the case of fixed ideal system. From late Joseon to modern times, ${\ll}$palpokbyeongpung(八幅屛風)${\gg}$ of various forms of folk painting is a corroborative evidence notifying that the cultural phenomenon of Gwandong-Palkyung has entered the universal period of embrace. Also, the 13 scenic beauties of Gangwon-Do appearing in the games of Namseungdo and Myeongseungyuramdo include Gwandong-Palkyung, which confirms the settlement of Gwandog-Palkyung even within the culture of games in late Joseon. Such results demonstrate the existence of awareness regarding Gwandong-Palkyung from the first half of the 15th century, which is presumed to have completely settled in the 17th century through the continuous development of formative process in the 16th century. Ultimately, Gwandong-Palkyung is the concrete formation of regional scenic beauties that individually gained its reputations as scenery from the Koryo Dynasty to late 17th century. Gwandong-Palkyung of the scenic beauty of Gwandong is a unique cultural scenery of the region that have germinated and formed through the process of cutting and polishing of long time to collect the best eight of scenic beauty from the many participation of sightseeing culture.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. 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 movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.600-616
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• 2018

The purpose of this study was to explore how primary school students develop their interest in science. A survey questionnaire was used to investigate students' interest, change of their interest, and engagement in science related activities three times a year. 201 students of two primary schools in Seoul Metropolitan City initially participated in this study. A follow-up case study was conducted with students who showed an increased interest in science. Finally, seven students were chosen in the case study. They were asked to keep a photo journal for 12 weeks, and were interviewed in every other week by one of the researchers. Among these seven participants, two (TK and QQ) were chosen for analyzing their data in this case study because they showed positive changes in developing science interest throughout the study. The results of two participants' survey, photo-journal and interview were analyzed qualitatively. First, TK, whose science interest developed from situational interest II to individual interest I, engaged in doing experiments at home, doing mathematics activities, raising pets or plants, observing phenomena, and visiting informal educational centers. He tended to participate in hands-on activities by himself in out-of-school settings. Second, QQ who developed from situational interest I to situational interest II, engaged in taking pictures as a representative activity at home and school. He tended to participate in activities with either his father or one of the researchers. Both students showed personal characteristics such as doing place-based activities, interaction with others and activity subjectivity. The goal of TK's interactions with others on the various places was to develop in cognitive domain. On the contrary, QQ's goal of interactions with others was to develop in emotional communication. This study reported the cases of characteristics of students who developed their interests in science including activities in- and out-of-school settings and their accompanying people.

• Journal of Korean Theatre Studies Association
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• no.52
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• pp.189-228
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• 2014

The purpose of this research was to summarize Korea and China's researches on Noh and to examine main domain in this field, by investigating the academic books and articles published in two countries. In 1960s, since Nohgaku has been introduced to China, academic articles on Zeami's theories and aesthetics have emphasized on aesthetic characteristics of Chinese plays and Japanese Nohgaku through the similarities of oriental plays. The number of researches on Kabuki is almost twice as that of researches on Noh in China. While most researches on Kabuki were compared with the styles and music of Pecking Opera and the theatrical theories of liyu[李漁], those on Noh has been highlighted the comparative studies on $Y{\bar{o}}kyoku$[謠曲], Chinese Noh plays. The main difference among the researches on $Y{\bar{o}}kyoku$ in Korea and China was the material regarding characters of Noh. Because song yuanzaju[宋 元雜劇]and Nohgaku in Chinese-Japanese plays were the mature form of the classic plays and those were representative of traditional nation plays, this researches tried to ascertain the cultural origins of two countries regarding the aesthetic characteristics by referencing lyrical and narrative features[曲詞] of yuanzaju[元雜劇]and the classic waka of Nohgaku. While the comparative studies on Noh and song yuanzaju and kunqu[昆劇] in China were prevalent, national researches have emphasized on the inner world of the main character and dramaturgy through the verbal description of Noh. Especially, this research tried to investigate the inner world of the main character and the intention of the writers through the verbal description of Noh authorized in the history of the works. Also, the researches on Buddhism in the Middle Ages and religious background were examined significantly. In addition, the $Y{\bar{o}}kyoku$ has influenced on European modern playwrights and the comparative studies between the materials of $Y{\bar{o}}kyoku$ and Western modern plays were concerned. In Korea, the comparative studies on Noh between Korea abd Japan has been most focused on the origin theory of Noh. The fact that appearance theory of Noh had originated from Sangaku was common opinion among Korean, Chinese, and Japanese scholars. However, they are agree with the opinion that according to the formation of the different genres, Noh's mainstream was different among three countries despite of the same origin. Yuan drama and Noh play have the same origin, but different branch. In relation to the Noh's origin theory, there are literature comparative studies in religious background, the studies presumed the origin of instrumental music related to those in mask plays, and the comparative studies between Korean mask plays and $ky{\bar{o}}gen$ of Nohgaku. Kyogen is the Comedy inserted among the stories in Nohgaku performed in just one day. Therefore, $ky{\bar{o}}gen$ must be discussed separately from the relations of 'shite[任手]'s inner action veiled with masks. This research figured out that the lacking points of the two countries' researches were the acting methods of Noh. Academic articles written by foreign scholars studying Korean and Chinese theatres should be included when this issue will be dealt with. In Korea and China, translation studies and writings regarding Nohgaku have studied by those who are major in Japanese literature or oriental literature. This case is the same in Korea in that scholars whose speciality is not theatre, but Japanese literature has studied. Therefore, this present study can give a good grasp of whole tendency on Nohgaku's research in theatre fields.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• 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.

• Information Systems Review
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• v.25 no.4
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• pp.131-159
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• 2023

Recently, amidst the backdrop of the COVID-19 pandemic shifting towards an endemic phase, there has been a rise in discussions and debates about the future of the metaverse. Simultaneously, major metaverse platforms like Roblox have been launching services integrated with generative AI, and Apple's mixed reality hardware, Vision Pro, has been announced, creating new expectations for the metaverse. In this situation where the outlook for the metaverse is divided, it is crucial to diagnose the metaverse from an ecosystem perspective, examine its key ecological features, driving forces for development, and future possibilities for advancement. This study utilized Wang's (2021) Information Ecology Theory (IET) framework, which is representative of ecosystem research in the field of Information Systems (IS), to derive the Metaverse Industrial Ecosystem (MIE). The analysis revealed that the MIE consists of four main domains: Tech Landscape, Category Ecosystem, Metaverse Platform, and Product/Service Ecosystem. It was found that the MIE exhibits characteristics such as digital connectivity, the integration of real and virtual worlds, value creation capabilities, and value sharing (Web 3.0). Furthermore, the interactions among the domains within the MIE and the four characteristics of the ecosystem were identified as driving forces for the development of the MIE at an ecosystem level. Additionally, the development of the MIE at an ecosystem level was categorized into three distinct stages: Narrow Ecosystem, Expanded Ecosystem, and Everywhere Ecosystem. It is anticipated that future advancements in related technologies and industries, such as robotics, AI, and 6G, will promote the transition from the current Expanded Ecosystem level of the MIE to an Everywhere Ecosystem level, where the connection between the real and virtual worlds is pervasive. This study provides several implications. Firstly, it offers a foundational theory and analytical framework for ecosystem research, addressing a gap in previous metaverse studies. It also presents various research topics within the metaverse domain. Additionally, it establishes an academic foundation that integrates concept definition research and impact studies, which are key areas in metaverse research. Lastly, referring to the developmental stages and conditions proposed in this study, businesses and governments can explore future metaverse markets and related technologies. They can also consider diverse metaverse business strategies. These implications are expected to guide the exploration of the emerging metaverse market and facilitate the evaluation of various metaverse business strategies.

• The Journal of the Petrological Society of Korea
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• v.27 no.3
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• pp.109-120
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• 2018

The distributional characteristics of fault segments in Cretaceous and Tertiary rocks from southeastern Gyeongsang Basin were derived. The 267 sets of fault segments showing linear type were extracted from the curved fault lines delineated on the regional geological map. First, the directional angle(${\theta}$)-length(L) chart for the whole fault segments was made. From the related chart, the general d istribution pattern of fault segments was derived. The distribution curve in the chart was divided into four sections according to its overall shape. NNE, NNW and WNW directions, corresponding to the peaks of the above sections, indicate those of the Yangsan, Ulsan and Gaeum fault systems. The fault segment population show near symmetrical distribution with respect to $N19^{\circ}E$ direction corresponding to the maximum peak. Second, the directional angle-frequency(N), mean length(Lm), total length(Lt) and density(${\rho}$) chart was made. From the related chart, whole domain of the above chart was divided into 19 domains in terms of the phases of the distribution curve. The directions corresponding to the peaks of the above domains suggest the directions of representative stresses acted on rock body. Third, the length-cumulative frequency graphs for the 18 sub-populations were made. From the related chart, the value of exponent(${\lambda}$) increase in the clockwise direction($N10{\sim}20^{\circ}E{\rightarrow}N50{\sim}60^{\circ}E$) and counterclockwise direction ($N10{\sim}20^{\circ}W{\rightarrow}N50{\sim}60^{\circ}W$). On the other hand, the width of distribution of lengths and mean length decrease. The chart for the above sub-populations having mutually different evolution characteristics, reveals a cross section of evolutionary process. Fourth, the general distribution chart for the 18 graphs was made. From the related chart, the above graphs were classified into five groups(A~E) according to the distribution area. The lengths of fault segments increase in order of group E ($N80{\sim}90^{\circ}E{\cdot}N70{\sim}80^{\circ}E{\cdot}N80{\sim}90^{\circ}W{\cdot}N50{\sim}60^{\circ}W{\cdot}N30{\sim}40^{\circ}W{\cdot}N40{\sim}50^{\circ}W$) < D ($N70{\sim}80^{\circ}W{\cdot}N60{\sim}70^{\circ}W{\cdot}N60{\sim}70^{\circ}E{\cdot}N50{\sim}60^{\circ}E{\cdot}N40{\sim}50^{\circ}E{\cdot}N0{\sim}10^{\circ}W$) < C ($N20{\sim}30^{\circ}W{\cdot}N10{\sim}20^{\circ}W$) < B ($N0{\sim}10^{\circ}E{\cdot}N30{\sim}40^{\circ}E$) < A ($N20{\sim}30^{\circ}E{\cdot}N10{\sim}20^{\circ}E$). Especially the forms of graph gradually transition from a uniform distribution to an exponential one. Lastly, the values of the six parameters for fault-segment length were divided into five groups. Among the six parameters, mean length and length of the longest fault segment decrease in the order of group III ($N10^{\circ}W{\sim}N20^{\circ}E$) > IV ($N20{\sim}60^{\circ}E$) > II ($N10{\sim}60^{\circ}W$) > I ($N60{\sim}90^{\circ}W$) > V ($N60{\sim}90^{\circ}E$). Frequency, longest length, total length, mean length and density of fault segments, belonging to group V, show the lowest values. The above order of arrangement among five groups suggests the interrelationship with the relative formation ages of fault segments.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.