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Jin-Taek Kim
Future IT Innovation Laboratory, Pohang University of Science and Technology, 77, Cheongam-ro, Nam-gu, Pohang-si, Gyeongsangbuk-do 37673, Korea

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Journal article
Published: 11 February 2021 in Applied Sciences
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This study aims to decompose plot structures of stories in narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia). Since a story is interwoven with main plots and subplots (i.e., primary and ancillary story lines), decomposing a story into multiple story lines enables us to analyze how events in the story are allocated and logically connected. For the decomposition, the existing studies employed character networks (i.e., social networks of characters that appeared in a story) and assumed that characters’ social relationships are consistent in a story line. However, these studies overlooked that social relationships significantly change around major events. To solve this problem, we attempt to use the changes for distinguishing story lines rather than suffer from the changes. We concentrate on the changes in characters’ social relationships being the result of changes in their personalities. Moreover, these changes gradually proceed within a story line. Therefore, we first propose features for measuring changes in personalities of characters: (i) Degrees of characters in character networks, (ii) lengths of dialogues spoken by characters, and (iii) ratios of out-degrees for in-degrees of characters in character networks. We supposed these features reflect importance, inner/outer conflicts, and activeness of characters, respectively. Since characters’ personalities gradually change in a story line, we can suppose that the features also show gradual story developments in a story line. Therefore, we conduct regression for each feature to discover dominant tendencies of the features. By filtering scenes that do not follow the tendencies, we extract a story line that exhibits the most dominant personality changes. We can decompose stories into multiple story lines by iterating the regression and filtering. Besides, personalities of characters change more significantly in major story lines. Based on this assumption, we also propose methods for discriminating main plots. Finally, we evaluated the accuracy of the proposed methods by applying them to the movies, which is one of the most popular narrative multimedia.

ACS Style

O-Joun Lee; Eun-Soon You; Jin-Taek Kim. Plot Structure Decomposition in Narrative Multimedia by Analyzing Personalities of Fictional Characters. Applied Sciences 2021, 11, 1645 .

AMA Style

O-Joun Lee, Eun-Soon You, Jin-Taek Kim. Plot Structure Decomposition in Narrative Multimedia by Analyzing Personalities of Fictional Characters. Applied Sciences. 2021; 11 (4):1645.

Chicago/Turabian Style

O-Joun Lee; Eun-Soon You; Jin-Taek Kim. 2021. "Plot Structure Decomposition in Narrative Multimedia by Analyzing Personalities of Fictional Characters." Applied Sciences 11, no. 4: 1645.

Journal article
Published: 08 December 2020 in Sustainability
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This study aims at discovering social desires and conflicts from subculture narrative multimedia. Since one of the primary purposes in the subculture consumption is vicarious satisfaction, the subculture works straightforwardly describe what their readers want to achieve and break down. The latent desires and conflicts are useful for understanding our society and realizing smart governance. To discover the social issues, we concentrate on that each subculture genre has a unique imaginary world that consists of inventive subjects. We suppose that the subjects correspond to individual social issues. For example, game fiction, one of the popular genres, describes a world like video games. Under game systems, everyone gets the same results for the same efforts, and it can be interpreted as critics for the social inequality issue. Therefore, we first extract subjects of genres and measure the membership degrees of subculture works for each genre. Using the subjects and membership degrees, we build a genealogy tree of subculture genres by tracing their evolution and differentiation. Then, we extract social issues by searching for the subjects that come from the real world, not imaginary. If a subculture work criticizes authoritarianism, it might include subjects such as government officials and bureaucrats. A combination of the social issues and genre genealogy tree will show diachronic changes in our society. We have evaluated the proposed methods by extracting social issues reflected in Korean web novels.

ACS Style

O-Joun Lee; Heelim Hong; Eun-Soon You; Jin-Taek Kim. Discovering Social Desires and Conflicts from Subculture Narrative Multimedia. Sustainability 2020, 12, 10241 .

AMA Style

O-Joun Lee, Heelim Hong, Eun-Soon You, Jin-Taek Kim. Discovering Social Desires and Conflicts from Subculture Narrative Multimedia. Sustainability. 2020; 12 (24):10241.

Chicago/Turabian Style

O-Joun Lee; Heelim Hong; Eun-Soon You; Jin-Taek Kim. 2020. "Discovering Social Desires and Conflicts from Subculture Narrative Multimedia." Sustainability 12, no. 24: 10241.

Journal article
Published: 12 May 2020 in Cancers
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Single-beam acoustic tweezers (SBAT) is a widely used trapping technique to manipulate microscopic particles or cells. Recently, the characterization of a single cancer cell using high-frequency (>30 MHz) SBAT has been reported to determine its invasiveness and metastatic potential. Investigation of cell elasticity and invasiveness is based on the deformability of cells under SBAT’s radiation forces, and in general, more physically deformed cells exhibit higher levels of invasiveness and therefore higher metastatic potential. However, previous imaging analysis to determine substantial differences in cell deformation, where the SBAT is turned ON or OFF, relies on the subjective observation that may vary and requires follow-up evaluations from experts. In this study, we propose an automatic and reliable cancer cell classification method based on SBAT and a convolutional neural network (CNN), which provides objective and accurate quantitative measurement results. We used a custom-designed 50 MHz SBAT transducer to obtain a series of images of deformed human breast cancer cells. CNN-based classification methods with data augmentation applied to collected images determined and validated the metastatic potential of cancer cells. As a result, with the selected optimizers, precision, and recall of the model were found to be greater than 0.95, which highly validates the classification performance of our integrated method. CNN-guided cancer cell deformation analysis using SBAT may be a promising alternative to current histological image analysis, and this pretrained model will significantly reduce the evaluation time for a larger population of cells.

ACS Style

Hae Gyun Lim; O-Joun Lee; K. Kirk Shung; Jin-Taek Kim; Hyung Ham Kim. Classification of Breast Cancer Cells Using the Integration of High-Frequency Single-Beam Acoustic Tweezers and Convolutional Neural Networks. Cancers 2020, 12, 1212 .

AMA Style

Hae Gyun Lim, O-Joun Lee, K. Kirk Shung, Jin-Taek Kim, Hyung Ham Kim. Classification of Breast Cancer Cells Using the Integration of High-Frequency Single-Beam Acoustic Tweezers and Convolutional Neural Networks. Cancers. 2020; 12 (5):1212.

Chicago/Turabian Style

Hae Gyun Lim; O-Joun Lee; K. Kirk Shung; Jin-Taek Kim; Hyung Ham Kim. 2020. "Classification of Breast Cancer Cells Using the Integration of High-Frequency Single-Beam Acoustic Tweezers and Convolutional Neural Networks." Cancers 12, no. 5: 1212.

Journal article
Published: 01 April 2020 in Sensors
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Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character’s roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies.

ACS Style

O-Joun Lee; Jason J. Jung; Jin-Taek Kim. Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia. Sensors 2020, 20, 1978 .

AMA Style

O-Joun Lee, Jason J. Jung, Jin-Taek Kim. Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia. Sensors. 2020; 20 (7):1978.

Chicago/Turabian Style

O-Joun Lee; Jason J. Jung; Jin-Taek Kim. 2020. "Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia." Sensors 20, no. 7: 1978.