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Dr. O-Joun Lee
Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang, South Korea

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Research Keywords & Expertise

0 Smart Cities
0 Social Network Analysis
0 smart governance
0 Multimedia content analysis
0 Computational narrative

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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.

Research article
Published: 11 February 2021 in Mobile Information Systems
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This study aims at forming research teams for interinstitutional collaborations. Research institutes have their own purposes and topics of interest. Thus, supporting joint research between multiple institutes, we have to consider not only synergies between scholars but also purposes of the institutes. To solve this problem, we propose a bibliographic network embedding method that can learn characteristics of institutes, not only of each scholar. First, we compose a bibliographic network that consists of scholars, publications, venues, research projects, and institutes. Collaboration styles and research topics of institutes and scholars are extracted by mining subgraphs from the bibliographic network. Then, vector representations of network nodes are learned based on occurrences of subgraphs on the nodes and neighborhoods of the nodes. Based on the vector representations, we train multilayer perceptrons (MLP) to assess collaboration probability between scholars affiliated in different institutes. For training the MLP, we suggest three strategies: (i) considering every collaboration, (ii) focusing on interinstitutional collaborations, and (iii) focusing on collaboration outcomes. To evaluate the proposed methods, we have analyzed research collaborations of POSTECH (Pohang University of Science and Technology) and RIST (Research Institute of Industrial Science and Technology) from 2011 to 2020. Then, we conducted the research team formation for joint research of the two institutes according to two purposes: pure research and commercialization research.

ACS Style

O-Joun Lee; Seungha Hong; Jin-Taek Kim. Interinstitutional Research Team Formation Based on Bibliographic Network Embedding. Mobile Information Systems 2021, 2021, 1 -12.

AMA Style

O-Joun Lee, Seungha Hong, Jin-Taek Kim. Interinstitutional Research Team Formation Based on Bibliographic Network Embedding. Mobile Information Systems. 2021; 2021 ():1-12.

Chicago/Turabian Style

O-Joun Lee; Seungha Hong; Jin-Taek Kim. 2021. "Interinstitutional Research Team Formation Based on Bibliographic Network Embedding." Mobile Information Systems 2021, no. : 1-12.

Journal article
Published: 19 January 2021 in Journal of Informetrics
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This study aims at representing research patterns of bibliographic entities (e.g., scholars, papers, and venues) with a fixed-length vector. Bibliographic network structures rooted in the entities are incredibly diverse, and this diversity increases in the outstanding entities. Thus, despite their significant volume, the outstanding entities obtain minimal learning opportunities, whereas low-performance entities are over-represented. This study solves the problem by representing the patterns of the entities rather than depicting individual entities in a precise manner. First, we describe structures rooted in the entities using the Weisfeiler–Lehman (WL) relabeling process. Each subgraph generated by the relabeling process provides information on the scholars, kinds of papers they published, standards of venues in which the papers were published, and types of their collaborators. We assume that a subgraph depicts the research patterns of bibliographic entities, such as the preference of a scholar in choosing either a few highly impactful papers or numerous papers of moderate impact. Then, we simplify the subgraphs according to multiple levels of detailedness. Original subgraphs represent the individuality of the entities, and simplified subgraphs represent the entities sharing the same research patterns. In addition, simplified subgraphs balance the learning opportunities of high- and low-performance entities by co-occurring with both types of entities. We embed the subgraphs using the Skip-Gram method. If the results of the embedding represent the research patterns of the entities, the obtained vectors should be able to represent various aspects of the research performance in both the short-term and long-term durations regardless of the performances of the entities. Therefore, we conducted experiments for predicting 23 performance indicators during four time periods for four performance groups (top 1%, 5%, 10%, and all entities) using only the vector representations. The proposed model outperformed the existing network embedding methods in terms of both accuracy and variance.

ACS Style

O-Joun Lee; Hyeon-Ju Jeon; Jason J. Jung. Learning multi-resolution representations of research patterns in bibliographic networks. Journal of Informetrics 2021, 15, 101126 .

AMA Style

O-Joun Lee, Hyeon-Ju Jeon, Jason J. Jung. Learning multi-resolution representations of research patterns in bibliographic networks. Journal of Informetrics. 2021; 15 (1):101126.

Chicago/Turabian Style

O-Joun Lee; Hyeon-Ju Jeon; Jason J. Jung. 2021. "Learning multi-resolution representations of research patterns in bibliographic networks." Journal of Informetrics 15, no. 1: 101126.

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.

Conference paper
Published: 13 October 2020 in Proceedings of the International Conference on Research in Adaptive and Convergent Systems
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This study aims to learn task-agnostic representations of narrative multimedia. The existing studies focused on only stories in the narrative multimedia without considering their physical features. We propose a method for incorporating multi-modal features of the narrative multimedia into a unified vector representation. For narrative features, we embed character networks as with the existing studies. Textual features can be represented using the LSTM (Long-Short Term Memory) autoencoder. We apply the convolutional autoencoder to visual features. The convolutional autoencoder also can be used for the spectrograms of audible features. To combine these features, we propose two methods: early fusion and late fusion. The early fusion method composes representations of features on each scene. Then, we learn representations of a narrative work by predicting time-sequential changes in the features. The late fusion method concatenates feature vectors that are trained for allover the narrative work. Finally, we apply the proposed methods on webtoons (i.e., comics that are serially published through the web). The proposed methods have been evaluated by applying the vector representations to predicting the preferences of users for the webtoons.

ACS Style

O-Joun Lee; Jin-Taek Kim. Learning Multi-modal Representations of Narrative Multimedia. Proceedings of the International Conference on Research in Adaptive and Convergent Systems 2020, 1 .

AMA Style

O-Joun Lee, Jin-Taek Kim. Learning Multi-modal Representations of Narrative Multimedia. Proceedings of the International Conference on Research in Adaptive and Convergent Systems. 2020; ():1.

Chicago/Turabian Style

O-Joun Lee; Jin-Taek Kim. 2020. "Learning Multi-modal Representations of Narrative Multimedia." Proceedings of the International Conference on Research in Adaptive and Convergent Systems , no. : 1.

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.

Journal article
Published: 13 January 2020 in Artificial Intelligence
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This study aims to learn representations of stories in narrative works (i.e., creative works that contain stories) using fixed-length vectors. Vector representations of stories enable us to compare narrative works regardless of their media or formats. To computationally represent stories, we focus on social networks among characters (character networks). We assume that the structural features of the character networks reflect the characteristics of stories. By extending substructure-based graph embedding models, we propose models to learn distributed representations of character networks in stories. The proposed models consist of three parts: (i) discovering substructures of character networks, (ii) embedding each substructure (Char2Vec), and (iii) learning vector representations of each character network (Story2Vec). We find substructures around each character in multiple scales based on proximity between characters. We suppose that a character's substructures signify its ‘social roles’. Subsequently, a Char2Vec model is designed to embed a social role based on co-occurred social roles. Since character networks are dynamic social networks that temporally evolve, we use temporal changes and adjacency of social roles to determine their co-occurrence. Finally, Story2Vec models predict occurrences of social roles in each story for embedding the story. To predict the occurrences, we apply two approaches: (i) considering temporal changes in social roles as with the Char2Vec model and (ii) focusing on the final social roles of each character. We call the embedding model with the first approach ‘flow-oriented Story2Vec.’ This approach can reflect the context and flow of stories if the dynamics of character networks is well understood. Second, based on the final states of social roles, we can emphasize the denouement of stories, which is an overview of the static structure of the character networks. We name this model as ‘denouement-oriented Story2Vec.’ In addition, we suggest ‘unified Story2Vec’ as a combination of these two models. We evaluated the quality of vector representations generated by the proposed embedding models using movies in the real world.

ACS Style

O-Joun Lee; Jason J. Jung. Story embedding: Learning distributed representations of stories based on character networks. Artificial Intelligence 2020, 281, 103235 .

AMA Style

O-Joun Lee, Jason J. Jung. Story embedding: Learning distributed representations of stories based on character networks. Artificial Intelligence. 2020; 281 ():103235.

Chicago/Turabian Style

O-Joun Lee; Jason J. Jung. 2020. "Story embedding: Learning distributed representations of stories based on character networks." Artificial Intelligence 281, no. : 103235.

Original research article
Published: 05 November 2019 in Frontiers in Big Data
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This study aims to validate whether the research performance of scholars correlates with how the scholars work together. Although the most straightforward approaches are centrality measurements or community detection, scholars mostly participate in multiple research groups and have different roles in each group. Thus, we concentrate on the subgraphs of co-authorship networks rooted in each scholar that cover (i) overlapping of the research groups on the scholar and (ii) roles of the scholar in the groups. This study calls the subgraphs “collaboration patterns” and applies subgraph embedding methods to discover and represent the collaboration patterns. Based on embedding the collaboration patterns, we have clustered scholars according to their collaboration styles. Then, we have examined whether scholars in each cluster have similar research performance, using the quantitative indicators. The coherence of the indicators cannot be solid proofs for validating the correlation between collaboration and performance. Nevertheless, the examination for clusters has exhibited that the collaboration patterns can reflect research styles of scholars. This information will enable us to predict the research performance more accurately since the research styles are more consistent and sustainable features of scholars than a few high-impact publications.

ACS Style

Hyeonju Jeon; O-Joun Lee; Jason J. Jung. Is Performance of Scholars Correlated to Their Research Collaboration Patterns? Frontiers in Big Data 2019, 2, 1 .

AMA Style

Hyeonju Jeon, O-Joun Lee, Jason J. Jung. Is Performance of Scholars Correlated to Their Research Collaboration Patterns? Frontiers in Big Data. 2019; 2 ():1.

Chicago/Turabian Style

Hyeonju Jeon; O-Joun Lee; Jason J. Jung. 2019. "Is Performance of Scholars Correlated to Their Research Collaboration Patterns?" Frontiers in Big Data 2, no. : 1.

Journal article
Published: 01 March 2019 in Future Generation Computer Systems
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Consideration of the stories included in the narrative works is important for analyzing and providing narrative works (e.g., movies, novels, and comics) to users. In this study, we analyzed the stories in a narrative work with three goals: (i) eliciting, (ii) modeling, and (iii) utilizing the stories. Based upon our previous studies regarding ‘character networks’ (i.e., social networks among characters in the stories), we elicited the stories with three methods: (i) composing affective character networks with affective relationships among the characters, (ii) measuring temporal changes in tension according to the flows of the stories, and (iii) detecting affective events which refer to dramatic changes in the tension. The affective relationships contain emotional changes of the characters on each segment of the stories. By aggregating the characters’ emotional changes, we measured the tension of each segment. We called it ‘Affective Fluctuation’ and represented it as a discrete function (Affective Fluctuation Function, AFF). The AFFs enable us to detect affective events by using gradients of them and measure similarities among the stories by comparing their shapes. Also, we proposed a computational model of the stories by annotating the affective events and characters involved in those events. Finally, we demonstrated a practical application with a recommendation method which exploited the similarities between stories. Additionally, we verified the reliabilities and efficiencies of the proposed method for narrative works in the real world.

ACS Style

O-Joun Lee; Jason J. Jung. Modeling affective character network for story analytics. Future Generation Computer Systems 2019, 92, 458 -478.

AMA Style

O-Joun Lee, Jason J. Jung. Modeling affective character network for story analytics. Future Generation Computer Systems. 2019; 92 ():458-478.

Chicago/Turabian Style

O-Joun Lee; Jason J. Jung. 2019. "Modeling affective character network for story analytics." Future Generation Computer Systems 92, no. : 458-478.

Journal article
Published: 21 February 2019 in Information Processing & Management
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This study aims to integrate diverse data within narrative multimedia (i.e., artworks containing stories and distributed through multimedia) into a unified character network (i.e., a social network between characters that appear in the story). By combining multiple data sources (e.g., the text, video, and audio), we attempted to enhance the accuracy and semantic richness of existing character networks that confine themselves to a particular data source. To merge various data, we propose story synchronization for (i) improving the accuracy of data extracted from the narrative multimedia and (ii) integrating the data into the unified character network. The story synchronization mainly consists of three steps: synchronizing (i) scenes, (ii) characters, and (iii) character networks. First, we synchronize dialogues in the text and audio, to discover speakers and time of dialogues. This enables us to segment the scene using time periods when dialogues (in the text and audio) and characters (in the video) do not commonly occur. Through the scene segmentation, we can discretize stories in the narrative work. By comparing the occurrence of dialogues and characters in each scene, we synchronize identities of the characters in the text and video (e.g., names and faces of characters). Thereby, we can more accurately estimate participants and time of a conversation between characters (i.e., a set of connected dialogues). Based on the conversation, the existing character networks are refined and integrated into the unified character network. In addition, we verified the efficacy of the proposed methods using movies in the real world, which are among the most accessible and popular narrative multimedia.

ACS Style

O-Joun Lee; Jason J. Jung. Integrating character networks for extracting narratives from multimodal data. Information Processing & Management 2019, 56, 1894 -1923.

AMA Style

O-Joun Lee, Jason J. Jung. Integrating character networks for extracting narratives from multimodal data. Information Processing & Management. 2019; 56 (5):1894-1923.

Chicago/Turabian Style

O-Joun Lee; Jason J. Jung. 2019. "Integrating character networks for extracting narratives from multimodal data." Information Processing & Management 56, no. 5: 1894-1923.

Conference paper
Published: 01 January 2019 in Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics - WIMS2019
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ACS Style

O-Joun Lee; Jason J. Jung. Character Network Embedding-based Plot Structure Discovery in Narrative Multimedia. Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics - WIMS2019 2019, 1 .

AMA Style

O-Joun Lee, Jason J. Jung. Character Network Embedding-based Plot Structure Discovery in Narrative Multimedia. Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics - WIMS2019. 2019; ():1.

Chicago/Turabian Style

O-Joun Lee; Jason J. Jung. 2019. "Character Network Embedding-based Plot Structure Discovery in Narrative Multimedia." Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics - WIMS2019 , no. : 1.

Conference paper
Published: 09 November 2018 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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In this paper, we want to introduce a new research area “network engineering”. The main research question is how the network configuration can be automatically and adaptively decided, given various dynamic contexts (e.g., network interference, heterogeneity and so on). The aim of this work is to design data-driven framework which is in three layer architecture (i.e., network entity layer, complex semantic analytics layer, and action provisioning layer).

ACS Style

Khac Hoai Nam Bui; Sungrae Cho; Jason J. Jung; Joongheon Kim; O-Joun Lee; Woongsoo Na. Network Engineering: Towards Data-Driven Framework for Network Configuration. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018, 60 -65.

AMA Style

Khac Hoai Nam Bui, Sungrae Cho, Jason J. Jung, Joongheon Kim, O-Joun Lee, Woongsoo Na. Network Engineering: Towards Data-Driven Framework for Network Configuration. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2018; ():60-65.

Chicago/Turabian Style

Khac Hoai Nam Bui; Sungrae Cho; Jason J. Jung; Joongheon Kim; O-Joun Lee; Woongsoo Na. 2018. "Network Engineering: Towards Data-Driven Framework for Network Configuration." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 60-65.

Original research
Published: 11 October 2018 in Journal of Ambient Intelligence and Humanized Computing
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Big data analytics is a growing trend for network and service management. Some approaches such as statistical analysis, data mining and machine learning have become promising techniques to improve operations and management of information technology systems and networks. In this paper, we introduce a novel approach for network management in terms of abnormality detection based on data analytics. Particularly, the main research focuses on how the network configuration can be automatically and adaptively decided, given various dynamic contexts (e.g., network interference, heterogeneity and so on). Specifically, we design a context-based data-driven framework for network operation in connected environment which includes three layer architecture: (i) network entity layer; (ii) complex semantic analytics layer and (iii) action provisioning layer. A case study on interference-based abnormal detection for connected vehicle explains more detail about our work.

ACS Style

Khac-Hoai Nam Bui; Sungrae Cho; Jason J. Jung; Joongheon Kim; O-Joun Lee; Woongsoo Na. A novel network virtualization based on data analytics in connected environment. Journal of Ambient Intelligence and Humanized Computing 2018, 11, 75 -86.

AMA Style

Khac-Hoai Nam Bui, Sungrae Cho, Jason J. Jung, Joongheon Kim, O-Joun Lee, Woongsoo Na. A novel network virtualization based on data analytics in connected environment. Journal of Ambient Intelligence and Humanized Computing. 2018; 11 (1):75-86.

Chicago/Turabian Style

Khac-Hoai Nam Bui; Sungrae Cho; Jason J. Jung; Joongheon Kim; O-Joun Lee; Woongsoo Na. 2018. "A novel network virtualization based on data analytics in connected environment." Journal of Ambient Intelligence and Humanized Computing 11, no. 1: 75-86.

Article
Published: 29 June 2018 in Mobile Networks and Applications
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With remarkable successes of sharing economy services (e.g., UBER (https://www.uber.com), Airbnb (https://www.airbnb.com), and so on), the amount of items which are distributed through these services is rapidly increasing. Therefore recommender systems for the sharing economy services are required. However, the existing recommenders are hard to support the sharing economy services, since they have focused on a ‘Item-User’ model that the recommenders provide satisfiable items to consumers (users) in accordance with only the consumers’ preferences. In this regard, we suggest a novel recommendation model, ‘Owner-Borrower’ model which considers the preferences of both sides: owners and borrowers of properties (items). Also, we propose a recommendation method based on the proposed model by applying a tensor factorization method and the Gale-Shapley algorithm. The tensor factorization is used for estimating preferences of the owners and the borrowers. With the estimated preferences, the Gale-Shapley algorithm makes optimal matches between the borrowers and the owners’ properties.

ACS Style

O-Joun Lee; Jai E. Jung. Owner-Borrower Model for Recommenders in O2O Services. Mobile Networks and Applications 2018, 23, 1089 -1096.

AMA Style

O-Joun Lee, Jai E. Jung. Owner-Borrower Model for Recommenders in O2O Services. Mobile Networks and Applications. 2018; 23 (4):1089-1096.

Chicago/Turabian Style

O-Joun Lee; Jai E. Jung. 2018. "Owner-Borrower Model for Recommenders in O2O Services." Mobile Networks and Applications 23, no. 4: 1089-1096.

Article
Published: 28 February 2018 in Concurrency and Computation: Practice and Experience
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Generally, texts on the social media (eg, Twitter and Facebook) are too short (microtexts) to understand the meaning and to search for relevant texts. It is difficult for the conventional information retrieval systems to conduct the searching tasks. Thereby, in this paper, we propose a novel approach on query contextualization by integrating all possible microtexts by considering spatio-temporal contexts. The proposed approach consists of two steps, which are (i) to understand and process microtexts in social media and (ii) to reformulate the queries for searching for relevant microtexts in these social media. To evaluate the performance of the query contextualization approach, microtexts from Twitter have been collected during 4 months.

ACS Style

Jae-Hong Park; O-Joun Lee; Jai E. Jung. Spatio-temporal query contextualization for microtext retrieval in social media. Concurrency and Computation: Practice and Experience 2018, 30, e4458 .

AMA Style

Jae-Hong Park, O-Joun Lee, Jai E. Jung. Spatio-temporal query contextualization for microtext retrieval in social media. Concurrency and Computation: Practice and Experience. 2018; 30 (15):e4458.

Chicago/Turabian Style

Jae-Hong Park; O-Joun Lee; Jai E. Jung. 2018. "Spatio-temporal query contextualization for microtext retrieval in social media." Concurrency and Computation: Practice and Experience 30, no. 15: e4458.

Conference paper
Published: 01 August 2017 in 2017 International Conference on Intelligent Environments (IE)
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Recently, most of context-aware services are trying to exploit the emotional contexts of the target users. The aim of this conceptual paper is to discuss affective lifelogging framework which can recognize the emotions by integrating multimodal information from multiple sources. Moreover, we will mention the open problems on affective lifelogging.

ACS Style

Jason J. Jung; Minsung Hong; O-Joun Lee; Jaehong Park; Chang Choi. Towards Affective Lifelogging with Information Fusion. 2017 International Conference on Intelligent Environments (IE) 2017, 153 -154.

AMA Style

Jason J. Jung, Minsung Hong, O-Joun Lee, Jaehong Park, Chang Choi. Towards Affective Lifelogging with Information Fusion. 2017 International Conference on Intelligent Environments (IE). 2017; ():153-154.

Chicago/Turabian Style

Jason J. Jung; Minsung Hong; O-Joun Lee; Jaehong Park; Chang Choi. 2017. "Towards Affective Lifelogging with Information Fusion." 2017 International Conference on Intelligent Environments (IE) , no. : 153-154.

Conference paper
Published: 07 June 2017 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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In this decade, the number of movies is increasing rapidly. Many studies have been proposed to assist users in movie understanding. In which, these methods are taken into account movie content analysis using social network for discovering relationships among characters and so on. However, these methods have shown some unsatisfactorily in dynamic changing of multimedia contents such as the character’s relationships over time. For overcoming this issue, we proposed a novel method for extracting dynamic character network from a movie.

ACS Style

Quang Dieu Tran; Dosam Hwang; O-Joun Lee; Jason J. Jung. A Novel Method for Extracting Dynamic Character Network from Movie. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017, 48 -53.

AMA Style

Quang Dieu Tran, Dosam Hwang, O-Joun Lee, Jason J. Jung. A Novel Method for Extracting Dynamic Character Network from Movie. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2017; ():48-53.

Chicago/Turabian Style

Quang Dieu Tran; Dosam Hwang; O-Joun Lee; Jason J. Jung. 2017. "A Novel Method for Extracting Dynamic Character Network from Movie." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 48-53.

Journal article
Published: 03 March 2017 in IEEE Access
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Since trust among entities can change according to various conditions, it is necessary for ambient services to determine when and how the trust has to be updated. Therefore, our contribution in this paper is to present: 1) a new definition of trust that can be extended to various domains; 2) a novel method based on social events and patterns to trigger trust refreshment in ambient services; and 3) a web application framework (called SocioScope) for collecting and analyzing data from multiple data sources. Finally, the case study suggests that this proposal could be applied to trust-aware ambient and recommendation systems.

ACS Style

Hoang Long Nguyen; O-Joun Lee; Jai E. Jung; Jaehwa Park; Tai-Won Um; Hyun-Woo Lee. Event-Driven Trust Refreshment on Ambient Services. IEEE Access 2017, 5, 4664 -4670.

AMA Style

Hoang Long Nguyen, O-Joun Lee, Jai E. Jung, Jaehwa Park, Tai-Won Um, Hyun-Woo Lee. Event-Driven Trust Refreshment on Ambient Services. IEEE Access. 2017; 5 ():4664-4670.

Chicago/Turabian Style

Hoang Long Nguyen; O-Joun Lee; Jai E. Jung; Jaehwa Park; Tai-Won Um; Hyun-Woo Lee. 2017. "Event-Driven Trust Refreshment on Ambient Services." IEEE Access 5, no. : 4664-4670.

Journal article
Published: 02 February 2017 in IEEE Access
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With various information sources (e.g., from IoT sensors to social media), it is difficult to provide users with trustworthy services in ambient environment. The aim of this paper is i) to design trust ontology for representing semantics of the ambient services and ii ) to compute trust measures among users by using a personalized trust ontology. In particular, given a large amount of data collected from ambient sensors, efficient trust computation and reasoning are required for the stability and reliability. Thereby, we propose trust ontology-based framework for deriving personalized ontologies for individual users according to their preference, perspective, and purpose. To evaluate the proposed model, we have figured out a method how the degree of trust is estimated based on the trust ontology. Furthermore, we have proved that the proposed method is reliable with a case study on a social media (Twitter) for a particular domain (restaurant).

ACS Style

O-Joun Lee; Hoang Long Nguyen; Jai E. Jung; Tai-Won Um; Hyun-Woo Lee. Towards Ontological Approach on Trust-Aware Ambient Services. IEEE Access 2017, 5, 1589 -1599.

AMA Style

O-Joun Lee, Hoang Long Nguyen, Jai E. Jung, Tai-Won Um, Hyun-Woo Lee. Towards Ontological Approach on Trust-Aware Ambient Services. IEEE Access. 2017; 5 ():1589-1599.

Chicago/Turabian Style

O-Joun Lee; Hoang Long Nguyen; Jai E. Jung; Tai-Won Um; Hyun-Woo Lee. 2017. "Towards Ontological Approach on Trust-Aware Ambient Services." IEEE Access 5, no. : 1589-1599.