• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.201-207
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• 2022

In this paper, we propose a movie recommendation model that uses the users' ratings as well as their reviews. To understand the user's preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.721-731
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• 2022

The purpose of this study is to examine and categorize the cases of luxury fashion brands and art collaboration and find out their effects. The research method examined the theoretical background of luxury fashion brand and art collaboration and proceeded with content analysis through major cases. The research results were classified into the following two types through case analysis. The first is art collaboration for product design, and the second type is art collaboration for exhibition works. Content analysis according to type was organized from the perspective of brands and artists, respectively, and the resulting collaboration effect was finally derived. The main effects are the integration of design and art, diversification of social and cultural backgrounds, and innovative vision of expression. This study is meaningful in examining the expanded design methods and effects of luxury fashion brands through grafting artworks and presenting basic data for future fashion art collaboration design research.

• Design Convergence Study
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• v.14 no.3
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• pp.61-72
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• 2015

This research attempts to provide greater understandings about 'Transbranding' theory by an analysis of , a 3D animation produced by Disney Studio released in 2013 partly in comparison with . Furthermore, this research shows the branding strategy of "Disney", reputed for its well-devised marketing strategy, in the era of Transmedia. This research analyzes the animation with four components of 「Transmedia Mix strategy」 and the 「2F(Flexible Fit) strategy」. In the 「2F(Flexible Fit) strategy」 analysis, is compared with another Disney-made animation . not just maintains the core values of Disney animations but also implements diverse strategies for developing the evolving interactions, the collaborative creation, and the multi-experience. has achieved successful branding identity/image by securing the strategic flexibility by differentiating itself from the existing Disney animations. Disney also establishes itself as a new and modern brand through 2F strategies.

• The Korean Journal of Emergency Medical Services
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• v.28 no.1
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• pp.7-19
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• 2024

Purpose: This study aimed to evaluate and assess the response capabilities and educational needs of 119 emergency medical technicians (EMTs) in chemical accidents. Methods: A self-reported questionnaire was completed by 167 119 EMTs between December 1st and December 31, 2023. The questionnaire comprised 8 questions on general characteristics, 2 on chemical accidents experienced by the participants, 29 on response capabilities, and 15 on educational needs. Data analysis was performed using t-tests, analysis of variance, Duncan's test for post-hoc analysis, and Pearson's correlation coefficient, using SPSS 27.0. Results: The participants scored 2.69 points on response capacity to chemical accidents. The EMT-Paramedics scored high in 'patient triage,' 'patient treatment,' 'patient transport,' and 'collaborative support' (F=3.924, p=.010; F=5.843, p=.001; F=3.698, p=.013; F=5.272, p=.002), followed by educational experience (t=-4.962, p<.001; t=-2.685, p=.008; t=-3.455, p=.001; t=-3.593, p<.001; t=-3.034, p=.003). The participants scored 4.19 points on educational needs, with high scores for 'patients treatment and transport' (4.280.93). The scores for 'patient triage competency', and 'patient triage' (r=.169, p=.024) correlated positively. Furthermore, the scores for 'patient treatment competency' and all sub-factors of educational needs (r=.185, p=.013; r=.215, p=.004; r=.199, p=.008; r=.190, p=.011; r=.197, p=.008) correlated positively. Conclusion: To strengthen the response capabilities of 119 EMTs, it is imperative to develop an educational program that focuses on first-aid responses.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.271-283
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• 2024

The study seeks to examine the alignment between the Model of Human Occupation (MOHO) and the Six Principles of the Individuals with Disabilities Education Act (IDEA) through qualitative analysis. The study utilizes a qualitative methodology that entails a comprehensive review of the existing literature to establish connections between MOHO and Individuals with Disabilities Education Act (IDEA) principles, with a specific focus on collaborative special education environments. Data collection involves examining academic literature on MOHO, Individuals with Disabilities Education Act (IDEA) principles, and the partnership between occupational therapists and special education teachers. Thematic analysis is employed to identify recurrent themes and relationships, offering valuable insights into the theoretical foundations of MOHO and its compatibility with the Individuals with Disabilities Education Act (IDEA)The Model of Human Occupation (MOHO) highlights the significance of active engagement and meaningful participation in inclusive education. It promotes the development of independence and self-determination in occupational performance for children with special needs. Moreover, MOHO stresses the importance of offering tailored support and adjustments for these children.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Journal of The Korean Association For Science Education
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• v.32 no.4
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• pp.555-569
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• 2012

Many science education research and practices are recently emphasizing the importance of collaborative learning. This study also understands learning in aspects of socio-cultural context, and regarded the creation of meaning in a same-age group as an important learning process. This is most especially true in the premise that the formation of roles in a collaborative learning is important for successful interactive learning. This study aims to find out how roles form in a group. For this purpose, university students participating in a group discussion activity about energy flow and circulation of material were selected as research participants. Discussions among the nine students in one group consisted of cognitive conversations on the topic and operational conversations for preparing a presentation. Video-clips of the discussions were made and transcribed. For the analysis, we developed a framework that includes four interaction functions (cognitive, organizational, meta-cognitive, operational), four action elements (question, simple answer, providing opinion, response to opinion), and two to four intention elements by each action elements. As a result, a total of nine roles were revealed through the interaction function and element; cognitive questioner, operational questioner, simple answerer, operational suggester, organizational commander, operational commander, cognitive explainer, terminator, reflective thinker. These roles are re-classified into seven utterance patterns by the utterance order and object, and they were categorized into three role groups (facilitating interaction, sustaining interaction, finishing interaction). The result means that role formation and function can have influence on learning and interaction. This study is meaningful to the suggestion to collaborative learning including project-based learning, investigation, club activity, and for the re-illumination of the role in an aspect of the interaction.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Molecules and Cells
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• v.24 no.3
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• pp.323-328
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• 2007

SOX (Sry-related HMG box) family proteins, which have an evolutionarily conserved DNA binding domain, have crucial roles in cell differentiation. However, their target genes remain enigmatic. Some members of the SOX family may have roles in regulation of cell proliferation. We established stable NT2/D1 cell lines overexpressing SOX15 (SOX15-NT2/D1), and a modified 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay showed that the SOX15-NT2/D1 cells exhibited significantly slower growth than the controls. Flow cytometry analysis revealed that an increased fraction of the SOX15-NT2/D1 cells were in G1-G0. In addition, a microarray analysis identified 26 genes that were up-regulated in the SOX15-NT2/D1 cells, but none that were down-regulated genes. Among the up-regulated genes, IGFBP5, S100A4, ID2, FABP5, MTSS1, PDCD4 have been shown to be related to cell proliferation and/or the cell cycle.