• Archives of design research
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• v.17 no.1
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• pp.63-76
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• 2004

Although information design principles had originated from the graphic design field including publishing design in 1970s, information design have taken one of the most important positions in multimedia design fields as the present since Richard Saul Wurmn started his researches on information design and its practical application on internet environments. However, relatively little researches have been performed for building identity of information design in multimedia design fields so far and moreover in some cases, information design is likely to be narrowly viewed as 'the representation of a piece of information and its visualization' which was defined in traditional graphic design field. This research circumstance leads the study to investigate characteristics of information design in current multimedia design with contingent perspective which is compared to the traditional information design. The study results suggest 5 characteristics of information design including 'suggesting context & finding out information hierarchy', 'access of integrated consideration & share of information control', 'using of multi-dimensional media & content first', 'stabilization of information quality & combinational understanding of meaning', 'bilateral information representation & user's knowledge expansion'. Future researches, based on the results of the study, are expected to be expanded to a degree with argument for inter-dependent and/or exclusive characteristics of adjacent fields of information design such as interface design, interaction design and experience design.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.213-223
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• 2019

This study aims to prove the relationship between a TPB(theory of planned behavior) variables (subjective norms, attitudes, and self-efficacy), need to belong and personal growth initiatives to explain the reasons for users' shared behavior. 959 participants who had shared online video were collected as a sample through an online survey and the collected data were analyzed through structural equation modeling. The study found that need to belong affected attitudes to online video sharing and subjective norms, and that personal growth initiative also affected attitudes to online video sharing and self-efficacy. In addition, all three variables of TPB were affect the intend of online video sharing, and attitudes to online video sharing were affecting subjective norms and self-efficacy. This study is meaningful in that it demonstrated the user's intention to share online video through variables of TPB along with their need to belong and personal growth initiatives.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.381-390
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• 2022

Influenced by a culture that prefers non-face-to-face activity during the COVID-19 pandemic, chatbot usage is accelerating. Chatbots have been used for various purposes, not only for customer service in businesses and social conversations for fun but also for mental health. Chatbots are a platform where users can easily talk about their depressed moods because anonymity is guaranteed. However, most relevant research has been on social media data, especially Twitter data, and few studies have analyzed the commercially used chatbots data. In this study, we identified the characteristics of depressive discourse in user-chatbot interaction data by analyzing the chats, including the word 'depress,' using the topic modeling algorithm and the text-mining technique. Moreover, we compared its characteristics with those of the depressive moods in the Twitter data. Finally, we draw several design guidelines and suggest avenues for future research based on the study findings.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.455-462
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• 2022

This study attempted to examine the problems of the expression and format of native advertisements appearing on various platforms and to prepare a plan to regulate them. To this end, in-depth interviews were conducted to those in charge of advertising practice and examined. First, as a problem with the expression and format of native advertisements, it was considered that the congestion and deception of indiscriminate native advertisements appearing on various platforms could bring negative perceptions to consumers. For the second user's interaction, it was considered that customized advertising expressions through targeting by platform should be produced. Third, regarding the regulation of native advertisements, it was suggested that regulatory measures for consumer protection should be prepared and that market autonomy should be left to it. A strategic operation plan for native advertising according to various platforms should be prepared.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.267-277
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• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.367-372
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• 2022

With the recent advent of the New Normal era, realistic technologies and non-contact technologies are receiving social attention. However, the hair styling field focuses on the direction of the hair itself, individual movements, and modeling, focusing on hair simulation. In order to create an improved practice environment and demand of the times, this study proposed a non-contact hair styling VR system. In the theoretical review, we studied the existing cases of hair cut research. Existing haircut-related research tend to be mainly focused on force-based feedback. Research on the interactive haircut work in the virtual environment as addressed in this paper has not been done yet. VR controllers capable of finger tracking the movements necessary for beauty enable selection, cutting, and rotation of beauty tools, and built a non-contact collaboration environment. As a result, we conducted two experiments for interactive hair cutting in VR. First, it is a haircut operation for synchronization using finger tracking and holding hook animation. We made position correction for accurate motion. Second, it is a real-time interactive cutting operation in a multi-user virtual collaboration environment. This made it possible for instructors and learners to communicate with each other through VR HMD built-in microphones and Photon Voice in non-contact situations.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.459-466
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• 2010

Recently, UCC, which stands for User Created Content, has emerged in various media industries including Internet. However, there exists little research regarding interaction to overcome the drawback of UCC, one way superficial form. In particular, little research has been done on the creation and effect of experiential UCC type. In the existing research, the interactive feature shows to bring the effectiveness in the field of education or promotion. So, in this study, we examine the problem of provider-centered UCC and to solve this problem, we propose a new experiential UCC form with interactive functionality by adapting the product test between UCCs. From the results of the analysis on the effectiveness of UCC after users experience the proposed UCC related to water industry, watching the interactive UCC represented the values of the higher levels in the aspect of recognition change than watching the existing UCCs. Also, the outcome showed that if this interactive UCC invigorates, UCC application will be very useful in eduction, industry, and promotion. From the analysis of the question instrument on whether an interactive UCC would be helpful or not, the positive response ratios was 84% in promotion, 70% in education, and 52% in industry, respectively.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.685-695
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• 2008

This paper presents a method to segment the object of interest from a set of multi-view images with minimal user interaction. Specifically, after the user segments an initial image, we first estimate the transformations between foreground and background of the segmented image and the neighboring image, respectively. From these transformations, we obtain regions in the neighboring image that respectively correspond to the foreground and the background of the segmented image. We are then able to segment the neighboring image based on these regions, and iterate this process to segment the whole image set. Transformation of foregrounds are estimated by feature-based registration with free-form deformation, while transformation of backgrounds are estimated by homography constrained to affine transformation. Here, both are based on correspondence point pairs. Segmentation is done by estimating pixel color distributions and defining a shape prior based on the obtained foreground and background regions and applying them to a Markov random field (MRF) energy minimization framework for image segmentation. Experimental results demonstrate the effectiveness of the proposed method.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.