• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.41-48
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• 2023

In this paper, we propose an analysis of recent trends in the NFT (Non-Fungible Token) industry using topic modeling techniques, focusing on their universal application across various industrial fields. For this study, patent data was utilized to understand industry trends. We collected data on 371 domestic and 454 international NFT-related patents registered in the patent information search service KIPRIS from 2017, when the first NFT standard was introduced, to October 2023. In the preprocessing stage, stopwords and lemmas were removed, and only noun words were extracted. For the analysis, the top 50 words by frequency were listed, and their corresponding TF-IDF values were examined to derive key keywords of the industry trends. Next, Using the LDA algorithm, we identified four major latent topics within the patent data, both domestically and internationally. We analyzed these topics and presented our findings on NFT industry trends, underpinned by real-world industry cases. While previous review presented trends from an academic perspective using paper data, this study is significant as it provides practical trend information based on data rooted in field practice. It is expected to be a useful reference for professionals in the NFT industry for understanding market conditions and generating new items.

• Journal of Gastric Cancer
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• v.23 no.4
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• pp.609-621
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• 2023

Purpose: Determination of optimal treatment strategies for HER2-positive advanced gastric cancer (AGC) in randomized trials is necessary despite difficulties in direct comparison between trastuzumab deruxtecan (T-DXd) and nivolumab as third or later-line treatments. Materials and Methods: This single-institution, retrospective study aimed to describe the real-world efficacy and safety of T-DXd and nivolumab as ≥ third line treatments for HER2-positive AGC between March 2016 and May 2022. Overall, 58 patients (median age, 64 years; 69% male) were eligible for the study (T-DXd group, n=20; nivolumab group, n=38). Results: Most patients exhibited a HER2 3+ status (72%) and presented metastatic disease at diagnosis (66%). The response rates of 41 patients with measurable lesions in the T-DXd and nivolumab groups were 50% and 15%, respectively. The T-DXd and nivolumab groups had a median progression-free survival of 4.8 months (95% confidence interval [CI], 3.3, 7.0) and 2.3 months (95% CI, 1.5, 3.5), median overall survival (OS) of 10.8 months (95% CI, 6.9, 23.8) and 11.7 months (95% CI, 7.6, 17.1), and grade 3 or greater adverse event rates of 50% and 2%, respectively. Overall, 64% patients received subsequent treatment. Among 23 patients who received both regimens, the T-DXd-nivolumab and nivolumab-T-DXd groups had a median OS of 14.0 months (95% CI, 5.0, not reached) and 19.3 months (95% CI, 9.5, 25.1), respectively. Conclusions: T-DXd and nivolumab showed distinct efficacy and toxicity profiles as ≥ third line treatments for HER2-positive AGC. Considering the distinct features of each regimen, they may help clinicians personalize optimal treatment approaches for these patients.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.597-603
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• 2023

The paradigm of military operations has evolved from platform-centric warfare to network-centric warfare and further to information-centric warfare, driven by advancements in information technology. In recent years, with the development of cutting-edge technologies such as big data, artificial intelligence, and the Internet of Things (IoT), military operations are transitioning towards knowledge-centric warfare (KCW), based on artificial intelligence. Consequently, the military places significant emphasis on integrating advanced information and communication technologies (ICT) to establish reliable C4I (Command, Control, Communication, Computer, Intelligence) systems. This research emphasizes the need to apply data mining techniques to analyze and evaluate various aspects of C4I systems, including enhancing combat capabilities, optimizing utilization in network-based environments, efficiently distributing information flow, facilitating smooth communication, and effectively implementing knowledge sharing. Data mining serves as a fundamental technology in modern big data analysis, and this study utilizes it to analyze real-world cases and propose practical strategies to maximize the efficiency of military command and control systems. The research outcomes are expected to provide valuable insights into the performance of C4I systems and reinforce knowledge-centric warfare in contemporary military operations.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.119-133
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• 2022

The COVID-19 pandemic is rapidly changing the behavior of members of society. Typically, the strong contagious power of the virus minimizes interaction between people in the real world, and they keep interaction activities to a minimum through online activities. Recently, as people demand online activities that enhance a sense of reality, the metaverse, which strengthens the 3D technology-centered sense of presence capability, is being chosen by people. The purpose of this study is to suggest a strategic direction for the establishment of the metaverse business model of startups by presenting factors for users' use and gratification of the metaverse. In detail, this study proposes the motivation for using the metaverse by reflecting the uses and gratification theory, and suggests a method to strengthen the motivation for the metaverse by reflecting the presences provided by the metaverse plotform and individual characteristics (normtive interpersonal influence). We surveyed people over 20 years of age who experienced metaverse and obtained 314 samples. In addition, we conducted the main effect analysis using the structural equation model and the moderating effect analysis using Process 3.1. As a result of hypothesis testing, we confirmed that metaverse presence (telepresence, social presence) has a positive effect on intention to continuous use by increasing metaverse's use and satisfaction factors (information, enjoyment, social interactivity). In addition, we found that individuals' normative interpersonal influence moderated the positive relationship between uses and gratification factors(enjoyment and social interactivity) intention to continuous use. Our study suggests strategies for establishing a user-centered business model for companies related to the metaverse.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.31-37
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• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

• Journal of Industrial Convergence
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• v.22 no.5
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• pp.11-21
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• 2024

In the steel industry, there is a perception that "collusion has become a long-standing practice" and it is expected that the authorities' legal response to collusion will be strengthened in the future. This necessarily requires improving the accuracy of the legal response, the most important of which is to accurately identify whether the allegedly colluding firms actually did collude. This study focuses on the recent iron scrap price-fixing case and examines whether a single accused firm actually engaged in price-fixing in a situation where there is a mix of firms that acted independently of the collusion and firms that actually engaged in price-fixing. The results of the analysis allow us to infer that the accused steelmaker did not actually collude, which is consistent with the authorities' final judgment against the steelmaker. In the real world, some collusions are carried out by only a subset of firms in a market, and in these cases, there are often disputed firms as to whether or not they carried out the collusion. This study can serve as an analytic guide for industries, including the steel industry, to verify the behavior of individual firms, especially those whose collusive practices are disputed.

• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.103-114
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• 2023

Augmented reality (AR) and virtual reality (VR) differ fundamentally, with AR overlaying computer-generated information onto the real world in a nonimmersive way. Despite extensive research on cybersickness in VR, its occurrence in AR has received less attention (Vovk et al., 2018). This study examines cybersickness and discomfort associated with AR usage, focusing on the impact of content intensity and exposure time. Participants viewed 30-minute racing simulation game clips through AR equipment, varying in racing speed to alter content intensity. Cybersickness was assessed subjectively using the Simulator sickness questionnaire (SSQ; Kennedy et al., 1993). Findings revealed a progressive increase in cybersickness with longer exposure, persisting even after removing the AR equipment. Contrarily, content intensity did not significantly influence cybersickness levels. Analysis of the SSQ subscales revealed higher oculomotor (O) scores compared to nausea (N) and disorientation (D), suggesting that discomfort primarily stemmed from oculomotor strain. The study highlights distinct differences in user experience between AR and VR, specifically in subjective responses.