• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.279-306
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• 2022

Public institutions and governments develop discussions on the premise that they can facilitate smooth public communication with the socially vulnerable by promoting citizens' welfare by providing voice-based service chatbots to citizens. The purpose of the study is to propose a plan for intelligent governments to provide quick and efficient administrative services by efficiently managing knowledge and information within and outside government organizations based on ICT and facilitating access and use of information for citizens, especially vulnerable groups. This paper confirms that citizens' attitudes, perceptions, and expectations for public institutions ahead of voice-based service provision are positive through small surveys and interviews with experts with knowledge of artificial intelligence, discuss the technical aspects of voice-based services, the significance and necessity of public institutions. In addition, the government and public institutions are considering the implications of using and providing voice-based services. As a result, chatbot's voice-based service is of great significance in providing an opportunity and platform for wider citizens to participate in intelligent government, to strengthen information accessibility, guarantee and strengthen human rights and basic rights of the socially vulnerable.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.219-240
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• 2023

Conversational AI which allows users to interact with satisfaction is a long-standing research topic. To develop conversational AI, it is necessary to build training data that reflects real conversations between people, but current Korean datasets are not in question-answer format or use honorifics, making it difficult for users to feel closeness. In this paper, we propose a conversation dataset (KOMUChat) consisting of 30,767 question-answer sentence pairs collected from online communities. The question-answer pairs were collected from post titles and first comments of love and relationship counsel boards used by men and women. In addition, we removed abuse records through automatic and manual cleansing to build high quality dataset. To verify the validity of KOMUChat, we compared and analyzed the result of generative language model learning KOMUChat and benchmark dataset. The results showed that our dataset outperformed the benchmark dataset in terms of answer appropriateness, user satisfaction, and fulfillment of conversational AI goals. The dataset is the largest open-source single turn text data presented so far and it has the significance of building a more friendly Korean dataset by reflecting the text styles of the online community.

• Journal of Service Research and Studies
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• v.12 no.1
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• pp.36-48
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• 2022

As the use of smartphones has rapidly increased due to the development of digital technology, the expansion of smartphones, and the COVID-19 incident, dependence on smartphones and the Internet is emerging as a serious social problem. As one of the solutions to the smartphone overdependence problem, the government and companies are releasing smartphone overdependence prevention applications. However, research on the effectiveness of smartphone overdependence prevention applications is insufficient. Therefore, this study selects 25 applications serviced in Korea as analysis targets and evaluates smartphone overdependence prevention applications in terms of function and service using the FGI survey method to identify problems and propose improvements. In the function evaluation, the functions of blocking illegal/harmful apps/websites, limiting smartphone usage time, and monitoring smartphone usage status are provided in most applications, so satisfaction scores are also highly evaluated. However, functions such as location check, smombie prevention, and body camphishing prevention served by some applications are evaluated low due to poor performance and poor accuracy. Classified by service provider, government-providing applications need to accurately perform functions and improve convenience of use. Mobile-Carrier-providing applications need to improve connectivity with other carriers and compatibility with other smart devices like smartphone, tablet, etc. Other private enterprise-providing applications need to open AS channels such as customer service centre and chatbot to improve service.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.455-460
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• 2023

Text augmentation is a methodology that creates new augmented texts by transforming or generating original texts for the purpose of improving the performance of NLP models. However existing text augmentation techniques have limitations such as lack of expressive diversity semantic distortion and limited number of augmented texts. Recently text augmentation using large language models and few-shot learning can overcome these limitations but there is also a risk of noise generation due to incorrect generation. In this paper, we propose a text augmentation method called TAGS that generates multiple candidate texts and selects the appropriate text as the augmented text. TAGS generates various expressions using few-shot learning while effectively selecting suitable data even with a small amount of original text by using contrastive learning and similarity comparison. We applied this method to task-oriented chatbot data and achieved more than sixty times quantitative improvement. We also analyzed the generated texts to confirm that they produced semantically and expressively diverse texts compared to the original texts. Moreover, we trained and evaluated a classification model using the augmented texts and showed that it improved the performance by more than 0.1915, confirming that it helps to improve the actual model performance.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.79-92
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• 2024

ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.687-692
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• 2024

The energy crisis is emerging as a serious problem around the world. In the case of Korea, there is great interest in energy efficiency research related to industrial complexes, which use more than 53% of total energy and account for more than 45% of greenhouse gas emissions in Korea. One of the studies is a study on saving energy through sharing facilities between factories using the same utility in an industrial complex called a virtual energy network plant and through transactions between energy producing and demand factories. In such energy-saving research, data collection is very important because there are various uses for data, such as analysis and prediction. However, existing systems had several shortcomings in reliably collecting time series data. In this study, we propose an intelligent IIoT platform to improve it. The intelligent IIoT platform includes a preprocessing system to identify abnormal data and process it in a timely manner, classifies abnormal and missing data, and presents interpolation techniques to maintain stable time series data. Additionally, time series data collection is streamlined through database optimization. This paper contributes to increasing data usability in the industrial environment through stable data collection and rapid problem response, and contributes to reducing the burden of data collection and optimizing monitoring load by introducing a variety of chatbot notification systems.