• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.219-220
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• 2023

현재 IoT 기기들은 일상생활에서 필수 가전기기가 되어가고 있다. 가정에서는 스마트홈으로 연결된 냉장고, 세탁기, 인공지능 스피커 등이 이미 많이 사용되고 있으며, 자율주행 차량과 키오스크 등 하루에도 매우 다양한 IoT 기기들을 가깝게 접하고 있다. 스마트 워치(Smart Watch)가 출시된 이후로는 IoT 기기가 매 순간 사용되며 사용자 개인정보와 사생활 등 중요하고 예민한 정보와 기업의 기밀 정보가 자동으로 기기에 저장되고 있다. 이러한 이유로 해커들의 타깃이 되어 새로운 해킹 수법이 발생하고 보안 취약점이 발견되고 있다. 본 논문에서는 IoT 기기에 관련하여 최근에 발생하는 해킹 사건들과 보안 취약점을 분석하고 이에 따른 대책을 알아보고자 한다.

• Information Systems Review
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• v.21 no.3
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• pp.111-129
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• 2019

Recently, artificial intelligence (AI)-enabled products and services such as smartphones, smart speakers, chatbots are being released due to advances in AI technology. Thus researchers making effort to reveal that consumers' intention to adopt AI-enabled products. Yet, little is known about the intended adoption of AI-enabled products. Because most of studies has been not consideredthe perceived utility value of consumers for each attribute by classified based on the characteristics of AI-enabled products. Therefore, the purpose of this study is to investigate the difference in importance between attributes that affect the intention to adopt of AI-enabled products. For this, first, identified and classified the attributes of AI-enabled products based on IS Success Model of DeLone and McLean. Second, measured the utility value of each attribute on the adoption of AI-enabled products through conjoint analysis. And we employed construal level theory to see whether there are differences in the relative importance of AI-enabled products attributes depending on the temporal distance. Third, we segmented the market based on the utility value of each respondent through cluster analysis and tried to understand the characteristics and needs of consumers in each segment market. We expect to provide theoretical implications for conceptually structured attributes and factors of AI-enabled products and practical implications for how development efforts of AI-enabled products are needed to reach consumers need for each segment.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.59-70
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• 2018

With the breakthrough of speech recognition technology, conversational agents have become pervasive through smartphones and smart speakers. The recognition accuracy of speech recognition technology has developed to the level of human beings, but it still shows limitations on understanding the underlying meaning or intention of words, or understanding long conversation. Accordingly, the users experience various errors when interacting with the conversational agents, which may negatively affect the user experience. In addition, in the case of smart speakers with a voice as the main interface, the lack of feedback on system and transparency was reported as the main issue when the users using. Therefore, there is a strong need for research on how users can better understand the capability of the conversational agents and mitigate negative emotions in error situations. In this study, we applied social strategies, "forewarning" and "apology", to conversational agent and investigated how these strategies affect users' perceptions of the agent in breakdown situations. For the study, we created a series of demo videos of a user interacting with a conversational agent. After watching the demo videos, the participants were asked to evaluate how they liked and trusted the agent through an online survey. A total of 104 respondents were analyzed and found to be contrary to our expectation based on the literature study. The result showed that forewarning gave a negative impression to the user, especially the reliability of the agent. Also, apology in a breakdown situation did not affect the users' perceptions. In the following in-depth interviews, participants explained that they perceived the smart speaker as a machine rather than a human-like object, and for this reason, the social strategies did not work. These results show that the social strategies should be applied according to the perceptions that user has toward agents.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.34-41
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• 2021

The purpose of this study is to explore any effects of primary English language learners' affective factors and satisfaction through AI-based speaking activity. In order to answer these questions, a total number of 46 ELLs from a public elementary school participated in this research. Survey questionnaire on affective factors and learning satisfaction were distributed and the results were analyzed quantitatively. The findings are as follows. First, participants could expand their knowledge on AI-based activity towards its educational advantages and capability. Second, overall affective factors of the participants on AI-based activity changed positively, with the improvement of the mean score. The paired samples t-test showed that there was a significant difference among interest, value and attitude. Third, the satisfaction degree on AI-based learning escalated, particularly in the sense of efficacy, academic achievement and involvement. Lastly, it was revealed that the satisfaction degree was correlated with learners' self-confidence, interest and attitude.

• Convergence Security Journal
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• v.23 no.5
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• pp.151-156
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• 2023

Currently, ICT is widely used in caring for the elderly living alone and preventing the disappearance of the elderly with dementia. Therefore, in this study, based on the government policy direction for the 4th industrial revolution, the use of AI technology-based care services, which are gradually increasing in community care, was sought to explore the current status and prospects for utilization and activation.AI speakers and caring robots, services that can be used for community care, help solve various problems experienced by the elderly, and are also used to relieve lack of conversation or loneliness by adding emotional functions. In order to activate community care using AI technology in the future: First, there is a need for continuous education to familiarize the elderly with AI devices and 'user experience (UX) design' for the elderly. Second, it is necessary to use human-centered technology that has a complementary relationship and enables emotional mutual relationships rather than using function-oriented technology. Third, it is necessary to solve ethical problems such as guaranteeing the user's right to self-determination and protecting privacy.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.213-216
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• 2018

As the advantages of smart devices based on artificial intelligence and voice recognition become more prominent, Virtual Assistant is gaining popularity. Virtual Assistant provides a user experience through smart speakers and is valued as the most user friendly IoT device by consumers. The purpose of this study is to investigate whether there are differences in people's perception of the key virtual assistant brand voice recognition. We collected tweets that included six keyword form three companies that provide Virtual Assistant services. The authors conducted semantic network analysis for the collected datasets and analyzed the feelings of people through sentiment analysis. The result shows that many people have a different perception and mainly about the functions and services provided by the Virtual Assistant and the expectation and usability of the services. Also, people responded positively to most keywords.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.17-25
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• 2020

Based on the successful use of chatbot technology, this study examined what business values each company is creating. The chatbot service contributes to improving the productivity of the company by helping to answer or respond to the questions of employees inside the company or customers. And in the field of education, Instead of instructor, AI technology responds the questions and feedback of the students to reduce the work of the instructor. In the field of commerce, offline stores provide convenient and new purchasing experiences to customers by providing product purchasing services through artificial intelligence speakers and personalization service. Although chatbot service is creating business value in some business cases, it is still limited to the process of a specific company, and the spread rate is still slowing because the service scope, convenience, and usefulness are not greater than expected. Therefore, some chatbot development service providers is providing an integrated development platform to improve usability, Chatbots have the features and advantages of providing convenience instead of answering human questions. However, there is a disadvantage that the level of communication can be lowered by reducing various human subjective views and giving mainly objective answers. Through this study, we will discuss the characteristics, advantages and disadvantages of chatbot services by comparing them.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.55-67
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• 2019

In this study, I researched whether consumers would adopt artificial intelligence speakers. A study was conducted on the motivations that arise when consumers want to use artificial intelligence speakers. Key motivational factors include needs and wants, and emotion is also included in the hypothesis as influencing the intended use. These factors have modeled the motivational process in which consumers want to use artificial intelligence speakers. In the empirical study, the survey was conducted and the survey data was analyzed by applying the method of analysis of the structural equation model. As a result of empirical research, consumers' expectations to meet their general needs for artificial intelligence speakers affected their expectations to meet their wants and their favorable perceptions. And consumers' expectations of meeting their quasi-desire for artificial intelligence speakers have affected their expectations of meeting the wants and affected their perception of favorability. Finally, consumers' expectations for satisfying their wants and their perception of favorability affected their intention to use artificial intelligence speakers. The implications of this study is that it helps to formulate strategies for information technology products with combined functionality. The specific components of motivation can play an important role in increasing consumers' intention to use artificial intelligence speakers.