• Journal of Venture Innovation
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• v.6 no.4
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• pp.83-97
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• 2023

The growth of the live commerce market allows you to conveniently and simply start live commerce anytime, anywhere with a smartphone. The use of smartphone services provides continuous communication and is used while feeling psychological attachment, and it leads to psychological attachment, self-consistency with consumers themselves, and self-identity. This study focuses on the motives and perceptions of consumers using live commerce. In other words, we will examine the relationship with service attachment through the moderating effect of self-efficacy and control focus tendency as consumers' personal and psychological characteristics. In other words, the tendency of regulatory focus, which determines the direction of behavior of consumers according to their motives and goals, affects the service attachment of live commerce. We believe that self-efficacy, which is personal confidence and belief that you can plan and execute on your own for the desired outcome in a given situation or task, will control this relationship. As a result of this research, consumers who highly perceive prevention focus were more likely to avoid negative consequences and pursue safety and obligations. Their attachment to live commerce services was stronger, offsetting their confidence and self-efficacy. When using live commerce services, the more they perceive that information acquisition is beneficial, the higher their belief, and self-efficacy, so service attachment, which is an emotional experience as well as a cognitive experience, is strongly formed for consumers with a preventive focus to avoid safety-seeking and negative consequences. Through the present research results, we believe that it will be helpful in operating strategies and management for companies and small business owners who want to understand the psychological behavior of consumers in using live commerce services.

• Korean Journal of Culture and Social Issue
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• v.9 no.spc
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• pp.35-55
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• 2003

The purpose of this study is to review the previous studies on the safety problems in Korea and to propose a psychological total safety system model. The model consisted of four agents; the government as the safety management agent, the suppliers of safety goods and services, consumer of safety goods and services, and civil movement institutions for safety. It was emphasized that the culture specific social representations of safety and accident have emerged in the course of rapid industrialization process in Korea during last 30 years. We delineated the social representations of the Korean people on safety and accident according to the model. A psychological analysis of drinking and driving behavior was performed as an application of the model. It was emphasized that safety psychologists have to develope and to apply the knowledge and the information from human engineering psychology and applied social psychology on safety and accidents.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of The Korean Association For Science Education
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• v.26 no.4
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• pp.568-580
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• 2006

The purpose of this study was to investigate the brain activities by 4-types of Generating Process of Scientific Emotion (GPSE) in the hypothesis-generating biological phenomena by using fMRI. Four-types of GPSE were involved in the Basic Generating Process (BGP), Retrospective Generating Process (RGP), Cognitive Generating Process (CGP) and Attributive Generating Process (AGP). For this study, we made an experimental design capable of validating the 4-types of generating process (e.g. BGP, RGP, CGP and AGP), and then measured BOLD signals of 10 pre-service teachers' brain activities by 3.0T fMRI system. Subjects were 10 healthy females majoring in biology education. As a result, there were clear differences among 4-types of GPSE. Brain areas activated by BGP were at right occipital lobe (BA 17), at left thalamus and left parahippocampal gyrus, while in the case of RGP, at left superior parietal lobe (BA 8, 9), at left pulvinar and left globus pallidus were activated. Brain areas activated by CGP were the right posterior cingulate and left medial frontal gyrus (BA 6). In the case of AGP, the most distinctively activated brain areas were the right medial frontal gyrus (BA 8) and left inferior parietal lobule (BA 40). These results would mean that each of the 4-types of GPSE has a specific neural networks in the brain, respectively. Furthermore, it would provide the basis of brain-based learning in science education.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.209-224
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• 2024

Speaker verification is becoming popular as a method of non-face-to-face identity authentication. It involves determining whether two voice data belong to the same speaker. In cases where the criminal's voice remains at the crime scene, it is vital to establish a speaker verification system that can accurately compare the two voice evidence. In this study, to achieve this, a new speaker verification system was built using a deep learning model for Korean language. High-dimensional voice data with a high variability like background noise made it necessary to use deep learning-based methods for speaker matching. To construct the matching algorithm, the ECAPA-TDNN model, known as the most famous deep learning system for speaker verification, was selected. A large dataset of the voice data, Voxceleb, collected from people of various nationalities without Korean. To study the appropriate form of datasets necessary for learning the Korean language, experiments were carried out to find out how Korean voice data affects the matching performance. The results showed that when comparing models learned only with Voxceleb and models learned with datasets combining Voxceleb and Korean datasets to maximize language and speaker diversity, the performance of learning data, including Korean, is improved for all test sets.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.159-165
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• 2007

In this paper, we propose a new pre-selection of candidate units that is suitable for the unit selection based Japanese TTS system. General pre-selection method performed by calculating a context-dependent cost within IP (Intonation Phrase). Different from other languages, however. Japanese has an accent represented as the height of a relative pitch, and several words form a single accentual phrase. Also. the prosody in Japanese changes in accentual phrase units. By reflecting such prosodic change in pre-selection. the qualify of synthesized speech can be improved. Furthermore, by calculating a context-dependent cost within accentual phrase, synthesis speed can be improved than calculating within intonation phrase. The proposed method defines AP. analyzes AP in context and performs pre-selection using accentual phrase matching which calculates CCL (connected context length) of the Phoneme's candidates that should be synthesized in each accentual phrase. The baseline system used in the proposed method is VoiceText, which is a synthesizer of Voiceware. Evaluations were made on perceptual error (intonation error, concatenation mismatch error) and synthesis time. Experimental result showed that the proposed method improved the qualify of synthesized speech. as well as shortened the synthesis time.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.466-476
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• 2006

The objective of this study was to compare and analyze the acceptability and consumption attitude for soy foods between Korean and Canadian university students as young consumers. This survey was carried out by questionnaire and the subjects were n=516 in Korea and n=502 in Canada. Opinions for soy foods in terms of general knowledge were that soy foods are healthy (86.5% in Korean and 53.4% in Canadian) or neutral (11.6% in Korean and 42.8% in Canadian), dairy foods can be substituted by soy foods (51.9% in Korean and 41.8% in Canadian), and soy foods are not only for vegetarians and milk allergy Patients but also for ordinary People (94.2% in Korean and 87.6% in Canadian). In main sources of information about soy foods, the rate by commercials on TV, radio or magazine was the highest (58.0%) for Korean students and the rate by family or friend was the highest(35.7%) for Canadian students. In consumption attitude, all of Korean students have purchased soy foods but only 55.4% of Canadian students have purchased soy foods, and soymilk was remarkably recognized and consumed then soy beverage and margarine in order. 76.4% of Korean students and 65.1% of Canadian students think soy foods are general and popular and can purchase easily, otherwise, in terms of price, soy foods were expensively recognized as 'more expensive than dairy foods' was 59.1% (Korean) and 54.7% (Canadian), and 'similar to dairy foods' was 36.8% (Korean) and 39.9% (Canadian). Major reasons for the rare consumption were 'I am not interested in soy foods' in Korean students (27.3%) and 'I prefer dairy foods to soy foods' in Canadian students (51.7%). However, consumption of soy foods in both countries are very positive and it will be increased.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.97-116
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• 2012

Conceptual modeling is an important step for successful system development. It helps system designers and business practitioners share the same view on domain knowledge. If the work is successful, a result of conceptual modeling can be beneficial in increasing productivity and reducing failures. However, the value of conceptual modeling is unlikely to be evaluated uniformly because we are lack of agreement on how to elicit concepts and how to represent those with conceptual modeling constructs. Especially, designing relationships between components, also known as part-whole relationships, have been regarded as complicated work. The recent study, "Representing Part-Whole Relations in Conceptual Modeling : An Empirical Evaluation" (Shanks et al., 2008), published in MIS Quarterly, can be regarded as one of positive efforts. Not only the study is one of few attempts of trying to clarify how to select modeling alternatives in part-whole design, but also it shows results based on an empirical experiment. Shanks et al. argue that there are two modeling alternatives to represent part-whole relationships : an implicit representation and an explicit one. By conducting an experiment, they insist that the explicit representation increases the value of a conceptual model. Moreover, Shanks et al. justify their findings by citing the BWW ontology. Recently, the study from Shanks et al. faces criticism. Allen and March (2012) argue that Shanks et al.'s experiment is lack of validity and reliability since the experimental setting suffers from error-prone and self-defensive design. They point out that the experiment is intentionally fabricated to support the idea, as such that using concrete UML concepts results in positive results in understanding models. Additionally, Allen and March add that the experiment failed to consider boundary conditions; thus reducing credibility. Shanks and Weber (2012) contradict flatly the argument suggested by Allen and March (2012). To defend, they posit the BWW ontology is righteously applied in supporting the research. Moreover, the experiment, they insist, can be fairly acceptable. Therefore, Shanks and Weber argue that Allen and March distort the true value of Shanks et al. by pointing out minor limitations. In this study, we try to investigate the dispute around Shanks et al. in order to answer to the following question : "What is the proper value of the study conducted by Shanks et al.?" More profoundly, we question whether or not using the BWW ontology can be the only viable option of exploring better conceptual modeling methods and procedures. To understand key issues around the dispute, first we reviewed previous studies relating to the BWW ontology. We critically reviewed both of Shanks and Weber and Allen and March. With those findings, we further discuss theories on part-whole (or part-of) relationships that are rarely treated in the dispute. As a result, we found three additional evidences that are not sufficiently covered by the dispute. The main focus of the dispute is on the errors of experimental methods: Shanks et al. did not use Bunge's Ontology properly; the refutation of a paradigm shift is lack of concrete, logical rationale; the conceptualization on part-whole relations should be reformed. Conclusively, Allen and March indicate properly issues that weaken the value of Shanks et al. In general, their criticism is reasonable; however, they do not provide sufficient answers how to anchor future studies on part-whole relationships. We argue that the use of the BWW ontology should be rigorously evaluated by its original philosophical rationales surrounding part-whole existence. Moreover, conceptual modeling on the part-whole phenomena should be investigated with more plentiful lens of alternative theories. The criticism on Shanks et al. should not be regarded as a contradiction on evaluating modeling methods of alternative part-whole representations. To the contrary, it should be viewed as a call for research on usable and useful approaches to increase value of conceptual modeling.