• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.435-452
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• 2023

This study experimented with automatic classification of subject headings using BERT-based transfer learning model, and analyzed its performance. This study analyzed the classification performance according to the main class of KDC classification and the category type of subject headings. Six datasets were constructed from Korean national bibliographies based on the frequency of the assignments of subject headings, and titles were used as classification features. As a result, classification performance showed values of 0.6059 and 0.5626 on the micro F1 and macro F1 score, respectively, in the dataset (1,539,076 records) containing 3,506 subject headings. In addition, classification performance by the main class of KDC classification showed good performance in the class General works, Natural science, Technology and Language, and low performance in Religion and Arts. As for the performance by the category type of the subject headings, the categories of plant, legal name and product name showed high performance, whereas national treasure/treasure category showed low performance. In a large dataset, the ratio of subject headings that cannot be assigned increases, resulting in a decrease in final performance, and improvement is needed to increase classification performance for low-frequency subject headings.

• Korean Journal of Culture and Social Issue
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• v.24 no.3
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• pp.385-410
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• 2018

Materialistic values can be a important variable to understand Koreans' psychological well-being and mental health. This study aimed to validate the Korean version of the Material Values Scale (K-MVS)(Richins & Dawson, 1992). In study 1, we performed confirmatory factor analysis(CFA) to ascertain the three factor model of the original MVS using 417 Korean undergraduate student data(sample 1). The CFA confirmed the three-factor model of the MVS. Yet, three items that yielded low factor loadings in this study as well as in other MVS validation studies were excluded from the final model. In study 2, content, construct, and concurrent validity of the K-MVS were examined with 650 undergraduate student data(Sample 2). We also tested measurement invariance across two groups(i.e., college student group of Sample 2 and employee group of Sample 3). The result revealed that the three-factor model of the K-MVS hold true across the two groups. Lastly test-retest reliability was calculated with 408 female college student data(Sample 4) that filled out K-MVS twice within 6 months. These findings suggest that the K-MVS is a reliable and valid measure for assessing materialistic values in Korea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.930-938
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• 2023

Maritime English is a specialized language system designed for ship operations, maritime safety, and external and internal communication onboard. According to the International Maritime Organization's (IMO) International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), it is imperative that navigational officers engaged in international voyages have a thorough understanding of Maritime English including the use of Standard Marine Communication Phrases (SMCP). This study measured students' proficiency in Maritime English using a learning and testing platform that includes voice recognition, translation, and word entry tasks to evaluate the resulting improvement in Maritime English exam scores. Furthermore, the study aimed to investigate the level of platform use needed for cadets to qualify as junior navigators. The experiment began by examining the correlation between students' overall English skills and their proficiency in SMCP through an initial test, followed by the evaluation of improvements in their scores and changes in exam duration during the mid-term and final exams. The initial test revealed a significant dif erence in Maritime English test scores among groups based on individual factors, such as TOEIC scores and self-assessment of English ability, and both the mid-term and final tests confirmed substantial score improvements for the group using the platform. This study confirmed the efficacy of a learning platform that could be extensively applied in maritime education and potentially expanded beyond the scope of Maritime English education in the future.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.71-80
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• 2023

This study primarily aimed to develop an automated stuttering identification and classification method using artificial intelligence technology. In particular, this study aimed to develop a deep learning-based identification model utilizing the convolutional neural networks (CNNs) algorithm for Korean speakers who stutter. To this aim, speech data were collected from 9 adults who stutter and 9 normally-fluent speakers. The data were automatically segmented at the phrasal level using Google Cloud speech-to-text (STT), and labels such as 'fluent', 'blockage', prolongation', and 'repetition' were assigned to them. Mel frequency cepstral coefficients (MFCCs) and the CNN-based classifier were also used for detecting and classifying each type of the stuttered disfluency. However, in the case of prolongation, five results were found and, therefore, excluded from the classifier model. Results showed that the accuracy of the CNN classifier was 0.96, and the F1-score for classification performance was as follows: 'fluent' 1.00, 'blockage' 0.67, and 'repetition' 0.74. Although the effectiveness of the automatic classification identifier was validated using CNNs to detect the stuttered disfluencies, the performance was found to be inadequate especially for the blockage and prolongation types. Consequently, the establishment of a big speech database for collecting data based on the types of stuttered disfluencies was identified as a necessary foundation for improving classification performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.439-449
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• 2024

Recent rapid advancements in voice generation technology have enabled the natural synthesis of voices using text alone. However, this progress has led to an increase in malicious activities, such as voice phishing (voishing), where generated voices are exploited for criminal purposes. Numerous models have been developed to detect the presence of synthesized voices, typically by extracting features from the voice and using these features to determine the likelihood of voice generation.This paper proposes a new model for extracting voice features to address misuse cases arising from generated voices. It utilizes a deep learning-based audio codec model and the pre-trained natural language processing model BERT to extract novel voice features. To assess the suitability of the proposed voice feature extraction model for voice detection, four generated voice detection models were created using the extracted features, and performance evaluations were conducted. For performance comparison, three voice detection models based on Deepfeature proposed in previous studies were evaluated against other models in terms of accuracy and EER. The model proposed in this paper achieved an accuracy of 88.08%and a low EER of 11.79%, outperforming the existing models. These results confirm that the voice feature extraction method introduced in this paper can be an effective tool for distinguishing between generated and real voices.

• Journal of The Korean Association For Science Education
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• v.28 no.6
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• pp.592-602
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• 2008

The purpose of this research is to explore types of pedagogical content knowledge (PCK, hereafter) for effective science teaching. In this research, we explored three science teachers' PCK on light, who were effective in teaching the topic with particular students. The data analysis consisted of identifying the three teachers' unique PCK and ways to improve each teaching episode through the teacher meetings. These analyses, which consisted of verbal exchanges among the participants, were identified on the basis of our understanding. Using grounded theory methods, the types of science PCK drawn from this research are: (1) teaching through curriculum reconstruction, (2) teaching to help students build their own explanation models about surrounding nature, (3) teaching for learning the social language of science, (4) teaching to motivate students' learning needs based on relevance of science to students, (5) teaching through lowering students' learning demand by providing scaffolding, (6) teaching based on the teacher's understanding of students, (7) teaching through inquiry with argumentation, (8) teaching through reification of abstract science concepts, and (9) teaching none marginalized science. Common features of science teachers with quality PCK and their professionalism in teaching are discussed.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.21-41
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• 2024

This study explores the relationship between users' attachment styles and their interactions with ChatGPT (Chat Generative Pre-trained Transformer), an advanced language model developed by OpenAI. As artificial intelligence (AI) becomes increasingly integrated into everyday life, it is essential to understand how individuals with different attachment styles engage with AI chatbots in order to build a better user experience that meets specific user needs and interacts with users in the most ideal way. Grounded in attachment theory from psychology, we are exploring the influence of attachment style on users' interaction with ChatGPT, bridging a significant gap in understanding human-AI interaction. Contrary to expectations, attachment styles did not have a significant impact on ChatGPT usage or reasons for engagement. Regardless of their attachment styles, hesitated to fully trust ChatGPT with critical information, emphasizing the need to address trust issues in AI systems. Additionally, this study uncovers complex patterns of attachment styles, demonstrating their influence on interaction patterns between users and ChatGPT. By focusing on the distinctive dynamics between users and ChatGPT, our aim is to uncover how attachment styles influence these interactions, guiding the development of AI chatbots for personalized user experiences. The introduction of the Perceived Partner Responsiveness Scale serves as a valuable tool to evaluate users' perceptions of ChatGPT's role, shedding light on the anthropomorphism of AI. This study contributes to the wider discussion on human-AI relationships, emphasizing the significance of incorporating emotional intelligence into AI systems for a user-centered future.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.729-736
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• 2024

In connection with the OECD's core competency proposal, we would like to consider an attempt to realize this in liberal arts education at Korean universities and examine what kind of education plan it is desirable to present to learners. Universities are expanding competency-based education into human and social fields by reconsidering new talent awards and the direction of education. In this way, each university selects and organizes core competencies and incorporates the core competencies that the university pursues into educational goals. Under the supervision of the Ministry of Education, education centered on core competencies is exploring its potential in liberal arts courses at universities above all else. We want to explore a methodology that can achieve learner-centered teaching and learning effects in the process of incorporating and accepting this. Language acquisition along with cross-cultural understanding is above all else a part that can promote learners' competencies in terms of diversity and mutual understanding. Therefore, we reflect this in French liberal arts education and explore teaching and learning processes by incorporating respect for diversity and mutual cultural understanding competency education related to learners' motivation into lectures. We aim to supplement this through collaboration and mutual cultural understanding processes as presentation tasks in order to overcome the existing competency-based evaluation while deriving acceptance results from learners. Therefore, they recognize that the direction of core competency education naturally shifts to value-centered education.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.53-59
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• 2024

This study presents the design and implementation of a camera-based 6DoF (6 Degrees of Freedom) tracking and pose estimation system. In particular, we propose a method for accurately estimating the positions and orientations of all fingers of a user utilizing a 6DoF robotic arm. The system is developed using the Python programming language, leveraging the Mediapipe and OpenCV libraries. Mediapipe is employed to extract keypoints of the fingers in real-time, allowing for precise recognition of the joint positions of each finger. OpenCV processes the image data collected from the camera to analyze the finger positions, thereby enabling pose estimation. This approach is designed to maintain high accuracy despite varying lighting conditions and changes in hand position. The proposed system's performance has been validated through experiments, evaluating the accuracy of hand gesture recognition and the control capabilities of the robotic arm. The experimental results demonstrate that the system can estimate finger positions in real-time, facilitating precise movements of the 6DoF robotic arm. This research is expected to make significant contributions to the fields of robotic control and human-robot interaction, opening up various possibilities for future applications. The findings of this study will aid in advancing robotic technology and promoting natural interactions between humans and robots.

• Phonetics and Speech Sciences
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• v.16 no.3
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• pp.39-48
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• 2024

Reading fluency refers to the ability to read sentences or paragraphs accurately, quickly, and with appropriate prosodic expression. Most reading fluency assessments exclude expressive ability because it is difficult to objectively measure. Therefore, in this study, we examined all elements of reading fluency by analyzing prosodic characteristics of reading scenario texts to maximize expressive reading. The subjects were 30 male students in the first and second grades of middle school (15 normal and 15 poor readers). To analyze the accuracy aspect, error types at the syllable level were analyzed for each group, and related acoustic variables were measured and examined in terms of prosodic aspects. The reading accuracy analysis showed that the poor reading group had a higher error rate than the normal. In terms of error types, the normal group showed the order of 'substitution>omission>correction>insertion>repetition', whereas the poor reading group was in the order of 'correction>substitution>repetition/insertion>omission'. For the speech tempo, the dyslexic students were slower than the typical students for all sentence types. The prosodic variables also showed a high frequency of accentual phrases (AP) and intonation phrases (IP) in sentences along with a wide intensity range.