• 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.26 no.2
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• pp.212-221
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• 2006

The purposes of this study were to examine the characteristics of teacher help in small group Thinking Science(TS) activities and analyze the way students respond to teacher help. For this study, twenty-four 5th grade and twenty-four 7th grade students were selected, to undertake TS activities. Out of the 8 activities students participated in, the verbal interactions in activity 4 and 6, by students in four small groups, which incorporated relatively active argumentation was analyzed. Students' cognitive level was identified through a science reasoning task and the students were grouped heterogeneously according to their cognitive level. This study showed that teachers predominately used simple confirmation questions in preference to metacognitive question. Also, teacher help varied according to one's personal traits, work experience and degree of activity recognition. It was discovered that when the teacher provided student appropriate metacognitive questions and sufficient feedback, students actively engaged in argumentation. On the other hand, when the teacher asked simple confirmation questions and interfered in the activity, students did not participate in argumentation actively.

• Korean Journal of Culture and Social Issue
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• v.30 no.2
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• pp.121-157
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• 2024

This study explored the essence of psychological burnout experiences due to emotional contagion using a hermeneutic phenomenological approach. In-depth interviews were conducted on 9 participants who work in fields that are subject to emotional contagion. Data analysis was conducted by using van Manen's methodology, insisting that the pure description of an experience can be enriched by adding interpretation. The emotional contagion experiences were identified through this process and the findings were categorized into 3 core themes, 8 essential themes, and 35 subthemes. The first core theme is "emotions in constant exchange". This theme included two essential themes: 'various channels of emotional contagion' and 'subjective states that change depending on the transmitted emotions'. The second core theme, "filtering the experience of emotional contagion" included the essential themes of 'the characteristics susceptible to the emotions of others', 'attitudes of spreading negative emotions' and 'situations that makes one feel overwhelmed by emotions'. The final core theme, "from burnout by emotional contagion to communication" was categorized into the following essential themes: 'burnout-inducing entangled interactions', 'moving toward communication and connection' and 'recovery after psychological burnout'. Finally, the implications and suggestions for future research were discussed by summarizing the core contents of each themes.

• Phonetics and Speech Sciences
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• v.16 no.2
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• pp.83-90
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• 2024

This study aimed to explore phonological retrieval and phonological memory skills in second to third graders with dyslexia, poor comprehension, and typical development. The participants included 17 children with dyslexia, 17 children with poor comprehension, and 24 typically developing children. Children with dyslexia scored below 85 on the word decoding test, poor comprehender scored above 90 on the word decoding, and below 85 on the reading comprehension test and typical children scored above 90 on both reading tests. All participants were assessed on rapid automatized naming (RAN) and nonword repetition (NWR). The result indicated that children with dyslexia performed significantly worse on RAN and NWR tasks than other groups. However, there was significant differences between poor comprehender and typically developing children. Furthermore, only RAN were significantly correlated with word decoding and reading comprehension in children with dyslexia. For typically developing children, RAN was correlated with word decoding and reading comprehension, while NWR had a significant correlation with reading comprehension. No correlations were found between these variables for poor comprehender. The finding suggests that children with dyslexia showed difficulties on phonological retrieval and phonological memory, which are essential for reading development while poor comprehender do not have difficulties with phonological processing skills. Phonological processing deficits may underlie word decoding difficulties in dyslexia.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.125-132
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• 2024

Recently, with immersive virtual reality(IVR) technologies, various services such as education, training, entertainment, industry, healthcare and remote collaboration have been applied. In particular, researches are actively being studied to visualize and interact with virtual humans, research on virtual pets in IVR is also emerging. For interaction with the virtual pet, similar to real-world interaction scenarios, the most important thing is to provide physical contact such as haptic and non-verbal interaction(e.g., gesture). This paper investigates the effects on factors (e.g., shape and texture) of passive haptic feedbacks using mapping physical props corresponding to the virtual pet. Experimental results show significant differences in terms of immersion, co-presence, realism, and friendliness depending on the levels of texture elements when interacting with virtual pets by passive haptic feedback. Additionally, as the main findings of this study by statistical interaction between two variables, we found that there was Uncanny valley effect in terms of friendliness. With our results, we will expect to be able to provide guidelines for creating interactive contents with the virtual pet in immersive VR environments.

• Journal of Korean Home Economics Education Association
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• v.23 no.3
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• pp.69-90
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• 2011

To develop the descriptive evaluation questionnaire with high validity and reliability on the Clothing and Textiles section of the middle school Technology & Hone Economics textbook, apply it to students and analyze its results. We made out a draft for descriptive evaluation questionnaire that was based upon the concrete establishment of the goal and the range of evaluation. We also made a rubric for scoring as well as sample answer-sheets. Finally, we completed a total of twenty three descriptive evaluation questions and we applied it to sixty five 2nd-grade students in two classes in a middle school. Descriptive evaluation questionnaire exhibited the relative high validity on each question. Moreover, three graders gave the same score on each question of descriptive evaluation, suggesting that descriptive evaluation questionnaire has the high inter-grader reliability and the strong correlation. But, low academic achievement was generally observed in the subjects. They had difficulty in describing their knowledge via their own language and drawing up accurate and detailed answers. They recognized the positive aspects of descriptive evaluation questionnaire, but they felt it uncomfortable due to study-burden and description itself. To overcome these limitations, it is required that students should experience various materials related to subject contents in classes as well as textbooks, concentrate themselves on finding solutions for problems, expand their scope, and practice describe them in advance. Therefore, the additional training for description evaluation questionnaire will be necessary for the more efficient and discriminative questionnaire. Also the questionnaire with high validity and reliability should be developed and the aggressive and voluntary participation of teachers will be needed.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.333-338
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• 2017

Ethnography is concerned about specifically-based behavior and belief and the learned pattern of language and aims to describe and interpret them. Therefore, it is a classical form of qualitative research that was developed by anthropologists who spent for long time in conducting fieldworks within the cultural group. The results of analyzing ethnographic research methods of doctoral dissertations in Korea are as follows. First, the number of research participants in data collection methods was 1-10(32 dissertations, 44.4%), 11-20(18, 25%), 21-30(13, 18.1%), 31-40(2, 2.7%), and others(7, 9.8%). Second, data collection methods were in-depth interview(71, 98.6%), participant observation(70, 97.2%), document data(38, 52.7%), engineering device(12, 16.6%), and others(8, 11.1%). Data collection periods were 3-5 months(7 dissertation, 9.8%), 6-8 months(15, 20.8%), 9-11 months(14, 19.6%), 12-14 months(13, 18.1%), more than 15 months(17, 23.6%), and unpresented(4, 5.4%). Third, trustworthiness criteria were triangulation(46 dissertation, 63.9%), research participants' evaluation of study results 44(61.1%), peer researchers' advice and indication(33, 45.8%), follow-up(25, 34.7%), use of reference(20, 27.8%), reflexive subjectivity(17, 23.6%), intensive observation for a sufficient period(10, 13.9%), in-depth description(7, 9.8%), and others(7, 9.8%).

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.39-58
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• 2014

As the importance of big data and related technologies continues to grow in the industry, it has become highlighted to visualize results of processing and analyzing big data. Visualization of data delivers people effectiveness and clarity for understanding the result of analyzing. By the way, visualization has a role as the GUI (Graphical User Interface) that supports communications between people and analysis systems. Usually to make development and maintenance easier, these GUI parts should be loosely coupled from the parts of processing and analyzing data. And also to implement a loosely coupled architecture, it is necessary to adopt design patterns such as MVC (Model-View-Controller) which is designed for minimizing coupling between UI part and data processing part. On the other hand, big data can be classified as structured data and unstructured data. The visualization of structured data is relatively easy to unstructured data. For all that, as it has been spread out that the people utilize and analyze unstructured data, they usually develop the visualization system only for each project to overcome the limitation traditional visualization system for structured data. Furthermore, for text data which covers a huge part of unstructured data, visualization of data is more difficult. It results from the complexity of technology for analyzing text data as like linguistic analysis, text mining, social network analysis, and so on. And also those technologies are not standardized. This situation makes it more difficult to reuse the visualization system of a project to other projects. We assume that the reason is lack of commonality design of visualization system considering to expanse it to other system. In our research, we suggest a common information model for visualizing text data and propose a comprehensive and reusable framework, TexVizu, for visualizing text data. At first, we survey representative researches in text visualization era. And also we identify common elements for text visualization and common patterns among various cases of its. And then we review and analyze elements and patterns with three different viewpoints as structural viewpoint, interactive viewpoint, and semantic viewpoint. And then we design an integrated model of text data which represent elements for visualization. The structural viewpoint is for identifying structural element from various text documents as like title, author, body, and so on. The interactive viewpoint is for identifying the types of relations and interactions between text documents as like post, comment, reply and so on. The semantic viewpoint is for identifying semantic elements which extracted from analyzing text data linguistically and are represented as tags for classifying types of entity as like people, place or location, time, event and so on. After then we extract and choose common requirements for visualizing text data. The requirements are categorized as four types which are structure information, content information, relation information, trend information. Each type of requirements comprised with required visualization techniques, data and goal (what to know). These requirements are common and key requirement for design a framework which keep that a visualization system are loosely coupled from data processing or analyzing system. Finally we designed a common text visualization framework, TexVizu which is reusable and expansible for various visualization projects by collaborating with various Text Data Loader and Analytical Text Data Visualizer via common interfaces as like ITextDataLoader and IATDProvider. And also TexVisu is comprised with Analytical Text Data Model, Analytical Text Data Storage and Analytical Text Data Controller. In this framework, external components are the specifications of required interfaces for collaborating with this framework. As an experiment, we also adopt this framework into two text visualization systems as like a social opinion mining system and an online news analysis system.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..