• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• Journal of the Korean Society of Earth Science Education
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• v.11 no.3
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• pp.182-192
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• 2018

The purpose of this study was to examine the effects of school forest activities program on elementary students' science process skill and attitude toward science to make suggestions to help develop and extend the program. The subjects of the study were 49 students of two classes. One class of 24 students, experimental class, took developed 10 periods of school forest activities program. While the other class of 25 students, comparative class, took ordinary teacher driven periods using photo materials and study papers. Before and after the program, pre and post test were done. The results of this study can be summarized as follows: First, the school forest activities program didn't have a meaningful effect on students' science process skill. Second, the school forest activities program had a meaningful effect on the improvement of students' attitude toward science. From the interview with experimental class, we could know that students had a favorable impression and high satisfaction level about the activities program.

• Journal of The Korean Association For Science Education
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• v.39 no.1
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• pp.45-57
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• 2019

NOS education through the history of science is regarded effective. However, science teaching has been criticized for not considering the interest of the learners enough and providing the context of learning themes that hinder the understanding of NOS. This study intends to convey the NOS element through the rich context of storytelling. The theme of the story is the history of continental drift, in which, the debate of many scientists and Wegener's creativity are prominent. Of the various media that deliver storytelling, the most powerful medium that leads to personal immersion is computer games, and among many kinds of games, the main genre of storytelling is role-playing games (RPGs). We developed the science history role-playing game (SHRPG) focusing on continental drift. The game development procedure followed Kim's 4F process (2017), which consists of the Figure Out, Focus, Fun Design, and Finalize. The story was constructed based on the NOS elements of Lederman et al. (2002), namely creativity and imagination demand, subjectivity, socio-cultural personality and tentativeness, which are all present in the story of the continental drift theory. The mechanics and rules of the RPG included quests, rewards, quizzes, NOS scores, and rankings. In the final phase of development, the game developed was pilot tested four times. The results of the tests showed that students' understanding of NOS through SHRPG has increased, especially in the creativity domain. The students' satisfaction with the fun, sympathy, and immersion during the game was very high.

• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.37-44
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• 2021

A study was conducted on the effects of improving concentration by obscuring the peripheral vision using a blindfold that only covers the left and right sides of the field of view. The blindfold was trapezoidal in shape (5 × 4.8 cm in length and width) and was fixed to the left and right sides of the glasses with fixing clips. The material was a black-colored polypropylene (PP) and weighed 2.3 g including the clip. Qualitative and quantitative evaluations were performed on 50 healthy college students during the 15 days of using a blindfold. The qualitative analysis was performed utilizing a questionnaire regarding the improvement of concentration and the structure of the blindfold. EEG was measured while watching a learning video that required attention for quantitative analysis, and signal power and ERD/S analyses were performed for the mid β band (15-20 Hz) at the F4 position, which was the frontal lobe. The results showed that 40 of the 50 people reported improved concentration when they wore a vision shield, and 80% of the total subjects found it to be effective. From the quantitative evaluation, the ERS peak (p = 0.023) and the ERD + ERS peak value showed a significant difference (p = 0.017). In conclusion, concentration still improved even if only the left and right visual fields were used. Thus, it is expected that blindfolding could be used in various environments that require concentration.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.200-220
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• 2021

The purpose of this study was to analyze the scope and content of high school textbooks based on the NCS competency unit criteria. The job competency level of hair beauty is included in the range of Level 1 to Level 5 according to the NCS standard. Among all the 40 units of NCS competency units, the number of them which can be acquired at the high school is 30 units. Among them, high school beauty textbooks were published around 19 competency units, 75% of which corresponded. Only about 85% of all the contents were contained in the text book. Based on the analysis contents, the improvement plan of the textbook is as follows. First, textbooks on 11 competency units that can be acquired at the high school level should be published and the professional curriculum with lessons related to this will be expanded at the same time. Second, there is a need for discussion to establish terminology in the field of Cosmetology. Third, it is necessary to improve the quality of the photographic and illustration materials. Fourth, it should be needed to correct typing errors and maintain a consistent editorial format. The results of this study can be used as basic data need to make curriculum and publish textbooks for high school graduates to perform their jobs at the same time as they get a job.

• Communications of Mathematical Education
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• v.35 no.4
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• pp.527-546
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• 2021

The purpose of this study is to reveal what mathematics teachers focus on and how they assess students' thinking during lessons enacted. For this purpose, we googled and searched internet sites to collect formative assessment materials for the year 2014 to 2019. The formative assessment tasks data were analyzed according to the levels cognitive demand levels and tasks suggested in textbooks in terms of degrees to which how they are related. The data analysis suggested as follows: a) most of the formative assessment tasks were at the low-level, in particular, PNC level tasks that require applying particular procedures without connections to concepts and meaning underlying the procedures, b) the assessment tasks appeared to be very similar to the tasks suggested in the secondary mathematics textbooks, and c) it seemed that 3 types of formative assessment, observation notes, self-assessment, and peer-assessment were dominantly utilized during mathematics lessons and these different types of formative assessment were employed apparently to find out whether students participated actively in class and in group activity, not how they go through understanding or thinking processes.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.33-48
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• 2023

In this paper we consider automatic speech recognition (ASR) for Korean speech data in which elderly persons randomly speak a sequence of words such as animals and vegetables for one minute. Most of the speakers are over 60 years old and some of them are dementia patients. The goal is to compare deep-learning based ASR models for such data and to find models with good performance. ASR is a technology that can recognize spoken words and convert them into written text by computers. Recently, many deep-learning models with good performance have been developed for ASR. Training data for such models are mostly composed of the form of sentences. Furthermore, the speakers in the data should be able to pronounce accurately in most cases. However, in our data, most of the speakers are over the age of 60 and often have incorrect pronunciation. Also, it is Korean speech data in which speakers randomly say series of words, not sentences, for one minute. Therefore, pre-trained models based on typical training data may not be suitable for our data, and hence we train deep-learning based ASR models from scratch using our data. We also apply some data augmentation methods due to small data size.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.8
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• pp.327-342
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• 2017

In this paper, we introduce an integrated processing system of smart sensor data for IoT service which collects sensor data and efficiently processes it. Based on the technology of collecting sensor data to the development of the IoT field and sending it to the network · Based on the receiving technology, as various projects such as smart homes, autonomous running vehicles progress, the sensor data is processed and effectively An autonomous control system to utilize has been a problem. However, since the data type of the sensor for monitoring the autonomous control system varies according to the domain, a sensor data integration processing system applying the autonomous control system to various different domains is necessary. Therefore, in this paper, we introduce the Smart Sensor Data Integrated Processing System, apply it and use the window as a reference to process internal and external sensor data 1) receiveData, 2) parseData, 3) addToDatabase 3 With the process of the stage, we provide and implement the automatic window opening / closing system "Smart Window" which ventilates to create a comfortable indoor environment by autonomous control system. As a result, standby information is collected and monitored, and machine learning for performing statistical analysis and better autonomous control based on the stored data is made possible.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.212-215
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• 2022

In the era of so-called digital transformation today, the need for the construction and utilization of big data in various fields has increased. Today, a lot of data is produced and stored in a digital device and media-friendly manner, but the production and storage of data for a long time in the past has been dominated by print books. Therefore, the need for Optical Character Recognition (OCR) technology to utilize the vast amount of print books accumulated for a long time as big data was also required in line with the need for big data. In this study, a system for digitizing the structure and content of a document object inside a scanned book image is proposed. The proposal system largely consists of the following three steps. 1) Recognition of area information by document objects (table, equation, picture, text body) in scanned book image. 2) OCR processing for each area of the text body-table-formula module according to recognized document object areas. 3) The processed document informations gather up and returned to the JSON format. The model proposed in this study uses an open-source project that additional learning and improvement. Intelligent OCR proposed as a system in this study showed commercial OCR software-level performance in processing four types of document objects(table, equation, image, text body).

• Journal of IKEEE
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• v.27 no.4
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• pp.486-493
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• 2023

In this paper, we propose an automatic hand gesture area extraction and recognition technique using FMCW radar-based point cloud and LSTM. The proposed technique has the following originality compared to existing methods. First, unlike methods that use 2D images as input vectors such as existing range-dopplers, point cloud input vectors in the form of time series are intuitive input data that can recognize movement over time that occurs in front of the radar in the form of a coordinate system. Second, because the size of the input vector is small, the deep learning model used for recognition can also be designed lightly. The implementation process of the proposed technique is as follows. Using the distance, speed, and angle information measured by the FMCW radar, a point cloud containing x, y, z coordinate format and Doppler velocity information is utilized. For the gesture area, the hand gesture area is automatically extracted by identifying the start and end points of the gesture using the Doppler point obtained through speed information. The point cloud in the form of a time series corresponding to the viewpoint of the extracted gesture area is ultimately used for learning and recognition of the LSTM deep learning model used in this paper. To evaluate the objective reliability of the proposed technique, an experiment calculating MAE with other deep learning models and an experiment calculating recognition rate with existing techniques were performed and compared. As a result of the experiment, the MAE value of the time series point cloud input vector + LSTM deep learning model was calculated to be 0.262 and the recognition rate was 97.5%. The lower the MAE and the higher the recognition rate, the better the results, proving the efficiency of the technique proposed in this paper.