• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.10
• /
• pp.373-380
• /
• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Journal of Broadcast Engineering
• /
• v.26 no.4
• /
• pp.429-440
• /
• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.

• Journal of Broadcast Engineering
• /
• v.27 no.1
• /
• pp.3-12
• /
• 2022

Image deblurring aims to remove image blur, which can be generated while shooting the pictures by the movement of objects, camera shake, blurring of focus, and so forth. With the rise in popularity of smartphones, it is common to carry portable digital cameras daily, so image deblurring techniques have become more significant recently. Originally, image deblurring techniques have been studied using traditional optimization techniques. Then with the recent attention on deep learning, deblurring methods based on convolutional neural networks have been actively proposed. However, most of them have been developed while focusing on better performance. Therefore, it is not easy to use in real situations due to the speed of their algorithms. To tackle this problem, we propose a novel deep learning-based deblurring algorithm that can be operated in real-time on HD resolution. In addition, we improved the training and inference process and could increase the performance of our model without any significant effect on the speed and the speed without any significant effect on the performance. As a result, our algorithm achieves real-time performance by processing 33.74 frames per second at 1280×720 resolution. Furthermore, it shows excellent performance compared to its speed with a PSNR of 29.78 and SSIM of 0.9287 with the GoPro dataset.

• Journal of Science Education
• /
• v.47 no.3
• /
• pp.251-262
• /
• 2023

In this study, we examine other factors that may contribute to the decline in students' academic performance and educational attainment. Many media reports, as well as previous studies, have suggested that virtual learning is the main reason for the decline in students' academic performance. However, the 2020 National Student Achievement Survey, which was conducted in conjunction with the COVID-19 Distance Learning Environment Student Survey, showed that students were highly satisfied with distance learning (70-80%), and the analysis of the National Assessment of Educational Achievement showed that students' academic performance had already been declining year by year since 2017, with a general downward curve. For further confirmation, we analyzed the performance of high school students on mock exams and found that their performance was not normally distributed, but rather a right-skewed U-shaped distribution with a shrinking number of medians and severe polarization. We found that this phenomenon is not simply because of the mode or quality of the virtual classroom, but to a variety of factors, including environmental influences such as care and management at home, changes in investment in private education, increased time spent on online devices while taking virtual classes at the bottom, and increased time spent watching online content, games, and videos that are not related to learning.

• The Journal of Bigdata
• /
• v.4 no.2
• /
• pp.23-34
• /
• 2019

Recently, the crime that utilizes the digital platform is continuously increasing. About 140,000 cases occurred in 2015 and about 150,000 cases occurred in 2016. Therefore, it is considered that there is a limit handling those online crimes by old-fashioned investigation techniques. Investigators' manual online search and cognitive investigation methods those are broadly used today are not enough to proactively cope with rapid changing civil crimes. In addition, the characteristics of the content that is posted to unspecified users of social media makes investigations more difficult. This study suggests the site-based collection and the Open API among the content web collection methods considering the characteristics of the online media where the infringement crimes occur. Since illegal content is published and deleted quickly, and new words and alterations are generated quickly and variously, it is difficult to recognize them quickly by dictionary-based morphological analysis registered manually. In order to solve this problem, we propose a tokenizing method in the existing dictionary-based morphological analysis through WPM (Word Piece Model), which is a data preprocessing method for quick recognizing and responding to illegal contents posting online infringement crimes. In the analysis of data, the optimal precision is verified through the Vote-based ensemble method by utilizing a classification learning model based on supervised learning for the investigation of illegal contents. This study utilizes a sorting algorithm model centering on illegal multilevel business cases to proactively recognize crimes invading the public economy, and presents an empirical study to effectively deal with social data collection and content investigation.

• Journal of the Korea Convergence Society
• /
• v.9 no.10
• /
• pp.303-313
• /
• 2018

In order to provide quality services across international airports, airline personnel must rapidly and effectively develop and share knowledge. Combining components of adaptive structuration theory (AST) and media synchronicity theory (MST), a research framework was developed to convey three distinct stages of knowledge sharing. We use the grounded theory research method for the qualitative data collected from audio transcripts of employees learning how to use and work with company issued smartphones with push-to-talk functionalities. Data was collected from 33 operations personnel. The results of the content analysis are recorded for the elements of each of the three concepts of our research framework. During the social interaction stage, the content of the audio conversations shifts mainly from conflict management to task management; for media synchronicity, from quality to quantity; for productive outcomes, from efficiency to commitment. New insights are uncovered from our analysis of data from the field as users advance from learning how to use the mobile devices, to using the devices for managing knowledge for their work in the airline industry.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.31 no.1
• /
• pp.67-87
• /
• 2020

The purpose of this study is to analyze current status and teacher librarians' perceptions of school library space composition and indoor environment, and propose some methods for the school library to be a basic educational facility. The space most secured by the 126 teacher librarians who participated in the survey was the free reading area, and the space with the lowest level was the media production and group project area. The most important types of spaces for teacher librarians are the teaching area and the free reading area, while the recognition of the importance of media production and group project areas is relatively low. Among the elements of indoor environmental assessment, they showed that safety and comfort were the most important but diversity and flexibility were relatively less important. The result of this analysis is different from the school and library policy direction that emphasizes the learning commons and maker spaces. Teacher librarians still seem to appreciate the importance of traditional library space. Therefore, it is necessary to include the establishment and operation of maker spaces and learning commons in the teacher librarians training and retraining process. In addition, it is necessary to increase the participation of users such as teachers, students, and parents in space composition and interior design initiatives to increase the user's interior environment satisfaction.

• Smart Media Journal
• /
• v.10 no.3
• /
• pp.31-38
• /
• 2021

It is hard to predict when and where a fall accident will happen. Also, if rapid follow-up measures on it are not performed, a fall accident leads to a threat of life, so studies that can automatically detect a fall accident have become necessary. Among automatic fall-accident detection techniques, a fall detection scheme using an IMU (inertial measurement unit) sensor attached to a wrist is difficult to detect a fall accident due to its movement, but it is recognized as a technique that is easy to wear and has excellent accessibility. To overcome the difficulty in obtaining fall data, this study proposes an algorithm that efficiently learns less data through machine learning such as KNN (k-nearest neighbors) and SVM (support vector machine). In addition, to improve the performance of these mathematical classifiers, this study utilized feature data aquired in the frequency space. The proposed algorithm analyzed the effect by diversifying the parameters of the model and the parameters of the frequency feature extractor through experiments using standard datasets. The proposed algorithm could adequately cope with a realistic problem that fall data are difficult to obtain. Because it is lighter than other classifiers, this algorithm was also easy to implement in small embedded systems where SIMD (single instruction multiple data) processing devices were difficult to mount.

• Smart Media Journal
• /
• v.8 no.2
• /
• pp.58-65
• /
• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• The Journal of Society for e-Business Studies
• /
• v.26 no.1
• /
• pp.43-65
• /
• 2021

With the advancement of information and communications technology, a means to recruit IT professional has fundamentally changed. Nowadays recruiters search for candidate information from the Web as well as traditional information sources such as résumés or interviews. Particularly, open-source software development (OSSD) platforms have become an opportunity for developers to demonstrate their IT capabilities, making it a way for recruiters to find the right candidates, whom they need. Therefore, this study aims to investigate the impact developers' profiles in an OSSD platform on their finding a job. This study examined four antecedents of developer information that can accelerate their job search: job-seeking status, personal-information posting, learning activities and knowledge contribution activities. For the empirical analysis, we developed a Web crawler and gathered a dataset on 4,005 developers from GitHub, which is a well-known OSSD platform. Proportional hazards regression was used for data analysis because shorter job-seeking period implies more successful result of job change. Our results indicate that developers, who explicitly posted their job-seeking status, had shorter job-seeking periods than those who did not. The other antecedents (i.e., personal-information posting, learning, and knowledge contribution activities) also contributed in reducing the job-seeking period. These findings imply values of OSSD platforms for recruiters to find proper candidates and for developers to successfully find a job.