• Informatization Policy
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• v.31 no.1
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• pp.32-52
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• 2024

This study investigates the effectiveness of virtual reality agents as foreign language instructors with focus on the impact of different native language backgrounds and instructional roles. The agents were first distinguished as native or non-native speakers treated as a between-subject factor, and then assigned roles as either teachers or salespersons considered within-subject factors. An immersive virtual environment was developed for this experiment, and a 2×2 mixed factorial design was carried out. In an experimental group of 72 university students, statistically significant interactions were found in learning satisfaction, memory, and recall between the native/non-native status of the agents and their roles. With regard to learning confidence and presence, however, no statistically significant differences were observed in both interaction effects and main effects. Contextual learning in a virtual environment was found to enhance learning effectiveness and satisfaction, with the nativeness and the role of agents influencing learners' memory; thus highlighting the effectiveness of using virtual reality agents in foreign language learning. This suggests that varied approaches can have positive cognitive and emotional impacts on learners, thereby providing valuable theoretical and empirical implications.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.259-264
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• 2019

In this paper, the prototyping process for developing syllable-initial consonant-based game 'Korean Guards' is described. Users may effectively learn Korean words using alphabetically sequential approaches, but the easiness of access bestowed on the smart environments and game algorithms could be fully utilized for the functional advantages for educational purposes. This functional game is developed on Android OS and the prototypical outcome is shown.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.3
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• pp.247-264
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• 2021

This study aims to create a deep learning-based classification model to classify suicide tendency by suicide corpus constructed for the present study. Also, to analyze suicide factors, the study classified suicide tendency corpus into detailed topics by using topic modeling, an analysis technique that automatically extracts topics. For this purpose, 2,011 documents of the suicide-related corpus collected from social media naver knowledge iN were directly annotated into suicide-tendency documents or non-suicide-tendency documents based on suicide prevention education manual issued by the Central Suicide Prevention Center, and we also conducted the deep learning model(LSTM, BERT, ELECTRA) performance evaluation based on the classification model, using annotated corpus data. In addition, one of the topic modeling techniques, LDA identified suicide factors by classifying thematic literature, and co-word analysis and visualization were conducted to analyze the factors in-depth.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.526-537
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• 2019

This study researched the effect of application systems for media industry by using machine learning method focusing on industrial organization theory. First, for applying the system successfully, formation of sympathy about needs is required. The introduction of machine learning can bring change in each stage of value chain especially, decision making process of investment and production process. In investment side, objective performance prediction data can enhance efficiency, and content diversity can decrease with concentrated investment phenomenon to secured content by the system. In production side, if the system support to make creators decrease simple repeat works, production efficiency will increase.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.527-537
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• 2022

As multimedia contents demand and supply increase, internet traffic around the world increases. Several standardization groups are striving to establish more efficient compression standards to mitigate the problem. In particular, research to introduce deep learning technology into compression standards is actively underway. Despite the fact that deep learning-based technologies show high performance, they suffer from the domain gap problem when test video sequences have different characteristics of training video sequences. To this end, several methods have been made to introduce content-adaptive deep video compression. In this paper, we will look into these methods by three aspects: codec information-aware methods, model selection methods, and information signaling methods.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.105-108
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• 2007

Bayesian learning network is employed for diverse applications. This paper discusses the Bayesian learning network algorithm structure which can be applied in the wireless sensor network environment for various online applications. First, this paper discusses Bayesian parameter learning, Bayesian DAG structure learning, characteristics of wireless sensor network, and data gathering in the wireless sensor network. Second, this paper discusses the important considerations about the online Bayesian learning network and the conceptual structure of the learning network algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.209-215
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• 2014

Sequence tagging is the task of predicting frame-wise labels for a given input sequence and has important applications to diverse domains. Conventional methods such as maximum likelihood (ML) learning matches global features in empirical and model distributions, rather than local features, which directly translates into frame-wise prediction errors. Recent probabilistic sequence models such as conditional random fields (CRFs) have achieved great success in a variety of situations. In this paper, we introduce a novel discriminative CRF learning algorithm to minimize local feature mismatches. Unlike overall data fitting originating from global feature matching in ML learning, our approach reduces the total error over all frames in a sequence. We also provide an efficient gradient-based learning method via gradient forward-backward recursion, which requires the same computational complexity as ML learning. For several real-world sequence tagging problems, we empirically demonstrate that the proposed learning algorithm achieves significantly more accurate prediction performance than standard estimators.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.139-145
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• 2018

This paper recommends flipped learning as a method to improve the learning abilities and the level of software utilization when it comes to using computers in children education institutes. Flipped learning enables a class fully making use of the up-to-date multimedia-related technology. Especially, flipped learning leads a participation-oriented class rather than lecture-based ones. Young child's teachers can, not only improve their capabilities to utilize multimedia, but also manage classes that follow the trend of the fourth industrial revolution. Therefore, this paper introduces the importance of media education when it comes to training preliminary teachers and suggests a flipped learning curriculum. This paper finds significance in future efficient education for raising creative and integrated thinking children.

• Annual Conference of KIPS
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• 2011.11a
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• pp.1532-1533
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• 2011

본 연구에서는 SQL를 배우고자 하는 개발자들에게 SQL 문법을 학습할 수 있는 교육용 Tool을 개발한다. 개발자가 예제와 설명을 통하여 SQL 문법을 배우고 ER-Diagram을 보면서 논리적인 DB의 개념을 이용하여 쉽게 학습할 수 있다. 예제는 초급과 중급으로 나누어져 있어 사용자의 수준에 맞는 학습이 선택가능하다. TPC-H 데이터는 DSS 환경에서 사용되는 표준 데이터 모델로 Database Generater를 통해 생성하며 본 연구에서 사용자가 데이터량의 조정이 가능하도록 구성하였다.