• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.676-684
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• 2003

eLearning can be defined as an approach to teaching and teaming that utilises Internet technologies to communicate and collaborate in an educational context. This includes technology that supplements traditional classroom training with web-based components and learning environments where the educational process is experienced online. The use of hypertext as an educational tool has a very rich history. The advent of the internet and one of its major application, the world wide web (WWW), has given a tremendous boost to the theory and practice of hypermedia systems for educational purposes. However, the web suffers from an inability to satisfy the heterogeneous needs of a large number of users. For example, web-based courses present the same static teaming material to students with widely differing knowledge of the subject. Adaptive hypermedia techniques can be used to improve the adaptability of eLearning. In this paper we report an approach to the design a unified implementation framework suitable for web-based eLearning that accommodates the three main dimensions of hypermedia adaptation: content, navigation, and presentation. The framework externalises the adaptation strategies using XML notation. The separation of the adaptation strategies from the source code of the eLearning software enables a system using the framework to quickly implement a variety of adaptation strategies. This work is a part of our more general ongoing work on the design of a framework for adaptive content delivery. parts of the framework discussed in this paper have been imulemented in a commercial eLearning engine.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.148-155
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• 2020

This study investigated learners' perceptions of using self-generated listening materials based on Text to Speech. After taking an online training session to learn how to make listening materials for extensive listening practice outside the classroom, the learners were engaged in practice with self-generated listening materials for 10 weeks in a self-directed way. The results show that a majority of the learners found the TTS-based listening materials helpful to reduce anxiety toward listening and enhance self-confidence and motivation, with a positive effect on improving their listening ability. The learners' general satisfaction can be attributed to some beneficial features of TTS-based listening material, including freedom to choose what they want to learn, convenient accessibility to the material, availability of various native speakers' voices, and novelty of digital tools. This suggests that TTS-based digital listening materials can be a useful educational tool to support learners' self-directed listening practice outside the classroom in EFL settings.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.97-107
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• 2014

The development of ICT has led positive aspects such as popularization of Internet. It, on the other hand, is causing a negative aspect, Cyber Terror. Although the causes for recent and continuous increase of cyber security incidents are various such as lack of technical and institutional security measure, the main cause which threatens the cyber security is the users' lack of awareness and attitude. The purpose of this study is the positive analysis of how the personal and job characteristics influence the cyber security training participation rate and the response ability to cyber terror response training with a sample case of K-corporation employees. In this paper, the relationship among career, gender, department, whether he/she is a cyber security specialist, whether he/she is a regular employee), "ratio of cyber security training courses during recent three years", "ratio that he/she has opened the malicious email in cyber terror response training during recent three years", "response index of virus active-x installation (higher index means poorer response)" is closely examined. Moreover, based on the examination result, the practical and political implications regarding K-corporation's cyber security courses and cyber terror response training are studied.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.201-207
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• 2012

A RAM-based neural network is a weightless neural network based on binary neural network(BNN) which is efficient neural network with a one-shot learning. RAM-based neural network has multiful information bits and store counts of training in BNN. Supervised learning based on the RAM-based neural network has the excellent performance in pattern recognition but in pattern categorization with unsupervised learning as unsuitable. In this paper, we propose a unsupervised learning algorithm in the RAM-based neural network to perform pattern categorization. By the proposed unsupervised learning algorithm, RAM-based neural network create categories depending on the input pattern by itself. Therefore, RAM-based neural network for supervised learning and unsupervised learning should proof of all possible complex models. The training data for experiments provided by the MNIST offline handwritten digits which is consist of 0 to 9 multi-pattern.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2824-2837
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• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4930-4951
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• 2018

The deep learning based speech enhancement has shown considerable success. However, it still suffers performance degradation under mismatch conditions. In this paper, an adaptation method is proposed to improve the performance under noise mismatch conditions. Firstly, we advise a noise aware training by supplying identity vectors (i-vectors) as parallel input features to adapt deep neural network (DNN) acoustic models with the target noise. Secondly, given a small amount of adaptation data, the noise-dependent DNN is obtained by using $L_2$ regularization from a noise-independent DNN, and forcing the estimated masks to be close to the unadapted condition. Finally, experiments were carried out on different noise and SNR conditions, and the proposed method has achieved significantly 0.1%-9.6% benefits of STOI, and provided consistent improvement in PESQ and segSNR against the baseline systems.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.26 no.6
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• pp.291-300
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• 2023

Purpose: The impact of coronavirus 2019 (COVID-19) on gastrointestinal (GI) endoscopy procedures in adults has been reported, with a drastic reduction in the number of procedures. However, there are no sufficient data regarding the impact on pediatric GI endoscopy. Here, we aimed to report that impact in the Asia-Pacific region. Methods: A questionnaire-based internet survey was conducted from June to November 2021 among pediatric endoscopy institutions in the Asia-Pacific region, with each institution providing a single response. Overall, 25 questions focused on the impact of the number of procedures conducted, the usage of personal protective equipment (PPE), and endoscopy training programs during the pandemic. Results: A total of 162 institutions across 13 countries in the Asia-Pacific region participated in the study, and 133 (82.1%) institutions underwent procedure changes since the emergence of COVID-19. The number of esophagogastroduodenoscopy and ileocolonoscopy procedures decreased in 118/133 (88.7%) and 112/133 (84.2%) institutions, respectively. Endoscopy for patient with positive COVID-19 in an emergency or urgent cases still carried out in 102/162 (62.9%) institutions. Screening of COVID-19 for all patients before endoscopy was done across 110/162 (67.9%) institutions. PPE recommendations varied among institutions. Pediatric gastrointestinal endoscopy training programs were discontinued in 127/162 (78.4%) institutions. Conclusion: This study reports the impact of the COVID-19 pandemic on pediatric gastrointestinal endoscopy in the Asia-Pacific region. There has been a significant reduction in the number of endoscopic procedures and relevant training programs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1433-1449
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• 2023

In recent years, the rapid development of social networks has led to a rapid increase in the amount of information available on the Internet, which contains a large amount of sensitive information related to pornography, politics, and terrorism. In the aspect of sensitive image detection, the existing machine learning algorithms are confronted with problems such as large model size, long training time, and slow detection speed when auditing and supervising. In order to detect sensitive images more accurately and quickly, this paper proposes a multiclassification sensitive image detection method based on lightweight Convolutional Neural Network. On the basis of the EfficientNet model, this method combines the Ghost Module idea of the GhostNet model and adds the SE channel attention mechanism in the Ghost Module for feature extraction training. The experimental results on the sensitive image data set constructed in this paper show that the accuracy of the proposed method in sensitive information detection is 94.46% higher than that of the similar methods. Then, the model is pruned through an ablation experiment, and the activation function is replaced by Hard-Swish, which reduces the parameters of the original model by 54.67%. Under the condition of ensuring accuracy, the detection time of a single image is reduced from 8.88ms to 6.37ms. The results of the experiment demonstrate that the method put forward has successfully enhanced the precision of identifying multi-class sensitive images, significantly decreased the number of parameters in the model, and achieved higher accuracy than comparable algorithms while using a more lightweight model design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2590-2606
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• 2017

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.