• The Journal of Korean Association of Computer Education
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• v.18 no.6
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• pp.81-88
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• 2015

Along with advent of Web 3.0 era and advanced technologies of IoT(Internet of Things), massive amounts of information are generated. Reflecting this trend, this paper developed a semi-automatic construction tool for named entity dictionary based on active learning. Our proposed method chose error candidates to verify among the preliminary results using initial trained model and re-trained the model for correctly labeled data by user. We adopt active learning approach for minimizing human effort utilized metadata features of Wikipedia. Based on experimental results using our tool, we show that 68.6% errors were automatically corrected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4214-4230
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• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3444-3457
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• 2021

In the field of anti-jamming based on dynamic spectrum, most methods try to improve the ability to avoid jamming and seldom consider whether the jammer would perceive the user's signal. Although these existing methods work in some anti-jamming scenarios, their long-term performance may be depressed when intelligent jammers can learn user's waveform or decision information from user's historical activities. Hence, we proposed a hidden anti-jamming method to address this problem by reducing the jammer's sense probability. In the proposed method, the action correlation between the user and the jammer is used to evaluate the hiding effect of the user's actions. And a deep reinforcement learning framework, including specific action correlation calculation and iteration learning algorithm, is designed to maximize the hiding and communication performance of the user synchronously. The simulation result shows that the algorithm proposed reduces the jammer's sense probability significantly and improves the user's anti-jamming performance slightly compared to the existing algorithms based on jamming avoidance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.911-931
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• 2021

Recently, most cloud services use Docker container environment to provide their services. However, there are no researches to evaluate the performance of communication libraries for multi-GPU based distributed deep learning in a Docker container environment. In this paper, we propose an efficient communication architecture for multi-GPU based deep learning in a Docker container environment by evaluating the performances of various communication libraries. We compare the performances of the parameter server architecture and the All-reduce architecture, which are typical distributed deep learning architectures. Further, we analyze the performances of two separate multi-GPU resource allocation policies - allocating a single GPU to each Docker container and allocating multiple GPUs to each Docker container. We also experiment with the scalability of collective communication by increasing the number of GPUs from one to four. Through experiments, we compare OpenMPI and MPICH, which are representative open source MPI libraries, and NCCL, which is NVIDIA's collective communication library for the multi-GPU setting. In the parameter server architecture, we show that using CUDA-aware OpenMPI with multi-GPU per Docker container environment reduces communication latency by up to 75%. Also, we show that using NCCL in All-reduce architecture reduces communication latency by up to 93% compared to other libraries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.44-52
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• 2022

With recent scientific advances and growing interest in AI technologies, AI-based chatbots have been viewed as a practical learning aid for English language development. The purpose of this study is to examine preservice teachers' perceptions on the potential benefits of employing AI chatbots in English instruction and its pedagogical aspects. 28 preservice teachers majoring in English education were asked to use Kuki chatbots for a week with a guidance of a researcher and then report on their perceptions of AI chatbots in terms of perceived usefulness after use, applicability, and educational benefits and drawbacks. Emerging codes and themes were identified and evaluated using Thematic Analysis(TA) based on qualitative data from surveys and interviews. The findings show that six emerging themes were identified, encompassing perspectives on teacher, learner, communication, linguistic, affective, and assessment. The overall findings of this study revealed that AI-based chatbots can play a significant role as learning tools for stimulating interactive communication in a target language. Most preservice primary teachers acknowledge that AI chatbots can be useful as teaching and learning aids for both teachers and students. Furthermore, when applying various learner data to chatbot technology, such as learner assessment and diagnosis, a guided approach is necessary to perform a conversation appropriate for the learner's level and characteristics. Finally, as chatbots have a variety of benefits in terms of affective aspects, they may improve EFL learners' confidence in speaking English and learning motivation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3780-3797
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• 2022

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.89-104
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• 2024

Cloud-based e-learning (CBEL) represents a promising technological frontier. Existing literature has presented a diverse array of findings regarding the determinants that influence the adoption of CBEL. The primary objective of this study is to conduct an exhaustive examination of the available literature, aiming to determine the key predictors of CBEL utilization by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. A comprehensive review of 35 articles was undertaken, shedding light on the status of CBEL as an evolving field. Notably, there has been a discernible downturn in related research output during the COVID-19 pandemic, underscoring the temporal dynamics of this subject. It is noteworthy that a significant portion of this research has emanated from the Asian continent. Furthermore, the dominance of the technology acceptance model (TAM) in research frameworks is affirmed by our findings. Through a rigorous thematic analysis, our study identified five overarching themes, each encompassing a diverse range of sub-themes. These themes encompass 1) technological factors, 2) individual factors, 3) organizational factors, 4) environmental factors, and 5) security factors. This categorization provides a structured framework for understanding the multifaceted nature of CBEL adoption determinants. Our study serves as a compass, guiding future research endeavours in this domain. It underscores the imperative for further investigations utilizing diverse theoretical frameworks, contextual settings, research methodologies, and variables. This call for diversity and expansion in research efforts reflects the dynamic nature of CBEL and the evolving landscape of e-learning technologies.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.212-220
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• 2024

A recently the advancement of society, AI technology has made significant strides, especially in the fields of computer vision and voice recognition. This study introduces a system that leverages these technologies to recognize users through a camera and relay commands within a vehicle based on voice commands. The system uses the YOLO (You Only Look Once) machine learning algorithm, widely used for object and entity recognition, to identify specific users. For voice command recognition, a machine learning model based on spectrogram voice analysis is employed to identify specific commands. This design aims to enhance security and convenience by preventing unauthorized access to vehicles and IoT devices by anyone other than registered users. We converts camera input data into YOLO system inputs to determine if it is a person, Additionally, it collects voice data through a microphone embedded in the device or computer, converting it into time-domain spectrogram data to be used as input for the voice recognition machine learning system. The input camera image data and voice data undergo inference tasks through pre-trained models, enabling the recognition of simple commands within a limited space based on the inference results. This study demonstrates the feasibility of constructing a device management system within a confined space that enhances security and user convenience through a simple real-time system model. Finally our work aims to provide practical solutions in various application fields, such as smart homes and autonomous vehicles.