• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3112-3127
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• 2018

Since time series clustering is performed without prior information, it is used for exploratory data analysis. In particular, clusters of time series with similar shapes can be used in various fields, such as business, medicine, finance, and communications. However, existing time series clustering algorithms have a problem in that time series with different shapes are included in the clusters. The reason for such a problem is that the existing algorithms do not consider the limitations on the size of the generated clusters, and use a dimension reduction method in which the information loss is large. In this paper, we propose a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series. In the data preprocessing step, we normalize the time series using z-transformation. Then, we use piecewise aggregate approximation (PAA) to reduce the dimension of the time series. In the clustering step, we use density-based spatial clustering of applications with noise (DBSCAN) to create a precluster. We then use a modified K-means algorithm to refine the preclusters containing differently shaped time series into subclusters containing only similarly shaped time series. In our experiments, our method showed better results than the existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권11호
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• pp.5530-5546
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• 2016

Automatic analysis and understanding of human activities is a challenging task in computer vision, especially for the surveillance scenarios which typically contains crowds, complex motions and occlusions. To address these issues, a Bag-of-words representation of videos is developed by leveraging information including crowd positions, motion directions and velocities. We infer the crowd activity in a motion field using Category Constrained Correlated Topic Model (CC-CTM) with latent topics. We represent each video by a mixture of learned motion patterns, and predict the associated activity by training a SVM classifier. The experiment dataset we constructed are from Crowd_PETS09 bench dataset and UCF_Crowds dataset, including 2000 documents. Experimental results demonstrate that accuracy reaches 90%, and the proposed approach outperforms the state-of-the-arts by a large margin.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.6190-6213
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• 2019

For small-scale vector data, restrictions on watermark scheme capacity and robustness limit the use of copyright protection. A watermarking scheme based on robust geometric features and capacity maximization strategy that simultaneously improves capacity and robustness is presented in this paper. The distance ratio and angle of adjacent vertices are chosen as the watermark domain due to their resistance to vertex and geometric attacks. Regarding watermark embedding and extraction, a capacity-improved strategy based on quantization index modulation, which divides more intervals to carry sufficient watermark bits, is proposed. By considering the error tolerance of the vector map and the numerical accuracy, the optimization of the capacity-improved strategy is studied to maximize the embedded watermark bits for each vertex. The experimental results demonstrated that the map distortion caused by watermarks is small and much lower than the map tolerance. Additionally, the proposed scheme can embed a copyright image of 1024 bits into vector data of 150 vertices, which reaches capacity at approximately 14 bits/vertex, and shows prominent robustness against vertex and geometric attacks for small-scale vector data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권6호
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• pp.1946-1963
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• 2014

Most background subtraction methods focus on dynamic and complex scenes without considering robustness against noise. This paper proposes a background subtraction algorithm based on dictionary learning and sparse coding for handling low light conditions. The proposed method formulates background modeling as the linear and sparse combination of atoms in the dictionary. The background subtraction is considered as the difference between sparse representations of the current frame and the background model. Assuming that the projection of the noise over the dictionary is irregular and random guarantees the adaptability of the approach in large noisy scenes. Experimental results divided in simulated large noise and realistic low light conditions show the promising robustness of the proposed approach compared with other competing methods.

• 한국인터넷방송통신학회논문지
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• 제16권2호
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• pp.1-8
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• 2016

정보통신기술의 발달 및 인터넷 이용의 활성화로 간편결제 등 금융과 정보통신기술의 융합된 핀테크 산업이 활성화 되고 있다. 하지만 현재 법규 상 금융사고 발생 시 금융회사, 핀테크 업체와 소비자 간의 책임이 모호하고 금융기관 또는 전자금융업자가 손해배상을 해야 하는 경우 전자금융거래법 제정('06년) 당시 지정된 전자금융사고 책임이행 보험 가입 최저한도와 현재 전자금융거래 규모, 사고 발생 추이, 보안 투자 규모 등을 비교했을 때 현실적으로 적정하다고 보기 어렵다. 이에 본 논문에서는 국내 금융사고의 현황과 사후처리를 파악하고 현재 사이버 배상책임보험의 한계와 변경 필요성을 지적하고자 한다.

• The Journal of Asian Finance, Economics and Business
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• 제6권1호
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• pp.169-175
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• 2019

Service quality has been a point of discussion from the decades as it is important for customer satisfaction, loyalty and retention. Various models have been proposed to measure the quality in the service sector. Models are modified in accordance with context and geography to assess the quality of service better. This study aims to investigate the impact of the modified e-SERVQUAL model on the customer perception about the existing relation and potential scope of doing business with a bank which in-turn will decide the performance of the bank. Statistical data was analyzed through various tests like reliability analysis, correlation and regression analysis using SPSS 25.0. The primary data of e-SQ and performance was gathered from 721 internet banking users using 32 item questionnaire, representing 72% response rates, of four selected Islamic banks of Malaysia. E-SERQUAL was modified by adding Shariah Compliance information about banks and products for Islamic banking customers. The finding specified that efficient & reliable services, fulfillment, security/trust, and Shariah compliance information have a significant association with the performance of Islamic banks. The research is original and its implications will be helpful for Islamic banks across the world to enhance the online experience of customers, which will help them to retain the customers in the rapid changing virtual environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2078-2093
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• 2019

Visual smoke recognition is a challenging task due to large variations in shape, texture and color of smoke. To improve performance, we propose a novel smoke recognition method by combining dual-encoded features that are extracted from both spatial and Curvelet domains. A Curvelet transform is used to filter an image to generate fifty sub-images of Curvelet coefficients. Then we extract Local Binary Pattern (LBP) maps from these coefficient maps and aggregate histograms of these LBP maps to produce a histogram map. Afterwards, we encode the histogram map again to generate Dual-encoded Local Binary Patterns (Dual-LBP). Histograms of Dual-LBPs from Curvelet domain and Completed Local Binary Patterns (CLBP) from spatial domain are concatenated to form the feature for smoke recognition. Finally, we adopt Gaussian Kernel Optimization (GKO) algorithm to search the optimal kernel parameters of Support Vector Machine (SVM) for further improvement of classification accuracy. Experimental results demonstrate that our method can extract effective and reasonable features of smoke images, and achieve good classification accuracy.

• 인터넷정보학회논문지
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• 제20권3호
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• pp.111-117
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• 2019

본 연구는 선행 연구 분석과 전문가 심층 인터뷰를 통해 스마트폰 기반 간편결제 서비스의 사용자 확산 정도를 결정하는 요인을 범용성, 보안성, 사용 편의성, 사용자 경제성, 가맹점 경제성으로 도출하였다. 또한 전문가 설문을 AHP 기법으로 분석한 결과, 각 요인별 중요도는 보안성, 범용성, 사용 편의성, 사용자 경제성, 가맹점 경제성 순으로 나타났다. 그러나 보안 관련 법규 준수 의무로 인해 보안성은 모델 간 평가 결과의 차이가 크지 않았고, 상대적으로 편차가 큰 범용성이 실질적으로 간편결제 서비스의 확산을 결정하는 요인임을 규명하였다. 또한 요인별 가중치를 주요 간편결제 서비스 모델인 MST, NFC, APP 카드에 적용한 결과, MST의 확산 가능성이 가장 높음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.347-370
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• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2589-2609
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• 2021

Segment routing (SR) is a highly implementable approach for traffic engineering (TE) with high flexibility, high scalability, and high stability, which can be established upon existing network infrastructure. Thus, when a network failure occurs, it can leverage the existing rerouting methods, such as rerouting based on Interior Gateway Protocol (IGP) and fast rerouting with loop-free alternates. To better exploit these features, we propose a high-performance and easy-to-deploy method SRUF (Segment Routing under Uncertain Failures). The method is inspired by the Value-at-Risk (VaR) theory in finance. Just as each investment risk is considered in financial investment, SRUF also considers each traffic distribution scheme's risk when forwarding traffic to achieve optimal traffic distribution. Specifically, SRUF takes into account that every link may fail and therefore has inherent robustness and high availability. Also, SRUF considers that a single link failure is a low-probability event; hence it can achieve high performance. We perform experiments on real topologies to validate the flexibility, high-availability, and load balancing of SRUF. The results show that when given an availability requirement, SRUF has greater load balancing performance under uncertain failures and that when given a demand requirement, SRUF can achieve higher availability.