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한국인터넷정보학회> KSII Transactions on Internet and Information Systems (TIIS)

KSII Transactions on Internet and Information Systems (TIIS) update

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수록범위 : 1권1호(2007)~14권6호(2020) |수록논문 수 : 2,666
KSII Transactions on Internet and Information Systems (TIIS)
14권6호(2020년 06월) 수록논문
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KCI등재 SCI SCOPUS

1Auxiliary Stacked Denoising Autoencoder based Collaborative Filtering Recommendation

저자 : Ruihui Mu , Xiaoqin Zeng

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2310-2332 (23 pages)

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In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

KCI등재 SCI SCOPUS

2Prioritized Data Transmission Mechanism for IoT

저자 : Changsu Jung

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2333-2353 (21 pages)

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This paper proposes a novel data prioritization and transmission mechanism to minimize the number of packets transmitted and reduce network overload using the constrained application protocol (CoAP) in resource-constrained networks. The proposed scheme adopts four classification parameters to classify and prioritize data from a sensor. With the packet prioritization scheme, the sensed data having the lowest priority is only delivered using the proposed keep-alive message notification to decrease the number of packets transmitted. The performance evaluation demonstrates that the proposed scheme shows the improvement of resource utilization in energy consumption, and bandwidth usage compared with the existing CoAP methods. Furthermore, the proposed scheme supports quality-of-service (QoS) per packet by differentiating transmission delays regarding priorities.

KCI등재 SCI SCOPUS

3A GQM Approach to Evaluation of the Quality of SmartThings Applications Using Static Analysis

저자 : Byeong-mo Chang , Janine Cassandra Son , Kwanghoon Choi

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2354-2376 (23 pages)

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SmartThings is one of the most popular open platforms for home automation IoT solutions that allows users to create their own applications called SmartApps for personal use or for public distribution. The nature of openness demands high standards on the quality of SmartApps, but there have been few studies that have evaluated this thoroughly yet. As part of software quality practice, code reviews are responsible for detecting violations of coding standards and ensuring that best practices are followed. The purpose of this research is to propose systematically designed quality metrics under the well-known Goal/Question/Metric methodology and to evaluate the quality of SmartApps through automatic code reviews using a static analysis. We first organize our static analysis rules by following the GQM methodology, and then we apply the rules to real-world SmartApps to analyze and evaluate them. A study of 105 officially published and 74 community-created real-world SmartApps found a high ratio of violations in both types of SmartApps, and of all violations, security violations were most common. Our static analysis tool can effectively inspect reliability, maintainability, and security violations. The results of the automatic code review indicate the common violations among SmartApps.

KCI등재 SCI SCOPUS

4A Robust and Device-Free Daily Activities Recognition System using Wi-Fi Signals

저자 : Enjie Ding , Yue Zhang , Yun Xin , Lei Zhang , Yu Huo , Yafeng Liu

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2377-2397 (21 pages)

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Human activity recognition is widely used in smart homes, health care and indoor monitor. Traditional approaches all need hardware installation or wearable sensors, which incurs additional costs and imposes many restrictions on usage. Therefore, this paper presents a novel device-free activities recognition system based on the advanced wireless technologies. The fine-grained information channel state information (CSI) in the wireless channel is employed as the indicator of human activities. To improve accuracy, both amplitude and phase information of CSI are extracted and shaped into feature vectors for activities recognition. In addition, we discuss the classification accuracy of different features and select the most stable features for feature matrix. Our experimental evaluation in two laboratories of different size demonstrates that the proposed scheme can achieve an average accuracy over 95% and 90% in different scenarios.

KCI등재 SCI SCOPUS

5RPSMDSM: Residential Power Scheduling and Modelling for Demand Side Management

저자 : Sheeraz Ahmed , Ali Raza , Shahryar Shafique , Mukhtar Ahmad , M. Yousaf Ali Khan , Asif Nawaz , Rohi Tariq

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2398-2421 (24 pages)

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In third world countries like Pakistan, the production of electricity has been quickly reduced in past years due to rely on the fossil fuel. According to a survey conducted in 2017, the overall electrical energy capacity was 22,797MW, since the electrical grids have gone too old, therefore the efficiency of grids, goes down to nearly 17000MW. Significant addition of fossil fuel, hydro and nuclear is 64.2%, 29% and 5.8% respectively in the total electricity production in Pakistan. In 2018, the demand crossed 20,223MW, compared to peak generation of 15,400 to 15,700MW as by the Ministry of Water and Power. Country faces a deficit of almost 4000MW to 5000MW for the duration of 2019 hot summer term. Focus on one aspect considering Demand Side Management (DSM) cannot oversea the reduction of gap between power demand and customer supply, which eventually leads to the issue of load shedding. Hence, a scheduling scheme is proposed in this paper called RPSMDSM that is based on selection of those appliances that need to be only Turned-On, on priority during peak hours consuming minimum energy. The Home Energy Management (HEM) system is integrated between consumer and utility and bidirectional flow is presented in the scheme. During peak hours of electricity, the RPSMDSM is capable to persuade less power consumption and accomplish productivity in load management. Simulations show that RPSMDSM scheme helps in scheduling the electricity loads from peak price to off-peak price hours. As a result, minimization in electricity cost as well as (Peak-to-Average Ratio) PAR are accomplished with sensible waiting time.

KCI등재 SCI SCOPUS

6Energy-Efficient Resource Allocation for Application Including Dependent Tasks in Mobile Edge Computing

저자 : Yang Li , Gaochao Xu , Jiaqi Ge , Peng Liu , Xiaodong Fu

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2422-2443 (22 pages)

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This paper studies a single-user Mobile Edge Computing (MEC) system where mobile device (MD) includes an application consisting of multiple computation components or tasks with dependencies. MD can offload part of each computation-intensive latency-sensitive task to the AP integrated with MEC server. In order to accomplish the application faultlessly, we calculate out the optimal task offloading strategy in a time-division manner for a predetermined execution order under the constraints of limited computation and communication resources. The problem is formulated as an optimization problem that can minimize the energy consumption of mobile device while satisfying the constraints of computation tasks and mobile device resources. The optimization problem is equivalently transformed into solving a nonlinear equation with a linear inequality constraint by leveraging the Lagrange Multiplier method. And the proposed dual Bi-Section Search algorithm Bi-JOTD can efficiently solve the nonlinear equation. In the outer Bi-Section Search, the proposed algorithm searches for the optimal Lagrangian multiplier variable between the lower and upper boundaries. The inner Bi-Section Search achieves the Lagrangian multiplier vector corresponding to a given variable receiving from the outer layer. Numerical results demonstrate that the proposed algorithm has significant performance improvement than other baselines. The novel scheme not only reduces the difficulty of problem solving, but also obtains less energy consumption and better performance.

KCI등재 SCI SCOPUS

7Video Saliency Detection Using Bi-directional LSTM

저자 : Yang Chi , Jinjiang Li

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2444-2463 (20 pages)

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Significant detection of video can more rationally allocate computing resources and reduce the amount of computation to improve accuracy. Deep learning can extract the edge features of the image, providing technical support for video saliency. This paper proposes a new detection method. We combine the Convolutional Neural Network (CNN) and the Deep Bidirectional LSTM Network (DB-LSTM) to learn the spatio-temporal features by exploring the object motion information and object motion information to generate video. A continuous frame of significant images. We also analyzed the sample database and found that human attention and significant conversion are time-dependent, so we also considered the significance detection of video cross-frame. Finally, experiments show that our method is superior to other advanced methods.

KCI등재 SCI SCOPUS

8Subjective Evaluation of Ultra-high Definition (UHD) Videos

저자 : Tariq Rahim , Soo Young Shin

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2464-2479 (16 pages)

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This paper presents a detailed subjective quality assessment for the ultra-high definition (UHD) videos having frame rates of 30fps and 60 fps. The subjective assessment is based on the ITU-R BT-500 recommendations, where double stimulus continuous quality scale (DSCQS-type II) test is performed for the evaluation of the perceived quality of the user's in terms of differential mean opinion score (DMOS). Encoding of the UHD videos by opting encoders i.e. H.264/AVC, H.265/HEVC, and VP9 at five different quantization parameter (QP) levels is done to investigate the perceived user's quality of experience (QoE) given as DMOS. Moreover, the encoding efficiency as the encoding time for each encoder and qualitative performance by employing full-reference (FR) quality metrics are presented in this work.

KCI등재 SCI SCOPUS

9Low-dose CT Image Denoising Using Classification Densely Connected Residual Network

저자 : Jun Ming , Benshun Yi , Yungang Zhang , Huixin Li

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2480-2496 (17 pages)

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Considering that high-dose X-ray radiation during CT scans may bring potential risks to patients, in the medical imaging industry there has been increasing emphasis on low-dose CT. Due to complex statistical characteristics of noise found in low-dose CT images, many traditional methods are difficult to preserve structural details effectively while suppressing noise and artifacts. Inspired by the deep learning techniques, we propose a densely connected residual network (DCRN) for low-dose CT image noise cancelation, which combines the ideas of dense connection with residual learning. On one hand, dense connection maximizes information flow between layers in the network, which is beneficial to maintain structural details when denoising images. On the other hand, residual learning paired with batch normalization would allow for decreased training speed and better noise reduction performance in images. The experiments are performed on the 100 CT images selected from a public medical dataset―TCIA(The Cancer Imaging Archive). Compared with the other three competitive denoising algorithms, both subjective visual effect and objective evaluation indexes which include PSNR, RMSE, MAE and SSIM show that the proposed network can improve LDCT images quality more effectively while maintaining a low computational cost. In the objective evaluation indexes, the highest PSNR 33.67, RMSE 5.659, MAE 1.965 and SSIM 0.9434 are achieved by the proposed method. Especially for RMSE, compare with the best performing algorithm in the comparison algorithms, the proposed network increases it by 7 percentage points.

KCI등재 SCI SCOPUS

10Deterministic Bipolar Compressed Sensing Matrices from Binary Sequence Family

저자 : Cunbo Lu , Wengu Chen , Haibo Xu

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 14권 6호 발행 연도 : 2020 페이지 : pp. 2497-2517 (21 pages)

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For compressed sensing (CS) applications, it is significant to construct deterministic measurement matrices with good practical features, including good sensing performance, low memory cost, low computational complexity and easy hardware implementation. In this paper, a deterministic construction method of bipolar measurement matrices is presented based on binary sequence family (BSF). This method is of interest to be applied for sparse signal restore and image block CS. Coherence is an important tool to describe and compare the performance of various sensing matrices. Lower coherence implies higher reconstruction accuracy. The coherence of proposed measurement matrices is analyzed and derived to be smaller than the corresponding Gaussian and Bernoulli random matrices. Simulation experiments show that the proposed matrices outperform the corresponding Gaussian, Bernoulli, binary and chaotic bipolar matrices in reconstruction accuracy. Meanwhile, the proposed matrices can reduce the reconstruction time compared with their Gaussian counterpart. Moreover, the proposed matrices are very efficient for sensing performance, memory, complexity and hardware realization, which is beneficial to practical CS.

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